Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures

• Autor(a) principal: Vieira, Alessandra Soares
• Data de Publicação: 2021
• Outros Autores: do Valle Junior, Renato Farias [UNESP], Rodrigues, Vinicius Silva, da Silva Quinaia, Thiago Luiz, Mendes, Rafaella Gouveia, Valera, Carlos Alberto [UNESP], Fernandes, Luís Filipe Sanches [UNESP], Pacheco, Fernando António Leal [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.scitotenv.2021.146019; http://hdl.handle.net/11449/205962
• topic: Brightness index; Environmental land use conflict; Geographic information system; Pasture degradation; Water erosion; “Polluter-pays principle”

The inadequate management of soils and the absence of conservation practices favor the degradation of pastures and can trigger adverse environmental alterations and damage under the terms of Brazilian Federal Law no. 6.938/1981. Based on this premise, this study aimed to estimate soil losses caused by water erosion in pasture areas using the brightness index (BI) from the annual series of Landsat 8 images in different geological formations. A specifically prepared Google Earth Engine (GEE) script automatically extracted the BI from the images. The study occurred in the Environmental Protection Area (EPA) of Uberaba River basin (Minas Gerais, Brazil). To accomplish the goal, 180 digital 500-wide random buffers were selected from 3 geologic types (60 points per type), and then analyzed for zonal statistics of USLE (Universal Soil Loss Equation) soil loss and BI in a Geographic Information System. The regression models BI versus USLE soil loss allowed estimating BI soil losses over the pastures of EPA. The model fittings were remarkable. The validation of soil loss maps in the EPA occurred in pasture phytophysiognomies through the probing of penetration resistance in 37 randomly selected locations. The results were satisfactory, mostly those based on the BI. The BI losses increased for greater resistances. Amplified losses also occurred in regions exposed to environmental land use conflicts (actual uses that deviate from land capability or natural use). Overall, the BI approach proved efficient to accurately track soil losses and pasture degradation over large areas, with the advantage of standing on a single parameter easily accessed through remote sensed data. From an environmental standpoint, this is an important result, because the accurate diagnosis and prognosis of degraded pastures is paramount to implement mitigation measures following the “polluter pays principle”, even more in Brazil where the areas occupied by degraded pastures are enormous.