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Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data

Author:
Gong Zhang  Sangram Ganguly  Ramakrishna R. Nemani  Michael A. White  Cristina Milesi  Hirofumi Hashimoto  Weile Wang  Sassan Saatchi  Yifan Yu  Ranga B. Myneni  


Journal:
Remote Sensing of Environment


Issue Date:
2014


Abstract(summary):

Abstract Accurate characterization of variability and trends in forest biomass at local to national scales is required for accounting of global carbon sources and sinks and monitoring their dynamics. Here we present a new remote sensing based approach for estimating live forest aboveground biomass (AGB) based on a simple parametric model that combines high-resolution estimates of leaf area index (LAI) from the Landsat Thematic Mapper sensor and canopy maximum height from the Geoscience Laser Altimeter System (GLAS) sensor onboard ICESat, the Ice, Cloud, and land Elevation Satellite. We tested our approach with a preliminary uncertainty assessment over the forested areas of California spanning a broad range of climatic and land-use conditions and find our AGB estimates to be comparable to estimates of AGB from inventory records and other available satellite-estimated AGB maps at aggregated scales. Our study offers a high-resolution approach to map forest aboveground biomass at regional-to-continental scales and assess sources of uncertainties in the estimates. Highlights • Estimation of regional Aboveground Biomass (AGB) at Landsat scale • Exploring the relationship between Landsat LAI and LiDAR based canopy heights • Assessing uncertainties of AGB estimates from input data and model parameters • Cross-scale validation of AGB maps with FIA data and other existing maps. • Demonstrate scale effects in comparison of AGB density estimates.


Page:
44-44


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