使用'sp'和'sf'使R脚本适应管理区域时出现问题`

我正在尝试在Qgis 3.14上运行一个R脚本，它在Qgis 2.18版中运行得非常好。它基于DEM地形高程光栅文件划定管理区域。 但是我有一些问题。 这是我的剧本

##Determina_zonas_de_manejo=group
##Raster_de_Entrada=raster
##Quantidade_de_Zonas_de_Manejo=number 2
##Poligono_Unidade_Produtiva=optional vector
##Ponto_Inicial=optional vector
##Inicial_do_identificador=string A
##Porcentagem_de_vertices_a_serem_mantidos_0_a_100=number 100
##Zonas_de_Manejo=output vector

library(raster)
library(sp)
library(rgdal)
library(e1071)
library(igraph)
library(rgeos)
library(TSP)

raster_in  <- Raster_de_Entrada[[1]]
groups <- Quantidade_de_Zonas_de_Manejo
pol_mask <- Poligono_Unidade_Produtiva
pts_inicial <- Ponto_Inicial
iniID <- Inicial_do_identificador

#Porcentagem_de_vertices_a_serem_mantidos_0_a_100 <- 100
porc_vert <- Porcentagem_de_vertices_a_serem_mantidos_0_a_100/100

#raster_in <- brick('G:\\Meu Drive\\Drive\\RemoteSensing\\UGD_v2\\correcoes14_02_2020\\ArquivosRenomearZM\\NDVI_Final_01.tif')[[1]]
#groups <- 5
#pol_file <- 'G:\\Meu Drive\\Drive\\RemoteSensing\\UGD_v2\\correcoes14_02_2020\\ArquivosRenomearZM\\01.shp'
#pol_mask <- readOGR(dirname(pol_file), strsplit(basename(pol_file), '[.]')[[1]][1] )
#iniID <- "B"
#pts_file <- 'G:\\Meu Drive\\Drive\\RemoteSensing\\UGD_v2\\correcoes14_02_2020\\ArquivosRenomearZM\\ponto_inicial_teste.shp'
#pts_inicial <- readOGR(dirname(pts_file), strsplit(basename(pts_file), '[.]')[[1]][1] )

#set.seed(1)
raster_in_pts <- as.data.frame(rasterToPoints(raster_in))
pc_calc <- prcomp(~ raster_in_pts[,1] + raster_in_pts[,2] + raster_in_pts[,3], scale = TRUE)
pc_data <- as.data.frame(pc_calc$x)
pc_kmeans <- cmeans(pc_data, groups, 500,verbose=TRUE, method="cmeans")

raster_in_pts <- data.frame(x = raster_in_pts$x, y = raster_in_pts$y, cluster = pc_kmeans$cluster)
cluster_raster <- rasterFromXYZ(raster_in_pts)
cluster_raster@crs <- raster_in@crs
#plot(cluster_raster, col = topo.colors(30))

# Converter falsas zonas de manejo em nodata
cluster_ref <- setValues(cluster_raster, NA)
for(i in 1:groups){
mask_i <- setValues(cluster_raster, NA)
mask_i[cluster_raster == i ] <- i
clump_i <- clump(mask_i, direction = 4)
clump_i_freq <- na.exclude(as.data.frame(freq(clump_i)))
valueID <- clump_i_freq$value[which.max(clump_i_freq$count)]
maxi <- max(clump_i_freq$count)
excludeID <- clump_i_freq$value[which(clump_i_freq$count < maxi)]
clump_i_mask <- clump_i
clump_i_mask[clump_i %in% excludeID] <- NA
clump_i_mask[clump_i == valueID] <- i
cluster_ref[clump_i_mask == i ] <- i
}
#plot(cluster_ref, col = topo.colors(30))

# Função para preencher nodata
func_viz <- function(x){
feq_class <- na.exclude(as.data.frame(table(x)))
if(nrow(feq_class)!=0) {
colnames(feq_class) <- c("Class","Freq")
value_noData <- as.numeric(as.character(feq_class$Class[feq_class$Freq==max(feq_class$Freq)]))
return( value_noData ) }
else {
return( NA ) }
}

# Converter poligono da unidade produtiva em raster para usar como mascara
if(class(pol_mask) == "SpatialPolygonsDataFrame"){
raster_in_copy <- raster_in
extent(raster_in_copy) <- extent(pol_mask)
res(raster_in_copy) <- res(raster_in)[1]
rp <- rasterize(pol_mask, raster_in_copy, 1)
raster_in_01 <- !is.na(rp)
}else{
raster_in_01 <- !is.na(raster_in)
}

# Preenchimento de nodata
repeat {
cluster_ref <- extend(cluster_ref, c(1, 1))
raster_in_01 <- extend(raster_in_01, c(1, 1))
cluster_ref_foc <- focal(cluster_ref, w=matrix(1,nrow=3,ncol=3),
fun=func_viz, pad=T)
in_s_out_01 <- is.na(cluster_ref_foc)
in_s_out_01[raster_in_01 != 1] <- NA
in_s_out_01[is.na(raster_in_01)] <- NA
#plot(in_s_out_01)
soma_vazio <- sum(in_s_out_01[raster_in_01 == 1], na.rm = TRUE)
print(soma_vazio)
#cluster_ref_foc[raster_in_01 != 1] <- NA
#plot(cluster_ref_foc)
#print(cluster_ref_foc)
if (soma_vazio == 0){
cluster_ref_foc <- focal(cluster_ref_foc, w=matrix(1,nrow=3,ncol=3),
fun=func_viz, pad=T)
cluster_ref_foc <- focal(cluster_ref_foc, w=matrix(1,nrow=3,ncol=3),
fun=func_viz, pad=T)
cluster_ref_foc <- focal(cluster_ref_foc, w=matrix(1,nrow=3,ncol=3),
fun=func_viz, pad=T)
break
} else{
cluster_ref_foc[raster_in_01 != 1] <- NA
#plot(cluster_ref_foc)
cluster_ref <- cluster_ref_foc
}
}

# Converter raster para poligono
pol_clust_ref <- rasterToPolygons(cluster_ref_foc, dissolve=TRUE)
#spplot(pol_clust_ref)
if(Porcentagem_de_vertices_a_serem_mantidos_0_a_100 != 100){
library(rmapshaper)
pol_clust_ref <- ms_simplify(pol_clust_ref, keep = porc_vert)#, keep_shapes = TRUE, explode = TRUE)
pol_clust_ref@data$rmapshaperid <- NULL
}

# Cortar poligono
if(class(pol_mask) == "SpatialPolygonsDataFrame"){
row.names(pol_mask) <- "1"
clip_mask <- gIntersection(pol_clust_ref, pol_mask, byid = T, drop_lower_td = TRUE)
row.names(clip_mask) <- gsub(" 1", "", row.names(clip_mask))
keep <- row.names(clip_mask)
clip_mask <- spChFIDs(clip_mask, keep)
pol_clust_ref <- SpatialPolygonsDataFrame(clip_mask, data.frame(layer = pol_clust_ref@data[keep, ]))
}else{
raster_in_1 <- raster_in
raster_in_1[!is.na(raster_in_1)] <- 1
pol_1 <- rasterToPolygons(raster_in_1, dissolve=TRUE)
row.names(pol_1) <- "1"
clip_mask <- gIntersection(pol_clust_ref, pol_1, byid = T, drop_lower_td = TRUE)
row.names(clip_mask) <- gsub(" 1", "", row.names(clip_mask))
keep <- row.names(clip_mask)
clip_mask <- spChFIDs(clip_mask, keep)
pol_clust_ref <- SpatialPolygonsDataFrame(clip_mask, data.frame(layer = pol_clust_ref@data[keep, ]))
}

# ur.area<-sapply(slot(pol_clust_ref, "polygons"), function(x) sapply(slot(x, "Polygons"), slot, "area"))
# mult_part <- unlist(lapply(ur.area, length))
# if(max(mult_part) > 1){
#     single_pol <- multitoone(pol_clust_ref)
#     #single_pol <- SpatialPolygonsDataFrame(single_pol, data.frame(layer = row.names(single_pol)))
#
# }

#caminho minimo

centroids <- gCentroid(pol_clust_ref, byid=TRUE)
centroids_pts <- SpatialPointsDataFrame(centroids, as.data.frame(centroids))
proj4string(centroids_pts) <- pol_clust_ref@proj4string

pts_xy <- as.data.frame(centroids@coords)
pts_xy$id_old <- 1:nrow(pts_xy)
dist_xy <- as.matrix(dist(pts_xy))
adj_matriz <- gTouches(pol_clust_ref, byid = TRUE)
dist_xy[!adj_matriz] <- dist_xy[!adj_matriz] + max(dist_xy) * 1000 # dist_xy[!adj_matriz] + max(dist_xy)
diag(dist_xy) <- 0.0
tsp <- TSP(dist_xy)

if(class(pts_inicial) == "SpatialPointsDataFrame"){
dists_viz <- sqrt((pts_xy$x - pts_inicial@coords[1])^2 + (pts_xy$y - pts_inicial@coords[2])^2)
start_pts_id <- which.min(dists_viz)
} else{
start_pts_id <- which.min(pts_xy$y) # miny
}

ptm <- proc.time()
dist_total <- 1e8
true_n <- 1
Numero_de_Repeticoes <- 1
while(true_n <= Numero_de_Repeticoes) {
tour <- solve_TSP(tsp, method =  "farthest_insertion", rep=20,
two_opt =TRUE, two_opt_repetitions = 10, start = start_pts_id)
tour_pts_temp <- pts_xy[tour,]
dist_total_temp <- sum(sqrt(diff(tour_pts_temp$x)^2+diff(tour_pts_temp$y)^2))
if(dist_total_temp < dist_total){
tour_pts <- tour_pts_temp
dist_total <- dist_total_temp
}
true_n <- true_n + 1
print(dist_total)
}
print("Tempo minimizar caminho:")
proc.time() - ptm

# Organizar poligono de saida
pol_resul <- pol_clust_ref[tour_pts$id_old,]
pol_resul@data$layer <- NULL
pol_resul@data$NomeID <- 1:nrow(pts_xy)
#width_str <- length(strsplit(as.character(nrow(pts_xy)), "")[[1]])
width_str <- 3
pol_resul@data$Nome <- paste(iniID, formatC(1:nrow(pts_xy), width = width_str, flag = "0"), sep = "")

mean_r <- extract(raster_in, pol_resul, fun = mean, na.rm = T, small = T)
sd_r <- extract(raster_in, pol_resul, fun = sd, na.rm = T, small = T)
cv <- sd_r/mean_r * 100
#len_r <- extract(raster_in, pol_resul, fun=function(x,...)length(x), na.rm = T, small = T)
#area_cell <- res(raster_in)[1]^2
#area_r <- len_r * area_cell
#area_total <- sum(area_r)

if(groups > 5) {
apt_kmeans <- kmeans(mean_r, 5)$cluster
apt_mean_cluster <- as.data.frame(cbind(mean_r, apt_kmeans))
apt_mean_cluster$ordem <- 1:nrow(apt_mean_cluster)
agreg <- aggregate( V1 ~ apt_kmeans, FUN = mean, data = apt_mean_cluster)
agreg <- agreg[order(agreg$V1),]
agreg$aptidao <- c("MUITO BAIXA", "BAIXA", "MEDIA", "ALTA", "MUITO ALTA")
agreg_merg <- merge(apt_mean_cluster, agreg, 'apt_kmeans', sort = FALSE)
ord <- agreg_merg$ordem
field_apt <- agreg_merg[order(ord), ]$aptidao
} else if(groups > 3){
apt_kmeans <- kmeans(mean_r, 3)$cluster
apt_mean_cluster <- as.data.frame(cbind(mean_r, apt_kmeans))
apt_mean_cluster$ordem <- 1:nrow(apt_mean_cluster)
agreg <- aggregate( V1 ~ apt_kmeans, FUN = mean, data = apt_mean_cluster)
agreg <- agreg[order(agreg$V1),]
agreg$aptidao <- c("BAIXA", "MEDIA", "ALTA")
agreg_merg <- merge(apt_mean_cluster, agreg, 'apt_kmeans', sort = FALSE)
ord <- agreg_merg$ordem
field_apt <- agreg_merg[order(ord), ]$aptidao

}else{
field_apt <- "MEDIA"
}

pol_resul@data$Media <- as.vector(mean_r)
pol_resul@data$Desv_Pad <- as.vector(sd_r)
pol_resul@data$CV <- as.vector(cv)
pol_resul@data$Area_ha <- area(pol_resul) / 10000
pol_resul@data$Aptidao <- field_apt
pol_resul@data$Constante <- 1

Zonas_de_Manejo =  pol_resul

##Determina_zonas_de_manejo=组
##光栅_de_Entrada=光栅
##Quantidade_de_Zonas_de_Manejo=2号
##Poligono_Unidade_Produtiva=可选向量
##Ponto_inical=可选向量
##INIACIAL_do_identificator=字符串A
##Porcentagem_de_顶点_a_serem_mantidos_0_a_100=数字100
##Zonas_de_Manejo=输出向量
图书馆（光栅）
图书馆（sp）
图书馆（rgdal）
图书馆（e1071）
图书馆（igraph）
图书馆（rgeos）
图书馆（TSP）
对于这个网站来说，这不是一个合适的问题。根据您提供的内容，我只能说您需要逐行运行脚本，找出错误发生的位置，并修复它。如果您不知道如何修复它，您可以在这里提供一个最小的、自包含的、可复制的示例。