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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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| Subjects: | |
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| _version_ | 1864030192523018243 |
|---|---|
| collection | WordPress RSS FRELIP Feed Integration |
| container_title | Environmental Systems Research |
| description | |
| discipline_display | Environmental Sciences |
| discipline_facet | Environmental Sciences |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:37672 |
| institution | FRELIP |
| journal_source_facet | Environmental Systems Research |
| 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 Environmental Management Environmental Sciences |
| sub_discipline_display | Environmental Management |
| sub_discipline_facet | Environmental Management |
| subject_display | — — — — — Environmental Studies Environmental Management Environmental Sciences — — — — — Environmental Studies Environmental Management Environmental Sciences |
| subject_facet | — — — — — Environmental Studies Environmental Management Environmental Sciences |
| title | Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution |
| title_auth | Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution |
| 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_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 Environmental Management Environmental Sciences |
| url | https://link.springer.com/article/10.1186/s40068-025-00444-0 |