Drought Monitoring By Remote Sensing Over India And Pakistan Based On The Temperature Vegetation Dryness Index

Drought is one of the world major phenomenon in recent years, the frequent occurrence of natural disasters, which not only threaten human survival of the natural environment, but also a serious impact on human society, sustainable economic development. Area affected by drought almost throughout the world, particularly in the monsoon region and arid region the most prominent. And in most parts of South Asia tropical monsoon climate with high temperatures, paper selects the major countries in South Asia, India and Pakistan as the research area, relying on the CAS-TWAS "Asia Drought Monitoring" project funded a study on India and Pakistan.In this paper, the temperature vegetation drought index(TVDI) for India and Pakistan to achieve a spatial and temporal dynamics of arid, TVDI method is based on surface temperature-vegetation index space will show the shape of the principle of the rule, the local table covering type from bare soil to a closed canopy, from the dry to wet soil moisture, then each scatterplot like surface temperature and vegetation index constituent element in the region appears as a trapezoid or triangle. Given the special nature of the study area, the third chapter of the first surface temperature made two improvements: one study area from north to south, the transition from mountain terrain to the plains, undulating terrain, can be effectively reduced by the correction because the terrain elevation NDVI under the same conditions that cause high elevation Ts corresponding pixel value of properties decreased; on the other hand for time series data, in the acquisition process, affected by various factors, such as: solar elevation angle, viewing angle, as well as water vapor, aerosols and clouds and other factors, resulting in time-series data are volatile, trend analysis and information can not be extracted in the time dimension, especially in coastal areas, often in cloudy weather, observations often missing, so research District frequent invalid value cell, which caused great impact on remote sensing real-time monitoring, so the land surface temperature time series data were reconstructed Hants, reduce the impact of these random factors. Secondly, the fourth chapter of the vegetation index has been improved, the vegetation index data TVDI method predecessors have achieved in commonly used normalized difference vegetation index(NDVI), but NDVI itself has some shortcomings, such as: high vegetation coverage easily saturated, limited correction for atmospheric disturbances did not consider the impact on vegetation indices crown background, does not eliminate the effects of soil background, etc., used in this study four typical vegetation indices: the normalized difference vegetation index(NDVI), Enhanced soil adjust vegetation index(MSAVI) vegetation index(EVI), ratio vegetation index(RVI), and amendments of the study area and four vegetation index improved after two surface temperature data to establish NDVI-Ts, respectively, EVI- Ts, RVI-Ts, MSAVI-Ts feature space to compare four screening the most suitable for the study area of surface temperature- vegetation index space, and then get the best feature space dry construction method based on feature space, the proposed wet edge fit equation into TVDI formula to give the vegetation growing season in the study area in recent years, TVDI image inversion. Finally, meteorological stations precipitation data for the study area TVDI verify the results, with good correlation, then the study area nearly a decade of drought temporal dynamics were analyzed, the proportion of area in the arid level statistics, time from watching the 2005 Year- in 2014 the proportion of arid area between India and Pakistan, the overall downward trend in volatility presents.