Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

Abstract

China is one of the countries with the most abundant bamboo forest resources in the world, and Zhejiang province is among the top-3 Chinese provinces with richest bamboo forests. For rational bamboo forests management, it is of great significance to study the spatiotemporal dynamic changes of Aboveground Carbon (AGC) stocks of bamboo forest in Zhejiang. In this study, remote sensing variables, such as spectral, vegetation indices and texture features of bamboo forest in Zhejiang, were extracted from 32 Landsat TM and OLI images got from four different years (2000, 2004, 2008 and 2014). These variables were subsequently selected with stepwise regression method to build an estimation model of AGC of the bamboo forests. The results showed that (1) the accuracy of bamboo forest remote sensing information extracted from the four different years was high with a classification accuracy of >76.26% and an accuracy of users of >91.62%. The classification area of bamboo forest was highly consistent with the area from forest resource inventory, and the area accuracy was over 96.50%; (2) the estimation model performed well in predicting the AGC in Zhejiang for different years. The correlation coefficient for estimated and measured AGC was between 63% and 72% with low root mean square error; (3) the derived AGC of the bamboo forests in Zhejiang province increased gradually from 2000 to 2014, with the AGC density of 6.75 Mg·ha −1 , 10.95 Mg·ha −1 , 15.25 Mg·ha −1 and 19.07 Mg·ha −1 respectively, and the average annual growth of 0.88 Mg·ha −1 . The spatiotemporal evolution of bamboo forest AGC in Zhejiang province had a close relationship with the gradual expansion of bamboo forest in the province and the differentiation of management levels in different regions.

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

“…The vegetation indices [57][58][59] included in this study included: NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), SR (Simple Ratio Index), DVI (Difference Vegetation Index), and EVI (Enhanced Vegetation Index). Additionally, the texture variables based on the gray-level co-occurrence matrix (GLCM) [60,61] included Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular second moment, and Correlation [27,58,59,[62][63][64] with different windows (3 × 3, 5 × 5, 7 × 7, 9 × 9, and 11 × 11) [27,65,66]. There were 251 total variables derived with the details that are shown in Table 2.…”

“…is the ith row of the jth column in the Nth moving [20,27] in Zhejiang province. The investigation method of NFI was systematic sampling, which is usually evenly placed at the intersection of kilometer grids of 1:50,000 topographic maps.…”

“…The determination coefficient (R 2 ), root mean square error (RMSE), and relative RMSE (RMSEr) [27,65] were used to evaluate the accuracy of the RF model. A higher accuracy is indicated by the higher values of R 2 and lower values of RMSE and RMSEr.…”

“…If the input data are insufficient or missing, it will have a great impact on the prediction results. Remote sensing technology has been commonly used in monitoring forest AGC over broad areas due to its wide coverage of observation, timeliness, and repetitive data availability [26,27]. As spectral characteristics of land cover show great differences [28][29][30][31][32], it is one of the important links in current research to accurately quantify various indicators of forest resources [33,34].…”

Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data

“…Based on the research of Li et al [60] and Xie et al [47], carbon density parameters for different carbon sources can only roughly estimate the carbon storage of a regional ecosystem and its socioeconomic value. Although the accuracy of these estimate must be further improved, this does not affect the overall research results of this paper, which could support decision-making in the region's relevant government departments.…”