基于TVDI与荧光的呼伦贝尔市干旱时空动态监测研究
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摘要
【目的】呼伦贝尔地区降水量少,年际和年内分布不均,灌溉条件不发达,干旱对当地农业和畜牧业生产造成了严重影响,结合干旱监模型与荧光数据可对呼伦贝尔干旱情况进行有效监测。【方法】文章使用MODIS归一化植被指数(NDVI)和地表温度(LST)产品,建立Ts-NDVI特征空间,计算温度植被干旱指数(TVDI),同时结合GOME-2 L2级荧光产品,综合分析评价2017—2018年呼伦贝尔地区干旱时空变化特征。【结果】该文建立的干旱指数模型能有效监测呼伦贝尔市干旱状况,干旱等级划分结果表明,2017—2018年呼伦贝尔地区存在不同程度的干旱问题,此模型在该研究区干旱监测上具有很好的适用性;通过SIF与NDVI的变化过程对比分析发现,SIF对水分胁迫等植被生理变化比较敏感,SIF较NDVI而言,能更有效地反映植物受胁迫状况。【结论】通过MODIS产品与GOME-2 L2荧光产品相结合,可以更准确地监测分析该研究区内的干旱时空动态变化,为呼伦贝尔市的旱情监测提供重要参考。
[Purpose]Drought,as the most common and widespread natural disaster in the world,occurs frequently and always lasts for a long time. Usually,the economic losses and impacts caused by drought far exceed those of other meteorological disasters. The Hulunbeier area has less precipitation,uneven distribution of precipitation,and underdeveloped irrigation conditions. Drought has seriously affected local agriculture and animal husbandry production.[Method]In this work,MODIS normalized vegetation index(NDVI)and surface temperature(LST)products were used to establish the Ts-NDVI feature space,Simultaneously,The Temperature Vegetation Dryness Index(TVDI)was calculated. Combining GOME-2 L2 grade fluorescent products,we comprehensively analyzed and evaluated the temporal and spatial variation characteristics of drought in Hulunbeier area from 2017 to 2018.[Result]The drought index model established in this paper can effectively monitor the drought situation in Hulunbeier City. Our division of drought grades indicate that there are different degrees of drought in the Hulunbeier area from 2017 to 2018. This model has a good applicability for drought monitoring in this study area. Comparing the changes of SIF and NDVI,we found that SIF is more sensitive to vegetation physiological changes such as water stress. Additionally,SIF can reflect the stress status of plants more effective than NDVI.[Conclusion]The combination of MODIS products and GOME-2 L2 fluorescent products can monitor and analyze the dynamic changes of drought in our study area accurately. This provides an important reference for drought monitoring and early warning in Hulunbeier City.
[Purpose]Drought,as the most common and widespread natural disaster in the world,occurs frequently and always lasts for a long time. Usually,the economic losses and impacts caused by drought far exceed those of other meteorological disasters. The Hulunbeier area has less precipitation,uneven distribution of precipitation,and underdeveloped irrigation conditions. Drought has seriously affected local agriculture and animal husbandry production.[Method]In this work,MODIS normalized vegetation index(NDVI)and surface temperature(LST)products were used to establish the Ts-NDVI feature space,Simultaneously,The Temperature Vegetation Dryness Index(TVDI)was calculated. Combining GOME-2 L2 grade fluorescent products,we comprehensively analyzed and evaluated the temporal and spatial variation characteristics of drought in Hulunbeier area from 2017 to 2018.[Result]The drought index model established in this paper can effectively monitor the drought situation in Hulunbeier City. Our division of drought grades indicate that there are different degrees of drought in the Hulunbeier area from 2017 to 2018. This model has a good applicability for drought monitoring in this study area. Comparing the changes of SIF and NDVI,we found that SIF is more sensitive to vegetation physiological changes such as water stress. Additionally,SIF can reflect the stress status of plants more effective than NDVI.[Conclusion]The combination of MODIS products and GOME-2 L2 fluorescent products can monitor and analyze the dynamic changes of drought in our study area accurately. This provides an important reference for drought monitoring and early warning in Hulunbeier City.
引文
[1]彭擎,王让会,蒋烨林,等.植被-地温指数(NDVI-LST)在新疆干旱监测中的适应性.生态学报,2018(13):1~9.
[2]庞治国.干旱遥感监测模型研究及墒情预报探索--以黑龙江省为例.中国水利水电科学研究院,2003.
[3]赵建苹,胡顺石,秦建新.基于TVDI的湖南省干旱监测分析.地理空间信息,2018,16(3):101~105.
[4]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.南京大学,2013.
[5]翁白莎,严登华.变化环境下中国干旱综合应对措施探讨.资源科学,2010,32(02):309~316.
[6]王劲松,李耀辉,王润元,等.我国气象干旱研究进展评述.干旱气象,2012,30(4):497~508.
[7]Jackson R D,Idso S B,Reginato R J,et al.Canopy temperature as a crop water stress indicator.Water Resources Research,1981,17(4):1133~1138.
[8]Kogon,F.N.Remote sensing ofweather impact S on vegetation in non-homogeneous areas.International Joumal of Remote Sensing.1990,11:1405~1420.
[9]Sandholt I,Rasmussen K,Andersen J.A Simple Interpretation of the Surface Temperature/Vegetation Index Space for Assessment of Surface Moisture Status.Remote Sensing of Environment,2002,79(2):213~224.
[10]Kimura,R.Estimation ofmoisture availability over the Liudaogou river basin ofthe Loess Plateau using new indices with surface temperature.Journal of Environments,2007,10:10~16.
[11]Price J C.Using spatial context in satellite data to infer regional scale evapotranspiration.IEEE Transactions on Geoscience and Remote Sensing,1990,28:940~948.
[12]杨彦荣,胡国强.基于植被供水指数的旱区土壤湿度反演方法研究.现代电子技术,2019,42(2):138~142,146.
[13]王鹏新,龚健雅,李小文.条件植被温度指数及其在干旱监测中的应用.武汉大学学报(信息科学版),2001(5):412~418.
[14]梁超,高永,党晓宏,等.呼伦贝尔市季节性干旱演变特征.内蒙古林业科技,2018,44(2):7~13.
[15]曲学斌.基于MCI指数的呼伦贝尔市干旱灾害风险区划.北方农业学报,2019,47(1):120~125.
[16]丁书萍.呼伦贝尔森林-草原生态交错带气候变化特征研究.东北农业大学,2012.
[17]孙福军.基于极端干旱年份遥感植被指数的半干旱地区土地评价研究.沈阳农业大学,2017.
[18]侯小丽.基于MODIS数据的全球干旱区植被变化及其与气候关系的研究(2002-2011年).山东农业大学,2016.
[19]李召良,唐伯惠,唐荣林,等.地表温度热红外遥感反演理论与方法.科学观察,2017,12(6):57~59.
[20]Zhao-Liang Li,Bo-Hui Tang,Hua Wu,et al.Satellite-Derived Land Surface Temperature:Current Status and Perspectives,Remote Sensing of Environment,2013(131):14~37.
[21]Bo-Hui Tang,Yuyun Bi,Zhao-Liang Li,et al.Generalized split-window algorithm for estimate of land surface temperature from Chinese geostationary FengYun meteorological satellite(FY-2C)data.Sensors,2008,8(2):933~951.
[22]郭佳.基于随机森林的遥感干旱监测模型及其应用研究.南京:南京信息工程大学,2016.
[23]高华.基于温度植被干旱指数印度和巴基斯坦干旱监测.北京:中国地质大学(北京),2016.
[24]宋扬.基于MODIS的辽西北地区干旱遥感监测研究.辽宁师范大学,2016.
[25]Zhuoya N,Hongyuan H,Shihao T,et al.Assessing the response of satellite sun-induced chlorophyll fluorescence and MODIS vegetation products to soil moisture from 2010 to 2017:a case in Yunnan Province of China.International Journal of Remote Sensing,2018:1~18.
[26]Yoshida Y,Joiner J,Tucker C,et al.The 2010 Russian drought impact on satellite measurements of solar-induced chlorophyll fluorescence:Insights from modeling and comparisons with parameters derived from satellite reflectances.Remote Sensing of Environment,2015,166:163~177.
[27]齐述华.干旱监测遥感模型和中国干旱时空分析.北京:中国科学院研究生院(遥感应用研究所),2004.
[28]刘啸添,周蕾,石浩,等.基于多种遥感植被指数、叶绿素荧光与CO_2通量数据的温带针阔混交林物候特征对比分析.生态学报,2018,38(10):3482~3494.
[2]庞治国.干旱遥感监测模型研究及墒情预报探索--以黑龙江省为例.中国水利水电科学研究院,2003.
[3]赵建苹,胡顺石,秦建新.基于TVDI的湖南省干旱监测分析.地理空间信息,2018,16(3):101~105.
[4]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.南京大学,2013.
[5]翁白莎,严登华.变化环境下中国干旱综合应对措施探讨.资源科学,2010,32(02):309~316.
[6]王劲松,李耀辉,王润元,等.我国气象干旱研究进展评述.干旱气象,2012,30(4):497~508.
[7]Jackson R D,Idso S B,Reginato R J,et al.Canopy temperature as a crop water stress indicator.Water Resources Research,1981,17(4):1133~1138.
[8]Kogon,F.N.Remote sensing ofweather impact S on vegetation in non-homogeneous areas.International Joumal of Remote Sensing.1990,11:1405~1420.
[9]Sandholt I,Rasmussen K,Andersen J.A Simple Interpretation of the Surface Temperature/Vegetation Index Space for Assessment of Surface Moisture Status.Remote Sensing of Environment,2002,79(2):213~224.
[10]Kimura,R.Estimation ofmoisture availability over the Liudaogou river basin ofthe Loess Plateau using new indices with surface temperature.Journal of Environments,2007,10:10~16.
[11]Price J C.Using spatial context in satellite data to infer regional scale evapotranspiration.IEEE Transactions on Geoscience and Remote Sensing,1990,28:940~948.
[12]杨彦荣,胡国强.基于植被供水指数的旱区土壤湿度反演方法研究.现代电子技术,2019,42(2):138~142,146.
[13]王鹏新,龚健雅,李小文.条件植被温度指数及其在干旱监测中的应用.武汉大学学报(信息科学版),2001(5):412~418.
[14]梁超,高永,党晓宏,等.呼伦贝尔市季节性干旱演变特征.内蒙古林业科技,2018,44(2):7~13.
[15]曲学斌.基于MCI指数的呼伦贝尔市干旱灾害风险区划.北方农业学报,2019,47(1):120~125.
[16]丁书萍.呼伦贝尔森林-草原生态交错带气候变化特征研究.东北农业大学,2012.
[17]孙福军.基于极端干旱年份遥感植被指数的半干旱地区土地评价研究.沈阳农业大学,2017.
[18]侯小丽.基于MODIS数据的全球干旱区植被变化及其与气候关系的研究(2002-2011年).山东农业大学,2016.
[19]李召良,唐伯惠,唐荣林,等.地表温度热红外遥感反演理论与方法.科学观察,2017,12(6):57~59.
[20]Zhao-Liang Li,Bo-Hui Tang,Hua Wu,et al.Satellite-Derived Land Surface Temperature:Current Status and Perspectives,Remote Sensing of Environment,2013(131):14~37.
[21]Bo-Hui Tang,Yuyun Bi,Zhao-Liang Li,et al.Generalized split-window algorithm for estimate of land surface temperature from Chinese geostationary FengYun meteorological satellite(FY-2C)data.Sensors,2008,8(2):933~951.
[22]郭佳.基于随机森林的遥感干旱监测模型及其应用研究.南京:南京信息工程大学,2016.
[23]高华.基于温度植被干旱指数印度和巴基斯坦干旱监测.北京:中国地质大学(北京),2016.
[24]宋扬.基于MODIS的辽西北地区干旱遥感监测研究.辽宁师范大学,2016.
[25]Zhuoya N,Hongyuan H,Shihao T,et al.Assessing the response of satellite sun-induced chlorophyll fluorescence and MODIS vegetation products to soil moisture from 2010 to 2017:a case in Yunnan Province of China.International Journal of Remote Sensing,2018:1~18.
[26]Yoshida Y,Joiner J,Tucker C,et al.The 2010 Russian drought impact on satellite measurements of solar-induced chlorophyll fluorescence:Insights from modeling and comparisons with parameters derived from satellite reflectances.Remote Sensing of Environment,2015,166:163~177.
[27]齐述华.干旱监测遥感模型和中国干旱时空分析.北京:中国科学院研究生院(遥感应用研究所),2004.
[28]刘啸添,周蕾,石浩,等.基于多种遥感植被指数、叶绿素荧光与CO_2通量数据的温带针阔混交林物候特征对比分析.生态学报,2018,38(10):3482~3494.