{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:44:11Z","timestamp":1783817051840,"version":"3.55.0"},"reference-count":86,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971577"],"award-info":[{"award-number":["31971577"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["31971577"],"award-info":[{"award-number":["31971577"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A natural reserve\u2019s forest is an important base for promoting natural education, scientific research, biodiversity conservation and carbon accounting. Dynamic monitoring of the forest type and forest aboveground biomass (AGB) in a nature reserve is an important foundation for assessing the forest succession stage and trend. Based on the Landsat images covering the National Nature Reserve of Yaoluoping in Anhui province spanning from 1987 to 2020, a total of 42 Landsat scenes, the forest cover product set was first developed by using the well-established vegetation change tracker (VCT) model. On this basis, a new vegetation index, NDVI_DR, which considers the phenological characteristics of different forest types, was proposed to distinguish coniferous forest from broad-leaved forest. Next, multiple modeling factors, including remote sensing spectral signatures, vegetation indices, textural measures derived from gray level co-occurrence matrix and wavelet analysis and topographic attributes, were compiled to model the AGB in 2011 by forest type separately by using the stochastic gradient boosting (SGB) algorithm. Then, using the 2011 Landsat image as the base, all the Landsat images in the other years involved in the modelling were relatively normalized by using the weighted invariant pixels (WIP) method, followed by an extrapolation of the 2011 AGB model to other years to create a time-series of AGB. The results showed that the overall accuracy of the VCT-based forest classification products was over 90%. The annual forest type classifications derived from NDVI_DR thresholding gained an overall accuracy above 92%, with a kappa coefficient above 0.8. The 2011 forest-type-dependent SGB-based AGB estimation model achieved an independent validation R2 at 0.63 and an RMSE at 11.18 t\/ha for broad-leaved forest, and 0.61 and 14.26 t\/ha for coniferous forest. The mapped time-series of AGB showed a gradual increasing trend over the past three decades. The driving factors responsible for the observed forest cover and AGB changes were analyzed to provide references for reasonable protection and development. The proposed methodology is a reliable tool for evaluating the management status, which can be extended to other similar regions.<\/jats:p>","DOI":"10.3390\/rs14122786","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2786","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Mapping Spatiotemporal Changes in Forest Type and Aboveground Biomass from Landsat Long-Term Time-Series Analysis\u2014A Case Study from Yaoluoping National Nature Reserve, Anhui Province of Eastern China"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1097-8312","authenticated-orcid":false,"given":"Boxiang","family":"Yang","sequence":"first","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yali","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xupeng","family":"Mao","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Lv","sequence":"additional","affiliation":[{"name":"Nanjing Institute of Environmental Sciences (NIES), Ministry of Environmental Protection (MEP), Nanjing 210042, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5689-5091","authenticated-orcid":false,"given":"Mingshi","family":"Li","sequence":"additional","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119220","DOI":"10.1016\/j.foreco.2021.119220","article-title":"Exploring the Influence of Large Trees on Temperate Forest Spatial Structure from the Angle of Mingling","volume":"492","author":"Liu","year":"2021","journal-title":"For. 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