INTEGRATED ANALYSIS OF MANGROVE CHANGES USING THE MANGROVE VEGETATION INDEX AND RANDOM FOREST CLASSIFICATION IN THE GAMBIA
Keywords: Mangrove forest, Random forest classification, Mangrove vegetation index, Sentinel-2 imagery, Google earth engine
Abstract. The extraction of mangrove forests from satellite imagery is usually accomplished using image classification algorithms. Recently, the mangrove vegetation index (MVI) was developed for rapid and accurate mapping of mangrove extent from remotely sensed imageries. In this study, we combine two techniques (random forest classification and the MVI) to improve the detection of mangrove changes within the Bintang Bolong Estuary in The Gambia. The two techniques were implemented on Sentinel-2 multi-spectral imageries covering the study area at two periods - 2017 and 2020. The random forest classifier was used to extract the full land cover information from which the mangroves were separated, while the MVI was implemented using the green, near-infrared and shortwave infrared bands. Subsequently, the results were extracted for interpretation and analysis. The image classification results showed an increase in mangroves from 38.6 km2 in 2017 to 41.5 km2 in 2020. The areal extent of mangroves from image classification was positively correlated with the MVI-generated extent. The findings prove the importance of combining image classification and spectral indices for gaining more comprehensive perspectives of mangrove changes.