Modelling areas for sustainable forest management in a mining and human dominated landscape: A Geographical Information System (GIS)- Multi-Criteria Decision Analysis (MCDA) approach

Tiamgne, Xavier Takam; Kalaba, Felix Kanungwe; Nyirenda, Vincent R.; Phiri, Darius

doi:10.1080/19475683.2022.2026469

Cited by 8 publications

(1 citation statement)

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“…Thus, geosimulation modelling approaches are seen as suitable for representing forest change processes. Accordingly, several geosimulation models have been implemented to represent the dynamics of forest changes as a complex spatial process including approaches based on cellular automata (CA) [17,18], and some were enhanced with techniques such as Markov chain [19,20], logistic regression [21,22], multi-criteria evaluation (MCE) [23,24], machine learning [25], and deep learning [26]. Several studies have also been incorporating human interactions to represent deforestation processes using agent-based geosimulation models (ABMs) [27,28].…”

Section: Introductionmentioning

confidence: 99%

Modelling Global Deforestation Using Spherical Geographic Automata Approach

Addae,

Dragićević

2023

IJGI

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Deforestation as a land-cover change process is linked to several environmental problems including desertification, biodiversity loss, and ultimately climate change. Understanding the land-cover change process and its relation to human–environment interactions is important for supporting spatial decisions and policy making at the global level. However, current geosimulation model applications mainly focus on characterizing urbanization and agriculture expansion. Existing modelling approaches are also unsuitable for simulating land-cover change processes covering large spatial extents. Thus, the objective of this research is to develop and implement a spherical geographic automata model to simulate deforestation at the global level under different scenarios designed to represent diverse future conditions. Simulation results from the deforestation model indicate the global forest size would decrease by 10.5% under the “business-as-usual” scenario through 2100. The global forest extent would also decline by 15.3% under the accelerated deforestation scenario and 3.7% under the sustainable deforestation scenario by the end of the 21st century. The obtained simulation outputs also revealed the rate of deforestation in protected areas to be considerably lower than the overall forest-cover change rate under all scenarios. The proposed model can be utilized by stakeholders to examine forest conservation programs and support sustainable policy making and implementation.

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