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Package `GSIF`

1. Tomislav Hengl and Gerard B M Heuvelink mpspline 51 References e Heuvelink G B M Bierkens MPP 1992 Combining soil maps with interpolations from point observations to predict quantitative soil properties Geoderma 55 1 2 1 15 mpspline Fits a mass preserving spline Description Fits a mass preserving spline to a soil profile data Usage HH S4 method for signature SoilProfileCollection mpspline obj var name lam 0 1 d t c 0 5 15 30 60 100 200 vlow 0 vhigh 1000 show progress TRUE Arguments obj object of class SoilProfileCollection var name character target variable name must be a numeric variable lam numeric lambda the smoothing parameter d numeric standard depths vlow numeric smallest value of the target variable smaller values will be replaced vhigh numeric highest value of the target variable larger values will be replaced show progress logical specifies whether to display the progress bar Value Returns a list with four elements idcol site ID column var fitted matrix are are spline estimated values of the target variable at observed depths upper and lower depths are indicated as attributes var std matrix are spline estimated values of the target variable at standard depths var 1cm matrix are spline estimated values of the target variable using the 1 cm increments Note Target variable needs to be a numeric vector measured at least 2 horizons for the splin
2. row names character giving the row names for the data frame missing values are not al lowed optional logical if TRUE setting row names and converting column names to syntac tic names see make names is optional optional arguments Details The advantage of converting the SoilProfileCollection data to a single table is that once both tables have been merged to a single data frame it can be more easily exported and visualized in a GIS and or imported into a data base Note Few profiles with a large number of horizons can make the whole data frame become large Con sider removing such locations or aggregating measured values per horizon to a lower number of horizons Author s Tomislav Hengl and Brendan Malone See Also as geosamples mpspline 6 as geosamples Examples library aqp library plyr library rgdal library sp sample profile from Nigeria lon 3 90 lat 7 50 id ISRIC NGQQ17 FAO1988 LXp top c 18 36 65 87 127 bottom c 18 36 65 87 127 181 ORCDRC c 18 4 4 4 3 6 3 6 3 2 1 2 prepare a SoilProfileCollection profl lt join data frame id top bottom ORCDRC data frame id lon lat FAO1988 type inner depths prof1 lt id top bottom site prof1 lt lon lat FAO1988 coordinates prof1 lt lon lat proj4string prof1 lt CRS proj longlat datum WGS84 convert to a simple table x lt as
3. randomForest quantregForest lme dimensions NULL fit family gaussian stepwise TRUE rvgm GLS FALSE steps subsample subsample reg Arguments formulaString object of class formula regression model rmatrix object of class data frame regression matrix produced as a result of spatial overlay predictionDomain object of class SpatialPixelsDataFrame spatial domain of interest method character family of methods considered e g GLM rpart regression trees randomForest random forest dimensions character 2D 3D 2D T or 3D T fit family family to be passed to the glm see examples below stepwise specifies whether to run step wise regression on top of GLM to get an optimal subset of predictors rvgm residual variogram to avoid fitting the variogram set as NULL 26 fit regModel methods GLS fit trend model using Generalized Least Squares implemented in the nlme pack age steps integer the maximum number of steps to be considered for step wise regression see stats step for more details subsample integer maximum number of observations to be taken for variogram model fit ting to speed up variogram fitting subsample reg integer maximum number of observations to be taken for regression model fit ting especially important for randomForest modelling other optional arguments that can be passed to gstat fit variogram Details Produces an object of
4. envir GSIF opts tmp file FALSE show output on console FALSE program object of class SpatialPixelsDataFrame or class RasterLayer character proj4string describing the target coordinate system pology optional grid topology from sp package e grid cell size in decimal degrees ling_method character resampling method see gdalwarp options character missing value flag le logical specifies whether a temporary file name should be generated utput on console logical specifies whether to print out the progress m full path to the GDAL warp program FWTools must be installed separately See also gdalUtils package Author s Tomislav Heng See Also make 3 Dgrid plotKML reproject WPS class 83 WPS class A class for a Web Processing Service Description A class for a Web Processing Service Can be used to overlay points or fetch grid values for rasters located remotely on a server and specified via the inRastername slot Slots server object of class list contains the location of the CGI script that executes WPS URI service name service name version version request type request identifier identifier inRastername object of class character name of the objects on the server Methods show signature object WPS gets the complete server capabilities getProcess signature x WPS gets a list of processes available from a server describe
5. 2 resampling_method bilinear NAflag get NAflag envir GSIF opts stdepths get stdepths envir GSIF opts tmp file TRUE show output on console TRUE Arguments obj object of class SpatialPixelsDataFrame or RasterBrick proj4s character proj4string describing the target coordinate system pixsize grid cell size in decimal degrees set by default at 1 1200 0 0008333333 or 100 m around equator resampling_method character resampling method to be passed the reprojection algorithm NAflag character missing value flag stdepths numeric list of standard depths tmp file logical specifies whether a temporary file name should be generated show output on console logical specifies whether to print out the progress optional arguments that can be passed to the reprojetion algorithm Value The output is list of objects of class SpatialPixelsDataFrame where the number of elements in the list corresponds to the number of standard depths Note If the input object is of class SpatialPixelsDataFrame the method by default uses FWTools warp command to resample grids otherwise the raster projectRaster command is passed FWTools must be installed separately Note this operation can be time consuming for large areas e g le6 pixels make 3Dgrid 45 Author s Tomislav Hengl References e Bivand R S Pebesma E J and G mez Rubio V 2008 Applied Spatial Data Analysis
6. I somewhat poorly drained and V very poorly drained Horizons table contains the following columns SOURCEID factor a short label to help a user identify a particular site UHDICM numeric upper horizon depth from the surface in cm LHDICM numeric lower horizon depth from the surface in cm MCOMNS factor Munsell color moist ORCDRC numeric soil organic carbon content in permilles PHIHOX numeric pH index measured in water solution SNDPPT numeric weight percentage of the sand particles 0 05 2 mm SLTPPT numeric weight percentage of the silt particles 0 0002 0 05 mm CLYPPT numeric weight percentage of the clay particles lt 0 0002 mm 4 afsp CRFVOL numeric volume percentage of coarse fragments gt 2 mm BLD numeric bulk density in tonnes per cubic meter CEC numeric Cation exchange capacity fine earth fraction in cmolc kg NTO numeric total N content in permille or g kg EMGX numeric exchangable Mg in cmolc kg Author s The Africa Soil Profiles Database have been prepared by Johan Leenaars lt johan leenaars wur nl gt This is a subset of the original database that can be downloaded via www isric org The AfSIS Sentinel Site database is one of the main deliverables of the Africa Soil Information Service project References e Leenaars J G B 2014 Africa Soil Profiles Database Version 1 2 A compilation of geo referenced and standardized legacy soil profile data for Sub Saharan Africa with dataset ISRIC
7. U unlist ov i grep PHIHOX U names ov PHIHOX pnt variable lt PHIHOX png paste PHIHOX_depth_ i png sep width 300 height 6 5 300 p lt xyplot top M 10 variable data PHIHOX pnt ylab Depth in cm xlab 5th and 95th percentiles xlim PHIHOX range 10 lower PHIHOX pnt L 10 upper PHIHOX pnt U 10 ylim c 150 0 panel panel depth_function alpha 0 25 sync colors TRUE par settings list superpose line list col Red lwd 3 strip strip custom bg grey 0 8 print p graphics off 3 plot in Google Earth library plotKML kml pnts colour id file PHIHOX_depth km1 shape paste PHIHOX_depth_ 1 nrow ov png sep size 6 points_names pnts id colour_scale rep FFFFFF 2 End Not run sample grid sample spatial points by grids Description Get a subset of a object of class SpatialPoints or SpatialPointsDataFrame avoiding spa tial clustering Usage S4 method for signature SpatialPoints sample grid obj cell size n bbox S4 method for signature SpatialPointsDataFrame sample grid obj cell size n bbox Arguments obj SpatialPointsx object cell size numeric the cell size of the overlayed SpatialGridDataFrame in the form of c x y n integer specifies maximum number points in each grid bbox matrix the bounding box of output SpatialPoints or SpatialPointsDataFrame it is set the same as the obj if missing
8. object The resulting predicted classes are then used to estimate class centres and variances per class 72 spmultinom Usage S4 method for signature HH formula SpatialPointsDataFrame SpatialPixelsDataFrame spmultinom formulaString observations covariates class stats TRUE predict probs TRUE Arguments formulaString formula string observations object of type SpatialPointsData occurrences of factors covariates object of type SpatialPixelsData list of covariate layers class stats logical species wether to estimate class centres predict probs logical species wether to predict probabilities per class optional arguments Value Returns an object of type SpatialMemberships with following slots predicted classes pre dicted by the multinomial logistic regression model the multinomial logistic regression model mu probabilities derived using the mutinom model class c derived class centres class sd derived class deviations confusion confusion matrix Author s Bas Kempen and Tomislav Hengl References e Multinomial logistic regression http en wikipedia org wiki Multinomial_logit e Nnet package http CRAN R project org package nnet See Also spfkm SpatialMemberships class Examples load data library plotKML library sp data eberg subset to 20 eberg lt eberg runif nrow eberg lt 2 data eberg_grid coordinates eberg lt X Y proj4
9. 0 1 0 2 EACKCL c 0 1 0 1 0 1 NA NA 0 5 EXB c 8 9 4 0 5 7 7 4 8 9 10 4 ORCDRC c 18 4 4 4 3 6 3 6 3 2 1 2 x lt LRICUHDICM UHDICM LHDICM LHDICM SNDPPT SNDPPT SLTPPT SLTPPT CLYPPT CLYPPT CRFVOL CRFVOL BLD BLD ORCDRC ORCDRC CEC CEC ENA ENA EACKCL EACKCL EXB EXB PHIHOX PHIHOX print thresholds TRUE D Most limiting BLD f and CRFVOL but nothing lt 20 Effective Rootable Depth sel lt x FALSE if all sel FALSE UHDICM which sel TRUE 1 else max LHDICM xI lt attr x minimum LRI derive Effective rooting depth ERDICM UHDICM UHDICM LHDICM LHDICM minimum LRI xI DRAINFAO M make 3Dgrid Methods to prepare 3D prediction locations Description Generates a list of objects of type SpatialPixelsDataFrame with longitude latitude and altitude coordinates these names are used by default for compatibility with the geosamples class 44 make 3Dgrid Usage S4 method for signature SpatialPixelsDataFrame make 3Dgrid obj proj4s get ref_CRS envir GSIF opts pixsize get cellsize envir GSIF opts 2 resampling_method bilinear NAflag get NAflag envir GSIF opts stdepths get stdepths envir GSIF opts tmp file TRUE show output on console TRUE S4 method for signature RasterBrick make 3Dgrid obj proj4s get ref_CRS envir GSIF opts pixsize get cellsize envir GSIF opts
10. 13 Note The farm is 37 ha stationed in the hilly Palouse region which receives an annual average of 550 mm of precipitation primarily as rain and snow in November through May Soils are deep silt loams formed on loess hills clay silt loam horizons commonly occur at variable depths Farming practices at Cook Farm are representative of regional dryland annual cropping systems direct seeded cereal grains and legume crops Author s Caley Gasch Tomislav Hengl and David J Brown References e Gasch C Hengl T Gr ler B Meyer H Magney T Brown D J 2015 Spatio temporal interpolation of soil water temperature and electrical conductivity in 3D T the Cook Agron omy Farm data set Spatial Statistics Journal accepted Examples An example for 3D T modelling applied to the cookfarm data set can be assesed via demo cookfarm_3DT_kriging demo cookfarm_3DT_RF Please note that the demo s might take 10 15 minutes to complete library rgdal library sp library spacetime library aqp library splines library randomForest library plyr library plotKML data cookfarm gridded data grid10m lt cookfarm grids gridded grid10m lt x y proj4string grid10m lt CRS cookfarm proj4string spplot grid10m DEM col regions SAGA_pal 1 soil profiles profs lt cookfarm profiles levels cookfarm profiles HZDUSD Bt horizon sel Bt lt grep Bt profs HZDUSD ignore
11. 2 points pnts lt data frame lon c 10 65 5 36 lat c 51 81 51 48 id c p1 p2 coordinates pnts lt lon lat proj4string pnts lt CRS proj longlat datum WGS84 pnts REST example soilgrids r lt REST SoilGrids c ORCDRC PHIHOX ov lt over soilgrids r pnts ORCDRC pnt1 lt data frame top unlist ov 1 grep depthCodesMeters names ov 100 M unlist ov 1 grep ORCDRC M names ov L unlist ov 1 grep ORCDRC L names ov U unlist ov 1 grep ORCDRC U names ov ORCDRC pnt1 variable lt ORCDRC plot the result library lattice library aqp data soil legends Soil organic carbon ORCDRC range range soil legends ORCDRC MIN soil legends ORCDRC MAX dev new width 5 height 6 xyplot top M variable data ORCDRC pnt1 ylab Depth in cm xlab 5th and 95th percentiles xlim ORCDRC range lower ORCDRC pnt1 L upper ORCDRC pnt1 U ylim c 150 0 panel panel depth_function alpha 0 25 sync colors TRUE par settings list superpose line list col RoyalBlue lwd 3 strip strip custom bg grey 8 Soil pH PHIHOX range range soil legends PHIHOX 1 MIN soil legends PHIHOX MAX for i in 1 nrow ov PHIHOX pnt lt data frame top unlist ovLi grep depthCodesMeters names ov 100 M unlist ov i grep PHIHOX M names ov 58 sample grid L unlist ov i grep PHIHOX L names ov
12. TRUE FALSE values where FALSE indicates rooting not possible Threshold values used to derive Limiting Rootability scores are set based on common soil agricultural pro ductivity tresholds e g in this case for maize and can be adjusted via the thresholds argument This functions also accounts for textural changes sudden changes in sand and clay content and saturated water content Note Horizons need to be sorted by depth e g 0 5 5 15 15 30 For each soil property at least three depths are needed otherwise the function reports an error Missing values are automatically replaced using smoothing splines Author s Johan Leenaars and Maria Ruiperez Gonzalez References e Driessen P M amp Konijn N T 1992 Land use systems analysis Wageningen Agricultural University e Rijsberman F R amp Wolman M G 1985 Effect of erosion on soil productivity an inter national comparison Journal of soil and water conservation 40 4 349 354 make 3Dgrid 43 See Also AWCPTF ERDICM Examples sample profile from Nigeria ISRIC NG0017 UHDICM c 0 18 36 65 87 127 LHDICM c 18 36 65 87 127 181 SNDPPT c 66 70 54 43 35 47 SLTPPT c 13 11 14 14 18 23 CLYPPT c 21 19 32 43 47 30 CRFVOL c 17 72 73 54 19 17 BLD c 1 57 1 60 1 52 1 50 1 40 1 42 1000 PHIHOX c 6 5 6 9 6 5 6 2 6 2 6 0 CEC c 9 3 4 5 6 0 8 0 9 4 10 9 ENA c 0 1 0 1 0 1 0 1
13. a go AE Ae dae TN iech 18 FAO SoilProfileCollection class o o a 19 fit gstatModel methods ee 21 fitregModel methods 25 fit vgmModel methods 2 0 0 2 ee 2I SEOCI ics A A EE A a A 28 geosamples class e s e c c eere ee 30 A AA A 32 gelSpatialTiles 2 4 ve Gea do es IS es ak odes 33 GlobalSoilMap class ee ee 34 Clem e bea oS Re a RS SK de a os a SR A ee Ss ee ago d 3D gstatModel class ee 36 ISIS sek Gad eae a eb Phe ee ree be Ye be bode a ee bd aoe fo 38 landmask 22 44 2ni 3454 2248 24064 RAE EES 24S St 39 ERE ua ee Pe ee oe Renee A ER Pe ee oe e a 41 make IDEA eri a A Ee A a a Bg B Bh ae wb 43 makeGstaCmd o s s ioa coeg mn Eee EE EE E E E AE 46 MaxEnt ona amp EE PAs SAA E AR E A ee 48 Merge mang a a Baie Gi A A y SS A A AA 50 at OTTEN 51 OCSKGM minima Bb eS Reh eS SEPNEER SE SEES a Se gl od 53 predict gstatModel method 54 REST SoilGrids class 42 92 be eRe ERE ans EME SRE Pe a Es 56 Sample Orid e a Se Ae ee CR ee ee eS 58 soll legends ona sa SA ee ES Re SH A SO E 59 SoilGrid validator EEN ee Re ER RS 61 SoilGrids class e i e e e E E E E E e a a E e e 62 SpatialComponents class a 65 Spatia lMemberships class ee 65 A 66 SP ie e a Sh e ESS EN eo ooh a 67 spline Krige soe roekan A a Ae ee 69 SPOQU LINOM i pos s a a A RR A eR RO de e Re a 21 spsample e BEE EE T3 summary methods ss sss 6 RA OY Ee A e i 793 test gstatModel metho
14. block x 5000 spplot line 1st 11112 gt polygons data eberg_zones this one requires ogr2ogr function pol 1st lt tile eberg_zones block x 5000 spplot pol 1st 11111 raster files via rgdal library rgdal fn system file pictures SP27GTIF TIF package rgdal obj lt GDALinfo fn ras lst lt getSpatialTiles obj block x 1000 offset lt c ras lst offset y 1 ras 1stfoffset x 1 region dim lt c ras lst region dim y 1 ras lst region dim x 1 read the first tile SP27GTIF_T1 lt readGDAL fn offset offset region dim region dim str SP27GTIF_T1 End Not run USDA TT im Probability density for texture triangle Description Probability density for texture triangle USDA system based on global soil profile data http soilprofiles org Usage data USDA TT im USDA TT im 81 Format The USDA TT im data frame contains the following columns v numeric probability density derived using the soiltexture TT kde2d function and global soil profile data TEXMHT factor USDA soil texture class estimated by hand one of the following CH SiC SC CL SiCL SCL E SiL SL Si ILS mS s1 numeric horizontal coordinate sand content 0 1 in the texture triangle system s2 numeric vertical coordinate 0 0 85 in the texture triangle system Note Texture by hand class can be converted to sand silt clay content fractions b
15. data frame prof1 str x horizons only horizons lt getHorizons x idcol id sel c top bottom ORCDRC horizons as geosamples Converts an object to geosamples class Description Converts an object of class SoilProfileCollection or SpatialPointsDataFrame to an ob ject of class geosamples with all measurements broken into individual records Geosamples are standardized spatially and temporally referenced samples from the Earth s surface Usage S4 method for signature SoilProfileCollection as geosamples obj registry as character NA sample area 1 mxd S4 method for signature SpatialPointsDataFrame as geosamples obj registry as character NA sample area 1 mxd 2 TimeSpan begin TimeSpan end 2 TimeSpan begin TimeSpan end Arguments obj object of class SoilProfileCollection optional arguments as geosamples H registry URI specifying the metadata registry web service that carries all metadata con nected to the certain method ID and or sample ID sample area standard sample area in square meters assumed to be by 1 m mxd maximum depth of interest in meters TimeSpan begin vector of class POSIXct begin of the measurement period TimeSpan end vector of class POSIXct end of the measurement period Value Returns an object of type geosamples Many columns required by the geosamples class might be not available and will result in NA
16. details Author s Tomislav Hengl Gerard B M Heuvelink and Bas Kempen References e Heng T Heuvelink G B M Rossiter D G 2007 About regression kriging from equations to case studies Computers and Geosciences 33 10 1301 1315 See Also gstatModel class fit gstatModel REST SoilGrids class A class for SoilGrids REST API Description A class for SoilGrids REST API Service Can be used to overlay points or fetch grid values from SoilGrids Soil Information System Slots server object of class character contains the location of the server that executes REST SoilGrids calls query object of class list contains parameters or REST SoilGrids query stream object of class character contains parameters or REST SoilGrids stream operation Methods over signature x REST SoilGrids y SpatialPoints overlays spatial points and the target grids defined via the REST SoilGrids class point by point and returns list of objects of SpatialPixelsDataFrame class Note More examples of overlay and download functions are available via http rest soilgrids org over method is not recommended for large point data sets REST SoilGrids class 57 Author s Tomislav Hengl amp Jorge S Mendes de Jesus References e SoilGrids a system for automated soil mapping http ww soilgrids org See Also SoilGrids class WPS class Examples Not run library rjson library sp
17. in tonnes per cubic meter Author s ISRIC World Soil Information Examples library rgdal library sp data isis sites lt isis sites coordinates sites lt LONWGS84 LATWGS84 proj4string sites lt proj longlat datum WGS84 Not run obtain country borders library maps country m map world plot FALSE fill TRUE IDs lt sapply strsplit country m names function x x 1 require maptools country lt as map2SpatialPolygons country m IDs IDs SpatialLines proj4string country proj longlat datum WGS84 overlay and plot points and maps plot country col darkgrey points sites pch 21 bg red cex 6 col black End Not run landmask Global coarse resolution land soil mask maps Description Land mask showing the 1 degree cells about 19 thousand in total in the geographical coordinates and the productive soils mask areas with a positive Leaf Area Index at least once in the period 2002 2011 The land mask is based on the Global Self consistent Hierarchical High resolution Shoreline Database data GSHHS 2 1 the productive soils mask on the MODIS Leaf Area Index monthtly product MOD15A2 and the water mask is based on the MOD44W product The map of the Keys to Soil Taxonomy soil suborders of the world at 20 km is based on the USDA NRCS map of the global soil regions 40 landmask Usage data landmask Format landmask data s
18. of the sampling period of class POSIXct sd1 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 2 5 cm 0 5 sd2 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 10 cm 5 15 sd3 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 22 5 cm 15 30 sd4 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 45 cm 30 60 sd5 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 80 cm 60 100 sd6 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 150 cm 100 200 Gridded data submitted to sd slots of the SoilGrids class must satisfy all of the following requirements class validity e All grids submitted must have the same grid topology identical grid slot in the object of class SpatialPixelsDataFrame e All grids must be projected in the referent coordinate system WGS84 geographical coordi nates with 3D dimension altitude expressed as distance from the land surface in meters e g altitude of 25 corresponds to the 2 5 cm depth e The grid cell size m
19. of tiles either as a list of SpatialPolygons or a data frame with with bounding box coordinates Author s Tomislav Heng See Also tile sp spsample 34 GlobalSoilMap class GlobalSoilMap class A class for GlobalSoilMap soil property maps Description A class containing predictions of target soil property at six standard depths following the Global SoilMap net specifications sd1 2 5 cm 0 5 sd2 10 cm 5 15 sd3 22 5 cm 15 30 sd4 45 cm 30 60 sd5 80 cm 60 100 sd6 150 cm 100 200 Slots varname object of class character abbreviated variable name registered in the Global Soil Data registry TimeSpan object of class list contains begin and end of the sampling period of class POSIXct sd1 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 2 5 cm 0 5 sd2 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 10 cm 5 15 sd3 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 22 5 cm 15 30 sd4 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 45 cm 30 60 sd5 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of
20. referenced samples Description A class for spatially and temporally referenced samples with fixed column names standardized geosamples Corresponds to the point Placemark in the KML schema Slots registry object of class character URI of the online registry i e the URL where the producerid column can be linked to all other connected metadata methods object of class data frame a table with method names methodid a one sentence description of each method description measurement units or levels units and associated detection limits detectionLimit geosamples class 31 data object of class data frame a standardized table with fixed column names observationid unique observation ID as specified in the data registry service sampleid producer s ID usually site ID and horizon ID or sequence number longitude longitude on the WGS84 ellipsoid latitude latitude on the WGS84 ellipsoid locationError error radius in meters TimeSpan begin begin of the measurement period TimeSpan end end of the measurement period altitude height above ground or above the sea level in meters altitudeMode one of the KML schema altitude modes sampleArea spatial support in square meters sampleThickness thickness of horizons in meters or vertical support observedValue measured value methodid method name see methods ta ble n
21. report 2012 03 Africa Soil Information Service AfSIS project and ISRIC World Soil Information Wageningen the Netherlands e Africa Soil Information Service http africasoils net Examples Not run library rgdal library aqp library sp data afsp sites lt afsp sites coordinates sites lt LONWGS84 LATWGS84 proj4string sites lt t tproj longlat datum WGS84 obtain country borders library maps country m map world plot FALSE fill TRUE IDs lt sapply strsplit country m names function x x 1 require maptools country lt as map2SpatialPolygons country m IDs IDs SpatialLines proj4string country proj longlat datum WGS84 overlay and plot points and maps plot country col darkgrey xlim c 25 3 57 8 ylim c 34 8 37 4 points sites pch 21 bg white cex 6 col black End Not run as data frame 5 as data frame Converts an object of class SoilProfileCollection to a data frame Description Converts an object of class SoilProfileCollection to an object of class data frame with both site and horizon data sorted in one row Each original column name in the horizons table receives a sufix _A B Z where alphabetic letters represent horizon sequence Usage S4 method for signature SoilProfileCollection as data frame x row names NULL optional FALSE Arguments D object of class SoilProfileCollection
22. spatial pixels Description Runs supervised fuzzy k means Hengl et al 2004 using a list of covariates layers provided as SpatialPixelsDataFrame class object If class centres and variances are not provided it first fits a multinomial logistic regression model spmultinom then predicts the class centres and variances based on the output from the nnet multinom Usage S4 method for signature HH formula SpatialPointsDataFrame SpatialPixelsDataFrame spfkm formulaString observations covariates class c NULL class sd NULL fuzzy e 1 2 Arguments formulaString formula string observations object of type SpatialPointsData occurrences of factors covariates object of type SpatialPixelsData or RasterBrick list of covariate lay ers class c object of type matrix class centres see examples below class sd object of type matrix class deviations see examples below fuzzy e object of type numeric fuzzy exponent 68 spfkm Value Returns an object of type SpatialMemberships with following slots predicted classes pre dicted either by the multinomial logistic regression or fuzzy k means model the multinomial lo gistic regression model if available mu memberships derived using the fuzzy k means class c submitted or derived class centres class sd submitted or derived class deviations confusion confusion matrix Note Although nnet multinom is consider to be robu
23. the GDA94 MGA zone 55 y numeric y coordinate in the GDA94 MGA zone 55 The edgeroi grids100 data frame contains a list of covariates at 100 m resolution prepared for the study area LNUABS6 factor Australian National scale land use data MVBSRT6 numeric SAGA GIS Multi resolution Index of Valley Bottom Flatness based on the SRTM DEM TI1LAN6 numeric principal component 1 for the Landsat band 7 thermal based on three periods of the Global Land Survey Landsat images GLS1990 GLS2000 GLS2005 TI2LAN6 numeric principal component 2 for the Landsat band 7 thermal based on three periods of the Global Land Survey Landsat images GLS1990 GLS2000 GLS2005 PCKGAD6 numeric percentage of Potassium estimated based on the gamma radiometrics radmap09 GADDS RUTGAD6 numeric ratio Uranium over Thorium estimated based on the gamma radiometrics radmap09 GADDS PCTGAD6 numeric parts per million of Thorium estimated based on the gamma radiometrics radmap09 GADDS x numeric x coordinate in the GDA94 MGA zone 55 y numeric y coordinate in the GDA94 MGA zone 55 Details The Edgeroi is one of the standard soil data sets used to test soil mapping methods in Australia Out of 359 profiles 210 sites were sampled on a systematic equilateral triangular grid with a spacing of 2 8 km between sites the other sites are distributed more irregularly or on transects The data set is described in detail in Malone et al 2010 and McGarry et a
24. values To ensure compatibility when building an object of type SoilProfilesCollection use some standard naming convention to attach attributes to each measurement horizons and sites slots in the SoilProfileCollection class locationError can be used to attach location errors in meters to each spatial location sampleArea can be used to attach spatial support to each measurement usually 1 by 1 meter measurementError can be used to attach specific measurement errors to each measurement in both site and horizons table IGSN can be used to attach the unique identifier International Geo Sample Number to each specific observation corresponds to the observationid column Author s Tomislav Heng and Hannes I Reuter See Also geosamples class as data frame aqp SoilProfileCollection Examples library aqp library plyr library rgdal library sp sample profile from Nigeria lon 3 90 lat 7 50 time as POSIXct 1978 format Y gt id ISRIC NG0017 TAXNFAO8 LXp top c 18 36 65 87 127 bottom c 18 36 65 87 127 181 ORCDRC c 18 4 4 4 3 6 3 6 3 2 1 2 methodid c TAXNFAO8 ORCDRC description c FAO 1988 classification system group Method of Walkley Black Org matter Org C x 1 72 units c FAO 1988 classes permille detectionLimit c as character NA 0 1 prepare a SoilProfileCollection profl l
25. 43 82 make 3Dgrid RasterBrick method make 3Dgrid 43 predict gstatModel method 54 print gstatModel gstatModel class 36 resample grid spline krige 69 REST SoilGrids REST SoilGrids class 56 INDEX REST SoilGrids class 56 sample 59 sample grid 58 sample grid SpatialPoints method sample grid 58 sample grid SpatialPointsDataFrame method sample grid 58 sample grid SpatialPoints sample grid 58 sample grid SpatialPointsDataFrame sample grid 58 show geosamples method geosamples class 30 show SpatialPredictions method summary methods 75 show WPS method WPS class 83 soil dom 87 soil dom soil legends 59 soil legends 59 soil vars soil legends 59 SoilGrid validator 61 SoilGrids SoilGrids class 62 SoilGrids class 62 sp3D make 3Dgrid 43 sp3D list method make 3Dgrid 43 sp3D SpatialPixelsDataFrame method make 3Dgrid 43 SpatialComponents class 65 SpatialMemberships class 65 spc 45 65 66 spc list list method spc 66 spc SpatialPixelsDataFrame formula method spc 66 spfkm 65 67 72 87 summary summary methods 75 summary SpatialPredictions method summary methods 75 summary methods 75 test gstatModel 22 37 test gstatModel test gstatModel methods 77 test gstatModel geosamples formula SpatialPixelsDataFrame test gstatModel methods 77 test gstatModel SpatialPointsDataFrame formula Spatial
26. 6 lat c 17 12721 9 363796 bbox 5 lt bbox 1 crs CRS proj longlat datum WGS84 x lt SpatialPolygons list Polygons list Polygon bbox ID 1 proj4string crs ID 1st lt getID x getSpatialTiles 33 getSpatialTiles Get a list of tiles regular blocks Description Creates a list of tiles SpatialPolygons for a given spatial domain i e extent Input can be any object of class Spatial or GDALobj Usage S4 method for signature Spatial getSpatialTiles obj block x block y block x overlap percent 0 limit bbox TRUE return SpatialPolygons S4 method for signature ANY getSpatialTiles obj block x block y block x overlap percent 0 limit bbox TRUE return SpatialPolygons FALSE TRUE Arguments obj object of class Spatialx block x numeric size of block in x direction meters or corresponding mapping units block y numeric size of block in y direction meters or corresponding mapping units overlap percent numeric percentage overlap must be a positive number limit bbox logical specifies whether to limit the extent of tiles to the bounding box only return SpatialPolygons logical specifies whether to return a list of tiles as SpatialPolygons or a data frame with bounding box coordinates Details The first output tile starts by default at the lower left corner getSpatialTiles method can only be used to generate regular tiles Value Returns a list
27. 8 e Hengl T 2009 A Practical Guide to Geostatistical Mapping 2nd Edt University of Ams terdam www lulu com 291 p See Also gstatModel class fit regModel test gstatModel geosamples class stats glm gstat fit variogram fit gstatModel methods Examples 2D model library sp library boot library aqp library plyr library rpart library splines library gstat library randomForest library quantregForest library plotKML load the Meuse data set demo meuse echo FALSE simple model omm lt fit gstatModel meuse om dist ffreq meuse grid family gaussian log om rk lt predict omm meuse grid plot om rk it was succesful fit a GLM with a gaussian log link omm lt fit gstatModel meuse om distt ffreq meuse grid fit family gaussian log summary omm regModel om rk lt predict omm meuse grid plot om rk fit a regression tree omm lt fit gstatModel meuse logip om dist ffreq meuse grid method rpart summary omm regModel plot a regression tree plot omm regModel uniform TRUE text omm regModel use n TRUE all TRUE cex 8 omm vgmModel fit a randomForest model omm lt fit gstatModel meuse om dist ffreq meuse grid method randomForest plot to see how good is the fit plot omm plot the estimated error for number of bootstrapped trees plot omm regModel omm vgmModel om rk lt predict omm m
28. OTE no transformation required m4 lt fit regModel om dist ffreq rmatrix ov meuse grid method randomForest plot m4 HH RF is very sensitive to the mtry argument m4b lt fit regModel om dist ffreq rmatrix ov meuse grid method randomForest mtry 2 plot m4b RF with uncertainty quantregForest package m5 lt fit regModel om dist ffreq rmatrix ov meuse grid method quantregForest plot m5 fit vgmModel methods Fits a 2D or 3D variogram model to spatial data Description Fits a 2D or 3D variogram model based on a regression matrix and spatial domain of interest Usage S4 method for signature formula data frame SpatialPixelsDataFrame fit vgmModel formulaString rmatrix predictionDomain vgmFun Exp dimensions list 2D 3D 2D T 3D T anis NULL subsample nrow rmatrix ivgm cutoff NULL width cressie FALSE Arguments formulaString object of class formula regression model rmatrix object of class data frame regression matrix produced as a result of spatial overlay predictionDomain object of class SpatialPixelsDataFrame spatial domain of interest vemFun character variogram function Exp by default dimensions character 3D 2D 2D T 3D T models 28 geochm anis vector containing 2 5 or more anisotropy parameters see gstat vgm for more info subsample integer size of the subset ivgm vem initial vari
29. Package GSIF July 20 2015 Type Package Title Global Soil Information Facilities Version 0 4 7 Date 2015 07 14 Maintainer Tomislav Hengl lt tom hengl isric org gt Depends R gt 2 15 0 Imports methods sp gt 1 0 8 RSAGA dismo rgdal raster aqp plotKML gstat stats plyr grDevices graphics Suggests rjson RCurl soiltexture spatstat stringr XML boot splines mda psych nortest rpart quantregForest randomPForest nlme reshape fossil AICcmodavg maptools nnet SDMTools rJava gt 0 5 0 spacetime gdalUtils tools maxlike Hmisc Description Global Soil Information Facilities tools standards and functions and sample datasets for global soil mapping License GPL URL http gsif r forge r project org LazyLoad yes NeedsCompilation no Author Tomislav Heng cre aut Bas Kempen ctb Gerard Heuvelink ctb Brendan Malone ctb Repository CRAN Date Publication 2015 07 20 18 08 46 R topics documented AS ba eee a hor Be A OG Mae a ee We AOE VA ee de ea ASAS ws a ac add e bw BA as Bae SMe ee ws Index R topics documented AS PCOSAMPIES E eer NEEN SEE EE ewe EEN E 6 autopredict methods s 2 0 0 00 0 ee 8 AWCPIR 22474 255054 Suh the hee bak 4b BOE ES Ree BAS A 9 Cookia ecse A GRE PE AN AAS EAR Oe eS ee Eee ee 11 Open wi pacea e Wee ERS a bee Be we SON Re he ORES es 14 ERDICM 3244 cidad 002 bs DASA Ge Hebe PASE EEAS Dba we ee 17 CXILACE ote AEN e a ade eS ip ee
30. Pixe test gstatModel methods 77 test gstatModel method test gstatModel methods 77 test gstatModel methods 77 tile 33 78 tile RasterLayer method tile 78 tile SpatialLinesDataFrame method tile 78 tile SpatialPixelsDataFrame method tile 78 tile SpatialPointsDataFrame method tile 78 tile SpatialPolygonsDataFrame method tile 78 TT2tri USDA TT im 80 USDA TT im 80 validate gstatModel class 36 validate gstatModel method gstatModel class 36 warp 19 55 82 warp RasterLayer method warp 82 pati alPixels PataFrame method spfkm formula SpatialPointsDataFrame SpatialPixell SEH d er spfkm 67 spline krige 69 spmultinom 8 9 67 68 71 wri spmultinom formula SpatialPointsDataFrame spatial Big 21808 ra spmultinom 71 spsample prob 73 spsample prob SpatialPoints BEE spsample prob 73 stack geosamples method geosamples class 30 subset geosamples method geosamples class 30 subset WPS method WPS class 83 WPS class 83 write Se 47 sam les el s 30 a EEN Sack geosamples class 30 ss er la aa OS method eosamples class 30 write data geosamples geosamples class 30 write data SpatialPoints geosamples class 30
31. RC PHIHOX SNDPPT SLTPPT CLYPPT CFRVOL CEC BLD TAXGWRB TAXOUSDA TimeSpan list begin as POSIXct 1950 01 01 end as POSIXct 2005 12 30 show env TRUE Arguments wps server ref_CRS NAf lag license_url project_url stdepths stsize cellsize REST server attributes TimeSpan show env character location of the WPS server the referent CRS proj4string tproj longlat datum WGS84 the default missing value flag usually 99999 the default license URL the default location of the package documentation numeric standard depths numeric standard horizon thicknesses numeric standard grid cell sizes on WGS84 geographical coordinates character location of the SoilGrids REST service character default soil variables of interest list default begin end times temporal coverage of SoilGrids logical specify whether to print all environmental parameters 36 gstatModel class Note To further customize the GSIF options consider putting library GSIF GSIF env show env FALSE in your etc Rprofile site Author s Tomislav Hengl Examples environmental variables GSIF env get cellsize envir GSIF opts gstatModel class A class for a geostatistical model Description A class containing fitted parameters of a geostatistical model to be used to run predictions by regression kriging It comprises regression model e g a GLM var
32. SAGA_pal 1 points iprob observations plot raster iprob2 1 zlim c 1 col SAGA_pal 1 points iprob2 observations 1 fit a weighted lm eberg xy lt eberg sel c SNDMHT_A X Y coordinates eberg xy lt X Y proj4string eberg xy lt CRS init epsg 31467 eberg xy iprob lt over eberg xy iprobL 1 iprob eberg xy data lt cbind eberg xy data over eberg xy covs fs lt as formula paste SNDMHT_A paste names covs collapse the lower the occurrence probability the higher the weight w lt 1 eberg xy iprob m lt Im fs eberg xy weights w summary m compare to standard Im m lt Im fs eberg xy summary m adj r squared summary m0 adj r squared all at once gm lt fit gstatModel eberg xy fs covs weights w plot gm summary methods Summarize an object of class SpatialPredictions Description Derives a statistical summary for an object of class SpatialPredictions Usage S4 method for signature SpatialPredictions summary object 76 summary methods Arguments object object of class SpatialPredictions Details The function creates a summary table with standard column names These tell us what is the sum mary accuracy of the spatial predictions and what are the effective bytes of information produced Value The summary returns a data frame with the following columns variable variable nam
33. SNDMHT t lt log eberg spc horizons SNDMHT 100 1 eberg spc horizons SNDMHT 100 convert to geosamples eberg geo lt as geosamples eberg spc load gridded data data eberg_grid gridded eberg_grid lt xty proj4string eberg_grid lt CRS init epsg 31467 derive spc s formulaString lt PRMGEO6 DEMSRT6 TWISRT6 TIRAST6 eberg_spc lt spc eberg_grid formulaString 64 SoilGrids class build a 3D gstatModel glm formulaString as formula paste SNDMHT t paste names eberg_spc predicted collapse ns altitude df 4 Not run SNDMHT m lt fit gstatModel observations eberg geo glm formulaString covariates eberg_spc predicted summary SNDMHT m regModel1 SNDMHT m vgmModel prepare new locations 6 standard depths new3D lt sp3D eberg_spc predicted Make predictions at six depths sd l lt lapply new3D FUN function x predict SNDMHT m predictionLocations x nfold back transformation function invlogit function x exp x 1 exp x 100 for the back transformation for the mean value see Diggle and Ribeiro 2007 p 148 invlogit m function x v 1 exp x 1 5 v exp x 1 exp x 1 exp x 3 100 back transform values from logits for j in 1 length sd 1 sd 1 Lj predicted M lt round invlogit m sd 1L j predicted SNDMHT t sd 1 j predicted var1 var sd 1 j lepredicted L lt round invlogit s
34. Sample Number http en wikipedia org wiki International_Geo_ Sample_Number KML Reference https developers google com km1 documentation kmlreference OGC Observations and Measurements standard http www opengeospatial org standards om SESAR the System for Earth Sample Registration http www geosamples org See Also as geosamples 32 getID getID Derive 1 degree cell IDs Description Derives ID s of the 1 degree cells in the default land mask for a given polygon defining the spatial domain of interest Usage S4 method for signature SpatialPolygons getID obj pixsize 3 3600 empty tif FALSE compress FALSE zipname set file extension tempfile tmpdir getwd zip Arguments obj object of class SpatialPolygons must be in geographical coordinates WGS84 pixsize grid cell size in decimal degrees set at 0 0008333333 or 100 m around equator empty tif logical specify whether a GeoTiff mask file should be created compress logical specify whether to compress GeoTiffs zipname optional zip archive file name Value The output is a vector of grid cell ID names e g W79_N83 These can be further used to automate digital soil mapping for large areas Note This operation can be time consuming for large areas e g continents Author s Tomislav Heng See Also landmask Examples library sp Bounding box for Malawi bbox expand grid lon c 32 67152 35 91504
35. SoilGrids org data validation protocol Note One SoilGrid layer 2D slice basically contains predictions on a regular grid for a specific soil depth at either point or block support The ground truth data must refer to the exactly the same depth and the same support size and should ideally be collected using some probability spatial sampling see e g sp spsample To estimate values of soil properties at standard depths consider using mpspline function Numeric resolution is derived as estimated RMSE 2 Numeric resolution can be best specified as Attribute_Measurement_Resolution the smallest unit increment to which an attribute value is measured Increasing N sample can lead to more precise results at the cost of higher computing time 62 SoilGrids class Author s Tomislav Hengl See Also plotKML spMetadata SoilGrids class A class for SoilGrids soil property and or class maps Description A class containing predictions and prediction error or multiple realizations of some of the tar get global soil property at six standard depths Standard depths used are based on the Global SoilMap net specifications sd1 2 5 cm 0 5 sd2 10 cm 5 15 sd3 22 5 cm 15 30 sd4 45 cm 30 60 sd5 80 cm 60 100 sd6 150 cm 100 200 Slots varname object of class character abbreviated variable name registered in the Global Soil Data registry TimeSpan object of class list contains begin and end
36. a vector in the form c xmin ymin xmax ymax sets bounding box of the kriging predictions file name character optional output file name pattern without any file extension silent logical specifies whether to print out the progress t_cellsize numeric target cell size output grid optN integer optimal number of prediction locations per sampling location e g 1 sampling location is used to predict values for 20 new pixels quant nndist numeric threshold probability to determine the search radius sigma 70 nmax predictOnly resample saga env saga lib saga module Value spline krige integer the number of nearest observations that should be used for kriging logical specifies whether to generate only predictions var1 pred column logical specifies whether to down or upscale SAGA GIS grids to match the grid system of newdata list path to location of the SAGA binaries extracted using rsaga env character names of the SAGA libraries used integer corresponding module numbers other optional arguments that can be passed to function gstat krige Returns an object of class SpatialGridDataFrame or an output file name Note This function adjusts grid density prediction locations in reference to the actual local sampling intensity High resolution grids are created where sampling density is higher and vice versa Heng 2006 Low resolution grids due to sparse data are then downscaled to the target re
37. ase Journal of Geophysical Research 101 8741 8743 See Also rworldmap rworldmapExamples maps map LRI 41 Examples library rgdal library sp data landmask gridded landmask lt x y proj4string landmask lt proj longlat datum WGS84 Not run plot maps library maps country m map world plot FALSE fill TRUE IDs lt sapply strsplit country m names function x x 1 library maptools country lt as map2SpatialPolygons country m IDs IDs SpatialLines spplot landmask mask col regions grey sp layout list sp lines country spplot landmaskL soilmask col regions grey sp layout list sp lines country End Not run also available in the Robinson projection at 20 km grid data landmask20km image landmask20km 17 summary landmask20km suborder summary landmask20km soilmask LRI Limiting Rootability Description Derive Limiting Rootability using observed soil properties at at least three depths Usage LRI UHDICM LHDICM SNDPPT SLTPPT CLYPPT CRFVOL BLD ORCDRC ECN CEC ENA EACKCL EXB PHIHOX CRB GYP tetaS fix values TRUE thresholds print thresholds FALSE Arguments UHDICM numeric upper horizon depth in cm LHDICM numeric lower horizon depth in cm SNDPPT numeric sand content in percent SLTPPT numeric silt content in percent CLYPPT numeric clay content in percent CRFVOL numeric volume percentage o
38. avy metal concetrations indicate a determination that is below the limit of detection for the analytic method used The magnitude of the negative number indicates the detection limit For example 10 ppm means the result should be regarded as lt 10 ppm 30 geosamples class Author s National Geochemical Survey database is maintaned by the USGS National Geochemical Survey Team contact Peter Schweitzer This subset has been prepared for the purpose of testing various geostatistical mapping algoriths by Tomislav Hengl tom hengl wur nl References e The National Geochemical Survey Team 2008 The National Geochemical Survey database and documentation U S Geological Survey Open File Report 2004 1001 U S Geological Survey Reston VA e National Geochemical Survey database http tin er usgs gov geochem Examples library sp Load the NGS data data geochm coordinates geochm lt LONGITUDE LATITUDE proj4string geochm lt CRS proj longlat ellps c1rk66 datum NAD27 no_defs Not run require plotKML data SAGA_pal replace the missing values with half the detection limit geochm PB_ICP40 lt ifelse geochm PB_ICP40 lt 2 geochm PB_ICP40 shape http maps google com mapfiles km1 pal2 icon18 png kml geochm shape shape colour loglp PB_ICP40 labels colour_scale SAGA_pal 1 kmz TRUE End Not run geosamples class A class for spatially and temporally
39. case FALSE fixed FALSE profs Bt lt profs Bt sel Bt lt 1 depths profs lt SOURCEID UHDICM LHDICM site profs lt TAXSUSDA Easting Northing coordinates profs lt Easting Northing proj4string profs lt CRS cookfarm proj4string profs geo lt as geosamples profs 14 edgeroi fit model for Bt horizon m Bt lt GSIF fit gstatModel profs geo Bt DEM TWI MUSYM Cook_fall_ECa Cook_spr_ECatns altitude df 4 grid10m fit family binomial logit plot m Bt fit model for soil pH m PHI lt fit gstatModel profs geo PHIHOX DEM TWI MUSYM Cook_fall_ECa Cook_spr_ECa ns altitude df 4 grid10m plot m PHI edgeroi The Edgeroi Data Set Description Soil samples and covariate layers for the Edgeroi area in NSW Australia ca 1500 square km Usage data edgeroi Format The edgeroi data set contains two data frames sites and horizons Sites table contains the following columns SOURCEID factor unique label to help a user identify a particular site ID in the NatSoil LONGDA94 numeric longitude in decimal degrees on the GDA94 datum LATGDA94 numeric latitude in decimal degrees on the GDA94 datum TAXGAUC factor Australian Great Soil Groups GSG see details NOTEOBS character free form observation notes Horizons table contains the following columns SOURCEID factor unique identifier used in the NatSoil DB LSQINT integer a layer sequence number 1 to N HZDUSD fact
40. class gstatModel that contains 1 fitted regression model e g a GLM cubist model or randomForest model 2 fitted variogram and c object of class SpatialPoints with observation locations To combine overlay and model fitting operations consider using fit gstatModel Author s Tomislav Hengl Mario Antonio Guevara Santamaria and Bas Kempen See Also fit gstatModel stats glm gstat fit variogram randomForest randomForest Examples Meuse data library sp library rpart library nlme library gstat library randomForest library quantregForest load the Meuse data set demo meuse echo FALSE prepare the regression matrix ov lt over meuse meuse grid ov lt cbind data frame meuse L om ov skip variogram fitting m lt fit regModel om disttffreq rmatrix ov meuse grid fit family gaussian log method GLM rvgm NULL m regModel m vgmModel plot m fit a GLM with variogram m1 lt fit regModel om dist ffreq rmatrix ov meuse grid fit family gaussian log method GLM m1 vgmModel fit vemModel methods 27 plot m1 fit a regression tree with variogram m2 lt fit regModel logip om distt ffreq rmatrix ov meuse grid method rpart plot m2 fit a lme model with variogram m3 lt fit regModel logip om dist rmatrix ov meuse grid method 1me random 1 ffreq plot m3 fit a randomForest model with variogram N
41. covariates data eberg_grid gridded eberg_grid lt xty proj4string eberg_grid lt CRS init epsg 31467 glm formulaString as formula paste SNDMHT paste names eberg_grid collapse ns altitude df 4 SNDMHT m lt fit gstatModel observations eberg geo glm formulaString covariates eberg_grid plot SNDMHT wi problems with the variogram Not run remove classes from the PRMGEO6 that are not represented in the model sel levels eberg_grid PRMGEO6 in levels SNDMHT m regModel mode1l PRMGE06 fix c levels eberg_grid PRMGEO6 sel summary eberg_grid PRMGE06 fit regModel methods 25 for j in fix c eberg_grid PRMGEO6Leberg_grid PRMGEO6 j lt levels eberg_grid PRMGEO6 7 J prepare new locations new3D lt sp3D eberg_grid regression only SNDMHT rk sd1 lt predict SNDMHT m new3D 1 vgmmodel NULL regression kriging SNDMHT rk sd1 lt predict SNDMHT m new3D 1 plot the results in Google Earth plotKML SNDMHT rk sd1 z lim c 5 85 End Not run fit regModel methods Fits a regression model to spatial data Description Fits a regression or a trend model e g a GLM and if not available a variogram for the response residuals using the default settings Usage S4 method for signature formula data frame SpatialPixelsDataFrame character fit regModel formulaString rmatrix predictionDomain method list GLM rpart
42. d 1 j 118predicted SNDMHT t 1 645xsqrt sd 1 j predicted var1 var sd 1 Lj predicted U lt round invlogit sd 1 j predicted SNDMHT t 1 645xsqrt sd 1 j predicted var1 var str sd 1 1 predicted data reproject to WGS84 system 100 m resolution p get cellsize envir GSIF opts 1 s get stdepths envir GSIF opts sd 11 lt sapply 1 length sd 1 FUN function x make 3Dgrid sd 1 x predicted c L M U pixsize p stdepths s x save to a SoilGrids object SNDMHT gsm lt SoilGrids obj sd 11 varname SNDPPT TimeSpan 1ist begin 1999 02 01 end 2001 07 01 str SNDMHT gsm max level 2 visualize all maps in Google Earth data R_pal z0 mean eberg_grid DEMSRT6 na rm TRUE export grids for j in 1 length sd 11 km1 slot SNDMHT gsm paste sd j sep folder name paste eberg_sd j sep file paste SNDMHT_sd j kml sep colour M z lim c 10 85 raster_name paste SNDMHT_sd j png sep altitude z0 5000 s j x2500 J End Not run SpatialComponents class 65 SpatialComponents class A class for gridded components derived using the spc method Description A class containing a list of gridded components and results of principal component analysis Slots predicted object of class SpatialPixelsDataFrame predicted values for components pca object of clas
43. d estimator of the trend model If not speficied otherwise subset observations by default selects only obserations within the spatial domain bounding box of the predictionLocations plus 50 of the one third of the ex tent of the area extend In the case of spatial duplicates in 2D or 3D subset observations will automatically remove all duplicates before running kriging All points in 3D that stand exactly above each other will be removed by default Predictions can be speed up by using a larger coarsening factor e g 2 to 5 in which case the or dinary kriging on residuals will run at a coarser resolution and the output would be then downscaled to the original resolution using splines via the warp method In the case of predict method RK the kriging variance is derived as a sum of the GLM variance and the OK variance which is statis tically sub optimal 56 REST SoilGrids class Note Predictions using predict method KED the default gstat setting can be time consuming for large data set and can result in instabilities singular matrix problems if the search radius is small and or if all covariates contain exactly the same values Predictions using predict method RK on the other hand can be speed up but will typically underestimate the prediction variance taken as a simple sum of the regression and ordinary kriging variances Compare to the KED variance that includes also a cross term see Heng et al 2007 for more
44. decimal degrees in this case 250 m Author s The original detailed profile description and laboratory analysis was funded by a Cotton Research and Development Corporation project in the mid late 1980 s by the CSIRO Division of Soils and available via the NatSoil DB The gamma radiometrics images are property of the NSW Department of Primary Industries Mineral Resources References e Malone B P McBratney A B Minasny B 2010 Mapping continuous depth functions of soil carbon storage and available water capacity Geoderma 154 138 152 e McGarry D Ward W T McBratney A B 1989 Soil Studies in the Lower Namoi Valley Methods and Data The Edgeroi Data Set 2 vols CSIRO Division of Soils Adelaide e Minty B Franklin R Milligan P Richardson L M and Wilford J 2009 The Radio metric Map of Australia Exploration Geophysics 40 4 325 333 Examples library rgdal library aqp library sp data edgeroi edgeroi sitesLedgeroi sites SOURCEID 399_EDGEROI_ed 95_1 edgeroi horizons Ledgeroi horizons SOURCEID 399_EDGEROI_ed095_1 spPoints sites lt edgeroi sites coordinates sites lt LONGDA94 LATGDA94 proj4string sites lt CRS proj longlat ellps GRS80 towgs84 0 0 0 0 0 0 0 no_defs sites lt spTransform sites CRS init epsg 28355 Not run ERDICM 17 plot points and grids pnts lt list sp points sites pch col black
45. ds 77 C he bbe eben ee debe a eee be eR owe eA eee 78 USDA TIIM ose ea Sw eK ee Sw e GS Se ad 80 WALD 25 9 e ed Stee a Se es Se be LD es A Seed Ae e A 82 WPS las 4 i4 s 94 pbb bade se bare bebe bb de be eos ba ee es 83 85 afsp 3 afsp Africa Soil Profiles Database Description A merge of the Africa Soil Profiles Database AFSP with 17 000 geo referenced legacy soil pro file records and AfSIS Sentinel Site database with 9000 sampling locations Usage data afsp Format The afsp data set contains two data frames sites and horizons Sites table contains the following columns SOURCEID factor unique label to help a user identify a particular site ProfileID in the AFSP SOURCEDB factor source data base LONWGS84 numeric longitude in decimal degrees on the WGS84 datum X_LonDD in the AFSP LATWGS84 numeric latitude in decimal degrees on the WGS84 datum Y_LatDD in the AFSP TIMESTRR character the date on which this particular soil was described or sampled T_Year in the AFSP TAXGWRB factor abbreviated soil group based on the WRB classification system WRBQ6rg in the AFSP TAXNUSDA factor Keys to Soil Taxonomy taxon name e g Plinthic Udoxic Dystropept USDA in the AFSP BDRICM numeric depth to bedrock in cm DRAINFAO factor drainage class based on the FAO guidelines for soil description E excessively drained S somewhat excessively drained W well drained M moderately well drained
46. e minium lowest value observed maximum largest value observed npoints number of observations area lowest value observed area units area units either square m or square arcdegrees covariates list of covariates used family GLM family if applicable RMSE RMSE derived using cross validation tvar variance percent explained by the model using the cross validation npixels total number of produced pixels breaks breaks based on the half RMSE bonds lower and upper boundaries for effective classes Bytes effective bytes produced see Heng et al 2012 for more details compress compression algorithm used Author s Tomislav Heng References e Hengl T Nikolic M MacMillan R A 2013 Mapping efficiency and information content International Journal of Applied Earth Observation and Geoinformation special issue Spatial Statistics Conference 22 127 138 See Also plotKML SpatialPredictions class test gstatModel methods 77 Examples load observations library sp library rgdal library gstat demo meuse echo FALSE HH fit a model omm lt fit gstatModel meuse om dist fit family gaussian link log meuse grid show omm regModel produce SpatialPredictions om rk lt predict omm predictionLocations meuse grid x summary om rk str x test gstatModel methods Methods to test predictability of a regression kriging model D
47. e consuming for large grids Author s Tomislav Hengl References e Baddeley A 2008 Analysing spatial point patterns in R Technical report CSIRO Australia Version 4 e Royle J A Chandler R B Yackulic C and J D Nichols 2012 Likelihood analysis of species occurrence probability from presence only data for modelling species distributions Methods in Ecology and Evolution See Also maxlike package spatstat package Examples library plotKML library maxlike library spatstat library maptools data eberg data eberg_grid existing sampling plan sel lt runif nrow eberg lt 2 eberg xy lt eberg sel c X Y coordinates eberg xy lt X Y proj4string eberg xy lt CRS init epsg 31467 Covariates gridded eberg_grid lt xty proj4string eberg_grid lt CRS tinit epsg 31467 convert to continuous independent covariates formulaString lt PRMGEO6 DEMSRT6 TWISRT6 TIRAST6 eberg_spc lt spc eberg_grid formulaString summary methods 75 derive occurrence probability covs lt eberg_spc predicted 1 8 iprob lt spsample prob eberg xy covs Note obvious omission areas hist iprobL 1 dataL 1 compare with random sampling rnd lt spsample eberg_grid type random n length iprob observations iprob2 lt spsample prob rnd covs compare the two par mfrow c 1 2 plot raster iprob 11 1 zlim c 0 1 col
48. e g c gstat Then add the program to your path see environmental variable under Windows gt Control panel gt System gt Advanced gt Environmental variables or copy the exe program directly to some windows system directory makeGstatCmd 47 Note The advantage of using gstat exe is that it loads large grids much faster to memory than if you use estat in R hence it is potentially more suited for computing with large grids The draw back is that you can only pass simple linear regression models to gstat exe The stand alone gstat is not maintained by the author of gstat any more Author s Tomislav Hengl References e Bivand R S Pebesma E J and G mez Rubio V 2008 Applied Spatial Data Analysis with R Springer 378 p e Pebesma E 2003 Gstat user s manual Dept of Physical Geography Utrecht University p 100 www gstat org See Also write data fit gstatModel gstat krige Examples Not run library sp library gstat Meuse data demo meuse echo FALSE fit a model omm lt fit gstatModel observations meuse formulaString om dist family gaussian log covariates meuse grid str omm vgmModel write the regression matrix to GeoEAS meuse log_om lt log1p meuse om write data obj meuse covariates meuse grid dist outfile meuse eas methodid log_om writeGDAL meuse grid dist dist rst drivername RST mvFlag 99999 gt makeGstatCmd log_o
49. e to be fitted Profiles with 1 horizon are accepted and processed as per output requirements but no spline is fitted as such Only positive numbers for upper and lower depths can be accepted It is assumed that soil variables collected per horizon refer to block support i e they represent averaged values for the whole horizon This operation can be time consuming for large data sets 52 mpspline Author s Brendan Malone and Tomislav Hengl References e Bishop T F A McBratney A B Laslett G M 1999 Modelling soil attribute depth func tions with equal area quadratic smoothing splines Geoderma 91 1 2 27 45 e Malone B P McBratney A B Minasny B Laslett G M 2009 Mapping continuous depth functions of soil carbon storage and available water capacity Geoderma 154 1 2 138 152 See Also stats spline Examples library aqp library plyr library sp sample profile from Nigeria lon 3 90 lat 7 50 id ISRIC NGQQ17 FAO1988 LXp top c 0 18 36 65 87 127 bottom c 18 36 65 87 127 181 ORCDRC c 18 4 4 4 3 6 3 6 3 2 1 2 munsell c 7 5YR3 2 7 5YR4 4 2 5YR5 6 5YR5 8 5YR5 4 10YR7 3 prepare a SoilProfileCollection prof1 lt join data frame id top bottom ORCDRC munsell data frame id lon lat FAO1988 type inner depths prof1 lt id top bottom site prof1 lt lon lat FAO1988 coordinates prof1 lt lon lat
50. easurementError estimated measurement error for that specific observation The column names in the data slot largely reflect the KML schema elements Geosamples are interoperable with the OGC Observations and measurements specifications but do not necessarily contain all required fields i e there is no validity check for the OGC specifications Geosamples class can be used to store and manipulate geological hydrological geochemical biodiversity soil science and similar field samples near or below land surface Geological and soil samples can also be registered via the geosamples org in which case the observationid will correspond to the unique sample identifier sampleid column allows linking geosamples to the original ID s Methods show signature obj geosamples summarize object by listing methods total number of observations total area covered etc subset signature obj geosamples subset to a single variable type returns a data frame over signature x SpatialPixelsDataFrame or RasterStack y geosamples overlay geosamples and spatial pixels stack signature x geosamples stacks all observed values into a single table using reshape function write data signature obj geosamples write geosamples to an external format e g GeoEAS Author s Tomislav Hengl References e Dyson E 2003 Online Registries The DNS and Beyond Edventure Vol 21 8 e International Geo
51. edictions by regression e g GLM and interpolation of residuals kriging via the Regression Kriging RK or Kriging with External Drift KED also known as Universal Kriging framework Usage S4 method for signature gstatModel predict object predictionLocations nmin 10 nmax 30 debug level 1 predict method c RK KED nfold 5 verbose FALSE nsim 0 mask extra TRUE block zmin Inf zmax Inf subsample length object sp coarsening factor 1 vgmmodel object vgmModel subset observations is na object sp coords 1 betas c 0 1 extend 5 S4 method for signature list predict object predictionLocations nmin 10 nmax 30 debug level 1 predict method c RK KED nfold 5 verbose FALSE nsim 0 mask extra TRUE block zmin Inf zmax Inf subsample length object sp Arguments object object of type gstatModel predict gstatModel method 55 predictionLocations object of type SpatialPixelsDataFrame prediction locations must contain all covariates from the model nmin integer minimum number of nearest observations sent to gstat krige nmax integer maximum number of nearest observations sent to gstat krige debug level integer default debug level mode sent to gstat krige predict method character mathematical implementation of the gstat krige interpolation method with covariates Regression Kriging RK or Kriging with E
52. eger gstat s setting to hide the progress output nfold integer number of folds for cross validation other optional arguments that can be passed to fit gstatModel Note Vector of sampling intensities if not provided will be estimated as sequence of 10 numbers on square root scale where N minimum is determined as 20 number of covariates times 10 and N maximum is the total number of observations Where no model can be fitted function returns an empty set This function can be time consuming for large data sets and is hence recommended only for testing a mapping algorithm using sample data Author s Tomislav Hengl Gerard B M Heuvelink See Also fit gstatModel gstatModel class Examples 2D model library sp load the Meuse data set demo meuse echo FALSE model diagnostics tl lt test gstatModel meuse om dist ffreq meuse grid fit family gaussian log Ns c 80 155 t1 1 tile Tiles subsets or clips a spatial object to regular blocks Description Tiles objects of class Spatial or RasterLayerx into regular blocks tile 79 Usage S4 method for signature SpatialPointsDataFrame tile x y block x S4 method for signature SpatialPixelsDataFrame tile x y block x S4 method for signature SpatialPolygonsDataFrame tile x y block x tmp file TRUE program show output on console FALSE S4 method for signature SpatialLinesDataFrame
53. eric variable or a multinomial logistic regression model via the spmultinom function factor type variable and generates predictions Usage S4 method for signature SpatialPointsDataFrame SpatialPixelsDataFrame autopredict target covariates auto plot TRUE Arguments target object of class SpatialPointsDataFrame containing observations of the tar get variable AWCPTF 9 covariates object of class SpatialPixelsDataFrame spatial covariates auto plot logical specifies whether to immediately plot the data via the plotKML function other optional arguments that can be passed to Ffit gstatModel or spmultinom Author s Tomislav Heng See Also fit gstatModel spmultinom Examples Ebergotzen data library sp library gstat library randomForest library plotKML load input data data eberg eberg lt eberg runif nrow eberg lt 1 coordinates eberg lt X Y proj4string eberg lt CRS t init epsg 31467 data eberg_grid gridded eberg_grid lt xty proj4string eberg_grid lt CRS init epsg 31467 predict sand content SNDMHT_A lt autopredict eberg SNDMHT_A eberg_grid auto plot FALSE plot SNDMHT_A predict soil types soiltype lt autopredict ebergL soiltype eberg_grid auto plot FALSE spplot soiltype predicted AWCPTF Available soil water capacity Description Derive available soil water capacity in cubic meter per c
54. escription Tests predictability of a regression kriging model on a sample data set Automates model fitting cross validation and prediction and prints out 1 RMSE at validation points under different sam pling intensities 2 number of predictions per second and 3 number of prediction failures failure predictions where cross validation z scores exceed value of 1 5 or cross validation residuals exceed three standard deviations of the observed values Usage S4 method for signature HH SpatialPointsDataFrame formula SpatialPixelsDataFrame test gstatModel observations formulaString covariates Ns predictionLocations save predictions TRUE debug level 0 nfold 5 S4 method for signature geosamples formula SpatialPixelsDataFrame test gstatModel observations formulaString covariates Ns predictionLocations save predictions TRUE debug level 0 nfold 5 Arguments observations object of type SpatialPointsDataFrame or geosamples class formulaString object of type formula or a list of formulas covariates object of type SpatialPixelsDataFrame or list of grids Ns vector list of sampling intensities maximum should not exceed the total num ber of samples 78 tile predictionLocations object of class SpatialPixelsDataFrame if not specified then passes the object covariates save predictions logical indicates whether the prediction results should also be saved debug level int
55. et is a data frame with the following columns mask percent land mask value soilmask boolean soil mask value watermask percent water mask value Lon_it indication of the longitude quadrant W or E Lat_it indication of the latitude quadrant S or N cell_id cell id code e g W79_N83 x longitudes of the center of the grid nodes y latitudes of the center of the grid nodes landmask20km data set is an object of class SpatialGridDataFrame with the following columns mask percent land mask value suborder factor Keys to Soil Taxonomy suborder class e g Histels Udolls Calcids soilmask factor global soil mask map based on the land cover classes see SMKISR3 Note The land mask has been generated from the layer GSHHS_shp h GSHHS_h_L1 shp level 1 bound aries References e Carroll M Townshend J DiMiceli C Noojipady P Sohlberg R 2009 A New Global Raster Water Mask at 250 Meter Resolution International Journal of Digital Earth 2 4 Global Self consistent Hierarchical High resolution Shoreline Database http en wikipedia org wiki GSHHS USDA NRCS Global Soil Regions Map http www nrcs usda gov Savtchenko A D Ouzounov S Ahmad J Acker G Leptoukh J Koziana and D Nickless 2004 Terra and Aqua MODIS products available from NASA GES DAAC Advances in Space Research 34 4 710 714 Wessel P Smith W H F 1996 A Global Self consistent Hierarchical High resolution Shoreline Datab
56. euse grid plot om rk Compare with quantregForest package omm lt fit gstatModel meuse om dist ffreq meuse grid method quantregForest 24 fit gstatModel methods Not run om rk lt predict omm meuse grid nfold 0 plot om rk plot the results in Google Earth plotKML om rk End Not run binary variable 0 1 meuse soil 1 lt as numeric I meuse soil 1 som lt fit gstatModel meuse soil 1 dist ffreq meuse grid fit family binomial logit summary som regModel som rk lt predict som meuse grid plot som rk Not run plot the results in Google Earth plotKML som rk End Not run 3D model library plotKML data eberg list columns of interest s lst lt c ID soiltype TAXGRSC X Y h 1st lt c UHDICM LHDICM SNDMHT SLTMHT CLYMHT sel lt runif nrow eberg lt 05 get sites table sites lt eberg sel s lst get horizons table horizons lt getHorizons eberg sel idcol ID sel h 1st create object of type SoilProfileCollection eberg spc lt join horizons sites type inner depths eberg spc lt ID UHDICM LHDICM site eberg spc lt as formula paste paste s lst 1 collapse sep coordinates eberg spc lt X Y proj4string eberg spc lt CRS init epsg 31467 convert to geosamples eberg geo lt as geosamples eberg spc
57. f coarse fragments gt 2 mm BLD numeric bulk density in kg per cubic meter for the horizon solum ORCDRC numeric soil organic carbon concentration in permille or g per kg 42 ECN CEC ENA EACKCL EXB PHIHOX CRB GYP tetaS fix values thresholds LRI numeric electrical conductivity in dS per m numeric Cation Exchange Capacity in cmol per kilogram numeric exchangable Na in cmol per kilogram numeric exchangable acidity in cmol per kilogram numeric exchangable bases in cmol per kilogram numeric soil pH in water suspension numeric CaCO3 carbonates in g per kg numeric CaSO4 gypsum in g per kg numeric volumetric percentage optional if not provided it will be derived us ing the AWCPTF Pedo Transfer Function logical specifies whether to correct values of textures and bulk density to avoid creating nonsensical values data frame optional table containing threshold values for CRFVOL tetaS volumetric percentage BLD f clay adjusted BLD SNDPPT CLY d dif ference in clay between horizons SND d difference in sand between hori zons PHIHOX L lower limits for pH PHIHOX H upper limits for pH ECN ENA TT exchangable saturated Na ENA EACKCL f exchangable saturated acidity CRB carbonates and GYP gypsum print thresholds Value logical specifies whether to attach the threshold values to the output object Returns a vector with
58. for more additional arguments see dismo predict Value Returns an object of type SpatialMaxEntOutput with the following slots sciname usually latin genus and species name occurrences occurrence only records TimeSpan begin begin of sampling TimeSpan end end of sampling maxent object of class MaxEnt produced as an output of the dismo maxent function sp domain assumed spatial domain and predicted results of prediction produced using the MaxEnt software Note MaxEnt is one of the standard tools used in ecology for Niche analysis and species distribution modelling What makes it especially robust is the fact that it can take both continuous and factor data as inputs and has no requirements considering the distribution of covariates Phillips et al 2006 In the example below I use MaxEnt to analyze representation of feature space by a given soil sampling pattern i e mis representation or the sampling preference by the surveyors For more information on how to install MaxEnt and use it in R see dismo package documentation MaxEnt 49 Author s Tomislav Hengl References e Phillips S J Anderson R P Schapire R E 2006 Maximum entropy modeling of species geographic distributions Ecological Modelling 190 231 259 e MaxEnt software http www cs princeton edu schapire maxent e Dismo package http CRAN R project org package dismo See Also dismo maxent plotKML SpatialMaxEn
59. he pre compiled binary gstat exe Usage makeGstatCmd formString vgmModel outfile easfile nsim 0 nmin 20 nmax 40 radius zmap 0 predictions var1 pred hdr variances var1 svar hdr xcol 1 ycol 2 zcol 3 vcol 4 Xcols Arguments formString vgmModel outfile easfile nsim nmin nmax radius zmap predictions variances xcol ycol zcol vcol Xcols Details object of class formula regression model object of class vgmmodel or data frame character output file for the command script character file name for the GeoEAS file with observed values integer number of simulations integer smallest number of points in the search radius see gstat user s manual integer largest number of points in the search radius see gstat user s manual numeric search radius see gstat user s manual numeric fixed value for the 3D dimension in the case of 3D kriging character output file name for predictions character output file name for kriging variances integer position of the x column in the GeoEAS file integer position of the y column in the GeoEAS file integer position of the z column in the GeoEAS file integer position of the target variable column in the GeoEAS file integer column numbers for the list of covariates To run the script under Windows OS you need to obtain the pre compiled gstat exe program from the www gstat org website and put it in some directory
60. hod getID 32 getProcess WPS class 83 getProcess WPS method WPS class 83 regModel formula data frame SpatialPixelsMaspFreee chhr tter method fit regModel methods 25 mpspline SoilProfileCollection method regModel methods 25 mpspline 51 vgmModel 22 munsell vgmModel fit vgmModel methods 27 FAO SoilProfileCollection class vgmModel formula data frame SpatialPixelsDataFrame9method fit vgmModel methods 27 vgmModel methods 27 OCSKGM 53 over RasterStack geosamples method geosamples class 30 over REST SoilGrids SpatialPoints method REST SoilGrids class 56 over SpatialPixelsDataFrame geosamples method geosamples class 30 over WPS SpatialPoints method WPS class 83 getSpatialTiles 33 79 getSpatialTiles ANY method getSpatialTiles Spatial method GlobalSoilMap GlobalSoilMap class 34 GlobalSoilMap class 34 GSIF env 35 GSIF opts GSIF env 35 gstatModel class 36 isis 38 landmask 32 39 83 plot gstatModel ANY method gstatModel class 36 plot gstatModel gstatModel class 36 predict gstatModel method predict gstatModel method 54 predict list method predict gstatModel method 54 predict MaxEnt method MaxEnt 48 predict gstatModel 37 predict gstatModel predict gstatModel method 54 predict gstatModel method 54 predict gstatModelList getSpatialTiles 33 getSpatialTiles 33 landmask20km landmask 39 LRI 18 41 make 3Dgrid
61. icubic splines for predictions i e nearest neighbor algorithm for simulations Weigths can be passed via the RMSE 1 argument otherwise they will be estimated from validation slot if objects are of the class SpatialPredictions Usage HH S4 method for signature SpatialPredictions SpatialPredictions merge x y RMSE 1 NULL silent TRUE Arguments D object of class SpatialPredictions or RasterBrickSimulations y object of class SpatialPredictions or RasterBrickSimulations additional objects of class SpatialPredictions or RasterBrickSimulations RMSE 1 numeric list of mean prediction errors for each object these are used as weights during the averaging silent logical specifies whether to print out the progress and used RMSE s Value Returns an object of type SpatialPixelsDataFrame or RasterBrickSimulations that con tains only the merged values Note Merging of multiple spatial predictions using weighted averaging is a heuristic approach to map ping This method assumes that the predictions are completely independent independent covari ates independent models but this not might be the case and hence the merged predictions will be sub optimal Merging multiple predictions is however attractive for situations where the predictions do not have the same extent so that spatial predictions with larger coverage can be used to fill in the gaps in locally produced predictions Author s
62. ifies whether to derive propagated error Value Soil organic carbon stock in kilograms per square meter To convert to tonnes per hectar multiply by 10 Note Propagated error attached as an attribute is estimated using the Taylor Series Method and shows only an approximate estimate A more robust way to estimate the propagated uncertainty would be to use geo statistical simulations See Heuvelink 1998 for more info Author s Tomislav Hengl Niels Batjes and Gerard Heuvelink References e Heuvelink G B 1998 Error propagation in environmental modelling with GIS CRC Press 150 p e Nelson D W and L E Sommers 1982 Total carbon organic carbon and organic matter p 539 580 In A L Page et al ed Methods of soil Analysis Part 2 2nd ed Agron Monogr 9 ASA and SSSA Madison WI 54 predict gstatModel method Examples Area lt 1E4 1 ha HSIZE lt 30 0 30 cm ORCDRC lt 50 5 ORCDRC sd lt 10 1 BLD lt 1500 1 5 tonnes per cubic meter BLD sd lt 100 1 tonnes per cubic meter CRFVOL lt 10 10 CRFVOL sd lt 5 5 x lt OCSKGM ORCDRC BLD CRFVOL HSIZE ORCDRC sd BLD sd CRFVOL sd x 20 25 4 41 kg m 2 in tonnes per ha x 1 Area 1000 predict gstatModel method Predict from an object of class gstatModel Description Predicts from an object of class gstatModel class using new prediction locations The function combines pr
63. iogram model and observation locations of sampled values used to fit the model Details Any model passed to the regModel slot must come with generic functions such as residuals fitted values summary formula and predict Slots regModel object of class ANY output of fitting a generalized linear model GLM or any similar regression model svgmModel object of class data frame sample variogram with semivariances and distances vgmModel object of class data frame the fitted gstat variogram model parameters containing variogram model nugget sill range and the five anisotropy parameters sp object of class SpatialPointsDataFrame observation locations gstatModel class 37 Methods predict signature obj gstatModel makes predictions for a set of given predictionLo cations gridded maps at block support corresponding to the cellsize slot in the object of class SpatialPixelsDataFrame to produce predictions at point support submit the predictionLocations as SpatialPointsDataFrame validate signature obj gstatModel runs n fold cross validation of the existing gstat Model it re fits the model using existing formula string and model data then estimates the mapping error at validation locations plot signature obj gstatModel plots goodness of fit and variogram model Note SpatialPredictions saves results of predictions for a single target variable which can be of type
64. l 1989 The edgeroi contains only a subset of the original NatSoil records Observed soil classes for TAXGAUC are alphabetically Alluvial soil A Brown clay BC Black earth BE Earthy sand ES Grey clay GC 16 edgeroi Grey earth GE No suitable group NSG Prairie soil PS Rendzina R Red brown earth RBE Red clay RC Red earth RE Red podzolic soil RP Solodic soil SC Soloth SH Solonchak SK Siliceous sand SS and Solonetz SZ Note The Landsat images and SRTM DEM have been obtained from the Global Land Cover Facility Scanned geology map paper sheets has been obtained from the Geoscience Australia then georef erenced and rasterized to 250 m resolution The land use map has been obtained from the Australian Collaborative Land Use and Management program The Radiometric Map of Australia grids has been downloaded using the Geophysical Archive Data Delivery System GADDS on the Australian Government s Geoscience Portal Mitny et al 2009 Listed gridded layers follow a standard naming convention used by WorlGrids org the standard 8 3 filename convention with at most eight characters first three letter are used for the variable type e g DEM digital elevation model the next three letters represent the data source or collection method e g SRT SRTM mission the 6th character is the effective scale e g 5 indicates the 5th standard scale i e 1 600
65. le horizons object of class data frame table containing observations at different depths site object of class data frame table containing observations at site locations sp object of class SpatialPoints locations of profiles diagnostic object of class data frame table containing diagnostic properties Data of class FAO SoilProfileCollection must satisfy all of the following requirements class validity e All variable names must be registered in the Global Soil Data Registry 20 FAO SoilProfileCollection class e All variable domains must correspond to the FAO Guidelines 2006 or later for soil descrip tion or similar e All values must pass the validity checks i e numeric values must be within physical limits defined in the SoilGrids Global Soil Data Registry Author s Tomislav Hengl References e Beaudette D E Roudier P amp O Geen A T 2013 Algorithms for quantitative pedology A toolkit for soil scientists Computers amp Geosciences 52 258 268 e FAO 2006 Guidelines for Soil Description Food and Agriculture Organization of the United Nations 4th Ed See Also SoilGrids class SpatialComponents class geosamples class Examples library aqp library sp LONWGS84 LATWGS84 UHDICM LHDICM SOURCEID SOURCEDB SPDFAO TEXMHT DCOMNS 3 90 7 50 30 ISRIC NG0017 AFSP DB bee Ba scl 7 5YR_3_2 sp1 lt new FAO SoilProfileCo
66. llection depthcols c UHDICM LHDICM metadata soil vars horizons data frame SOURCEID UHDICM LHDICM TEXMHT site data frame SOURCEID SPDFAO SOURCEDB sp SpatialPoints data frame LONWGS84 LATWGS84 proj4string CRS proj longlat datum WGS84 str sp1 DCOMNS fit gstatModel methods 21 fit gstatModel methods Methods to fit a regression kriging model Description Tries to automatically fit a 2D or 3D regression kriging model for a given set of points object of type SpatialPointsDataFrame or geosamples and covariates object of type SpatialPixelsDataFrame It first fits a regression model e g Generalized Linear Model regression tree random forest model or similar following the formulaString then fits variogram for residuals usign the fit variogram method from the gstat package Creates an output object of class gstatModel class Usage S4 method for signature HH SpatialPointsDataFrame formula SpatialPixelsDataFrame fit gstatModel observations formulaString covariates method list GLM rpart randomForest quantregForest dimensions list 2D 3D 2D T 3D T fit family gaussian stepwise TRUE vgmFun Exp subsample 5000 subsample reg 10000 S4 method for signature geosamples formula SpatialPixelsDataFrame fit gstatModel observations formulaString covariates method list GLM rpart randomFores
67. load the 250 m grids con lt url http gsif isric org lib exe fetch php media edgeroi grids rda load con str edgeroi grids gridded edgeroi grids lt xty proj4string edgeroi grids lt CRS t init epsg 28355 spplot edgeroi grids 1 sp layout pnts load the 100 m grids con2 lt url http gsif isric org lib exe fetch php media edgeroi grids10Q rda load con2 str edgeroi grids100 gridded edgeroi grids100 lt xty proj4string edgeroi grids100 lt CRS init epsg 28355 spplot edgeroi grids100 TIILANG sp layout pnts End Not run ERDICM Effective Rooting Zone depth Description Derive Effective Rooting Zone depth i e an effective depth suitable for plant growth Usually minimum depth of soil out of three standard rooting depths limiting soil properties depth to water stagnating layer and depth to bedrock Usage ERDICM UHDICM LHDICM minimum LRI DRAINFAO BDRICM threshold LRI 20 srd 150 drain depths smooth LRI TRUE Arguments UHDICM numeric upper horizon depth in cm LHDICM numeric lower horizon depth in cm minimum LRI numeric minimum Limiting Rootability index DRAINFAO factor FAO drainage class eg V P I M NW en E BDRICM numeric depth to bedrock in cm threshold LRI numeric treshold index for LRI srd numeric maximum depth of interest drain depths data frame estimate effective rooting depth per drainage class DRAINFAO smooth LRI
68. logical specify whether to smooth LRI values using splines 18 extract Value Returns a vector of effective rooting depth in cm Author s Johan Leenaars Maria Ruiperez Gonzalez and Tomislav Hengl See Also LRI extract Extracts values at points from a list of files Description Overlays and extracts values at points from a list of raster layers defined as file names e g Geo Tiffs Extends the extract function from the raster package Especially suitable for extracting values of a large list of rasters that have not been organized into a mosaick a virtual stack for example a list of Landsat scenes Usage S4 method for signature SpatialPoints character extract x y path ID SOURCEID method simple is pattern FALSE force projection TRUE NAflag show progress TRUE isFactor FALSE S4 method for signature SpatialPointsDataFrame character extract x y path ID SOURCEID method simple is pattern FALSE force projection TRUE NAflag show progress TRUE isFactor FALSE Arguments D object of class SpatialPointsx y character list of files that can be read using the raster function path optional working directory where the files are stored ID character column name for the unique identifier if object is of class SpatialPoints SOURCEID column is automatically generated method character resampling method see raster e
69. m 11 describe WPS class 83 describe WPS method WPS class 83 edgeroi 14 ERDICM 17 43 extract 18 extract SpatialPoints character method extract 18 extract SpatialPointsDataFrame character method extract 18 extract list extract 18 FAO SoilProfileCollection 8 FAO SoilProfileCollection FAO SoilProfileCollection class 19 FAO SoilProfileCollection class 19 fit gstatModel 8 9 26 28 47 56 78 fit gstatModel fit gstatModel methods 21 86 fit fit Fit INDEX gstatModel geosamples formula list methodmake 3Dgrid SpatialPixelsDataFrame method fit gstatModel methods 21 make 3Dgrid 43 gstatModel geosamples formula SpatialPixehskeGstabtiedm thod fit gstatModel methods 21 makeSAGAlegend soil legends 59 gstatModel geosamples list list method makeTiles getSpatialTiles 33 fit gstatModel methods 21 MaxEnt 48 fit gstatModel SpatialPointsDataFrame Formula St adobe xShsdadbPrantbshetsbdame method fit gstatModel methods 21 MaxEnt 48 fit gstatModel method merge 50 fit gstatModel methods 21 merge RasterBrickSimulations RasterBrickSimulations method fit gstatModel methods 21 merge 50 fit regModel 22 28 merge SpatialPredictions SpatialPredictions method fit regModel fit regModel methods 25 merge 50 fit fit fit fit fit fit geochm 28 geosamples class 30 getHorizons as data frame 5 getID 32 getID SpatialPolygons met
70. m dist vgmModel omm vgmModel outfile meuse_om_sims cmd easfile meuse eas nsim 50 nmin 20 nmax 40 radius 1500 compare the processing times system time system gstat meuse_om_sims cmd vemModel omm vgmModel class vgmModel lt c variogramModel data frame system time om rk lt krige log_om dist meuse is na meuse log_om 1 meuse grid nmin 20 nmax 40 model vgmModel nsim 50 End Not run 48 MaxEnt MaxEnt Prediction and cross validation using the Maximum Entropy Description Runs MaxEnt algorithm on a set of observations ppp class from the spatstat package and envi ronmental covariates of SpatialPixelsDataFrame class and returns predicted probability of occurrence and cross validation of models with presence absence data Usage S4 method for signature ppp SpatialPixelsDataFrame MaxEnt occurrences covariates nfold 5 Npoints 1000 sciname as character NA period c Sys Date 1 Sys Date Arguments occurrences object of type ppp occurrences covariates object of type SpatialPixelsData list of covariate layers nfold object of type integer number of folds used for cross validation Npoints object of type integer number of points used for cross validation sciname object of type character usually species latin name it can also be a sur veyor s team name or a sampling design period object of type Date sampling period
71. nts to the output object Returns a data frame with the following columns e AWCh1 available soil water capacity volumetric fraction for h1 e AWCh2 available soil water capacity volumetric fraction for h2 e AWCh3 available soil water capacity volumetric fraction for h3 e WWP available soil water capacity volumetric fraction until wilting point e tetaS saturated water content Note Pedotransfer coefficients PTF coef developed by Hodnett and Tomasella 2002 fix values will correct sand silt and clay fractions so they sum up to 100 and will replace bulk density values using global minimum maximum values Author s Johan Leenaars Maria Ruiperez Gonzalez and Tomislav Hengl cookfarm 11 References e Hodnett M G amp Tomasella J 2002 Marked differences between van Genuchten soil water retention parameters for temperate and tropical soils a new water retention pedo transfer functions developed for tropical soils Geoderma 108 3 155 180 e Wosten J H M Verzandvoort S J E Leenaars J G B Hoogland T amp Wesseling J G 2013 Soil hydraulic information for river basin studies in semi arid regions Geoderma 195 79 86 Examples SNDPPT 30 SLTPPT 25 CLYPPT 48 ORCDRC 23 BLD 1200 CEC 12 PHIHOX 6 4 x lt AWCPTF SNDPPT SLTPPT CLYPPT ORCDRC BLD CEC PHIHOX str x attr x coef predict AWC for AfSP DB profile data afsp names afs
72. numeric or factor Multiple variables can be combined into a list When using nsim argument with the predict method the output result will be of type plotKML RasterBrickSimulations class i e N number of equiprobable realizations To generate an object of type plotKML SpatialPredictions class set nsim 0 Author s Tomislav Hengl and Gerard B M Heuvelink See Also predict gstatModel test gstatModel plotKML SpatialPredictions class plotKML RasterBrickSimulation gstat gstat stats glm Examples load observations library plotKML library sp demo meuse echo FALSE data meuse coordinates meuse lt x ty proj4string meuse lt CRS init epsg 28992 load grids data meuse grid coordinates meuse grid lt x y gridded meuse grid lt TRUE proj4string meuse grid lt CRS init epsg 28992 38 isis fit a model omm lt fit gstatModel meuse om dist ffreq fit family gaussian link log meuse grid plot omm produce SpatialPredictions om rk lt predict omm predictionLocations meuse grid plot om rk run a proper cross validation rk cv lt validate omm RMSE sqrt mean rk cv validation var1 pred rk cv validation observed 2 isis ISRIC Soil Information System Description ISRIC s collection of global soil monoliths that represent the main soil reference groups of the World Reference Base for Soil Resources WRB Includes
73. ogram model cutoff numeric distance up to which point pairs are included in semivariance estimates width numeric sample variogram bin width cressie logical specifies whether to use cressie robust estimator other optional arguments that can be passed to gstat fit variogram Details It will try to fit a variogram to multidimensional data If the data set is large this process can be time consuming hence one way to speed up fitting is to subset the regression matrix using the subsample argument 1 e randomly subset observations Author s Tomislav Heng See Also fit regModel fit gstatModel gstat fit variogram Examples library sp library gstat fit variogram to the Meuse data demo meuse echo FALSE produce a regression matrix ov lt over meuse meuse grid ov lt cbind data frame meuse om ov fit a model v lt fit vgmModel om 1 rmatrix ov meuse grid dimensions 2D plot variogram om 1 meuse is na meuse om v vgm geochm NGS database samples for Indiana State Description A subset of the National Geochemical Survey NGS samples covering the Indiana and Illinois State Contains a total of 2681 point samples Usage data geochm geochm 29 Format Data frame contains the following columns REC_NO factor unique record identifier DATASET factor abbreviated dataset group e g AK MI TYPEDESC factor abbreviated description of sample type stream
74. or horizon designation primary letter UHDICM numeric lower horizon depth from the surface in cm LHDICM numeric upper horizon depth from the surface in cm CLYPPT numeric weight percentage of the clay particles lt 0 0002 mm SNDPPT numeric weight percentage of the silt particles 0 0002 0 05 mm SLTPPT numeric weight percentage of the sand particles 0 05 2 mm PHIHO5 numeric pH index measured in water solution ph_h20 in the NSCD ORCDRC numeric soil organic carbon content in permille edgeroi 15 The edgeroi grids data frame contains a list of covariates at 250 m resolution DEMSRT5 numeric SRTM DEM TWISRT5 numeric SAGA Topographic Wetness Index based on the SRTM DEM PMTGEO5 factor parent material class based on the National Geological map at scale 1 250 000 sand with minor silty sand Qd alluvium gravel sand silt clay Qrs quartz sand stone obscured by quartenary sands Qrt Jp quartz sandstone obscured by talus material Qrt Rn basalt obscured by talus material Qrt Tv mottled clay silt sandstone and gravel Ts and basalt dolerite trachyte techenite Tv EV1MOD5 numeric first principal component of the MODIS EVI MOD 13Q1 time series data year 2011 EV2MOD5 numeric second principal component of the MODIS EVI MOD13Q1 time series data year 2011 EV3MOD5 numeric third principal component of the MODIS EVI MOD13Q1 time series data year 2011 x numeric x coordinate in
75. other optional arguments that can be passed to over soil legends 59 Value Returns a list of two objects 1 an object of type SpatialPoints or SpatialPointsDataFrame that contains a subset of the obj and 2 resulting grid Note Spatial points are overlayed with spatial grids with a specified cell size and then get a subset from each grid with a specified number at most If one grid has less points than the specified number all the points are taken If one grid has more points than the specified number only this number of points are taken by sample This function can be used when there are too much point observations to be handled especially for spatially clustered observations The total number of sampled points are determined by cell size and n together You will get fewer the sampled points when cell size is larger or and when n is smaller Similar sample sizes can be achieved by differen combination of cell size and n Author s Wei Shangguan Examples library sp data isis profs lt isis sites coordinates profs lt LONWGS84 LATWGS84 proj4string profs lt CRS proj longlat datum WGS84 sample SpatialPointsDataFrame bbox lt matrix c 180 90 180 90 nrow 2 profl lt sample grid profs cell size c 5 5 n 1 10 lt list sp points profs pch 1 col red 11 lt list sp points profl subset pch col black cex 1 2 spplot prof1 grid scales list d
76. p horizons profile of interest sel lt afsp horizons SOURCEID NG 28440_Z5 hor lt afsp horizons sel replace missing values BLDf lt ifelse is na hor BLD mean hor BLD na rm TRUE hor BLD hor lt cbind hor AWCPTF hor SNDPPT hor SLTPPT hor CLYPPT hor ORCDRC BLD BLDfx1000 hor CEC hor PHIHOX str hor cookfarm The Cook Agronomy Farm data set Description The R J Cook Agronomy Farm cookfarm is a Long Term Agroecosystem Research Site operated by Washington State University located near Pullman Washington USA Contains spatio temporal 3D T measurements of three soil properties and a number of spatial and temporal regression covariates Usage data cookfarm 12 cookfarm Format The cookfarm data set contains four data frames The readings data frame contains measurements of volumetric water content cubic m cubic m temperature degree C and bulk electrical conduc tivity dS m measured at 42 locations using 5TE sensors at five standard depths 0 3 0 6 0 9 1 2 1 5 m for the period 2011 01 01 to 2012 12 31 SOURCEID factor unique station ID Date date observation day PortxVW numeric volumetric water content measurements at five depths Port C numeric soil temperature measurements at five depths PortxEC numeric bulk electrical conductivity measurements at five depths The profiles data frame contains soil profile descriptions from 142 sites SOURCEID factor uniq
77. p1 lt data frame lon 15 lat 15 coordinates p1 lt lontlat proj4string p1 lt CRS proj longlat datum WGS84 p1 over glcesa3 wps p1 fetch grids and load the to R glcesa3 lt subset glcesa3 wps bbox matrix c 20 40 22 42 nrow 2 image glcesa3 End Not run Index Topic Classes FAO SoilProfileCollection class 19 geosamples class 30 GlobalSoilMap class 34 gstatModel class 36 REST SoilGrids class 56 SoilGrids class 62 SpatialComponents class 65 SpatialMemberships class 65 WPS class 83 Topic datasets afsp 3 cookfarm 11 edgeroi 14 geochm 28 isis 38 landmask 39 soil legends 59 USDA TT im 80 Topic methods as data frame 5 as geosamples 6 extract 18 fit gstatModel methods 21 getID 32 getSpatialTiles 33 make 3Dgrid 43 makeGstatCmd 46 MaxEnt 48 merge 50 mpspline 51 predict gstatModel method 54 sample grid 58 spc 66 spfkm 67 spmultinom 71 test gstatModel methods 77 tile 78 85 warp 82 Topic options GSIF env 35 afsp 3 as data frame 5 7 as data frame SoilProfileCollection method as data frame 5 as geosamples 5 6 31 as geosamples SoilProfileCollection method as geosamples 6 as geosamples SpatialPointsDataFrame method as geosamples 6 autopredict autopredict methods 8 autopredict SpatialPointsDataFrame SpatialPixelsDataFrame autopredict methods 8 autopredict methods 8 AWCPTF 9 43 cookfar
78. pond spring soil etc COLL_DATE integer sampling date LONGITUDE numeric longitude in decimal degrees NAD27 datum LATITIUDE numeric latitude in decimal degrees NAD27 datum DATUM factor geodetic datum if different from NAD83 RELIEF factor relief in drainage basin from which sample was collected FORMATION factor code or name of geologic formation in which sample area was located ROCK_TYPE factor rock type in area of sample collection e g carbonate SOIL_HORIZ factor soil horizon from which the sample was collected COLOR factor observed color of powdered sample during splitting MEDIUM factor sample medium rock sediment standard or unknown SOURCE factor geological source of the sample medium that was collected e g Beach AS_ICP40 numeric As ppm by Inductively Coupled Plasma Spectrometry ICP after acid disso lution CD_ICP4Q numeric Cd ppm CR_ICP4 numeric Cr ppm CU_ICP4Q numeric Cu ppm NI_ICP40 numeric Ni ppm ZN_ICP4 numeric Zn ppm AS_AA numeric As ppm by Hydride Atomic Absorption HG_AA numeric Hg ppm by Hydride Atomic Absorption PB_ICP40 numeric Pb ppm C_TOT numeric total carbon weight percentage by combustion C_ORG numeric organic carbon weight percentage as a difference between C_TOT and C_C03 C_C03 numeric carbonate carbon weight percentage by Coulometric Titration S_TOT numeric total sulfur weight percentage by combustion Note Negative values of the he
79. proj4string prof1 lt CRS proj longlat datum WGS84 fit a spline ORCDRC s lt mpspline prof1 var name ORCDRC str ORCDRC s Example with multiple soil profiles Make some fake but reasonable profiles rand prof lt ldply 1 20 random_profile n c 6 7 8 n_prop 1 method LPP promote to SPC and plot depths rand prof lt id top bottom plot rand prof color p1 fit MP spline by profile try m lt mpspline rand prof p1 gt OCSKGM 53 OCSKGM Soil organic carbon stock Description Derive soil organic carbon stock storage in kilograms per square meter and propagated un certainty for a given horizon solum depth and based on soil organic carbon concentration hori zon solum thickness bulk density and percentage of coarse fragments Usage OCSKGM ORCDRC BLD 1400 CRFVOL 0 HSIZE ORCDRC sd 10 BLD sd 100 CRFVOL sd 5 se prop TRUE Arguments ORCDRC numeric soil organic carbon concentration in permille or g kg BLD numeric bulk density in kg cubic meter for the horizon solum CRFVOL numeric percentage of coarse fragments above 2 mm in diameter in the sample HSIZE numeric thickness of the horizon solum in cm ORCDRC sd numeric standard error of estimating ORCDRC must be positive number BLD sd numeric standard error of estimating BLD must be positive number CRFVOL sd numeric standard error of estimating CRFVOL must be positive number se prop logical spec
80. psg 31467 prepare prediction locations for spline krige grd lt resample grid locations eberg SNDMHT_A t_cellsize 25 newdata eberg_grid25 optN 5 quant nndist 9 plot resampled grid plot raster grd density plot grd newlocs points eberg pch 19 col red cex 7 env lt rsaga env if exists env A env version 2 1 0 HH compare processing time system time SND sok lt spline krige locations ebergl SNDMHT_A t_cellsize 25 newdata eberg_grid25 newlocs grd newlocs model m nmax 30 gt system time SND ok lt krige SNDMHT_A 1 ebergL is na eberg SNDMHT_A newdata eberg_grid m debug level 1 nmax 30 system time SND ok25 lt krige SNDMHT_A 1 ebergL is na eberg SNDMHT_A newdata eberg_grid25 m debug level 1 nmax 30 HH compare outputs visually par mfrow c 1 3 plot raster SND sok 1 main spline krige 25 m plot raster SND ok25 1 main krige 25 m plot raster SND ok 1 main krige 100 m End Not run conclusion spline krige produces less artifacts and is at order of magnitude faster than simple krige spmultinom Multinomial logistic regression on spatial objects Description Runs the multinomial logistic regression via nnet multinom to produce spatial predictions of the target factor type variable It requires point locations of observed classes and a list of covariate layers provided as SpatialPixelsDataFrame class
81. raw TRUE col regions grey sp layout list 1 11 Subsampling ratio round length prof1 subset length profs 100 1 soil legends Standard color palettes for soil properties and classes Description Standard color palettes for soil properties and classes that can be used to display global soil data Usage data soil legends 60 soil legends Format Contains a list of color palettes data frames with class names break points and cumulative prob abilities for ORCDRC numeric soil organic carbon content in permille PHIHOX numeric pH index measured in water solution PHIKCL numeric pH index measured in KCI solution BLD numeric bulk density in kg per cubic meter CEC numeric Cation Exchange Capacity SNDPPT numeric weight percentage of the sand particles 0 05 2 mm SLTPPT numeric weight percentage of the silt particles 0 0002 0 05 mm CLYPPT numeric weight percentage of the clay particles lt 0 0002 mm CRFVOL numeric volumetric percentage of coarse fragments gt 2 mm TAXGWRB factor World Reference base groups TAXOUSDA factor Keys to Soil Taxonomy suborders Note Breaks for continuous soil properties were determined using the quantiles function and by visually inspecting the histograms to maximize the contrast in output maps Based on a compilation of global soil profile data http soilprofiles org Author s Tomislav Heng References e Global Soil Information Facilities ht
82. s list output objects from the stats prcomp process contains objects stdev rotation center and scale Author s Tomislav Heng See Also spc SpatialMemberships class A class for membership maps derived using the fkmeans classification Description A class containing a list of gridded maps and results of model fitting Slots predicted object of class SpatialPixelsDataFrame predicted values factor model object of class multinom output object from the nnet multinom method mu object of class SpatialPixelsDataFrame a list of predicted memberships class c object of class matrix class centres class sd object of class matrix class deviations confusion object of class matrix confusion matrix Author s Tomislav Heng See Also spfkm SpatialComponents class 66 spc spc Derive Spatial Predictive Components Description Derives Spatial Predictive Components for a given set of covariates It wraps the stats prcomp method and predicts a list principal components for an object of type SpatialPixelsDataFrame Usage S4 method for signature SpatialPixelsDataFrame formula spc obj formulaString scale TRUE silent FALSE HH S4 method for signature list list spc obj formulaString scale TRUE silent FALSE Arguments obj object of class SpatialPixelsDataFrame must contain at least two grids or a list of objec
83. signature x WPS lists parameters specific to some service identifier over signature x WPS y SpatialPoints overlays spatial points and the target grids defined via the WPS class point by point subset signature x WPS subsets a grid from server and loads it to R use bbox argument to specify the bounding box Note More examples of overlay subset and aggregation functions are available via WorldGrids org WPS WorldGrids org uses the PyWPS module on a Debian system with Webserver GDAL Python and Scipy The standard format for the gridded data on the WorldGrids org repository is GeoTiff Use of the bbox object to obtain grids that cover more than 30 percent of the global coverage is not recommended Consider instead downloading the compressed images directly from World Grids org Author s Tomislav Hengl 8 Hannes I Reuter References e PyWPS module http pywps wald intevation org e WorldGrids org http worldgrids org See Also landmask 84 WPS class Examples Not run library XML library sp URI http wps worldgrids org pywps cgi server lt list URI URI request execute version version 1 0 0 service name service wps identifier identifier sampler_local1pt_nogml glcesa3 wps lt new WPS server server inRastername glcesa3a show biocl15 wps prl lt getProcess glcesa3 wps pr1 7 describe glcesa3 wps identifier overlay
84. solution us ing spline interpolation This allows for speeding up the kriging with minimal loss in precision whilst reducing generation of artifacts Spline interpolation is implemented via the SAGA GIS v2 1 function Multilevel B Spline Interpolation using the default settings This function is es pecially suitable for producing predictions for large grids where the sampling locations show high spatial clustering It is NOT intended for predicting using point samples collected using sampling designs with constant spatial sampling intensity e g point samples collected using simple random sampling or grid sampling Author s Tomislav Heng References e Heng T 2006 Finding the right pixel size Computers and Geosciences 32 9 1283 1298 e SAGA GIS http sourceforge net projects saga gis e SpatStat package http www spatstat org Examples Not run library plotKML library spatstat library RSAGA library gstat library raster data eberg data eberg_grid data eberg_grid25 library sp spmultinom 71 coordinates eberg lt X Y proj4string eberg lt CRS t init epsg 31467 m lt vegm psill 320 model Exp range 1200 nugget 160 plot variogram SNDMHT_A 1 eberg is na eberg SNDMHT_A 1 m prediction locations gridded eberg_grid lt xty proj4string eberg_grid lt CRS init epsg 31467 gridded eberg_grid25 lt xty proj4string eberg_grid25 lt CRS init e
85. some 950 monoliths 785 with coordi nates from over 70 countries with detailed soil profile and environmental data Usage data isis Format The isis data set contains two data frames sites and horizons Sites table contains the following columns SOURCEID factor unique ISIS code LONWGS84 numeric longitude in decimal degrees on the WGS84 datum LATWGS84 numeric latitude in decimal degrees on the WGS84 datum TIMESTRR Date the date on which this particular soil was described or sampled TAXGWRB factor soil group based on the WRB classification system TAXNUSDA factor Keys to Soil Taxonomy taxon name e g Natraqualf BDRICM numeric depth to bedrock R horizon if observed SOURCEDB factor source data base Horizons table contains the following columns SOURCEID factor unique ISIS code UHDICM numeric upper horizon depth from the surface in cm LHDICM numeric lower horizon depth from the surface in cm CRFVOL numeric volume percentage of coarse fragments gt 2 mm PHIHOX numeric pH index measured in water solution landmask 39 PHIKCL numeric pH index measured in KCI solution ORCDRC numeric soil organic carbon content in permilles SNDPPT numeric weight percentage of the sand particles 0 05 2 mm SLTPPT numeric weight percentage of the silt particles 0 0002 0 05 mm CLYPPT numeric weight percentage of the clay particles lt 0 0002 mm CEC numeric Cation Exchange Capacity in cmol kg BLD bulk density
86. st and suited for large data sets function might not converge in some cases or result in artifacts If this happens try setting up the class centres and variances manually Author s Tomislav Hengl and Bas Kempen References e Burrough P A Gaans P F M and Van Hootsmans R 1997 Continuous classification in soil survey spatial correlation confusion and boundaries Geoderma 77 2 4 115 135 e Heng T Walvoort D J J Brown A 2004 A double continuous approach to visualisation and analysis of categorical maps Int Jou of Geographical Information Science 18 2 183 202 See Also spmultinom SpatialMemberships class nnet multinom Examples load data library plotKML library sp data eberg subset to 20 eberg lt eberg runif nrow eberg lt 2 data eberg_grid coordinates eberg lt X Y proj4string eberg lt CRS t init epsg 31467 gridded eberg_grid lt xty proj4string eberg_grid lt CRS init epsg 31467 derive soil predictive components eberg_spc lt spc eberg_grid PRMGEO6 DEMSRT6 TWISRT6 TIRAST6 predict memberships formulaString soiltype PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 eberg_sm lt spfkm formulaString eberg eberg_spc predicted Not run plot memberships pal seq 1 1 50 spline krige 69 spplot eberg_sm mu col regions grey rev pal predict soil properties using memberships glm formulaString as formula pa
87. ste SNDMHT_A paste names eberg_sm mu collapse 1 SNDMHT m2 lt fit gstatModel observations eberg glm formulaString covariates eberg_sm mu summary SNDMHT m2 regMode1 Coefficients correspond to the class centres End Not run spline krige Kriging combined with splines Description Combines kriging and spline interpolation to speed up the kriging with minimal loss in precision whilst reducing generation of artifacts Spline interpolation is implemented via the SAGA GIS function Multilevel B Spline Interpolation SAGA GIS needs to be installed separately Usage spline krige formula locations newdata newlocs NULL model te as vector newdata bbox file name silent FALSE t_cellsize newdata grid cellsize 1 optN 20 quant nndist 5 nmax 30 predictOnly FALSE resample TRUE saga env saga lib c grid_spline grid_tools saga module c 4 0 Arguments formula formula that defines the dependent variable as a linear model of independent variables usually in the form z 1 locations object of class SpatialPoints sampling locations newdata object of class SpatialPixels spatial domain of interest newlocs object of class Spatial Pointss prediction locations produced using the resample grid function if missing it will be generated using the resample grid function model variogram model of dependent variable or its residuals see gstat krige te numeric
88. string eberg lt CRS t init epsg 31467 gridded eberg_grid lt xty spsample prob 73 proj4string eberg_grid lt CRS init epsg 31467 tt derive soil predictive components eberg_spc lt spc eberg_grid PRMGEO6 DEMSRT6 TWISRT6 TIRAST6 predict memberships formulaString soiltype PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 eberg_sm lt spmultinom formulaString eberg eberg_spc predicted Not run plot memberships pal seq 1 1 50 spplot eberg_sm mu col regions pal image eberg_sm mu 1 col pal text eberg coords paste eberg soiltype cex 6 col black classes predicted Ls length levels eberg_sm predicted soiltype pnts list sp points eberg pch cex 6 col black spplot eberg_sm predicted col regions rainbow Ls rank runif Ls 1 sp layout pnts End Not run spsample prob Estimate occurrence probabilities of a sampling plan points Description Estimates occurrence probabilities as an average between the kernel density estimation spreading of points in geographical space and MaxLike analysis spreading of points in feature space The output iprob indicates whether the sampling plan has systematically missed some important locations features and can be used as an input for geostatistical modelling e g as weights for regression modeling Usage S4 method for signature SpatialPoints SpatialPixelsDataFrame spsample prob observations covaria
89. t quantregForest dimensions list 2D 3D 2D T 3D T fit family gaussian stepwise TRUE vemFun Exp subsample 5000 subsample reg 10000 S4 method for signature geosamples formula List fit gstatModel observations formulaString covariates method list GLM rpart randomForest quantregForest dimensions list 2D 3D 2D T 3D T fit family gaussian stepwise TRUE vgmFun Exp subsample 5000 subsample reg 10000 HH S4 method for signature geosamples list list fit gstatModel observations formulaString covariates method list GLM rpart randomForest quantregForest dimensions list 2D 3D 2D T 3D T fit family gaussian stepwise TRUE vgmFun Exp subsample 5000 subsample reg 10000 Arguments observations object of type SpatialPointsDataFrame or geosamples class formulaString object of type formula or a list of formulas covariates object of type SpatialPixelsDataFrame or list of grids 22 fit gstatModel methods method character family of methods considered e g GLM dimensions character 3D 2D 2D T 3D T models fit family character string defyning the GLM family for more info see stats glm stepwise specifies whether to run step wise regression on top of GLM to get an optimal subset of predictors vemFun variogram function E
90. t join data frame id top bottom ORCDRC data frame id lon lat time TAXNFAO8 type inner 8 autopredict methods depths prof1 lt id top bottom site prof1 lt lon lat time TAXNFAO8 amp coordinates prof1 lt lon lat time proj4string prof1 lt CRS proj longlat datum WGS84 add measurement errors attr prof1 horizons ORCDRC measurementError lt c 1 5 0 5 0 5 0 5 0 5 0 5 attr prof1 sp coords locationError lt 1500 add the metadata profl metadata lt data frame methodid description units detectionLimit convert to geosamples x lt as geosamples prof1 D print only the sampled values of ORCDRC ORCDRC lt subset x ORCDRC ORCDRCL c sampleid altitude observedValue convert object of type SpatialPointsDataFrame data meuse prepare columns names meuse which names meuse x longitude names meuse which names meuse y latitude meuse altitude 15 meuse time unclass as POSIXct 1992 01 01 coordinates meuse lt longitude latitude altitude time proj4string meuse lt CRS t init epsg 28992 library plotKML hm lt reproject meuse c zinc copper hm geo lt as geosamples hm hm geo autopredict methods Auto predict numeric or factor type variables Description Fits either geostatistical model via the fit gstatModel function in the case of num
91. tO0utput class Examples load data library plotKML library rJava library spatstat library maptools library dismo library rgdal data eberg data eberg_grid prepare data for spatial analysis eberg xy lt eberg runif nrow eberg lt 0 3 coordinates eberg xy lt X Y proj4string eberg xy lt CRS init epsg 31467 format gridded data gridded eberg_grid lt xty proj4string eberg_grid lt CRS init epsg 31467 convert to a ppp object eberg ppp lt as ppp eberg xy run MaxEnt analysis evaluates sampling bias or mis representation jar lt paste system file package dismo java maxent jar sep if file exists jar me eberg lt MaxEnt occurrences eberg ppp covariates eberg_grid NOTE MaxEnt can be time consuming plot the results par mfrow c 1 2 mar c 0 5 0 5 0 5 0 5 oma c 0 0 0 0 image as me eberg predicted SpatialPixelsDataFrame col rev heat colors 25 xlab ylab gt points me eberg occurrences pch cex 7 image me eberg sp domain col grey xlab ylab 50 merge merge Merge multiple predictions Description Merges objects of class SpatialPredictions or RasterBrickSimulations and produces average predictions where the two objects overlap spatially If the predictions are available at dif ferent resolutions then it downscales all other grids to the smallest grid cell size using b
92. tes quant nndist 95 n sigma Arguments observations object of class SpatialPoints sampling locations covariates object of class SpatialPixelsDataFrame list of covariates of interest quant nndist numeric threshold probability to determine the search radius sigma n sigma numeric size of sigma used for kernel density estimation optional other optional arguments that can be passed to function spatstat density Value Returns a list of objects where iprob SpatialPixelsDataFrame is the map showing the estimated occurrence probabilities 74 spsample prob Note Occurrence probabilities for geographical space are derived using kernel density estimator The sampling intensities are converted to probabilities by deviding the sampling intensity by the max imum sampling intensity for the study area Baddeley 2008 The occurrence probabilities for feature space are determined using MaxLike algorithm Royle et al 2012 The lower the average occurrence probability for the whole study area the lower the representation efficiency of a sam pling plan MaxLike function might fail to produce predictions e g if not at least one continuous covariate is provided and if the optim function is not able to find the global optima in which case an error message is generated Running Principal Component analysis i e standardizing the covariates prior to running spsample prob is thus highly recommended This function can be tim
93. the target variable at depth 80 cm 60 100 sd6 object of class SpatialPixelsDataFrame predictions and variances or number of real izations of the target variable at depth 150 cm 100 200 References e Hartemink A E Hempel J Lagacherie P McBratney A McKenzie N MacMillan R A amp Zhang G L 2010 GlobalSoilMap net A New Digital Soil Map of the World In Digital Soil Mapping pp 423 428 Springer Netherlands e Sanchez P A S Ahamed F Carre A E Hartemink J Hempel J Huising P Lagacherie A B McBratney N J McKenzie M L de Mendon a Santos et al 2009 Digital Soil Map of the World Science 325 5941 680 681 See Also SoilGrids class SpatialComponents class geosamples class GSIF env 35 GSIF env GSIF specific environmental variables paths Description Sets the environmental package specific parameters and settings URLs names default cell size and similar that can be later on passed to other functions Usage GSIF env wps server http wps worldgrids org ref_CRS proj longlat datum WGS84 NAflag 99999 license_url http creativecommons org licenses by 3 0 project_url http gsif r forge r project org stdepths c 2 5 10 22 5 45 80 150 100 stsize c 5 10 15 30 40 100 100 cellsize rev c 6 120 3 120 1 120 1 240 1 600 1 1200 1 3600 REST server http rest soilgrids org attributes c ORCD
94. tile x y block x tmp file TRUE program show output on console FALSE HH S4 method for signature RasterLayer tile x y block x tmp file TRUE program show output on console FALSE Arguments D object of class Spatialx Raster aver y list of SpatialPolygons if missing will be derived based on block x block x numeric size of block in meters or corresponding mapping units tmp file logical specifies whether to generate a temporary file program character location of the auxiliary program in the system show output on console logical specifies whether to print the progress of a function optional arguments that can be passed to the getSpatialTiles Details When working with objects of type SpatialLinesDataFrame SpatialPolygonsDataFrame and or RasterLayer the function looks for FWTools binary files ogr2ogr and warp FWTools is a Separate program and must be installed separately Value Returns a list of objects of the same class as the input object Author s Tomislav Hengl See Also getSpatialTiles 80 USDA TT im Examples spatial pixels library sp data meuse grid gridded meuse grid lt xty tl lt getSpatialTiles meuse grid block x 1000 image meuse grid lines as tl SpatialLines all at once pix 1st lt tile meuse grid block x 1000 Not run lines library plotKML data eberg_contours line 1st lt tile eberg_contours
95. tp gsif isric org Examples data soil legends pal lt soil legends ORCDRC COLOR names pal lt signif soil legends ORCDRC MAX soil legends ORCDRC MIN 2 3 pal data soil vars soil vars soil vars varname ORCDRC make SAGA GIS palette makeSAGAlegend x as factor names pal col_pal pal filename ORCDRC txt SoilGrid validator 61 SoilGrid validator Validate SoilGrid spatial predictions Description Validate SoilGrid spatial predictions i e soil property maps following the GSIF validation proto col Usage SoilGrid validator obj domain ground truth N sample 2000 xml file z lim md type INSPIRE test URL FALSE Arguments obj GDALobj object i e a pointer to a spatial layer of interest single slice domain GDALobj object i e a pointer to a spatial layer contain soil mask ground truth SpatialPointsDataFrame contains values of the target variable at exactly the same depth same support size sampled either using Simple Random Sam pling or regular sampling on a grid N sample integer random sampling size xml file character metadata file should have the same name as obj file z lim numeric upper and lower physical limits md type character metadata standard currently INSPIRE test URL logical specifies whether to validate XML schema test download times and proj4 string Value Returns a list with validation results Explanation of codes is available in the
96. ts of type SpatialPixelsDataFrame formulaString object of class formula or a list of formulas scale object of class logical specifies whether covariates need to be scaled silent object of class logical specifies whether to print the progress additional arguments that can be passed to stats prcomp Value spc returns an object of type SpatialComponents This is a list of grids with generic names PC1 PCp where p is the total number of input grids Note This method assumes that the input covariates are cross correlated and hence their overlap can be reduced The input variables are scaled by default and the missing values will be replaced with 0 values to reduce loss of data due to missing pixels This operation can be time consuming for large grids Author s Tomislav Heng See Also stats prcomp SpatialComponents class spfkm 67 Examples load data library plotKML library sp pal rev rainbow 65 1 48 data eberg_grid gridded eberg_grid lt xty proj4string eberg_grid lt CRS tinit epsg 31467 formulaString lt PRMGEO6 DEMSRT6 TWISRT6 TIRAST6 eberg_spc lt spc eberg_grid formulaString names eberg_spc predicted 11 components on the end Not run plot maps rd range eberg_spcepredictededata 1 na rm TRUE sq seq rd 1 rd 2 length out 48 spplot eberg_spc predicted 1 4 at sq col regions pal End Not run spfkm Supervised fuzzy k means on
97. ubic meter based on a Pedo Transfer Function developed using the Africa Soil Profile Database Hodnett and Tomasella 2002 W sten et al 2013 10 Usage AWCPTF AWCPTF SNDPPT SLTPPT CLYPPT ORCDRC BLD 1400 CEC PHIHOX h1 10 h2 20 h3 31 6 pwp 1585 PTF coef fix values TRUE print coef TRUE Arguments SNDPPT SLTPPT CLYPPT ORCDRC BLD CEC PHIHOX h1 h2 h3 pwp PTF coef fix values print coef Value numeric sand content in percent numeric silt content in percent numeric clay content in percent numeric soil organic carbon concentration in permille or g kg numeric bulk density in kg cubic meter for the horizon solum numeric Cation Exchange Capacity in cmol per kilogram numeric soil pH in water suspension numeric moisture potential in kPa e g 10 pF 2 0 numeric moisture potential in kPa e g 20 pF 2 3 numeric moisture potential in kPa e g 31 6 pF 2 5 numeric moisture potential at wilting point in kPa e g 1585 pF 4 2 data frame optional conversion coefficients Pedo Transfer Function with rows i1 w sand silt clay oc bd cec ph silt 2 clay 2 sandx xsilt sandx clay and colums 1nAlfa InN tetaS and tetaR see W sten et al 2013 for more details logical specifies whether to correct values of textures and bulk density to avoid creating nonsensical values logical specifies whether to attach the PTF coefficie
98. ue station ID Easting numeric x coordinate in the local projection system Northing numeric y coordinate in the local projection system TAXNUSDA factor Keys to Soil Taxonomy taxon name e g Caldwel11 HZDUSD factor horizon designation UHDICM numeric upper horizon depth from the surface in cm LHDICM numeric lower horizon depth from the surface in cm BLD bulk density in tonnes per cubic meter PHIHOX numeric pH index measured in water solution The grids data frame contains values of regression covariates at 10 m resolution DEM numeric Digital Elevation Model TWI numeric SAGA GIS Topographic Wetness Index MUSYM factor soil mapping units e g Thatuna silt loam NDRE M numeric mean value of the Normalized Difference Red Edge Index time series of 11 RapidEye images NDRE sd numeric standard deviation of the Normalized Difference Red Edge Index time series of 11 RapidEye images Cook_fall_ECa numeric apparent electrical conductivity image from fall Cook_spr_ECa numeric apparent electrical conductivity image from spring X2011 factor cropping system in 2011 X2012 factor cropping system in 2012 The weather data frame contains daily temperatures and rainfall from the nearest meteorological station Date date observation day Precip_wrcc numeric observed precipitation in mm MaxT_wrcc numeric observed maximum daily temperature in degree C MinT_wrccc numeric observed minimum daily temperature in degree C cookfarm
99. ust correspond to some standard resolution e g 0 0008333333 1 1200 or about 100 m 0 0016666667 1 600 or about 250 m or similar e Only standard abbreviated names registered in the Global Soil Data registry can be used in the varname slot SoilGrids class 63 Methods summary signature x SoilGrids generates summary statistics for the object Author s Tomislav Hengl and Robert A MacMillan References e SoilGrids a system for automated soil mapping http ww soilgrids org See Also GlobalSoilMap class SpatialComponents class geosamples class Examples tt load soil samples from the plotKML package library plotKML library aqp library plyr library splines library rgdal library raster data eberg subset data to 10 eberg lt eberg runif nrow eberg lt 1 sites table s lst lt c ID soiltype TAXGRSC X Y h 1st lt c UHDICM LHDICM SNDMHT SLTMHT CLYMHT sites lt ebergl s 1st get horizons table horizons lt getHorizons eberg idcol ID sel h 1st create object of type SoilProfileCollection eberg spc lt join horizons sites type inner depths eberg spc lt ID UHDICM LHDICM site eberg spc lt as formula paste paste s lst 1 collapse sep coordinates eberg spc lt X Y proj4string eberg spc lt CRS init epsg 31467 convert to logits eberg spc horizons
100. with R Springer 378 p FWTools http fwtools maptools org gdalUtils package http CRAN R project org package gdalUtils e Raster package http CRAN R project org package raster See Also spc geosamples class plotKML reproject Examples grids Ebergotzen library plotKML library rgdal library raster data eberg_grid gridded eberg_grid lt xty proj4string eberg_grid lt CRS init epsg 31467 convert to spatial components formulaString lt PRMGEO6 DEMSRT6 TWISRT6 TIRAST6 eberg_spc lt spc eberg_grid formulaString create 3D locations in the original coordinate system eberg_3Dxy lt sp3D eberg_spc predicted Not run wrapper function to create 3D locations in the default WGS84 system eberg_3D lt make 3Dgrid eberg_spc predicted image eberg_3D 1 1 PcC1 7 downscale 100 m resolution imagery to 25 m data eberg_grid25 gridded eberg_grid25 lt x y proj4string eberg_grid25 lt CRS init epsg 31467 eberg_grid25 data lt cbind eberg_grid25 data warp eberg_grid pixsize eberg_grid25 grid cellsize 1 GridTopology eberg_grid25 grid resampling_method cubicspline data this function requires FWTools End Not run makeGstatCmd makeGstatCmd Make a gstat command script Description Generates a command script based on the regression model and variogram This can then be used to run predictions simulations by using t
101. xp by default subsample integer maximum number of observations to be taken for variogram model fit ting to speed up variogram fitting subsample reg integer maximum number of observations to be taken for regression model fit ting currently only used for randomForest other optional arguments that can be passed to glm and or fit variogram Details The GLM method by default assumes that the target variable follows a normal distribution fit family gaussian Other possible families are normal distribution fit family gaussian default setting log normal distribution fit family gaussian log binomial variable fit family binomial logit variable following a poisson distribution fit family poisson log Note Residuals response residuals from the model will be checked for normality and problems reported by default The warning messages should be taken with care as when the sample size is small even big departures from normality will not be reported when the sample size is large even the smallest deviation from normality might lead to a warning Likewise if the variogram fitting fails consider fitting a variogram manually or using the fit vgmModel method Author s Tomislav Hengl Gerard B M Heuvelink and Bas Kempen References e chapter 8 Interpolation and Geostatistics in Bivand R Pebesma E Rubio V 2008 Applied Spatial Data Analysis with R Use R Series Springer Heidelberg pp 37
102. xternal Drift KED nfold integer n fold cross validation sent to gstat krige cv verbose logical specifies whether to supress the progress bar of the gstat krige cv nsim integer triggers the geostatistical simulations mask extra logical specifies whether to mask out the extrapolation pixels prediction vari ance exceeding the global variance block numeric support size block support for objects of type SpatialPixelsDataFrame is chosen by default zmin numeric lower physical limit for the target variable zmax numeric upper physical limit for the target variable subsample integer sub sample point observations to speed up the processing coarsening factor integer coarsening factor 1 5 to speed up the processing vgmmodel object of class data frame corresponding to the gstat vgm variogram subset observations logical vector specifying the subset of observations used for interpolation extend numeric fraction of the range for which the spatial domain should be extended when searching for observations for kriging betas numeric vector of the beta coefficients to be passed to the gstat krige other optional arguments that can be passed to gstat krige and or predict glm Details Selecting predict method KED invokes simple kriging with external drift with betas set at O intercept and 1 regression predictions used as the only covariate This assumes that the regres sion model already results in an unbiase
103. xtract is pattern logical specifies whether the list is a pattern force projection logical specifies whether the reprojection should be ignored FAO SoilProfileCollection class 19 NAflag character missing value flag all missing values are removed by default show progress logical specifies whether to display the progress bar isFactor logical turns aggregation on off for factor type variable additional arguments that can be passed to the raster extract function Note The method will try to reproject the values to the native coordinate system hence it is highly ad visible to embed the proj4 string into the GeoTiffs If both x and y are in the same coordinate system then reprojection can be turned off by setting force projection FALSE In the case is pattern TRUE search by pattern missing values are removed by default and if multiple rasters covering the same area are found values are aggregated to the mean value Author s Tomislav Heng See Also raster extract warp FAO SoilProfileCollection class A class for FAO SoilProfileCollection Description A class for harmonized FAO soil profile records Extends the SoilProfileCollection class from the aqp package Slots idcol object of class character column name containing IDs depthcols object of class character two element vector with column names for horizon top bottom depths metadata object of class data frame metadata tab
104. y using the TT2tri function This function uses the v column in the USDA TT im i e prior probability densities to adjust for texture fraction combinations that are more probable Author s Tomislav Hengl References e Skaggs T H Arya L M Shouse P J Mohanty B P 2001 Estimating Particle Size Distribution from Limited Soil Texture Data Soil Science Society of America Journal 65 4 1038 1044 See Also FAO SoilProfileCollection soil dom Examples plot prior probabilities library sp data USDA TT im gridded USDA TT im lt s1 s2 spplot USDA TT im v Not run library soiltexture convert textures by hand to sand silt and clay TEXMHT lt c CL C SiL ein missing x lt TT2tri TEXMHT D End Not run 82 warp warp GDAL warp function from FWTools Description Reproject and resample using GDAL warp program Usage DI S4 method for signature SpatialPixelsDataFrame warp obj proj4s proj4string obj S4 warp o Arguments obj proj4s GridTo pixsiz resamp NAflag tmp fi show o progra Note GridTopology NULL pixsize resampling_method bilinear NAflag get NAflag envir GSIF opts tmp file FALSE show output on console FALSE program method for signature RasterLayer bj proj4s proj4string obj GridTopology NULL pixsize resampling_method bilinear NAflag get NAflag

Package `GSIF`

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