Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Machine learning and geospatial modeling of forest loss, drivers, and risk areas: advancing continuous cover forestry as a nature-based solution
Saved in:
| Published in: | Environmental Systems Research |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867301676030885890 |
|---|---|
| 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 |
| 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 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_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 General Environmental Studies |
| url | https://link.springer.com/article/10.1186/s40068-025-00444-0 |