vph: Volume Per Hectares Prediction Model
In wslerry/tRee: Volume/Ha Prediction Model of Acacia Mg.
Usage
1
Arguments
data
img
poly
model
Examples
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##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (data, img, poly, model)
{
require(raster)
require(rgdal)
require(rgeos)
traindata <- base::sample(nrow(data), size = 0.7 * nrow(data),
replace = FALSE)
TrainSet <- data[traindata, ]
ValidSet <- data[-traindata, ]
if (model == "mod1") {
mod <- lm(log(VUB) ~ I(log(DBH)) + I(log(H)) + I(log(BA)),
data = data)
coef(mod)
vol <- function(x) {
exp(coef(mod)[1]) * (23.1^coef(mod)[2]) * (x^coef(mod)[3]) *
(0.046^coef(mod)[4])
}
vol.agg <- aggregate(img, fact = 4/res(img))
volimg <- raster::calc(vol.agg, vol)
x <- xres(volimg)
y <- yres(volimg)
ha <- (x * y)/10000
volha.func <- function(x) {
x/ha
}
volha <- raster::calc(volimg, volha.func)
par(mar = rep(0.5, 4))
compsRas <- rasterize(poly, volha)
zoneStat <- zonal(volha, compsRas, "mean")
poly[["volumeperha"]] <- zoneStat[, "mean"]
colRamp <- colorRampPalette(c("lightgoldenrod1", "tomato2"))(10)
polyCols <- colRamp[as.numeric(cut(poly[["volumeperha"]],
breaks = 10))]
options(repr.plot.width = 4, repr.plot.height = 4)
plot(poly, col = polyCols, xlab = "", ylab = "", xaxt = "n",
yaxt = "n", main = "Volume/Ha")
text(gCentroid(poly, byid = TRUE), round(poly[["volumeperha"]],
1), col = "blue", font = 2)
writeRaster(volha, "./output/volperha_mod1.tif", format = "GTiff",
overwrite = TRUE)
writeOGR(poly, "output", "vhamod1", driver = "ESRI Shapefile",
overwrite = TRUE)
}
else if (model == "mod2") {
mod <- lm(log(VUB) ~ I(log(DBH)) + I(log(H)) + I(log(DBH)^2) +
I(log(H)^2), data = data)
coef(mod)
vol <- function(x) {
exp(coef(mod)[1]) * (23.1^coef(mod)[2]) * (x^coef(mod)[3]) *
((23.1^2)^coef(mod)[4]) * ((x^2)^coef(mod)[5])
}
vol.agg <- aggregate(img, fact = 3/res(img))
volimg <- raster::calc(vol.agg, vol)
x <- xres(volimg)
y <- yres(volimg)
ha <- (x * y)/10000
volha.func <- function(x) {
x/ha
}
volha <- raster::calc(volimg, volha.func)
par(mar = rep(0.5, 4))
compsRas <- rasterize(poly, volha)
zoneStat <- zonal(volha, compsRas, "mean")
poly[["volumeperha"]] <- zoneStat[, "mean"]
colRamp <- colorRampPalette(c("lightgoldenrod1", "tomato2"))(10)
polyCols <- colRamp[as.numeric(cut(poly[["volumeperha"]],
breaks = 10))]
plot(poly, col = polyCols, xlab = "", ylab = "", xaxt = "n",
yaxt = "n", main = "Volume/Ha")
text(gCentroid(poly, byid = TRUE), round(poly[["volumeperha"]],
1), col = "blue", font = 2)
writeRaster(volha, "./output/volperha_mod2.tif", format = "GTiff",
overwrite = TRUE)
writeOGR(poly, "output", "vhamod2", driver = "ESRI Shapefile",
overwrite = TRUE)
}
else if (model == "mod3") {
mod <- lm(log(VUB) ~ I(log(DBH^2 * H)), data = data)
coef(mod)
vol <- function(x) {
exp(coef(mod)[1]) * (((23.1^2) * x)^coef(mod)[2])
}
vol.agg <- aggregate(img, fact = 4/res(img))
volimg <- raster::calc(vol.agg, vol)
x <- xres(volimg)
y <- yres(volimg)
ha <- (x * y)/10000
volha.func <- function(x) {
x/ha
}
volha <- raster::calc(volimg, volha.func)
par(mar = rep(0.5, 4))
compsRas <- rasterize(poly, volha)
zoneStat <- zonal(volha, compsRas, "mean")
poly[["volumeperha"]] <- zoneStat[, "mean"]
colRamp <- colorRampPalette(c("lightgoldenrod1", "tomato2"))(10)
polyCols <- colRamp[as.numeric(cut(poly[["volumeperha"]],
breaks = 10))]
plot(poly, col = polyCols, xlab = "", ylab = "", xaxt = "n",
yaxt = "n", main = "Volume/Ha")
text(gCentroid(poly, byid = TRUE), round(poly[["volumeperha"]],
1), col = "blue", font = 2)
writeRaster(volha, "./output/volperha_mod3.tif", format = "GTiff",
overwrite = TRUE)
writeOGR(poly, "output", "vhamod3", driver = "ESRI Shapefile",
overwrite = TRUE)
}
else if (model == "mod4") {
mod <- lm(log(VUB) ~ I(log(DBH^2)) + I(log(H^2)), data = data)
coef(mod)
vol <- function(x) {
exp(coef(mod)[1]) * ((23.1^2)^coef(mod)[2]) * ((x^2)^coef(mod)[3])
}
vol.agg <- aggregate(img, fact = 4/res(img))
volimg <- raster::calc(vol.agg, vol)
x <- xres(volimg)
y <- yres(volimg)
ha <- (x * y)/10000
volha.func <- function(x) {
x/ha
}
volha <- raster::calc(volimg, volha.func)
par(mar = rep(0.5, 4))
compsRas <- rasterize(poly, volha)
zoneStat <- zonal(volha, compsRas, "mean")
poly[["volumeperha"]] <- zoneStat[, "mean"]
colRamp <- colorRampPalette(c("lightgoldenrod1", "tomato2"))(10)
polyCols <- colRamp[as.numeric(cut(poly[["volumeperha"]],
breaks = 10))]
plot(poly, col = polyCols, xlab = "", ylab = "", xaxt = "n",
yaxt = "n", main = "Volume/Ha")
text(gCentroid(poly, byid = TRUE), round(poly[["volumeperha"]],
1), col = "blue", font = 2)
writeRaster(volha, "./output/volperha_mod4.tif", format = "GTiff",
overwrite = TRUE)
writeOGR(poly, "output", "vhamod4", driver = "ESRI Shapefile",
overwrite = TRUE)
}
else if (model == "mod5") {
mod <- lm(log(VUB) ~ I(log(H)), data = data)
coef(mod)
vol <- function(x) {
exp(coef(mod)[1]) * (x^coef(mod)[2])
}
vol.agg <- aggregate(img, fact = 4/res(img))
volimg <- raster::calc(vol.agg, vol)
x <- xres(volimg)
y <- yres(volimg)
ha <- (x * y)/10000
volha.func <- function(x) {
x/ha
}
volha <- raster::calc(volimg, volha.func)
par(mar = rep(0.5, 4))
compsRas <- rasterize(poly, volha)
zoneStat <- zonal(volha, compsRas, "mean")
poly[["volumeperha"]] <- zoneStat[, "mean"]
colRamp <- colorRampPalette(c("lightgoldenrod1", "tomato2"))(10)
polyCols <- colRamp[as.numeric(cut(poly[["volumeperha"]],
breaks = 10))]
plot(poly, col = polyCols, xlab = "", ylab = "", xaxt = "n",
yaxt = "n", main = "Volume/Ha")
text(gCentroid(poly, byid = TRUE), round(poly[["volumeperha"]],
1), col = "blue", font = 2)
writeRaster(volha, "./output/volperha_mod5.tif", format = "GTiff",
overwrite = TRUE)
writeOGR(poly, "output", "vhamod5", driver = "ESRI Shapefile",
overwrite = TRUE)
}
}
wslerry/tRee documentation built on Oct. 5, 2019, 10:30 p.m.
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Usage
1 |
Arguments
data |
|
img |
|
poly |
|
model |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (data, img, poly, model)
{
require(raster)
require(rgdal)
require(rgeos)
traindata <- base::sample(nrow(data), size = 0.7 * nrow(data),
replace = FALSE)
TrainSet <- data[traindata, ]
ValidSet <- data[-traindata, ]
if (model == "mod1") {
mod <- lm(log(VUB) ~ I(log(DBH)) + I(log(H)) + I(log(BA)),
data = data)
coef(mod)
vol <- function(x) {
exp(coef(mod)[1]) * (23.1^coef(mod)[2]) * (x^coef(mod)[3]) *
(0.046^coef(mod)[4])
}
vol.agg <- aggregate(img, fact = 4/res(img))
volimg <- raster::calc(vol.agg, vol)
x <- xres(volimg)
y <- yres(volimg)
ha <- (x * y)/10000
volha.func <- function(x) {
x/ha
}
volha <- raster::calc(volimg, volha.func)
par(mar = rep(0.5, 4))
compsRas <- rasterize(poly, volha)
zoneStat <- zonal(volha, compsRas, "mean")
poly[["volumeperha"]] <- zoneStat[, "mean"]
colRamp <- colorRampPalette(c("lightgoldenrod1", "tomato2"))(10)
polyCols <- colRamp[as.numeric(cut(poly[["volumeperha"]],
breaks = 10))]
options(repr.plot.width = 4, repr.plot.height = 4)
plot(poly, col = polyCols, xlab = "", ylab = "", xaxt = "n",
yaxt = "n", main = "Volume/Ha")
text(gCentroid(poly, byid = TRUE), round(poly[["volumeperha"]],
1), col = "blue", font = 2)
writeRaster(volha, "./output/volperha_mod1.tif", format = "GTiff",
overwrite = TRUE)
writeOGR(poly, "output", "vhamod1", driver = "ESRI Shapefile",
overwrite = TRUE)
}
else if (model == "mod2") {
mod <- lm(log(VUB) ~ I(log(DBH)) + I(log(H)) + I(log(DBH)^2) +
I(log(H)^2), data = data)
coef(mod)
vol <- function(x) {
exp(coef(mod)[1]) * (23.1^coef(mod)[2]) * (x^coef(mod)[3]) *
((23.1^2)^coef(mod)[4]) * ((x^2)^coef(mod)[5])
}
vol.agg <- aggregate(img, fact = 3/res(img))
volimg <- raster::calc(vol.agg, vol)
x <- xres(volimg)
y <- yres(volimg)
ha <- (x * y)/10000
volha.func <- function(x) {
x/ha
}
volha <- raster::calc(volimg, volha.func)
par(mar = rep(0.5, 4))
compsRas <- rasterize(poly, volha)
zoneStat <- zonal(volha, compsRas, "mean")
poly[["volumeperha"]] <- zoneStat[, "mean"]
colRamp <- colorRampPalette(c("lightgoldenrod1", "tomato2"))(10)
polyCols <- colRamp[as.numeric(cut(poly[["volumeperha"]],
breaks = 10))]
plot(poly, col = polyCols, xlab = "", ylab = "", xaxt = "n",
yaxt = "n", main = "Volume/Ha")
text(gCentroid(poly, byid = TRUE), round(poly[["volumeperha"]],
1), col = "blue", font = 2)
writeRaster(volha, "./output/volperha_mod2.tif", format = "GTiff",
overwrite = TRUE)
writeOGR(poly, "output", "vhamod2", driver = "ESRI Shapefile",
overwrite = TRUE)
}
else if (model == "mod3") {
mod <- lm(log(VUB) ~ I(log(DBH^2 * H)), data = data)
coef(mod)
vol <- function(x) {
exp(coef(mod)[1]) * (((23.1^2) * x)^coef(mod)[2])
}
vol.agg <- aggregate(img, fact = 4/res(img))
volimg <- raster::calc(vol.agg, vol)
x <- xres(volimg)
y <- yres(volimg)
ha <- (x * y)/10000
volha.func <- function(x) {
x/ha
}
volha <- raster::calc(volimg, volha.func)
par(mar = rep(0.5, 4))
compsRas <- rasterize(poly, volha)
zoneStat <- zonal(volha, compsRas, "mean")
poly[["volumeperha"]] <- zoneStat[, "mean"]
colRamp <- colorRampPalette(c("lightgoldenrod1", "tomato2"))(10)
polyCols <- colRamp[as.numeric(cut(poly[["volumeperha"]],
breaks = 10))]
plot(poly, col = polyCols, xlab = "", ylab = "", xaxt = "n",
yaxt = "n", main = "Volume/Ha")
text(gCentroid(poly, byid = TRUE), round(poly[["volumeperha"]],
1), col = "blue", font = 2)
writeRaster(volha, "./output/volperha_mod3.tif", format = "GTiff",
overwrite = TRUE)
writeOGR(poly, "output", "vhamod3", driver = "ESRI Shapefile",
overwrite = TRUE)
}
else if (model == "mod4") {
mod <- lm(log(VUB) ~ I(log(DBH^2)) + I(log(H^2)), data = data)
coef(mod)
vol <- function(x) {
exp(coef(mod)[1]) * ((23.1^2)^coef(mod)[2]) * ((x^2)^coef(mod)[3])
}
vol.agg <- aggregate(img, fact = 4/res(img))
volimg <- raster::calc(vol.agg, vol)
x <- xres(volimg)
y <- yres(volimg)
ha <- (x * y)/10000
volha.func <- function(x) {
x/ha
}
volha <- raster::calc(volimg, volha.func)
par(mar = rep(0.5, 4))
compsRas <- rasterize(poly, volha)
zoneStat <- zonal(volha, compsRas, "mean")
poly[["volumeperha"]] <- zoneStat[, "mean"]
colRamp <- colorRampPalette(c("lightgoldenrod1", "tomato2"))(10)
polyCols <- colRamp[as.numeric(cut(poly[["volumeperha"]],
breaks = 10))]
plot(poly, col = polyCols, xlab = "", ylab = "", xaxt = "n",
yaxt = "n", main = "Volume/Ha")
text(gCentroid(poly, byid = TRUE), round(poly[["volumeperha"]],
1), col = "blue", font = 2)
writeRaster(volha, "./output/volperha_mod4.tif", format = "GTiff",
overwrite = TRUE)
writeOGR(poly, "output", "vhamod4", driver = "ESRI Shapefile",
overwrite = TRUE)
}
else if (model == "mod5") {
mod <- lm(log(VUB) ~ I(log(H)), data = data)
coef(mod)
vol <- function(x) {
exp(coef(mod)[1]) * (x^coef(mod)[2])
}
vol.agg <- aggregate(img, fact = 4/res(img))
volimg <- raster::calc(vol.agg, vol)
x <- xres(volimg)
y <- yres(volimg)
ha <- (x * y)/10000
volha.func <- function(x) {
x/ha
}
volha <- raster::calc(volimg, volha.func)
par(mar = rep(0.5, 4))
compsRas <- rasterize(poly, volha)
zoneStat <- zonal(volha, compsRas, "mean")
poly[["volumeperha"]] <- zoneStat[, "mean"]
colRamp <- colorRampPalette(c("lightgoldenrod1", "tomato2"))(10)
polyCols <- colRamp[as.numeric(cut(poly[["volumeperha"]],
breaks = 10))]
plot(poly, col = polyCols, xlab = "", ylab = "", xaxt = "n",
yaxt = "n", main = "Volume/Ha")
text(gCentroid(poly, byid = TRUE), round(poly[["volumeperha"]],
1), col = "blue", font = 2)
writeRaster(volha, "./output/volperha_mod5.tif", format = "GTiff",
overwrite = TRUE)
writeOGR(poly, "output", "vhamod5", driver = "ESRI Shapefile",
overwrite = TRUE)
}
}
|
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