{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T20:45:56Z","timestamp":1771015556574,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31860207"],"award-info":[{"award-number":["31860207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32260390"],"award-info":[{"award-number":["32260390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["YNWR-QNBJ-2020-164"],"award-info":[{"award-number":["YNWR-QNBJ-2020-164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u201cYoung Top Talents\u201d special project of the high-level talent training support program of Yunnan province, China","award":["31860207"],"award-info":[{"award-number":["31860207"]}]},{"name":"\u201cYoung Top Talents\u201d special project of the high-level talent training support program of Yunnan province, China","award":["32260390"],"award-info":[{"award-number":["32260390"]}]},{"name":"\u201cYoung Top Talents\u201d special project of the high-level talent training support program of Yunnan province, China","award":["YNWR-QNBJ-2020-164"],"award-info":[{"award-number":["YNWR-QNBJ-2020-164"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 R2 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 R2 of 0.87, an RMSE of 3.82 t-C\u00b7ha\u22121, and a MAE of 1.78 t-C\u00b7ha\u22121. (3) The direct estimation results of the dynamic model showed that the carbon storage of Pinus densata in Shangri-La decreased in 1987\u20131992 and 1997\u20132002, and increased in 1992\u20131997, 2002\u20132007, 2007\u20132012, and 2012\u20132017. (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.<\/jats:p>","DOI":"10.3390\/rs14246244","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T06:14:00Z","timestamp":1670566440000},"page":"6244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Modelling the Dynamics of Carbon Storages for Pinus densata Using Landsat Images in Shangri-La Considering Topographic Factors"],"prefix":"10.3390","volume":"14","author":[{"given":"Yi","family":"Liao","sequence":"first","affiliation":[{"name":"College of Forestry, Southwest Forestry University, Kunming 650224, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6969-3656","authenticated-orcid":false,"given":"Jialong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Forestry, Southwest Forestry University, Kunming 650224, China"}]},{"given":"Rui","family":"Bao","sequence":"additional","affiliation":[{"name":"Institute of Southwest Survey and Planning, National Forestry and Grassland Administration, Kunming 650021, China"}]},{"given":"Dongfan","family":"Xu","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai 200438, China"}]},{"given":"Dongyang","family":"Han","sequence":"additional","affiliation":[{"name":"Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing 100091, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shao, W., Cai, J., Wu, H., Liu, J., Zhang, H., and Huang, H. 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