- Related Research Areas
- Carbon Cycle & Ecosystems
Lidar remote sensing provides measurements of horizontal and vertical vegetation structure of ecosystems which will be critical for estimating global carbon storage and assessing ecosystem response to climate change and natural and anthropogenic disturbances. However, no consistent approach currently exists to derive the lidar based vegetation structure information required by terrestrial ecosystem models. Further, when available, this information has rarely been incorporated into ecosystem models. The overall goal of this project is to use a physical approach to derive vegetation structure from the ICESat (Ice Cloud and Land Elevation Satellite) data and generate structure datasets compatible with the Ent Dynamic Global Terrestrial Ecosystem Model (DGTEM) constrained by MODIS land cover for improved estimates of terrestrial carbon stocks and fluxes. In particular we seek to answer the following two questions: What level of accuracy can we achieve for a vegetation structure dataset derived from remote sensing for inputs to a dynamic global terrestrial ecosystem model? Does the vegetation structure dataset improve estimates of carbon stocks, and what is the sensitivity of land-atmosphere CO2 exchange to error ranges in the vegetation structure, as simulated by a dynamic global terrestrial ecosystem model? To answer these questions, we will build upon our past Ent DGTEM modeling effort and the ongoing development of a physical approach to retrieve 3D vegetation structure from lidar at local scales. We aim to develop a fusion approach to make full use of lidar vegetation structure data for a DGTEM. We have four objectives to achieve our goal: 1) Develop a physical approach to derive accurate vegetation structure including height, foliage profile and vegetation cover from archived ICESat and National Elevation Data (NED, US only) or Shuttle Radar Topography Mission (SRTM, globally) slope data. 2) Fully evaluate these products using airborne Lidar Vegetation Imaging Sensing (LVIS) and ground data collected in different forest types. 3) Derive Ent structure inputs using ICESat vegetation structure data and MODIS land cover maps. 4) Run Ent with aprior and the new structure inputs and assess the improvement of CO2 flux and carbon stocks estimates using Fluxnet data and USDA Forest Inventory Analysis (FIA) data. This proposal is directly responsive to this NRA in that it develops an innovative approach to use archived ICESat data for a dynamic global terrestrial ecosystem model for improved estimates of continental carbon stocks and terrestrial ecosystem carbon fluxes. It directly contributes to NASA’s overall Earth Science goals of improving the scientific utility of existing NASA observational datasets to study our changing planet. It also addresses the US Climate Change Program (CCSP) Goal 4: Understand the sensitivity and adaptability of different natural and managed ecosystems and human systems to climate and related global changes through fusion of accurate ecosystem structure characteristics into a GCM coupled global vegetation dynamic model for the study of ecosystem and climate feedback.
Project PI: Wenge Ni-Meister/Hunter College of The City University of New York
Department of Geology and Geography, Hunter College of City University of New York, 695 Park Avenue, New York, NY 10021.
Phone: (212) 772–5321
Fax: (212) 772–5268
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