Source Materials

The following materials are based on materials developed by Dr. Chris Kibler for the UCSB Geography Department.

Background

Monitoring the distribution and change in land cover types can help us understand the impacts of phenomena like climate change, natural disasters, deforestation, and urbanization. Determining land cover types over large areas is a major application of remote sensing because we are able to distinguish different materials based on their spectral reflectance.

Classifying remotely sensed imagery into land cover classes enables us to understand the distribution and change in land cover types over large areas.

There are many approaches for performing land cover classification:

Supervised approaches use training data labeled by the user
Unsupervised approaches use algorithms to create groups which are identified by the user afterward

Task

In this lab, we are using a form of supervised classification – a decision tree classifier.

Decision trees classify pixels using a series of conditions based on values in spectral bands. These conditions (or decisions) are developed based on training data.

In this lab, we will create a land cover classification for southern Santa Barbara County based on multi-spectral imagery and data on the location of 4 land cover types:

green vegetation
dry grass or soil
urban
water

To do so, we will need to:

Load and process Landsat scene
Crop and mask Landsat data to study area
Extract spectral data at training sites
Train and apply decision tree classifier
Plot results

Getting started

To get started, fork and clone this repository to access all necessary data.

1. Data

Landsat 5 Thematic Mapper

Landsat 5
1 scene from September 25, 2007
Bands: 1, 2, 3, 4, 5, 7
Collection 2 surface reflectance product

Data files:

landsat-data/LT05_L2SP_042036_20070925_20200829_02_T1_SR_B1.tif
landsat-data/LT05_L2SP_042036_20070925_20200829_02_T1_SR_B2.tif
landsat-data/LT05_L2SP_042036_20070925_20200829_02_T1_SR_B3.tif
landsat-data/LT05_L2SP_042036_20070925_20200829_02_T1_SR_B4.tif
landsat-data/LT05_L2SP_042036_20070925_20200829_02_T1_SR_B5.tif
landsat-data/LT05_L2SP_042036_20070925_20200829_02_T1_SR_B7.tif

Study area

Polygon representing southern Santa Barbara county

Data file: SB_county_south.shp

Training data

Polygons representing sites with training data - type: character string with land cover type

Data file: trainingdata.shp

Workflow

1. Set up

To train our classification algorithm and plot the results, we’ll use the rpart and rpart.plot packages.

install.packages("rpart")
install.packages("rpart.plot")

Let’s load all necessary packages:

library(sf) # vector data
library(terra) # raster data
library(here) # file path management
library(tidyverse)
library(rpart) # recursive partitioning and regression trees
library(rpart.plot) # plotting for rpart
library(tmap) # map making

2. Load Landsat data

Let’s create a raster stack. Each file name ends with the band number (e.g. B1.tif).

Notice that we are missing a file for band 6
Band 6 corresponds to thermal data, which we will not be working with for this lab

To create a raster stack, we will create a list of the files that we would like to work with and read them all in at once using the terra::rast() function. We’ll then update the names of the layers to match the spectral bands and plot a true color image to see what we’re working with.

# list files for each band, including the full file path
filelist <- list.files(here::here("data", "landsat-data"), full.names = TRUE)

# read in and store as a raster stack
landsat <- rast(filelist)

# update layer names to match band
names(landsat) <- c("blue", "green", "red", "NIR", "SWIR1", "SWIR2")

# plot true color image
plotRGB(landsat, r = 3, g = 2, b = 1, stretch = "lin")

3. Load study area

We want to constrain our analysis to the southern portion of the county where we have training data, so we’ll read in a file that defines the area we would like to study.

# read in shapefile for southern portion of SB county
SB_county_south <- st_read(here::here("data", "SB_county_south.shp")) %>%
      st_transform(SB_county_south, crs = crs(landsat))

Code

tm_shape(SB_county_south) +
  tm_borders()

4. Crop and mask Landsat data to study area

Now, we can crop and mask the Landsat data to our study area.

Why? This reduces the amount of data we’ll be working with and therefore saves computational time
Bonus: We can also remove any objects we’re no longer working with to save space

# crop Landsat scene to the extent of the SB county shapefile
landsat_cropped <- crop(landsat, SB_county_south)

# mask the raster to southern portion of SB county
landsat_masked <- mask(landsat_cropped, SB_county_south)

# remove unnecessary object from environment
rm(landsat, SB_county_south, landsat_cropped)

plotRGB(landsat_masked, r = 3, g = 2, b = 1, stretch = "lin")

5. Convert Landsat values to reflectance

Now we need to convert the values in our raster stack to correspond to reflectance values. To do so, we need to remove erroneous values and apply any scaling factors to convert to reflectance.

In this case, we are working with Landsat Collection 2.

The valid range of pixel values for this collection goes from 7,273 to 43,636…
- with a multiplicative scale factor of 0.0000275
- with an additive scale factor of -0.2

Let’s reclassify any erroneous values as NA and update the values for each pixel based on the scaling factors. Now the pixel values should range from 0-100%!

# reclassify erroneous values as NA
rcl <- matrix(c(-Inf, 7273, NA,
                 43636, Inf, NA), ncol = 3, byrow = TRUE)

landsat <- classify(landsat_masked, rcl = rcl)

# adjust values based on scaling factor
landsat <- (landsat * 0.0000275 - 0.2) * 100

# check values are 0 - 100
summary(landsat)

      blue           green            red             NIR       
 Min.   : 1.11   Min.   : 0.74   Min.   : 0.00   Min.   : 0.23  
 1st Qu.: 2.49   1st Qu.: 2.17   1st Qu.: 1.08   1st Qu.: 0.75  
 Median : 3.06   Median : 4.59   Median : 4.45   Median :14.39  
 Mean   : 3.83   Mean   : 5.02   Mean   : 4.92   Mean   :11.52  
 3rd Qu.: 4.63   3rd Qu.: 6.76   3rd Qu.: 7.40   3rd Qu.:19.34  
 Max.   :39.42   Max.   :53.32   Max.   :56.68   Max.   :57.08  
 NA's   :39856   NA's   :39855   NA's   :39855   NA's   :39856  
     SWIR1           SWIR2      
 Min.   : 0.10   Min.   : 0.20  
 1st Qu.: 0.41   1st Qu.: 0.60  
 Median :13.43   Median : 8.15  
 Mean   :11.88   Mean   : 8.52  
 3rd Qu.:18.70   3rd Qu.:13.07  
 Max.   :49.13   Max.   :48.07  
 NA's   :42892   NA's   :46809

6. Training classifier

Let’s begin by extracting reflectance values for training data!

We will load the shapefile identifying locations within our study area as containing one of our 4 land cover types.

# read in and transform training data
training_data <- st_read(here::here( "data", "trainingdata.shp")) %>%
  st_transform(., crs = crs(landsat))

Now, we can extract the spectral reflectance values at each site to create a data frame that relates land cover types to their spectral reflectance.

# extract reflectance values at training sites
training_data_values <- terra::extract(landsat, training_data, df = TRUE)

# convert training data to data frame
training_data_attributes <- training_data %>%
  st_drop_geometry()

# join training data attributes and extracted reflectance values
SB_training_data <- left_join(training_data_values, training_data_attributes,
                              by = c("ID" = "id")) %>%
                    mutate(type = as.factor(type)) # convert landcover type to factor

Next, let’s train the decision tree classifier!

To train our decision tree, we first need to establish our model formula (i.e. what our response and predictor variables are).

The rpart() function implements the CART algorithm
The rpart() function needs to know the model formula and training data you would like to use
Because we are performing a classification, we set method = "class"
We also set na.action = na.omit to remove any pixels with NAs from the analysis.

# establish model formula
SB_formula <- type ~ red + green + blue + NIR + SWIR1 + SWIR2

# train decision tree
SB_decision_tree <- rpart(formula = SB_formula,
                          data = SB_training_data,
                          method = "class",
                          na.action = na.omit)

To understand how our decision tree will classify pixels, we can plot the results!

Interpreting decision trees

Note: The decision tree is comprised of a hierarchy of binary decisions. Each decision rule has 2 outcomes based on a conditional statement pertaining to values in each spectral band.

Code

# plot decision tree
prp(SB_decision_tree)

7. Classify image

Now that we have a rule set for classifying spectral reflectance values into landcover types, we can apply the classifier to identify the landcover type in each pixel.

The terra package includes a predict() function that allows us to apply a model to our data. In order for this to work properly, the names of the layers need to match the column names of the predictors we used to train our decision tree. The predict() function will return a raster layer with integer values. These integer values correspond to the factor levels in the training data. To figure out what category each integer corresponds to, we can inspect the levels of our training data.

# classify image based on decision tree
SB_classification <- terra::predict(landsat, SB_decision_tree, type = "class", na.rm = TRUE)

# inspect level to understand the order of classes in prediction
levels(SB_training_data$type)

[1] "green_vegetation" "soil_dead_grass"  "urban"            "water"

8. Plot results

Now we can plot the results and check out our land cover map!

Code

# plot results
tm_shape(SB_classification) +
  tm_raster(palette = c("#8DB580", "#F2DDA4", "#7E8987", "#6A8EAE"),
labels = c("green vegetation", "soil/dead grass", "urban", "water"),
title = "Landcover type") +
  tm_layout(legend.position = c("left", "bottom"),
            main.title = "Santa Barbara Landcover")

Critical thinking

Do these results make sense?