DINÂMICA ESPAÇO-TEMPORAL DA COBERTURA E USO DA TERRA NO CENTRO-OESTE BRASILEIRO (1985–2023): PRESSÕES CLIMÁTICAS E ANTRÓPICAS, IMPACTOS HÍDRICOS E IMPLICAÇÕES PARA A CONSERVAÇÃO

Abstract

This study addresses the spatiotemporal dynamics of land use and land cover (LULC) in the Brazilian Midwest, encompassing the states of Mato Grosso, Mato Grosso do Sul, and Goiás from 1985 to 2023. The main objectives were to identify conversion patterns among land use classes, assess temporal trends, and explore the implications of these changes, regarding water resources and regional sustainability. Historical Landsat series harmonized with the MapBiomas dataset (Collection 9) were processed in the Google Earth Engine environment with statistical trend tests (Mann–Kendall and Sen’s Slope) applied at the state level. The results showed accelerated conversion of natural formations into agricultural, silvicultural, and livestock activities, resulting in a significant reduction of forest areas and water resources. Mato Grosso State exhibited strong expansion of agricultural and pastureland, while Mato Grosso do Sul State recorded substantial forest loss associated with silvicultural growth. Goiás State presented an expressive increase in agriculture oscillating in water areas associated with both reservoirs and climatic variability. Statistical analyses showed significant trends with Sen’s slope coefficients indicating negative annual rates for forests and water resources, and positive rates for pasture and agriculture, often at a 99% confidence level (p<0.01). Despite the inherent limitations in the use of remote sensing products and the statistical nature of the analyses, the findings underscore the urgency of conservation and water management policies, especially in light of intensified anthropogenic and climatic pressures. In practical terms, the results may support land-use planning, the definition of restoration targets, and the formulation of water management instruments tailored to each state. This study also highlights the need to integrate historical satellite series, process-based hydrological modeling, and field validation to strengthen the scientific foundation of future recommendations.