Annual vegetation maps in Qinghai-Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, Mengzi Zhou
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引用次数: 0

Abstract

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.
基于 MODIS 系列卫星图像的 2000 至 2022 年青藏高原(QTP)年度植被图
摘要青藏高原素有 "地球第三极 "和 "亚洲水塔 "之称,在全球气候调节、生物多样性保护和区域社会经济发展中发挥着重要作用。连续的年度植被类型及其地理分布数据对于研究植被对气候变化的响应和适应至关重要。然而,由于自然环境恶劣,有关青藏高原植被类型及其地理分布的数据非常有限。目前,土地覆被/地表植被(LCSV)数据通常是根据遥感信息,采用独立的分类方法获得每个时期的产品。这些方法没有考虑植被存在的时间连续性,导致误分类像素的数量和位置的不确定性逐渐增加,从而降低了长时间序列遥感产品的可解释性。针对这一问题,本研究开发了一种利用遥感影像进行长时连续年度植被测绘的新方法,并通过 MOD09A1 产品以 500 米的空间分辨率对 2000 年至 2022 年的 QTP 植被进行了测绘。根据 733 个文献样本,2000 年至 2022 年 QTP 连续年度植被绘图的总体准确率达到 80.9%,2020 年参考年度的准确率达到 86.5%,Kappa 系数为 0.85。该研究支持利用遥感数据绘制长期连续的年度植被图。
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, 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.
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