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Assessing above-ground biomass in reforested urban landscapes using machine learning and remotely sensed data.
- Source :
-
Journal of Spatial Science . Sep2024, Vol. 69 Issue 3, p1047-1073. 27p. - Publication Year :
- 2024
-
Abstract
- Urban reforestation mitigates climate change by sequestering carbon, but quantifying carbon gains requires accurate aboveground biomass estimation. This study estimated carbon sequestration in a reforested urban landscape using PlanetScope, Sentinel-1A, Sentinel-2A, SRTM data, and field measurements. Non-parametric machine learning algorithms (k-nearest neighbor, support vector machines, extreme gradient boosting, random forests) with 39 predictor features generated aboveground biomass density maps. The extreme gradient boosting model performed best, predicting 4.1–286.5t ha-1 aboveground biomass, demonstrating its effectiveness for modeling reforested biomass with multi-source data. Findings highlight extreme gradient boosting's promise for urban biomass estimation, the importance of multi-source data, and machine learning's potential in addressing environmental challenges like climate change. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14498596
- Volume :
- 69
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Journal of Spatial Science
- Publication Type :
- Academic Journal
- Accession number :
- 180040696
- Full Text :
- https://doi.org/10.1080/14498596.2024.2343764