Estimating Forest and Woodland Aboveground Biomass Using Active and Passive Remote Sensing
| Autor: | Zhuoting Wu, Dennis G. Dye, Barry R. Middleton, John M. Vogel |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Biomass (ecology)
010504 meteorology & atmospheric sciences 0211 other engineering and technologies 02 engineering and technology Woodland Land cover 01 natural sciences Geography Lidar Remote sensing (archaeology) Spatial ecology Ecosystem Computers in Earth Sciences Aboveground biomass 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Photogrammetric Engineering & Remote Sensing. 82:271-281 |
| ISSN: | 0099-1112 |
| DOI: | 10.14358/pers.82.4.271 |
| Popis: | Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R 2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14 Mg ha −1 across all woodland and forest species. Land-sat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha −1 . Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States. |
| Databáze: | OpenAIRE |
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