This paper calculated the eco-compensation standard in China's 14 contiguous destitute areas based on eco-service assessment using the remote sensing data (Landsat TM/ETM+ and HJ-1 CCD) combined with the ground observation data consisting of soil, vegetation and climate datasets. The results are indicated as follows: (1) The ecosystem in China's 14 contiguous destitute areas was important to the national ecological security showing a high eco-service value up to 2062.7 billion per year, which was roughly the same as the annual gross domestic product (GDP). In certain west areas, the annual eco-service values were 2 to 12 times higher than their annual GDP, reflecting their significant contributions to the national ecological security. (2) The eco-compensation standard in China's 14 contiguous destitute areas was overall about 197.1 billion per year. The average eco-compensation standard for each impoverished county was about 0.3 billion theoretically, which indicated that the central government's financial transfer payment could not cover this amount currently. At present, the impoverished county which benefits from the financial transfer could receive 0.09 billion per year averagely, and this only covers 32.4% of the necessary eco-compensation. This situation brings enormous pressure to the poverty alleviation work and increases the ecological risk in China's 14 contiguous destitute areas as well. (3) Increasing the eco-compensation standard for the contiguous destitute areas might be the key measure to solve the problem of poverty. Besides, local government should develop a new poverty alleviation strategy, in which the ecological products development is considered to be predominant and the economic development to be auxiliary.
The significance of the ideas and directions on the equalization development of rural basic public services among contiguous destitute areas has been reflected in current rural poverty alleviation and development. In this paper, we selected 67 key counties in the poverty alleviation of Wuling Mountain contiguous poor area, designed a comprehensive evaluation index system of rural basic public services for the poor areas, and used the subjective and objective weight method that is based on the game theory to evaluate the comprehensive development level of the rural basic public services. In addition, we conducted an advanced analysis by adopting the Gini coefficient and the minimum variance method to multi-dimensionally address the development diversities of their rural basic public services at a multiple area-province-county scale. As a result, it reveals the spatial differences of rural basic public services in the poor counties and provides a reference for the local governments by providing the optimal allocation plan for the rural basic public service resources. The case study results for the study area are concluded as follows: (1) The overall quality of the rural basic public services in the study area is not high, showing a trend which is high in the northwest but low in the southeast, while the overall development level of the area which is under the jurisdiction of Hubei Province and Chongqing City is relatively high; (2) The development disparity of the rural public security service index show an evident variant trend among the counties of the area, while the other dimensions are relatively average; (3) In the aspect of development disparity types, most counties are five-dimension dominated, while Mayang county is affected by two dimensions, and Dejiang and Shiqian county are dominated by the jointed seven-dimension type.
Most of the poverty areas in China are mainly distributed in the mountainous areas, which are also prone to several mountain disasters. These disasters may lead or enhance the poverty to some extent. Current research on mountain disaster mainly focuses on the kinetics whereas pays less attention on the risks, especially on the risk of poverty. The authors distinguished the specific mountain disasters from the common disasters, and summarized the characteristics of mountain disasters, then give out the explanation of poverty vulnerability and poverty risk induced by mountain disasters. The poverty risk model is set up from the perspectives of two aspects, the hazard and the vulnerability, where the hazard is a function of susceptibility, and the vulnerability is consisted of the copping ability and exposure. With this concept, a demonstrative research is carried out for Enshi in Hubei province, which is located in the central China and is a minority mountainous area in severe poverty condition. In this demonstration, the hazard susceptibility is obtained from the landslide historical records and geological environments background analysis. As the vulnerability, the building structure intensity and completeness of infrastructure, and the net income of the farmers were factors of the copping ability, while the density of cultivated land ratio, the poverty population rate and the GDP were factors of the exposure. The final poverty risk map was produced with the upper dual-factor model and was divided into 5 levels for usability. In the end, some suggestions about poverty-reduction and disaster mitigation were introduced by the authors from the perspective of poverty risk, such as the integrate disaster and poverty reduction, and some other management policies were also recommended.
With the rapid growth of global economy and population, the ecological environment was severely influenced by the human activity, and the excessive human exploitation of resources has brought continuously increasing pressure to resources and environment. As a result, a series of global ecological problems were caused, such as resource wars, environmental pollution, ecological refugees and environmental health problems. The ecological environmental problem has been a serious threat to our country's economic development and human life security. Meanwhile, the ecological security has gradually become a hot issue which drew increasing attentions of the international communities. Based on the physical, social and economic characteristics of the research area, an evaluation system of ecological security was built up, and the grey relational analysis method was used to calculate the ecological security index of the mountainous area of Wuling. This research analyzed the study area's ecological security pattern, and the results are showed as follow: (1) there was a huge increase in the ecological security index between 2000 to 2010, and the Qiandong area presented the fastest increasing trend; (2) a significant positive correlation was presented between the ecological security index and the space in 2000 and 2010, and the spatial aggregation effect of 2010 was stronger; (3) a high accumulation and a low accumulation pattern was revealed in the spatial distribution of ecological security index within the mountainous area of Wuling, and the spatial distribution pattern of ecological security changed greatly between 2000 and 2010. Because of the rich natural resources of Wuling area, the scientific and reasonable evaluation of the regional ecological environment is necessary to provide specific and feasible suggestions to the relevant functional departments for environmental protection.
On the basis of establishing the index-system using the pressure-state-response model, we combined the gray correlation degree analysis with the entropy weighting method to calculate the ecological safety indices of the comprehensive, pressure, state, and response factors, and made a comprehensive evaluation for the poor areas of Inner Mongolia in 2012. And we used the geographical spatial analysis method and spatial analysis software of GeoDA to analyze the spatial correlation of index on the comprehensive safety for the research area. Meanwhile, by combining the physical geographical characteristics and the socioeconomic status of the research area, we diagnostically analyzed the ecological safety status of the research area. In this research, we made the following conclusions: comprehensively, the ecological safety in the poor areas of Inner Mongolia is in the general state condition, and the ecological safety level distribution has presented characteristics of majorly concentrated, and a few scattered. The distribution of ecological safety status was not completely random, it shows a spatial aggregation pattern within values that have similarity, and indicate a positive correlation. The ecological safety level distribution presents the regional difference, that the west and east poor areas were in the general state condition, and the poor areas of the central Inner Mongolia is in the good state condition overall. The ecological safety level has revealed obvious internal differences among counties. There are obvious contrasts on the ecological safety level between cities in the better state and the general state, which is reflected in the indicators of pressure, state, and response, and the effects are different regarding to different factors.
Selecting the contiguous destitute regions in Wuling Mountain as the study area, this paper build an evaluation index system based on the AHP in order to calculate the poverty alleviation index for each county in the Wuling Mountains area from 2010 to 2012. Using the auto-correlation theory, this research conducts some analyses about the spatial pattern and the influence factors. Conclusions can be found as follows: (1) the spatial pattern of poverty alleviation exhibits a pattern of aggregation within the same province, and the poverty alleviation effect of the north region is higher than the central region; (2) in different provinces, the southeast of Chongqing has the best poverty alleviation effect and a moderate growth. All indices of the four aspects, which are the economic, social, production and living conditions, and the poverty alleviation program are the best. The poverty alleviation level in the northeast of Guizhou is poor, but the growth rates of the four aspects are higher than the average level of the best region. In the southwest of Hubei, the poverty alleviation and growth rate are also good, which ranks the second best in the Wuling Mountain area. In the northwest of Hunan, the poverty alleviation level is low, and its index level is the lowest until 2012. In addition, its growth rate is the slowest; therefore, this region should be focused in the future for further development.
Rainstorm-induced geological disasters happen frequently in recent years and cause great loss in some regions of China. Enshi Autonomous Prefecture which located in Wuling Mountain Region is one of the fourteen Continuous Extremely Poverty Areas (CEPA) in China. The region is characterized with more frequent geological disasters, large amount of minority population and wide range of poor people distribution. Rainstorm-induced geological disaster threatens the communities usually, which pushes the people into a worse condition. Based on the theory of disaster system and natural disaster risk assessment, the rainstorm-induced geological disaster risk indicator system about Enshi Autonomous Prefecture was established considering the hazard affected bodies, hazard-formative environment and hazard bodies of this region. A comprehensive risk assessment model system is constructed and the sensitivity, vulnerability and risk of the rainstorm-induced geological disaster hazard in Enshi Autonomous Prefecture were explored at a l km × l km scale. The results are concluded as follows: (1) The inducing factor of the disaster is heavy rainfall, and the high hazard mainly occurs in the central of Enshi city and the southeast of Hefeng county. (2) By applying the Information Value Method and Analytical Hierarchy Process (AHP), the hazard-formative environment sensitivity evaluation index system was set up, including the topography, geomorphology, basic geology, hydrology and human activity. The less high and high sensitivity areas are mainly distributed in the Badong county, Enshi city and Hefeng county. (3) The building construction, population, social economy, arable land were selected for vulnerability assessment of hazard-affected bodies, and Lichuan city and Laifeng county were concluded to be occupied with more high vulnerability areas. (4) Rainstorm-induced geological disasters risk in Enshi Autonomous Prefecture is high according to the sensitivity and vulnerability assessment, and the areas having the highest risk mainly distributed in Badong county and Enshi city, which is consistent with the actual situation.
To meet the actual needs of refined land-cover information in poverty-stricken areas' ecological environment assessment and resource survey, this paper studies the technology and application of accurate land-cover classification based on the high-resolution satellite images. This article focuses on the technologies of high-resolution image segmentation using the mean shift algorithm, multiple feature extraction and analysis, automatic object-sample selection, and supervised classification. Then, we give a complete realization of the streamlined object-based land-cover classification by lining up the above technologies. Finally, taking Qianjiang district of Chongqing city as an example, we carry out a land cover information extraction experiment in its poverty-stricken areas. The experimental results show that the accurate and detailed land-cover maps can be obtained using the provided technology. The meticulous and reliable land-cover information can promptly provide a base-data-support for the eco-environmental assessment and carbon accounting applications in the poverty-stricken areas.
Well-planned evacuation routes definitely contribute to reducing traffic congestions and mitigating logistics costs during an emergency evacuation. With an objective to minimize the total evacuation time, an approach to seek optimum evacuation routes in a capacity constrained road network was presented in this paper, and then it is carried out based on a scenario with severe surge floods affecting an island county in Zhejiang province, China. The classical Dijkstra algorithm is improved by addressing the issues aroused with respect to evacuees' demands, traffic congestion, and the equal accessibility to and assignment of relief resources. Besides, regarding the fact that, since the evacuee units contain a large population, it is hard to find a closest shelter with enough capacity at one time, thus the mixed split delivery model is adopted. Namely, a unit might be divided into several groups and sent to different shelters separately. Using C#, an applet with built-in algorithms named MiniGIS is developed for the visualization of the dynamic evacuation flow. As presented in this paper, the ideal algorithm, though being capable of finding out the optimal evacuation routes, is based on a highly ordered scenario in which only simple traffic congestion is considered. Next, the additional delay caused by mode transferring and slow down at road cross-sections, turnings and crowded segments is taken into consideration. In addition, the delay-improved algorithm, which records the state of each group at road conjunctions, is able to calculate the total delay if given the time when evacuation state swifts from one to another. Undoubtedly, it provides more reliable results than the ideal algorithm does; however, due to route adjustments, several retrogressions are captured in the routes planned by delay-improved algorithm using the tracking animation in MiniGIS, implying that a certain vehicle is required to return to a former earlier location before heading toward the destination according to the new planned route. Considering routes with retrogressions are not common and easily being identified and adjusted, such routes are artificially fixed and then input to the retrogression-improved algorithm, which would calculate the new evacuation time and meanwhile avoid redundant retrograde motions. Basically, though the total travel time would slightly exceed that of delay-improved algorithm, results of retrogression-improved algorithm is still regarded as the optimum solution giving priority to efficient evacuation management and resource economization.
Considering the necessity of integrity certification and the lack of certification methods for DEM, an integrity certification algorithm based on fragile watermarking for DEM was proposed, which had fully taken the terrain features into account. The characteristic of DEM data is analyzed in order to propose the special requirements of the fragile watermarking technology. To reduce the impact of watermarked data, in the process of fragile watermark embedding, based on the feature lines extracted from the original DEM data, the data was firstly divided into two different parts: the feature region and non-feature region. Secondly, the fragile watermarking information was generated according to the number of raster in feature region and watermarking sequence segments, and then it was embedded into the non-feature region by LSB-based method. In the integrity certification process, the same feature region was extracted from DEM data, and the regenerated watermark information was compared with the extracted one in order to determine the integrity of the data. Finally, experiments using the proposed algorithms were presented. Experimental results show that the presented algorithm not only is able to effectively certificate the integrity of DEM data, but also has little influence on the data accuracy. In addition, the algorithm has high security and suitability.
Accurate three-dimensional road information has important significance in the fields of transportation, urban planning, road network construction, 3D road modeling and intelligent vehicle. For instance, in the field of intelligent vehicle, accurate three-dimensional road information can provide lane level road information for autonomous navigation. As a high-tech tool of surveying and mapping, mobile laser scanning can obtain 3D laser point cloud quickly and accurately and is suitable for road extraction and modeling in large-scale scenes. A robust method for extracting the refined three-dimensional road and its boundary from mobile laser scanning point clouds is proposed. First, the point cloud is self-adaptively partitioned according to the spatial feature of mobile laser point cloud. Then, the candidate road and its boundary are extracted based on the prior knowledge and rules. And the extracted road boundary is tracked and vectorized according to the linear feature of the road boundary. Finally, the road model parameters are computed by the extracted road and its boundary. Experiments were undertaken to evaluate the validities of the proposed method with three different scene datasets, including the highway, urban area and campus. The highway dataset contains a steep ramp and its point density is low, the urban dataset contains flower beds in the middle of the road, the campus dataset contains a great many of objects such as trees, pedestrians, cars et al and its point density is very high. The completeness, correctness, and quality of the extracted roads are over 91.29%, 93.15%, and 90.08%, respectively, which proves the proposed method is capable in various complex scenes with different point density, which is fast and robust to extract road and its boundary in large-scale scenes.
The textural analysis methods have advantage and potential in macro topographic analysis. Currently, based on the texture features of DEM and remote sensing image, some researches are conducted, including quantitative analysis of terrain features, landform classification and the pattern recognition of physiographic units. However, little literatures focus on the difference evaluation of texture features between DEM and image data, which making the theory and methodology scarce for data selection, data fusion and results evolution. In this paper, Gray Level Co-occurrence Matrix (GLCM) model is used for texture analysis in eight sample areas representing different landform types. Second angular moment, contrast, variance and entropy are selected as the quantitative indices. Based on the texture features derived from two different data types, a series of experiments are conducted, including the contrastive analysis of texture features, the stability analysis within same sample area and the divergence analysis among different sample areas. Coefficient of variation is used to evaluate the discrete degree. The results suggest a strong correlation between DEM based contrast and image based contrast. It also proves that the texture features derived from DEM are more evident in high-relief landform, while the image data takes advantage in small-relief area, however, could be affected by the land surface. Considering the stability of topographic analysis, DEM data are more suitable for hill and mountain areas, while the image data achieves better results in the plain area and tableland area. Considering the divergence of topographic analysis, the texture features derived from DEM data vary more obvious among different test areas, with the second angular moment, contrast and entropy getting higher values. These phenomena indicate that the texture features derived from DEM data have stronger discrimination ability. Hillshade data and slope data are employed to enhance the texture information in further analysis, which proves that such two land surface parameters can improve the discrimination ability among different landforms, giving a greater superiority compared with image data.
Comparison of the same study area between two time points on the same categorical variable can reveal changes occurred among categories over time, such as transitions among land categories. Therefore, it is an effective method to aggregate a large number of categories into a smaller number of broader categories in order to simplify interpretation and give insights concerning categorical change over time. We use an algorithm to aggregate categories in a sequence of steps based on the categories' behaviors in terms of gross losses and gross gains. The behavior-based algorithm aggregates net gaining categories with net gaining categories, and aggregates net losing categories with net losing categories, but doesn't aggregates a net gaining category with a net losing category. The algorithm's steps are summarized into three phases. The first phase is the Exclusive Zero phase, when our algorithm aggregates pairs of Exclusive Zero categories until all Exclusive Zero categories are aggregated into one comprehensive Exclusive Zeroes category, which remains as one of the three categories at the end of the algorithm. The second phase is the Exclusive Loser and Gainer phase, when the algorithm aggregates pairs of Exclusive Gainer categories and pairs of Exclusive Loser categories. The third phase is the swapping phase. In this final phase, our algorithm computes the transition sum for every possible pair of categories that have the same direction of net change. Moreover, the behavior-based algorithm at each step in the sequence keeps the values of net change and maximizes swap change. This article introduces three mathematical principles and defines six aggregation behaviors. In the last section of this article, we present a case study, in which the data are obtained in Yunnan Province between 1990 and 2010 for 20 land categories, in order to prove the advantage and feasibility of this algorithm in terms of area change and intensity change. The results show that the behavior-based algorithm produces a set of 9 categories that retains almost the original amount of change, while giving a total area change of 5.10%, which is only 0.06% lower than the original change. In contrast, the common used classification system of 6 categories produces a total area change of 4.7%, which is 0.4% lower than the results of our algorithm. It has been found that the behavior-based algorithm is an effective method to retain land use dynamic change information.
As a major factor of smart city, 3D building model has been widely applied in many fields such as city planning, navigation and virtual geographic environments. Level of detail (LOD) technology is often used to visualize these models in complex urban environment. In order to reduce the high production cost, 3D building model generalization is gradually attracting attention. 3D building model generalization includes single model simplification, which has been studied widely and deeply, and multiple models synthesis, which is still at its early stages. In this paper, a self-organizing map (SOM) based classification algorithm is used to analyze the texture, and then the texture is combined with geometry feature to generalize 3D building models. In addition, a cognition-based hierarchical clustering algorithm is proposed. Firstly, a constrained Delaunay triangulation (CDT) is constructed from the 2D footprints of building models, and a connected network is established by visibility analysis based on the CDT, which is divided based on the road network to make it conform to the urban morphology theory. An initial proximity graph is constructed from the CDT. Secondly, the proximity graph is segmented according to the texture feature. Finally, a minimum spanning tree (MST) is created from the proximity graphic based on the minimum distance, and after linear detection and discrete model conflation, the model groups are conflated and visualized. The texture feature is used to synthesize 3D model that is adaptive to people's cognitive habits. The experimental results confirm the effectiveness of this method.
Accurately obtaining the land use and land cover information has been a hot research focus in the field of remote sensing. It is a feasible way to utilize the new remote sensing data source and the effective classification algorithms. In this study, five decision tree classifiers and the MISR multi-angle data have been implemented to study the land cover classification in the lower Tarim River. Six datasets of different bands and observation angles are classified and compared, and the findings are presented as follows: (1) Compared to the Nadir angle observation, the multi-angle observation can achieve higher classification accuracy and significantly improve the classification accuracy of the shrubs, woodland and grassland in particular. It showed that the multi-angle observation can effectively reveal the anisotropic feature of surface reflectance and get a better classification result. (2) Compared with the MLC method, the classification accuracy of the decision tree algorithms is higher, which is especially evident for the random forest and C5.0 methods, implying that the classification ability of decision tree is better than MLC and is more effective when using multi-angle datasets. (3) The classification effect of near infrared band is better than the red band, which indicates that the near infrared band can provide more information about the anisotropic feature of the surface reflectance.
Buildings are most prone to change and the most in need of update in the urban geographic database, but its workload is heavy. Therefore, it is of great significance to study the automatic extraction and change detection of buildings in high-resolution remotely sensed imagery. In order to extract the precise locations and boundaries of the changed buildings, this article proposed an approach for building change detection from the high-resolution remotely sensed imagery based on the graph-cut segmentation. Firstly, each pixel in the remotely sensed imagery was mapped into the vertex of a graph. The edges of the graph were constructed using a distance threshold between pixels, and their weight values were set according to the comprehensive characteristic calculation using the position, intensity and contour. The segmentation of remotely sensed imagery was transformed into graph partitioning, and the segmentation objects set was obtained through normalized graph-cut segmentation method. Then, buildings were extracted by selecting the segmentation objects sets of two temporal remotely sensed imageries with constraint integrating aspect ratio and rectangularity. Last, the change types of buildings were identified, including the newly added buildings, building disappearance and building reconstruction, and their visualizations were expressed, according to the relationships of spaces, areas and configurations between buildings from the two temporal remotely sensed imageries. For verifying the validity of the proposed method, 13 representative sub-image pairs were chosen from the WorldView images which cover the Shenzhen city and the QuickBird panchromatic images which cover the Beijing city, respectively. Experimental results show that the proposed method has revealed certain adaptability to the image pairs with low registration accuracy, which allows a permissible registration error up to 20 pixels (10 meters), and the average precision rate and recall rate of the proposed method are 93.16% and 87.90% respectively.
