Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.

[1]  L. Zhang,et al.  Local Modeling of Tree Growth by Geographically Weighted Regression , 2004 .

[2]  Duan Zhugen,et al.  Estimation of the Forest Aboveground Biomass at Regional Scale Based on Remote Sensing , 2015 .

[3]  Tian Qingjiu,Min Xiangjun,et al.  ADVANCES IN STUDY ON VEGETATION INDICES , 1998 .

[4]  Jingyun Fang,et al.  FOREST BIOMASS OF CHINA: AN ESTIMATE BASED ON THE BIOMASS–VOLUME RELATIONSHIP , 1998 .

[5]  Nie Zhi-long Geometric Correction of Remote Sensing Image , 2007 .

[6]  Weiliang Fan,et al.  Satellite-based carbon stock estimation for bamboo forest with a non-linear partial least square regression technique , 2012 .

[7]  Dieu Tien Bui,et al.  Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran) , 2018, Remote. Sens..

[8]  R. Xingu ABOVEGROUND BIOMASS ESTIMATES FOR TROPICAL MOIST FORESTS OF THE BRAZILIAN AMAZON , 2004 .

[9]  Liu Shuguang,et al.  General Review on Remote Sensing-Based Biomass Estimation , 2012 .

[10]  Calvin A. Farris,et al.  Incorporating spatial non-stationarity of regression coefficients into predictive vegetation models , 2007, Landscape Ecology.

[11]  S. Puliti,et al.  Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat 8 , 2020, 2010.14262.

[12]  A. J. Richardsons,et al.  DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION , 1977 .