A landscape ecology approach

Modeling forest fragmentation with artificial neural networks and cellular‐automata Markov‐chain for improved environmental policy in the southwestern Brazilian Amazon

Bibliographic Data

ID21648312
AuthorsRodrigo Martins Moreira (0000-0001-6794-6026, Department of Environmental Engineering Federal University of Rondônia Ji‐Paraná Brazil, corresponding author), Marcos Lana (0000-0002-1733-1100, Swedish University of Agricultural Sciences Uppsala Sweden), Stefan Sieber (0000-0002-4849-7277, Leibniz Centre of Agricultural Landscape Research (ZALF) Müncheberg Germany), Tadeu Fabrício Malheiros (0000-0002-9455-4199, Department of Hydraulics and Sanitation University of São Paulo São Paulo Brazil)
Year2024
Volume35
Issue2
Pages687-704
Publication date2024-01-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLand Degradation and Development (JOURNAL)
Journal identifiersISSN: 1085-3278 • E-ISSN: 1099-145X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ldr.4945
OpenAlexW4388935802
LanguageEN
Citations received1
References cited57

Land degradation and forest fragmentation have been prominent issues in the Amazon since the 1970s, primarily driven by the suppression of primary forests due to land use changes. In this study, we propose an innovative approach by integrating artificial neural networks (ANN) and cellular automata Markov chain (CA‐MA) models to predict future land use and assess the associated forest fragmentation using landscape ecology metrics in the Jamari River Basin. The analysis reveals a significant increase in fragmentation between 1985 and 2018, as evidenced by a rise in the number of fragments from 7162 to 28,170. Moreover, we observed a decrease in the core area from approximately 2.5 million hectares to less than 1 million hectares, accompanied by an increase in edge density from 6 to 18 m.ha −1 . Additionally, the average distance between fragments expanded from 66 to 95 m. Highlighting the urgency of addressing this issue, our study emphasizes the necessity of exploring effective strategies within the Brazilian public environmental management system. By utilizing various rural policy planning tools, we aim to tackle unsustainable development and mitigate the adverse impacts of land use changes in the region. In conclusion, this research offers an innovative approach that combines ANN and CA‐MA models to predict land use and assess forest fragmentation, shedding light on the alarming trend of land degradation and providing valuable insights for informed decision‐making and sustainable land management practices

Amazon rainforest · Biodiversity · Biology · Cellular automaton · Environmental planning · Environmental resource management · Forest fragmentation · Geography · Land use · Landscape ecology · Machine learning · Markov chain · Computer Science · Conservation, Biodiversity, and Resource Management · Environmental Science · Land Rights and Reforms · Land Use and Ecosystem Services · Artificial Intelligence · Ecology

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Open DOIOpen Access