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Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution
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| Published in: | Environmental Systems Research |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
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| _version_ | 1868553050555154432 |
|---|---|
| collection | WordPress RSS FRELIP Feed Integration |
| container_title | Environmental Systems Research |
| description | |
| discipline_display | Environmental Studies |
| discipline_facet | Environmental Studies |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:72566 |
| institution | FRELIP |
| journal_source_facet | Environmental Systems Research |
| last_indexed | 2026-06-20T21:30:51.459Z |
| publishDate | 2026 |
| publishDateSort | 2026 |
| record_format | rss_article |
| spellingShingle | Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution Environmental Studies General Environmental Studies |
| sub_discipline_display | General |
| sub_discipline_facet | General |
| subject_display | Environmental Studies General Environmental Studies |
| subject_facet | Environmental Studies General Environmental Studies |
| title | Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution |
| title_alt | Aprendizaje automático y modelado geoespacial de la pérdida forestal, los impulsores y las áreas de riesgo: avanzando en la silvicultura de cobertura continua como una solución basada en la naturaleza Apprentissage automatique et modélisation géospatiale de la perte forestière, des moteurs et des zones à risque : promouvoir la foresterie à couvert continu comme solution fondée sur la nature Aprendizado de máquina e modelagem geoespacial de perda florestal, condutores e áreas de risco: avançando na silvicultura de cobertura contínua como uma solução baseada na natureza |
| title_auth | Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution |
| title_es_txt | Aprendizaje automático y modelado geoespacial de la pérdida forestal, los impulsores y las áreas de riesgo: avanzando en la silvicultura de cobertura continua como una solución basada en la naturaleza |
| title_fr_txt | Apprentissage automatique et modélisation géospatiale de la perte forestière, des moteurs et des zones à risque : promouvoir la foresterie à couvert continu comme solution fondée sur la nature |
| title_full | Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution |
| title_fullStr | Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution |
| title_full_unstemmed | Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution |
| title_pt_txt | Aprendizado de máquina e modelagem geoespacial de perda florestal, condutores e áreas de risco: avançando na silvicultura de cobertura contínua como uma solução baseada na natureza |
| title_short | Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution |
| title_sort | machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution |
| topic | Environmental Studies General Environmental Studies |
| url | https://link.springer.com/article/10.1186/s40068-025-00444-0 |