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A Segmentation Approach for Combining RaDAR Backscater, InSAR and LiDAR Measurements to Determine Vegetation 3D Structure and Biomass from Space
Project Funding: 2009 - 2012
NRA: 2008 NASA: Terrestrial Ecology
Funded by NASA
Abstract:This proposal addresses the need to develop algorithms for estimating vegetation three-dimensional (3D) structure and biomass relevant to the ecological applications of the DESDynI mission specified in the National Research Council’s Decadal Survey. These algorithms will serve to provide i.) a systematic methodology for combining the SAR, InSAR and LiDAR measurement capabilities of the DESDynI mission, and ii.) a mechanism to explore the observational tradespace in terms of the overall DESDynI instrument complexity versus the accuracy of the delivered products of vegetation 3D structure and estimates of above-ground biomass. The algorithm described in this proposal is based on a SAR segmentation approach that will be used to extend LiDAR observations over the DESDynI swath. By relying on segmentation, the algorithm allows for regions of a like response to the RaDAR, whether the backscatter sensitivity to biomass is saturated or not, to be aggregated and associated with LiDAR observations over similar regions. This algorithm will be initially exercised over local scales for the purpose of detailed refinement and providing short-term feedback to the overall instrument configuration. The results and experience obtained will then be broadened to address in the first instance, swath-wide scales and then include a sample of swaths over sites representing the major biomes and those with different vegetation 3D structure and biomass. The intent of the work will be to provide timely evaluative feedback to the overall mission configuration for the purpose of finalizing the ultimate DESDynI configuration as it applies to the stated needs of NASA’s Terrestrial Ecology program. By taking the multi-scale approach described here, first results will be available from the work within one year, with an increase in the breadth of analysis achieved over the duration of the project.
Publications:
Ahmed, R., Siqueira, P., Hensley, S., Bergen, K. 2013. Uncertainty of Forest Biomass Estimates in North Temperate Forests Due to Allometry: Implications for Remote Sensing. Remote Sensing. 5(6), 3007-3036. DOI: 10.3390/rs5063007
Dickinson, C., Siqueira, P., Clewley, D., Lucas, R. 2013. Classification of forest composition using polarimetric decomposition in multiple landscapes. Remote Sensing of Environment. 131, 206-214. DOI: 10.1016/j.rse.2012.12.013
Lei, Y., Siqueira, P. 2014. Estimation of Forest Height Using Spaceborne Repeat-Pass L-Band InSAR Correlation Magnitude over the US State of Maine. Remote Sensing. 6(11), 10252-10285. DOI: 10.3390/rs61110252
Lei, Y., Siqueira, P. 2015. An Automatic Mosaicking Algorithm for the Generation of a Large-Scale Forest Height Map Using Spaceborne Repeat-Pass InSAR Correlation Magnitude. Remote Sensing. 7(5), 5639-5659. DOI: 10.3390/rs70505639
2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
2013 NASA Terrestrial Ecology Science Team Meeting Poster(s)
2011 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
2010 NASA Terrestrial Ecology Science Team Meeting Poster(s)
More details may be found in the following project profile(s):