Flood is one of natural disasters with the worst loss in the world. It needs to assess flood disaster risk so that we can reduce the loss of flood disaster. Disaster management practical work needs the dynamic risk results of building. Rainstorm flood disaster system is a typical complex system. From the view of complex system theory, flood disaster risk is the interaction result of hazard effect objects, rainstorm flood hazard factors, and hazard environments. Agent-based modeling (ABM) is an important tool for complex system modeling. Rainstorm-flood building risk dynamic assessment method (RFBRDAM) was proposed using ABM in this paper. The interior structures and procedures of different agents in proposed meth had been designed. On the Netlogo platform, the proposed method was implemented to assess the building risk changes of the rainstorm flood disaster in the Huaihe River Basin using Agent-based simulation (ABS). The results indicated that the proposed method can dynamically assess building risk of the whole process for the rainstorm flood disaster. The results of this paper can provide one new approach for flood disaster building risk dynamic assessment and flood disaster management.
The Qingjiang River Basin, which is 423 km long in the Hubei province, China, is the first large tributary of the Yangtze River below the Three Gorges. The Qingjiang River Basin surface water area monitoring plays an important role in the water resource management strategy and regular monitoring management of the Yangtze River watershed. Hydropower cascade exploitation, which started in 1987, has formed three reservoirs including the Geheyan reservoir, the Gaobazhou reservoir, and the Shuibuya reservoir in the midstream and downstream of the Qingjiang River Basin. They have made a great impact on surface water area changes of the Qingjiang River Basin and need to be taken into account. We monitor the Qingjiang River Basin surface water area changes from 1973 to 2010. Ten scenes from the Multispectral Scanner System (MSS), seven scenes from the Thematic Mapper (TM), and two scenes from the Enhanced Thematic Mapper Plus (ETM+) remote sensing data of Landsat satellites, the normalized different water index (NDWI), the modified NDWI (MNDWI), and Otsu image segmentation method were employed to quantitatively estimate the Qingjiang River Basin surface water area in the 1970s, 1980s, 1990s, and 2000s, respectively. The results indicate that the surface water area of the Qingjiang River Basin shows a growing trend with the hydropower cascade development from the 1980s to the first decade of the 21st century. The study concluded the significance of human activities impact on surface water spatiotemporal distribution. Surface water accretion is significant in most parts of the Qingjiang River Basin and might be related to the constructed cascade hydropower dams.
Drought is one of major nature disaster in the world and China. China has a vast territory and very different spatio-temporal
distribution weather condition. Therefore, drought disasters occur frequently throughout China, which may
affect large areas and cause great economic loss every year. In this paper, geostationary meteorological remote sensing
data, FY-2C/D/E VISSR and three quantitative remotely sensed models including Cloud Parameters Method (CPM),
Vegetation Supply Water Index (VSWI), and Temperature Vegetation Dryness Index (TVDI) have been used to
dynamically monitor severe drought in southwest China from 2009 to 2010. The results have effectively revealed the
occurrence, development and disappearance of this drought event. The monitoring results can be used for the relevant
disaster management departments' decision-making works.
Wild fire of forest and grassland is one of the major natural disasters in the world. The fire of forest and grassland occurs
frequently due to global changing and uneven precipitation distribution, which may affect large areas and cause great
economic loss. With the advantages of wide-coverage, high spatio-temporal resolution, remote sensing technology has
been used for the wild fire monitoring activities. In this paper, the fire point detection model and burned area monitoring
model, for HJ-1A/B data, have been developed and applied for the southeast Australian wild fire monitoring in 2009.
The application shows that the models and the data are efficient for the wild fire monitoring.
Vegetation coverage and its changes are one of the hottest research branches in regional ecological environment change.
An important research content of vegetation coverage is vegetation fraction. Hydropower exploitation cascade in a river
basin inevitably makes vegetation fraction change. In this paper, the sub-pixel decomposition model and NDVI have
been applied to quantitatively estimate vegetation fraction change of Qingjiang River downstream based on remotely
sensed data. The results show that the overall vegetation environment has been greatly improved and the hydropower
cascade development has a greater influence on vegetation fraction change in Qingjiang River basin form 1987 to 2004.
KEYWORDS: Solar radiation models, Data modeling, Near infrared, RGB color model, Vegetation, Reflectivity, Neodymium, Image analysis, Atmospheric modeling, Statistical analysis
Hydropower cascade exploitation in a river basin inevitably makes the spatiotemporal distribution of water resource to
change. With Qingjiang River as the test in this paper, two kinds of normalized water indexes have been employed to
extract water area from TM/ETM+ imagery so as to quantitatively research the impact on hydropower cascade
exploitation for water resource distribution. The results show two models can accurately extract water resource area and
both can be used to monitor water resources dynamic change. Before undeveloped in 1987, the total water resource area
of Qingjiang River is 60.33km2, and that reaches 110.90km2 in 2004. So, the hydropower cascade exploitation project
has a significant impact on the spatiotemporal distribution of water resources in Qingjiang watershed.
The original principle of image fusion based on pixel level is using the spatial and spectral information from different remotely sensed data to generate a new image. So, the fusion method with high preservation is the main issue. In this paper, modified Brovey transform (MBT) has been proposed based on the principle of the Brovey transform model. Three fusion methods of MBT, wavelet transform (WT) and smoothing filter-based intensity modulation (SFIM), which can merge each band images directly, are applied to respectively merge multi-spectral data with panchromatic image of ETM+ and QB sensors. The qualitative evaluation and quantitative computation analysis show that MBT has the highest high frequency information preservation and SFIM model enjoys the best low frequency information preservation, and both of them can be used to deal with large numbers of images fusion for their fast computation capability. The WT has a suboptimal spectral maintenance and the lowest high spatial frequency gain among the mentioned three data fusion algorithms, and it takes more time to finish the progress of data fusion.
The water body information is accurately extracted from remotely sensed images with the method of normalized ratio
index, and the water information is greatly enhanced through restricting the brightness of backgrounds. What's more,
there is no noise formed by shadows in results. However, the spatial resolution of most images used for water extraction
is usually not high enough to identify water body clearly. Fusion of remotely sensed images with different spatial
resolution can solve this problem. Four data fusion methods such as Modified Brovey Transform (MBT), Multiplication
Transform (MLT), Smoothing Filter-based Intensity Modulation Transform (SFIMT) and High Pass Filter Transform
(HPTF) have been applied to merge ETM+ panchromatic band with multi-spectral band data. Normalized ration method
is adopted to extract water body information from both original and merged images. The effect of data fusion and
extracting result are validated and evaluated by qualitative analysis and quantitative statistical calculation. SFIMT model
enjoys the best maintenance of spectral quality from the multi-spectral bands. On the other hand, MLT model has the
highest spatial frequency information gain. In the data fusion algorithms, SFIMT is the optimization data fusion method
appropriate to the normalized ration water extracting model.
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