文献类型：期刊文献

英文题名：A new method for estimating forest stand carbon stock: Segmentation and modeling based on forest aboveground imagery

作者：Chen, Xingjing[1,2] Guo, Ying[1,2] Chen, Zhulin[1,2] Luo, Xin[1,2] Wang, Peng[1,2] Shi, Mengmeng[1,2] Wang, Xuefeng[1,2]

第一作者：Chen, Xingjing

通信作者：Wang, XF[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forest Management & Growth Modelling, Beijing 100091, Peoples R China

年份：2024

卷号：167

外文期刊名：ECOLOGICAL INDICATORS

收录：;EI(收录号：20244117172257);Scopus(收录号：2-s2.0-85205924042);WOS:【SCI-EXPANDED(收录号:WOS:001331924100001)】；

基金：This study was funded by the China National Key Research and Development Program (grant number 2023YFE0105100-5) and Na-tional Natural Science Foundation of China (grant number 32401581) .

语种：英文

外文关键词：Aboveground imagery; Fully convolutional networks; Weight least squares methods; Stand Carbon Stock

摘要：Estimating forest stand carbon stock (SCS) is crucial for forest management and achieving carbon neutrality. However, traditional forest surveys are time-consuming, laborious and may posing the additional risk of harming the forest habitats. Remote sensing technology provides a faster and non-destructive method for SCS estimation. But these methods are hampered by issues like expensive equipment and complex data processing. To address these challenges, this study proposes a novel method to estimate SCS based on forest aboveground imagery (AGI) by integrating semantic segmentation techniques with SCS statistical modeling. Following the comparative analysis of three Fully Convolutional Network models (FCNs) for segmenting AGI, the optimized FCN8s semantic model was selected to segment AGI, capturing the pixel ratio of tree trunks within the imagery. Moreover, this study validated the enhanced precision and efficiency in segmentation models achieved through transfer learning. Subsequently, utilizing the foreground proportion of AGI derived by FCN8s as the independent variable and SCS as the dependent variable, ordinary least squares model and weight least squares model were developed. Through comparative analysis, the optimal SCS estimation model was determined. The experimental results demonstrated high segmentation accuracy with a validation set of 96.98 % for accuracy, 96.74 % for mean pixel accuracy, and 96.63 % for intersection over union. The optimal SCS model was the weight least squares model with R-2 of 0.7989, RMSE of 34.41 Mg hm(-2), TRE of 1.74 %, rRMSE of 0.137 %, MPE of 3.94 %, MPSE of 10.71 %. Overall, the proposed the method could estimate SCS in a low-cost and effective way.