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Comparison of statistical modelling approaches for estimating tropical forest aboveground biomass stock and reporting their changes in low-intensity logging areas using multi-temporal LiDAR dataAuthor(s): Franciel Eduardo Rex; Carlos Alberto Silva; Ana Paula Dalla Corte; Carine Klauberg; Midhun Mohan; Adrian Cardil; Vanessa Sousa da Silva; Danilo Roberti Alves de Almeida; Mariano Garcia; Eben North Broadbent; Ruben Valbuena; Jaz Stoddart; Trina Merrick; Andrew Thomas Hudak
Source: Remote Sensing. 12: 1498.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionAccurately quantifying forest aboveground biomass (AGB) is one of the most significant challenges in remote sensing, and is critical for understanding global carbon sequestration. Here, we evaluate the effectiveness of airborne LiDAR (Light Detection and Ranging) for monitoring AGB stocks and change (ΔAGB) in a selectively logged tropical forest in eastern Amazonia. Specifically, we compare results from a suite of different modelling methods with extensive field data. The calibration AGB values were derived from 85 50 × 50m field plots established in 2014 and which were estimated using airborne LiDAR data acquired in 2012, 2014, and 2017. LiDAR-derived metrics were selected based upon Principal Component Analysis (PCA) and used to estimate AGB stock and change. The statistical approaches were: ordinary least squares regression (OLS), and nine machine learning approaches: random forest (RF), several variations of k-nearest neighbour (k-NN), support vector machine (SVM), and artificial neural networks (ANN). Leave-one-out cross validation (LOOCV) was used to compare performance based upon root mean square error (RMSE) and mean difference (MD). The results show that OLS had the best performance with an RMSE of 46.94 Mg/ha (19.7%) and R² = 0.70. RF, SVM, and ANN were adequate, and all approaches showed RMSE ≤ 54.48 Mg/ha (22.89%). Models derived from k-NN variations all showed RMSE ≥ 64.61 Mg/ha (27.09%). The OLS model was thus selected to map AGB across the time-series. The mean (± sd - standard deviation) predicted AGB stock at the landscape level was 229.10 (± 232.13) Mg/ha in 2012, 258.18 (±106.53) in 2014, and 240.34 (sd±177.00) Mg/ha in 2017, showing the effect of forest growth in the first period and logging in the second period. In most cases, unlogged areas showed higher AGB stocks than logged areas. Our methods showed an increase in AGB in unlogged areas and detected small changes from reduced-impact logging (RIL) activities occurring after 2012. We also detected that the AGB increase in areas logged before 2012 was higher than in unlogged areas. Based on our findings, we expect our study could serve as a basis for programs such as REDD+ and assist in detecting and understanding AGB changes caused by selective logging activities in tropical forests.
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CitationRex, Franciel Eduardo; Silva, Carlos Alberto; Corte, Ana Paula Dalla; Klauberg, Carine; Mohan, Midhun; Cardil, Adrian; da Silva, Vanessa Sousa; de Almeida, Danilo Roberti Alves; Garcia, Mariano; Broadbent, Eben North; Valbuena, Ruben; Stoddart, Jaz; Merrick, Trina; Hudak, Andrew Thomas. 2020. Comparison of statistical modelling approaches for estimating tropical forest aboveground biomass stock and reporting their changes in low-intensity logging areas using multi-temporal LiDAR data. Remote Sensing. 12: 1498.
KeywordsAmazon, forest structure, remote sensing, modelling, mapping
- Impacts of airborne lidar pulse density on estimating biomass stocks and changes in a selectively logged tropical forest
- Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes
- Monitoring selective logging in western Amazonia with repeat lidar flights
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