THE IMPORTANCE OF SEASONAL TEXTURAL FEATURES FOR OBJECT-BASED CLASSIFICATION OF WETLANDS: NEW YORK STATE CASE STUDY
Keywords: GLCM, Machine Learning, Random Forest, Google Earth Engine, Sentinel-2, Sentinel-1, DEM, SNIC
Abstract. Seasonal variations result in hydrophytes and undrained hydric soil changes in wetland areas, which lead to a dynamic environment that makes wetland classification challenging. This study aims to explore the applicability of multi-seasonal Gray-Level Co-Occurrence Matrix (GLCM) texture-derived features for object-based wetland classification over large-extent for the first time. We attempted to enhance the performance of the random forest classifier by incorporating multi-source remote sensing data, including Sentinel-2, Sentinel-1, Alos-Palsar, and topographic features. A total of 47 features were extracted from multi-source remote sensing data. In this context, we assessed the applicability of multi- versus mono-seasonal derived features for the wetland's classes with low within-class separability. We investigated the mean decrease in the Gini impurity index for each GLCM feature. We observed that including GLCM features enhanced overall accuracy by 7.38% when using imagery from one season and 4.21% for multi-season imagery. The multi-season scenario that included GLCM measures (93.49%) attained the highest overall accuracy. For this scenario, the means of decrease in Gini impurity index suggested that Soil Adjusted Vegetation Index, Modified Normalized Difference Water Index, slope, correlation in summer (GLCM feature), and Sentinel-1 VH are the most important features in increasing the random forest's classifier performance. In looking at the GLCM features, the separability analysis suggested that Entropy, Sum of Average, and Variance calculated from the summer imagery improve the classifier's performance while other textural features from spring imagery better contributed to classifier accuracy.