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干旱区地理 ›› 2016, Vol. 39 ›› Issue (2): 395-404.

• 地球信息科学 • 上一篇    下一篇

基于SDI指数的南非共和国2001-2014年干旱监测时空分布

雷步云1,2,3, 赵书河1,2,3, 覃志豪4, Peter JOHNSTON5, 贺可勋1,2,3, 张振克1,2   

  1. 1 南京大学非洲研究所, 江苏 南京 210023;
    2 南京大学地理与海洋科学学院, 江苏 南京 210023;
    3 江苏省地理信息资源开发与利用协同创新中心, 江苏 南京 210023;
    4 中国农业科学院农业资源与农业区划研究所, 北京 100081;
    5 南非开普敦大学环境与地理科学学院, 南非共和国
  • 收稿日期:2015-12-21 修回日期:2016-02-19 出版日期:2016-03-25
  • 通讯作者: 赵书河(1971-), 男, 博士, 副教授. Email: zhshe@163.com E-mail:zhshe@163.com
  • 作者简介:雷步云(1988-), 男, 硕士研究生, 研究方向为全球变化遥感. Email: lbyun0908@126.com
  • 基金资助:

    外交部非洲司“中非联合研究交流计划”项目: “气候变化条件下非洲农业旱灾对粮食生产的影响”(JL201309); 江苏省高校国际问题研究中心建设项目联合资助

Drought temporal-spatial distribution of South Africa based on MODIS SDI index from 2001-2014

LEI Bu-yun1,2,3, ZHAO Shu-he1,2,3, QIN Zhi-hao4, Peter JOHNSTON5, HE Ke-xun1,2,3, ZHANG Zhen-ke1,2   

  1. 1 Center of African Studies of Nanjing University, Nanjing 210023, Jiangsu, China;
    2 Department of Geographic Information Science, Nanjing University, Nanjing 210023, Jiangsu, China;
    3 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, Jiangsu, China;
    4 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
    5 Department of Environmental and Geographical Science, University of Cape Town, Cape Town, South Africa
  • Received:2015-12-21 Revised:2016-02-19 Online:2016-03-25

摘要: 干旱在南非共和国地区是一种发生频率较高的自然灾害, 干旱的发生会对粮食安全产生影响. 根据南非共和国的自然气候特点和内部地表覆盖及地形特点, 找出了一种合适的干旱监测指数. 即通过NDVI的分级对南非共和国农业区不同地表覆盖度和不同地形植被进行单独计算归一化植被供水指数(VSWI), 得到监测干旱的合成干旱监测指数(SDI). 用于干旱的监测数据是2001-2014年MOD13A1和MOD11A2数据, 采用SDI 指数结合农业种植区得到14a南非共和国高时空分辨率农业干旱监测分布数据集. 通过数据分析得出, 南非共和国干旱主要发生于春季末、夏季初, 一般在夏季末期影响达到最大. 14a间, 干旱影响较严重的年份是2002年、2007年和2013年, 各省发生干旱时, 一般是以轻度干旱和中度干旱为主, 夏季末出现重度干旱的影响. 干旱发生时往往是由降雨稀少的中部地区出现, 进而向东部和南部扩张, 并且中部地区出现轻度干旱的频率较高. SDI 得到的干旱监测结果与南非共和国干旱发生事件基本一致, 弥补了气象干旱监测的不足, 因此SDI 干旱指数可用于南非共和国地区干旱监测.

关键词: 干旱监测, MODIS数据, SDI, 南非共和国

Abstract: MODIS data has been widely used for dynamic drought monitoring in large scale for their high time resolution, high spectral resolution, and moderate spatial resolution. Over 930 MODIS images have been used in the study for agro-drought monitoring events. The monitoring data for drought was MOD13A1 and MOD11A2 data from 2001 to 2014. Sixteen days'synthetic data product was used for the analysis of drought sequence. Drought is a natural disaster with higher frequency in South Africa, and can affect food security seriously. Agriculture is an important economic sector in South Africa, where precipitation distributes unevenly in both spatial and temporal dimensions. In this paper, based on the characteristics of South Africa's natural climate and internal land cover and terrain features, a suitable drought monitoring index is presented. By using NDVI classification, this paper calculated separately the Vegetation Supply Water Index(VSWI)for vegetation in different land covers and terrains in agricultural areas of South Africa and worked out the synthetic drought index(SDI)for monitoring drought. Combined SDI and agricultural planting area information, a 14-years'high spatial-temporal resolution data set for drought monitoring distribution was obtained. Results showed that drought in South Africa occurred mainly in late spring and early summer, usually reached maximum in late summer. In the 14 years, severe drought occurred mainly in 2002, 2007 and 2013, and each state was generally dominated by light drought and moderate drought, but turned to severe drought in late summer. Drought often occurred first in the central region where the rainfall is rather scarce, and then expanded towards east and south. Above all, the SDI drought monitoring results are basically consistent with the drought fact in South Africa, indicating that the SDI drought index can be used for drought monitoring in South Africa, and make up for the insufficient through meteorological drought monitoring, as well as provide references for making policies in responding to drought warning.

Key words: drought monitoring, MODIS, SDI, South Africa

中图分类号: 

  • TP79