Exploring spatiotemporal dynamics and drivers of forest ecosystems in southern Europe with explainable machine learning
The condition of forests plays a crucial role in environmental balance and the sustainability of ecosystems. In this context, the study of forest health trends emerges as an essential element to comprehend and address the impacts of environmental drivers. This study explores relationships between fo...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003528 |
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| author | Patricia Arrogante-Funes Fátima Arrogante-Funes Dina Osuna Feijoo Ariadna Álvarez-Ripado Carlos J. Novillo Adrián G. Bruzón |
| author_facet | Patricia Arrogante-Funes Fátima Arrogante-Funes Dina Osuna Feijoo Ariadna Álvarez-Ripado Carlos J. Novillo Adrián G. Bruzón |
| author_sort | Patricia Arrogante-Funes |
| collection | DOAJ |
| description | The condition of forests plays a crucial role in environmental balance and the sustainability of ecosystems. In this context, the study of forest health trends emerges as an essential element to comprehend and address the impacts of environmental drivers. This study explores relationships between forest health (assessed via NDVI) and environmental drivers across Alpine, Atlantic, and Mediterranean biogeographical regions in Spain during 2001–2016. Spatiotemporal dynamics, defined here as changes in forest NDVI trends over time and across geographical areas, are analyses using machine learning techniques (Random Forest and SHAP). The results provide identified key environmental and climatic drivers, which are essential insights for sustainable forest management and policy-making under climate change scenarios. The study period, the national scale employed, and the understanding of these trends make this study a knowledge source for forest managers in Spain, enabling them to identify regions and develop strategies and policies that help alleviate, prevent, and protect forest ecosystems and their associated ecosystem services in this era of global change. |
| format | Article |
| id | doaj-art-a5559efa39e4473c97534e403fe2037d |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-a5559efa39e4473c97534e403fe2037d2025-08-20T05:05:54ZengElsevierEcological Informatics1574-95412025-12-019010334310.1016/j.ecoinf.2025.103343Exploring spatiotemporal dynamics and drivers of forest ecosystems in southern Europe with explainable machine learningPatricia Arrogante-Funes0Fátima Arrogante-Funes1Dina Osuna Feijoo2Ariadna Álvarez-Ripado3Carlos J. Novillo4Adrián G. Bruzón5Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C/Tulipán s/n, Móstoles, 28933 Madrid, Spain; Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Rey Juan Carlos University, C/Tulipán s/n, Móstoles, 28933 Madrid, SpainDepartment of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C/Tulipán s/n, Móstoles, 28933 Madrid, Spain; Corresponding author.Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C/Tulipán s/n, Móstoles, 28933 Madrid, SpainDepartment of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C/Tulipán s/n, Móstoles, 28933 Madrid, SpainDepartment of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C/Tulipán s/n, Móstoles, 28933 Madrid, Spain; Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Rey Juan Carlos University, C/Tulipán s/n, Móstoles, 28933 Madrid, SpainDepartment of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C/Tulipán s/n, Móstoles, 28933 Madrid, SpainThe condition of forests plays a crucial role in environmental balance and the sustainability of ecosystems. In this context, the study of forest health trends emerges as an essential element to comprehend and address the impacts of environmental drivers. This study explores relationships between forest health (assessed via NDVI) and environmental drivers across Alpine, Atlantic, and Mediterranean biogeographical regions in Spain during 2001–2016. Spatiotemporal dynamics, defined here as changes in forest NDVI trends over time and across geographical areas, are analyses using machine learning techniques (Random Forest and SHAP). The results provide identified key environmental and climatic drivers, which are essential insights for sustainable forest management and policy-making under climate change scenarios. The study period, the national scale employed, and the understanding of these trends make this study a knowledge source for forest managers in Spain, enabling them to identify regions and develop strategies and policies that help alleviate, prevent, and protect forest ecosystems and their associated ecosystem services in this era of global change.http://www.sciencedirect.com/science/article/pii/S1574954125003528Forest remote sensingMain driversMachine learningNDVI trendsTime series analysesForest health |
| spellingShingle | Patricia Arrogante-Funes Fátima Arrogante-Funes Dina Osuna Feijoo Ariadna Álvarez-Ripado Carlos J. Novillo Adrián G. Bruzón Exploring spatiotemporal dynamics and drivers of forest ecosystems in southern Europe with explainable machine learning Ecological Informatics Forest remote sensing Main drivers Machine learning NDVI trends Time series analyses Forest health |
| title | Exploring spatiotemporal dynamics and drivers of forest ecosystems in southern Europe with explainable machine learning |
| title_full | Exploring spatiotemporal dynamics and drivers of forest ecosystems in southern Europe with explainable machine learning |
| title_fullStr | Exploring spatiotemporal dynamics and drivers of forest ecosystems in southern Europe with explainable machine learning |
| title_full_unstemmed | Exploring spatiotemporal dynamics and drivers of forest ecosystems in southern Europe with explainable machine learning |
| title_short | Exploring spatiotemporal dynamics and drivers of forest ecosystems in southern Europe with explainable machine learning |
| title_sort | exploring spatiotemporal dynamics and drivers of forest ecosystems in southern europe with explainable machine learning |
| topic | Forest remote sensing Main drivers Machine learning NDVI trends Time series analyses Forest health |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003528 |
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