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 Aboveground biomass estimation with airborne hyperspectral and LiDAR data in Tesinske Beskydy Mountains

Beskydy ◽  
2015 ◽  
Vol 8 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Olga Brovkina ◽  
František Zemek ◽  
Tomáš Fabiánek
Keyword(s):  
Aboveground Biomass ◽  
Field Measurements ◽  
Airborne Lidar ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Predicting Model ◽  
The Third ◽  
Study Results ◽  
Airborne Lidar Data ◽  
Forest Aboveground Biomass

The study presents three models for estimation of forest aboveground biomass (AGB) for plot level using different categories of airborne data. The first and the second models estimate AGB from metrics of airborne LiDAR data. The third model estimates AGB from integration of metrics of airborne hyperspectral and LiDAR data. The results are compared with plot level biomass estimated from field measurements. The results show that the best AGB estimate is obtained from the model utilizing a fusion of hyperspectral and LiDAR metrics. Study results expand existing research on the applicability of airborne hyperspectral and LiDAR datasets for AGB assessment. It evidences the efficiency of using a predicting model based on hyperspectral and LiDAR data for study area.

scholarly journals Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR

2019 ◽  
Vol 11 (6) ◽  
pp. 722 ◽  
Author(s):  
Xiaofang Sun ◽  
Guicai Li ◽  
Meng Wang ◽  
Zemeng Fan
Keyword(s):  
Random Forest ◽  
Aboveground Biomass ◽  
Cross Validation ◽  
Field Measurements ◽  
Random Forest Model ◽  
Carbon Accounting ◽  
Prediction Methods ◽  
Sample Sizes ◽  
Forest Model ◽  
Forest Aboveground Biomass

Accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. Forest AGB estimation has been conducted with a variety of data sources and prediction methods, but many uncertainties still exist. In this study, six prediction methods, including Gaussian processes, stepwise linear regression, nonlinear regression using a logistic model, partial least squares regression, random forest, and support vector machines were used to estimate forest AGB in Jiangxi Province, China, by combining Geoscience Laser Altimeter System (GLAS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, and field measurements. We compared the effect of three factors (prediction methods, sample sizes of field measurements, and cross-validation settings) on the predictive quality of the methods. The results showed that the prediction methods had the most considerable effect on the prediction quality. In most cases, random forest produced more accurate estimates than the other methods. The sample sizes had an obvious effect on accuracy, especially for the random forest model. The accuracy increased with increasing sample sizes. The random forest algorithm with a large number of field measurements, was the most precise (coefficient of determination (R2) = 0.73, root mean square error (RMSE) = 23.58 Mg/ha). Increasing the number of folds within the cross-validation settings improved the R2 values. However, no apparent change occurred in RMSE for different numbers of folds. Finally, the wall-to-wall forest AGB map over the study area was generated using the random forest model.

scholarly journals Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1282
Author(s):  
Parth Naik ◽  
Michele Dalponte ◽  
Lorenzo Bruzzone
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Temporal Information ◽  
Observation Data ◽  
Multispectral Data ◽  
Sensing Data ◽  
Multispectral Remote Sensing ◽  
Forest Aboveground Biomass

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal information.

scholarly journals Comparative Analysis of Seasonal Landsat 8 Images for Forest Aboveground Biomass Estimation in a Subtropical Forest

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 45 ◽  
Author(s):  
Chao Li ◽  
Mingyang Li ◽  
Jie Liu ◽  
Yingchang Li ◽  
Qianshi Dai
Keyword(s):  
Aboveground Biomass ◽  
Regional Scale ◽  
Coniferous Forest ◽  
Mixed Forest ◽  
Subtropical Forest ◽  
Acquisition Time ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Forest Types ◽  
Forest Aboveground Biomass

To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has, therefore, been widely used for regional scale AGB estimation; however, most studies have been based solely on peak season images without performance comparison of other seasons; this may ultimately affect the accuracy of AGB estimation. To explore the effects of utilizing various seasonal images for AGB estimation, we analyzed seasonal images collected using Landsat 8 OLI for a subtropical forest in northern Hunan, China. We then performed stepwise regression to estimate AGB of different forest types (coniferous forest, broadleaf forest, mixed forest and total vegetation). The model performances using seasonal images of different forest types were then compared. The results showed that textural information played an important role in AGB estimation of each forest type. Stratification based on forest types resulted in better AGB estimation model performances than those of total vegetation. The most accurate AGB estimations were achieved using the autumn (October) image, and the least accurate AGB estimations were achieved using the peak season (August) image. In addition, the uncertainties associated with the peak season image were largest in terms of AGB values < 25 Mg/ha and >75 Mg/ha, and the quality of the AGB map depicting the peak season was poorer than the maps depicting other seasons. This study suggests that the acquisition time of forest images can affect AGB estimations in subtropical forest. Therefore, future research should consider and incorporate seasonal time-series images to improve AGB estimation.

scholarly journals Examination of the Quantitative Relationship between Vegetation Canopy Height and LAI

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yongwei Yuan ◽  
Xinsheng Wang ◽  
Fang Yin ◽  
Jinyan Zhan
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Quantitative Relationship ◽  
Field Measurements ◽  
Canopy Height ◽  
Accurate Estimation ◽  
Vegetation Canopy ◽  
Area Index

Accurate estimation of vegetation biophysical variables such as the vegetation canopy height (H) is of great importance to the applications of the land surface models. It is difficult to obtain the data ofHat the regional scale or larger scale, but the remote sensing provides the most useful and most effective method. The leaf area index (LAI) is closely related to theH, and we analyzed its relationship with the correlation analysis based on the dataset at 86 site-years of field measurements from sites worldwide in this study. The result indicates that there is significant positive exponent correlation between these two parameters and the change of LAI would exert great impacts onH. The higher the LAI is, the higher theHis, and vice versa. Besides, the coefficients of different land cover types are very heterogeneous, and LAI of the needleleaf forest shows strong correlation withH, while that of the cropland shows weak correlation withH. The results may provide certain reference information for the extraction of the data ofHat the regional scale with the remote sensing data.

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