Forest aboveground biomass mapping in Mexico using SAR, optical and airborne LiDAR data
- Paper number
IAC-16,B1,6,1,x35850
- Coauthor
Mr. Mikhail Urbazaev, Friedrich-Schiller-Universität Jena, Germany
- Coauthor
Dr. Christian Thiel, Friedrich-Schiller-Universität Jena, Germany
- Coauthor
Dr. Mirco Migliavacca, Max Planck Institute for Biogeochemistry, Germany
- Coauthor
Dr. Markus Reichstein, Max Planck Institute for Biogeochemistry, Germany
- Coauthor
Dr. Bruce Cook, NASA GSFC, United States
- Coauthor
Prof. Ralph Dubayah, University of Maryland, United States
- Coauthor
Mrs. Christiane Schmullius, University of Jena, Germany
- Year
2016
- Abstract
Information on the spatial distribution of aboveground biomass (AGB) over large areas is needed for understanding and managing the processes involved in the carbon cycle, and supporting international policies for climate change mitigation and adaption. Furthermore, these products provide local stakeholders with important baseline data for the development of sustainable management strategies. Using remote sensing techniques it is possible to provide spatially explicit information of AGB from local to global scales. In this work we present a two-stage up-scaling approach to estimate forest aboveground biomass in Mexico at national scale based on multi-sensor remote sensing data. For this, we estimate firstly AGB along the airborne LiDAR transects using Mexican National Forest Inventory data collected by CONAFOR and very high resolution NASA G-LiHT LiDAR data. We calculated from discrete-return LiDAR data 88 LiDAR metrics that are then related to field-estimated AGB. In the next step, we calibrate active (ALOS PALSAR) and passive satellite imagery (Landsat) with LiDAR-based AGB estimates in a non-parametric Random Forest model to create a national wall-to-wall AGB map. Finally, the generated AGB product is validated using independent Mexican National Forest Inventory (NFI) data that were not used for model training. Furthermore, we modelled AGB at national scale using satellite imagery and NFI data only and compared to the results from the two-stage up-scaling approach. The estimated AGB products showed similar goodness-of-fit statistics at different scales compared to the independent validation data set. However, we observed different AGB spatial patterns in two products, especially in regions where NFI data are not available, but where high AGB values occur.
- Abstract document
- Manuscript document
IAC-16,B1,6,1,x35850.pdf (🔒 authorized access only).
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