| 青藏高原月NDVI时空动态变化及其对气候变化的 响应 |
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| 引用本文： | 郑海亮,房世峰,刘成程,吴金华,杜加强.青藏高原月NDVI时空动态变化及其对气候变化的 响应[J].地球信息科学,2019,21(2):201-214. |
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| 作者姓名： | 郑海亮 房世峰 刘成程 吴金华 杜加强 |
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| 作者单位： | 1. 中国环境科学研究院 国家环境保护区域生态过程与功能评估重点实验室,北京 1000122. 首都医科大学附属北京世纪坛医院,北京 1000383. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101 |
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| 基金项目： | 国家重点研发计划项目（2016YFC0500401-5）;国家自然科学基金项目（41001055） |
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| 摘 要： | 青藏高原脆弱的高寒植被对外界干扰十分敏感,使其成为研究植被对气候变化响应的理想区域之一。青藏高原气候变化剧烈,在较短的合成时间研究气候变化对植被的影响十分必要。因此,本文利用GIMMS NDVI时间序列数据集,研究了1982-2012年青藏高原生长季月尺度植被生长的时空动态变化,探讨了其与气温、降水量和日照时数等气候因子的响应关系。结果表明：在区域尺度上,除8月外,其他各月份植被均呈增加趋势,显著增加多发生在4-7月和9月;大部分月份的NDVI增加速率随着时段的延长显著减小,表明NDVI增加趋势放缓;在像元尺度上,月NDVI显著变化的区域多呈增加趋势,但显著减少范围的扩张多快于显著增加。4月和7月植被生长主要是受气温和日照时数共同作用,6月和9月受气温的控制,而8月则主要受降水量的影响。长时间序列NDVI数据集的出现为采用嵌套时段研究植被生长变化趋势奠定了前提,而植被活动变化趋势的持续性则有助于形象表征植被活动变化过程、深入理解植被对气候变化的响应和预测植被未来生长变化趋势。由此推测,青藏高原月NDVI未来增加趋势总体上趋于缓和,但在像元尺度显著变化的区域趋于增加。
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| 关 键 词： | NDVI 月尺度 时空变化 气候影响 嵌套时间序列趋势分析 青藏高原 |
| 收稿时间： | 2018-09-11 |
Dynamics of Monthly Vegetation Activity and Its Responses to Climate Change in the Qinghai-Tibet Plateau |
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| Hailiang ZHENG,Shifeng FANG,Chengcheng LIU,Jinhua WU,Jiaqiang DU.Dynamics of Monthly Vegetation Activity and Its Responses to Climate Change in the Qinghai-Tibet Plateau[J].Geo-information Science,2019,21(2):201-214. |
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| Authors: | Hailiang ZHENG Shifeng FANG Chengcheng LIU Jinhua WU Jiaqiang DU |
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| Institution: | 1. State Environmental Protection Key Laboratory of Regional Eco-process and Function Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China2. Beijing Shijitan Hospital affiliated to Capital Medical University, Beijing 100038, China3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | Understanding the spatial pattern and dynamic processes of vegetation changes and their causes is one of the key topics in research on global change of terrestrial ecosystems. Characterized by vulnerable alpine vegetation, which is sensitive to external disturbance, the Qinghai-Tibet Plateau is one of the ideal areas for studying the response of vegetation to climate change. It is necessary to investigate the impacts of climate change on vegetation in a short synthetic period because of the intense climate variations in the Qinghai-Tibet Plateau. Previous studies have not sufficiently investigated NDVI change comparisons between various periods and the persistence of NDVI trends. In this study, we investigated monthly vegetation dynamics in the Qinghai-Tibet Plateau and their relationships with climatic factors over 15 progressive periods of 18-32 years starting in 1982. This was accomplished by using the updated Global Inventory Modeling and Mapping Studies (GIMMS) third generation global satellite Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset and climate data. The NDVI time-synthesis method of each season masks the trends of NDVI variations within the single month. Except for August, vegetation increased in other six months, with a significant increase occurring in April-July and September. The increase rate of NDVI in most months decreased significantly with the extension of the period, indicating that the increasing trend of NDVI slowed down. At pixel scale, the regions with significant changes (including both increase and decrease) in NDVI showed increasing trends in most months, but the range of significant decreases in NDVI expanded faster than that of significant increases. Vegetation activities in the Qinghai-Tibet Plateau are generally controlled by temperature changes, but the dominant climatic factors affecting vegetation are varied in different months and regions. The vegetation activities in April and July were mainly promoted by temperature and sunshine hours, and those in June and September were controlled by temperature, and in August were mainly affected by precipitation. The emergence of long time series NDVI data sets provides a precondition for application of nested time series to study the trend analysis of vegetation growth and change. The persistence of the trend of vegetation activity may help to visualize the process of vegetation change, understand the vegetation response to climate change, and to predict thevegetation growth trend. It is inferred that the increases of NDVI in the future tend to be more moderate in general, but areas with significant pixel-scale changes in NDVI tend to increase in most months. |
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| Keywords: | NDVI monthly scale spatiotemporal changes climate influences trends in nested periods the Qinghai-Tibet Plateau |
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