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

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 Chapeco 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.

Automated summary

The study focused on simulation and analysis of future land use and land cover scenarios using artificial neural networks for environmental and territorial policy making. Results showed the potential disappearance of natural fields in the ecological corridor if proper measures are not taken to protect them.