scholarly journals A map of African humid tropical forest aboveground biomass derived from management inventories

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Pierre Ploton ◽  
Frédéric Mortier ◽  
Nicolas Barbier ◽  
Guillaume Cornu ◽  
Maxime Réjou-Méchain ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
Humid Tropical Forest ◽  
Forest Aboveground Biomass

scholarly journals Symbiotic N fixation is sufficient to support net aboveground biomass accumulation in a humid tropical forest

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
E. N. J. Brookshire ◽  
Nina Wurzburger ◽  
Bryce Currey ◽  
Duncan N. L. Menge ◽  
Michael P. Oatham ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
Biomass Accumulation ◽  
N Fixation ◽  
Humid Tropical Forest

scholarly journals 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

2020 ◽  
Vol 12 (9) ◽  
pp. 1498 ◽  
Author(s):  
Franciel Eduardo Rex ◽  
Carlos Alberto Silva ◽  
Ana Paula Dalla Corte ◽  
Carine Klauberg ◽  
Midhun Mohan ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
Ordinary Least Squares ◽  
Forest Growth ◽  
Selective Logging ◽  
Airborne Lidar ◽  
Support Vector ◽  
Lidar Data ◽  
Field Plots ◽  
Forest Aboveground Biomass

Accurately 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 square field plots sized 50 × 50 m 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.

scholarly journals Combination of SAR Polarimetric Parameters for Estimating Tropical Forest Aboveground Biomass

2020 ◽  
Vol 29 (5) ◽  
pp. 3353-3365
Author(s):  
Truong Tuong ◽  
Hiroshi Tani ◽  
Xiufeng Wang ◽  
Nguyen Thang ◽  
Ha Bui
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
Forest Aboveground Biomass

scholarly journals New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

2021 ◽  
Vol 13 (15) ◽  
pp. 2892
Author(s):  
Zhongbing Chang ◽  
Sanaa Hobeichi ◽  
Ying-Ping Wang ◽  
Xuli Tang ◽  
Gab Abramowitz ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Aboveground Biomass ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Random Forest Regression ◽  
Multiple Datasets ◽  
Optimal Weighting ◽  
Two Hybrid ◽  
Forest Aboveground Biomass

Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (−4.6 and −3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales.

scholarly journals Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 914
Author(s):  
Adeel Ahmad ◽  
Hammad Gilani ◽  
Sajid Rashid Ahmad
Keyword(s):  
High Resolution ◽  
Literature Review ◽  
Satellite Imagery ◽  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Independent Variables ◽  
Non Linear ◽  
Multiple Variables ◽  
Forest Aboveground Biomass

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted across six continents in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear (multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

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