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.
Highlights
Forests play a crucial role in the global carbon budget
Goodness-of-fit statistics (R2, root mean squared errors (RMSE) and bias) for the ∆aboveground biomass (AGB) estimations were computed over the full range of possible stand heights, demonstrating an asymmetry between gains and losses
It was found that a direct, linear ∆H-to-∆AGB relationship only provides accurate predictions under limited conditions and can lead to large prediction biases when applied over a wide range of stand heights
Summary
Forests play a crucial role in the global carbon budget. Carbon stocks of forests worldwide are estimated to be around 350–600 Gt [1,2,3]. To quantify the aboveground biomass and carbon stocks of forests, passive optical sensors suffer from saturation and can be used only for forests with relatively low biomass [5,6]. Active sensors, such as light detection and ranging (lidar) [7,8] and synthetic aperture radar (SAR), [9] enable measurements of the canopy height structure of forests, which can be used to derive information about the standing aboveground biomass (AGB). Among several possible metrics that describe average canopy height (e.g., height quantiles, mean profile height), the so-called mean top-of-canopy height (TCH) [15] has become one of the most frequently used metrics [16,17,18]
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