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      Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+

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          Abstract

          Background

          Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing.

          Results

          We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (~20 returns m −2) data was highly accurate (mean signed error of 0.19 ± 0.97 m), while those derived from reduced-density datasets (8 m −2, 4 m −2, 2 m −2 and 1 m −2) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4 m −2, the bias in height estimates translated into errors of 80–125 Mg ha −1 in predicted aboveground biomass.

          Conclusions

          Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13021-015-0013-x) contains supplementary material, which is available to authorized users.

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          Most cited references48

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          LiDAR remote sensing of forest structure

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            Determination of terrain models in wooded areas with airborne laser scanner data

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              High-resolution forest carbon stocks and emissions in the Amazon.

              Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at 0.1-ha resolution over 4.3 million ha of the Peruvian Amazon, an area twice that of all forests in Costa Rica, to reveal the determinants of forest carbon density and to demonstrate the feasibility of mapping carbon emissions for REDD. We discovered previously unknown variation in carbon storage at multiple scales based on geologic substrate and forest type. From 1999 to 2009, emissions from land use totaled 1.1% of the standing carbon throughout the region. Forest degradation, such as from selective logging, increased regional carbon emissions by 47% over deforestation alone, and secondary regrowth provided an 18% offset against total gross emissions. Very high-resolution monitoring reduces uncertainty in carbon emissions for REDD programs while uncovering fundamental environmental controls on forest carbon storage and their interactions with land-use change.
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                Author and article information

                Contributors
                veronika.leitold@nasa.gov
                mkeller.co2@gmail.com
                douglas.morton@nasa.gov
                bruce.cook@nasa.gov
                yosio@dsr.inpe.br
                Journal
                Carbon Balance Manag
                Carbon Balance Manag
                Carbon Balance and Management
                Springer International Publishing (Cham )
                1750-0680
                3 February 2015
                3 February 2015
                December 2015
                : 10
                : 1
                : 3
                Affiliations
                [ ]Remote Sensing Division, National Institute for Space Research (INPE), São José dos Campos, SP CEP 12201-970 Brazil
                [ ]Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA
                [ ]International Institute of Tropical Forestry, USDA Forest Service, San Juan, 00926 Puerto Rico
                [ ]EMBRAPA Satellite Monitoring, Campinas, SP CEP 13070-115 Brazil
                Article
                13
                10.1186/s13021-015-0013-x
                4320300
                c242f222-876b-4c14-8118-6f354dd50890
                © Leitold et al.; licensee Springer. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

                History
                : 21 October 2014
                : 15 January 2015
                Categories
                Research
                Custom metadata
                © The Author(s) 2015

                Environmental change
                tropical montane forest,airborne lidar,digital terrain model,elevation accuracy,data thinning,canopy height,biomass estimation,redd+

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