基于多源数据的西藏东南部历史干旱监测与分析
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摘要
干旱作为频发的全球性自然灾害之一,造成了严重的社会、经济和生态环境问题。以西藏主要耕作区为研究区,2001—2015年MODIS、TRMM和SRTM-DEM数据为数据源,利用植被状态指数(VCI)、温度条件指数(TCI)和降水状态指数(PCI)等模型参量,采用空间主成分分析方法构建区域干旱综合监测模型,对模型精度和可靠性进行检核验证,并以所建模型对研究区2001—2015年逐月干旱进行识别,采用地理时空分析方法对研究区干旱变化特征及趋势进行研究。结果表明,干旱综合监测指数(DCMI)能够较好地反映区域土壤相对湿度与标准化降水蒸散指数(SPEI)的变化,干旱综合监测模型具有较好的适用性;研究区全年干旱频率在空间分布上呈现出西高东低的特征,大部分地区干旱频率小于20%,约12.41%的区域干旱频率超过20%;从不同等级干旱发生频率来看,日喀则市为轻旱、中旱易发区,重旱易发区则集中于日喀则市和昌都市的中部及东部地区;区域月际干旱频率空间格局差异较大,全年干旱易发生于1、8、11月等月份,局部地区干旱易发月份存在差异;区域年内旱情变化趋势差异性较大,10月～次年9月,旱情加剧区域呈现出随月份变化由耕作区东部向西部逐渐转移的趋势。
As one of the global natural disasters which take place frequently,drought has caused problems in society,economy,and ecological environment. Based on the MODIS,TRMM and SRTM-DEM data from 2001 to 2015 as the data sources,this paper takes the main farming area of Tibet Province,China as the research area and constructed a regional drought comprehensive monitoring model by using the spatial principal component analysis method and adopting the vegetation state index(VCI),temperature condition index(TCI) and precipitation state index(PCI).The accuracy and reliability of the model is verified.The model was then used to identify the monthly drought in the study area from 2001 to 2015,and the geospatial-temporal analysis method was used to study the characteristics and trends of drought changes in the study area.The results show that the drought comprehensive monitoring index(DCMI) can better reflect the changes of regional soil relative humidity and standardized precipitation evapotranspiration index(SPEI),and the comprehensive drought monitoring model has good applicability.The spatial distribution of annual drought frequency in the west of the study area was higher than that in the east,and the drought frequency in most areas was less than 20%,and about 12.41% of the regions had a drought frequency more than 20%.From the perspective of the frequency of droughts in different grades,Shigatse City was a light drought and moderate drought area,while the severe drought areas were concentrated in the central and eastern parts of Shigatse City and Changdu City.The spatial pattern of monthly drought frequency in the study area was quite different.The drought in the whole year was prone to occur in January,August and November,and there were differences in the drought-prone months in some areas.The change trend of drought in the study area was quite different during a year.From October to September,the drought-intensified area showed a trend of gradually shifting from the east to the west of the cultivated area along the month.
As one of the global natural disasters which take place frequently,drought has caused problems in society,economy,and ecological environment. Based on the MODIS,TRMM and SRTM-DEM data from 2001 to 2015 as the data sources,this paper takes the main farming area of Tibet Province,China as the research area and constructed a regional drought comprehensive monitoring model by using the spatial principal component analysis method and adopting the vegetation state index(VCI),temperature condition index(TCI) and precipitation state index(PCI).The accuracy and reliability of the model is verified.The model was then used to identify the monthly drought in the study area from 2001 to 2015,and the geospatial-temporal analysis method was used to study the characteristics and trends of drought changes in the study area.The results show that the drought comprehensive monitoring index(DCMI) can better reflect the changes of regional soil relative humidity and standardized precipitation evapotranspiration index(SPEI),and the comprehensive drought monitoring model has good applicability.The spatial distribution of annual drought frequency in the west of the study area was higher than that in the east,and the drought frequency in most areas was less than 20%,and about 12.41% of the regions had a drought frequency more than 20%.From the perspective of the frequency of droughts in different grades,Shigatse City was a light drought and moderate drought area,while the severe drought areas were concentrated in the central and eastern parts of Shigatse City and Changdu City.The spatial pattern of monthly drought frequency in the study area was quite different.The drought in the whole year was prone to occur in January,August and November,and there were differences in the drought-prone months in some areas.The change trend of drought in the study area was quite different during a year.From October to September,the drought-intensified area showed a trend of gradually shifting from the east to the west of the cultivated area along the month.
引文
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[17] 王莺,王静,姚玉璧,等.基于主成分分析的中国南方干旱脆弱性评价[J].生态环境学报,2014,23(12):1897-1904.[WANG Ying,WANG Jing,YAO Yubi,et al.Evaluation of drought vulnerability in Southern China based on principal component analysis[J].Ecology & Environmental Sciences,2014,23(12):1897-1904.]
[18] DU L,TIAN Q,YU T,et al.A comprehensive drought monitoring method integrating MODIS and TRMM data[J].International Journal of Applied Earth Observation & Geoinformation,2013,23(1):245-253.
[19] LI X,HE B,QUAN X,et al.Use of the standardized precipitation evapotranspiration index (SPEI) to characterize the drying trend in southwest China from 1982—2012[J].Remote Sensing,2015,7(8):10917-10937.
[20] 中华人民共和国国家质量监督检验检疫总局.中华人民共和国国家标准:气象干旱等级GB/T 20481-2017)[S].北京:中国标准出版社,2017.[General Administration of Quality Supervision,Inspection and Quarantine of the People’s Republic of China.Classification of meteorological drought (GB/T 20481-2017)[S].Beijing:Standards Press of China,2017.]
[21] 何鑫,吴吉东,李颖,等.基于SPEI的辽西地区气象干旱时空分布特征[J].干旱区地理,2017,40(2):340-347.[HE Xin,WU Jidong,Li Ying,et al.Spatio-temporal distribution characteristics of meteorological drought in western region of Liaoning Province based on standardized precipitation evapotranspiration index[J].Arid Land Geography,2017,40(2):340-347.]
[2] KOGAN F N.Application of vegetation index and brightness temperature for drought detection[J].Advances in Space Research,1995,15(11):91-100.
[3] TADESSE T,BROWN J F,HAYES M J.A new approach for predicting drought-related vegetation stress:Integrating satellite,climate,and biophysical data over the U.S.central plains[J].Isprs Journal of Photogrammetry & Remote Sensing,2005,59(4):244-253.
[4] RAJSEKHAR D,SINGH V P,MISHRA A K.Multivariate drought index:An information theory based approach for integrated drought assessment[J].Journal of Hydrology,2015,526(11/12):164-182.
[5] ZHANG L,JIAO W,ZHANG H,et al.Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices[J].Remote Sensing of Environment,2017,190:96-106.
[6] 郑杰,冯文兰,王凤杰,等.岷江上游干旱河谷范围的界定及其变化分析[J].干旱区地理,2017,40(3):541-548.[ZHENG Jie,FENG Wenlan,WANG Fengjie,et al.Spatial definition and its range variation of arid valley in the upper reaches of Minjiang River[J].Arid Land Geography,2017,40(3):541-548.]
[7] 张婧娴.综合多源遥感数据的干旱监测模型研究[D].南京:南京信息工程大学,2015.[ZHANG Jingxian.Study on drought monitoring modeling integrated multi-source remote sensing date-Taking Shanxi as an example[D].Nanjing:Nanjing University of Information Science & Technology ,2015.]
[8] 雷步云,赵书河,覃志豪,等.基于SDI指数的南非共和国2001—2014年干旱监测时空分布[J].干旱区地理,2016,39(2):395-404.[LEI Buyun,ZHAO Shuhe,QIN Zhihao,et al.Drought temporal-spatial distribution of South Africa based on MODIS SDI index from 2001—2014[J].Arid Land Geography,2016,39(2):395-404.]
[9] 杜灵通.基于多源空间信息的干旱监测模型构建及其应用研究[D].南京:南京大学,2013.[DU Lingtong.Drought monitoring model based on multi-source spatial information and its application[D].Nanjing:Nanjing University,2013.]
[10] 陈建军.基于EOS/MODIS的江西省水稻长势遥感监测研究[D].南京:南京信息工程大学,2012.[CHEN Jianjun.Study on monitoring of paddy rice growth based on EOS/MODIS in Jiangxi province[D].Nanjing:Nanjing University of Information Science & Technology ,2012.]
[11] 李红军,郑力,雷玉平,等.基于EOS/MODIS数据的NDVI与EVI比较研究[J].地理科学进展,2007,26(1):26-32.[LI Hongjun,ZHENG Li,LEI Yuping,et al.Comparison of NDVI and EVI based on EOS/MODIS data[J].Progress in Geography,2007,26(1):26-32.]
[12] 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
[13] 张尧.基于遥感方法的2010年西南干旱监测评价[D].杨凌:西北农林科技大学,2014.[ZHANG Yao.Monitoring and estimating the 2010 drought impact in southwestern China based on remote sensing[D].Yangling:Northwest A&F University,2014.]
[14] 王俊霞,朱秀芳,刘宪锋,等.基于多源遥感数据的旱情评价研究——以河南省为例[J].国土资源遥感,2018,30(1):180-186.[WANG Junxia,ZHU Xiufang,LIU Xianfeng,et al.Research on agriculture drought monitoring method of Henan Province with multi-sources data[J].Remote Sensing for Land & Resources,2018,30(1):180-186.]
[15] 曾波.湖南省农业干旱空间分布研究[D].长沙:中南林业科技大学,2014.[ZENG Bo.Research on spatial distribution of agriculture drought in Hunan[D].Changsha:Central South University of Forestry and Technology,2014.]
[16] 夏文韬,王莺,冯琦胜,等.甘南地区MODIS土地覆盖产品精度评价[J].草业科学,2010,27(9):11-18.[XIA Wentao,WANG Ying,FENG Qisheng,et al.Accuracy assessment of MODIS land cover product of Gannan Prefecture[J].Pratacultural Science,2010,27(9):11-18.]
[17] 王莺,王静,姚玉璧,等.基于主成分分析的中国南方干旱脆弱性评价[J].生态环境学报,2014,23(12):1897-1904.[WANG Ying,WANG Jing,YAO Yubi,et al.Evaluation of drought vulnerability in Southern China based on principal component analysis[J].Ecology & Environmental Sciences,2014,23(12):1897-1904.]
[18] DU L,TIAN Q,YU T,et al.A comprehensive drought monitoring method integrating MODIS and TRMM data[J].International Journal of Applied Earth Observation & Geoinformation,2013,23(1):245-253.
[19] LI X,HE B,QUAN X,et al.Use of the standardized precipitation evapotranspiration index (SPEI) to characterize the drying trend in southwest China from 1982—2012[J].Remote Sensing,2015,7(8):10917-10937.
[20] 中华人民共和国国家质量监督检验检疫总局.中华人民共和国国家标准:气象干旱等级GB/T 20481-2017)[S].北京:中国标准出版社,2017.[General Administration of Quality Supervision,Inspection and Quarantine of the People’s Republic of China.Classification of meteorological drought (GB/T 20481-2017)[S].Beijing:Standards Press of China,2017.]
[21] 何鑫,吴吉东,李颖,等.基于SPEI的辽西地区气象干旱时空分布特征[J].干旱区地理,2017,40(2):340-347.[HE Xin,WU Jidong,Li Ying,et al.Spatio-temporal distribution characteristics of meteorological drought in western region of Liaoning Province based on standardized precipitation evapotranspiration index[J].Arid Land Geography,2017,40(2):340-347.]