我提出了同样的问题elsewhere。现在我带来数据,以便您尝试重现错误。
我只是想知道到1997年有多少小面积被砍伐。小区域在setoresp.zip。砍伐森林是在d97.zip。文件为here。将它们解压缩到工作目录以及代码:
# This script reads sectors of investigated cities and calculates the
# deforestated area (up to 1997) for the first sector
library(rgdal)
library(rgeos)
library(raster)
setp <- readOGR(".","setoresp")
# to make it faster, I'll try to intersect with the bounding box,
# instead of the actual polygon
rect <- extent(setp[1,]@bbox)
rect <- as(rect, 'SpatialPolygons')
# Deforestation of brazilian Amazon, clipped to the interest area
# and projected to UTM (so I'll get the intersection area in meters)
d97 <- readOGR(".","d97")
rect@proj4string <- d97@proj4string
if (gIntersects(rect, d97)) {
print("Intersects!")
flush.console() # so I'll know the error is below, not above
rect97 <- gIntersection(rect, d97)
}
#Error in RGEOSBinTopoFunc(spgeom1, spgeom2, byid, id, "rgeos_intersection") :
# TopologyException: found non-noded intersection between LINESTRING (533036 -314770, 533036 -314770) and LINESTRING (533036 -314780, 533036 -314770) at 533035.88110651996 -314769.97350772272
答案 0 :(得分:0)
由于PRODES多边形是MESS,我最终使用了它们的光栅文件。这是我的解决方案:
# This script reads sectors of investigated cities and calculates the
# deforestated area (in different periods) for each sector
library(rgdal)
library(rgeos)
library(raster)
desmat <- raster('Prodes2012_AM_90m.tif')
set <- readOGR(".","setoresp",encoding="UTF-8",stringsAsFactors=F)
set@proj4string
#classes
d97 <- 17 #cinza
d06 <- c(2,3,5,6,8,9,11:13,15,16,18,19,24,29,36:39,41,42,44,47,50,54,55,60,62,66,68:71,74,79,81) #rosa
d11 <- c(4,7,14,20:23,25:28,30,33:35,40,43,45,46,48,49,51:53,56:59,61,63,65,72,73,75:78,82,84) #vermelho
d12 <- 80 #roxo
dagua <- 67 #azul
dnuv <- c(32,64) #azul claro (nuvem e resíduo)
dflor <- 31 #verde escuro
dnfl <- 10 #laranja
detc <- c(1,83) #marrom (DV? e DSF_ANT?)
par(mar=c(0,0,0,0))
d <- data.frame(codsetor=character(),
codmun=character(),
nomemun=character(),
areaset=numeric(),
a97=numeric(), #areas
a06=numeric(),
a11=numeric(),
a12=numeric(),
afl=numeric(),
anfl=numeric(),
anuv=numeric(),
aagua=numeric(),
p97=numeric(), #percentages
p06=numeric(),
p11=numeric(),
p12=numeric(),
pfl=numeric(),
pnfl=numeric(),
pnuv=numeric(),
pagua=numeric(),
stringsAsFactors=FALSE)
for (i in 1:length(set)) {
r <- raster(ext=extent(set[i,]))
setR <- rasterize(set[i,],r)
desmatP <- projectRaster(from=desmat,to=setR,method='ngb')
setI <- mask(desmatP,set[i,])
plot(setI)
freqI <- freq(setI,useNA='no')
areas <- freqI[,2] * prod(res(setI))
freqI <- cbind(freqI,areas)
print(set@data[i,c(2,19,20)],row.names=F)
flush.console()
gArea(set[i,])
d[nrow(d)+1,] <- c(as.character(set@data[i,2]), #codsetor
as.character(set@data[i,19]), #codmun
as.character(set@data[i,20]), #nomemun
gArea(set[i,])/1e6, #areaset (all areas in Km2)
sum(freqI[freqI[,1] %in% d97,3])/1e6, #a97
sum(freqI[freqI[,1] %in% d06,3])/1e6, #a06
sum(freqI[freqI[,1] %in% d11,3])/1e6, #a11
sum(freqI[freqI[,1] %in% d12,3])/1e6, #a12
sum(freqI[freqI[,1] %in% dflor,3])/1e6, #afl
sum(freqI[freqI[,1] %in% dnfl,3])/1e6, #anfl
sum(freqI[freqI[,1] %in% dnuv,3])/1e6, #anuv
sum(freqI[freqI[,1] %in% dagua,3])/1e6, #aagua
sum(freqI[freqI[,1] %in% d97,3])/gArea(set[i,]), #p97
sum(freqI[freqI[,1] %in% d06,3])/gArea(set[i,]), #p06
sum(freqI[freqI[,1] %in% d11,3])/gArea(set[i,]), #p11
sum(freqI[freqI[,1] %in% d12,3])/gArea(set[i,]), #p12
sum(freqI[freqI[,1] %in% dflor,3])/gArea(set[i,]), #pfl
sum(freqI[freqI[,1] %in% dnfl,3])/gArea(set[i,]), #pnfl
sum(freqI[freqI[,1] %in% dnuv,3])/gArea(set[i,]), #pnuv
sum(freqI[freqI[,1] %in% dagua,3])/gArea(set[i,])) #pagua
}
write.table(d,"mapsdesm/tabela.txt",quote=FALSE,sep="\t",row.names=FALSE,col.names=T)