Estimating forest and woodland aboveground biomass using active and passive remote sensing
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Abstract
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 (R2 = 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 14Mg ha –1 across all woodland and forest species. Landsat-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.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Estimating forest and woodland aboveground biomass using active and passive remote sensing |
| Series title | Photogrammetric Engineering and Remote Sensing |
| DOI | 10.14358/PERS.82.4.271 |
| Volume | 82 |
| Issue | 4 |
| Year Published | 2016 |
| Language | English |
| Publisher | Ingenta |
| Contributing office(s) | Western Geographic Science Center |
| Description | 11 p. |
| First page | 271 |
| Last page | 281 |
| Online Only (Y/N) | N |
| Additional Online Files (Y/N) | N |
| Google Analytic Metrics | Metrics page |