Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil

Juliana Mio de Souza, Paulo Morgado, Eduarda Marques da Costa and Luiz Fernando de Novaes Vianna
Additional contact information
Juliana Mio de Souza: Agricultural Research and Extension Service Institution of the State of Santa Catarina, Rua Admar Gonzaga, 1347, Itacorubi, Florianópolis 88034-901, Brazil
Paulo Morgado: Centre of Geographical Studies, Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
Eduarda Marques da Costa: Centre of Geographical Studies, Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
Luiz Fernando de Novaes Vianna: Agricultural Research and Extension Service Institution of the State of Santa Catarina, Rua Admar Gonzaga, 1347, Itacorubi, Florianópolis 88034-901, Brazil

Sustainability, 2022, vol. 14, issue 7, 1-23

Abstract: The simulation and analysis of future land use and land cover—LULC scenarios using artificial neural networks (ANN)—has been applied in the last 25 years, producing information for environmental and territorial policy making and implementation. LULC changes have impacts on many levels, e.g., climate change, biodiversity and ecosystem services, soil quality, which, in turn, have implications for the landscape. Therefore, it is fundamental that planning is informed by scientific evidence. The objective of this work was to develop a geographic model to identify the main patterns of LULC transitions between the years 2000 and 2018, to simulate a baseline scenario for the year 2036, and to assess the effectiveness of the Chapecó River ecological corridor (an area created by State Decree No. 2.957/2010), regarding the recovery and conservation of forest remnants and natural fields. The results indicate that the forest remnants have tended to recover their area, systematically replacing silviculture areas. However, natural fields (grassland) are expected to disappear in the near future if proper measures are not taken to protect this ecosystem. If the current agricultural advance pattern is maintained, only 0.5% of natural fields will remain in the ecological corridor by 2036. This LULC trend exposes the low effectiveness of the ecological corridor (EC) in protecting and restoring this vital ecosystem.

Keywords: LULC change; machine learning; simulation; spatial planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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