Vehicle-borne laser scanning system can quickly and accurately obtain 3D point cloud data of the street scene by multiple sensors and data processing technology. Extracting street curb point clouds from vehicle-borne laser scanning data is important for many applications such as road panning and maintenance, but it is difficult to directly extract curb point clouds from the original data due to the high point density, large amount of data, uneven spatial distribution of point clouds, object obscuring each other and complex urban street scenes. A novel algorithm for extracting curb point clouds from vehicle-borne laser scanning data based on Support Vector Machine （SVM） is proposed in this paper. The algorithm is described as followings: firstly, the original data is thinned and segmented into a series of point cloud blocks to improve efficiency. Secondly, a point clouds feature vector is constructed including the relative elevation, normal vector direction, multi-scale height difference and height variance, through the analysis of spatial distribution and local geometric features of curb point clouds. Thirdly, a new method is proposed to refine the point cloud feature of the border of ground and object on the ground such as point clouds of the bottom of tree, building facade, fence, and so on, which avoids errors in dividing the border point clouds into curb point clouds. Fourthly, the obtained training samples are processed by artificial choice. Radial Basis Function （RBF） is selected as kernel. Particle Swarm Optimization （PSO） is used to optimize penalty factor C and kernel function parameter γ, then the feature vector is taken as the input to train SVM. The obtained SVM is used to extract curb point clouds from original data. Finally, cluster analysis is performed on the extracted results for eliminating noisy point clouds when the number of point clouds in cluster is less than threshold. Experiments were undertaken to evaluate the validities of the proposed algorithm with four different street scene datasets acquired by Street Mapper 360 System and Lynx Mobile Mapper System, respectively. The completeness of the results of curb point clouds extraction is over 94.99%, correctness is above 91.88%, and quality is above 90.55%. This proves that the proposed algorithm not only has high accuracy, but also has strong robustness to extract curb point clouds with regular or irregular shape from complex urban street scenes of vehicle-borne laser scanning data.
Human beings live in a ubiquitous electromagnetic geographical environment. To evaluate how the electromagnetic environment influence human's daily life, it is significant to monitor and analyze the temporal, spatial and frequent characteristics of electromagnetic environment. At present, only a few studies focus on data acquisition methods and spatial representation of electromagnetic radiation. Traditional spatial interpolation methods are effective means for representing spatial distribution patterns of geographical phenomenon and have been widely used in various academic fields. However, these spatial interpolation methods are not suitable for representing electromagnetic phenomenon because of its unique characteristics of spatial propagation and attenuation. Electromagnetic environment monitoring system of full band vehicle can collect dense spatial samples of electromagnetic radiation intensity data when the car is driving along the roads and streets. However, sampling data cannot describe the spatial pattern in the whole region. To describe the spatial distribution pattern at regional scale, it is necessary to interpolate the collected electromagnetic data into the whole research area. According to the electromagnetic radiation propagation law, we proposed and implemented a new spatial interpolation method based on electromagnetic radiation propagation model. Using this interpolation method, sampling data are interpolated in the entire region to implement the spatialization representation of electromagnetic radiation field. Also, the new spatial interpolation method is compared with two traditional spatial interpolation approaches, i.e. IDW and Kriging. Experimental results indicated that the proposed method is more suitable for the reconstruction of electromagnetic radiation field than other spatial interpolation methods.
Simulating the channel flow process can provide suggestion to flood disaster forecasting and warning. The hydraulics and hydrology model presently used in simulating channel flow process have many disadvantages, for example, many parameters need to be input and the operation process is complex. What is more, there are high requirements for data precision and it is inapplicable to the ungagged catchments where the condition of river section flow is unknown. In this study, the cellular automata model of the overland runoff and channel confluence rules was constructed by combining the cellular automata model with the hydrological model. Through establishing the river slope topology, we used the SCS-CN (Soil Conservation Service-Curve Number) to calculate the slope inflow of each channel cellular. Then, we used Manning equation to simulate river confluence process. In the end, the process was visualized using ArcEngine. The basin of Maolin Creek in Xiamen was taken as a study case, in which the rainfall-runoff process during May 6-7 in 1997 was simulated to conform this model. Compared with other scholars’ study results, our results can not only simulate the small flood peak in each rain interval but also increase 5 times in the maximum peak flow precision and 5 times in the time precision under the same condition of the input data and hydrologic model parameters. Using the cellular automation can get higher accuracy and it is suitable for channel flow visualization, which can provide reference to flood disaster forecastingand warning.
In recent years, a large number of lost persons have aroused the attention of all sectors of society because the collection and query of information is not easy. The network tracing platform is fast in information acquisition and has widely used in the application. However, the information management of lost persons are scattered, and it is insufficient in the spatial and temporal category analysis. To solve the problems of the inaccuracy and ambiguity of information, we made the memory fuzziness analysis of different age groups of lost persons based on the query of their attribute information. Then, combining with the partition of Chinese language and the fuzzy range of space and time, we set threshold and weight for matching algorithm. Finally, we set up the fuzzy matching model for spatial-temporal information of lost persons. Considering several characteristics of the lost people information such as names, gender, blood types, date of birth, missing time, missing place, dialect accent and missing age, we computed the information matching index among the lost persons. In addition, we used the time geography method to design the time correction method of the model and we also verified the intersection of spatiotemporal reachable range of matching results. The results of case verification indicated that the model can consider the known items of matching index and select the information that has higher matching degree.
This paper aims to optimize the performance of a previously developed hydrodynamic model for urban flood simulation. Our major task includes calibration of two key parameters in the runoff generation and flood routing modules, and verification of the precision of the model output. In the runoff producing module, we focused on optimization of Curve Number (CN) values. To achieve this purpose, the method of Linear Spectral Mixture Analysis (LSMA) was employed to extract terrestrial information of vegetation coverage, soil categories and impervious land use from Landsat TM images, based on which a specific CN value could be defined for each unit in the hydrodynamic model. As for the flood routing module, we reset the manning coefficient via integrating previous empirical value and findings from calibration experiments conducted in this study. Verification experiments show both the calibration of CN values and manning coefficient promotes the model's simulation precision. Using the Vegetation-Impervious Surface-Soil (V-I-S) raster layers, in which the CN values incorporate more accurate information of vegetation coverage and soil categories, as input for the hydrodynamic model, are able to lower the extreme abnormal values of simulated water depth, and provide more reasonable estimation of water volume and inundation area. After resetting the manning coefficient for different land uses, the simulated maximum water depth increased notably (almost 100 mm), compared with previous model outputs without calibration of this parameter. Through our calibration study, it is safe to say that manning coefficient is a sensitive and critical parameter and deserves further attention in the extension research for optimization of the flood routing module.
Navigation electronic map is the only platform for operators to monitor unmanned vehicles. It is also an important information source for route planning as well as auto decision making. A navigation electronic map which covers wide territory and of high precision can provide more details and is an indispensable guarantee for a ground station task. Nowadays, the implementations of navigation electronic maps for unmanned vehicles are mainly based on secondary development on both online and offline map server platforms. These implementations may bring inevitable drawbacks. For one thing, map data and corresponding software such as MapX are very expensive. Even so, the map data always lacks flexibility for personal extensions such as altitude. Besides, the installation and configuration increases much difficulty for the ground station software developers. Finally, online platforms always rely on stable and high-speed Internet network environment, which is not always satisfied. With the development of free and open source geographic data and tools, individuals can easily modify and even construct their own map servers based on their individual needs. In this paper, we provide a new method to implement a navigation electronic map based on a widely crowd sourced map, which is called OpenStreetMap. Firstly, we set up a background tile service using raw OSM data. Then, we render 90 m SRTM data to a bunch of hill-shading and color coding raster files and define the rendering formats and layers in the configure file of the service style for each raster hill-shading and color coding file. Finally, we implement a client module in ground station to request tiles from the background service and present the map in the user interface. The navigation electronic map we implement covers all 0-18 zoom levels of the whole China and it provides 0-13 zoom levels of vivid topographic information and free of Internet limit. Besides, this navigation map we implement can also render other DEM sources and any other raster-format files, such as population, vegetation, precipitation and so on.
During the last decade, the global temperature has a clear rise. The rising temperature may affect ecosystems, public health and socioeconomic systems. It is important to understand how the temperature change. Velocity of temperature change proposed by Loarie in 2009 is a suitable index to quantify the change in temperature. Based on national monthly mean temperature dataset, spatial difference about velocity of temperature change is analyzed in Northeast China and North China in the past 53 years. Here, we use slope of linear regression for each pixel to calculate linear trends of temperature firstly. Then, spatial gradients is calculated using the average maximum technique from a 3×3 grid cell neighborhood. Finally, we calculate velocity of temperature change as the ratio of the linear trends of temperature to the spatial gradients. The results show that: (1) there is an obvious increasing trend in temperature across all of the study area. The characteristic is that the higher the latitude, the more obvious the increasing trend. (2) Spatial gradients is greatly affected by terrain, which is higher in Da Hinggan Mountains and Taihang Mountain, but relatively lower in Northeast China Plain and Inner Mongolia Plateau. (3) The velocity of temperature change mainly varies between 0 km/year and 9 km/year, and the mean is 5.60 km/year. For spatial difference, the velocity of temperature change in Northeast China (5.85 km/year) is higher than that in North China (5.41 km/year); the velocity of temperature change in Heilongjiang Province and Jilin Province is higher than that in Liaoning Province over Northeast China; velocity in Inner Mongolia Plateau and a small part of Hebei and Tianjin is higher than that in other part of North China.
A region is a complex system of human, nature and society. The quantitative modeling and simulation of the ecology of the region are the key to realize the strategy of regional sustainable development. Traditional methods of machine learning have made some achievements in the modeling of regional ecosystems, but it is difficult to determine the learning characteristics and realize the simulation of time and space. Deep learning does not need to determine the training characteristics and has excellent feature learning ability and higher accuracy of model prediction. In this paper, we used the net primary productivity (NPP), aerosol optical thickness (AOD) and population grid data to make full use of the advantages of depth learning. The optimal deep neural network is used to simulate the spatial and temporal patterns of Henan Province. The spatial distribution map of ecological deficit and the forecast of ecological deficit in Henan province from 2015 to 2020 are generated and analyzed. Our work provides relevant basic scientific support and reference for the scientific management and construction of regional ecology.
Ecological security is one of the main goals to the reconstruction of urban ecological civilization and an important foundation for the sustainable development of urban economy. Therefore, it is significant to evaluate the ecological security pattern of urban for urban planning. This paper selected several typical indexes, made a comparative analysis between objective analysis and subjective analysis method and established an evaluation system of ecological security pattern. To verify the effectiveness of the evaluation system, this paper made an evaluation of the ecological security pattern for the study area using the evaluation system. The result shows that: (1) The Tanglang mountain, Merlin mountain, Yinhu mountain, ecological corridors of Dashahe park and Xiangsilin park all have a low level of security pattern of the value of ecosystem service and ecological sensitivity. (2) The ecological corridors of Dashahe Park and Xiangsilin Park and the dams of Merlin and Changlingpi have a complete biodiversity with a low level of security pattern of the protection of biodiversity and ecological sensitivity. (3) On the basic line of ecological control, the area of the low level and lower level of ecological security pattern increased. Also, the area of the intermediate-level ecological security pattern had a decrease in the area outside the line. On the whole, the high-level ecological security pattern area was substantially constant. (4) The percentage of high level and intermediate level of security pattern is the same in the result made by taking the objective analysis method and the result by taking the subjective analysis method. The former percentage of low-level security pattern is less than the latter. In spite of the difference, they show the similar tendency and results.
Reasonably demarcating mountain types and mountainous areas can provide reasonable basis for exploring land resource and carrying out policies in mountainous regions, respectively. In this study, mean change point method was used for best statistical window. We obtain the slope and the degree of fluctuation form SRTM data by using spatial analysis tools, and extract spatial scope and scale in Qian-Gui karst areas. In addition, we also divide Qian-Gui karst areas into different types of mountainous area at county level. The results are as follows: (1) quadratic mean change point method is used to determine the logarithmic curve inflection point of which the best statistical window is demonstrated to be 6.50 km2 between changing window size and mean relief degree in Qian-Gui karst areas. (2) Qian-Gui karst areas have high proportion of mountainous land, and the ratio of mountainous land to non-mountainous land is 89:11. However, there is an obvious spatial distribution difference between Guangxi and Guizhou province. Karst mountain region in Guizhou is mainly consisted of middle-low mountain and middle mountain which account for 57%. Instead, the hill which accounts for 59% is the primary part of Guangxi karst mountain region. (3) Qian-Gui karst areas are all mountainous counties, including 18 pure hilly counties, 10 semi-mountainous county, 15 quasi-mountainous counties, 21 apparent mountainous counties and 32 entire mountainous counties. The entire mountainous county that is almost national key counties for poverty alleviation and development distribute majorly in Wumeng mountainous areas and Qian-Gui karst peak-cluster depressions.
Landslide disaster is serious in Sichuan province. This influence is more obvious after the year 2008. How to prevent landslide disaster is an effective way to reduce landslide disaster losses and protect people's lives and property. Research on early warning models of landslide hazard is the core issue of landslide disaster prevention. This study collected the landslide data, precipitation data between 2008 and 2013, digital elevation data, geological lithology data and seismic intensity data. Our research can be divided into the following two parts: (1) Evaluation of landslide hazard in Sichuan Province. This study used the method of deterministic coefficient to quantify the slope, relief amplitude, hydrogeological lithology vegetation coverage, seismic intensity and annual rainfall factor. We also established a logistic regression model to quantitatively analyze the risk of landslide disaster in Sichuan Province. The results were also verified. The results indicated that the high risk area of landslide disaster in Sichuan is similar to the shape of the letter "Y". The risk value of landslide disaster is as high as 0.97. In addition, the risk of landslide disaster in northeastern Sichuan is very high, with a maximum of 0.8. According to the statistical analysis of the frequency of landslide and the analysis of risk zoning area, the area of region where the value of landslide hazard is between 0.1 and 0.2 accounted for 22% of the whole province's area. The area of the risk value exceeding 0.9 occupies only 5% of the area of the whole province. 35% of the historical disaster points are located in this area, indicating that the degree of landslide disaster risk is high. The spatial distribution characteristics of landslide hazards in Sichuan Province is as follows: the landslide is zonally distributed along the Longmenshan fault zone, the Xianshui River fault zone and the Anning River fault zone, and clustered in the northeastern Sichuan, which is consistent with the model results.(2) Research on early warning model of the meteorological risk of landslide disaster. Based on the statistical analysis of the early landslide disaster and rainfall, and the risk assessment of landslide disaster, this study took the landslide risk assessment as the static factor and the daily rainfall data as the dynamic factor to determine the precipitation probability value, the zoning value of the landslide disaster risk, precipitation probability value of one day ahead, precipitation probability value of two days ahead as the influence factor of the model. The influence degree of each factor to the warning results is decreasing in the order above. Finally, we established the meteorological coupling warning model of the landslide hazards. According to the verification of 2139 disaster points, 80% of the landslide disaster can be successfully predicted, among which 30% of the landslide disaster warning values are more than 0.75. 18% warning values of the landslide disaster are higher than 0.99. 90% of the large and super large landslide disasters can be successfully predicted. 40% of large-scale landslide disaster warning results are greater than 0.75. 12% of large-scale landslide disaster warning results are more than 0.99. On July 10th, 2003, there was a case of group-occurring landslide. It shows that the warning area decreased greatly. Empty quote rate and missing quote rates are greatly reduced compared with the current model results of Sichuan province.
Land surface energy information of remote sensing describes the ecological process of regional ecosystem elements. The distribution and variation trends of land surface energy reflect structure and quality of regional ecosystem element. This study is based on the theory of ecology and aims to provide a scientific basis of preservation and restoration of forests in decision-making, prediction, implementation, verification and other aspects. In this study, we extracted the information about the comprehensive responses and interactive relationship between tropical rain forest and land surface energy in Sanya, using classes of vegetation greenness, land surface energy and the vegetation-energy relationship index to evaluate the quality of forest ecosystem. Vertical and horizontal distributions of tropical rain forest of 30 years (1987-2016) were used to discuss a change of spatial-temporal zonality. The following results are noted: (1) With around 90% of vegetation coverage in the past 30 years, classes of vegetation greenness are mainly composed of high and medium values, and has an increasing trend. (2) The low vegetation greenness and high land surface energy shifts to high vegetation greenness and low land surface energy from coastal area to mountain area. (3) The fluctuation of land surface energy distribution at all levels was less than 10%. Regions with medium energy expanded to low energy areas. (4) Tropical rain forest of high vegetation greenness increases with elevation increasing associated with land surface energy decreasing. (5) The ecological quality of the planted vegetation regions below 200 meters height, declined faster than that of planted vegetation regions above 400 meters height. Compared with planted vegetation regions, tropical rain forest regions have high spatial-temporally stability in both surface energy and vegetation greenness. In general, comprehensive response characteristics of remote sensing and their interactive relationship provide quantitative basis for evaluating the tropical rain forest ecosystems.
With the development of technology, the Unmanned Aerial Vehicle (UAV) is widely used for scientific activities. Image acquisition is its main function. The bad image quality can affect processing results. Image blur is a key indicator of image evaluation. It determines quality and accuracy of the image. If the blurred UAV images are used in the subsequent calculation and processing, the results will be unreliable and unrobustness. This is even a serious error. As a result, the detection of blurred image is of great significance to use. The reason of image blurring has 4 kinds of factors. They are weather conditions, UAV platforms, camera systems and environments. The weather conditions mainly include rain and wind. The UAV platforms mainly are GPS signal intensity. They can make unstable position for UAV. The camera systems mainly include parameters setting of camera, such as focal length, ISO, shutter speed and aperture value. Environments mainly include terrain and illumination. Undulating terrain and different illumination intensity maybe lead to focus inaccuracy. These factors also make image blurring. According to the cause of blurring, there are motion blur and defocus blur. The traditional methods of detecting image blur are mainly based on manual inspection and qualified reference images in the process of evaluation. However, this method is considerably time-consuming and laborious. It is not suitable for a great number of UAV images processing. This paper used the four directions of Sobel edge detection algorithm for building basic evaluation principle, finding blur neighborhood width of each Sobel edge detection point in whole image. Finally, constructing the calculation guidelines of blur neighborhood width. The average value of the blur neighborhood width is calculated by sum and average operation for each sobel edge detection point, and used this value as a direct basis of detecting image blur. The average blur neighborhood width is a dimensionless value. It is affected by richness of image information. So it cannot be used to direct comparison as an absolute reference value. It can also be used for relatively comparison between the similar images, which has approximate richness of image information. Meanwhile, taking into account the characteristics of the UAV images, they have a certain overlap and series. We put time-adjacent images as mutual referenced images because the time-adjacent images have the similar richness of image information. By relatively comparing the changes of blur neighborhood width, when the change is more than a certain threshold, the blurred images have been determined. According to this method, the whole image has been detected. Through a number of experiments, the whole 4 thresholds have been determined. There are m=5, T=5, T1=0.2 and T2=0.167. These thresholds can also be used to UAV image blur detection. Finally, we processed 2322 images of different feature types. There are hill, urban, mountain and plain. They were divided into 7 groups with automatic detection. 151 images was blurred while 158 images was blurred by manual inspection as the correct detection results. The average rate of detection was 95.57%. The detection method has a strong applicability.
The lakes on the high-altitude plateau play an essential role in the local water cycle. Alpine lakes on the Tibetan Plateau have experienced a rapid expansion since 1990s under the global climate change. In order to understand the changing pattern of alpine lakes on the Tibetan Plateau in recent years, this study monitored 127 lakes larger than 50 square kilometers annually on the endorheic Tibetan basin from 2009 to 2014. Based on a semi-automatic lake extraction method, we combined multi-temporal HJ-1A/1B imagery and Landsat imagery to extract lake boundaries accurately. The results have shown that the surface area of large lakes experienced a significant expansion with an overall rate of 231.89 km2 yr-1 (0.87 %yr-1) and the trend of lake expansion is slowing down during the study period. 104 lakes expanded at a rate of 271.08 km2 yr-1 (1.02 %yr-1), while 23 lakes shrunk at a rate of -39.19 km2 yr-1 (-0.15 %yr-1). The spatial pattern of the lake area dynamics also have shown significant regional difference. The expanded lakes are distributed in the most of the east and north study area, while the stable lakes are mainly distributed in the south basin. Besides, the shrunken lakes are scattered at the border of the study area. Based on the comparison between the changing rates of glacier-fed and non-glacier-fed lakes, glacier-fed lakes have shown a much rapid expansion trend than non-glacier-fed lakes, which indicates that the increase of glacier wastage is one of the main factors that contributed to the expansion of Tibetan lakes.
Soil salinization is the process of increasing the salt content in the soil. Salinization occurs when the groundwater table is between two and three meters from the surface of the soil in arid lands. The salts from the groundwater are raised by capillary action to the surface of the soil, and it affects human and natural resources, such as native vegetation and crops, animals, infrastructure, agricultural inputs, biodiversity, aquatic ecosystems and water supply quality in the environment. Factors such as climate, features of landscape, soils, drainage, aspect and the effects of human activities all impact on the severity and occurrence of salinization. Therefore, it is an important concern to evaluate and monitor soil salinity in order to take protective measures against further deterioration of the soil. Traditionally, soil salinity evaluation and monitoring are often carried out with intensive field work and sampling. Most previous studies have focused on differentiating salinized and non-salinized soil qualitatively by analyzing the salinity distribution and monitoring its dynamics. Remote Sensing (RS), Geographical Information Systems (GIS) and modeling have recently outperformed the traditional methods. Remotely sensed data has great potential for monitoring dynamic processes, including salinization. Remote sensing of surface features using aerial photography, videography, infrared thermometry, and multispectral scanners has been used intensively to identify and map salt-affected areas. Salinization has seriously restricted the sustainable development of agriculture and ecological security in the Yanqi Basin, Xinjiang, China. Therefore, accurate assessment and monitoring of soil salinization is particularly important. In this paper, based on the Landsat 8 OLI remote sensing data and measured data, the soil salinization evaluation model was established by using the four evaluation indexes of groundwater depth (GD), salinity index (SI), surface evapotranspiration (SET) and modified temperature vegetation dryness index (MTVDI) in Yanqi basin, Xinjiang. Results demonstrate that: (1) BP neural network model for training was combined with the field measured soil salinity data and the best performance was archived in 4-4-1 architecture (R2=0.864, RMSE=0.569) in the three networks. Compared with traditional multiple linear regression model (R2=0.741, RMSE=0.767), the artificial neural network can improve the predictive accuracy of soil salinization. (2) Soli salinization is strongly associated with GD、SI、SET and MTVDI, and the soil salinization are the results of different combinations of different combination effect factors. Salinization is mainly distributed in low groundwater level and unused area. (3) Most of the study area was salinized in different degrees of salinization, and the degradation of farmland led to further soil salinization and secondary soil salinization.
Atmospheric water vapor content is an important parameter for quantitative remote sensing. In this paper, the CE-318 sun-photometer was mounted on top of our building in Nanning, South China to measure the solar irradiance in the pre-set wavelength, i.e. 440 nm, 670 nm, 870 nm, 936 nm and 1020 nm for retrieval of atmospheric water vapor or perceptible water (PW) during June 2014 to May 2016. After calibration, the solar irradiance measurements from the CE-318 Sun-photometer were used to retrieve the atmospheric water vapor (CE-318 PW) for systematical analysis of its seasonal variation during the measuring period. Comparison has been made to correlate CE-318 PW with the radiosonde data and MODIS near infrared water vapor products. The results showed that: (1) the retrieved CE-318 PW in Nanning is characterized with remarkable seasonal variation. High values (4~6 g/cm2) of the PW were observed in summer. As a contrast, the PW was observed to be relatively low (usually ~2 g/cm2) in winter. This is mainly attributed to the performance of subtropical monsoon in the region. In summer, the monsoon performs actively, making the atmosphere to be wet and hot. In winter, performance of the monsoon becomes weak, leading to a relatively dry atmosphere dominating the region. (2) Good correlation was found between CE-318 PW and the radiosonde data from meteoroidal station, with correlation coefficient of 0.877, average absolute deviation of 0.42 g/cm2 and average absolute relative deviation of 10.96%. But the precision of MOD05 PW was low, its average absolute error was 0.74 g/cm2 and the average relative error was 18.74%.
