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Estimation of the Aboveground Carbon Storage of Dendrocalamus giganteus Based on Spaceborne Lidar Co-Kriging

文献类型： 外文期刊

作者： Yang, Huanfen 1 ; Qin, Zhen 1 ; Shu, Qingtai 1 ; Xi, Lei 2 ; Xia, Cuifen 1 ; Wu, Zaikun 1 ; Wang, Mingxing 1 ; Duan, Dandan 3 ;

作者机构： 1.Southwest Forestry Univ, Coll Forestry, Kunming 650224, Peoples R China

2.Chinese Acad Forestry, Inst Desertificat Studies, Beijing 100091, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

关键词： Dendrocalamus giganteus; aboveground carbon storage; GEDI; ICESat-2/ATLAS; Landsat 9; Co-Kriging; Stacking-RR

期刊名称：FORESTS （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2024 年 15 卷 8 期

页码：

收录情况： SCI

摘要： Bamboo forests, as some of the integral components of forest ecosystems, have emerged as focal points in forestry research due to their rapid growth and substantial carbon sequestration capacities. In this paper, satellite-borne lidar data from GEDI and ICESat-2/ATLAS are utilized as the main information sources, with Landsat 9 and DEM data as covariates, combined with 51 pieces of ground-measured data. Using random forest regression (RFR), boosted regression tree (BRT), k-nearest neighbor (KNN), Cubist, extreme gradient boosting (XGBoost), and Stacking-ridge regression (RR) machine learning methods, an aboveground carbon (AGC) storage model was constructed at a regional scale. The model evaluation indices were the coefficient of determination (R-2), root mean square error (RMSE), and overall estimation accuracy (P). The results showed that (1) The best-fit semivariogram models for c(dem), f(dem), f(ndvi), p(dem), and a(ndvi) were Gaussian models, while those for h1(b7), h2(b7), h3(b7), and h4(b7) were spherical models; (2) According to Pearson correlation analysis, the AGC of Dendrocalamus giganteus showed an extremely significant correlation (p < 0.01) with c(dem) and p(dem) from GEDI, and also showed an extremely significant correlation with a(ndvi), h1(b7), h2(b7), h3(b7), and h4(b7) from ICESat-2/ATLAS; moreover, AGC showed a significant correlation (0.01 < p < 0.05) with f(dem) and f(ndvi) from GEDI; (3) The estimation accuracy of the GEDI model was superior to that of the ICESat-2/ATLAS model; additionally, the estimation accuracy of the Stacking-RR model, which integrates GEDI and ICESat-2/ATLAS (R-2 = 0.92, RMSE = 5.73 Mg/ha, p = 86.19%), was better than that of any single model (XGBoost, RFR, BRT, KNN, Cubist); (4) Based on the Stacking-RR model, the estimated AGC of Dendrocalamus giganteus within the study area was 1.02 x 10(7) Mg. The average AGC was 43.61 Mg/ha, with a maximum value of 76.43 Mg/ha and a minimum value of 15.52 Mg/ha. This achievement can serve as a reference for estimating other bamboo species using GEDI and ICESat-2/ATLAS remote sensing technologies and provide decision support for the scientific operation and management of Dendrocalamus giganteus.

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