The precisely extraction of construction areas in remote sensing images can play an important role in territorial planning, land use management, urban environments and disaster reduction. In this article, we propose a method for extracting construction areas using Gaofen-1 panchromatic remote sensing images by adopting the improved Pantex (a procedure for the calculation of texture-derived built-up presence index) and unsupervised classification. First of all, texture cooccurrence measures of 10 different directions and displacements are calculated. In this step, we improve the built-up presence index that we use the windows size of 21*21 to calculate the GLCM contrast measure instead of 9*9 according to the spatial resolution of Gaofen-1 panchromatic image. Then we use the intersection operator “MIN” to combine the 10 different anisotropic GLCM contrast measure to generate the final built-up presence index result. At last, we use the unsupervised classification method to classify the Pantex result into two classes and the one with larger cluster center is the construction area class. Confusion matrix of Beijing-Tianjin-Hebei region experiment shows that this method can effectively and accurately extract the construction areas in Gaofen-1 panchromatic images with the overall accuracy of more than 92%.
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.
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.
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.
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.