The semantic heterogeneity of geospatial data is the main bottleneck for the realization of data association, the intelligent recommendation and the accurate discovery of data. Geospatial data ontology is known as an effective approach to solve the semantic heterogeneity of geospatial data. The morphological characteristic is an important feature of geospatial data besides its temporal, spatial and attribute characteristics, and it is the primary research content of geospatial data ontology. Based on the systematic analysis on the morphological characteristics of geospatial data, this paper studies and puts forward a concept system. Furthermore, this paper creates the morphology-ontology model of geospatial data, defines the ontology representation method of morphological information, and ultimately constructs the morphology-ontology. In the last part, a prototype system for the semantic retrieval of geospatial data has been programmed by taking the morphology-ontology product as the ontology library and using the Jena Java framework. The constructed morphology-ontology have been applied to the retrieval of metadata from the National Earth System Science Data Sharing Infrastructure. Verification test shows that the morphology-ontology of geospatial data can effectively solve the semantic heterogeneity existing in the morphological characteristics of geospatial data and improve the precision and recall rate of data discovery result. The research methods and results of this paper have great reference values in solving the semantic heterogeneity problems occurred in other research fields.
For the damaged or disappeared buildings which cannot be reconstructed by laser scanning technique or from multi-pictures, this paper proposed a method for the single view architecture's reconstruction based on the structured scene. Firstly, the vanishing lines and vanishing points were calculated and extracted based on the RANSAC algorithm and minimum distance method. Then, their three dimensional coordinates were calculated from the parallel plane or any plane that contains the parallel lines or vertical lines. Experiments were undertaken to evaluate the validities of the proposed method with two datasets, one of which is featured by a university's library and the other is the Sichuan Bailu Lingbao Seminary Church. The minimum reconstruction error of the university library is 0 while the maximum is 5.8%, and the overall accuracy is about 1.9%, which satisfies the accuracy requirement. At the same time, this method was applied for reconstructing the damaged buildings, which provides a good reconstruction result for Sichuan Lingbao Seminary Church's three dimensional reconstruction and modeling. This method is applicable to building scenarios containing the parallel, perpendicular, vanishing point and plane structure, which is used to obtain the three-dimensional geometric wireframe building model. Furthermore, the geometric details are replaced by texture mapping. This method also can be applied to the three-dimensional reconstruction of the existing buildings and ancient cultural relics.
Image is a natural representation of the real world, which has the characteristics of intuitionistic expression and rich content. It has become an important visual data source in GIS data acquisition and application. With the rapid development of hardware terminal, the sensors integrated in mobile terminal become more abundant, and multi-sensor data fusion has become a trend in mobile GIS data collection. Aiming at the problem of obtaining azimuth deviation of mobile terminal, this paper emphasized on the acquisition algorithm of camera shooting directions, which was based on a gravity sensor and a magnetic sensor embedded in mobile terminals. Based on the EXIF format, locatable images were generated by the integrated collection of image metadata, location and direction. To overcome the disadvantages of existing modeling method of taking a surface as a point for locatable image, this paper proposed a method to build image viewshed by using image metadata, location and direction based on the model of lens’ angle of view. Multi-level grid was used to build the spatial index for the polygon feature of the image viewshed. Based on this method, a case study was conducted in Fujian province. In the experiment, 8022 locatable images were collected by field verification of remote sensing. Point based modeling and polygon based modeling were utilized to build the retrieved image set. Retrieval speed and results were compared and analyzed. It showed that the image retrieval results could be more accurate based on image viewshed modeling. It could retrieve images of which the actual shooting position located inside and outside of the retrieval area. Meanwhile, the image retrieval speed could increase 6.22 to 8.64 times by using the multi-level grid spatial index.
Among the researches on watermarking algorithms for vector geographic data, more attentions have been paid on the attacking methods such as increasing points, deleting points, translation, rotation and so on. However, there is scarce research on watermarking algorithms resisting the attack of projection transformation. As a common data processing method in geography, projection transformation would affect and ruin the watermark embedded by normal ways in vector geographic data. Therefore this paper proposes an anti-projection watermarking algorithm for this kind of data based on the correspondence points matching. First, feature points of the data are extracted using Douglas-Peucker compression and quad-tree partition. And the points are stored with their attribute information together. Then the watermark is embedded based on coordinate mapping and quantization mechanism, which would enhance the robustness of the watermarking algorithm. Data containing watermark can now be distributed and shared. When it is needed to detect the watermark from the suspect data, the detected one will be compared and matched with the points stored according to their attribute information. If the corresponding points are matched, the project transformation of them can be realized by the bivariate cubic polynomial. When calculating the coefficients of the polynomial, the least square method and QR decomposition are used to improve its accuracy. After the calculation, the polynomial will help to transform the projection coordinate system from the detected data to the original data. And the watermark will be able to be detected after the transformation if enough accuracy is ensured. Experiments have been conducted to prove the proposed algorithm is robust against the attacks of projection transformation, adding data, deleting data, ordinary geometric transform and their compound attacks. Results of the experiments show that the algorithm has a good feasibility and can be taken into practice easily.
Incremental information extraction of geospatial data is the foundation of geospatial database incremental updating, and geometric matching of features is one of the key processes in the incremental information extraction of geospatial data. In order to solve the problems that the single matching operator couldn't perform accurately in the polygon geometric matching, and the negative impacts of low efficiency, redundant matching, low accuracy of weighted multi-operator matching algorithm, etc, a polygon geometric matching method based on hierarchical matching is proposed. To improve the efficiency of the algorithm and guarantee the accuracy of matching results, this algorithm was formulated based on the centroid matching operator, areal overlapping degree matching operator, and steering angle function matching operator which could reflect the location, size and shape feature of polygon respectively, and it was designed with the hierarchical matching rules according to the complexity of each operator. In this paper, a contrast experiment was carried out between two methods, the results show that the integrity and accuracy of the hierarchical matching method can be higher than 90%, and the method works better in the geometric matching of polygon. Compared with the weighted matching method, this method has a high efficiency and low cost; at the same time, the result is more accurate than the weighted matching method.
Raster data model is one of the primary data models applied in the geographical information science, and the cost distance analysis is an important application of the raster data model. When applying the traditional Dijkstra's algorithm or A* algorithm to solve the cost distance problems, it will be confronted with a high time complexity and low computational efficient if the data size is huge. In order to avoid the disadvantage of high time consumption, this paper proposed an improved ant colony algorithm (ACA) to compute the cost distance based on the raster data. In this work, firstly, we construct a new raster data model and a coding method, which meets the requirement of artificial intelligence algorithms. Secondly, the number of iterations and the size of ants were initialized, and the transition probability mechanism is used to calculate the cost between raster cells and to choose the direction of cost path. Meanwhile, the ants can associate with each other by adopting the pheromone updating strategy in the iteration process, so as to accelerate the convergence of the algorithm and attain the optimal result. Finally, the crossover operator of the genetic algorithm is introduced to the general ant colony algorithm, which expands the diversity of the solutions, improves the global superiority of the algorithm, and optimizes the result of the cost distance path. The experiment that computes the optimal Arctic sailing route cost, which adopts the Arctic raster data of sea ice concentration, is produced using the improved ant colony algorithm, the traditional Dijkstra's algorithm and the advanced A* algorithm respectively. The results indicate that, by combining the advantages of ant colony algorithm and genetic algorithm, the improved ant colony algorithm can quickly and effectively construct the optimal route and compute the total ice concentration cost of path based on the raster data with an overall better performance.
Due to the lack of bus operating data, some scholars put forward a method, which uses the orthogonal transfer information between different bus routes, to derive the boarding station information of bus passengers. However, a lot of the bus single trips lack the orthogonal transfer information in practical applications, which lead to the difficulty in deriving the boarding station information of bus passengers. To solve this issue, based on the existing research, this paper put forward a method for deriving the boarding station information of bus passengers based on the comprehensive transfer information mined from IC card data. This method uses the comprehensive transfer information between the subways and buses information mined from the IC card data to confirm the driving direction of buses, and then, divide the passengers of the same single trip into groups. These groups would match the corresponding boarding stations more accurately. We fully take use of the orthogonal transfer information and non-orthogonal transfer information between different bus routes and the transfer information between subways and buses to recognize the fiducial groups and their boarding station information, then, we use the minimum error rule to derive the pending groups′ boarding stations. Using the transfer information to confirm the driving direction could enlarge existing research on how to determine a single trip's driving direction, which makes the method of driving the boarding station information of bus passengers more complete and scientific. In the end, through example analysis, this study greatly increases the number of fiducial groups. This method has a higher reliability and accuracy compared to the method which only uses the orthogonal transfer information between different bus routes to derive the boarding station information of bus passengers. In this study, we use the transfer information to confirm the driving direction of some single trips with the absence of the location data. In addition, we use the transfer information mined from three transfer approaches to derive the boarding station information of bus passengers as well as put forward a series of constraint rules to make the result more accurate and feasible, which are considered to be the most significant innovation and contribution. Deriving the boarding station information of bus passengers is the up-front work in getting the bus passengers' demands and the bus service ability data, which has a guiding significance to urban planning.
The positive effects of ecological conservation and construction projects on ecosystem restoration were obvious in the headwater of the Yangtze River. This paper estimated the vegetation coverage (VC) and net primary productivity (NPP) during the period of 1997-2012 in this region by applying the GLOPEM-CEVSA model and time series Normalized Difference Vegetation Index (NDVI) dataset. Then, the changes of ecological conditions before and after the implementation of projects were assessed. The results showed that the ecological conditions in the headwater of the Yangtze River were significantly improved after implementing the projects, with obviously increased VC and NPP. From the annual average levels of VC and NPP, we can see that by comparing the conditions before and after implementing the projects, the area with increased VC and NPP had accounted for 72.10% and 73.82% of the total area respectively. And by looking into the variation trends, we can also see that the area with restored VC and NPP had increased by 13.02% and 24.62% respectively. The vegetation restorations were varied in different watersheds. Compared the conditions before and after implementing the project, the restoration of VC in the headwater of the Chumaer River showed an obvious improvement and NPP had increased significantly in the Tongtian River watershed. The restoration of the ecological condition in the headwater of the Yangtze River was benefited from the combined effects of the humid climate and the ecological projects, while continuous efforts are still needed to improve the ecological environment sustainably and effectively in the future.
Analyzing the characteristics of spatio-temporal variability of precipitation is very important to prepare against droughts and floods. Based on the monthly rainfall data from 1954 to 2014 collected from 32 meteorological sites in Yunnan province, the spatio-temporal variability of the precipitation and drought-flood regional responses were analyzed using the linear regression, RBF spatial interpolation, wavelet analysis, R/S analysis and Z-index. The results show that the precipitation of 3 seasons, except for the spring, has indicated a fluctuant downward trend, and the annual rainfall tends to decrease at a rate of 8.1 mm/10a in the last 61 years in Yunnan province, moreover, the results of wavelet analysis and Hurst index reveal that the precipitation will maintain a decreasing trend in the future. The average annual rainfall decreases gradually from Xishuangbanna to Lijiang-Diqing. Furthermore, the historical precipitation has a primarily downward trend, while secondly an increasing trend in partial areas. Interestingly, the spatial distribution of the decreasing trend has shown a characteristic of symmetry, but the spatial distribution of the increasing trend seems randomly scattered. The periodic oscillation of the annual rainfall is concentrated in the expectant of 2 years, 6 years, 8 years, 18 years and 28 years, and especially in expectant 28 years it has the strongest energy. The rate of drought trend (KL=0.359) is faster than that of flood trend (KI= -0.071), and the downward trend of the annual precipitation can also explain this phenomenon There are 5 sub-prefectures that the droughts appear more often than the other states in Yunnan province, and which are Diqing, Dehong, Xishuangbanna, Honghe and Chuxiong. In addition, there are 3 sub-prefectures and 2 cities where floods appear frequently, which are Nujiang, Dali, Wenshan, Puer and Shaotong.
Shahekou and Xigang District of Dalian was the subject of this study. Based on remote sensing images of SPOT5 and ETM7 sensors, we obtained the spatial pattern and evolution characteristics of green space and surface temperature in this area from 1999 to 2013 by remote sensing interpretation and inversion. This work adopted the TVX Model to accurately measure evolution of green space and thermal environmental effects in order to put forward a more targeted climate change adaptation planning for urban. The results showed that: (1) in 1999-2013, the greenbelt area decreased 43.8% and greenbelt patch increased 42.86%. The urban green space has evolution characteristic of decreasing area and the spatial distribution fragmentation. The contribution of forest green land, park green land and neighborhood green land transformation into construction is 43.8%, 42.9% and 13.3%, respectively. The occupation of neighborhood green land for construction is the most serious because the original neighborhood green land area is small and the conversion rate high; (2) in 1999-2013, the highest rise of surface temperature is 5.2 K and the surface temperature difference increased 1.7 K. The geothermal aggregation interval increased from 301.6~304.7 K to 306.5~310.9 K and the thermal environmental effects deteriorated. (3) During the period studied, the landscape of land cover and land surface temperature changed significantly. TVX model tracking results indicated that urban green space transformation into urban construction land caused vegetation coverage decreased and surface temperature raised due to the rapid urbanization. Neighborhood green land encroachment caused more serious surface thermal environmental effects than park and forest green land.
Heatwave has become an extreme meteorological disaster which occurred frequently during the summer. Moreover, heatwave could evidently affect the healthy conditions of residents. Thus, study the spatial pattern of heatwave health risk would be helpful for us to prevent from and respond to the impacts of heatwaves. Using the historically meteorological datum of Xiamen, this study built a database of heatwave cases and analyzed the basic characteristics of heatwaves in Xiamen. Taking a heatwave event occurred in 2010 as a case, we analyzed the spatial pattern of heatwave health risk by using both the remote sensing data and the demographic data. It is concluded as the following statements. (1) The intensity of heatwaves in Xiamen is quite low, but its frequency is rather high. An intensive heatwave occurred occasionally. (2) The regions with high health risk are located in Xiamen Island, lying from the northeast toward the southwest. The regions with the highest healthy risk are located in the northern and southeastern Jiangtou sub-district, Huli district, and the most area of Xiagang sub-district and Siming district. (3) The human health risk pattern of Heatwave is associated with the spatial distribution of environmental and demographic factors. Generally, this study promotes and extends the scientific knowledge on the health risk of heatwaves.
In China, the biodiversity conservation priority area is an ecological protection system that promotes and enhances the ecosystem and biodiversity. Methods, which are based on the remote sensing data with a high temporal/spatial resolution and wide coverage and are designed for the supervision of human disturbance in the biodiversity conservation priority area, are currently expected to be established. The quantification and specialization of human activity intensity are the key issues for a supervision system to the environmental protection department on biodiversity conservation. Human disturbance index was proposed to achieve the fast dynamic monitoring of biodiversity conservation priority area. Through establishing the new index system and assessment method, and extracting and calculating the spatial distribution data by raster calculation function of geographical information system, the degree of human disturbance and dynamic change is assessed by the proposed index system using the GIS method. Firstly, according to the necessity of supervising the biodiversity conservation priority area, the classification system for human disturbance was established to describe its type and spatial pattern. Secondly, the human disturbance index system, which was derived from the classification data, was established to achieve a deeper level of assessment on human disturbance. Then, in order to determine the level of human disturbance, the model and method for disturbance assessment of biodiversity conservation priority area was established. The human disturbance index (HSI) was calculated based on the main monitoring index. The five-level disturbance assessment was used to differentiate the disturbance degree. Finally, the biodiversity conservation priority area in western Guangxi and southern Guizhou was taken as a case study to validate the proposed method. The results revealed the potential of this method to be applied to various biodiversity conservation priority areas. This method and technique process can provide a technical support to biodiversity conservation.
Vegetation index time series have difficulty to depict the detailed response of vegetation dynamics to coal mining and the signals of change trend and periodic oscillation at an inter-annual scale. While at the monthly scale, the signals are so weak that they are hard to be extracted due to the disturbance of vegetation phenology. In addition, the physical significance of the transformation detection algorithm is still unclear. In order to solve these problems and to reveal the change trend and periodic oscillation of vegetation growth in the disturbance area of grassland open-cast mine, this paper selected Shengli open-cast mine area as an study area to extract, amplify and quantitatively compute the signals of the monthly change trend and periodic oscillation based on the MODIS NDVI time series from January 2001 to December 2013 in the mining disturbance area and the unchanged feature region. SSA-Man Kendall was adopted to extract the change trend and periodic oscillation. Moreover, we quantitatively analyzed the significant degree and jump time. For the change trend, the Sen slope of the change components were calculated, which could indicates the change direction. Then the trend significance was measured based on the results of Mann Kendall trend analysis. Combined with the moving-t test for some fuzzy catastrophe points, the time points of abrupt changes were also detected. For the periodic oscillation, it was estimated based on the utilization of power spectrum analysis. The evolution characteristics of NDVI time series′ periodic oscillation in different feature regions were also studied. The results show that SSA-Mann Kendall can effectively extract the signals of change trend and be competent in depicting the periodic oscillation at different time scales, as well as quantitatively express the change trend signals. A downward trend of NDVI time series in the stopes is significant, and it is more significant than the waste dumps within the same mine area, while the trend in the unchanged feature region is relative stable. In addition, damage of vegetation is a sudden event in the grassland opencast mine area, and the catastrophe points of NDVI time series usually occur at the beginning of the mine construction. In the partial open-cast mines, NDVI time series′ periodic oscillation in the stopes and waste dumps are different, which are related to the different disturbance forms within these areas. In particular, the vegetation almost vanishes in the stopes with coal mining, however, the dynamics of vegetation growth are more complex in the waste dumps due to the effective vegetation restoration.
Spectral transformation is an essential pre-treatment technique as it can eliminate the effects of background and noise, and it plays a vital role in extracting spectral features′ information and constructing the optimal model. In order to explore the effects of different spectral transformation methods on the accuracy of the PLSR model in monitoring soil nitrogen and determining the optimal spectral transformation, the raw spectrum was transformed with respect to fifteen transformation algorithms and the correlations between each pair of transformed spectrum and soil nitrogen were analyzed. Furthermore, the performances of the PLSR models in monitoring the soil nitrogen based on different transformed spectra were evaluated. The results showed that, for cases involving the first or second-order differential reprocessing transformations, the correlation coefficient between the soil nitrogen and the relevant transformed spectrum increased more significantly than with the raw spectrum, especially when applying the transformation algorithms of square root (T8 and T11) and logarithm (T6 and T12) firstly. Also, fewer optimal factors for these pre-treatments were needed and selected to achieve the threshold of 98% in explaining the dependent variable. Moreover, the first-order differential reprocessing of the square root of raw spectrum (T8) had a higher accuracy (R2=0.985022, RMSEC=0.000132; R2=0.9853, RMSEV=0.000162, Fn=6) for the calibrated model and the validated model respectively, after the comprehensive evaluation of the predicting performance and the complexity of different models. Finally, the first-order differential reprocessing of the square root of raw spectrum (T8) was determined as the recommended transformation method to evaluate the soil nitrogen. In addition, the first-order and second-order differential of the logarithm of raw spectrum (T6 and T12), the first-order differential of the logarithmic reciprocal of raw spectrum (T7), the first-order differential of raw spectrum (T9), as well as the second-order differential of the square root of raw spectrum (T11) could also be considered and chosen as alternatives. The study would provide some theoretical techniques and references to the evaluation of soil nitrogen and spectrum processing.
It has been a hot study field to extract forest structure parameter using Airborne LiDAR. This paper evaluated the validity of random forests technique (RF) in the estimation of forest height, based on both of the physical and statistical features of airborne LiDAR data with the utilization of a new detection method to find the crown height. The study area was selected to be the Zhuxi river basin of Changting county in Fujian Province. At first, the ground point dataset, vegetation and elevation normalized vegetation point dataset of stands were generated by using the progressive TIN filter algorithm. Then, 24 independent variables, such as the percentile of heights and the statistical metrics of points, were derived from the normalized vegetation point dataset. Based on the 24 laser-derived features and the field data, the estimation model for the random forest regression of the mean canopy height in the study area was established. 29 of the samples were used to construct the prediction model, and the remaining 11 samples were used to verify the accuracy of the model. Finally, we compared the average value of the estimated tree heights in each plot with the measured values. The result showed that they were highly correlated with each other, the regression coefficient between them was 0.938, and the correlation coefficient was 0.968. The accuracies of all plots were higher than 87% and the total average accuracy was 93.17%. Moreover, the importance of each variable was calculated in this paper to evaluate the accuracy of model estimation closely. And a conclusion was drawn that the importance of the variable sand the model estimation accuracy were positive correlated, which implies that the greater the importance of the variables, the greater their impact on the accuracy of the model estimation. Among all variables, the Mean_P90 and the percentiles between 70%~95% were representatively having a great influence on the accuracy of model estimation. According to the results, it was concluded that the estimation model of forest height based on random forest technique (RF) with multi-factor was proved to be feasible and efficient.
Cotton is an important economic crop in our country, and also it is one of the main sources of income to the farmers of Jianghan plain in Hubei Province. Therefore, it is very important to obtain the information about cotton planting area, and accurately and timely get the spatial distribution for optimizing the cotton planting spatial pattern, scientifically guiding the cotton production and improving the management level. To explore the feasibility of middle and high resolution remote sensing images in monitoring the cotton plantation area, we took Jianghan plain as the study area in this paper. According to the growth period duration and the phonological characteristics of cotton, we selected the multi-temporal HJ-1A/1B images during the critical period of cotton growth of Jianghan plain in 2012 and 2014 as the data source. By analyzing the spectral property and normalized difference vegetation index (NDVI) time series variation during the cotton growing period in the study area, we use the threshold configurations and the mask processes to build the decision trees for the estimation of cotton planting area. Finally, the planting areas of cotton were extracted using the decision trees method in 2012 and 2014. With respect to the confusion matrix calculations, the overall accuracy of the study area reached 95.96% and the Kappa coefficient was 0.93. Then, by taking the statistics data provided by the Agricultural Bureau as a reference, the accuracies of area extraction results from HJ data are 97.91% and 91.27% in 2012 and 2014, respectively. Results indicated that this method can accurately reflect the distribution of the cotton area in Jianghan plain. In addition, it is concluded that the cotton planting area can be accurately extracted as long as we have the HJ satellite CCD data for the critical period of cotton growth and the image data is not affected by factors such as clouds and precipitation.
