With the constant enhancement of data acquisition capability, an increasing amount of spatial data is available to WebGIS. However, the efficiency of spatial data transmission cannot meet the current need for fast access to spatial data. In such a context, the compression of spatial data is important for reducing the space of data storage and improving the efficiency of data transmission and processing for WebGIS. Traditional methods of vector data compression are usually based on spatial relations, such as the distances or angles between different vertices, to determine which vertices are redundant. Whereas in the field of image compression, the method based on the techniques of transform coding (in frequency domain) is more frequently-used. This paper proposes a novel method of spatial vector data compression in frequency domain. This method is composed of several steps. To be specific, we transform the coordinate values of x and y to frequency domain coefficients respectively using Discrete Cosine Transform (DCT), and quantize these coefficients according to the relevant specific thresholds, which could restrict the average distortions (root-mean-square error, RMSE) of the reconstructed vector data. This quantizing method does not need quantization tables and is adaptive to large thresholds that often cause problems such as compressing a polygon or line feature into a point or a polygon feature into a line. In the final stage of the proposed vector data compression method, the Huffman coding is used in a similar way to the corresponding part of JPEG standard. The proposed compression method was implemented by C#.NET and was applied to compress vector data to test its performance from the aspects of compression ratio and geometric shape distortion. The results of the tests show that the proposed method is a feasible solution for vector data compression. It is flexible in terms of restricting distortion, and it can achieve large compression ratios as well as retain the main geographic shape´s characteristics which are derived from the original vector data, within the compressed vector data.
With the rapid development of geographical information acquisitions methods, 3D city models are widely used now. Level of detail (LOD) is used to improve the performance of three-dimensional (3D) visualization. Management of multi-scale building data is important in 3D building generalization, and many scholars have studied the indexing model of multi-scale spatial data for 2D map. In order to satisfy the demands of massive spatial data in 3D city modeling, a new spatial indexing model named as GAMR tree (Generalization Area Multi-representation R Tree) is proposed to deal with the multi-scale model of 3D building. Based on the characteristics of 3D building generalization, impact factors for constructing the 3D building generalization areas are analyzed in this paper. Next, the generalization areas are divided into four types which are denoted as big city, city, town and village. As a result, different generalization methods are adopted for the generalization of different areas. In this research, GAMR tree is defined based on the structure of R-Tree while assigned with different node types. Two indexing trees, which are named respectively as the generalization R tree and the child tree of generalization area, are included in the new indexing model. The leaf node of the child tree is consisted of two items, one is the MBR that representing the building ground area, and the other is the corresponding 3D building model for that area. While the branch node is consist of a MBR that accounts for the total area of its child nodes, and pointers that connect to its child nodes. The children trees are generated by a bottom-up method, and the height of a tree is determined by the threshold calculated in generalization. Multi-scale querying method of GAMR tree is furthermore proposed in this paper, and the steps of a rectangle querying instance are presented. Considering the variations of projected length caused by different sight directions, the corresponding LODs of building models that organized by GAMR tree is well determined and chosen to be displayed on the screen, so that the projection of building features can be recognized by users. Generally, the experiment has proved that this spatial indexing tree works well in 3D building generalization and multi-scale representation.
High Accuracy Surface Modeling (HASM) method has theoretically solved issues of hill peak smoothing and oscillation phenomenon at edges, and its modeling accuracy is much better than the traditional interpolation methods such as Inverse Distance Weighting (IDW), Spline and Kriging. HASM has been successfully applied to the spatial mapping in multiple fields, such as population density, soil properties and climatic elements, etc. However, as the number and distribution of meteorological rain gauges are limited, getting the accurate precipitation distribution maps based on HASM is still a challenge. Additionally, remote sensing rainfall estimation data, which can provide better spatial information of the precipitation, but without accurate rainfall values, may play an important role. Therefore, in this study, we combine these two data sets together based on HASM model to estimate regional rainfall. Central and western China (25°~35°N, 105°~115°E), which are featured by extensive high mountains and plains, is chosen as the study area to model the spatial distribution of its total precipitation in 2010. Using satellite rainfall estimation, the Tropical Rainfall Measuring Mission (TRMM) 3B43 data is chosen as the background field for HASM modeling. Then, we compare its results with respect to the classical methods (including IDW, Spline and Kriging) based (also used as background fields) HASM modeling. Results show that TRMM based HASM method has higher accuracy and its results exhibit a better spatial pattern for precipitation simulation than those from the other methods. The MAE and RMSE of TRMM based HASM simulation results are 125.15 mm and 155.80 mm, respectively. The simulation errors of the best simulation results using the other methods are respectively 53.6% and 54.5% higher than TRMM based HASM simulation results. Besides, its relative error in each sub-region is also smaller than the other methods. In the multiple applications of spatial elements modeling, e.g. meteorological elements modeling, where there is not enough sampling sites to characterize the spatial structure of an element, the accuracy of HASM modeling will be limited. Therefore, combining it with supplementary information to compensate the deficiency of limited sampling sites will contribute to the production of better results for HASM applications.
The problem of Large-scaled Point Feature Cartographic Label Placement (LS-PFCLP) is one of map labeling difficulties, which is mainly limited by time efficiency and labels’ quality. Changing this plight will accelerate the application development of Intelligent Cartography. The article firstly contraposes the form simplification of traditional potential label position scheme and puts forward an oval multi-orientations and multi-levels cartographic potential label position scheme to make a better conformation of the potential label position scheme to the actual demand, and make it parameterized and multiplex. Secondly, the article combines the space distribution features of point features to adopt an Ant Colony Algorithm based on cluster grouping (C-ACA), whose core framework is decreasing the large scale of points to plural mini-scaled point collections through DBSCAN clustering, then integrating Ant Colony Algorithm to C-ACA. In the process of C-ACA’s implementation, we discuss and optimize the core parameters, such as Eps and MinPts of DBSCAN, ant colony size parameters (
Since land subsidence was firstly discovered in the 1950s in Beijing city, it has revealed a rapid developing trend. In the past few decades, land subsidence has been increasing in both its range and speed year by year and become one of the most serious disasters affecting the safety and development of Beijing [1]. In this paper, we choose four typical subsidence locations as the study area. The land subsidence information of the typical area in Beijing plain during 2004-2010 was used as the primary data source. This is acquired by Permanent Scatterers Synthetic Aperture Radar Interferometry (PS-InSAR) method, which has high accuracy and been widely used in deformation monitoring and land subsidence research. Meanwhile, the conventional monitoring data (1955-2010) was adopted as the supplement data. Then, we analyzed subsidence evolution characteristics from two aspects: spatial distribution and temporal evolution. Combing with the groundwater dynamic monitoring data, the land-use data from remote sensing and the using of GIS spatial analysis, we study the spatial and temporal respond and the important factors affecting land subsidence. The result shows that the seriously affected areas of ground subsidence in Beijing had expanded unceasingly, and the degree of unevenness has been gradually increasing. During the study period, the change of groundwater level had a high consistency with land subsidence in temporal and spatial distribution. In the process of urban development, engineering activities were considered another factor affecting the characteristics of spatial and temporal distribution of land subsidence. In this paper, our research indicated that the excessive exploitation of groundwater is the main influence factor for the subsidence in Beijing; and the rapid development of city leading to large-scale construction is also one of the factors that make the subsidence worse and more serious. The results have considerable referential importance and can provide a scientific basis for the prevention of land subsidence in Beijing.
Confirming the existence of spatial differences among different parts of urban areas, based on the 1:1 million digital geomorphic database and DMSP/OLS nighttime light data from 1992 to 2012, we explored the spatial characteristics of urban development in Beijing-Tianjin-Hebei region for recent 21 years, including the change of urban barycenter migration, the development difference of internal urban area and the relative development rate. Besides, we compared the development difference of urban areas among different macro-scale geomorphologies. The results show that: the barycenter of Beijing-Tianjin-Hebei region has moved south-west, north-east and south-west in sequence, and different cities have different barycenter migration routes and directions; the development level of Beijing-Tianjin-Hebei region has been steadily increasing, while the disparities between cities in this region have been reduced; for cities with different types of geomorphologies, the disparities between their own municipal districts have different changing patterns over time. For plain cities, the disparity basically keeps stable; for mountainous-plain or plain-mountainous cities, an implicit downtrend is indicated; and for mountainous cities, the disparity between their municipal districts declines gradually. The migration of mountainous cities’ urban barycenter is more obvious than cities dominated by other geomorphologic types, but these cities have an overall low development level. At last, we discussed the advantages of using DMSP/OLS nighttime light data, with the premise that heterogeneity exists in each city. We also pointed out that with regard to a long time series, there is evident relevance between urban development and urban geomorphology. A deep investigation into the relevance between the spatial characteristics of urban development and urban geomorphology is significant to understand the nationwide changes of urban development.
Urban expansion, in which the non-urban land use is converted to urban land use, is an important aspect of urbanization. Three Landsat time-series images for 2000, 2006 and 2013 of Zhangzhou city, which is located in Southern Fujian Golden Triangle Region, have been used to study the urban expansion of Zhangzhou urban area with the assistance of remote sensing and GIS technology. The index-based built-up index (IBI) was used to extract urban build-up land information of Zhangzhou from the three images. The extended urban range and expanded urban area were obtained by applying overlay analysis on the vectorized maps allocated in eight quadrants. The result indicated that during the 13-year study period, the urban built-up area of Zhangzhou had a net increase of 13.69 km2, which was 66.9% more than the area in 2000. The calculation showed that the built-up land expanded more rapidly in the period between 2000 and 2006. The urban area expanded toward the northwest direction in a planar and linear way, which was mainly along the three main traffic lines of Zhangzhou. The buffer analysis showed that the traffic line distribution had a significant impact on urban expansion. Driving force analysis indicated that the fast economic development and population increase were the driving forces to the expansion of Zhangzhou urban area. Socio-economic data showed that Zhangzhou´s urban expansion was closely related to the growth rate of the city´s secondary industry. Its expansion pattern belonged to the industry oriented expansion. Besides, the different growth rates of population in different regions also played an important role in Zhangzhou urban area growth, which resulted in the expansion of urban area toward the north-west district. Overall, the imbalanced growth ratio between urban land area and population implied the irrational urban land development in this city. The urban expansion derived by industrial development can be represented with a traditional urban-development model. Therefore, a faster development of tertiary industry for Zhangzhou is crucial to improve the city´s developing quality. Nevertheless, when shifting the planning focus from industrial structure to tertiary industry, the decision makers should pay attention to the intelligent use of urban land.
The impact of tsunami hazard to the coastal countries and regions is a hot topic within the field of tsunami research. One of the main reasons that cause a tsunami is submarine earthquake. Since the Manila Trench and a part of China´s territory cover across the South China Sea, and the trench has frequent seismic activity, it becomes a potential tsunami source which may influence the south China. A hypothetical tsunami is simulated by adopting the COMCOT numerical tsunami model, to research its effects on the area of Hainan Island and Beibu Gulf. The tsunami is triggered by an earthquake in the Manila Trench, and the entire source is discretized into 33 rectangular elements, which would produce an earthquake with a magnitude of Mw=9.0. The tsunami wave characteristics and its impacts on this region are analyzed with the help of records from the virtual tidal stations installed in the study area. The majority of tsunami energy is directed northward to the coast of China. The results indicate that the tsunami waves propagating to the Beibu Gulf Zone have distinct nonlinear characteristics, and spread more slowly than in the deep ocean. After 2 hours and 10 minutes when the earthquake occurs, the head wave propagates to the east coast of Hainan Island with the amplitude of 2.6 meters, which could bring great impacts or hazards to this area. However, because of sea bottom friction and the blocking of Hainan Island, it takes another six hours for the head wave to reach Beihai offshore, and the wave amplitude then is reduced to be less than 50 centimeters. The energy of tsunami undergoes a great loss, and it may have little impacts on the southern coast of Guangxi Province and the Beibu Gulf area. And yet, the additive effects of the tsunami and the tidal currents in the gulf area require further research.
With the implementation of "coordinating urban and rural construction land" policy and "people linked to land" policy, it is of great significance for relieving the contradiction existing in the construction land between supply and demand, and in addition to grasp the laws of spatial pattern and arrange reasonable layouts for rural residential patches that have large renovation potential. Based on the "Land Use Change Survey Database 2010" and "Jilin Statistical Yearbook 2011", the laws of spatial pattern of the present rural residential land for different administrative districts in Changchun-Jilin consolidated metropolitan area were quantitatively analyzed using GIS method combined with landscape ecology method. The spatial relationships between the layouts of rural residential land and slope, river, road, and railway were also discussed in the same way. As a multi-factor comprehensive evaluation method was used to evaluate the locational suitability of rural residential patches, we divided rural settlements into different types of renovation, which could provide a theoretical support and reference for the comprehensive management of territory. The results indicate that: (1) the land distribution of rural settlements is uneven, and both the quantity and density have big deviations in different administrative regions. (2) The terrain conditions (slope), water conditions (river), traffic conditions (roads and railways) all have effects on the spatial layouts of the rural residential land in the study area, but the effects are different. Different grades of road (trunk road and branch) and different forms of railway (rail lines and stations) have different effects on the spatial pattern of rural settlements. (3) On this basis, according to the role of influence factors that have effects on rural settlements, we divided the spatial locations of rural residential patches into different suitability grades. Therefore, the rural residential patches were further divided into corresponding types, including the priority development areas, the retaining development areas and the limited development areas. Then, we pointed out and discussed the improvement directions for each rural residential type.
This paper delineates the relationship between remote sensing monitoring indexes and malaria incidences using genetic programming (GP) method based on factors derived from remote sensing data. Thus, the spatial distribution of malaria incidence is predicted, the prediction results are analyzed, and the modeling precision is evaluated. Malaria is considered to be the severest parasite disease and Anhui Province is one of the typical mid-latitude areas coping with high malaria risk. This paper studies the issue of predicting malaria spatial distribution using GP method, as GP is a striking optimization method which has the capability of exploring a proper solution for sophisticated issues through evolutionary algorithms. And this process is further explained with an example adopting the monthly average malaria incidences in each county of Anhui Province from 2004 to 2010. Also, remote sensing data is regarded to be the main source of factors, considering its large spatial scale and fast data acquisition, and that various meteorological and environmental indexes, could be converted from remote sensing data. These factors include remote sensing indexes, such as normalized difference vegetation index (NDVI) and land surface temperature (LST), plus natural attribute (elevation) and social attributes (population, immigrant and GDP data) in the county level. Results demonstrate that NDVI and LST have influences of two months’ and one month’s lag respectively. Compared with the result of linear regression (R2 = 0.470 for training data and R2 = 0.408 for test data), the predicting precision is improved using GP method (R2 = 0.558 for training data and R2 = 0.429 for test data), which is benefited from illustrating the non-linear relation between remote sensing indexes and malaria incidences. GP method contributes to increase the precision of predicting the spatial distribution of malaria incidence. Conclusively, this paper provides a basis for future scientific research on predicting spatial distribution and mapping malaria using remote sensing data.
There are a large number of earthen sites that have important historical and cultural values in Turpan region, Xinjiang. It is very meaningful to study the spatial-temporal change of earthen sites and their environment. Aiming at the exploration of earthen sites conservation, using methods of archaeology and field investigation, remote sensing (RS), geographic information system (GIS), and global positioning system (GPS) techniques, we analyzed the spatial-temporal change of earthen sites and their internal-external environment. The results showed that the persistence of the earthen sites has a close relationship with the regional environment, that the regional environment has an influence on the persistence of earthen sites. The earthen sites and their internal environment had changed greatly from the 1960s to 2010. We found that the main driving forces of earthen sites and their internal environment change were diseases and human destructions. The main forms of diseases in earthen sites are fracture and collapse, wind erosion, water erosion, alkali disease, surface peeling, and biological erosion. Among the above factors, fracture, collapse and wind erosion are the dominant forms. From 1990 to 2010, great changes in the external environment of earthen sites had taken place in the study area. The cultivated land and residential land had increased significantly, while the grassland and unused land had decreased obviously. The changes in the external environment were especially obvious around Jiaohe and Gaochang ruins, while the intensity of human activities around these earthen sites had increased. The changes in surface environment types of earthen sites in different regions were different. The changes in environment types of earthen sites that located in the interior of oasis were more intense than those located at the edge of oasis; earthen sites that located in the interior of oasis are more vulnerable to the impacts of human activities. The development of cultivated land and residential land were the main driving forces of the external environment change of earthen sites, thus it is necessary to limit human activities for the purpose of earthen sites conservation.
Water surface temperature (including sea surface temperature and lake water surface temperature) is a key parameter for studying global or regional climate change and numerical weather prediction (NWP), as well as being an essential controlling variable in the exchange of heat, moisture and gases between water surface and atmosphere. It has important significance for understanding the biophysical processes of water body. Satellite measurements of water surface temperature are well established with 30 years' collection of practical data and have incomparable advantages over traditional observations. Their limitations and challenges are also identified at the same time. There are more than 30 types of infrared/microwave radiometers which can be used for measuring SST/LWST, and their resolutions, advantages and disadvantages are summarized and compared in this paper. SST/LWST measurements depend on a combination of atmospheric properties and water surface radiances. Therefore, it is necessary to adjust and correct the atmospheric effect and water surface processes. The basic principles and the main types of algorithms for water surface temperature inversion using infrared and microwave data are illuminated and reviewed briefly. There are many uncertainties associated with SST/LWST measurements, and the magnitude of these uncertainties has put restrictions on the application or interpretation of SST/LWST measurements. A detailed analysis about these uncertainties in both infrared and microwave SST/LWST retrieval including undetected cloud, water vapor, aerosols, emissivity and skin effect is conducted. In order to determine the uncertainties in satellite-derived surface temperature, the validation of surface temperature retrieval is an indispensable step. Finally, a prospection about the trend of water surface temperature retrieval is proposed. Additionally, a strategy is advised for assimilating measurements from multi-sensor data in order to take the advantage of their complementary strengths.
Endmember extraction is a key step for unmixing hyperspectral mixed pixels and an important prerequisite in the further analysis of hyperspectral imagery. The traditional N-FINDR algorithm is a classical and effective algorithm among various endmember extraction methods. However, the N-FINDR algorithm need to compute all the possible pixel combinations, thus it is time consuming. In order to improve the time efficiency of the N-FINDR algorithm, this paper proposed an improved N-FINDR algorithm on hyperspectral endmember extraction based on spectral Shannon entropy theory and the convex geometry, meanwhile we utilize the characteristic that in spectral feature space, all pixels of the hyperspectral imagery could compose a single shape body, in which the pure pixels are located at the apex and the mixed pixels in the interior or at the surface. The spectral Shannon entropies of all pixels are calculated according to the pixel gray probability, and are used to determine the purity of pixels. The pixel is removed if its spectral Shannon entropy is greater than the threshold value of the spectral Shannon entropy, otherwise it is preserved. Next, the N-FINDR was used to search the largest volume from the single shape body composed by the preserved pixels, and the pixels at the apex of the body with the largest volume would be the endmembers. Finally, we use the Hyperion data of a copper mine in Dexing city from Jiangxi province to testify the improved N-FINDR algorithm. By analyzing the experimental results, the improved algorithm ensured a high accuracy as well as improved the data processing efficiency very greatly in the course of extracting hyperspectral endmembers.
Machine learning technology has been widely used in remote sensing image classification. Extreme learning machine (ELM) is proposed recently for image classification, but the regularization and kernel parameters (C, σ) of ELM have significant influence on classification performance. In this paper, an ELM classifier with firefly algorithm (FA)-based parameter optimization is proposed for hyperspectral image classification. Firstly, FA algorithm is used for band selection in order to reduce the computational load of hyperspectral image classification. Then, the parameters (C, σ) of ELM are optimized by FA with respect to the classification accuracy. In our experiments, the firefly algorithm is also compared with other parameter optimization algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the support vector machine (SVM)-based classification algorithm is also implemented for comparison purpose. The experiments are conducted on three classical hyperspectral remote sensing data. Results indicate that the performance of ELM method is better than SVM method from the aspects of classification accuracy and running time. Our experiments successfully prove that the proposed algorithm can provide a better performance for hyperspectral image classification.
There is a pressing need to extract building height rapidly based on high-resolution remote sensing images without parameters in the field of urban construction and land management. Current studies are mostly based on remote sensing images with parameters, however the images used for extraction are difficult to get, and current extraction methods have a lot of restrictions. In this paper, a new method is proposed with the use of four types of characteristic lines. The characteristic lines are comprehensively formed by the characteristic points on a single image, which is used to convert the building height based on high-resolution images without parameters. The four types of characteristic lines include: the connection line of the roof displacement point and roof shadow point, the displacement of the roof image point caused by the building height, the full-length shadow, the remaining length of the shadow excluding the part occluded by building. Four types of calculation model for acquiring building height based on the corresponding characteristic lines are deduced. According to the known heights of a small amount of constructions and using the four calculation models, the relevant parameters of remote sensing images can be derived conversely. Then, we can select the characteristic lines that are extracted most accurately on each building , and use the corresponding model to convert the building height. With the application of this method, a large number of building heights can be calculated quickly and accurately. The method is verified based on Google Earth image in Nanjing city and the results show: the images used in this approach are easy to acquire; the method of comprehensive measurement and calculation does not merely rely on the use of shadow lengths to calculate building height, so it significantly increases the practicality of extracting building height on a single image; it solves the issue that there is no related angle parameters of the sun and the satellite position when calculating the building height. Case study indicated that the precision of the proposed method is high, and it can extract the building height quickly in a large area. Generally, the method proposed in this paper has significant practical value in production applications.
Vegetation dynamics and their coupled relations with climate are current research hot spots in exploring how terrestrial ecosystems respond to climate systems. Beijing-Tianjin-Hebei Metropolis Circle, one of the three major economic circles located in the eastern part of China, has been experiencing a rapid development as well as a severe change in eco-environment. The analysis of NDVI’s (Normalized Difference Vegetation Index) spatial-temporal dynamics and the exploration of relevant key climatic driving factors have special significance in the research of ecological environment change in Beijing-Tianjin-Hebei Metropolis Circle. In order to explore the vegetation dynamics and the impact of climate change on vegetation cover in Beijing-Tianjin-Hebei Metropolis Circle from 2001 to 2013, we adopted NDVI data of MOD13A3 to study the trend and spatial pattern of this region using linear regression, with the assistance of precipitation and average temperature data from the growing season. The climatic driving forces of vegetation cover change were discussed using partial correlation analysis and multiple correlation analysis. The results indicate that NDVI had increased gradually during this period, which showed a good development trend for the vegetation cover in this region. The average partial correlation coefficients between NDVI and precipitation, and between NDVI and average temperature are 0.20 and -0.14 respectively, which indicate that NDVI is positively correlated with precipitation and negatively correlated with the average temperature during the growing season at the annual variation level. Furthermore, the impact of precipitation on NDVI is greater than temperature. Analysis of the driving factors on vegetation change shows that about 89.63% of the study area was impacted by non-climatic driving factor, while 5.68% was driven by a combined climatic factor of both precipitation and temperature, and 4.51% and 0.18% was driven by precipitation and temperature separately. As a result, it is evident to conclude that the vegetation change in this region is mainly affected by human activities.
