Urban Expansion and Agricultural Land Conflict in Southern Brazil: Implications for Sustainable Land Use Policy

Guilherme Peterle Schmitz; Emanuelle Goellner; Brian William Bodah; Marcos Roberto dos Santos; Giana Mores; Grace Tibério Cardoso; Richard Thomas Lermen; Alcindo Neckel

doi:10.63335/j.hp.2026.0033

Abstract

This study adopts an integrated simulation framework based on Cellular Automata and Artificial Neural Networks (CA-ANN) to model land use and land cover (LULC) transitions driven by urban expansion. By combining machine learning with spatial modeling, the approach enables the forecasting of urban growth dynamics and supports data-driven urban planning. The objective is to assess urban sprawl in the city of Passo Fundo, southern Brazil, using LULC change simulations from 2002 to 2043. Satellite imagery from 2002 to 2023 indicate for supervised classification of three land cover classes—urbanized areas, forests, and non-urbanized areas—alongside key spatial variables, including hypsometry, proximity to water bodies, railways, central business districts, and road networks. These variables served as inputs to the CA-ANN model to simulate future land use scenarios for 2033 and 2043. Results indicate a 45% increase in urbanized areas from 2002 to 2023, with projections reaching 66% growth by 2043, absolute land area expansion. This urban expansion primarily occurs at the expense of agricultural and forest areas, underscoring the risks of landscape fragmentation, biodiversity loss, and pressure on agricultural lands. The findings highlight the urgency of integrating spatial intelligence into sustainable land governance strategies, particularly in regions where urbanization intersects with agribusiness territories and food security systems.

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