The Importance of Dunnian Runoff in Atlantic Forest Remnants: An Integrated Analysis Between Machine Learning and Spectral Indices

Abstract

This study investigated Dunnian runoff in the Sauípe River basin, Bahia, Brazil, analyzing the relationship between soil moisture, terrain slope, and land use. It utilized Landsat satellite images, annual water balance data, and rainfall data from the last 10 days. The Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) were calculated, along with image classification using the Random Forest machine learning algorithm. (1) Saturated zones with potential for Dunnian runoff were identified, especially on steeper slopes, with a notable negative influence of eucalyptus on soil moisture, except in areas with steeper slopes. (2) Dunnian runoff was predominantly observed from the middle course to the mouth, following the east-west direction of the watershed. (3) Higher areas exhibited Dunnian runoff with high soil moisture values, while areas with less steep slopes showed low moisture levels. (4) The results indicate a positive correlation between steeper slopes and Dunnian runoff and a negative correlation between eucalyptus plantations and soil moisture. (5) Forest fragments exhibited high NDVI and NDWI values, suggesting dense forests with high moisture, especially in areas with steep slopes. This suggests that forest fragments are in good moisture conditions, acting to delay Dunnian runoff. (6) In areas with savannization or without vegetation, significant moisture content was not observed, indicating the absence of intense rainfall in the last ten days of image acquisition. This confirms the importance of this runoff for forest remnants.