Tornado disaster is not the main natural disasters in China, but it could cause serious casualties and economic losses. As a means of large-scale and dynamic monitoring, remote sensing could monitor the loss of housing and facility agriculture in tornado disaster, and could dynamic monitor the reconstruction of infrastructure such as post-disaster houses. Based on high-resolution optical remote sensing pre-disaster images, basic geographic data, GF-1, GF-2, and TripleSat Constellation satellite images, damaged house and temporary resettlement site and damaged facility agriculture have been monitored during disaster, transitional settlement site and centralized settlement site have been dynamic monitored after disaster. Through the dynamic monitoring of the tornado disaster in Yancheng City of Jiangsu Province in June 2016, the monitoring result shows that satellite remote sensing technology could help to monitor the degree of disasters and resettlement, as well as the dynamic monitor the resettlement and production recovery of residents after the disaster, which can be used as an objective way to judge the process of post-disaster recovery and reconstruction.
KEYWORDS: Floods, Image fusion, Remote sensing, Data modeling, Satellites, Data processing, Data fusion, Multispectral imaging, Data acquisition, Satellite imaging
Flood is one of the major disasters in China. There are heavy intensity and wide range rainstorm during flood season in eastern part of China, and the flood control capacity of rivers is lower somewhere, so the flood disaster is abrupt and caused lots of direct economic losses. In this paper, based on BJ-2 Spatio-temporal resolution remote sensing data, reference image, 30-meter Global Land Cover Dataset(GlobeLand 30) and basic geographic data, forming Dam break monitoring model which including BJ-2 date processing sub-model, flood inundation range monitoring sub-model, dam break change monitoring sub-model and crop inundation monitoring sub-model. Case analysis in Poyang County Jiangxi province in 20th, Jun, 2016 show that the model has a high precision and could monitoring flood inundation range, crops inundation range and breach.
Earthquake is one of the major natural disasters in the world. Since the twentieth century, it caused a large number of casualties and lots of direct economic losses. With the advantage of wide-coverage, high spatial-temporal resolution, remote sensing technology has been used for residential distribution monitoring of different earthquake intensity. In this paper, based on interpretation of GF-1 remote sensing data, Digital Elevation Model (DEM), reference image, earthquake intensity, resident population statistics data, residential distribution analyzing model has been formed which including: GF-1 remote sensing data processing sub-model, residential distribution monitoring sub-model and residential distribution analyzing sub-model. Case analysis in Nie lamu, Ji long, Ding ri in Tebet during Nepal's 8.1 magnitude earthquake shows that: the proposal model has a high precision and could be used in residential distribution monitoring, combined with resident population statistics data, affected population in earthquake intensity influence region can be acquired, quickly assessment the possibly influence degree of earthquake can be qualitative analyzed.
Earthquake is one major nature disasters in the world. At 8:02 on 20 April 2013, a catastrophic earthquake with Ms 7.0 in surface wave magnitude occurred in Sichuan province, China. The epicenter of this earthquake located in the administrative region of Lushan County and this earthquake was named the Lushan earthquake. The Lushan earthquake caused heavy casualties and property losses in Sichuan province. After the earthquake, various emergency relief supplies must be transported to the affected areas. Transportation network is the basis for emergency relief supplies transportation and allocation. Thus, the road losses of the Lushan earthquake must be monitoring. The road losses monitoring results for Lushan earthquake disaster utilization multisource remote sensing images were reported in this paper. The road losses monitoring results indicated that there were 166 meters' national roads, 3707 meters' provincial roads, 3396 meters' county roads, 7254 meters' township roads, and 3943 meters' village roads were damaged during the Lushan earthquake disaster. The damaged roads mainly located at Lushan County, Baoxing County, Tianquan County, Yucheng County, Mingshan County, and Qionglai County. The results also can be used as a decision-making information source for the disaster management government in China.
Remote sensing is one of important methods on the agricultural drought monitoring for its long-term and wide-area observations. The detection of soil moisture and vegetation growth condition are two widely used remote sensing methods on that. However, because of the time lag in the impact of water deficit on the crop growth, it is difficulty to indicate the severity of drought by once monitoring. It also cannot distinguish other negative impact on crop growth such as low temperature or solar radiation. In this paper, the joint use of soil moisture and vegetation growth condition detections was applied on the drought management during the summer of 2013 in Liaoning province, China, in which 84 counties were affected by agricultural drought. MODIS vegetation indices and land surface temperature (LST) were used to extract the drought index. Vegetation Condition Index (VCI), which only contain the change in vegetation index, and Vegetation Supply Water Index (VSWI), which combined the information of vegetation index and land surface temperature, were selected to compare the monitoring ability on drought during the drought period in Liaoning, China in 2014. It was found that VCI could be a good method on the loss assessment. VSWI has the information on the change in LST, which can indicate the spatial pattern of drought and can also be used as the early warning method in the study.
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