To elucidate the mechanisms of vegetation dynamics, this study primarily investigates the spatiotemporal variations in fractional vegetation cover (FVC) on the Loess Plateau and its responses to topography. Leveraging the high-resolution and temporal continuity of Landsat imagery, this study utilized the Google Earth Engine (GEE) platform to acquire imagery from 1995 to 2024. The normalized difference vegetation index (NDVI) dataset was synthesized, and FVC was retrieved using the pixel dichotomy model. Methods including linear regression, Sen + MK analysis, coefficient of variation (CV), Hurst exponent, and multiscale geographically weighted regression (MGWR) were employed to examine long-term FVC dynamics and terrain-driven influences. Results show a significant upward FVC trend (0.449%/a, p < 0.01), with 69.74% of the area showing vegetation improvement (p < 0.05) and 54.32% exhibiting large fluctuations. Future predictions indicate spatial heterogeneity, with 52.99% of the area projected to improve. Under categorized conditions, FVC initially decreases and then increases with elevation and topographic position index, while it exhibits a fluctuating upward trend with increasing slope, terrain roughness, and topographic complexity index. FVC responses to terrain factors are significant (p < 0.05), except for aspect (p > 0.05). These findings provide valuable insights for ecological restoration policies by elucidating the spatial heterogeneity of vegetation and the driving mechanisms of topography.

Exploring the spatial and temporal dynamics of vegetation cover in different regions of China and its topographic effect is crucial for maintaining the ecological environment and preventing soil erosion. Based on the MODIS NDVI data from 2000 to 2023, the vegetation cover of China for 24 years was calculated by using the pixel binary model. The spatial and temporal trends of vegetation cover in different regions of China and the influence of topographic factors on the spatial distribution of vegetation cover were investigated by dividing the study area into seven typical climate zones. The results showed that： ① From 2000 to 2023, the vegetation cover in China showed a fluctuating upward trend, with a growth rate of 0.207%·a-1 and a basic pattern of "low in the northwest, high in the southeast, and spatially differentiated in the central part of the country." The proportion of areas with improved vegetation cover over the years was 46.7%, with a risk of continuous degradation in local areas. ② With the increase in altitude, the trend of vegetation cover changes in various climatic zones was not the same. With the increase in slope, the vegetation cover of the climatic zones showed a fluctuating upward trend, and the proportion of vegetation cover of different slope direction was relatively stable. ③ The vegetation cover in the same climatic zone had a significant difference in the response to the topographic factors, and the experiment showed that topographic factors had a significant influence on the vegetation cover. The experiment showed that the influence of terrain factors on vegetation cover was as follows： elevation &gt; slope &gt; slope direction. The study of the spatial differentiation of vegetation and the driving law of topography in typical climatic zones can provide scientific basis for the improvement of China's ecological environment.

The global ecosystem dynamics investigation (GEDI) mission aims to provide large-scale, high-precision, and high-frequency measurements of the Earth's three-dimensional structures. However, uncertain geolocation error may hinder or restrict the further application of GEDI products. Based on the error matrix, the laser spot center positioning method was employed to quickly evaluate and quantify the geolocation errors of GEDI L2A (version 2) data in the high-latitude region of China, thereby reducing the impact of systematic geolocation errors on elevation and canopy height detection performance. Combining with high-resolution airborne light detection and ranging data, we provided the geolocation offset characteristics at footprint, beam, and orbit scales, while monitoring their performance over nearly a year. Correcting geolocation errors at the footprint scale can significantly enhance the elevation accuracy, butit has the largest standard deviation (approximately 15 m). Conversely, at the orbit and beam scales, the standard deviations of along- and cross-track error are smaller and the ability to improve elevation accuracy is lower. At beam scale, the level of improvement in elevation accuracy increases with the slope but diminishes when the slope exceeds 20 degrees. Unfortunately, the effect of geolocation correction on canopy height accuracy in this study area is not obvious. Over time, GEDI's elevation detection performance exhibits greater stability, whereas canopy height accuracy decreases as the leaf on season transitions to the leaf off season. The proposed approach provides a rapid solution for preliminary evaluation (beam and orbit scale) and detailed assessment (footprint scale) of GEDI's geolocation errors, thereby laying the groundwork for its future applications.

Currently, scientifically and reasonably specifying carbon emission reduction measures in the context of "double carbon" has become a common concern worldwide. China's administrative divisions have a notable impact on the formulation and implementation of relevant policies. Therefore the carbon emissions must be calculated accurately under China's administrative divisions at different scales. The spatiotemporal change characteristics of absorption and carbon emissions can provide scientific basis for the formulation of reasonable and differentiated carbon emission reduction policies in different administrative regions in China. To this end, this study used multi-source data such as remote sensing and statistics and integrated ecological models, statistics, and GIS space analysis and other methods to analyze the spatiotemporal dynamic change characteristics of carbon emissions and carbon absorption at different administrative scales （provinces, cities, and counties） in China. The results showed that： ① The total carbon absorption of vegetation in China continued to increase from 2000 to 2021 and the average value gradually increased. Differences were observed in spatiotemporal changes in carbon emissions at different administrative scales. The spatiotemporal changes at smaller scales were more evident. Carbon emissions showed obvious spatial differences of "high in the north and low in the south, high in the east and low in the west." ② The spatiotemporal distribution of CPI at the administrative scale was similar to that of carbon emissions and the overall trend was increasing annually. The pressure of carbon emissions on carbon absorption gradually weakened from the east to the central and western regions. ③ Spatiotemporal hotspot analysis showed that the overall spatial distribution of cold and hot spots in China's carbon absorption was as follows： In the spatial pattern of "hot in the east and cold in the west," the spatial distribution of cold and hot spots of carbon emissions showed agglomeration characteristics. The provincial scale was primarily oscillating hotspot whereas municipal and county scales were majorly continuous hot spots. Further results revealed that： ① Carbon absorption in different regions and periods in China showed significant variability, especially in the central and eastern regions. The possibility of offsetting carbon emissions by increasing carbon absorption remains. ② At the same scale, administrative regions （such as different provinces） and lower-level administrative regions at another scale （such as different cities in the same province） showed varying degrees of variability in carbon absorption and carbon emissions. Therefore, taking provincial administrative regions as an example for subsequent formulation considering carbon trading, emission reduction, and other policies, we should first consider the coordination of emissions between different cities in the province and then consider the coordination between provinces, which is expected to better promote the implementation of relevant policies.

A new digital elevation model (DEM) upscaling method based on high accuracy surface modeling (HASM) is proposed by combining the elevation information of DEM and the valley lines extracted from DEM with different flow accumulation thresholds. The proposed method has several advantages over traditional DEM upscaling methods. First, the HASM ensures the smoothness of the upscaled DEM. Secondly, several DEMs with different topographic details can be obtained using the same DEM grid size by incorporating the valley lines with different flow accumulation thresholds. The Jiuyuangou watershed in China’s Loess Plateau was used as a case study. A DEM with a grid size of 5 m obtained from the local surveying and mapping department was used to verify the proposed DEM upscaling method. We established the surface complexity index to describe the complexity of the topographic surface and quantified the differences in the topographic features obtained from different upscaling results. The results show that topography becomes more generalized as grid size and flow accumulation threshold increase. At a large DEM grid size, an increase in the flow accumulation threshold increases the difference in elevation values in different grids, increasing the surface complexity index. This study provides a new DEM upscaling method suitable for quantifying topography.

The extraction of urban road features provides indispensable support to numerous high-accurate applications such as autonomous driving and urban high-definition mapping. However, approaches mainly focus on road connectivity, while often overlooking finer details of urban road constituent structures. Data that captures road details, such as LiDAR, may not be always readily available. This article proposes an operational framework for mapping fine-grained urban road features by integrating open-source data (OSD). The geometric measurement method is successively presented using projective geometry and prior knowledge for urban road sections. And a feature generation strategy is introduced to achieve and express the fine-grained road features. Compared with the corresponding large-scale topographic map (LTM) and available optical remote sensing image (AORSI), the proposed method regenerated fine-grained features of urban roads with m-level. It provides a cost-effective innovative and alternative method to acquiring fine-grained road datasets in other data-scarce regions.

Ergodic reasoning is a concept that has been widely applied, but not thoroughly tested, in some fields of geomorphology. This study aims to test whether ergodic reasoning is valid in reconstructing the evolution of a special type of gully called spoon-shaped gully (SG) in China's Loess Plateau. We use unmanned aerial vehiclebased digital elevation model data to analyze the morphometry of a sequence of SGs ordered in terms of increasing gully length. The morphological model of the SG evolution that we can propose from this analysis is similar to the established models in the literature. Therefore, time can be substituted by space when reconstructing the evolution of SGs in the Loess Plateau. By extracting morphometric information from the application of the ergodic reasoning model to our data, we identify a series of morphological patterns as SG evolves in the Loess Plateau. We also observed through detailed field studies that SG formation is strongly associated with loess piping and tunnel erosion. SG can be considered a special initial form of a hillside gully that is widely distributed in the collapsible sandy loess area. This type of hillside gully is dominated by piping and tunnel, surface fluvial, and gravity erosion.

With the rising popularity of portable mobile positioning equipment, the volume of mobile trajectory data is increasing. Therefore, trajectory data compression has become an important basis for trajectory data processing, analysis, and mining. According to the literature, it is difficult with trajectory compression methods to balance compression accuracy and efficiency. Among these methods, the one based on spatiotemporal characteristics has low compression accuracy due to its failure to consider the relationship with the road network, while the one based on map matching has low compression efficiency because of the low efficiency of the original method. Therefore, this paper proposes a trajectory segmentation and ranking compression (TSRC) method based on the road network to improve trajectory compression precision and efficiency. The TSRC method first extracts feature points of a trajectory based on road network structural characteristics, splits the trajectory at the feature points, ranks the trajectory points of segmented sub-trajectories based on a binary line generalization (BLG) tree, and finally merges queuing feature points and sub-trajectory points and compresses trajectories. The TSRC method is verified on two taxi trajectory datasets with different levels of sampling frequency. Compared with the classic spatiotemporal compression method, the TSRC method has higher accuracy under different compression degrees and higher overall efficiency. Moreover, when the two methods are combined with the map-matching method, the TSRC method not only has higher accuracy but also can improve the efficiency of map matching.

The automatic extraction of gullies from digital elevation models(DEMs)has great application value in GIS and hydrology.Many types of algorithms have been developed to address this problem,and the well-known D8(Deterministic eight-node)algorithm has been widely applied and implemented in some commercial GIS software such as ArcGIS.However,a key parameter called flow accumulation threshold(FAT)must be determined in this process.Numerous studies focus on how to determine an optimal value for this parameter but ignore that the optimal threshold varies for different gullies,so the universality of a different optimal threshold parameter determined by different methods is poor.To address this problem,this study designs a parameter called surface concavity index(SC-index)that can describe the shape of gullies from the perspective of surface morphology.Based on this index,the positions of different gullies'heads are identified,and then the flow accumulation matrix calculated by the D8 algorithm is used as auxiliary data to extract the gully network in the research area.In this study,six small watersheds in the Loess Plateau in northern Shaanxi,China,were used as test areas to verify the validity of the proposed method in areas with various landform types.Experimental results show that gully heads in different test areas can be effectively identified by setting different SC-index thresholds that are related to the types of terrain in the test areas.Then,the entire gully network can be extracted in watersheds with the help of a D8 algorithm.The accuracy of the gully network extracted by the new method is better than the contrast method in all test areas.In test areas with a large area of flat land(e.g.,Chunhua),the difference between the total length of gullies extracted by the new method and the reference value is-2.77 km,while the corresponding value of the contrast method is 14.50 km.In test areas with large numbers of short gullies(e.g.,Jiuyuangou),the difference between the total length of gullies extracted by the new method and the reference value is-2.61 km while the corresponding value of the contrast method is-27.9 km.It is pointed out that the new method can not only avoid the extraction of pseudo gullies,but also extract short gullies effectively.Further experimental analysis shows that the dependence of the new method on DEM resolution is significantly weaker than that of the contrast method.Taking Jiuyuangou test area as an example,when the DEM cell size increases from 5 m to 30 m,the total length of gullies extracted by the new method changes only about 1 km,while the corresponding value of the contrast method exceeds 20 km.

Roads are a type of typical artificial terrain, and are key components of urban terrain. Road networks formed by connections between different roads not only form the skeleton of urban terrain, but also plays an important role in transmitting energy and matter on the urban surface. Therefore, how to consider characteristics when constructing the digital road elevation model (DEM) has become an important research topic in the field of geographic information and mapping. Using high-definition unmanned aerial vehicle (UAV) images as the basic data source, this study proposes a new method for constructing the road DEM by analyzing semantic features such as road shape and function. This method first takes the sideline and centerline of the road as the macroscopic undulation morphological constraints. It uses the shape control equation of the local domain to constrain the morphological change characteristics of the road surface in the transverse and longitudinal directions, in order to construct the road DEM with high fidelity to the surface shape characteristics. Then, in terms of the water catchment function of the road surface, a road DEM correction method considering surface flow direction characteristics is designed to ensure that the water catchment path of the road surface conforms to the actual situation. For this paper, several typical roads in Chuzhou University in Anhui Province, China, were selected as the experimental objects to carry out a DEM construction experiment. The results indicate the following: (1) compared with the traditional construction method, the DEM shape of the road constructed by this research method is more consistent with the actual road shape, and the smoothness of the road surface is better; (2) due to the high density and high elevation accuracy of the point cloud used in modeling, the elevation adjustment strategy of the sideline and centerline of the road implemented in this study does not reduce elevation accuracy, indicating that an adjustment to the elevation information is necessary for constructing the DEM of special artificial terrain; and (3) the DEM correction method proposed in this paper to find the correct catchment path can ensure that the processed DEM can accurately simulate the surface catchment process, and the correction of the elevation of the road DEM is also controlled within a small range without affecting the elevation accuracy of the regional DEM. This study has reference value for implementing projects such as urban terrain expression in the construction of 3D China.

利用数理统计及空间分析方法,对皖西大别山传统村落空间分布及历史演变过程进行研究,并探讨其影响因素,以期为美丽乡村建设和传统村落可持续发展提供支撑.研究表明:(1)皖西大别山区传统村落整体呈聚集型分布,各区县分布不均衡;从宋前时期到抗战后期逐渐形成了以岳西县与潜山市交界处为中心的主核心区和以金寨县为中心的次级核心区.(2)主核心区传统村落在不同历史阶段呈现"东北—西南"的空间聚集方向;次级核心区聚集方向则为"西北—东南".(3)传统村落的选址前期以坡度平缓的平原和丘陵为主,抗战时期偏向丘陵和山区,坡向则保持着阳坡为主,同时具有近水特征.(4)交通的便利性在开始阶段促进了村落的形成和发展,在后期又加速了村落的消亡;而较低的人口密度、经济和城市化水平在一定程度上阻碍了村落的消亡.

In 2002 and 2020–2022, KH-9 HEXAGON mapping camera system (MCS) and panoramic camera system (PCS) images were made available to the public, respectively. Although great efforts have been made by the scientific community to develop applications that utilize KH-9 HEXAGON images, little attention has been paid to de-noising and contrast enhancement of these images particularly over urban landscapes. This paper focuses on developing a de-noising and contrast enhancement pipeline for KH-9 HEXAGON MCS and PCS over urban regions. The proposed approach employs first a wavelet transform trained using a suite of ‘degree of over-smoothing’ metrics (DOSM) for image de-noising. These metrics are sensitive to structure, texture, edges and local homogeneity of image objects. Then the de-noised image is subjected to the multi-resolution Top-hat to optimize the contrast. This method incorporates a range of shapes and neighborhoods at multiple scales. The method was applied to a KH-9 HEXAGON MCS image (acquired in 1975) and PCS image (acquired in 1974) representing a complex urban landscape, to support comprehensive evaluation under a range of settings. Performance was assessed against three state-of-the-art benchmark approaches: residual learning (deep learning), blind deconvolution and spatial filtering. To evaluate the performance of the proposed pipeline against the benchmarks, we employed the saturation image edge difference standard-deviation, co-occurrence metrics and the semivariogram. Additionally, the potential applications of pre-processed results were demonstrated using change detection, identification reference points and stereo images. The proposed method not only improved the quality of the KH-9 image across the different urban landscape types, but also preserved the original spatial characteristics of the image in comparison with the benchmark methods. At a time when understanding the nature of our changing planet is paramount, the proposed pipeline should be of great benefit to investigators wishing to use KH program images to extend their historical or time-series analyses further back in time.

A peak is an important topographic feature crucial in quantitative geomorphic feature analysis,digital geomorphological mapping,and other fields.Most peak extraction methods are based on the maximum elevation in a local area but ignore the morphological characteristics of the peak area.This paper proposes three indices based on the morphological characteristics of peaks and their spatial relationship with ridge lines:convexity mean index(CM-index),convexity standard deviation(CSD-index),and convexity imbalance index(CIB-index).We develop computation methods to extract peaks from digital elevation model(DEM).Subsequently,the initial peaks extracted by neighborhood statistics are classified using the proposed indices.The method is evaluated in the Qinghai Tibet Plateau and the Loess Plateau in China.An ASTER Global DEM(ASTGTM2 DEM)with a grid size of 30 m is chosen to assess the suitability of the proposed mountain peak extraction and classification method in different geomorphic regions.DEM data with grid sizes of 30 m and 5 m are used for the Loess Plateau.The mountain peak extraction and classification results obtained from the different resolution DEM are compared.The experimental results show that:(1)The CM-index and the CSD-index accurately reflect the concave or convex morphology of the surface and can be used as supplements to existing surface morphological indices.(2)The three indices can identify pseudo mountain peaks and classify the remaining peaks into single ridge peak(SR-Peak)and multiple ridge intersection peak(MRI-Peak).The visual inspection results show that the classification accuracy in the different study areas exceeds 75％.(3)The number of peaks is significantly higher for the 5 m DEM than for the 30 m DEM because more peaks can be detected at a finer resolution.

Lane-level road maps are crucial for urban traffic management, autonomous driving, and vehicle navigations. Optical remote sensing image suffers from trees and buildings occlusion for lane-level road mapping due to the top-down view. While street view images (SVIs) have been used for road detection, however, most of the previous articles focused on extracting road in image space. The reconstruction of lane-level road maps with measurability in geographic space remains challenging. Hence, this article proposed an operational framework for extracting and reconstructing lane-level road maps from urban open access data. First, a sample strategy was used to collect SVIs based on OpenStreetMap (OSM) road central lines. Then, a deep-learning-based method was adopted to identify lanes accurately, and road width was extracted based on design knowledge and OSM information. Finally, the lane-level road map was reconstructed by integrating the lane and its width information. The proposed framework achieves the transformation from image space to geographic space. The case study shows that 82.43% of the roadway is accurately reconstructed in lane-level. The difference between the reconstructed width of the roadway and the reference true value is within the m-level and the RMSE is 0.32 m. The proposed method is cost-effective and accurate-acceptable for acquiring lane-level road datasets in cities.

Extracting a channel network based on the Digital Elevation Model (DEM) is one of the key research topics in digital terrain analysis. However, when the channel area is wide and flat, it is easy to form parallel channels, which seriously affect the accuracy of channel network extraction. To solve this problem, this study proposes a method to identify and eliminate parallel channels extracted by classical methods. First, the channel level in the study area is marked based on the flow accumulation data, and the parallel channels are then identified using the positional relationship between the different channel levels. Finally, the modification point of the identified parallel channels is determined to eliminate the parallel channels, with the help of the change relationship between the parallel channel and its upper-level channel. In this study, two watersheds in southeast China are selected as examples for method verification and analysis. Experimental results show that the parallel channel identification method proposed in this paper can accurately identify all parallel channels and eliminate the identified parallel channels one by one. The location relationship of the modified channels is consistent with the actual situation, indicating that the proposed method has good application potential in DEM-based channel extraction networks.

为支撑我国地理信息产业迅速扩张和顺应新技术发展趋势,解决满足行业需求的地理信息科学人才紧缺的问题,本文针对当前应用型人才培养模式存在的主要问题,结合"双万计划"专业建设要求和滁州学院地理信息科学专业的办学实际,从课程体系改革、实践教学体系创新、质量标准体系建设等方面入手,探索构建产教协同地理信息科学一流应用型专业人才培养模式,并从与企业深入交流、校企合作人才培养、产教协同实践教学平台建设等方面开展实践,阐述了近5年人才培养实践效果,为我国GIS专业一流应用型人才培养提供了有效参考.

当前数字高程模型(DEM)无法有效表达突变地形真实地表形态,严重制约了突变地形DEM在这些区域的应用.本文选取南京市某一区域为研究区,对特征线分别采用建模可用高程点加密法和平行特征线法,在特征线处理参与的情况下,建立分辨率为1 m规则格网DEM进行对比分析,验证不同方法在突变地形处DEM构建效果.实验结果表明,本研究两种思路构建的DEM与传统DEM相比,无论是高程精度还是形态精度都具有明显的优势.在突变地形两侧高程信息突变不明显的情况下,传统构建法和建模可用高程点加密法平均误差相接近,平行特征线法平均误差仅0.42 m;地形复杂的区域,传统构建法和建模可用高程点加密法平均误差均超过1 m,平行特征线法平均误差仅0.94 m,高程精度验证结果理想.研究表明,无论是地形相对简单的区域还是地形复杂的区域,平行特征线法构建突变地形效果更优.

Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed. Based on MODIS phenology, meteorology, land cover and other data from 2001 to 2019, this paper analyzes the phenology change characteristics of the Yangtze River Delta from three dimensions: time, plane space and elevation. Then, the spatiotemporal heterogeneity of phenology and its driving factors are explored with random forest and geographic detector methods. The results show that (1) the advance of start of season (SOS) is insignificant—with 0.17 days per year; the end of season (EOS) shows a significant delay—0.48 days per year. The preseason temperature has a greater contribution to SOS, while preseason precipitation is main factor in determining EOS. (2) Spatial differences of the phenological index do not strictly obey the change rules of latitude at a provincial scale. The SOS of Jiangsu and Anhui is earlier than that of Zhejiang and Shanghai, and EOS shows an obvious double-clustering phenomenon. In addition, a divergent response of EOS with elevation grades is found; the most significant changes are observed at grades below 100 m. (3) Land cover (LC) type is a major factor of the spatial heterogeneity of phenology, and its change may also be one of the insignificant factors driving the interannual change of phenology. Furthermore, nighttime land surface temperature (NLST) has a relatively larger contribution to the spatial heterogeneity in non-core urban areas, but population density (PD) contributes little. These findings could provide a new perspective on phenology and its complex interactions between natural or anthropogenic factors.

借助GIS的空间分析方法,研究了我国三甲医院空间分布特征及其可达性(港澳台未统计),同时构建了省级行政区相互支持潜力模型,得到全国三甲医院空间分布图和省级行政区相互支持潜力等级图.分析结果表明:(1)2020年全国共有三甲医院1 620家,三甲医院空间分布总体呈现出以东部最为密集、中部次之、西部最为稀疏的格局;(2)全国三甲医院医疗服务范围大体上受医院空间分布影响,医疗服务覆盖率最好的地区为上海市,北京、天津、山东、安徽、江苏、辽宁、浙江和广东等省市覆盖率较好,其余地区覆盖率较低;(3)各省级行政区受自身居民需求影响,全国不同地区三甲医院相互支持潜力等级以中低级为主,支持潜力等级最高的地区为安徽省.

本文针对应用型本科院校地理信息科学专业《空间分析原理与方法》课程建设中存在的课程学习和思政育人脱离、课程知识和工程应用脱节、课堂教学和课外拓展脱钩等现象进行分析,在此基础上探讨课程改革内容及相应措施,阐述课程改革执行情况和效果.实践证明:课程改革在很大程度上提高了学生的学习积极性及教师的教学质量,学生专业技能和综合素质有较大的提升,契合了知识、技能、素质三位一体应用型人才培养目标,有望为地理信息科学应用型人才培养和专业发展提供支撑.