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.
Ms8.0 Wenchuan earthquake that occurred on May 12, 2008 brought huge casualties and property losses to the Chinese people, and Beichuan County was destroyed in the earthquake. In order to leave a site for commemorate of the people, and for science propaganda and research of earthquake science, Beichuan National Earthquake Ruins Museum has been built on the ruins of Beichuan county. Based on the demand for digital preservation of the earthquake ruins park and collection of earthquake damage assessment of research and data needs, we set up a data set of Beichuan National Earthquake Ruins Museum, including satellite remote sensing image, airborne remote sensing image, ground photogrammetry data and ground acquisition data. At the same time, in order to make a better service for earthquake science research, we design the sharing ideas and schemes for this scientific data set.
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.
Snow disaster in pastoral area is a meteorological disaster which due to snowfall and snow covers, in relation to cowman’s product and life, seriously affects development of animal husbandry in China. So it has been paid high degree attention in the field of disaster prevention and reduction. Risk warning of snow disaster is an effective way to reduce the loss. Space technology, as an important way to monitor snow cover in pastoral area, could be used in risk warming of snow disaster. In this article, based on interpretation of remote sensing image, combined with geo-spatial, meteorological and land use data, research on three indicators have been carried out, which are: possibly degree of buried graze by snow, continuous days of perpetual snow and rate of perpetual snow, model on risk warning of snow disaster has been set up successfully. Case analysis in Inner Mongolia during 2012 shows that: the proposal model has a high precision and could be used in risk warning of snow disaster in pastoral area.
Drought is one kind of nature disasters in the world. It has characteristics of temporal-spatial inhomogeneity, wide affected areas and periodic happening. The economic loss and affected population caused by different droughts are the largest in all natural disasters. Remote sensing has the advantages of large coverage, frequent observation, repeatable observation, reliable information source and low cost. These advantages make remote sensing a vital contributor for drought disaster risk assessment and monitoring. In this paper, three drought monitoring models, such as Vegetation Condition Index (VCI), Temperature Vegetation Dryness Index (TVDI), and Water Supplying Vegetation Index (WSVI) had been selected to monitor the drought occurred from January 2012 to June 2012 in Hubei province, China. Two kinds of remote sensing data, including HJ-1A/B CCD/IRS and ZY-3, had been employed to assess the integrated risk of Hubei drought based on three drought monitoring models. The results shown that the risk of northwest regions and middle regions in Hubei province were higher than that in the other regions. The results also indicated that the extreme risk regions were located in Shiyan, Xiangyang, Suizhou and Jingmen.
Drought is one major nature disaster in the world. The affected population and agriculture loss caused by drought are the
largest in all natural disasters. Drought has the characteristics of wide affected areas, long duration and periodic strong
feature. Remote sensing has the advantages of large coverage, frequent observation, repeatable observation, reliable
information source and low cost. These advantages make remote sensing a vital contributor for drought disaster
monitoring and forecasting. So, remote sensing data have been widely used and delivered significant benefits in drought
prevention and reduction in China. Three drought monitor models including Vegetation Condition Index (VCI),
Temperature Condition Index (TCI) and Temperature Vegetation Dryness Index (TVDI) had been used to monitor
southwest drought occurred in China from 2009 to 2011 based on the small satellite constellation for environment and
disaster monitoring and forecasting A/B satellites (HJ-1AB) and Landsat remote sensing data. The results shown that
five regions including Sichuan province, Chongqing, Guizhou province, Yunnan province, Guangxi province in
southwest of China had suffered different degrees agricultural drought disaster in 2010 and 2011. The comprehensive
agricultural disaster situation of five affected areas in 2010 was more serious than drought events occurred in 2011. The
many regions in Guizhou province were hardest-hit areas cased by the two consecutive year drought events in southwest
China.
This paper analyzed the evolution of drought and the spectral response of the crop at different growing seasons focuses
on the irrigated agricultural areas of northen Henan using the HJ-1 data and MODIS data,associated with relevant
meteologic data, regional geographical data and the social economic data.The Spatial and temporal distribution of the
risk of disaster-causing factors and the fragility of the disaster-affected body was conducted and the comprehensive
index of agricultral drought risk was built up.Then, trend of the agricultural drought was analyzed and the irrigated
agricultural drought risk class was performed and the possible hazard and influence of agricultural drought and the
performance of appropriate strategy to reduce agricultral drought have been estimated.At last,verification of the results
and improvement of the model have been carried out supported by the historic cases, expert system and the on-site
investigation data.
Taking Shandong province as study area, based on CCD and IRS data obtained by HJ-1 secondary planet, recurring to
the VSWI, this work studied the agriculture drought distribution on Mar. 7th, 2011, using geographic information system
technology the monitoring results were suitably expressed and it is coincident with the late average rainfall of the cities
of this province. This result not only provides a scientific basis in assessing the drought disaster and making mitigation
measures, but also affirms the effect of HJ-1 satellite data in drought monitoring.
Drought has been a frequently happened type of disaster in China, and it has caused massive losses to people's lives.
Especially the drought happened in Shandong province in the late 2010, which was recognized as the severest in the past
five hundred years in some areas. Evaluation must be done in order to make proper rescue plans. Instead of collecting
data site by site, remote sensing is an efficient way to acquire data in a large area, which is very helpful for drought
identification. Some normal ways in remote sensing for drought analysis are explained and compared in this paper, and
then the VSWI method is chosen to evaluation the drought in Shandong province. Because of its free data policy and
wide availability, the data sets acquired by Terra-MODIS are chosen to identify the drought severity in Shandong
province. From the drought severity level images we can see that almost the whole area of Shandong province was lack
of water except the Weishan Lake and eastern coastline regions where large area of water exists. The southwest region,
including Heze and Jining, is in moderate drought condition, where it is used to be an important grain-producing area.
This drought condition will inevitably put a negative effect on its grain production. The central and southern areas were
in severe drought condition, but fortunately these areas are of hills and mountains, so the drought will only affect the
lives of residents. The northern parts, including Dezhou and Bingzhou areas, were also in severe drought condition, and
these regions are also important for grain-producing, so the severe drought disaster will lead to a sharp grain output cut.
This analysis results will not only shed light on the rescue process, but also give the government some clues on how to
maintain the grain supply safety.
In the paper, the parameters of hyperspectral data of the environment disaster reduction satellites have been introduced,
firstly. Then the pro-processing methods for hyperspectral data have been elaborated according to the characteristics of
the hyperspectral sensor of the environment disaster reduction satellites. After analysis the problems existing in the pre-processing
of hyperspectral data, the hyperspectral data have been employed to classify the land features. The
experimental evaluation shows that the performance of classifying the hyperspectral data of environment disaster
reduction satellites is excellent.
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