Modelling Global Deforestation Using Spherical Geographic Automata Approach
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 dec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/12/8/306 |
| _version_ | 1797584508521807872 |
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| author | Bright Addae Suzana Dragićević |
| author_facet | Bright Addae Suzana Dragićević |
| author_sort | Bright Addae |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:53:40Z |
| format | Article |
| id | doaj.art-bef6da1a1b674fc08cc4e73dd83323d8 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T23:53:40Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-bef6da1a1b674fc08cc4e73dd83323d82023-11-19T01:23:34ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-07-0112830610.3390/ijgi12080306Modelling Global Deforestation Using Spherical Geographic Automata ApproachBright Addae0Suzana Dragićević1Spatial Analysis and Modeling Laboratory, Department of Geography, Simon Fraser University, Burnaby, BC V5A 1S6, CanadaSpatial Analysis and Modeling Laboratory, Department of Geography, Simon Fraser University, Burnaby, BC V5A 1S6, CanadaDeforestation 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.https://www.mdpi.com/2220-9964/12/8/306spherical geographic automatamodelling global deforestationland-cover changecomplex spatial systemsgeographic information systems |
| spellingShingle | Bright Addae Suzana Dragićević Modelling Global Deforestation Using Spherical Geographic Automata Approach ISPRS International Journal of Geo-Information spherical geographic automata modelling global deforestation land-cover change complex spatial systems geographic information systems |
| title | Modelling Global Deforestation Using Spherical Geographic Automata Approach |
| title_full | Modelling Global Deforestation Using Spherical Geographic Automata Approach |
| title_fullStr | Modelling Global Deforestation Using Spherical Geographic Automata Approach |
| title_full_unstemmed | Modelling Global Deforestation Using Spherical Geographic Automata Approach |
| title_short | Modelling Global Deforestation Using Spherical Geographic Automata Approach |
| title_sort | modelling global deforestation using spherical geographic automata approach |
| topic | spherical geographic automata modelling global deforestation land-cover change complex spatial systems geographic information systems |
| url | https://www.mdpi.com/2220-9964/12/8/306 |
| work_keys_str_mv | AT brightaddae modellingglobaldeforestationusingsphericalgeographicautomataapproach AT suzanadragicevic modellingglobaldeforestationusingsphericalgeographicautomataapproach |