Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence
Peruvian Amazonian rainforests are constantly threatened by forest loss. Understanding changes in forest cover and assessing the level of risk is a permanent concern for numerous scientists and forest authorities. There are many conservation programs for Peruvian forests that involve collaborative e...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Trees, Forests and People |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666719323000729 |
| _version_ | 1827630296811438080 |
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| author | Gianmarco Goycochea Casas Juan Rodrigo Baselly-Villanueva Mathaus Messias Coimbra Limeira Carlos Moreira Miquelino Eleto Torres Hélio Garcia Leite |
| author_facet | Gianmarco Goycochea Casas Juan Rodrigo Baselly-Villanueva Mathaus Messias Coimbra Limeira Carlos Moreira Miquelino Eleto Torres Hélio Garcia Leite |
| author_sort | Gianmarco Goycochea Casas |
| collection | DOAJ |
| description | Peruvian Amazonian rainforests are constantly threatened by forest loss. Understanding changes in forest cover and assessing the level of risk is a permanent concern for numerous scientists and forest authorities. There are many conservation programs for Peruvian forests that involve collaborative efforts and employ diverse methodologies for forest monitoring. In this study, we propose an alternative approach to decision-making for forest preservation, aiming to classify the risk of forest loss in districts within the Peruvian Amazon rainforest. This classification enables sustainable forest management. To accomplish this, we utilized unsupervised learning artificial intelligence through Kohonen's neural network. The network was trained using a historical database spanning from 2001 to 2021, which includes variables such as forest cover and loss, climate, topography, hydrographic networks, and timber forest concessions. Through this approach, the network successfully established five clusters. Following preliminary analysis, we designated these clusters as: low, medium, high, very high, and extremely high risk of forest loss. Kohonen networks demonstrated their effectiveness in clustering forest loss and forest cover. The results indicate a shifting trend among the classes over time, with an increase in the categories exhibiting high and very high risk of forest cover loss. This study provides valuable information for decision-making in the prevention and conservation of Peruvian forests. We strongly recommend maintaining vigilance, particularly in districts classified as a very high or extremely high risk of losing forest cover. |
| first_indexed | 2024-03-09T14:03:51Z |
| format | Article |
| id | doaj.art-5bfec294552c458b888890aff28e90ab |
| institution | Directory Open Access Journal |
| issn | 2666-7193 |
| language | English |
| last_indexed | 2024-03-09T14:03:51Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Trees, Forests and People |
| spelling | doaj.art-5bfec294552c458b888890aff28e90ab2023-11-30T05:11:36ZengElsevierTrees, Forests and People2666-71932023-12-0114100440Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligenceGianmarco Goycochea Casas0Juan Rodrigo Baselly-Villanueva1Mathaus Messias Coimbra Limeira2Carlos Moreira Miquelino Eleto Torres3Hélio Garcia Leite4Department of Forest Engineering, Federal Univdaersity of Viçosa, Av. Purdue, s/n, Viçosa Campus, Zip Code 36570-900, Viçosa, MG, Brazil; Corresponding author.Estación Experimental Agraria San Roque, Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Calle San Roque 209, San Juan Bautista, Maynas, Loreto 16430, PeruDepartment of Forest Engineering, Federal Univdaersity of Viçosa, Av. Purdue, s/n, Viçosa Campus, Zip Code 36570-900, Viçosa, MG, BrazilDepartment of Forest Engineering, Federal Univdaersity of Viçosa, Av. Purdue, s/n, Viçosa Campus, Zip Code 36570-900, Viçosa, MG, BrazilDepartment of Forest Engineering, Federal Univdaersity of Viçosa, Av. Purdue, s/n, Viçosa Campus, Zip Code 36570-900, Viçosa, MG, BrazilPeruvian Amazonian rainforests are constantly threatened by forest loss. Understanding changes in forest cover and assessing the level of risk is a permanent concern for numerous scientists and forest authorities. There are many conservation programs for Peruvian forests that involve collaborative efforts and employ diverse methodologies for forest monitoring. In this study, we propose an alternative approach to decision-making for forest preservation, aiming to classify the risk of forest loss in districts within the Peruvian Amazon rainforest. This classification enables sustainable forest management. To accomplish this, we utilized unsupervised learning artificial intelligence through Kohonen's neural network. The network was trained using a historical database spanning from 2001 to 2021, which includes variables such as forest cover and loss, climate, topography, hydrographic networks, and timber forest concessions. Through this approach, the network successfully established five clusters. Following preliminary analysis, we designated these clusters as: low, medium, high, very high, and extremely high risk of forest loss. Kohonen networks demonstrated their effectiveness in clustering forest loss and forest cover. The results indicate a shifting trend among the classes over time, with an increase in the categories exhibiting high and very high risk of forest cover loss. This study provides valuable information for decision-making in the prevention and conservation of Peruvian forests. We strongly recommend maintaining vigilance, particularly in districts classified as a very high or extremely high risk of losing forest cover.http://www.sciencedirect.com/science/article/pii/S2666719323000729Kohonen neural networkForest conservationForest preventionRisk classification |
| spellingShingle | Gianmarco Goycochea Casas Juan Rodrigo Baselly-Villanueva Mathaus Messias Coimbra Limeira Carlos Moreira Miquelino Eleto Torres Hélio Garcia Leite Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence Trees, Forests and People Kohonen neural network Forest conservation Forest prevention Risk classification |
| title | Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence |
| title_full | Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence |
| title_fullStr | Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence |
| title_full_unstemmed | Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence |
| title_short | Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence |
| title_sort | classifying the risk of forest loss in the peruvian amazon rainforest an alternative approach for sustainable forest management using artificial intelligence |
| topic | Kohonen neural network Forest conservation Forest prevention Risk classification |
| url | http://www.sciencedirect.com/science/article/pii/S2666719323000729 |
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