文献类型：期刊文献

英文题名：Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

作者：Chen, Zhao[1,3] Sun, Zhibin[1,3] Zhang, Huaiqing[2] Zhang, Huacong[2] Qiu, Hanqing[2]

第一作者：Chen, Zhao

通信作者：Zhang, HQ[1]

机构：[1]Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Natl Forestry & Grassland Adm, Engn Res Ctr Forestry Oriented Intelligent Informa, Beijing 100083, Peoples R China

年份：2023

卷号：15

期号：24

外文期刊名：REMOTE SENSING

收录：;Scopus(收录号：2-s2.0-85180618256);WOS:【SCI-EXPANDED(收录号:WOS:001131486700001)】；

基金：We would like to express our special thanks to Huacong Zhang from the Subtropical Forestry Experimental Center of the Chinese Academy of Forestry for help with data collection.

语种：英文

外文关键词：Landsat 8 OLI; Sentinel-1A; combined optical and SAR indices; tent mapping atom search optimized BP neural network (Tent_ASO_BP); aboveground biomass

摘要：Accurate forest biomass estimation serves as the foundation of forest management and holds critical significance for a comprehensive understanding of forest carbon storage and balance. This study aimed to integrate Landsat 8 OLI and Sentinel-1A SAR satellite image data and selected a portion of the Shanxia Experimental Forest in Jiangxi Province as the study area to establish a biomass estimation model by screening influencing factors. Firstly, we extracted spectral information, vegetation indices, principal component features, and texture features within 3 x 3-pixel neighborhoods from Landsat 8 OLI. Moreover, we incorporated Sentinel-1's VV (vertical transmit-vertical receive) and VH (vertical transmit-horizontal receive) polarizations. We proposed an ensemble AGB (aboveground biomass) model based on a neural network. In addition to the neural network model, namely the tent mapping atom search optimized BP neural network (Tent_ASO_BP) model, partial least squares regression (PLSR), support vector machine (SVR), and random forest (RF) regression prediction techniques were also employed to establish the relationship between multisource remote sensing data and forest biomass. Optical variables (Landsat 8 OLI), SAR variables (Sentinel-1A), and their combinations were input into the four prediction models. The results indicate that Tent_ ASO_ BP model can better estimate forest biomass. Compared to pure optical or single microwave data, the Tent_ASO_BP model with the optimal combination of optical and microwave input features achieved the highest accuracy. Its R2 was 0.74, root mean square error (RMSE) was 11.54 Mg/ha, and mean absolute error (MAE) was 9.06 Mg/ha. Following this, the RF model (R2 = 0.54, RMSE = 21.33 Mg/ha, MAE = 17.35 Mg/ha), SVR (R2 = 0.52, RMSE = 17.66 Mg/ha, MAE = 15.11 Mg/ha), and PLSR (R2 = 0.50, RMSE = 16.52 Mg/ha, MAE = 12.15 Mg/ha) models were employed. In conclusion, the BP neural network model improved by tent mapping atom search optimization algorithm significantly enhanced the accuracy of AGB estimation in biomass studies. This will provide a new avenue for large-scale forest resource surveys.