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基于MODIS NDVI和气候信息的草原植被变化监测
Grassland Vegetation Change Based on MODIS NDVI Data and Climate Information
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摘要: 对植被的动态监测可以从一定程度上反映气候变化趋势。该文利用2000—2005年MODI S NDVI数据对锡林郭勒盟典型草原植被变化进行动态监测，在此基础上，以降水量、水汽压、 平均气温、最高气温、最低气温、日照时数作为气候指标，分析锡林郭勒盟典型草原和荒漠 草原MODIS NDVI与同期及前期气候因子的相关性，探讨草原植被变化的气候驱动因子。结果 表明：2000—2005年锡林郭勒盟植被改善面积大于退化面积，植被退化面积最大的区域为荒 漠草原，占全盟面积的12.84%，植被改善面积最大的区域为典型草原，占全盟面积29.09%。4 类草原改善趋势由强到弱的顺序为草甸草原、典型草原、沙地草原、荒漠草原。对于 典型草原，其NDVI与最高气温关系最密切，其次为水汽压；对于荒漠草原，其NDVI与最高气 温关系最为密切，其次为最低气温。此外，NDVI对气候因子的响应表现出明显的时滞效应。Abstract: Monitoring vegetation change is an important aspect to study global climate chan ge. The Normalized Difference Vegetation Index (NDVI) is the most frequently us ed vegetation index to discuss environmental change. It's common to study vegetati on change using the NOAA Advanced Very High Resolution Radiometer (AVHRR) data which are available for more than 20 years, and the EOS Moderate Resolution Im aging Spectroradiometer (MODIS) data with improved quality are also widely used now. MODIS NDVI dataset during 2000—2005 in Xilingol are analyzed. Precipitation, vapor pressure, mean temperature, max temperature, min temperatur e, hours of sunshine are taken as climatic elements to explore the relationship between MODIS NDVI and climatic factors over different types of grassland. Due t o the narrow spectra bands of infrared and near infrared satellite sensors, MOD IS NDVI is sensitive for dynamically monitoring the four types of steppe: Meadow steppe, typical steppe, sandy steppe, desert steppe. As the water conditions of these four typical grassland vary widely, their spectral responses are also sig nificantly different. The vegetation increases on the whole in Xilingol during t he period of 2000 to 2005. Desert steppe decreases while typical steppe increase s for the research area. The global warming trend is obvious in Xilingol. Althou gh the rain doesn't increase much from 2000 to 2005, the grassland grows for t he meadow steppe and the typical steppe, promoted by the increasing temperature and better water conditions. For the sandy steppe and the desert steppe where wa ter conditions are poor, as the increasing temperature accelerates water evapora tion, the vegetation degradation occurs as a result of the worse environment. MODIS NDVI series exhibit obvious correlation with climatic factors. For typical steppe, max temperature is the most related factor with NDVI, and vapor pressur e is the second. For desert steppe, max temperature is the most related factor w ith NDVI, and min temperature is in the second place. Responses of MODIS NDVI to climatic factors lag in time obviously. The lag time for all climatic factors i s -1 stage for typical steppe, and for desert steppe, the lag time for both wate r and mean temperature climatic factors is -1 stage, while the lag time is incon sistent for max temperature and min temperature, which may be caused by the diff erences of soil texture and vegetation types.
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Key words:
- NDVI;
- vegetation change;
- climate change;
- Xilingol
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图 1 锡林郭勒盟4类草原MODISNDVI16d平均值年内变化
Fig. 1 16 day schange of MODIS NDVI average values in four kinds of typical grassland in Xilingol
图 2 2000—2005年锡林郭勒盟YNDVI变化趋势分类图
Fig. 2 Classification image of vegetation change trend simulated during 2000-2005 in Xilingol
图 3 典型草原和荒漠草原全时间序列NDVI值与前期水汽压、降水量的相关性
Fig. 3 The relationship between completed NDVI series and vapor pressure, precipitation with different time at typical steppe and desert steppe
图 4 典型草原和荒漠草原全时间序列NDVI值与前期平均气温、最高气温、最低气温的相关性
Fig. 4 The relationship between completed NDVI series and mean temperature, max temperature, min temperature with different time at typical steppe and desert steppe
表 1 气象站点位置及其10km范围内主要植被类型
Table 1 Location of meteorological stations in Xilingol and vegetation types with in their 10-km buffer areas
表 2 2000—2005年锡林郭勒YNDVI变化趋势图分类级别表
Table 2 Classification of vegetation change trend image during 2000—2005 in Xilingol
表 3 锡林郭勒盟各类草原植被变化趋势类统计表
Table 3 Statistic of vegetation change trend in different grassland in Xilingol
表 4 2000—2005年全时间序列NDVI与同期气象因子的相关系数表
Table 4 Correlation analysis between completed NDVI series and climate variables during 2000—2005
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