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Study On Typhoon Impact Based On Multi-source Remote Sensing Data And GIS

Posted on:2011-12-20Degree:DoctorType:Dissertation
Country:ChinaCandidate:R DengFull Text:PDF
GTID:1118330332475948Subject:Use of agricultural resources
Abstract/Summary:PDF Full Text Request
China is one of the countries in the world most affected by the typhoon, and the typhoon affecting China is mainly from Northwest Pacific. Northwest Pacific is the only sea in which typhoon formation all the year round. The number of typhoon is largest, and the distribution of typhoon is most widely. Typhoon has a wide range, and it can bring disasters such as wind, rainstorm and storm surge. On the other hand, it can bring some benefits. For example, it can relieve drought and make temperature drop substantially. With the rapid development of economic, China is more vulnerable to typhoon due to the high concentration of population and social wealth. It is significant to study the impact of typhoons on China.In the study, the spatio-temporal distribution of typhoon was analyzed based on the best track data of tropical cyclones in 1971-2008 and GIS (Geographic Information System). Then, MODIS 1B (Moderate Resolution Imaging Spectroradiometer) image was preprocessed and land surface temperature (LST) was retrieved from MODIS 1B. Next, the benefit of drought relief of typhoon was studied combining vegetation supply water index (VSWI) and AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) soil moisture data, and the benefit of temperature drop of typhoon was studied by daily maximum temperatures of meteorological stations and AMSR-E land surface temperature data. Finally, disasters caused by typhoon including the water body change, landslide, debris flow and vegetation disturbance were assessed by HJ-1 images. The mainly methods and conclusions are as follows:1 Spatio-temporal distribution of tropical cyclones landfalling in ChinaAccording to the best tracks of landfalling tropical cyclones in 1971-2008, landfalling frequency, intension and place were analyzed. The results showed that landfalling tropical cyclone kept high frequency in the early 1970s, and kept low frequency in the late 1990s. Tropical cyclone concentrated in the July to September, and August is as the center. Tropical cyclones with wind level above 10 acounted for 77% of the landfalling tropical cyclones. Tropical cyclones landed mainly in the southeast coast of China including Guangdong, Taiwan, Hainan, Fujian, Zhejiang and Guangxi provinces.Wind radius of historical tropical cyclone was reconstructed by studying the relationship between wind radius and maximum wind speed near the center at 6 hourly intervals of every tropical cyclone in 2004-2008, and the spatial distribution of tropical cyclone was mapped with the support of GIS. It can be seen from the spatial distribution map, the impact reduced when tropical cyclone moving inland, and the southeast coast provinces comprising Hainan, Taiwan and so on suffered the severest impact.2 Preprocess of MODIS 1B image and LST retrievalMODIS 1B is the level 1 product, and it writes a 16-bit scaled integer representation of the calibrated digital signals measured by the MODIS. Reflectance can be retrieved from SI through radiometric correction (radiometric calibration and atmospheric correction), geometric correction, bow-tie removal. Then, cloud detection and mosaic were applied for getting the data of the whole study area. LST was retrieved by split window algorithm combining visible, near infrared with thermal infrared band. Brightness temperature was calculated by radiance through Planck function. Atmospheric transmittance was gotten based on atmospheric water vapor, temperature correction function and perspective correction function. Surface emissivity was computed by vegetation coverage. Finally, LST was calculated by brightness temperature, atmospheric transmittance and surface emissivity.3 Remote sensing assessments on the drought relief and temperature drop cuased by typhoonTyphoon brings rain to relieve drought condition. As the typhoon has thick clouds, and the absorption of solar radiation reduces, temperature decreases. In the study, drought relief and temperature drop caused by typhoon were assessed using multi-source remote sensing data.(1) First, precipitation anomaly calculated on the basis of measured rainfall was adopted to analyze drought condition before typhoon Haitang. Then, vegetation supply water index (VSWI) integrating NDVI (Normalized Difference Vegetation Index) with LST was used to monitor the drought change. Impact of typhoon on drought was studied through comparing VSWI before and after typhoon Haitang. On the other hand, AMSR-E soil moisture change was also used to reflect the drought change. After typhoon Haitang, average VSWI and soil moisture increased from 18.62 to 20.67 and from 35.70% to 52.37%, respectively. It indicated that drought was relieved.(2) During the typhoon Haitang, daily maximum temperature of meteorological stations all dropped, the average is 8.6℃. Due to the daily maximum temperature of meteorological stations is point data, it can not reflect the temperature of a polygon, in addition, optical Images such as MODIS, are vulnerable to contamination by clouds, microwave data of AMSR-E were used in the study. Average AMSR-E LST before and after typhoon Haitang was compared to reflect the impact of typhoon on temperature. Moreover, because there had difference between AMSR-E LST and daily maximum temperature, AMSR-E LST was revised by daily maximum temperature. After typhoon Haitang, revised average AMSR-E LST decreased from 34.51℃to 29.53℃.4 Remote sensing assessments on the disasters caused by typhoonAfter a series of preprocess on HJ-1, disasters caused by typhoon were shown from three aspects, including change of water body, landslide and debris flow, and wind disturbance on vegetation.(1) Using multi-temporal HJ-1 images before and after typhoon Morakot, decorrelation stretch (DS) was applied to decrease the correlations among bands, and water body of the Tseng-Wen reservoir was extracted through maximum likelihood classification (MLC). The overall accuracie of each phase was all above 90%, Kappa coefficient was above 0.9, and they were better than that of direct MLC without DS spectral enhancement. The area of the water body was 10.72km2 before typhoon Morakot, and after the typhoon, the area went up to 14.81km2. It can provide a basis for decision-making in scheduling flood discharge and ensure the safety of reservoir downstream by monitoring water body impacted by typhoon.(2) Using multi-temporal HJ-1 images before and after typhoon Morakot, land types change in Xiaolin village caused by typhoon was extracted by MLC based on DS. TRMM data were adopted to know precipitation brought by typhoon, and 3D view was used to analyze landslide and debris flow with the aid of DEM. The overall accuracie of each phase was all above 90%, Kappa coefficient was above 0.9. After the typhoon, vegetation became bare land, and it appeared clear boundaries and morphological characteristics of landslide. Xiaolin village buried by landslide and debris flow, and clear alluvial fan deposition zone of debris flow was formed at the river. The whole area of landslide and debris flow (including alluvial fan deposition zone of debris flow) was about 2.92km2. It can reflect landslide and debris flow more directly from 3D view.(3) Due to HJ-1 images are vulnerable to contamination by clouds, the study area was selected in a cloudless area along the typhoon track. The NDVI is saturated in the study area, according to land cover type of MODIS MCD12Q1, the change of enhanced vegetation index (EVI) was used to reflect wind disturbance on vegetation through comparing multi-temporal HJ-1 images before and after typhoon Morakot. Vegetation was in the growing season at that time, but wind blew down leaves and broken branches, resulting in EVI decrease. After one month, vegetation gradually recovered and EVI rebounded too.
Keywords/Search Tags:drought relief, high temperature drop, disaster, spatio-temporal distribution, typhoon, multi-source remote sensing data, GIS
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