Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, Mengzi Zhou
{"title":"基于 MODIS 系列卫星图像的 2000 至 2022 年青藏高原(QTP)年度植被图","authors":"Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, Mengzi Zhou","doi":"10.5194/essd-2024-193","DOIUrl":null,"url":null,"abstract":"<strong>Abstract.</strong> The Qinghai Tibet Plateau (QTP), known as the \"Third Pole\" of the Earth\" and the \"Water Tower of Asia,\" plays a crucial role in global climate regulation, biodiversity conservation, and regional socio-economic development. Continuous annual vegetation types and their geographical distribution data are essential for studying the response and adaptation of vegetation to climate change. However, there is very limited data on vegetation types and their geographical distributions on the QTP due to harsh natural environment. Currently, land cover/surface vegetation (LCSV) data are typically obtained using independent classification methods for each period's product, based on remote sensing information. These approaches do not consider the time continuity of vegetation to presence, and leads to a gradual increase in the number of misclassified pixels and the uncertainty of their locations, consequently decreasing the interpretability of the long-time series remote sensing products. To address this issue, this study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the QTP from 2000 to 2022 at a 500 m spatial resolution through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping from 2000 to 2022 reached 80.9 % based on 733 samples from literature, with the reference annual 2020 reaching an accuracy of 86.5 % and a Kappa coefficient of 0.85. The study supports the use of remote sensing data to mapping a long-term continuous annual vegetation.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"22 1","pages":""},"PeriodicalIF":11.2000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Annual vegetation maps in Qinghai-Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery\",\"authors\":\"Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, Mengzi Zhou\",\"doi\":\"10.5194/essd-2024-193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<strong>Abstract.</strong> The Qinghai Tibet Plateau (QTP), known as the \\\"Third Pole\\\" of the Earth\\\" and the \\\"Water Tower of Asia,\\\" plays a crucial role in global climate regulation, biodiversity conservation, and regional socio-economic development. Continuous annual vegetation types and their geographical distribution data are essential for studying the response and adaptation of vegetation to climate change. However, there is very limited data on vegetation types and their geographical distributions on the QTP due to harsh natural environment. Currently, land cover/surface vegetation (LCSV) data are typically obtained using independent classification methods for each period's product, based on remote sensing information. These approaches do not consider the time continuity of vegetation to presence, and leads to a gradual increase in the number of misclassified pixels and the uncertainty of their locations, consequently decreasing the interpretability of the long-time series remote sensing products. To address this issue, this study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the QTP from 2000 to 2022 at a 500 m spatial resolution through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping from 2000 to 2022 reached 80.9 % based on 733 samples from literature, with the reference annual 2020 reaching an accuracy of 86.5 % and a Kappa coefficient of 0.85. 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Annual vegetation maps in Qinghai-Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery
Abstract. The Qinghai Tibet Plateau (QTP), known as the "Third Pole" of the Earth" and the "Water Tower of Asia," plays a crucial role in global climate regulation, biodiversity conservation, and regional socio-economic development. Continuous annual vegetation types and their geographical distribution data are essential for studying the response and adaptation of vegetation to climate change. However, there is very limited data on vegetation types and their geographical distributions on the QTP due to harsh natural environment. Currently, land cover/surface vegetation (LCSV) data are typically obtained using independent classification methods for each period's product, based on remote sensing information. These approaches do not consider the time continuity of vegetation to presence, and leads to a gradual increase in the number of misclassified pixels and the uncertainty of their locations, consequently decreasing the interpretability of the long-time series remote sensing products. To address this issue, this study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the QTP from 2000 to 2022 at a 500 m spatial resolution through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping from 2000 to 2022 reached 80.9 % based on 733 samples from literature, with the reference annual 2020 reaching an accuracy of 86.5 % and a Kappa coefficient of 0.85. The study supports the use of remote sensing data to mapping a long-term continuous annual vegetation.
Earth System Science DataGEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍:
Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.