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Remote Sens. 2018, 10(5), 731;

Model-Assisted Estimation of Tropical Forest Biomass Change: A Comparison of Approaches

Department of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany
Institute for Environmental Systems Research, Department of Mathematics/Computer Science, University of Osnabrück, 49076 Osnabrück, Germany
German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, 04103 Leipzig, Germany
Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
Field Museum of Natural History, Chicago, IL 60605, USA
Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095, USA
Author to whom correspondence should be addressed.
Received: 27 February 2018 / Revised: 30 April 2018 / Accepted: 7 May 2018 / Published: 9 May 2018
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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Monitoring of changes in forest biomass requires accurate transfer functions between remote sensing-derived changes in canopy height (ΔH) and the actual changes in aboveground biomass (ΔAGB). Different approaches can be used to accomplish this task: direct approaches link ΔH directly to ΔAGB, while indirect approaches are based on deriving AGB stock estimates for two points in time and calculating the difference. In some studies, direct approaches led to more accurate estimations, while, in others, indirect approaches led to more accurate estimations. It is unknown how each approach performs under different conditions and over the full range of possible changes. Here, we used a forest model (FORMIND) to generate a large dataset (>28,000 ha) of natural and disturbed forest stands over time. Remote sensing of forest height was simulated on these stands to derive canopy height models for each time step. Three approaches for estimating ΔAGB were compared: (i) the direct approach; (ii) the indirect approach and (iii) an enhanced direct approach (dir+tex), using ΔH in combination with canopy texture. Total prediction accuracies of the three approaches measured as root mean squared errors (RMSE) were RMSEdirect = 18.7 t ha−1, RMSEindirect = 12.6 t ha−1 and RMSEdir+tex = 12.4 t ha−1. Further analyses revealed height-dependent biases in the ΔAGB estimates of the direct approach, which did not occur with the other approaches. Finally, the three approaches were applied on radar-derived (TanDEM-X) canopy height changes on Barro Colorado Island (Panama). The study demonstrates the potential of forest modeling for improving the interpretation of changes observed in remote sensing data and for comparing different methodologies. View Full-Text
Keywords: aboveground biomass change; lidar; synthetic aperture radar; tropical rainforests; forest model; simulation aboveground biomass change; lidar; synthetic aperture radar; tropical rainforests; forest model; simulation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Knapp, N.; Huth, A.; Kugler, F.; Papathanassiou, K.; Condit, R.; Hubbell, S.P.; Fischer, R. Model-Assisted Estimation of Tropical Forest Biomass Change: A Comparison of Approaches. Remote Sens. 2018, 10, 731.

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