We all take pictures, don’t we? Similarly, satellite images are pictures of our planet taken by satellites from space. These images are incredibly useful for various purposes making it a very important tool to understand and manage our planet better. Using these images, we can identify different land cover and land types like water bodies, agricultural lands, forests, etc., on the earth's surface. We can detect land cover changes across time which can help in monitoring the environment. As we focus on mangrove carbon today, I’ll take you through a method of identifying mangroves using these satellite images. To identify mangroves, I used images from satellites Landsat 8 and Landsat 9 for 2018 and 2022 especially focusing on a small patch of mangrove in Merces, Goa. Satellite images are captured using sensors that detect various parts of the light spectrum i.e., various bands with various information. These bands are like different colors of the rainbow. When combined, bands like red, green, and blue give us regular color photos that we can see from our eyes. There are infrared bands that capture temperatures of the earth's surface, there are also near-infrared bands that can be used to understand the health of vegetation, and many other bands with specific purposes, and colors that are captured beyond what our eyes can see. For example, you can see, on the left layer panel in Step 1, there are several files loaded and each one has a number ‘B2, B3, B4...’ assigned at the end. These are satellite images with different bands that I downloaded for my area of interest i.e., Merces.
Step 1: Download and load the images Now to start the process of classifying different land cover types including mangroves, we install a plugin called Semi-Automatic Classification Plugin (SCP). This plugin helps us identify the different things in the images quickly on command. So firstly, we load the bands or images in the SCP for the plugin to detect the files for processing. The next thing the plugin does is create a combined image with all the bands together.
Step 2: Open the SCP, load the satellite images, and run it We now have one composite image of all the bands. Objects reflect or absorb light differently in different bands so we can create a number of these band combinations that can be used to identify what we are looking for.
Step 3: Setting band combinations
For example- if we use a natural band combination i.e., 4-3-2 i.e., red-green-blue, it gives us an image almost similar to a natural image that we see from our eyes, see example 1. However, in this combination, it is not very convenient to identify the different land covers easily.
Example 1: A true color composite combination band 4-3-2 Vegetation reflects a lot of light in the Near-infrared band, so it is much easier to identify it using false color combinations rather than natural color combinations. Band combination 5-6-7 (infrared-midinfrared1-midinfrared2) is the most useful combination for identifying mangroves, see example 2. In this combination, we can differentiate the mangroves in striking orange color from the rest of the vegetation.
Example2: A false-color composite combination band 5-6-7 Once we have set the preferred combination of bands, we can move to the process of classification. We use SCP to create a new training set. In the training set, we are then going to name the MC ID which is the Macro class name which will be the feature that we want to identify like in this case, mangroves.
Step 4: Creating a training set
Example 3: ROI (black patch) created on a mangrove patch We then create regions of interest (ROIs) or samples for each class or land cover type. It is basically like training the computer to identify different things in the image. The ROIs that I created are like examples to the computer of what different things look like, say mangroves, waterbodies, agricultural lands, etc. The computer will then be able to classify areas based on the resemblance to the examples I showed.
Step 5: Creating samples of ROIs for different classes To run the classification, we need to first specify an algorithm for the computer. Here we selected the maximum likelihood algorithm which is simply a way for the computer to make the best guess about what is in a particular area of a satellite image by considering the characteristics of the objects in the image.
Step 6: Choosing the algorithm for classification
Step 7: Run an accuracy assessment Once the classification is done, we can also check the accuracy of the output using the same SCP plugin. A report is generated where overall accuracy tells us how much of the classification is done right by the system or person. In this case, I got an 87% overall accuracy meaning 87% of my classification is correctly classified.
Example 4: Accuracy assessment report Once the classification is done, we can change the colors of the different land types accordingly. These are the classified outputs of the mangroves in Merces from 2018 and 2022. A clear change in mangrove cover is visible from 2018 to 2022. We can see that the mangrove cover has decreased in 2022 for some reasons.
Classification of land covers in 2018
Classification of land cover in 2022