| Autor: |
Sarika Khanwilkar, Chris Galletti, Pinki Mondal, Johannes Urpelainen, Harini Nagendra, Yadvendradev Jhala, Qamar Qureshi, Ruth DeFries |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Scientific Data, Vol 10, Iss 1, Pp 1-8 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2052-4463 |
| DOI: |
10.1038/s41597-023-02634-w |
| Popis: |
Abstract Satellite imagery has been used to provide global and regional estimates of forest cover. Despite increased availability and accessibility of satellite data, approaches for detecting forest degradation have been limited. We produce a very-high resolution 3-meter (m) land cover dataset and develop a normalized index, the Bare Ground Index (BGI), to detect and map exposed bare ground within forests at 90 m resolution in central India. Tree cover and bare ground was identified from Planet Labs Very High-Resolution satellite data using a Random Forest classifier, resulting in a thematic land cover map with 83.00% overall accuracy (95% confidence interval: 61.25%–90.29%). The BGI is a ratio of bare ground to tree cover and was derived by aggregating the land cover. Results from field data indicate that the BGI serves as a proxy for intensity of forest use although open areas occur naturally. The BGI is an indicator of forest health and a baseline to monitor future changes to a tropical dry forest landscape at an unprecedented spatial scale. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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