Zhuowei Hu

A damage assessment model of oil spill accident combining historical data and satellite remote sensing information: a case study in Penglai 19-3 oil spill accident of China.

Oil spills are one of the major sources of marine pollution; it is important to conduct comprehensive assessment of losses that occur as a result of these events. Traditional methods are required to assess the three parts of losses including cleanup, socioeconomic losses, and environmental costs. It is relatively slow because assessment is complex and time consuming. A relatively quick method was developed to improve the efficiency of assessment, and then applied to the Penglai 19-3 accident. This paper uses an SAR image to calculate the oil spill area through Neural Network Classification, and uses historical oil-spill data to build the relationship between loss and other factors including sea-surface wind speed, and distance to the coast. A multiple regression equation was used to assess oil spill damage as a function of the independent variables. Results of this study can be used for regulating and quickly dealing with oil spill assessment.

Texture feature analysis in oil spill monitoring by SAR image

This paper introduces the oil spill monitoring in SAR image by texture analysis and spectral information. In texture analysis, it discusses the parameters of texture extraction, and makes experiment. Then it determines the 17*17 as the windows size, 90 as the angle and 5 as distance. Through neural network classification, these parameters are suitable for oil spill monitoring. It can distinguish with oil and oil-like well. Its accuracy is satisfactory. These parameters are not only used for oil spill monitoring, but also provide a foundation for deeper SAR image classification.

Estimation of vegetation water content based on MODIS: Application on forest fire risk assessment

Forest fire is one of serious and universal natural disasters with the characteristics of wide distribution, high frequency, and uncertainty. Because of these features, the traditional manual methods to monitor forest fire become difficult. With the development of the remote sensing monitoring techniques, the forest fire monitoring becomes more effective. The ignition of forest fire needs some special weather and forest conditions concerned. Fuel moister content is a critical factor to induce the occurrence of forest fires. It is decided by the vegetation water content. In This paper the Great Khingan Mountains Region which locate in Heilongjiang Province were taken as the study area. And it includes the flowing contents: 1.Using MODIS NDVI data to reveal the growth situation of vegetation, and its relationship with vegetation water. 2. Using the band 2 and band 6 of MODIS data to calculate the global vegetation moisture index. 3. Using global vegetation moisture index to retrieve vegetation water content. 4. Considering the relation of NDVI, and vegetation water content comprehensively, obtained high fire risk period and areas of experimental area.

Method and Implementation of Oil Spill Detection in SAR Image

The Ocean is an important component part of the earth; it provides people the richest and the most valuable material resources. However, it is under various degrees of pollution every year. One of the most harmful pollution among them is the pollution by oil, and these oil pollutions are mainly come from ships oil leakage and explosions of oil platforms or submarine pipelines etc. The direct economic losses of each time accidents caused could be millions or tens of millions of upper and lower, so the action of monitoring oil leakage with regard to the ocean becomes significantly important. This paper aims the monitoring of oil leakage of the ocean which based on the ASAR DATA of ENVISAT remote sensing data. It contains introductions to the basic steps and implementations of monitoring oil leakage by SAR image, and also analysis of them. By comparing different methods of filtering, the enhanced Lee filter is ultimately been selected and settled as the filtering method, and then we extract and contract the area of oil leakage by using single threshold method, maximum entropy method and unsupervised classification method respectively. Finally, we bring up the developmental direction of oil spill detection in SAR image.

Application of the relief degree of land surface in landslide disasters susceptibility assessment in China

Chinese mountainous area takes 2/3 of its total land area, making it one of the countries most seriously effected by landslide disasters, and the impact of landslide on peoples lives and property becomes increasingly serious. Considering the grim situation of landslide disasters and the resulting huge losses, it is a very important work to assess the susceptibility of the landslide disasters under national scale in order to provide the scientific basis for landslide disasters prevention. In many factors affecting the appearance of landslide disasters, such as lithology, geological structure, earthquake zone distribution, etc, the geomorphologic form is one of the main factors to control the spatial distribution of landslide disasters in China, also plays an important role as the main factor in the assessment of the landslide susceptibility. The relief degree of land surface is an important reference factor of geomorphologic form classification used to describe and reflect the macroscopic characteristics of topography of the surface in a large area. This paper extracts the relief degree of land surface of China based on the SRTM DEM covering the whole country. Based on the extraction, classify the national geomorphologic form into slow undulation, the low undulation, the moderate undulation, mountainous undulation and alpine undulation based on the classification to the relief degree of land surface with the range of 0~25m, 25~75m, 75~200m, 200~600m and >600m. It analyses the spatial correlation between different landscape patterns and the spatial distribution of the landslide disasters using the information content model. The results show that there is a very good correlation between the relief degree of land surface and the landslide susceptibility, with the increase of the value of relief degree of land surface, its information content gradually increases.

A distributed geospatial information services sharing technology based on SaaS thought: In the application of biodiversity conservation

Biodiversity conservations are often comprehensive, dynamic and complex problem that require professionals to work in teams while dealing with large and decentralized of project areas. However, the varieties of scattered thematic geospatial information can not be used directly and effectively by users — it impedes rather facilitates collaboration, and increases the project cost. To solve this problems, an implementation method of providing distributed geospatial information services (GIServices) based on SaaS (Software as a Service) thought and the Service-oriented Web Service technology, which is called the Distributed GIServices Sharing Technology (DGISST), is presented as a key technology for the proposed scheme. This paper explores that develops a distributed GIServices sharing platform to provide users with a shared collaboration environment with the DGISST as the core technology. First of all, all the existing geoprocessing function applications, geospatial information data and specialized business models are published as Web services. Different from the traditional WebGIS, this platform not only provides geodata services but also focuses more on providing geoprocessing function services and model services, and offers powerful geoprocessing functions and specialized model functions. Therefore, it is very critical to help users in geodata manipulation online. Secondly, some important concepts such as perfect metainformation system and GIServices application mode are introduced. Therefore, the different particle sizes of GIServices can be called flexibly and simply via Internet. Particularly, it also permits users to mashup their private geodata services with the public geodata services. Then it allows users to combine geodata service and geoprocessing function service (or specialized model service) to meet the different needs. Thirdly, this platform is designed to allow geodata services to be distributed in the Internet and be accessible at client sites. Therefore, to ensure the security of information and data, a multi-user application model is presented as a core module in this platform. This model implements the R-F-RBAC (Role-Function-Resource Based Access Control) model by introducing Centralized Identity Authentication, the RBAC model and Log Management. Through the above work, this platform provides a multi-user geospatial information application environment to meet the needs of geodata manipulation and collaboration for teams. A prototype implementation has been developed to put into use in TNC (The Nature Conservancy) China, satisfying the demands of sharing geospatial information and the daily work.

Village-level multidimensional poverty measurement in China: Where and how

Village is an important implementation unit of national poverty alleviation and development strategies of rural China, and identifying the poverty degree, poverty type and poverty contributing factors of each poverty-stricken village is the precondition and guarantee of taking targeted measures in poverty alleviation strategies of China. To respond it, we construct a village-level multidimensional poverty measuring model, and use indicator contribution degree indices and linear regression method to explore poverty factors, while adopting Least Square Error (LSE) model and spatial econometric analysis model to identify the villages’ poverty types and poverty difference. The case study shows that: (1) Spatially, there is obvious territoriality in the distribution of poverty-stricken villages, and the poverty-stricken villages are concentrated in contiguous poverty-stricken areas. The areas with the highest VPI, in a descending order, are Gansu, Yunnan, Guizhou, Guangxi, Hunan, Qinghai, Sichuan, and Xinjiang. (2) The main factors contributing to the poverty of poverty-stricken villages in rural China include road construction, terrain type, frequency of natural disasters, per capita net income, labor force ratio, and cultural quality of labor force. The main causes of poverty include underdeveloped road construction conditions, frequent natural disasters, low level of income, and labor conditions. (3) Chinese poverty-stricken villages include six main subtypes, and most poverty-stricken villages are affected by multiple poverty-forming factors, reflected by a relatively high proportion of the three-factor dominant type, four-factor coordinative type, and five-factor combinative type. (4) There exist significant poverty differences in terms of geographical location and policy support, and the governments still need to carry out targeted poverty alleviation measures according to local conditions. The research can not only draw a macro overall poverty-reduction outline of impoverished villages in China, but also depict the specific poverty characteristics of each village, helping the government departments of poverty alleviation at all levels to mobilize all kinds of anti-poverty resources.

Comparison of spatial structures of urban agglomerations between the Beijing-Tianjin-Hebei and Boswash based on the subpixel-level impervious surface coverage product

Under the background of China’s rapid urbanization, study on comparative analysis of the spatial structure of urban agglomerations between China and the US can provide the policy proposals of space optimization for the Chinese government. Taking the Beijing-Tianjin-Hebei (BTH) and Boswash as study area, we mapped the subpixel-level impervious surface coverage of the BTH and Boswash, respectively, from 1972 to 2011. Further, landscape metrics, gravitational model and spatial analysis were used to analyze the differences of the spatial structures between the BTH and Boswash. The results showed that (1) the area of the impervious surface increased rapidly in the BTH, while those remained stable in the Boswash. (2) The spatial structure of the BTH experienced different periods including isolated cities stage, dual-core cities stage, group cities stage and network-style cities stage, while those of the Boswash was more stable, and its spatial pattern showed a “point-axis” structure. (3) The spatial pattern of high-high assembling regions of the impervious surface exhibited a “standing pancake” feature in the BTH, while those showed a “multi-center, local aggregation and global discrete” feature in the Boswash. (4) All the percentages of the impervious surface of ecological, living, and production land of the BTH were higher than those of the Boswash. At last, from the perspective of space optimization of urban agglomeration, the development proposals for the BTH were proposed.

An integrated soft and hard classification approach for evaluating urban expansion from multisource remote sensing data: a case study of the Beijing–Tianjin–Tangshan metropolitan region, China

ABSTRACT Integrating soft and hard classification to monitor urban expansion can effectively provide comprehensive urban growth information to urban planners. In this study, both the impervious surface coverage (as a soft classification result) and land cover (as a hard classification result) in the Beijing–Tianjin–Tangshan metropolitan region (BTTMR), China, were extracted from multisource remote sensing data from 1990 to 2015. Then, we evaluated urban expansion based on centre migration, standard deviation ellipse, and spatial autocorrelation metrics. Furthermore, the differences between the soft and hard classification results were analysed at the landscape scale. The results showed that (1) the impervious surface area increased considerably over the past 25 years. Notably, the areas of urban built-up land and industrial production land increased rapidly, while those of ecological land and agricultural production land seriously decreased. (2) The distribution of impervious surfaces was closely related to the regional economic development plan of ‘One Axis, Two Wing, and Multi-Node’ in the BTTMR. (3) The contributions of different land use types to impervious surface growth ranked from high to low as follows: urban built-up land, rural residential land, industrial production land, agricultural production land, and ecological land. (4) The landscape metrics varied considerably based on the hard and soft classification results and were sensitive to different factors.

The preliminary research on the multidimensional poor information spatial processing and measuring

Poverty is an important country to solve the livelihood problems. Poverty identification is mainly based on a single income standard. Sens poverty thinking that poverty is determined by a number of dimensions. Main source of data in the 2000 census in the years 1949-2005 Lan national economic statistics Lan county neighborhood population, the use of multidimensional poverty theory, using the inverse distance weighted interpolation(IDW), the Multidimensional poverty spatial processing of information, research Lan county River Township, East towns and counties, the Lan towns townships multidimensional Poverty level, the weighting factors of each dimension of poverty measurement considering the three dimensions of poverty, and poverty grading classification of thematic mapping. The result shows that: single income dimension of poverty and multidimensional analysis of the extent of poverty is different, and more accurately identify the extent of poverty of the multidimensional.