Classification of Semideciduous Seasonal Forest successional stages using Sentinel-1-2 and SRTM data on Google Earth Engine

Remote sensing data used in this study included MSI (Multispectral Instrument) Sentinel -2, SAR (Synthetic Aperture Radar) Sentinel -1, GLCM (Grey Level Co -Occurrence Matrix) texture data derived from Sentinel -1, and geomorphometric data derived from SRTM (Shuttle Radar Topography Mission) images. The input data was divided into separate groups for machine learning algorithms, including Support Vector Machine (SVM), Classification and Regression Tree (CART), and Random Forest (RF), which were implemented on the Google Earth Engine platform. RF showed the highest overall accuracies (93 to 97%), regardless of the dataset used as input, with the Kappa index ranging from 0.89 (optical and SAR data) to 0.95 (optical, SAR, and geomorphometric data). CART showed identical overall accuracy values (92.5%) except for the dataset supplemented with SAR texture data, which showed slightly lower accuracy (91.7%), with the Kappa index ranging from 0.89 to 0.91. The worst performance was classifying optical data by SVM, resulting in 59% accuracy and a Kappa index of 0.37. However, the synergy of optical, SAR, and geomorphometric data classified by SVM achieved 75% accuracy.