ORCID Profile
0000-0002-2569-1595
Current Organisation
Oregon State University
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Publisher: Wiley
Date: 08-04-2021
DOI: 10.1002/RSE2.203
Abstract: Forest biomass estimation at large scale is challenging and generally entails large uncertainty in tropical regions. With their wall‐to‐wall coverage ability, passive remote sensing signals are frequently used to extrapolate field estimates of forest aboveground biomass (AGB). However, studies often use limited reference data and/or flawed validation schemes and thus report unreliable extrapolation error estimates. Here, we compared the ability of three medium‐ to high‐resolution passive satellite sensors, Landsat‐8 (L8), Sentinel‐2B (S2) and Worldview‐3 (WV3), to map AGB in a forest landscape of Thailand. We used a large airborne LiDAR‐derived AGB dataset as a reference to train and validate a random forest algorithm and conducted robust error assessments and variable selection using spatialized cross‐validations. Our results indicate that the selected predictors strongly varied among the three sensors and between analyses restricted to low (≤200 Mg ha −1 ) and high ( Mg ha −1 ) AGB areas. WV3 and S2 data outperformed L8 data to extrapolate AGB (RMSE of 68 and 72 against 84 Mg ha −1 , respectively) due to the inclusion of the red‐edge band and, probably, to their higher spatial and spectral resolution. Sensitivity to large AGB values was higher for WV3 than for S2 and L8 with saturation point of 247 Mg ha −1 against 204 and 192 Mg ha −1 . AGB values above these saturation points remained poorly predictable, especially for L8, indicating that several tropical forest AGB maps should be interpreted with extreme caution. However, predicted gradients of lower AGB values (≤200 Mg ha −1 ), i.e., in early forest successional stages, were fairly consistent among sensors ( r 0.70), even if the mean absolute difference between estimates was large when AGB predictions were extrapolated out of the calibration area at regional level (34%). We finally showed that calibrating the model only within the sensitivity AGB domain (e.g., Mg ha −1 ) minimizes the risk of induced bias for estimating small AGB values. These results provide important benchmarks for interpreting previously published maps and to improve future validation schemes.
Publisher: Copernicus GmbH
Date: 14-01-2020
Abstract: Abstract. Half of Asian tropical forests were disturbed in the last century resulting in the dominance of secondary forests in Southeast Asia. However, the rate at which biomass accumulates during the recovery process in these forests is poorly understood. We studied a forest landscape located in Khao Yai National Park (Thailand) that experienced strong disturbances in the last century due to clearance by swidden farmers. Combining recent field and airborne laser scanning (ALS) data, we first built a high-resolution aboveground biomass (AGB) map of over 60 km2 of forest landscape. We then used the random forest algorithm and Landsat time series (LTS) data to classify landscape patches as non-forested versus forested on an almost annual basis from 1972 to 2017. The resulting chronosequence was then used in combination with the AGB map to estimate forest carbon recovery rates in secondary forest patches during the first 42 years of succession. The ALS-AGB model predicted AGB with an error of 14 % at 0.5 ha resolution (RMSE=45 Mg ha−1) using the mean top-of-canopy height as a single predictor. The mean AGB over the landscape was 291 Mg ha−1, showing a high level of carbon storage despite past disturbance history. We found that AGB recovery varies non-linearly in the first 42 years of the succession, with an increasing rate of accumulation through time. We predicted a mean AGB recovery rate of 6.9 Mgha-1yr-1, with a mean AGB gain of 143 and 273 Mg ha−1 after 20 and 40 years, respectively. This rate estimate is about 50 % larger than the rate prescribed for young secondary Asian tropical rainforests in the 2019 refinement of the 2006 IPCC guidelines for national greenhouse gas inventories. Our study hence suggests that the new IPCC rates, which were based on limited data from Asian tropical rainforests, strongly underestimate the carbon potential of forest regrowth in tropical Asia. Our recovery estimates are also within the range of those reported for the well-studied Latin American secondary forests under similar climatic conditions. This study illustrates the potential of ALS data not only for scaling up field AGB measurements but also for predicting AGB recovery dynamics when combined with long-term satellite data. It also illustrates that tropical forest landscapes that were disturbed in the past are of utmost importance for the regional carbon budget and thus for implementing international programs such as REDD+.
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