Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China

Fugen Jiang; Muli Deng; Jie Tang; Liyong Fu; Hua Sun

doi:10.1186/s13021-022-00212-y

Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China

Carbon Balance Manag. 2022 Sep 1;17(1):12. doi: 10.1186/s13021-022-00212-y.

Authors

Fugen Jiang^{1

2

3}, Muli Deng^{1

2

3}, Jie Tang^{1

2

3}, Liyong Fu^{1

4}, Hua Sun^{5

6

7}

Affiliations

¹ Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China.
² Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, 410004, Hunan, China.
³ Key Laboratory of State Forestry Administration On Forest Resources Management and Monitoring in Southern Area, Changsha, 410004, Hunan, China.
⁴ Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China.
⁵ Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China. sunhua@csuft.edu.cn.
⁶ Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, 410004, Hunan, China. sunhua@csuft.edu.cn.
⁷ Key Laboratory of State Forestry Administration On Forest Resources Management and Monitoring in Southern Area, Changsha, 410004, Hunan, China. sunhua@csuft.edu.cn.

Abstract

Background: Fast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China.

Results: The results show that stacking achieved the best AGB estimation accuracy among the models, with an R² of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively.

Conclusion: Compared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring.

Keywords: Aboveground biomass; Carbon cycle and management; Google earth engine; ICESat-2; Machine learning; Remote sensing.

Abstract

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