Shuttle Radar Topography Mission(SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM have a high spatial resolution and wide spatial coverage, which play an important role in many Earth researches. However, their error distributions are heterogeneous on different terrain types. In order to assess the elevation accuracy of the two DEMs, data from Geoscience Laser Altimeter System (GLAS) carried on the Ice, Cloud, and land Elevation Satellite (ICESat) are often used as the checkpoints due to their high accuracy. Taking Shandong Province as the research area, the accuracy of SRTM and ASTER GDEM are first evaluated by ICESat/GLAS in the years of 2003-2010 in this paper. Results indicate that the root mean squared errors (RMSEs) of SRTM and ASTER are 5.57 m and 7.20 m, respectively, which are much lower than the nominated accuracy. We further analyzed the effect of terrain slope and landscape type on the accuracy of SRTM and ASTER GDEM. Specifically, the study area was first divided into different sub-regions according to slope ranges (0~5°, 5~10°, 10~15°, 15~20°, 20~25°, 30~35°, 35~40°, 40~45°) and landscape types (farmland, shrub, forest, grassland, wetlands, water body), respectively. Then, the RMSE of each sub-region was computed and analyzed. We found that with the increasing of terrain slope, the accuracy of the two DEMs decreases, and under different land cover types, they also have different accuracy. More specifically, the two DEMs have a higher accuracy on farmland and shrub; while have a lower accuracy on forest and wet lands. To improve the accuracy of SRTM and ASTER, their error surfaces were first produced by interpolating the elevation differences between the DEM and randomly selected ICESat/GLAS data with the proportion of 90%. The interpolation methods include Inverse Distance Weight (IDW), Ordinary Kriging (OK), terrain-to-grid method (T2G) and Natural Neighborhood (NN). Then, the interpolated error surfaces were added to the original DEMs. Accuracy assessment of the improved SRTM and ASTER using the remaining 10% ICESat/GLAS demonstrates that IDW with the RMSEs of 2.20 m and 5.31 m is more accurate than the other interpolation methods. IDW is closely followed by T2G and NN. It is surprised to see that OK produces the worst results. Hence, SRTM and ASTER GDEM are improved with the IDW-based error surfaces. The ICESat-2 satellite was launched on September 15, 2018. It can emit 10,000 laser pulses per second, monitoring the height of glaciers and land in unprecedented detail. ICESat-2 collects elevation data over all surfaces spanning the world's frozen regions, forests, lakes, urban areas, and more. Thus, further researches will focus on improving the accuracy of SRTM and ASTER with the ICESat2 data.

As a key issue of sustainable development, scientific assessment of sustainable development potential at county scale provides a solid support for policy making of regional planning. The existing studies have mostly evaluated development potentials of counties using the aggregation of multi-dimensional indicators based on actual development conditions, but rarely focused on the evolution of development potentials in future. Here, we construct an indicator system for the evaluation of sustainable development potential at county scale based on the 2030 Sustainable Development Goals (SDGs), and project the changes in evaluation indicators based on the integration of System Dynamics model (SD) and Future Land Use Simulation model (FLUS). The Zhaoyuan City in Shandong Province, one of China's top 100 economic counties and famous of its gold mining, was selected as a case study to explore the potential of its transition from the mining-dependent to the sustainable development mode. To examine the impacts of different development modes on sustainable development potentials of the study area, we designed five simulation scenarios based on multiple Shared Socioeconomic Pathways (SSPs), i.e., business-as-usual scenario, SSP1, SSP2, SSP3, and SSP5, and performed the evaluation under different pathways from a simulation perspective. The results show that: (1) A majority of indicators on economic and social dimensions are likely to be improved under all scenarios, while ecological indicators, e.g. carbon sequestration, forest, grass, water shape index, and number of forest, grass, and water patches, will be significantly declined; (2) The changing rate of development potentials during the period of 2018-2030 will be less than that from 2009 to 2018 due to the development transition from extensive to the high-quality mode; (3) Compared with the year of 2018, the development potential on average in 2030 under SSP1 and SSP2 scenarios will be increased by 17.36% and 9.8%, respectively, while those under SSP3 and SSP5 will be decreased by 0.5% and 4.20%, respectively. The SSP1 can maximize the development sustainability of the study area, but SSP5 may exert significantly negative impact; (4) future development of Zhaoyuan City should focus on the promotion of SSP1 scenario and cope with backward indicators such as the labor force proportion in different industries, aging population, and carbon sequestration. Overall, we aim to clarify the mapping relationship between 2030 Sustainable Development Goals and development potentials at county scale and provide a comprehensive evaluation framework for development potentials under multiple simulation scenarios. Our work is expected to provide scientific guidance for development policy making and high-quality development transition of Zhaoyuan City.

Cities with different land use types influenced by rapid urbanization and urban expansion support various human activities, such as shopping, eating, living, working, and recreation. The mixed use of land can stimulate the vitality of the city, enable the city togather enough people at different points in time, thus producing more interaction, promoting diversified consumption, and improving the economic and social benefits of the city.Mixed characteristics of land use types in cities gain more popularity in many researches due to the huge practical meanings. However, previous researches on mixed characteristics calculation mainly focused on POI data,and there is a lack of consideration for detecting urban topics. Human activities usually take place in different types of points of interest, the potential relationships and spatial interactions between the different types of adjacent POIs can work together to express the potential semantics of locations. In this paper, from an urban topic perspective, a method for the consideration of the relationship between POIs was proposed, and the Hill Numbers Diversity Index was applied to calculate the mixed degree of topics at the block level. Specifically,LDA (Latent Dirichlet Allocation) topic model was firstly used to generate topic vectors of the block and the co-occurrence patterns of POIs. Secondly, the diversity index was introduced to measure the mixed degree of blocks. Then, according to the Goodness of Variance Fit (GVF) and the nature break method, the blocks wereclassified into three groups: (1) high mixed blocks, (2) medium mixed blocks, and (3) low mixed blocks. Finally, multiple linear regression was applied based on mixed degree and topics in the blocksto uncover the significant topics and mixed pattern.Results show that different mixed blocks haddifferent mixed patterns.For high mixed blocks, the topic of teahouse restaurant was significant; the topics of company, enterprise, and residence weresignificant in medium mixed blocks; and the most typical two patterns in low mixed blocks werethe existence of landscape and famous scenery topic and teahouse restaurant topic. To sum up,starting from the urban topic, this paper reveals the mixed pattern of block, and the results show thatdifferent mixed patterns reflect the characteristics of different mixed areas and present certain rules in spatial distribution, which is conducive to the deep understanding of the cityareas, so as to provide a reference for the construction of Beijing mixed city, and also provide suggestions for other mixed cities.

Map generalization is in essence a spatial similarity transformation of maps. Studying the Douglas-Peucker algorithm and its parameter setting is in essence studying the relationship between the optimal distance threshold of the algorithm and map scale change. However, the quantitative relationship between them is still unknown, which leads to strong subjectivity in parameter setting and selection of simplification results. Therefore, in order to realize the automated simplification of polyline based on DP algorithm, this paper proposes to take the spatial similarity evaluation model of multi-scale polylines as the coincidence point, and determine the quantitative relationship between them using the principle of threshold parameter optimization. The results indicate that quadratic function is the optimal function to describe the quantitative relationship between the optimal distance threshold and map scale change. It is feasible to use the same optimal distance to automatically simplify the polylines from the same geographical feature area based on the Douglas-Peucker algorithm, such as the polylines from the Lower Yangtze River plain. The simplification results match well with the existing target scale data. However, it is unreasonable to use the same optimal distance threshold to simplify the polylines from different geographical feature areas, such as polylines from the Lower Yangtze River plain and the Jianghuai plain. Therefore, different optimal distance thresholds should be selected to realize full automated simplification of DP algorithm for polylines from different geographical feature areas.

Rapid and accurate classification of wetland features is the basis of accurate wetland monitoring. The key to improve the classification accuracy is to select the best polarization characteristics combination among many polarization characteristics. And in order to further study the influence of significant polarization characteristics of wetland features on classification results, a classification method based on the polarization decomposition characteristics of typical features in this area is proposed. In this method, the optimal polarization characteristics are selected and combined from a variety of polarization decomposition methods under the criteria of feature selection factors and so on by using the box plots, and then the classification is realized on this basis. Firstly, in order to simplify and reduce the speckle noises of PolSAR (Polimertice Synthetic Aperture Radar) image, the original four polarization images are processed by reciprocity, and the three polarization images after reciprocity are processed by multi-looks processing and Refined Lee filtering. Secondly, the data are decomposed into six kinds of polarization decompositions, such as Cloude-Pottierde decomposition and Paulide decomposition, and the polarization characteristics are extracted according to the decomposition results. Thirdly, the correlation between the above polarization characteristics and the scattering mechanism of typical features of Shuangtai Estuary wetland is analyzed in detail by using the box plots, Cloude-Pottier plane scatter plots and power mean scatter plots, and some polarization characteristics are selected under the criteria of feature selection factor, feature judgment factor, H/α plane, A/α plane, H/A plane, mean and standard variance. The selected polarization characteristics are combined. Finally, on the basis of the optimal polarization characteristics combination, the Support Vector Machine (SVM) classifier is designed to achieve the optimal classification of wetland features. Shuangtai Estuary, located at the estuary of Liaohe River in Panjin, Liaoning Province, is known as the "world's largest reed field". In order to verify the effectiveness of the optimal polarization characteristics combination, the C-band Radarsat-2 full polarization data in July, 2016 are utilized as experimental data. Through the qualitative and quantitative analysis of the proposed and the compared algorithm, the conclusions are as follows: the polarimetric entropy H, average alpha angle α and anisotropy A of the Cloude-Pottier decomposition, the single-bounce scattering of MCSM (Multiple-Component Scattering Model) decomposition, T₃₃ of Pauli decomposition, the single-bounce and the double-bounce scattering of Yamaguchi3 decomposition are the optimal polarization characteristics on the one hand, and on the other hand, the optimal polarization characteristics combination can not only reduce the data redundancy and the calculation, and improve the classification efficiency, but also accurately represent the features and improve the producer's accuracy of each wetland category, the overall accuracy and kappa coefficient. Among them, the producer's accuracy of the wetland features has increased by 1% to 5%, the overall accuracyand kappa coefficient can reach 94.25% and 93.63% respectively.

The formation mechanism of landslide disasters is complicated and there are many influencing factors. It is imperative to explore a low-cost and highly applicable method to manage and prevent landslide disasters. As a hot spot in the current artificial intelligence field, deep learning can better simulate the formation of landslide disasters and accurately predict potential slopes. Thus, to explore the application potential of deep learning, this paper constructs one-dimensional, two-dimensional, and three-dimensional forms of landslide data, and then introduces three Convolutional Neural Networks (CNN)-based landslide susceptibility analysis frameworks, including CNN-based classifiers, integrated models, and ensemble models. The proposed deep learning methods were applied to Yanshan County, Jiangxi Province for experiments. 16 landslide influencing factors were first selected for modelling based on the geomorphological, hydrological, and geological environment conditions of the study area. These factors include altitude, aspect, distance to faults, land use, lithology, normalized difference vegetation index, plan curvature, profile curvature, rainfall, distance to rivers, distance to roads, slope, soil, stream power index, sediment transport index, and topographic wetness index. Then, the multi-collinearity analysis and relief-F algorithm were used to analyze and screen the influencing factors. All CNN-based methods were constructed and validated based on several statistical measures of accuracy, root mean square error, mean absolute error, sensitivity, specificity, and the receiver operation characteristic curve. Finally, the susceptibility value of each pixel in the study area was predicted based on the CNN-based methods, and the entire study areas were reclassified into five susceptibility categories: very low, low, moderate, high, and very high. The factor analysis results show that the plan curvature, profile curvature, stream power index, and sediment transport index are redundant factors and should be removed from further modelling process. The model evaluation results demonstrate that all CNN-based models can obtain accurate and reliable landslide susceptibility mapping results. The two-dimensional CNN model achieved the highest prediction accuracy of 78.95% among single CNN models. Moreover, the performance of logistic regression was effectively improved by combining the two-dimensional CNN for feature extraction, with an accuracy improvement of 7.9%. Besides, the heterogeneous ensemble strategy can greatly improve landslide prediction accuracy when using CNN classifiers, with an accuracy improvement between 4.35% and 8.78%. Generally, the CNN has been proven to have huge application potential in landslide susceptibility analysis and can be implemented in other landslide-prone areas with similar geo-environmental conditions.

Compared with the observation data from meteorological stations, remote sensing precipitation products can be used to well present the spatial distribution of precipitation. So it is of great significance to make a comparative study on the differences of remote sensing precipitation products in different climatic regions. This paper selects the typical climatic regions in China for comparison analysis. Based on the corrected observations of 649 meteorological stations, the performance of five typical remote sensing precipitation products (CHIRPS v2.0, CMORPH v1.0, MSWEP v2.0, PERSIANN-CDR, and TRMM 3B42v7) are evaluated in different climatic regions. Results show that: (1) The performance of each product varies in different climatic regions; (2) The MSWEP product shows higher CC, KGE, and RMSE values in each climatic region. Based on BIAS, the MSWEP, CHIRPS, PERSIANN, and TRMM have better performance in arid, humid regions, the Tibetan Plateau, and transition regions, respectively; (3) In terms of POD, CSI, and ACC, the MSWEP has better performance in all climatic regions. In humid region, TRMM and CMORPH have advantages with lower FAR, while MSWEP is better in other climatic regions. Above all, MSWEP, the multi-source precipitation product, has better basic statistical performance and category performance in all climatic regions and can be used as a reliable precipitation data source for various hydrometeorological studies in China, which indicates that multi-source data fusion has a good application prospect in future.

The research on car-following behavior aims to explore the impact of the leading vehicle's movement on the following vehicle's driving state on a one-way road. By establishing corresponding car-following models for simulation studies, it can reveal the underlying mechanism of traffic congestion, traffic flow oscillation, and other traffic phenomena, which is helpful for evaluating the stability, road capacity, and operational efficiency of traffic flow. Due to differences in driving experience, personality, and other characteristics, drivers may exhibit different car-following characteristics. Moreover, under the same conditions, the car-following behavior of different drivers may differ, and the car-following behavior of the same driver may also vary at different times. However, traditional car-following models often assume that drivers' driving behavior is homogeneous and rarely consider differences in driving styles among passing vehicles, which is inconsistent with actual situations. Therefore, this paper first extracts four driving behaviors of passing vehicles on the road (lane changing, starting, braking, and smooth driving), develops a Weight-based Adaptive Data Stream Gravity Clustering (WAStream) algorithm based on weights, and conducts clustering analysis on the time-series data of different driving behavior characteristics. Then, according to the driving style scoring model, the aggressiveness of different driving behaviors of drivers is quantified, the effective classification of driving styles of passing vehicles is achieved, and the overall driving behavior characteristics of different style driver groups are obtained. Next, by analyzing the car-following data of drivers with different styles, a speed expectation function for different style vehicles is constructed. Furthermore, the proposed car-following model considers the impact of speed and acceleration differences between the leading vehicle and multiple front vehicles in the driver's field of vision, which considers the driver's driving style. Finally, based on the NGSIM vehicle trajectory data, the key parameters of the car-following model considering the driver's driving style are calibrated using genetic algorithms, and the model's validation and numerical simulation analysis are achieved. The experimental results show that compared with the classical FVD model, the proposed car-following model can better fit the car-following data, and the MAE, MAPE, and RMSE are reduced by 1.511 m/s², 6.122%, and 1.064 m/s², respectively. At the same time, the model can effectively reduce the delay of vehicles in car-following behavior, construct traffic flow scenarios closer to reality, and improve the stability of traffic flow. The car-following model proposed in this study can provide effective decision-making information for transportation planning and management departments and provide model references for micro-traffic simulation studies.

Terrorist attack is violent and destructive, resulting in casualties and property losses; it also involves social unrests, causing significant psychological pressure and hindering normal economic development. The data of this paper came from the global terrorism database, spanning from 2013 to 2017. GIS technology and the statistical theory were used to process and analyze the data of global terrorist attacks, and to analyze the spatial evolution of global terrorist attacks and the overall situation. The attributes selected for the data processing include latitude and longitude, regional information, casualties, etc., which were used for the hotspot analysis of casualties, hierarchical clustering of regional event frequency, and the time and space of global terrorist attacks. The evolution characteristics and situation were analyzed and studied. The spatial distribution and changes of the global high-injury hotspots in the five years were discussed, and the frequency of attacks in different regions was counted and the severity of incidents was classified. Specifically, based on the number of casualties, we used the ArcGIS software to draw the 2013-2017 casualty hotspot map and cold spot map to analyze the spatial trend of terrorism, and used the SPSS software to draw hierarchical clustering pedigree maps for regions of different severity levels. Results show: (1) In the 5 years, the number of casualties reached 202 099 in 2014, and then decreased year by year; the frequency of attacks showed a jagged pattern of “maniac-governance-convergence-no governance-again mania”. (2) The Middle East and North Africa regions were the main sources of terrorist attacks and also the hot spots with high casualties. The average annual casualties accounted for about 49% of the world's total, and the frequency of incidents accounted for about 40%, while the number of casualties in South Asia wass about 22.8%, the attack frequency was about 31.1%, followed by sub-Saharan Africa. By contrast, Southeast Asia, Western Europe, Eastern Europe, and South America were the emerging areas of active terrorism. (3) Global terrorism in general centered on the border area of the Middle East, North Africa, and sub-Saharan Africa, and gradually spreaded to South Asia, Southeast Asia, and Western Europe. Our findings can inform the decision-makers of anti-terrorism organizations to help enhance global security.