The Importance of Dunnian Runoff in Atlantic Forest Remnants: An Integrated Analysis Between Machine Learning and Spectral Indices  
[PERSON]  
1 Department of Exact and Earth Sciences II DCET II, State University of Bahia UNEB, Alagoinhas/Salvador Highway, BR110, Km 03, Alagoinhas 48000-000, BA, Brazil; [EMAIL_ADDRESS] (M.R.B.d.M. ); [EMAIL_ADDRESS] (M.B.F.) [PERSON]  
1 Department of Exact and Earth Sciences II DCET II, State University of Bahia UNEB, Alagoinhas/Salvador Highway, BR110, Km 03, Alagoinhas 48000-000, BA, Brazil; [EMAIL_ADDRESS] (M.R.B.d.M. ); [EMAIL_ADDRESS] (M.B.F.) [PERSON]  
1 Department of Exact and Earth Sciences II DCET II, State University of Bahia UNEB, Alagoinhas/Salvador Highway, BR110, Km 03, Alagoinhas 48000-000, BA, Brazil; [EMAIL_ADDRESS] (M.R.B.d.M. ); [EMAIL_ADDRESS] (M.B.F.) Tresse Avenne, 338 University City, Sao Paulo 05508-000, SP, Brazil; lusanira@usp. these areas through efficient and accurate methods is essential. In this context, satellite imagery and digital image processing gain relevance, allowing for robust and low-cost mapping, ideal for analyzing complex biomes such as the Atlantic Forest. Remote sensing technologies, such as the use of satellite images, play a fundamental role in monitoring important hydrological processes, especially in fragmented biomes like the Atlantic Forest. One of these processes is Dunnian runoff, also known as saturation overland flow. This phenomenon occurs in areas where the soil reaches its maximum infiltration capacity, becoming saturated with water after intense precipitation. Dunnian runoff typically begins in riparian zones, areas close to watercourses where the water table is shallow, and is particularly common in areas with steep slopes. The importance of this runoff lies in its ability to significantly influence the hydrological dynamics and behavior of watercourses in watersheds, many of which are located in the Atlantic Forest. With the use of satellite images, it is possible to accurately map and monitor these saturated areas, providing a detailed view of the interactions between terrain and vegetation. This includes analyzing how the remaining fragments of the Atlantic Forest impact the behavior of Dunnian runoff. This type of monitoring is essential for developing conservation strategies, helping to better understand the role of native vegetation and the impact of biome fragmentation on hydrological processes, and offering support for more sustainable and effective environmental management practices. It is widely recognized that wetlands play a crucial ecological role, and with their importance is globally acknowledged by the scientific community [3]. Wetlands are among the most significant, dynamic, and diverse ecosystems on the planet, characterized by a unique combination of land and water in both inland and coastal environments [3]. The study of these areas becomes even more essential, given that they are increasingly being degraded [4]. Significant studies have been conducted in various regions of the world using spectral indices, machine learning, and hydrology to analyze wetlands. One example is the work by [3], which employed unsupervised machine learning algorithms (K-means--KMC) and supervised (Support Vector Machine classifier--SVMc) applied to Landsat images to model NDVI and NDWI in the Selisoo Bog area, Estonia. The researchers concluded that the bog areas stood out in terms of coverage over the swamp. Another relevant study was conducted by [5], who used MODIS sensor images to analyze land cover changes in the Poyang Lake wetlands, China. The authors applied vegetation indices, the ISODATA method to cluster the detected differences, and decision trees to classify the data. They concluded that the classification was effective in mapping this ecosystem. Other works focusing on wetlands and remote sensing, particularly incorporating machine learning, include the study by [6], which investigated the health of wetlands in the moribund delta region of India.