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https://github.com/bleutner/RStoolbox
Remote Sensing Data Analysis in R 🛰
https://github.com/bleutner/RStoolbox
cran ggplot2 land-cover-mapping r r-package remote-sensing spectral-unmixing supervised-classification unsupervised-classification
Last synced: about 2 months ago
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Remote Sensing Data Analysis in R 🛰
- Host: GitHub
- URL: https://github.com/bleutner/RStoolbox
- Owner: bleutner
- Created: 2014-05-05T15:08:49.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2024-04-25T09:24:47.000Z (2 months ago)
- Last Synced: 2024-05-15T12:15:53.265Z (about 2 months ago)
- Topics: cran, ggplot2, land-cover-mapping, r, r-package, remote-sensing, spectral-unmixing, supervised-classification, unsupervised-classification
- Language: R
- Homepage:
- Size: 198 MB
- Stars: 254
- Watchers: 25
- Forks: 79
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
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- awesome-stars - bleutner/RStoolbox - Remote Sensing Analysis in R (R)
README
# RStoolbox 
[](https://github.com/bleutner/RStoolbox/actions/workflows/rcmdcheck.yaml)
[](https://CRAN.R-project.org/package=RStoolbox)
[](https://app.codecov.io/gh/bleutner/RStoolbox)
[](https://www.r-pkg.org:443/pkg/RStoolbox)
`RStoolbox` is an R package providing a wide range of tools for your every-day remote sensing processing needs. The available tool-set covers many aspects from data import, pre-processing, data analysis, image classification and graphical display. `RStoolbox` builds upon the `terra` package, which makes it suitable for processing large data-sets even on smaller workstations.
Find out more on the [`RStoolbox` webpage](https://bleutner.github.io/RStoolbox/).
## Installation
The package is available on CRAN and can be installed as usual via
```R
install.packages("RStoolbox")
```
To install the latest version from GitHub you need to have r-base-dev (Linux) or Rtools (Windows) installed.
Then run the following lines:
```R
library(devtools)
install_github("bleutner/RStoolbox")
```
## Get started
`RStoolbox` implements a variety of [remote sensing methods and workflows](https://bleutner.github.io/RStoolbox/reference/RStoolbox.html). Below are a few examples to get started. Further examples can be found in the [documentation of the respective functions](https://bleutner.github.io/RStoolbox/reference/index.html).
### Example 1: Classifications
The example below shows an unsupervised classification workflow based on *kmeans* clustering:
``` r
library(RStoolbox)
# unsupervised classification with 3 classes
uc <- unsuperClass(lsat, nClasses = 3)
# plot result using ggplot
ggR(uc$map, geom_raster = T, forceCat = T) +
scale_fill_manual(values = c("darkgreen", "blue", "sandybrown"))
```
If training data are available, e.g. labeled polygons, `RStoolbox` can be used to conduct a supervised classification. The workflow below employs *randomForest* to train a classification model:
``` r
library(RStoolbox)
library(caret)
library(randomForest)
library(ggplot2)
library(terra)
# example: training data from digitized polygons
train <- readRDS(system.file("external/trainingPolygons_lsat.rds", package="RStoolbox"))
# plot input data
ggRGB(lsat, r = 3, g = 2, b=1, stretch = "lin") +
geom_sf(data = train, aes(fill = class)) +
scale_fill_manual(values = c("yellow", "sandybrown", "darkgreen", "blue"))
#> Coordinate system already present. Adding new coordinate system, which will
#> replace the existing one.
```
``` r
# fit random forest (splitting training into 70\% training data, 30\% validation data)
sc <- superClass(lsat, trainData = train, responseCol = "class",
model = "rf", tuneLength = 1, trainPartition = 0.7)
# print model performance and confusion matrix
sc$modelFit
#> [[1]]
#> TrainAccuracy TrainKappa method
#> 1 0.9992293 0.9988032 rf
#>
#> [[2]]
#> Cross-Validated (5 fold) Confusion Matrix
#>
#> (entries are average cell counts across resamples)
#>
#> Reference
#> Prediction cleared fallen_dry forest water
#> cleared 141.6 0.0 0.0 0.0
#> fallen_dry 0.0 22.0 0.0 0.0
#> forest 0.4 0.0 255.0 0.0
#> water 0.0 0.0 0.0 99.4
#>
#> Accuracy (average) : 0.9992
# plotting: convert class IDs to class labels (factorize) and plot
r <- as.factor(sc$map)
levels(r) <- data.frame(ID = 1:4, class_supervised = levels(train$class))
ggR(r, geom_raster = T, forceCat = T) + scale_fill_manual(values = c("yellow", "sandybrown", "darkgreen", "blue"))
```
Created on 2024-04-19 with [reprex v2.1.0](https://reprex.tidyverse.org)
### Example 2: Spectral Unmixing
`RStoolbox` offers spectral unmixing by implementing the Multiple Endmember Spectral Mixture Analysis (MESMA) approach for estimating fractions of spectral classes, such as spectra of surfaces or materials, on a sub-pixel scale. The following workflow shows a simple Spectral Mixture Analysis (SMA) with single endmembers per class, extracted from the `lsat` example image by cell id:
``` r
library(RStoolbox)
library(terra)
# to perform a SMA, use a single endmember per class, row by row:
em <- data.frame(lsat[c(5294, 47916)])
rownames(em) <- c("forest", "water")
# umix the lsat image
probs <- mesma(img = lsat, em = em)
plot(probs)
```
Instead, one can define multiple endmembers per class to conduct a Multiple Endmember Spectral Mixture Analysis (MESMA):
``` r
library(RStoolbox)
library(terra)
# to perform a MESMA, use multiple endmembers per class, differntiating them
# by a column named 'class':
em <- rbind(
data.frame(lsat[c(4155, 17018, 53134, 69487, 83704)], class = "forest"),
data.frame(lsat[c(22742, 25946, 38617, 59632, 67313)], class = "water")
)
# unmix the lsat image
probs <- mesma(img = lsat, em = em)
plot(probs)
```
``` r
# MESMA can also be performed on more than two endmember classes:
em <- rbind(
data.frame(lsat[c(4155, 17018, 53134, 69487, 83704)], class = "forest"),
data.frame(lsat[c(22742, 25946, 38617, 59632, 67313)], class = "water"),
data.frame(lsat[c(4330, 1762, 1278, 1357, 17414)], class = "shortgrown")
)
# unmix the lsat image
probs <- mesma(img = lsat, em = em)
plot(probs)
```
### Example 3: Cloud Masking
`RStoolbox` comes with a suite of pre-processing functions, including `cloudMask` to identify clouds in optical satellite imagery:
``` r
library(ggplot2)
# lsat example scene, with two tiny clouds in the east
ggRGB(lsat, stretch = "lin")
```
``` r
# calculate cloud index
cldmsk <- cloudMask(lsat, blue = 1, tir = 6)
ggR(cldmsk, 2, geom_raster = TRUE)
```
``` r
# mask by threshold, region-growing around the core cloud pixels
cldmsk_final <- cloudMask(cldmsk, threshold = 0.1, buffer = 5)
## plot cloudmask
ggRGB(lsat, stretch = "lin") +
ggR(cldmsk_final[[1]], ggLayer = TRUE, forceCat = TRUE, geom_raster = TRUE) +
scale_fill_manual(values = c("red"), na.value = NA)
#> Warning: Removed 88752 rows containing missing values or values outside the scale range
#> (`geom_raster()`).
```
### Example 4: Radiometric and atmospheric correction
With `radCor`, users can compute radiometric and simple atmospheric corrections (based on dark object substraction):
``` r
library(terra)
# import Landsat meta data
mtlFile <- system.file("external/landsat/LT52240631988227CUB02_MTL.txt", package="RStoolbox")
metaData <- readMeta(mtlFile)
lsat_t <- stackMeta(mtlFile)
# convert DN to top of atmosphere reflectance and brightness temperature
lsat_ref <- radCor(lsat_t, metaData = metaData, method = "apref")
# correct DN to at-surface-reflecatance with DOS (Chavez decay model)
lsat_sref <- radCor(lsat_t, metaData = metaData)
# correct DN to at-surface-reflecatance with simple DOS and automatic haze estimation
hazeDN <- estimateHaze(lsat_t, hazeBands = 1:4, darkProp = 0.01, plot = FALSE)
lsat_sref <- radCor(lsat_t, metaData = metaData, method = "sdos",
hazeValues = hazeDN, hazeBands = 1:4)
# plot result
ggRGB(lsat_sref, r = 3, g = 2, b = 1, stretch = "lin")
```
Created on 2024-04-19 with [reprex v2.1.0](https://reprex.tidyverse.org)