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.
On 20 April 2013, a catastrophic earthquake of magnitude 7.0 struck the Lushan County, northwestern Sichuan Province, China. This earthquake named Lushan earthquake in China. The Lushan earthquake damaged many buildings. The situation of building loss is one basis for emergency relief and reconstruction. Thus, the building losses of the Lushan earthquake must be assessed. Remote sensing data and geographic information systems (GIS) can be employed to assess the building loss of the Lushan earthquake. The building losses assessment results for Lushan earthquake disaster utilization multisource remote sensing dada and GIS were reported in this paper. The assessment results indicated that 3.2% of buildings in the affected areas were complete collapsed. 12% and 12.5% of buildings were heavy damaged and slight damaged, respectively. The complete collapsed buildings, heavy damaged buildings, and slight damaged buildings mainly located at Danling County, Hongya County, Lushan County, Mingshan County, Qionglai County, Tianquan County, and Yingjing County.
Snow disaster is a natural phenomenon owning to widespread snowfall for a long time and usually affect people's life, property and economic. During the whole disaster management circle, snow disaster in pastoral area of northern china which including Xinjiang, Inner Mongolia, Qinghai, Tibet has been paid more attention. Thus do a good job in snow cover monitoring then found snow disaster in time can help the people in disaster area to take effective rescue measures, which always been the central and local government great important work. Remote sensing has been used widely in snow cover monitoring for its wide range, high efficiency, less conditions, more methods and large information. NOAA/AVHRR data has been used for wide range, plenty bands information and timely acquired and act as an import data of Snow Cover Monitoring Model (SCMM). SCMM including functions list below: First after NOAA/AVHRR data has been acquired, geometric calibration, radiometric calibration and other pre-processing work has been operated. Second after band operation, four threshold conditions are used to extract snow spectrum information among water, cloud and other features in NOAA/AVHRR image. Third snow cover information has been analyzed one by one and the maximum snow cover from about twenty images in a week has been selected. Then selected image has been mosaic which covered the pastoral area of China. At last both time and space analysis has been carried out through this operational model ,such as analysis on the difference between this week and the same period of last year , this week and last week in three level regional. SCMM have been run successfully for three years, and the results have been take into account as one of the three factors which led to risk warning of snow disaster and analysis results from it always play an important role in disaster reduction and relief.
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.
Yushu Earthquake of magnitude 7.1 Richter in 2010 has brought a huge loss of personal lives and properties to China.
National Disaster Reduction Center of China implemented the disaster assessment by using remote sensing images and
field investigation. Preliminary judgment of disaster scope and damage extent was acquired by change detection. And the
building region of hard-hit area Jiegu town was partitioned into 3-level grids in airborne remote sensing images by street,
type of use, structure, and about 685 girds were numbered. Hazard assessment expert group were sent to implement field
investigation according to each grid. The housing damage scope and extent of loss were defined again integrated field
investigation data and local government reported information. Though remote sensing technology has played an
important role in huge disaster monitoring and assessment, the automatic capability of disaster information extraction
flow, three-dimensional disaster monitoring mode and bidirectional feedback mechanism of products and services should
still be further improved.
On August 8, 2010 morning, a large debris flow occurred in Zhouqu County, Gannan Tibetan
Autonomous Prefecture, Gansu Province, China, which has damaged Zhouqu County and its
surrounding area seriously. An UAV and airplane were sent there the day after to acquire images of
disaster area; UAV image of 0.2 meter resolution and aerial remote sensing image of 1 meter
resolution were acquired. NDRCC compared pre-disaster and post-disaster remote sensing images of
disaster area, preliminary analyzed and judged the damage condition and disaster trend. We
partitioned the coverage and affected area of debris flow into 2457 girds in high resolution remote
sensing images, hazard assessment expert group were sent to implement field investigation
according to each grid. The disaster scope and extent of loss were defined again combined with field
investigation data. Then we assessed the physical quantity of housing, infrastructure, land resource
in detail and assessed the direct economic losses. It is for the first time that remote sensing images
are integrated into the national catastrophe assessment flow of China as a major data source.
Vegetation is a fundamental component of urban environment and its abundance is determinant of urban climate and
urban ground energy fluxes. Based on the radiometric normalization of multitemporal ASTER imageries, the objectives
of this study are: firstly, to estimate the vegetation abundance based on linear spectral mixture model (LSMM), and to
compare it with NDVI and SDVI; secondly, to analyze the spatial distribution patterns of urban vegetation abundance in
different seasons combined with some landscape metrics. The result indicates that both the vegetation abundance estimation based on LSMM and SDVI can reach high accuracy; however, NDVI is not a robust parameter for vegetation abundance estimation because there is significant non-linear effect between NDVI and vegetation abundance. This study reveals that the landscape characteristics of vegetation abundance is most complicated in summer, with spring and autumn less complicated and simplest in winter. This provides valuable information for urban vegetation abundance estimation and its seasonal change monitoring using remote sensing data.