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Title (Primary) Model-assisted estimation of tropical forest biomass change: a comparison of approaches
Author Knapp, N.; Huth, A.; Kugler, F.; Papathanassiou, K.; Condit, R.; Hubbell, S.P.; Fischer, R.;
Journal Remote Sensing
Year 2018
Department OESA; iDiv;
Volume 10
Issue 5
Language englisch;
POF III (all) T53;
Keywords aboveground biomass change; lidar; synthetic aperture radar; tropical rainforests; forest model; simulation
Abstract 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
ID 20289
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Knapp, N., Huth, A., Kugler, F., Papathanassiou, K., Condit, R., Hubbell, S.P., Fischer, R. (2018):
Model-assisted estimation of tropical forest biomass change: a comparison of approaches
Remote Sens. 10 (5), art. 731