文献类型：期刊文献

英文题名：Modelling the Dynamics of Carbon Storages for Pinus densata Using Landsat Images in Shangri-La Considering Topographic Factors

作者：Liao, Yi[1] Zhang, Jialong[1] Bao, Rui[2] Xu, Dongfan[3] Han, Dongyang[4]

第一作者：Liao, Yi

通信作者：Zhang, JL[1]

机构：[1]Southwest Forestry Univ, Coll Forestry, Kunming 650224, Peoples R China;[2]Natl Forestry & Grassland Adm, Inst Southwest Survey & Planning, Kunming 650021, Peoples R China;[3]Fudan Univ, Inst Biodivers Sci, Key Lab Biodivers Sci & Ecol Engn, Minist Educ, Shanghai 200438, Peoples R China;[4]Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China

年份：2022

卷号：14

期号：24

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20225313320130);Scopus(收录号：2-s2.0-85144829121);WOS:【SCI-EXPANDED(收录号:WOS:000902843000001)】；

基金：This research was funded by the National Natural Science Foundation of China (Nos. 31860207 and 32260390), and the “Young Top Talents” special project of the high-level talent training support program of Yunnan province, China, in 2020 (No. YNWR-QNBJ-2020-164).

语种：英文

外文关键词：Landsat; Pinus densata; terrain niche index; dynamic model; carbon storage

摘要：Accurate estimation of forest carbon storage is essential for understanding the dynamics of forest resources and optimizing decisions for forest resource management. In order to explore the changes in the carbon storage of Pinus densata in Shangri-La and the influence of topography on carbon storage, two dynamic models were developed based on the National Forest Inventory (NFI) and Landsat TM/OLI images with a 5-year interval change and annual average change. The three modelling methods used were partial least squares (PLSR), random forest (RF) and gradient boosting regression tree (GBRT). Various spectral and texture features of the images were calculated and filtered before modelling. The terrain niche index (TNI), which is able to reflect the combined effect of elevation and slope, was added to the dynamic model, the optimal model was selected to estimate the carbon storage, and the topographic conditions in areas of change in carbon storage were analyzed. The results showed that: (1) The dynamic model based on 5-year interval change data performs better than the dynamic model with annual average change data, and the RF model has a higher accuracy compared to the PLSR and GBRT models. (2) The addition of TNI improved the accuracy, in which R-2 is improved by up to 10.48% at most, RMSE is reduced by up to 7.32% at most, and MAE is reduced by up to 8.89% at most, and the RF model based on the 5-year interval change data has the highest accuracy after adding TNI, with an R-2 of 0.87, an RMSE of 3.82 t-C center dot ha(-1), and a MAE of 1.78 t-C center dot ha(-1). (3) The direct estimation results of the dynamic model showed that the carbon storage of Pinus densata in Shangri-La decreased in 1987-1992 and 1997-2002, and increased in 1992-1997, 2002-2007, 2007-2012, and 2012-2017. (4) The trend of increasing or decreasing carbon storage in each period is not exactly the same on the TNI gradient, according to the dominant distribution, as topographic conditions with lower elevations or gentler slopes are favorable for the accumulation of carbon storage, while the decreasing area of carbon storage is more randomly distributed topographically. This study develops a dynamic estimation model of carbon storage considering topographic factors, which provides a solution for the accurate estimation of forest carbon storage in regions with a complex topography.