Forest mapping and classification from satellite imagery

Abstract

In recent decades, the availability of remote sensing data for tree species classification, has been increasingly utilised for forest management, and especially so with the increased urgency incurred by climate change. Academia and commercial entities are investing in research to achieve more reliable classification models. This research aims to provide a detailed background of the existing literature, emerging trends, potential areas of research that can be explored further to improve results, and evaluating a further comparison between various classification models over a single contiguous region at the center of Metropolitan France. Feature analysis was made with results rating statistically derived features for Sentinel-2 optical indices for mean and variance within a range of adjacent pixels. Pixel-level supervised classifiers were evaluated comparatively with random forest classifier achieving a kappa score of about 40%, with basic neural network implementations such as deep feed-forward networks, temporal convolutional models, and LSTM models, reaching a kappa score, at best, of around 65% to 70%. A significant portion of this dissertation also investigates the use of U-Net image segmentation, and outlining the results exhibited, under a limited feature set and sparse reference data available, with the results of such models providing reasonable evaluations, ranging from kappa scores of 52% to 83%, depending on the level of detail required from the classifier.