Affected by global warming, extreme heat has become a serious climate disaster in the recent years. Human health, ecological environment, and socioeconomic development have all been affected by heat waves to varying degrees. This study describes the selection of risk factors for heat wave risk assessment and the method of model construction in the recent years. According to the existing literature, we sorted out the basic steps of risk identification and assessment of heat waves. Then we discussed the framework of heat wave risk assessment. The spatial assessment system, which can be used to construct heat wave risk in the future, was proposed. This space system was based on remote sensing technology. The exposure of hazard-bearing body,the hazard of disaster, the vulnerability of pregnant environment and the comprehensive prevention capability of the region should be combined to build a comprehensive evaluation system. Moreover, the selection of evaluation factors was discussed. Based on the literature, we compared various methods for determining the weight of each indicator, such as the graph stacking method, subjective weighting methods, objective weighting methods, and subjective-objective combined weighting methods. We discussed the merits and demerits of each approach. Through comparing the methods, stacking method combined with H-AHP, stood out with its simple addition and subtraction or the multiplication and division, and can be used in the construction of comprehensive evaluation models. And the TOPSIS method, which determines the relative merits of the object based on the distance between the evaluation object and the ideal target,will be the optimal choice for multi-city heat wave risk rating in the future.We provided a reference for constructing a reasonable heat wave disaster risk assessment system in the future. This paper was intended to serve as a basis for further understanding the heat wave hazard and establishing the monitoring, evaluation, and reporting system, as well as for further fostering the establishment of a heat wave disaster warning system in the future.

The Shanxi-Shaanxi-Inner Mongolia region is China's energy production base and also a major carbon emission area. It is very difficult to estimate the carbon emissions of in Shanxi, Shaanxi, Inner Mongolia. How to obtain the spatiotemporal dynamic information of carbon emissions accurately and quickly has important application value for making more informed regional carbon emission reduction plans. In this paper, the Shanxi-Shaanxi-Inner Mongolia region was selected as the study area. Based on nighttime light imagery, the spatial distribution of carbon emissions in Shanxi、Shaanxi and Inner Mongolia were simulated, and then the spatial distribution characteristics and rules of carbon emissions were systematically described. The results showed that the correlation coefficient between the total value of night light pixels and energy consumption carbon emissions was relatively high in the three provinces of Shanxi, Shaanxi and Mongolia during 1997-2016, which all passed the significance test of 1%. From 1997 to 2016, the carbon emissions increased year by year in Shanxi、Shaanxi and Inner Mongolia. Ordos is a "high carbon" area; Tongchuan, Ankang, Shangluo, Hanzhong, Alashan, and Yangquan are "low carbon" areas. The distribution of the carbon emissions presented a distinct pattern ? smaller in southern Shaanxi than in the central areas. The analysis of spatial distribution patterns of carbon emissions in energy rich areas can better inform the formulation of feasible carbon emission reduction policies.

Analyzing the spatial heterogeneity of land surface temperature (LST) is important for understanding the spatial structure of LST. This study retrieved LST by the atmospheric correction method, and constructed a spatial heterogeneity model of LST by using the semi-variogram function. It then took a multi-scale perspective to discuss LST’s spatial heterogeneity in the study area of Chongqing. A Landsat 8 OLI imagery in June 16, 2013 was the primary data source. Results show that: ① The LST’s spatial heterogeneity was exponentially distributed at different spatial scales. ② At the 30 m spatial scale, the spatial heterogeneity was mainly caused by spatial structure, though the proportion of spatial variation caused by random factors accounted for 0.45, showing obvious nugget effect; thus, random factors cannot be ignored at this scale. ③ On the whole spatial scale (30~1500 m), the spatial heterogeneity was mainly caused by spatial structure, and showed obvious spatial scale effect. As the spatial scale increases, the nugget (C₀), the partial sill (C), the sill (C₀+C), and the nugget-sill ratios (C₀/(C₀+C)) gradually decreased, indicating that the spatial heterogeneity declined and the spatial autocorrelation gradually increased. Meanwhile, the range (A) gradually increased, indicating that spatially autocorrelated regions gradually enlarged. ④ On one hand, the proportion of spatial variation caused by random factors ranged from 0.23 to 0.46, showing obvious volatility, because the LST also had spatial heterogeneity within each pixel. On the other hand, the spatial variability caused by spatial structure was relatively flat, because the change of spatial scale did not affect the topographic structure. ⑤ From the scale effect perspective, both sill and nugget showed large fluctuations, and the trend was similar from 690 m to 1500 m, indicating that the change of the LST's spatial heterogeneity was related to random factors. In summary, choosing the appropriate spatial scale is very important for analyzing the spatial structure of LST. When the scale is small, the spatial distribution of LST is easily disturbed by random factors, which affects the variability in spatial structure. When the scale is large, the spatial heterogeneity of LST is weak and unstable.

Land Use and Land Cover (LULC) classification products play an indispensable role in ecosystem assessment, climate change simulation, national geographical condition monitoring, and macro-control policy analysis at the global scale; consistency analysis is the precondition of applying various LULC classification products. This paper assessed the area consistency and spatial consistency of five LULC classification products - MCD12Q1-2010, GlobCover2009, CCI-LC2010, FROM-GLC2010 and GlobeLand30-2010- in the global coastal zones. The five products were compared in terms of the deviation coefficient, correlation coefficient, error matrix, and spatial confusion of LULC types. The main findings are as follows: (1) The spatial patterns of LULC in five products demonstrate relatively strong overall consistency, but can have significant local inconsistency. (2) The five products are qualitatively consistent yet quantitatively inconsistent in classifying the LULC in the global coastal zones ? in terms of structure, water ranks top one, followed by forest and unused land, next are farmland, grassland and shrubland, and lastly wetland and artificial surface, yet the exact area of each LULC type differs among different products. (3) For the correlation coefficient, overall accuracy and Kappa coefficient, MCD12Q1-2010/GlobCover2009 have the minimum values, 0.8814, 67.46% and 0.5748, respectively; while GlobCover2009/CCI-LC2010 have the maximum values, 0.9869, 81.50% and 0.7505, respectively; it is because GlobCover2009 and CCI-LC2010 obtained from the same production organization have the same classification system, while MCD12Q1-2010 is different from GlobCover2009 in terms of the production organization, data source, classification system, and classification method. (4) For the spatial confusion/misclassification between any two different products, grassland, shrubland, and wetland have the highest mix-up ratios, followed by farmland and artificial surface, and lastly forest, unused land, and water; this difference is because forest, unused land, and water have distinctive spectral characteristics and clear spatial textures, while grassland, shrubland, and wetland have similar spectral characteristics and fuzzy spatial distributions. (5) There are 28.81% land area in the global coastal zones with relatively low consistency, i.e., with severe spatial confusion; specifically, the misclassification of farmland, forest, grassland, shrubland, wetland, and unused land has direct influence on the spatial consistency of the five products. This paper is hoped to serve as a reference of selecting data from the five available LULC products for researching coastal zones.

The Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) nighttime light (NTL) imagery can objectively reflect the impacts of human activities on the scope and intensity of urban built-up areas. Therefore, the DMSP/OLS imagery have been widely used in monitoring urban expansion dynamics. In this paper, the invariant region method was used to calibrate the DMSP/OLS time series NTL imagery. Then, we used the calibrated DMSP/OLS imagery to extract the urban built-up areas in China's east coast from 2001 to 2013. The result shows that the built-up areas in China's east coast increased from 7550 km² in 2001 to 21 650 km² in 2013, with a net increase of 14 100 km². Although the built-up areas have increased year by year, the increase rate has slowed down. The gravity center of the built-up areas has gradually moved south. The Beijing-Tianjin-Tangshan area, the Yangtze River Delta, and the Pearl River Delta are the three major urban agglomerations in the east coast. The Beijing-Tianjin-Tangshan area was unbalanced in regional development, where the small and medium sized cities faced shortage of development resources. The relationship between urban expansion and economic growth was explored. We conclude that population and economy are the two main driving factors for the expansion of urban built-up areas in China's east coast. The rapid urban growth in China's east coast has caused land resource waste to a certain extent. Moreover, we also found that the edges of urban built-up areas were easily mis-extracted, due to the coarse spatial resolution and saturation problem in DMSP/OLS NTL imagery. The new generation of Suomi NPP/VIIRS NTL imagery has greatly improved in spatial and spectral resolutions. In future studies, the advantages of Suomi NPP/VIIRS should be fully explored to provide more accurate monitoring of urban expansion dynamics.

The warming effects caused by urban expansion have always been a hot topic in climate thermal research. Firstly, this study obtains the average proportion and expansion rate of urban-land area of 58 meteorological stations in Beijing-Tianjin-Hebei region within the radius of 2 km based on the 7 periods of remote sensing monitoring data of urban expansion from 1980 to 2015; builds expansion degree index of each meteorological station by entropy method. Then divide these meteorological stations into three types (these are lowly urban-expanded meteorological stations, moderately urban-expanded meteorological stations and highly urban-expanded meteorological stations) by quartering the expansion degree index of all stations. Finally, this study reveals the effects and its contributions of urban expansion through the comparative analysis of the annual and seasonal average temperature change trend of three types of meteorological stations. The results show that: (1) the surrounding of almost all meteorological stations has experienced urban expansion in Beijing-Tianjin-Hebei region during 1980-2015, the average of urban expansion degree index of 58 meteorological stations is 0.377, and the average of urban expansion degree index of C3 meteorological stations is 0.650; (2) the annual and seasonal average warming rates of three types of meteorological stations show as C1 < C2 < C3, and the annual average warming rate of C3 meteorological stations is 0.536℃/10a; the seasonal average warming rate of C1, C2 and C3 meteorological stations are the highest in spring, and the seasonal average warming rate of C3 meteorological stations is 0.637 ℃/10a, while the three types of meteorological stations have the lowest warming rate in summer or autumn; (3) The effects and its contribution rate of urban expansion on annual and seasonal mean warming rate of C3 meteorological stations are higher than that of C2 meteorological stations, the effects and its contribution rate of urban expansion on annual mean warming rate of C3 meteorological stations are 0.342 ℃/10a and 63.81% respectively; the effects of urban expansion on the seasonal average warming rate of C2 and C3 meteorological stations are the highest in winter, these are 0.229 ℃/10a and 0.410 ℃/10a respectively, while that of C2 and C3 meteorological stations are the lowest in spring or summer; the contribution rate of urban expansion on the seasonal average warming rate of C2 and C3 meteorological stations are the highest in autumn, these are 73.24% and 82.96% respectively, while that of C2 and C3 meteorological stations are the lowest in spring.

With the acceleration of urbanization, urban heat island (UHI) effect has become an increasingly serious problem, which poses a great threat to public health and urban sustainability. Vegetation can lower the air temperature by reflecting direct sunlight and through the process of evapotranspiration, and hence plays a key role in improving local thermal environments. Investigating the effect of vegetation on regulating building temperature is very useful for understanding the principle of urban heat island and mitigating the deterioration of urban thermal environment. However, most previous studies are based on remote sensing imagery, which lacks three-dimensional information on vegetation structure. Additionally, these studies are mainly carried out at the urban scale due to the limitation of spatial resolution. Therefore, it remains challenging to quantitatively investigate the effects of vegetation canopy structure on building temperature at small and medium scales. In this paper, we quantitatively investigated the relationship between the LiDAR-derived 3D vegetation structure (canopy density, CD) and the rooftop surface temperature (RST) at the city-block (medium) and individual building (small) scale. We improved the Building Thermal Functional Area model (BTFA). Considering the spatial and quantity characteristics of buildings in Santa Rosa, the optimal sizes of the small and medium thermal function areas were estimated. Then the vegetation canopy density around the buildings at two scales were calculated. The cooling capacity of CD was analyzed by nonlinear fitting model and other statistical methods. Moreover, we used spatial autoregression model to analyze the contribution of CD to lower the rooftop temperature under the interaction of various factors. Results show that the cooling effect of vegetation on buildings is closely related to the canopy density around them: the minimum threshold of 17% is required to achieve effective cooling effect, while 30% and 40% are the optimal thresholds at medium and small scales, respectively. Additionally, changes of RST vary at different scales with the same canopy density. The decrease of RST at the medium scale is on average 0.89 ℃ lager than that at the small scale. The findings suggest that the planning of urban green space should be considered comprehensively in different scales. Moreover, the RST changes at small and medium scales are affected by not only the vegetation structure nearby the buildings but also the overall thermal environment. The methods and results of this paper are helpful to better plan green spaces on the limited urban land resources and build a more sustainable human livable environment.

Traditional methods for monitoring goaf landslides cost manpower and material resources, while optical remote sensing is difficult for quantitatively identifying potential landslides. This paper used InSAR to monitor the slopes of a mountane area in Kafang Town of Yunnan Province that is affected by goafs. To date, there have been some methods for identifying the occurrence of (potential) landslides, but most of them are based on the line-of-sight (LOS) direction deformation or slope direction that is converted from the LOS direction. Yet, when landslide is monitored based on the LOS direction, the actual deformation trend of the landslide cannot be captured. The deformation based on the slope direction is limited by the different slopes of mountains during the conversion process, and cannot reflect the specific landslide deformation. In this context, this paper proposed a new method that integrated the ground monitoring data of SBAS-InSAR, slope, and aspect. The deformation rate of LOS was obtained by SBAS-InSAR, and then it was converted into the vertical deformation rate. Based on the SRTM DEM data of 30 m resolution in the study area, the GIS analysis tool was used to generate the slope and aspect maps. Combined with the satellite parameters of Sentinel-1A, the radar visibility of the study area was partitioned to obtain effective observation values. The slope and aspect were re-extracted to detect areas where landslide is likely to occur, and then they were integrated into the vertical deformation rate to identify potential landslides. The identified potential landslide areas were compared with historical records to evaluate the accuracy of our method. The result showed that the surrounding areas of Kafang town were notably affected by goafs, and that the vertical deformation rate of most areas was more than 10 mm/a. With the proposed method, we found that 16 of the 21 historical landslide points in the study area were identified as potential landslides while 5 were not identified (but also located in the deformation region). We conclude that our proposed method for identifying potential landslides was highly accurate and feasible. This study provides a way to detect potential landslides near the goafs, by determining whether mountain slopes are in a potential and inconspicuous sliding state or not, and accordingly, helps provide early warnings of landslide disasters.

As the cutting-edge technology of modern information technology, remote sensing has the advantages of large coverage, short detection period, strong current situation, and low cost, which makes it possible to quickly and accurately extract large-scale crop planting information. Accurate crop type identification and spatial distribution information can provide basic and necessary information for subsequent crop monitoring applications. Identification of crop spatial distribution by remote sensing is the frontier and a hotspot of multidisciplinary research in geography, ecology, and agronomy. Multi-source remote sensing data plays an important role. Combining the characteristics of winter wheat and rapeseed during their planting and growing stages, this study took Hefei City in Anhui Province as the study area, and used multi-source remote sensing imagery such as ZY-3, Sentinel-2, and GF-1, with elevation and slope data as auxiliary information. Utilizing the object-oriented classification method with multi-scale segmentation, nearest neighbor method, and threshold method as the main steps, the spatial distribution information of winter wheat and rapeseed planting in Hefei City was extracted. Ground truth data from the GVG agricultural sampling system and Google Earth high-resolution imagery were combined to verify the accuracy of the classification results. By confusion matrix analysis, the overall accuracy and the kappa Coefficient were calculated, the values of which were 94.43% and 0.914, respectively. The results show that, to a large extent, the proposed method can effectively distinguish the planting areas of winter wheat and rapeseed in the mixed planting regions and the combination of those various strategies can be applied to the crop classification in other regions with similar characteristics and at even larger scales. Future research can explore the feasibility of using multi-source remote sensing data to map winter wheat and rapeseed for remote sensing monitoring, and can establish a suitable technical system.

Reasonable construction of PM_2.5 concentration prediction models is the key to scientifically and accurately predict PM_2.5 concentration dynamics. The traditional EEMD-GRNN model predicts PM_2.5 concentration with good prediction accuracy, but focuses more on research data and less on its physical meaning. Based on the time series data of PM_2.5 concentration in Nanjing duing 2014-2017, this study analyzed the multi-scale variations of PM_2.5 concentration and its scale response to meteorological and atmospheric pollution factors. Additionally, the EEMD-GRNN model was reconstructed and validated based on time scale reconstruction. Results show that: ① based on scale reconstruction, the improved EEMD-GRNN model was scientific in predicting the PM_2.5 concentration as it considered the variation of PM_2.5 concentration and its multi-scale responses to atmospheric pollutants and meteorological factors. The PM_2.5 concentration of Nanjing’s sample data had obvious inter-daily and inter-monthly variations. Thus, it is more practical to construct GRNN modeling at the reconstruction scales (i.e., daily and monthly). Meanwhile, PM_2.5 was sensitive to factors such as PM₁₀, NO₂, O₃, RH, and MinT. Thus, choosing these factors as input variables in the GRNN model can be more explanatory for time-series. ② the improved EEMD-GRNN model was more accurate in predicting PM_2.5 concentration. The MAE, MAPE, RMSE, and R² of the improved models are 6.17, 18.41%, 8.32, and 0.95, respectively, which are superior to the validity test results of the traditional EEMD-GRNN model. Furthermore, for the prediction of high-concentration days (i.e., PM_2.5 concentration greater than 100 μg/m³), the improved model was more comprehensive than the traditional EEMD-GRNN model. With the new EEMD-GRNN model, the MAPE is 12.02%, 9.03% higher than the traditional model. Our findings indicate that the improved EEMD-GRNN based on scale reconstruction is an efficient method to scientifically and accurately predict PM_2.5 concentration, especially in days with high PM_2.5 concentration.

With the advent of the era of UAV,the real-time requriements for massive data processing are getting higher.Achieve parallel processing of Retinex image enhancement algorithm on the GPU (Graphic Processing Unit) platform, which improves the processing speed of Retinex image enhancement algorithm for processing high resolution digital images.Firstly, by data combine-accessing and memory data interaction technology realize fast access of data, shorten the transmission time of data between different kinds of memory, and improve the efficiency of data access. Then, using kernel instruction optimization and data parallel computing technology, the multi-core programming of Retinex image enhancement algorithm on GPU platform is realized.Finally, the asynchronous execution mode of the host and the device is used to perform parallel calculation of the kernel data while data transmission, and the execution time of the algorithm on the GPU platform is further shortened by the parallel of the task level. With the powerful parallel computing power of the GPU, the processing speed of the Retinex algorithm is greatly improved. For images of different resolutions, the processing speed of the Retinex image enhancement algorithm is tens of times higher than that of the CPU platform. Processing an image with a resolution of 2048×2048 pixels requires only 38.04 ms, and the processing speed of the algorithm is 40 times higher than that of the CPU.

With the progress of computer vision and RS, dense matching based on remote sensing images has also become one of the important means to obtain high-precision point clouds. Like point clouds of LiDAR, filtering is the fundamental step. Dense matching point cloud is similar with LiDAR point cloud, but have different feature. In this paper, the feature condition is added to the progressive morphological filtering algorithm, point clouds and images are combined into RGB-Depth images, and depth images are semantically segmented according to typical object types, so that point clouds which coordinate correspond with image coordinate are marked and filtered for the first time. Then divide point clouds by grid, then do simply classified according to geometric features, and the improved irregular triangular network of ground points is constructed by filter parameters corresponding to the classification results. Finally, use and intergraded the pre-filtering results and the semantic segmentation results, the regions with similar features are optimized and confirmed by predefined parameter, and the final filtering results are obtained. The results are compared with results of the Cloth Simulated Filtering algorithm. The test result was show that type I error less than 1.98%, type II error less than 2.33% of the progressive morphological filtering algorithm, that algorithm is suitable for higher precision application, especially mixed terrain points cloud filtering.

LiDAR is an active topographic mapping technique for obtaining high precision surface geometric information. In recent years, LiDAR point cloud has been widely used and gradually become a new standard geospatial information product, because of its efficiency, directness and easy availability. Due to its high accuracy, LiDAR point cloud data can be used as georeference data for aerial triangulation of unmanned aerial vehicle (UAV) imagery with sparse ground control points (GCPs) or even without GCP. Because the accuracy of aerial triangulation strongly depends on the accuracy of georeference data itself, so it is of great practical significance to evaluate the accuracy of LiDAR point cloud as georeference data. In this study, we proposed a method for evaluating the accuracy of airborne LiDAR point cloud data using high precision digital line graphic (DLG) as reference data. This method realizes not only the assessment of the vertical accuracy of LiDAR point cloud, but also the reliable assessment of the horizontal accuracy of LiDAR point cloud. Firstly, the vertical accuracy was evaluated by comparing the elevations of the elevation points in DLG with the elevations of the LiDAR points at corresponding positions. Secondly, the facade points of LiDAR buildings were extracted and projected into a horizontal plane, and their distances from the outlines of buildings in DLG were calculated to evaluate the horizontal accuracy of LiDAR point cloud. Due to the existence of a large number of elevation points and building contours in the DLG, the accuracy assessment samples were abundant, and the assessment results could reflect the true accuracy level of LiDAR point cloud. The experiments showed that the horizontal and vertical accuracy of LiDAR point cloud in the test area could reach 7.2 cm and 8.3 cm, respectively, which proved that LiDAR can be used as an effective control data for large scale UAV aerial remote sensing purpose.

In recent years, unmanned aerial vehicle (UAV) have become a means of civilization and universalization. The UAV image is gradually replacing aerospace remote sensing data and is widely used in many fields. The limitation that the orthophotos can only be taken from a vertical angle in the past has been broken nowadays by oblique photogrammetry which has wide application prospect in 3D modeling. Aiming to ensure three-no-image (i.e., no camera calibration parameters, no strip information (disordered), and no POS (Position and orientation System) information) in some oblique images, the paper proposes a method of automatic aerial triangulation and 3D reconstruction for the three-no-image. This method is based on the content-based image retrieval method and improved progressive SFM (Structure from Motion) method in computer vision. Firstly, the method retrieves similar images and establishes the network through extracted features. Secondly, the correspondence between the two images is enhanced by matching the images and the tie points are tracked. Thirdly, the 3D point cloud of image is obtained by bundle adjustment. The algorithm improves the accuracy and robustness of reconstruction and makes a great progress in large scale image retrieval and image matching. Finally, , the stability, reliability, and accuracy of the proposed method was tested and validated with three-test experiments by using large scale real oblique images over three test areas. The test-1 area has 1190 images, from the project construction to the final aerial triangulation calculation without control, the total time is 4.3 hours, and the error is 0.4 pixels. The test-2 has 3685 images and no POS is used in experiment. From the project construction to the final aerial triangulation calculation without control, it takes 8 hours and the error is within 0.32 pixels. The two experimental results verified the stability and applicability of the proposed algorithm. The test-3 area has 1346 images, after 5 hours processing, the error of the free network adjustment is within 0.42 pixels, 9 ground control points are used for check points, the error is within 0.16 m in plane and 0.18 m in elevation. The experimental results verified the accuracy and reliability of the proposed algorithm.

The UAV (Unmanned Aerial Vehicle) photography is a new remote sensing system emerging in recent years. It plays an important role in the rapid emergency response of natural disasters. However, due to the large amount of UAV image data, the traditional method for image matching and mosaic is low accuracy and time-consuming. Feature matching is one of key steps in UAV image mosaic. Traditional matching algorithms have several problems, including less feature points, feature maldistribution, and time-consuming. To solve these problems, a fast image mosaic algorithm based on Dense SIFT feature matching is proposed. Firstly, the connection matrix is build based on POS (Position and Orientation System) data to conduct the matching process. The UAV images are then down-sampled. Secondly, image segmentation is performed on the down-sampled images. Then the Dense SIFT operator is used in overlap area of down-sampled images to obtain the initial matching points which are eliminated through matching by the RANSAC (Random Sample Consensus) algorithm and refined by the NCC algorithm on the original and down-sampled images, respectively. Finally, processed images are projected to the object coordinate system based on collinear equation which is calculated by the bundle adjustment method. By contrast, the SIFT (Scale-Invariant Feature Transform) and SURF (Speeded Up Robust Feature) algorithms and the Pix4Dmapper Photogrammetry software are used to test the quality and efficiency of the Dense SIFT algorithm. Two groups of UAV images mosaic experiment results indicate: (1) The Dense SIFT algorithm can be used to obtain about five times more evenly distributed matching points than the SIFT and SURF algorithm at the same time; (2) The Dense SIFT algorithm can be used to effectively improve the quality of the images mosaic by removing the phenomenon of ghosting; (3) It takes about half the time of Pix4D mapper software to complete the same image mosaic test using the Dense SIFT algorithm. This indicates that the presented algorithm has a high image mosaic quality and fast processing speed, which can play an important role in the rapid emergency response of natural disasters.

For geometric correction of SAR image without ground control point in large area, method of geometric positioning parameters correction based on DEM is proposed in this paper. The basis of this method is that the static error of geometric positioning parameters is stable in short time and certain area. Firstly, simulation SAR Image is generated based on DEM. Then, feature points are extracted on the Simulation SAR Image. Corresponding image point of these feature points on original SAR image are extracted by image matching between simulation Image and original SAR image. And the geographic coordinates of these feature points are got by indirect geolocation with DEM. The image points on original SAR image with geographic coordinates are used for reference points of geometric positioning parameters correction. And then, the correction model is established according to rigorous geometric imaging model of SAR image and correction values of geometric positioning parameters are solved with the reference points. Finally, geometric positioning parameters of other SAR images in certain area are corrected with the values, and geometric correction accuracy of SAR images in this area is improved. GF-3 SAR images were used in experiments. The correction values of geometric positioning parameters were obtained from one image. Geometric positioning parameters of other images in the same orbit and in different orbits were corrected. The geometric positioning accuracy before and after parameters correction was evaluated. Geometric positioning accuracy of the image in the same orbit improved from 66.0 m before parameters correction to 9.7 m after parameters correction, and that of the image in different orbit improved from 65.0m to 13.5 m. The results showed that geometric positioning accuracy can improved significantly by parameters correction using the method of this paper.

The ever-increasing numbers of UAVs and their free-flying route planning have brought great challenges to national aviation safety. In order to build a safe and efficient aviation flight environment, it is possible to establish an isolated airspace for the UAV activities, and also plan UAV low-altitude public air routes within it. If established, this would increase safe airspace utilization and provide a decision-basis for UAV traffic management. Taking full account of the geographic characteristics of near-surface flight and the near-instant messaging capabilities of UAVs, this study built a low-altitude flight environment for UAVs in Tianjin, China based on multi-source geospatial data using geographic information technologies, and constructed a low-altitude public air route network using an improved Ant Colony Optimization (ACO) algorithm. The study had five major components. Firstly, we developed a path-searching model by improving the traditional ACO algorithm from search space and local target selection. The improved algorithm can be used to search paths in eight directions along a line between the start and end points in order to shorten the search time, and the search radium was determined by an obstacles ratio. Then, local target selection was optimized by introducing evaluation function of A* algorithm and random roulette method. Secondly, we compared the calculating efficiency and path length between the traditional algorithm and the improved one, and found that the improved algorithm was three times more efficient and shorter than the traditional one. Thirdly, the low-altitude flight environment for UAVs included a cellular network, and climatological condition and airspace-policy can be taken into account. The cellular network environment was determined by the distribution of mobile communication base stations and signal attenuation principles. Climatological conditions included wind shear, thunderstorms, glaciation, and low-visibility weather events, and all of which have a significant impact on UAV flight safety. The airspace-policy factors included populated areas, key buildings, and civil airport clearances. Fourthly, we constructed a digital low-altitude airspace by establishing UAV flight principles within air routes and quantifying a grid cost for each kind of constraint. Lastly, the fifth component is verifying the outcomes’ reliability by comparing air-route length with the most realistic distance that the UAV currently exhibits. In summary, we found that the improved algorithm greatly shortened search time, and reduced path redundancy. The air-route lengths also comply with the farthest-distance requirement for UAVs currently on the markets. The study described basic ideas and key technologies of the UAV's low-altitude public air route research and can provide the core technical support for the UAV control systems.