【作者】 贺可勋；

【导师】 赵书河；

【作者基本信息】 南京大学 ， 地图学与地理信息系统， 2014， 硕士

【摘要】 本文利用站点土壤相对湿度数据,探讨一种适合全国区域的干旱遥感监测指数,利用1982-2010年AVHRR和MODIS中分辨率遥感数据,实现全国逐月农业干旱制图,并对不同等级干旱空间分布和时间变化差异进行了分析。首先,利用站点土壤水分数据对常用的旱情监测指数进行敏感性分析,发现温度植被干旱指数(TVDI)对干旱具有较高的敏感性;然后,利用高程数据和TVDI的干边校正参数对监测指数进行优化,提出了一种适合全国区域的改进的温度植被干旱指数(M-TVDI),并实现基于该指数的干旱等级划分;利用提出的M-TVDI,实现1982-2010年全国及黄淮海平原典型区的干旱制图;最后,对得到的干旱长时间序列数据集进行了不同等级干旱空间特征分析,并利用趋势倾向率分析干旱的时间变化差异。主要的研究内容和结论如下:(1)利用土壤水分数据与遥感数据,分析距平植被指数(AVI)、植被状态指数(VCI)、温度条件指数(TCI)、植被供水指数(VSWI)、温度植被干旱指数等常用指数对干旱的敏感性。结果表明:与其它常用干旱指数相比较,温度植被干旱指数对站点土壤湿度有较好的敏感性,可以用于全国区域的干旱监测。(2)利用高程数据和TVDI的干边校正参数对监测指数进行优化,提出了一种适合全国区域的改进的温度植被干旱指数(M-TVDI)并利用M-TVDI与土壤相对湿度的关系实现基于该指数的干旱等级划分。研究发现:与TVDI相比,M-TVDI与土壤相对湿度的有更大的相关性,该指数比TVDI更加适合用于全国区域干旱遥感监测。(3)利用提出的M-TVDI,实现1982-2010年全国及黄淮海平原典型区的干旱制图;最后,对得到的干旱长时间序列数据集,进行了不同等级干旱空间特征分析。分析发现:近30年,西北地区、青藏地区西南部干旱发生频率高;黄淮海平原为北方地区干旱中心;云贵高原为南方地区干旱中心。其中,黄淮海农业干旱典型区干旱多发区域分布在于河北东部、河北东南、山东中部区域以及河南东北部。(4)利用干旱时空分布,获得了 1982-2010年不同等级干旱的频率分布,通过干旱频率的趋势倾向率以及年际标准差,分析了干旱的时间变化特征。结果显示:近30年中国干旱发生的频率总体上呈现上升趋势,其中严重干旱发生频率上升趋势明显,轻旱频率上升趋势要缓于中旱和严重干旱。中国西北地区、云贵高原干旱频率上升趋势明显且变化波动大。其中,黄淮海典型农业干旱区域干旱总体呈上升趋势,但其时间变化趋势特征与全国干旱差别很大。本文以中国为研究区,选用AVHRR09C1数据以及MODIS植被指数和温度数据,按照"干旱遥感监测方法——干旱空间分布特征——干旱时序变化特征"的技术思路,对1982-2010年中国农业干旱发生的空间分布和时间变化特征进行分析,可为中国的干旱研究提供参考,为气候变化研究和气候变化与粮食产量的关系研究提供数据支持。更多还原

【Abstract】 Basing on the research project,the present study used AVHRR and MODIS data,set the index system to describe the drought.According to the index system,we analyzed the Spatial Distribution and Temporal Variation of Drought in China and typical districts of drought.Firstly,correlational analyses were conducted between soil moisture and drought monitoring index to find the index system.We found that temperature vegetation dryness index(TVDI)is the most sensitive index to drought.Then,the study improved the TVDI by DEM correction and dry edge correction.Combining with the drought class index,the study formed the dry index system.Secondly,the study obtained the dataset of the long time series of drought.At last,analyzed the distribution characteristics of drought and get the evolutionary trend of drought by using the tendency rate analysis.The result of the study as followed:(1)The study used moisture data and remote sensing data to analyze the sensibility of TVDI and other drought monitoring index to drought.The results show that comparing to other drought monitoring index,Temperature vegetation dry index is the most sensitive index to drought,and can be the index system of drought.(2)The study improved the TVDI by DEM correction and dry edge correction.The result shows that after improving,the TVDI is more sensitive to soil moisture than before.(3)The study obtaining drought class-based thresholds by fitting function that came from improved index and soil moisture.According to the thresholds,the study obtained the space-time distribution and frequency of drought.The results indicate that Xibei area,southwest of Xizang are the high rate of drought occurrence places in China during the past 30 years.Yunnan-guizhou plateau is the drought center of south west of China.Drought centers are located in east of Hebei,south east of Hebei and middle part of Shandong in Huang HuaiHai Plain.(4)The study obtained the average frequency of the study area and then gained the trend fate of drought frequency.Results indicate that,drought occurrence increase during the past thirty years,but it is not sustained growth year by year.The frequency of severe drought increased significantly,while increase of light drought is slow than moderate drought and severe drought.Drought increase in Huanghuaihai Plain,But the tendency is different to increase of the whole China.The present study using AVHRR and MODIS vegetation and temperature data,according to the methodology of "Index system set-the analysis of Spatial distribution-drought Temporal Variation",analyzed the Spatial distribution characteristic and Temporal Variation of China drought.The results of the study can provide the reference of China drought research and provide data support for climate change and the relationship between climate change and food production.更多还原