遥感干旱指数的时空格局适应性研究
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摘要
干旱因其持续时间长、影响范围广、灾害损失重等特点成为在世界范围内影响最为严重的自然灾害之一。旱灾的频繁发生使我国经济发展、人民生活等遭受了巨大损失,因此对干旱进行监测,掌握其发生、发展规律及影响因子之间的关系,科学合理地预报旱情,对于减少灾害损失、社会的可持续发展等具有重要的实际意义,而遥感干旱监测则以其监测范围广、数据更新快等优点在干旱监测中发挥了重要作用。本文以河南省为例,以MODIS影像为基础,分别计算了归-化植被指数(NDVI)、距平植被指数(AVI)、植被状态指数(VCI)、温度状态指数(TCI),并分析了在不同时空格局下精度较高的遥感干旱指数,以提高遥感干旱监测的精度。
本文首先对气象数据进行处理,利用气象数据中的站点坐标、月降水量等信息计算出降水量距平百分率,然后对河南省近十年的干旱状况进行了一系列的分析,同时对获取的MODIS影像数据运用MRT、ENVI进行重投影、剪切等预处理得到河南省的影像图,根据常用遥感干旱指数的计算公式分别计算出常用遥感干旱指数的影像图。为了对比分析不同时域各遥感干旱指数的精度,按照行政区划对河南省分为豫中、豫东、豫南、豫西、豫北五个区域,分别统计各分区的遥感干旱指数值及传统干旱指数降水量距平百分率。然后分别选取月、季度、年为时间尺度,以各个分区为地域条件分析传统干旱指数与常用遥感干旱指数之间的相关性,以确定不同时空格局条件下精度较高的遥感干旱指数。从研究结果来看,除豫东地区是AVI与平均降水量间的相关性最大外,其余均为NDVI与降水量的相关性最强。
因此当以多年平均得到的月均遥感干旱指数值为尺度研究各地区干旱时用NDVI来预测干旱情况具有较高的准确性,而以季度为研究时间尺度时可以得到在豫中地区四个季节中遥感干旱指数与降水量相关性最高的依次为：VCI、VCI, TCI、TCI;豫东地区依次为：AVI、VCI、TCI、NDVI;豫南地区依次为：VCI、AVI、AVI、NDVI;豫西地区依次为：TCI、VCI、VCI、NDVI;豫北地区依次为：AVI、VCI、AVI、TCI。
Drought is one of the severest nature disasters for its long duration, a wide range affect, disaster losses weight and other characteristics, and for the frequent occurrence of drought in China, the country's economic development and daily life suffered a great loss. Therefore, drought monitoring and control the relationship between the development and its influencing factors, forecast drought in scientific and reasonable, has important practical significance to the reduction of disaster losses and sustainable development of society. Remote Sensing is playing an increasingly important role in drought monitoring, with its wide range of monitoring and quickly data updates. Henan province was taken as the case, on the basis of MODIS image, Normalized Difference Vegetation Index (NDVI), Anomaly Vegetation Index (AVI), Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) was calculated separately, and compare and analyze precision of the indices under the same spatial and temporal patterns, to select the Remote Sensing of drought index of high precision under different spatial and temporal patterns, and improve the precision of remote sensing of drought monitoring.
This paper process the meteorological data of each measure site from China monthly surface climate data set, select the meteorological data site of Henan province, calculate precipitation anomaly percentage of Henan province using site coordinates and monthly precipitation of meteorological data and other information, and analyze the drought condition of Henan province in nearly decade; re-project and cut the MODIS image(monthly vegetation index of lkm resolution, land surface temperature of lkm resolution) and other pretreatment by MRT and ENVI to obtain the image of Henan province, calculate popular Remote Sensing image of drought index base on the formula. According to administrative divisions, Henan Province is divided into five regions (mid Henan, eastern Henan, southern Henan, western Henan and northern Henan) to analyze the precision of popular Remote Sensing of drought index of different spatial and temporal condition, statistic Remote Sensing of drought index and precipitation anomaly percentage of sub area. Take month, quarter and year as time scale, each sub area as spatial condition to analyze the correlation of traditional drought index and Remote Sensing of drought index, and select the Remote Sensing of high precision in different spatial and temporal condition. From the research results, the best correlation with precipitation is AVI in eastern Henan while the other is NDVI, so the use of NDVI to predict drought conditions with high accuracy when research drought condition of each sub area with monthly Remote Sensing of drought index value of annual average, and when research drought condition by quarter, the highest correlation of Remote Sensing of drought index and precipitation in four seasons is VCI, VCI, TCI and TCI in mid Henan; the follow is AVI, VCI, TCI and NDVI in eastern Henan; VCI, AVI, AVI and NDVI in southern Henan; TCI, VCI, VCI and NDVI in western Henan; AVI, VCI, AVI and TCI in northern Henan.
本文首先对气象数据进行处理,利用气象数据中的站点坐标、月降水量等信息计算出降水量距平百分率,然后对河南省近十年的干旱状况进行了一系列的分析,同时对获取的MODIS影像数据运用MRT、ENVI进行重投影、剪切等预处理得到河南省的影像图,根据常用遥感干旱指数的计算公式分别计算出常用遥感干旱指数的影像图。为了对比分析不同时域各遥感干旱指数的精度,按照行政区划对河南省分为豫中、豫东、豫南、豫西、豫北五个区域,分别统计各分区的遥感干旱指数值及传统干旱指数降水量距平百分率。然后分别选取月、季度、年为时间尺度,以各个分区为地域条件分析传统干旱指数与常用遥感干旱指数之间的相关性,以确定不同时空格局条件下精度较高的遥感干旱指数。从研究结果来看,除豫东地区是AVI与平均降水量间的相关性最大外,其余均为NDVI与降水量的相关性最强。
因此当以多年平均得到的月均遥感干旱指数值为尺度研究各地区干旱时用NDVI来预测干旱情况具有较高的准确性,而以季度为研究时间尺度时可以得到在豫中地区四个季节中遥感干旱指数与降水量相关性最高的依次为：VCI、VCI, TCI、TCI;豫东地区依次为：AVI、VCI、TCI、NDVI;豫南地区依次为：VCI、AVI、AVI、NDVI;豫西地区依次为：TCI、VCI、VCI、NDVI;豫北地区依次为：AVI、VCI、AVI、TCI。
Drought is one of the severest nature disasters for its long duration, a wide range affect, disaster losses weight and other characteristics, and for the frequent occurrence of drought in China, the country's economic development and daily life suffered a great loss. Therefore, drought monitoring and control the relationship between the development and its influencing factors, forecast drought in scientific and reasonable, has important practical significance to the reduction of disaster losses and sustainable development of society. Remote Sensing is playing an increasingly important role in drought monitoring, with its wide range of monitoring and quickly data updates. Henan province was taken as the case, on the basis of MODIS image, Normalized Difference Vegetation Index (NDVI), Anomaly Vegetation Index (AVI), Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) was calculated separately, and compare and analyze precision of the indices under the same spatial and temporal patterns, to select the Remote Sensing of drought index of high precision under different spatial and temporal patterns, and improve the precision of remote sensing of drought monitoring.
This paper process the meteorological data of each measure site from China monthly surface climate data set, select the meteorological data site of Henan province, calculate precipitation anomaly percentage of Henan province using site coordinates and monthly precipitation of meteorological data and other information, and analyze the drought condition of Henan province in nearly decade; re-project and cut the MODIS image(monthly vegetation index of lkm resolution, land surface temperature of lkm resolution) and other pretreatment by MRT and ENVI to obtain the image of Henan province, calculate popular Remote Sensing image of drought index base on the formula. According to administrative divisions, Henan Province is divided into five regions (mid Henan, eastern Henan, southern Henan, western Henan and northern Henan) to analyze the precision of popular Remote Sensing of drought index of different spatial and temporal condition, statistic Remote Sensing of drought index and precipitation anomaly percentage of sub area. Take month, quarter and year as time scale, each sub area as spatial condition to analyze the correlation of traditional drought index and Remote Sensing of drought index, and select the Remote Sensing of high precision in different spatial and temporal condition. From the research results, the best correlation with precipitation is AVI in eastern Henan while the other is NDVI, so the use of NDVI to predict drought conditions with high accuracy when research drought condition of each sub area with monthly Remote Sensing of drought index value of annual average, and when research drought condition by quarter, the highest correlation of Remote Sensing of drought index and precipitation in four seasons is VCI, VCI, TCI and TCI in mid Henan; the follow is AVI, VCI, TCI and NDVI in eastern Henan; VCI, AVI, AVI and NDVI in southern Henan; TCI, VCI, VCI and NDVI in western Henan; AVI, VCI, AVI and TCI in northern Henan.
引文
[1].樊任华.基于MODIS数据的草地干旱指数对比研究[D].南京大学建筑工程学院.2008,12
[2].赵旭春.中国北方干旱区干旱指标的应用研究[D].兰州大学大气科学.2007,11
[3].张芳.基于MODIS的陕西省干旱遥感监测研究[D].陕西师范大学自然地理学.2008,5
[4].阎娜娜.基于遥感指数的旱情监测方法研究[D].中国科学院遥感应用研究所.2005,6
[5].李星敏,郑有飞,刘安麟.我国用NOAA/AVHRR资料进行干旱遥感监测的方法综述[J].中国农业气象.2003,24(3)：38-41
[6].干小平,郭铌.遥感监测干旱的方法及研究进展[J].干旱气象.2003,21(4)：76-81
[7].夏虹,武建军,刘雅妮等.中国用遥感方法进行干旱监测的研究进展[J].遥感信息.2005,1：55-58
[8].王树东,陈曦,刘素红等.区域干旱遥感临测模型研究进展[J].遥感信息.2006,6：72-78
[9].刘良明.EOSMODIS数据的遥感干旱预警模型研究[D].武汉大学摄影测量与遥感.2004,11
[10].Kogan F N. Application of vegetation index and brightness temperature for drought detection [J].Advances in Space Research,1995,15:91
[11].Jackson R D, Reginato R J, Idso S B. Wheat canopy temperature:A practical tool for evaluating water requirements [J]. Water Resource Research,1997,13:651
[12].Idso S B, Jackson R D, Printer P J, et al. Normalizing the stress degree day for environmental variability [J]. Agricultural Meteorology,1981,24:45
[13].Jupp D L B, Tian G, McVicar T R, et al. Monitoring soil moisture and drought using AVHRR satellite data, I:theory[A] CSIRO Earth Observation Technical Report[C]. Canberra, ACT 1998
[14].Price J C. Using spatial context in satellite data to infer regional scale evapotranspiration [J].IEEE Transactions on Geosciences and Remote Sensing,1990(28):940-948
[15].Gillies R R, Carlson T N, Gui J, et al. A verification of the triangle method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index and surface radiant temperature [J]. International Journal of Remote Sensing,1997,18(15):3145-3166
[16].Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status [J].Remote Sensing Of Environment,2002(79):213-224
[17].韩丽娟,王鹏新,王锦地等.植被指数——地表温度构成的特征空间研究[J].中国科学：D辑,2005,35(4)：371
[18].王鹏新,黄健雅,李小文.条件植被温度指数及其在干旱监测中的应用[J].武汉大学学报：信息科学版,2001,26：412
[19].Wan Z, Wang P, Li X. Using MODIS land surface temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA [J].International Journal of Remote Sensing,2004,25(1):61
[20].中广荣,田国良.基于GIS的黄淮海平原旱灾遥感监测研究——作物缺水指数模型的实现[J].生态学报,2000,20(2)：224-228
[21].许国鹏,李仁东,梁守真.基于改进型温度植被干旱指数的旱情监测研究[J].世界科技研究和发展,2006,28(6)：51-55
[22].蔡斌,陆文杰,郑新江.气象卫星条件植被指数监测十壤状况[J].国土资源遥感,1995,(4)：45-51
[23].杨绍锷,吴灿方,熊隽等.基于TRMM降水产品计算月降水量距平百分率[J].遥感信息.2010,5：62-66
[24].Seiler R. A., Hayes M., Bressan L. Using the standardized precipitation index for flood risk monitoring [J]. International Journal of Climatology,2002,22(1):1365-1376
[25].Hayes M. J., Svoboda M. D., Wilhite D. A., et al. Monitoring the 1996 drought using the Standardized Precipitation Index [J]. Bulletin of American Meteorological Society,1999,80:429-438
[26].黄晚华,杨晓光,李茂松等.基于标准化降水指数的中国南方季节性干旱近85a演变特征[J].农业工程学报.2010,26(7)：50-59
[27].张战睦,芮杰.遥感技术基础[M].北京：科学出版社.2007,7：11-303
[28].Zhou L, Tucker C J, Kaufmann R K, et al. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981-1999[J]. Journal of Geophysical Research.2001,106(D 17):20069-20083
[29].Fang J Y, Piao S L, He J S, et al. Increasing terrestrial vegetation activity in China, 1982-1999[J]. Science in China (Series C),2004,47(3):229-240
[30].邢文渊.基于MODIS影像数据反演干旱区十壤湿度方法研究[D].新疆大学.2006,6
[31].陈维英,肖乾广,盛永伟.距平植被指数在1992年特大干旱监测中的应用[J].环境遥感.1994,9(2)：106-112
[32].Kogan F N. Remote Sensing of weather impacts on vegetation in non-homogeneous areas[J]. International Journal of Remote Sensing,1990,11(8):1405-1419
[33].千鹏新,Wan Zheng-Ming,龚健雅.基于植被指数和十地表面温度的干旱监测模型[J].地球科学进展,2003,18(4)：527-533
[34].蒙继华.农作物长势遥感监测指标研究[D].中国科学院遥感应用研究所.2006.5
[35].付祥建,余卫东,杜子璇.河南省气候资源分析[C].中国气象学会2008年年会气候资源应用研究分会场论文集.2008
[36].刘国锋,胡细涓.河南气候变化对农业气象灾害的影响[J].当代经理人.2005,4：218-219
[37].席荣胱.河南自然资源特点及其优劣势分析[J].中原地理研究.1984,2：41-48
[38].中国天气网河南站.河南气候[EB/OL]. http://henan.weather.com.cn/hnqh/hnqh.shtml.2010
[39].陈辉,于向云,王志强.河南近10年主要农业灾害及其影响[J].河南气象.2001,2：28-29
[40].黄家洁,万幼川,刘良明MODIS的特性及其应用[J].地理空间信息.2003,1(4)：20-23
[2].赵旭春.中国北方干旱区干旱指标的应用研究[D].兰州大学大气科学.2007,11
[3].张芳.基于MODIS的陕西省干旱遥感监测研究[D].陕西师范大学自然地理学.2008,5
[4].阎娜娜.基于遥感指数的旱情监测方法研究[D].中国科学院遥感应用研究所.2005,6
[5].李星敏,郑有飞,刘安麟.我国用NOAA/AVHRR资料进行干旱遥感监测的方法综述[J].中国农业气象.2003,24(3)：38-41
[6].干小平,郭铌.遥感监测干旱的方法及研究进展[J].干旱气象.2003,21(4)：76-81
[7].夏虹,武建军,刘雅妮等.中国用遥感方法进行干旱监测的研究进展[J].遥感信息.2005,1：55-58
[8].王树东,陈曦,刘素红等.区域干旱遥感临测模型研究进展[J].遥感信息.2006,6：72-78
[9].刘良明.EOSMODIS数据的遥感干旱预警模型研究[D].武汉大学摄影测量与遥感.2004,11
[10].Kogan F N. Application of vegetation index and brightness temperature for drought detection [J].Advances in Space Research,1995,15:91
[11].Jackson R D, Reginato R J, Idso S B. Wheat canopy temperature:A practical tool for evaluating water requirements [J]. Water Resource Research,1997,13:651
[12].Idso S B, Jackson R D, Printer P J, et al. Normalizing the stress degree day for environmental variability [J]. Agricultural Meteorology,1981,24:45
[13].Jupp D L B, Tian G, McVicar T R, et al. Monitoring soil moisture and drought using AVHRR satellite data, I:theory[A] CSIRO Earth Observation Technical Report[C]. Canberra, ACT 1998
[14].Price J C. Using spatial context in satellite data to infer regional scale evapotranspiration [J].IEEE Transactions on Geosciences and Remote Sensing,1990(28):940-948
[15].Gillies R R, Carlson T N, Gui J, et al. A verification of the triangle method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index and surface radiant temperature [J]. International Journal of Remote Sensing,1997,18(15):3145-3166
[16].Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status [J].Remote Sensing Of Environment,2002(79):213-224
[17].韩丽娟,王鹏新,王锦地等.植被指数——地表温度构成的特征空间研究[J].中国科学：D辑,2005,35(4)：371
[18].王鹏新,黄健雅,李小文.条件植被温度指数及其在干旱监测中的应用[J].武汉大学学报：信息科学版,2001,26：412
[19].Wan Z, Wang P, Li X. Using MODIS land surface temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA [J].International Journal of Remote Sensing,2004,25(1):61
[20].中广荣,田国良.基于GIS的黄淮海平原旱灾遥感监测研究——作物缺水指数模型的实现[J].生态学报,2000,20(2)：224-228
[21].许国鹏,李仁东,梁守真.基于改进型温度植被干旱指数的旱情监测研究[J].世界科技研究和发展,2006,28(6)：51-55
[22].蔡斌,陆文杰,郑新江.气象卫星条件植被指数监测十壤状况[J].国土资源遥感,1995,(4)：45-51
[23].杨绍锷,吴灿方,熊隽等.基于TRMM降水产品计算月降水量距平百分率[J].遥感信息.2010,5：62-66
[24].Seiler R. A., Hayes M., Bressan L. Using the standardized precipitation index for flood risk monitoring [J]. International Journal of Climatology,2002,22(1):1365-1376
[25].Hayes M. J., Svoboda M. D., Wilhite D. A., et al. Monitoring the 1996 drought using the Standardized Precipitation Index [J]. Bulletin of American Meteorological Society,1999,80:429-438
[26].黄晚华,杨晓光,李茂松等.基于标准化降水指数的中国南方季节性干旱近85a演变特征[J].农业工程学报.2010,26(7)：50-59
[27].张战睦,芮杰.遥感技术基础[M].北京：科学出版社.2007,7：11-303
[28].Zhou L, Tucker C J, Kaufmann R K, et al. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981-1999[J]. Journal of Geophysical Research.2001,106(D 17):20069-20083
[29].Fang J Y, Piao S L, He J S, et al. Increasing terrestrial vegetation activity in China, 1982-1999[J]. Science in China (Series C),2004,47(3):229-240
[30].邢文渊.基于MODIS影像数据反演干旱区十壤湿度方法研究[D].新疆大学.2006,6
[31].陈维英,肖乾广,盛永伟.距平植被指数在1992年特大干旱监测中的应用[J].环境遥感.1994,9(2)：106-112
[32].Kogan F N. Remote Sensing of weather impacts on vegetation in non-homogeneous areas[J]. International Journal of Remote Sensing,1990,11(8):1405-1419
[33].千鹏新,Wan Zheng-Ming,龚健雅.基于植被指数和十地表面温度的干旱监测模型[J].地球科学进展,2003,18(4)：527-533
[34].蒙继华.农作物长势遥感监测指标研究[D].中国科学院遥感应用研究所.2006.5
[35].付祥建,余卫东,杜子璇.河南省气候资源分析[C].中国气象学会2008年年会气候资源应用研究分会场论文集.2008
[36].刘国锋,胡细涓.河南气候变化对农业气象灾害的影响[J].当代经理人.2005,4：218-219
[37].席荣胱.河南自然资源特点及其优劣势分析[J].中原地理研究.1984,2：41-48
[38].中国天气网河南站.河南气候[EB/OL]. http://henan.weather.com.cn/hnqh/hnqh.shtml.2010
[39].陈辉,于向云,王志强.河南近10年主要农业灾害及其影响[J].河南气象.2001,2：28-29
[40].黄家洁,万幼川,刘良明MODIS的特性及其应用[J].地理空间信息.2003,1(4)：20-23