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Today's Topics:
1. Error running codes (Enoch Gyamfi Ampadu)
2. Re: regression-kriging and co-kriging (Edzer Pebesma)
----------------------------------------------------------------------
Message: 1
Date: Thu, 15 Aug 2019 06:51:49 +0200
From: Enoch Gyamfi Ampadu <egamp...@gmail.com>
To: r-sig-geo@r-project.org
Subject: [R-sig-Geo] Error running codes
Message-ID:
<cac4+n+yravrhc_mcoatv-ao_-sh7qyo8zukzaojqbnabgjx...@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"
Dear List,
Please I have been running a certain the codes below to read my image,
shapeffile to enable me classify for cover with Random forest. I have
gotten to the point of extracting the raster values and the raster
information. However I keep getting errors. though I have tried
different
combination and other codes like readOGR for importing the training
polygon. I will be glad if you could be of help as I am new to using R.
#import clip image of study area
TestImg3 <- brick("C:\\Users\\hp\\Desktop\\Data collection\\Nkandla
Images\\Landsat\\2019\\08052019\\Corrected\\New
Folder\\Image08052019_Clip.tif")
#Assign name of bands
names(TestImg3) <- c(paste0("B", 1:7, coll=""), "B9")
plotRGB(TestImg3, r=4, g=3, b=2, stretch ="lin")
#Import training shapefile
sample <- shapefile("C:/Users/hp/Desktop/Data collection/Nkandla
Images/Landsat/2019/08052019/Corrected/Train08052019_2.shp")
responseCol <- "class"
#(I have tried options of changing "class" to "classname" as reflect
actaul
name assigned in ArcMap)
# Overlap the sample polygons on the image (combine the class
information
with extracted values).
pixels = data.frame(matrix(vector(), nrow = 0, ncol =
length(names(img)) +
1))
for (i in 1:length(unique(sample[[responseCol]]))){
category <- unique(sample[[responseCol]])[i]
categorymap <- sample[sample[[responseCol]] == category,]
dataSet <- extract(img, categorymap)
dataSet <- dataSet[!unlist(lapply(dataSet, is.null))]
if(is(sample, "SpatialPointsDataFrame")){
dataSet <- cbind(dataSet, class = as.numeric(category))
pixeles <- rbind(pixeles, dataSet)
}
if(is(sample, "SpatialPolygonsDataFrame")){
dataSet <- lapply(dataSet, function(x){cbind(x, class =
as.numeric(rep(category,
nrow(x))))})
df <- do.call("rbind", dataSet)
pixels <- rbind(pixeles, df)
}
}
THIS IS THE ERROR I GET FROM RUNNING THE ABOVE CODES
for (i in 1:length(unique(samples[[responseCol]]))){
+ category <- unique(samples[[responseCol]])[i]
+ categorymap <- samples[samples[[responseCol]] == category,]
+ dataSet <- extract(img, categorymap)
+
+ if(is(sample, "SpatialPointsDataFrame")){
+ dataSet <- cbind(dataSet, class = as.numeric(category))
+ pixeles <- rbind(pixeles, dataSet)
+ }
+ if(is(sample, "SpatialPolygonsDataFrame")){
+ dataSet <- lapply(dataSet, function(x){cbind(x, class =
as.numeric(rep(category,
+
nrow(x))))})
+ df <- do.call("rbind", dataSet)
+ pixels <- rbind(pixeles, df)
+ }
+
+ }
Error in y[i, ] :
cannot get a slot ("Polygons") from an object of type "NULL"
Please help me out.
Thank you.
Best regards,
Enoch
--
*Enoch Gyamfi - Ampadu*
*Geography & Environmental Sciences*
*College of Agriculture, Engineering & Science*
*University of KwaZulu-Natal, Westville Campus*
*Private Bag X54001*
*Durban, South Africa **– 4000**.*
*Phone: +27 835 828255*
*email: egamp...@gmail.com <egamp...@gmail.com>*
*skype: enoch.ampadu*
*The highest evidence of nobility is self-control*.
*A simple act of kindness creates an endless ripple*.
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------------------------------
Message: 2
Date: Thu, 15 Aug 2019 08:33:59 +0200
From: Edzer Pebesma <edzer.pebe...@uni-muenster.de>
To: r-sig-geo@r-project.org
Subject: Re: [R-sig-Geo] regression-kriging and co-kriging
Message-ID: <bdae5226-7023-7e30-1927-5fb66ff41...@uni-muenster.de>
Content-Type: text/plain; charset="utf-8"
On 8/12/19 8:21 PM, Emanuele Barca wrote:
Dear Edzer,
maybe I found the solution. I found this in the predict function help:
"When a non-stationary (i.e., non-constant) mean is used, both for
simulation and prediction purposes the variogram model defined should
be
that of the residual process, and not that of the raw observations"
Since my data were, actually, non-stationary, I applied the universal
co-kriging instead usual co-kriging.
now the maps of regression-kring and co-kriging are actually similar s
expected.
did I understand correctly the quoted sentence?
I think so, but hard to be sure given the information you provide.
regards
emanuele barca
------------------------------
Message: 2
Date: Sat, 10 Aug 2019 10:41:38 +0200
From: Edzer Pebesma <edzer.pebe...@uni-muenster.de>
To: r-sig-geo@r-project.org
Subject: Re: [R-sig-Geo] regression-kriging and co-kriging
Message-ID: <da76da51-e46b-8a8d-4952-7c3b85c19...@uni-muenster.de>
Content-Type: text/plain; charset="utf-8"
Hard to tell from your script. Maybe give a reproducible example?
On 8/6/19 1:07 PM, Emanuele Barca wrote:
Dear r-sig-geo friends,
I produced two maps garnered in the following way:
# for regression-kriging
Piezo.map <-autoKrige(LivStat ~ Z, input_data = mydata.sp, new_data
= covariates, model = "Ste")
Piezork.pred <- Piezo.map$krige_output$var1.pred
Piezork.coords <- Piezo.map$krige_output@coords
Piezork.out <- as.data.frame(cbind(Piezork.coords, Piezork.pred))
colnames(Piezork.out)[1:2] <- c("X", "Y")
coordinates(Piezork.out) = ~ X + Y
gridded(Piezork.out) <- TRUE
spplot(Piezork.out, main = list(label = "R-k Hydraulic head", cex =
1.5))
#for co-kriging
g <- gstat(id = "Piezo", formula = LivStat ~ 1, data = mydata.sp,
set
= list(nocheck = 1))
g <- gstat(g, id = "Z", formula = Z ~ 1, data = mydata.sp, set =
list(nocheck = 1))
v.g <- variogram(g)
#g <- gstat(g, id = "Piezo", model = vgm(150, "Mat", 1350, 0.0,
kappa
= 1.9), fill.all = T)#
g <- gstat(g, id = "Piezo", model = vgm(0.7, "Ste", 1300, 18, kappa
=
1.9), fill.all = T)#
g.fit <- fit.lmc(v.g, g, fit.lmc = TRUE, correct.diagonal = 1.01) #
fit multivariable variogram model , fit.lmc = TRUE, correct.diagonal
= 1.01
g.fit
plot(v.g, model = g.fit, main = "Fitted Variogram Models - Raw
Data")#
#gridded(covariates) <- TRUE
g.cok <- predict(g.fit, newdata = covariates)#grid
g.cok.pred <- g.cok@data$Piezo.pred
aaaa <- na.omit(g.cok.pred)
g.cok.coords <- g.cok@coords
g.cok.out <- as.data.frame(cbind(g.cok.coords, g.cok.pred))
colnames(g.cok.out)[1:2] <- c("X", "Y")
coordinates(g.cok.out) = ~ X + Y
gridded(g.cok.out) <- TRUE
spplot(g.cok.out, main = list(label = "Hydraulic head with
Co-kriging", cex = 1.5))
###########################################################################################################################
I am unable to understand why the first map appears as a raster and
the second not, notwithstanding the fact that they are both computed
on the same "covariates" DEM???
where is the mistake???
regards
emanuele
________________________________________________________
Emanuele Barca Researcher
Water Research Institute (IRSA-CNR)
Via De Blasio, 5 70123 Bari (Italy)
Phone +39 080 5820535 Fax +39 080 5313365
Mobile +39 340 3420689
_________________________________________________________
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Edzer Pebesma
Institute for Geoinformatics
Heisenbergstrasse 2, 48151 Muenster, Germany
Phone: +49 251 8333081
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Phone: +49 251 8333081
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