From pixels to objects: Integrated indicators for balancing sustainable management in protected areas
Balancing human activities with environmental protection is a critical challenge in managing Protected Areas (PAs). Spatial zoning serves as the cornerstone of PA management and plays a crucial role in harmonizing conservation with development. This study introduces an integrated land-use management...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-11-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003802 |
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| author | Reza Peykanpour Fard Alireza Soffianian Mohsen Ahmadi Saeid Pourmanafi |
| author_facet | Reza Peykanpour Fard Alireza Soffianian Mohsen Ahmadi Saeid Pourmanafi |
| author_sort | Reza Peykanpour Fard |
| collection | DOAJ |
| description | Balancing human activities with environmental protection is a critical challenge in managing Protected Areas (PAs). Spatial zoning serves as the cornerstone of PA management and plays a crucial role in harmonizing conservation with development. This study introduces an integrated land-use management strategy that supports conservation, rehabilitation, tourism, and multilateral objectives. The approach combines pixel-based optimization with AI-enhanced object-based evaluation. Using GIS, we compiled and mapped a comprehensive set of biological, physical, infrastructural, and socio-economic criteria. Two pixel-based methods, Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA), were applied to assess land-use suitability. A decision space was established using the Multi-Objective Land Allocation (MOLA) method. Object-based land allocation was subsequently performed using a suite of artificial intelligence models, including Boosted Regression Trees (BRT), Artificial Neural Networks (ANN), Classification and Regression Trees (CART), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The results showed that the OWA method outperformed WLC in land suitability analysis. When integrated into the object-based approach, the RF model demonstrated the highest allocation performance, followed by ANN. Notably, RF and MOLA models showed the highest spatial agreement. This integrative framework underscores the potential of combining advanced AI-driven object-based methods with conventional pixel-based techniques to strengthen land management in PAs. The findings offer actionable insights for sustainable spatial planning in protected areas where land-use goals may be diverse and competing. |
| format | Article |
| id | doaj-art-5092f5b10ddd4f839bd55eb5048e9643 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-5092f5b10ddd4f839bd55eb5048e96432025-08-23T04:47:48ZengElsevierEcological Informatics1574-95412025-11-019110337110.1016/j.ecoinf.2025.103371From pixels to objects: Integrated indicators for balancing sustainable management in protected areasReza Peykanpour Fard0Alireza Soffianian1Mohsen Ahmadi2Saeid Pourmanafi3Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, IranCorresponding author.; Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, IranDepartment of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, IranDepartment of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, IranBalancing human activities with environmental protection is a critical challenge in managing Protected Areas (PAs). Spatial zoning serves as the cornerstone of PA management and plays a crucial role in harmonizing conservation with development. This study introduces an integrated land-use management strategy that supports conservation, rehabilitation, tourism, and multilateral objectives. The approach combines pixel-based optimization with AI-enhanced object-based evaluation. Using GIS, we compiled and mapped a comprehensive set of biological, physical, infrastructural, and socio-economic criteria. Two pixel-based methods, Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA), were applied to assess land-use suitability. A decision space was established using the Multi-Objective Land Allocation (MOLA) method. Object-based land allocation was subsequently performed using a suite of artificial intelligence models, including Boosted Regression Trees (BRT), Artificial Neural Networks (ANN), Classification and Regression Trees (CART), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The results showed that the OWA method outperformed WLC in land suitability analysis. When integrated into the object-based approach, the RF model demonstrated the highest allocation performance, followed by ANN. Notably, RF and MOLA models showed the highest spatial agreement. This integrative framework underscores the potential of combining advanced AI-driven object-based methods with conventional pixel-based techniques to strengthen land management in PAs. The findings offer actionable insights for sustainable spatial planning in protected areas where land-use goals may be diverse and competing.http://www.sciencedirect.com/science/article/pii/S1574954125003802Land allocationArtificial intelligenceEnvironmental planningDecision-making |
| spellingShingle | Reza Peykanpour Fard Alireza Soffianian Mohsen Ahmadi Saeid Pourmanafi From pixels to objects: Integrated indicators for balancing sustainable management in protected areas Ecological Informatics Land allocation Artificial intelligence Environmental planning Decision-making |
| title | From pixels to objects: Integrated indicators for balancing sustainable management in protected areas |
| title_full | From pixels to objects: Integrated indicators for balancing sustainable management in protected areas |
| title_fullStr | From pixels to objects: Integrated indicators for balancing sustainable management in protected areas |
| title_full_unstemmed | From pixels to objects: Integrated indicators for balancing sustainable management in protected areas |
| title_short | From pixels to objects: Integrated indicators for balancing sustainable management in protected areas |
| title_sort | from pixels to objects integrated indicators for balancing sustainable management in protected areas |
| topic | Land allocation Artificial intelligence Environmental planning Decision-making |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003802 |
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