Spacialization of the Degradation Risk in the Natural Monument of the São Francisco River

Abstract

Understanding changes related to environmental degradation by parameters such as Normalized Difference Vegetation Index (NDVI), Surface Albedo (α) and Moving Standard Deviation Index (MDSI) has been of great relevance in the study of environmental impacts. The objective of the present study was to analyze the evolution of soil degradation and also of the soil use and occupation in the San Francisco River Natural Monument, using surface data and images from Landsat-5 and Landsat-8, for the 1987, 1997, 2007 and 2017 years. Remote sensing techniques were used to estimate indices such as NDVI, albedo (α) and MDSI. The change detection technique and decision tree classification based on predefined rules in NDVI, albedo and MDSI were applied to infer degradation, soil use and occupation. There was a significant increase in degradation, especially for areas with high degradation. Vegetation indices showed the lowest values for areas of low vegetation and exposed soil, being the highest values found for Caatinga dense vegetation. It was concluded that the change detection technique and decision tree classification were efficient in identifying the degradation during the study period. The change detection technique algorithm was more sensitive to water bodies than the change intensity technique.