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notes.Rmd
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---
title: "Analysing spatial patterns of the landscape"
subtitle: "[Intro2R-spatial](https://github.com/xp-song/Intro2R-spatial) workshop"
author: "[Xiao Ping (XP) Song](https://xp-song.github.io)"
date: "Updated: `r Sys.Date()`"
output:
html_document:
toc: true
toc_depth: 3
toc_float: true
number_sections: true
theme: paper
---
# Set up
Install necessary packages:
```{r install_packages, eval = FALSE}
install.packages("tidyverse", dependencies = TRUE)
install.packages("raster", dependencies = TRUE)
install.packages("sf", dependencies = TRUE)
install.packages("landscapetools", dependencies = TRUE)
install.packages("landscapemetrics", dependencies = TRUE)
```
---
# Prepare data {.tabset .tabset-fade .tabset-pills}
## Quick visualisation
Import only the RGB color bands as individual `RasterLayer` objects:
```{r import images, message=FALSE}
library(raster)
#blue
b2 <- raster('data/Landsat 8 OLI_TIRS C1 Level-1/LC08_L1TP_125059_20180524_20180605_01_T1/LC08_L1TP_125059_20180524_20180605_01_T1_B2.tif')
#green
b3 <- raster('data/Landsat 8 OLI_TIRS C1 Level-1/LC08_L1TP_125059_20180524_20180605_01_T1/LC08_L1TP_125059_20180524_20180605_01_T1_B3.tif')
#red
b4 <- raster('data/Landsat 8 OLI_TIRS C1 Level-1/LC08_L1TP_125059_20180524_20180605_01_T1/LC08_L1TP_125059_20180524_20180605_01_T1_B4.tif')
```
Combine the `RasterLayer` objects and visualise the satellite image:
```{r combine and plot, fig.align='center', dpi= 100}
landsatRGB <- stack(b4, b3, b2) #order is impt
plotRGB(landsatRGB,
stretch = "lin") #scale the values (try using "hist" also)
```
<center>Landsat-8 true color composite (RGB). Source: U.S. Geological Survey.</center>
---
## Import data
Import all 5 bands from the satellite data as a `RasterStack` object named `landsat`:
```{r import all bands}
filenames <- paste0('data/Landsat 8 OLI_TIRS C1 Level-1/LC08_L1TP_125059_20180524_20180605_01_T1/LC08_L1TP_125059_20180524_20180605_01_T1_B', 1:5, ".tif")
landsat <- stack(filenames)
#rename bands
names(landsat) <- c('ultra-blue', 'blue', 'green', 'red', 'NIR')
```
Check coordinate reference system of `landsat`:
```{r check crs of landsat}
crs(landsat)
```
---
## Crop data
Import polygon of city boundaries as `sgshp` and check if their coordinate reference systems match:
```{r import polyon of Singapore, results='hide', message=FALSE}
library(sf)
sgshp <- st_read("data/master-plan-2014-region-boundary-web-shp/MP14_REGION_WEB_PL.shp")
```
Check coordinate reference system of `sgshp`:
```{r check crs of sgshp}
crs(sgshp)
```
Transform `sgshp` to the match the coordinate reference system of the `landsat`:
```{r transform sgshp}
sgshp <- st_transform(sgshp, crs = crs(landsat))
```
Crop `landsat` according to city boundaries:
```{r crop landsat}
landsat <- crop(landsat, sgshp) #crop to rectangle
landsat <- mask(landsat, sgshp) #mask values according to shape of sgshp
```
Plot the cropped image using only the RGB bands:
```{r plot cropped bands, fig.align='center', dpi= 100}
landsatRGB <- subset(landsat, c(4,3,2)) #Red, Green, Blue
plotRGB(landsatRGB,
stretch = "lin")
```
<center>Landsat-8 true color composite (USGS, 2018) cropped to city boundaries (URA, 2014)</center>
---
# Classify land cover {.tabset .tabset-fade .tabset-pills}
## Calculate NDVI
Create a function that calcuates the [Normalized Difference Vegetation Index (NDVI)](https://gisgeography.com/ndvi-normalized-difference-vegetation-index/) for each pixel:
```{r ndvi function}
ndvi <- function(x, y) {
(x - y) / (x + y)
}
```
Apply function to the NIR and Red bands of `landsat`
```{r calculate NDVI}
landsatNDVI <- overlay(landsat[[5]], landsat[[4]],
fun = ndvi)
```
Limit the range of values to be from -1 to 1:
```{r reclassify NDVI}
landsatNDVI <- reclassify(landsatNDVI, c(-Inf, -1, -1)) # <-1 becomes -1
landsatNDVI <- reclassify(landsatNDVI, c(1, Inf, 1)) # >1 becomes 1
```
---
## Visualise NDVI
Map out the NDVI values:
```{r plot NDVI, dpi= 100}
plot(landsatNDVI,
col = rev(terrain.colors(10)),
main = "Landsat 8 NDVI",
axes = FALSE, box = FALSE)
```
Plot histogram of NDVI values:
```{r plot NDVI histogram, dpi= 100}
hist(landsatNDVI,
main = "Distribution of NDVI values", xlab = "NDVI",
xlim = c(-1, 1), breaks = 100, yaxt = 'n')
abline(v=0.2, col="red", lwd=2)
abline(v=0, col="red", lwd=2)
```
---
## Define NDVI threshold
Set 0.2 as the threshold; reclassify values below this threshold to `NA`:
```{r reclassify based on threshold}
landsatGreen <- reclassify(landsatNDVI, c(-1, 0.2, NA)) #-1 to 0.2 becomes NA
```
Plot values of NDVI larger than 0.2
```{r plot vegetation cover, dpi= 100}
plot(landsatGreen,
main = 'Vegetation cover',
col = "darkgreen",
axes = FALSE, box = FALSE, legend = FALSE)
```
---
## Classify using NDVI
Create a matrix to be used as an argument in the `reclassify()` function:
```{r create matrix with thresholds for reclassification}
reclass_m <- matrix(c(-Inf, 0, 1, #water
0, 0.2, 2, #urban
0.2, Inf, 3), #veg
ncol = 3, byrow = TRUE)
reclass_m
```
Classify land cover using the defined threshold values:
```{r classify by NDVI}
landsatCover <- reclassify(landsatNDVI, reclass_m)
```
Plot the land cover classes:
```{r echo = FALSE, fig.align='center', dpi= 100}
plot(landsatCover,
col = c("blue", "grey", "darkgreen"),
legend = FALSE,
axes = FALSE,
box = FALSE,
main = "Land cover (mosaic) in Singapore")
legend("bottomright",
legend = c("Water", "Urban", "Vegetation"),
fill = c("blue", "grey", "darkgreen"),
border = FALSE,
bty = "n")
```
---
## Save raster
Save the reclassified raster `landsatcover` in the GeoTiff format:
```{r save file}
writeRaster(landsatCover,
filename = "clean_data/landsat_landcover.tif",
overwrite = TRUE)
```
---
# Landscape metrics {.tabset .tabset-fade .tabset-pills}
## Quick visualisation
```{r plot land cover}
library(landscapemetrics)
library(landscapetools)
#landsatCover <- raster('clean/landsat_landcover.tif') #reload saved raster
show_landscape(landsatCover, discrete = TRUE)
```
Check if the raster data is in the right format:
```{r check data}
check_landscape(landsatCover)
```
---
## Patch-level
E.g. Area of each patch in the landscape:
```{r}
lsm_p_area(landsatCover)
```
---
## Class-level
E.g. For each class, the total area of all patches:
```{r}
lsm_c_ca(landsatCover)
```
E.g. For each class, the average area of patches:
```{r}
lsm_c_area_mn(landsatCover)
```
---
## Landscape-level
E.g. Total area of the landscape (all three land cover classes):
```{r}
lsm_l_ta(landsatCover)
```
---
# Credits
Spatial data used in this document:
- Landsat-8 satellite images from the [U.S. Geological Survey](https://earthexplorer.usgs.gov/)
- Singapore [Regional Master Plan 2014](https://data.gov.sg/dataset/master-plan-2014-region-boundary-web) from the Urban Redevelopment Authority
---
<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons Licence" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a>
© XP Song