This PDF file contains the front matter associated with SPIE Proceedings Volume 12988, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Taking Guangdong as the research area, using 2022 Landsat remote sensing data and based on geographic information system (GIS) technology, five evaluation factors were selected: slope, aspect, elevation, water system and vegetation index, analyze the sensitivity of Guangdong’s ecological environment. The results show: Single-factor ecological sensitivity analysis shows that among the five evaluation factors, land use category and elevation are important factors that affect the degree of ecological vulnerability in Guangdong Province. The weights are: land use type (0.32), elevation (0.104). Comprehensive ecological sensitivity analysis shows that Guangdong has obvious differences in ecological sensitivity, with low-sensitivity areas accounting for the largest proportion, followed by sensitive areas. The extremely sensitive areas are mainly distributed in Heyuan and Meizhou; the sensitive areas are mainly distributed in Zhaoqing, Qingyuan and Shaoguan; the low-sensitive areas are mainly distributed in Zhanjiang, Maoming, Yangjiang, Jiangmen and Shanwei; the insensitive areas are mainly distributed in central Guangdong (Guangzhou, Foshan, Zhongshan, Dongguan, Shenzhen) and the eastern region (Chaozhou and Jieyang). The comprehensive evaluation results of the ecological environment sensitivity in Guangdong Province can provide relevant suggestions for regional ecological environment protection and construction.

The physical structure of rivers and lakes is an important component of the indicator system for river and lake health assessment. This study focuses on the Yellow River and Nansi Lake basins, carries out the interpretation of physical form remote sensing images, and evaluates and analyzes their physical form characteristics. The results show that: (1) The expansion of urban construction and water conservancy planning and construction in the water systems of the Yellow River and Nansi Lake basins have seriously affected the connectivity of the rivers. (2) The vegetation coverage around the Yellow River and Nansi Lake basins is relatively low. More than half of the rivers and lakes (reservoirs) have vegetation coverage less than 5%, and most of them are vegetation-free rivers and lakes (reservoirs). (3) The average value of the ecological buffer zone index in the Nansi Lake Basin is greater than that in the Yellow River Basin. The main reasons for the decline in the ecological buffer index are the large width of the water level fluctuation zone, the small width of the land buffer zone, and insufficient delineation and protection of the ecological buffer zone. (4) River coastlines are dominated by natural coastlines, and the proportion of natural coastlines on both sides of most rivers is greater than 80%. The proportion of natural coastlines in the Yellow River Basin is lower than that in the Nansi Lake Basin. Coastline protection and utilization are important for ecological protection and protection in the Yellow River Basin.

In this research, a vector road data matching approach exclusively targeted at road data is proposed to suit the needs of quick and real-time updates of geographic databases. The location and kind of alterations to the road construction have to be precisely identified using this method. We suggest a technique for feature matching of vector road data from several eras under the same scale and geographic coordinate system by using the properties of road alignments. This helps us track changes in road development by allowing us to recognize how different road sections vary over time. Moreover, it forms the foundation for gradual updates of spatial data.

This study attempts to use the spatial distribution information provided by SMAP remote sensing soil moisture data to estimate the water storage capacity curve parameters in the Hymod model, and study the impact of this method on the simulation effect of the hydrological model. Then, based on the calculated soil water storage capacity, the empirical distribution curve of soil water storage capacity can be obtained. After testing the goodness of fit of Pareto distribution and Lognormal distribution, this study proposes to use Lognormal distribution to describe the space of water storage capacity. distributed. Finally, the Hymod model with different water storage capacity curves was used to conduct hydrological simulations in the four study basins, and the simulation results of the models were compared and analyzed. Judging from the runoff simulation effect of the model on the four study watersheds, the Hymod model using Lognormal distribution is better than the results using Pareto distribution in both the rate period and the inspection period, with the improvement effect in the Zishui watershed being the most obvious.

The wetland is one of the most important ecosystems in the world, and monitoring its evolution is of great significance to protect wetland biodiversity and maintain ecosystem stability. This study aims to address the problem of low accuracy in salt marsh vegetation monitoring due to the difficulty of obtaining measured data in complex wetland areas. The Yellow River Delta serves as the test area in this study. The method for discriminating samples of dual-polarization time-series datasets is constructed by combining the time-weighted dynamic temporal warping algorithm and the DBSCAN clustering algorithm, using the intensity information provided by time-series Sentinel-1A images. On this basis, the support vector machine classifier is used to classify different polarized temporal datasets. Research shows that the sample discrimination method based on this article can extract wetland feature information. Among the classification results, the VV+VH polarization dataset exhibits the highest accuracy, with overall accuracy and Kappa coefficient of 88.734% and 0.857, respectively. These values are 2.43% and 0.025 higher than the classification accuracy of the VH polarization time series dataset relying only on visual discrimination samples. The research results in this paper provide a basis for decision-making on the restoration and protection of wetlands in the Yellow River Delta.

Realistic 3D model is a data set that is consistent with human visual perception and reflects the spatial relationship and attribute information of terrain and ground objects in a three-dimensional way. Its mathematical model has the function of measurement, which is conducive to the convenient and rapid extraction and processing of various terrain and geographic elements. This paper studies the technology and method of rapid extraction of elements and index parameters based on Realistic 3D model, which provides support for large-scale production of Realistic 3D China.

Water body detection plays an important role in the field of ecological research, but efficient, accurate and automatic long-time water body detection methods still need to be explored. Therefore, this paper proposes a water body detection method based on Google earth engine, which is based on the automatic threshold recognition principle of the OTSU method, and conducts automatic water detection of a total of 135 Sentinel-1 images of South Dongting Lake from 2018 to 2022. The study shows that the overall classification accuracy of the method is 92.61%, and the Kappa coefficient is 0.86, and the results reflect the impact of extreme climate change on the seasonal water distribution of South Dongting Lake.

This article takes Shaoguan City, a resource-exhausted city, as the research object. Using multi temporal remote sensing images, statistical yearbooks, and other data, based on the analysis of indicators such as biological abundance index, vegetation coverage index, water network density index, land stress index, and pollution load index, the ecological environment status index is used for comprehensive evaluation of the ecological environment status. Through research, the following conclusions can be drawn: (1) From 2000 to 2020, the ecological environment quality of various districts and counties in Shaoguan City generally decreased first and then increased. The ecological environment quality of Zhenjiang District, Qujiang District, Wengyuan County, and Nanxiong City is "good", while the ecological environment quality of Shixing County and Xinfeng County is "excellent", while the ecological environment quality of Lechang City, Ruyuan Yao Autonomous County, and Renhua County first decreases and then increases. (2) From 2000 to 2020, the changes in various ecological environment indicators in Shaoguan City were divided into two stages. From 2000 to 2010, with the gradual depletion of mineral resource development, the biological abundance, vegetation coverage, water network density, and land stress index of most districts and counties gradually decreased, while the pollution load index gradually increased, and the ecological environment condition continued to deteriorate. From 2010 to 2020, with the country's support for resource depleted cities and the success of industrial transformation and upgrading, the biological abundance, vegetation coverage, and water network density index gradually increased, while the land stress index and pollution load index decreased, and the ecological environment quality significantly improved. (3) To continue improving the ecological environment quality of Shaoguan City, it is necessary to increase the efforts of industrial transformation and upgrading, vigorously develop high-tech industries, characteristic agriculture, and modern service industries, and create a demonstration city for rural revitalization in Guangdong Province.

With China’s development into the world’s second-largest economy, power supply shortages have become a difficult problem during its economic growth. Pumped storage power stations can solve the power supply shortage through peak regulation and valley filling of the power grid. In recent years, pumped storage power stations have become a key construction area in China. External deformation monitoring of the dam of pumped storage power stations is gradually becoming an important means of ensuring the normal operation of power stations. This paper develops a method for monitoring the external deformation of a dam based on GNSS technology, which ensures the normal operation of a pumped storage power station through data processing, analysis, and warning. The effectiveness of the method is demonstrated through an example of the Tianchi pumped storage power station in Henan province.

Climate change has a particularly significant impact on agricultural ecosystems. In order to ensure food security, it is necessary to master the change law. Remote sensing can be used to quickly and efficiently obtain the spatial-temporal pattern characteristics of planting systems in a large region, providing an important basis for evaluating regional food security. Firstly, the Savitzky-Golay filtering method was used to smoothly reconstruct the GLASS (Global LAnd Surface Satellite) LAI (Leaf Area Index) data from 2011 to 2020. Secondly, the cultivated land in the North China Plain was extracted with the dynamic threshold method, and the cultivated land maturity system was extracted with the quadratic difference method, and the annual multiple cropping index was calculated. Finally, the changes of the reforestation index in the North China Plain were analyzed based on factors such as climate and elevation. The analysis results showed that the cultivated land area in the North China Plain was slowly decreasing, and the area of two-crop cultivated land was much larger than that of single-crop cultivated land. The recultivation index showed an overall upward trend except for a significant decrease in 2013 and 2018, and the center of gravity of single-crop cultivated land and two-crop cultivated land showed a northward trajectory.

The Open Data Cube (ODC) is an open-source platform for managing, analyzing, and visualizing Earth observation data, especially satellite Remote Sensing data. We proposed the Data Hub system because a single Data Cube cannot undertake the analysis and research works across various remote sensing domains. The Data Hub is an integrated system built upon multiple Data Cubes. This system is specifically designed to manage multiple Data Cubes and perform tasks such as data management, indexing, and processing of remote sensing data. This paper mainly focuses on the strategy of Data Hub communication with multiple Data Cubes. Communication includes two parts: KeepALive (heartbeat) transfer and metadata synchronization. In order to minimize the network workload on the Data Hub system and identify the optimal communication strategy, we conducted two experiments: experiments on KeepALive frequency and experiments on metadata synchronization strategies. We designed comparative experiments to simulate the computational and metadata synchronization tasks between the Data Hub server and multiple Data Cube servers. Based on the experimental results, we identified the optimal frequency for sending KeepALive signals and gained insights into the impact of network conditions on metadata synchronization strategies (Pull strategy, Push strategy, and Pull-Push hybrid strategy). Finally, we determined the sending frequency of KeepALive packets to be once every three hours and selected the pull-push hybrid strategy as the transfer strategy for the metadata synchronization module.

In the study of changes in vegetation in Baoji City, Shaanxi Province, the new-generation computer, within the research span between 2005 and 2019, applies MODIS remote sensing image data is processed and analyzed with the technical support of next-generation computers for processing and analyzing, calculates the surface vegetation index, dissects changes and characteristics of vegetation in Baoji City, extracts the change distribution types, and outputs a complete thematic remote sensing image map. Fast response to types of changes in the vegetation area and the distribution of vegetation changes and characteristics, which can quickly visually reflect and present the true ecological status of the vegetation in the surface area of Baoji City, thus satisfying the ecological environment monitoring of Baoji City for surface ecology and laying the foundation for the construction and development of Baoji City's surface ecological monitoring network in recent years. Research outcomes proved a trend of substantial improvement in surface vegetation coverage in Baoji City in 2019 compared with that in 2005. Very few cities and regions registered large areas of vegetation degradation. In January 2019, compared with January 2005, the vegetation in more than half of the area showed obvious improvements in moderate degradation; Compared with April 2005, in April 2019, due to the construction of urban infrastructure or other public facilities, 4.97% of the vegetation coverage in some areas showed moderate degradation; Compared with July 2005, in July 2019, the moderately degraded vegetation coverage in nearly half of the areas remained basically unchanged, and 7.09% of the regional degraded coverage area had significantly improved vegetation coverage; Compared with October 2005, in October 2019, the area of some areas where vegetation degradation coverage remained basically unchanged accounted for 38.04%. Although the area of some areas where vegetation coverage showed moderate degradation and improvement accounted for 47.60%, it was still only 3.66% of the area. Area vegetation coverage showed significant improvement. Accelerated industrial and urbanization construction in Baoji City, the southern part of Fengxiang County in Anhui Province, the central part of Baoji County, the central part of Qishan County, most of Fufeng County and the northern part of Emei County in Sichuan came with striking vegetation degradation. Other counties showed no obvious improvement in vegetation coverage at all.

This paper addresses the problems of traditional surface current inversion methods, including low model accuracy and insufficient spatial and temporal resolution of the corresponding data, especially the challenges in processing remote sensing image data bearing cloud cover and performing large-scale time series inversion. In this study, several machine learning methods, including Random Forest, LightGBM, and Deep Neural Network, based on Himawari-8 satellite data and AVIS measurements, are employed to realize surface currents' inversion in the South China Sea. The experimental results fully demonstrate that using Himawari-8 full-band feature data for the inversion of sea surface currents even under cloud coverage is feasible. Moreover, the accuracy of the LightGBM model is better than other algorithms, with a correlation coefficient of 0.8856 and a root-mean-square error (RMSE) of 6.15 cm/s. These conclusions clearly show that the LightGBM algorithm is able to overcome the limitations of the traditional methods and realize the inversion of surface currents for the whole region.

This paper introduces an innovative asymmetric hurricane intensity retrieval method based on data from the Cyclone Global Navigation Satellite System (CYGNSS). The proposed method demonstrates an advanced approach for characterizing the structural complexities of hurricanes, thereby providing essential support for hurricane monitoring and forecasting. The process begins by leveraging distinctive features within CYGNSS observational data to ascertain the hurricane's maximum wind speed direction and asymmetry. Subsequently, the method employs a central equidistant weighting technique in conjunction with weighted least squares calculations on observational data. This leads to a precise inversion of both the hurricane's maximum wind speed and the maximum wind speed radius across varying wind speed conditions, significantly enhancing the accuracy of the inversion results. In the final phase, this research conducts an in-depth study on eleven hurricanes occurring within the period spanning from 2018 to 2021. A comprehensive analysis of a dataset encompassing forty distinct hurricane events is carried out, and these findings are systematically compared against the International Best Track Archive for Climate Stewardship. The assessment criterion, based on the root mean square error, highlights a notable enhancement in both the fitting accuracy of maximum wind speeds and the precision of maximum wind speed radius inversion. This method, thus, emerges as a pivotal advancement in hurricane science and adds to the nuanced understanding of hurricane structures while contributing valuable insights to the field of hurricane monitoring and prediction.

As an important tropical fruit, the yield and quality of dragon fruit play a key role in stabilizing the fruit market. Remote sensing technology can quickly and efficiently extract dragon fruit information over a wide area, providing important data support for orchard management, disease prevention and control, and ecological environment monitoring, and thus contributing to the sustainable development of agriculture. However, the small spectral difference between dragon fruit and other fruit trees on remote sensing images makes the extraction of dragon fruit a challenge. In this study, feature combination and the Swin Transformer deep learning model have been selected as techniques for extracting dragon fruit of the study area. The meticulous band selection is carried out for the original Sentinel-2A multispectral image, and a feature combination scheme is generated after adding the vegetation indices NDVI, EVI, RVI and DVI, which is a dataset including 14 features for the Swin Transformer model to extract dragon fruit. Based on the semantic segmentation of the Swin Transformer model, the dragon fruit is extracted and its effect is compared with other classical deep learning models FCN, Unet and DeepLabV3. The results show that the Swin Transformer model obtains the best extraction accuracy for dragon fruit compared with the compared models.

Change detection of dual-temporal remote sensing images is widely employed in natural disaster monitoring and land resource planning. Recently, most change detection methods aim at combining the complementary features at different scales to enhance the detection accuracy. However, it is challenging to obtain the complete change regions due to the general differences between semantic concept information and location representation in different feature layers. In this letter, we propose a multi-level attention-based network for change detection of high-resolution optical satellite imagery, namely MLANet. It combines Siamese network in the encoding structure and integrates multi-level attention mechanism (MLAM) in the decoding structure to improve the UNet++. MLANet focuses on strengthening the natural aggregation and spatial matching in multi-layer feature information fusion, and utilizes MLAM to enhance the effective change features and alleviate the impact of interference factors to refine the feature mapping information. Experimental results on two representative change detection datasets demonstrate that the proposed method outperforms most of the state-of-the-art change detection methods.

The acquisition of spatial distribution information on active offshore oil/gas platforms (AOOP) is of importance for effective ocean management and the comprehensive study on marine carbon emissions. In recent years, the methodological framework for AOOP extraction using low-light remote sensing imagery has emerged as a novel and promising approach. Consequently, in this study, we utilized high-resolution low-light images obtained from the Glimmer Imager (GLI) payload aboard the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) to facilitate the automatic extraction of AOOP. We applied a time-series low-light remote sensing (TLLRS) method for automatic extraction of AOOP in the northern Gulf of Mexico for the year 2022. The results indicate that the TLLRS method achieves a remarkable precision rate of 95.31% in the automatic extraction of AOOP, with an overall accuracy of 84.92%. Our findings demonstrate the feasibility and efficacy of applying SDGSAT-1 GLI data to monitor AOOP at large-scale.

This paper takes 155 tropical marine cultural landscapes in Hainan as the research subject. It employs methods such as Average Nearest Neighbor Index, Kernel Density Estimation, and Geographical Detectors, and utilizes the ArcGIS 10.8 software to analyze the spatial distribution characteristics and development influencing factors of Hainan's tropical marine cultural landscape. The results show that: 1. The tropical marine cultural landscapes in Hainan exhibit a clustered spatial distribution within the province. 2. The tropical marine cultural landscapes are relatively concentrated at the city and county scale, revealing an imbalanced distribution across various cities and counties in Hainan. 3. According to the Kernel Density Estimation results, the spatial distribution of Hainan's tropical marine cultural landscape is summarized as having Haikou and Sanya as the "dual cores," with Lingshui, Qionghai, Wanning, Wenchang, Danzhou, and Dongfang forming the "Eastern and Western belts," thereby exhibiting a "two cores and two belts" spatial characteristic. 4. The main factors influencing the development of the tropical marine cultural landscape are the density of graded roads, total tourism revenue, the GDP of the tertiary industry, and the number of tourist arrivals. Enhancing the accessibility and convenience of transportation, and the high-quality development of the tourism industry, can effectively promote the rapid development of the tropical marine cultural landscape.

In order to study the ecological sensitivity of the Jiulian Mountain scenic area and provide a basis for promoting the development and implementation of ecological planning in this region, we assessed the current state of the local environment. This article takes the Jiulian Mountain Scenic Spot in Hui County, Xinxiang City, Henan Province, as the research object and selects altitude, slope, aspect, water body, land use type, and vegetation coverage. 6 Each ecological factor is used as an ecological sensitivity evaluation index, which is divided into four evaluation levels: insensitive, mildly sensitive, moderately sensitive, and highly sensitive. The analytic hierarchy process is used to determine the weight, and the spatial distribution of single-factor and multi-factor ecological sensitivity is analyzed through GIS spatial analysis technology and multi-source DEM remote sensing data. The results show that the overall ecological sensitivity of the Jiulian Mountain scenic area is high. The highly sensitive area accounts for 28.52% of the total area; the medium sensitive area accounts for 26.25% of the total area; the low sensitivity area accounts for 21.51% of the total area, distributed in the northwest of the scenic spot; and the insensitive area accounts for 18.48% of the total area, evenly distributed in scenic spots. Finally, in light of the current conditions in the Jiulian Mountain scenic area, I propose targeted recommendations and strategies for the various sensitive regions. My goal is to advance ecological planning practices and sustainable development across the Jiulian Mountain region.

Under the background of rapid urbanization, the food security is threatened by the problem of democratization of cultivated land, and it is the basis for formulating related policies on cultivated land protection to accurately identify the degree and change law of cultivated land non-grain cultivation. In this paper, the long time series (1991-2021) multisource remote sensing data was used to obtain the typical temporal trajectory of cultivated land change. The continuous change detection and classification (CCDC) algorithm was used to identify the temporal trajectory breakpoints of each pixel, and the non-grain distribution and spatial-temporal evolution of regional cultivated land were obtained. The method was verified by taking Pujiang County of Chengdu as the research area. The results show that the democratization of cultivated land in Pujiang County mainly consists of forest and fruit land and water surface of pits and ponds, which presents a radiative development. From 2015 to 2018, the non-grain increased at the fastest rate, reaching a peak of 2662.11 hectares in 2017, and decreasing from 2019 to 2021, which is closely related to the stage economic and social conditions. This method can accurately identify the time nodes of cultivated land democratization, reduce the misjudgment caused by seasonal changes, and is suitable for large-scale and long time series change analysis, and can provide countermeasures and suggestions for curbing cultivated land democratization.

The absorption and release of carbon dioxide by different types of land cover, with activities like deforestation or urbanization releasing CO2, while afforestation or natural ecosystems act as carbon sinks, affecting the overall carbon balance in the atmosphere. This paper analyzes the spatial and temporal characteristics of land use and carbon emissions in Shanghai from 2000 to 2020 using the land use transfer matrix, the carbon emission estimation model, and the standardized error ellipse method. The results indicate that the total carbon emissions from land use in Shanghai have exhibited an upward trend from 2000 to 2020, with an average annual growth rate of 3.055%. The expansion of construction land has been identified as the main source of carbon emissions, while forests serve as the primary carbon sink. Spatial analysis reveals that areas with high-intensity carbon emissions are mainly concentrated in Pudong New Area, while regions with moderate carbon emissions are in Jiading and Minhang districts, gradually expanding towards the northeast. Based on these findings, it is recommended that carbon emission policies consider the characteristics of regional differences, control land use intensity appropriately, and guide low-carbon and efficient land utilization as the primary direction to achieve Shanghai's energy low-carbon transformation.

Water is crucial for all life on Earth and is essential for human survival and development. In nature, the Earth's hydrological cycle relies on the evaporation of both marine and terrestrial water as the only means of water entering the atmosphere. This is one of the primary links in the hydrological cycle. The study of evapotranspiration is becoming increasingly important in the management of water resources and environmental protection. The current research on the spatial and temporal characteristics of evapotranspiration in the Dawen River Basin is limited, lacking in-depth analysis and systematisation. This study aims to fill the research gap and provide a comprehensive understanding of evapotranspiration in the Dawen River Basin. This information will be useful in optimising water resource management. The study area's relevant information was obtained through first collecting GLDAS meteorological data and Landsat8 remote sensing image data. The SEBS model was used to estimate and analyze evapotranspiration next. According to the results, the evapotranspiration in the study area displays a seasonal pattern indicating a relative increase from winter to summer. Furthermore, various land cover areas present similar seasonal change characteristics, with a consistent overall pattern. However, the magnitude of seasonal changes in evapotranspiration varies significantly among different land cover types. Evapotranspiration changes across different land cover types are most striking during the summer, and the dynamics pattern can be classified as follows: Water > Cropland > Wasteland > Grassland > Forest > Shrub > Impervious.

In recent years, with the intensification of global climate change, the risk of urban waterlogging and regional floods in China has significantly increased. Shanghang County in Fujian Province is a typical small watershed city in the mountainous areas of western Fujian, with increasingly severe problems such as flood control and drainage, soil erosion prevention and control. This study is based on the needs of sponge city planning and development, selecting small watershed cities in typical mountainous areas of western Fujian as the research area. With the help of geographic information platform (GIS), a geographic database is established. SCS-CA hydrological model is used to quantitatively simulate the runoff generation and concentration process of watersheds around the city which would flow into the urban area under the scenario of extremely heavy rainstorm. The security pattern of flood disaster in the urban area was found out on the basis of comprehensive risk evaluation of flood disaster in Shanghang County. Furthermore, in the planning of sponge cities, the green and gray infrastructure in the urban area should be reasonably arranged, which can store and drain the simulated flood and rainfall amount, also Optimizing the urban landscape pattern and ensuring the ability of small watershed cities in mountainous areas to prevent and control flood disasters.

Spatial assessment of Nicotine of flue-cured tobacco leaves is essential for agricultural management. This study was conducted to quantity the spatial variability of nicotine of flue-cured tobacco leaves and soil fertility factors in tobacco planting fields in Central Henan province. Geostatistics method, with geographic information system (GIS),was applied to conducted this study. The Analysis of the isotropic variogram indicated that the contents of nicotine in flue-cured tobacco leaves in the study regions are well described with exponential model, which are mainly induced by structure factors. The semivariogram for soil O.M, available P and available k were best fitted by Spherical models, while soil pH was best fitted by Gaussian model, soil CEC was best fitted by Exponential model, soil available N was best fitted by Linear model. The nugget/ sill ratio indicates a strong spatial dependence for soil pH and CEC, moderate spatial dependence for available P and available k, weak spatial dependence for available N. Correlation matrix of soil nutrients and nicotine in tobacco leaves reveal that there are extremely significant positive correlation between nicotine in tobacco leaves and soil pH and organic matter. There are significant positive correlation between nicotine in tobacco leaves and soil available N. The maps generated by ordinary kriging also revealed the distribution of nicotine in tobacco leaves and soil fertility factors in the study regions, which provided the basis for precise fertilization.

Remote sensing technology enables the observation of terrestrial features on the Earth's surface at specific time intervals and over particular swaths, facilitating the analysis of land use/cover changes and landscape patterns in a study area over time, thereby enhancing planning and utilization. In this study, we calculated the area, dynamic degree, and land use transition matrix for different land use types in the Yunzhou District of Datong City, and discovered the following: the rate of change in the area of construction residential land and forest land in the Yunzhou District of Datong City is significant. The area of construction residential land has gradually increased, exhibiting a trend of continuous outward expansion, with all increases being a result of the conversion from cultivated land. The river surface of the Sanggan River is continually shrinking, with cultivated land year by year engulfing riparian wetlands. From the perspective of the landscape pattern index, the class area (CA) is highest for forest land, indicating that forest land has a higher degree of fragmentation. Special land uses (landscape, cultural and historical sites) are also more dispersed. The largest patch index (LPI) is highest for cultivated land, indicating that cultivated land is the dominant land use matrix type in the study area, with a high degree of dominance and concentrated distribution. In addition, the landscape shape index (LSI) is highest for forest land, making its shape and distribution the most complex.

The sensitivity assessment of ecological environment is an important foundation for analyzing the current situation of regional ecological environment and formulating relevant urban planning. This article is based on Hangzhou, an important node city in the Yangtze River Delta urban agglomeration, the capital of Zhejiang Province, and the nationally renowned tourist city. Five sensitive evaluation indicators are selected, including vegetation coverage, slope, water environment, nighttime lighting, and land use type, to analyze the distribution pattern of land ecological environment sensitive areas throughout Hangzhou. The results indicate that water environment, population density, and vegetation coverage are the main influencing factors for the comprehensive evaluation of land ecological sensitivity in Hangzhou. The ecologically sensitive areas of land in Hangzhou are mainly moderately sensitive, mainly located in densely developed areas outside the main urban area; Low sensitivity areas are mainly located in the main urban area; Non sensitive areas are mainly located in low-density development areas outside the main urban area. In order to optimize the ecological sensitivity of land in Hangzhou, it is necessary to implement corresponding protection and development measures based on the research status, in order to achieve a win-win situation between urban development and ecological protection.

In this study, the offshore sea area is taken as the research object, and the suitability evaluation set of sea sand mining is established. The analytic hierarchy process (subjective weighting method) and the structural entropy weight method (objective weighting method) are used to calculate the weight of each index in the evaluation set, and the game theory is used to organically combine the two to determine the combined weight of the index. The weighted index layers are superimposed by factors, and the natural breakpoint method is used to divide the suitability of sea sand mining in the study area into five categories: suitable, more suitable, generally suitable, less suitable and unsuitable, and the suitability of sea sand mining in the coastal waters of Guangdong Province is analyzed.

Geologic hazards have attracted more attention around the world. This paper tries to comb the relevant literature through Web Of Science, lists the leading indicators and methods of geological hazard susceptibility assessment, discusses the appropriate, suitable means of geological hazards, and gives the future development direction of the existing evaluation in this field based on Remote Sensing images research work is systematically reviewed. The study shows that the most critical highway slope geological hazards - landslides and mudslides - negatively impact human lives. Specifically, remote sensing technology is the primary method of acquiring image data for studying highway slope geological hazards. In addition, the application of emerging technologies and models, the fusion of multi-technology tools, and the integration of multiple models may enhance the accuracy of vulnerability assessment related to geological hazards, which should be emphasized more in future studies.

With the support of geostatistics and GIS, this study investigated the spatial variability of water-soluble total sugar content and soil fertility factor in a flue-cured tobacco producing area in central Henan Province, with the semi-variance function. The best unbiased linear interpolation was performed by the ordinary Krings method. The result indicated that the average water-soluble total sugar content in flue-cured tobacco leaves from the research regions was 23.82%, with a growing distribution trend from southeast to northwest. The amount of water-soluble sugar in this region’s tobacco leaf was consistent with the exponential model, accompanied by an auto-correlation distance of 39600m. The C₀ /C₀+C of the semi-variance variogram was 0.16, and the spatial variability was mainly influenced by structural factors. Among the soil fertility factors, total phosphorus, available nitrogen, and available potassium had moderate spatial variability, while soil pH, cation exchange capacity, and organic matter content showed weak spatial variability. The water-soluble total sugar content of tobacco leaves in this area was highly positively correlated with soil pH cation exchange capacity and available potassium, but it had an exceedingly negative correlation with soil available phosphorus, according to the correlation analysis. The findings of this study can provide a basis for the precise fertilizer application in this tobacco-growing area.

The 2015 Mw7.8 Gorkha earthquake in Nepal resulted in 9,084 fatalities, the collapse of thousands of buildings, and a large number of landslides. Field investigations revealed the poor seismic performance of structures in the affected area, which can be categorized as self-built reinforced concrete frame structures, brick and concrete structures, brick and timber structures, and rubble structures. Interpretation of earthquake-induced landslides was conducted based on high-resolution satellite imagery. The landslides triggered by the Mw7.8 Gorkha earthquake were primarily distributed in the boundary area between the High Himalaya and the Low Himalaya, and basically spread along the Main Central Thrust (MCT). The distribution of landslides was consistent with the characteristics of topography, seismic intensity, and aftershocks. The intensity map of the Mw7.8 Gorkha earthquake was revised based on the investigation of building damage and secondary hazards. The overall intensity circle featuring two macro-seismic epicenters, appeared elliptical with a NWW orientation on the long axis, and a highest intensity reaching Ⅸ.

PM_2.5 particulate matter is one of the important pollutants in the atmosphere, which has a serious impact on human health and environment. Therefore, the study of the change of PM_2.5 particulate matter concentration has attracted more and more attention. This paper selected the monitoring data set of PM_2.5 particulate matter concentration from 2014 to 2022 at various detection points in Urumqi, aiming to deeply understand the change rule of PM_2.5 particulate matter concentration and provide a basis for future environmental management and prevention measures. In terms of data preprocessing, this paper fills in the vacant values, carries out statistical analysis on the daily average concentration of PM_2.5 particles at each data collection point, and calculates the mean value, variance, standard difference and other statistical indicators of PM_2.5 particle concentration in different regions. At the same time, the paper also calculates the daily average concentration of PM_2.5 particles at each data collection point, which provides a data basis for the subsequent time series analysis and prediction. In the aspect of time series analysis, the ARIMA model is used to predict the change of PM_2.5 particle average daily concentration in the next five years. Firstly, the ADF test and the data comparison graph before and after difference were used to judge whether the sequence was stationary. Then, the p and q values of the ARIMA model were determined by autocorrelation analysis. Finally, the order of backward prediction is obtained according to the model parameter table and the time series analysis diagram. The prediction of the ARIMA model can accurately analyze and predict the change of the average daily concentration of PM_2.5 particles in the next five years, and provide a scientific basis for environmental management and prevention measures. In conclusion, this paper makes a detailed analysis and prediction on the monitoring data set of PM_2.5 particulate matter concentration at various detection points in Urumqi from 2014 to 2022, which provides a basis for studying the change rule of PM_2.5 particulate matter concentration and a reference for future environmental management and preventive measures.

This paper integrates small baseline subset-interferometric synthetic aperture rada(SBAS-InSAR) and probabilistic integral model(PIM) for deformation monitoring in mining area, on the one hand, it solves the limitation that SBAS-InSAR technology cannot obtain the large gradient deformation in the center of the mining area, and enhances the ability of InSAR technology to monitor the large gradient deformation; on the other hand, it confirms the feasibility of the InSAR results for solving the parameters of PIM, and improves the time-consuming and labor-consuming shortcomings of the model based on the leveling data. The method firstly utilizes the SBAS-lnSAR technique to obtain the deformation of the mining area, and secondly constructs the fitness function through a hybrid genetic algorithm-simulated annealing (GA-SA) algorithm based on the side-view imaging principle of the radar system, and finally obtains the results of the deformation and subsidence of the mine area. Taking a north mine in Heze, Shandong Province as the study area, the Sentinel-1A data with a time span of 29 months are used for verification, and the results show that the inversion of PIM parameters from SAR data and the construction of the model with the hybrid GA-SA algorithm have small errors in the L1 and L2 lines, which are in line with the accuracy of engineering applications.

The Xiliaohe River basin, located in the agro-pastoral ecotone of northeast China, is characterized by a fragile environment and especially vulnerable to the combination of climate change and irrational human activities, while the Land Use and Coverage Change (LUCC) is an important indicator used to reflect the regional environmental change. This paper took the Xiliaohe River basin as the research object, and investigated the spatial-temporal pattern and dynamic change characteristics of land use during 2000~2020. The results indicated that, firstly, the distribution area of grassland was the largest, followed by cropland, which accounted for 43% and 27% of the total area respectively; while that of water body and construction land was quite small, accounted for 2% respectively. Secondly, the distribution areas of forest and construction land had continuously increased by 674.12km² and 507.14km² respectively, while that of water body continuously decreased by 527.6km²; besides, the distribution areas of cropland, grassland and uncultivated land had fluctuated during the research period. Thirdly, the centroids of grassland, dryland and construction land were concentrated on the center, while the centroids of water body and uncultivated land were located in the lower plains of Xiliaohe River basin. Finally, there was a continually slight decline in the land use structure equilibrium, which indicated the land use structure tended to be unstable.

Obtaining accurate landslide susceptibility models for Yingshan County poses a challenge due to the presence of spatially heterogeneous landslide influencing factors (LIF) with significant uncertainties. Previous studies have utilized frequency ratio (FR) methods and XGBoost for landslide susceptibility mapping, but the results can still be improved. To address the limitations imposed by a single method, this paper proposes an improved frequency ratio (IFR) and XGBoost integrated model (IFR-XGBoost) and selects 12 LIFs for landslide susceptibility modeling. The results of accuracy, specificity, sensitivity, and ROC analysis indicate that all four models demonstrate accurate prediction of landslide susceptibility maps and the IFR-XGBoost model performs the best (AUC=0.93). These findings suggest that the IFR-XGBoost model proposed in this study is expected to produce accurate landslide susceptibility maps and provide guidelines for future scholars.

Karst collapse columns are prone to cause severe water surges during mining activities, resulting in many casualties and property losses. In order to assess the impact of karst collapse columns on the excavation of the No. 3 coal seam of coal industry in Shanxi, this study used the Controlled Source Audio Geodetic Electromagnetic Method (CSAMT) to investigate the structural characteristics and hydrogeological conditions of two undisclosed karst collapse columns. It then scientifically evaluated the hydraulic conductivity hazards of the karst collapse columns. According to the line-view resistivity cross-section, the X1 karst collapse column mainly developed the Rockfill segment and slurry section, there is the possibility of sudden water. The DX17 karst collapse column presents relatively high resistivity electrically, the overall water-bearing performance is poor, and there is no sudden water hazard for mining and digging. The study results are of guiding significance to the water control work of traversing downhole and exposing karst collapse columns in the Ordovician high-bearing pressure water production workings.

Many existing studies focused on monitoring the construction scale of prefecture-level or metropolitan cities using optical remote sensing images with a medium resolution of 15-80m. However, in many urban towns of cities especially in the eastern coastal areas, the rapid and speeding up developments of construction are happening. Therefore, it is necessary to examine the adaptability on construction scale monitoring using remote sensing images with different resolutions, and to monitor construction scale expansion in order to analyze the driving force of urban development and to support decision making in the management of county-level cities. Taking Fenghua District, Ningbo City, Zhejiang Province as the study area, the construction scale expansion monitoring of urban towns is carried out. GF1 remote sensing images with different spatial resolutions (i.e., 2m, 8m and 16m) are used to compare their capacity in monitoring the construction scale expansion of those areas. Results show that images of 8m resolution is more suitable for monitoring the construction scale of urban towns considering both data size and processing cost. With these imageries, the construction scale of the central urban area of Fenghua and Xikou town in 2009 and 2013 are monitored, and the expansion area and expansion rate from 2009 to 2013 are calculated. The results indicate that the construction expansion scale of Fenghua from 2009 to 2013 is small, and the expansion area is mainly from small block of land construction without any large-area and contiguous block.

Earthquakes are one of the natural disasters with the greatest threat to human life, and since earthquake forecasting is not accurate, one of the important tasks of post-earthquake emergency response is to assess the earthquake damage in time and guide the decision-making of emergency rescue and relief materials distribution. The 3D seismic information sharing platform provided by Cesium indeed provides an effective application for 3D visualization of seismic information in the existing network. In this paper, Vue and Cesium jointly build the front end of the platform, Java programming language, and Spring Boot framework to build the back end, using MySQL as the database and MyBatis-plus ORM framework to achieve data persistence. At the same time, Redis is used to store cached data to improve read and write speed and performance. The platform can capture real-time earthquake information and perform intensity estimation based on elliptic attenuation model equations after the earthquake, use empirical models to assess economic losses and casualties, and obtain the Gaode API for route planning to rescue points. The platform not only helps the government to carry out post-earthquake activities but also allows the public to understand the effects of earthquakes more clearly.

Based on GF-1 high-resolution remote sensing images, POI and statistical data, this paper adopts object-oriented classification, land function identification and coupling coordination model to quantitatively analyze the dynamic changes of land use and industrial development in characteristic water towns. The results show that agricultural land is mainly distributed in Seagull Island and south of Qinghe Road in Shilou Town, mainly aquaculture and flower planting. In comparing results of 2013 and 2020, some aquaculture lands were transformed into flower planting lands, and significant changes of land use types happened in the northeast and township; Coupling and coordination degree showed different spatial heterogeneity. Seagull Island and South of Qinghe Road villages group had the high coupling coordination degree, and should continue to develop aquaculture and flower planting. Northeast villages group achieved the moderate coordination between land use and industrial development by changing land use types. What needs to be paid attention to were Northwest villages group and township. Northwest villages group had the low coordination degree with low land and industry contribution. It should optimize the industrial structure and change the type and layout of land use. The township should increase the investment of industrial lands because land use obviously lagged behind the industrial development.

Risk monitoring in mining areas is an indispensable part of the process of developing and managing natural resources in mining areas, and it involves the assessment of the safety of mining areas. Accurate acquisition and analysis of spatial geographic information of mining areas can better reduce the labour intensity of the mining industry, reduce the damage to the natural environment, and reduce the disasters that may be encountered in the mining process. With the continuous progress of space information technology, the emerging global navigation satellite system GNSS (Global Navigation Satellite System), geographic information processing system GIS (Geographic Information System) and remote sensing system RS (Remote Sensing) common role of 3S technology in the mining industry. 3S technology in the field of mine monitoring shows a strong potential and advantages. The purpose of this paper is to discuss the characteristics, specific operational details, and modes of mutual cooperation and joint collaboration of 3S technologies in mine monitoring, in order to provide suggestions for future mine risk monitoring.

Satellite-based SMAP (Soil Moisture Active and Passive), ASCAT (Advanced Scatterometer) and ESACCI (European Space Agency-Climate Change Initiative) soil moisture products were widely used in evapotranspiration estimation, water balance research and drought monitoring etc. It is very important to evaluate these three soil moisture products before using them. Due to significant scale differences between in-site soil moisture measurement data and satellite-based (i.e., SMAP, ASCA and ESACCI) soil moisture products with coarse spatial grid resolution (i.e., 9km, 0.1°and 0.25°), it is difficult to obtain the robust evaluation result of these three satellite-based SM products in heterogeneous areas only using in-situ soil moisture measurements with single scale observation network. In this study, the seven multi-scale (10km, 25km, 50km and 100km) in-situ observation networks collected were selected and collected from ISMN (International Soil Moisture Network) for evaluation of SMAP, ASCAT and ESACCI soil moisture products. The five metrics of Mean Absolute Error (MAE, cm³/cm³), Root Mean Square Error (RMSE, cm³/cm³), Unbiased Root Mean Square Error (ubRMSE, cm3/cm3), Bias (cm³/cm³) and correlation coefficient (R) were used to evaluate the SMAP, ASCAT and ESACCI soil moisture products. Results showed that overall, ESACCI soil moisture product was more consistent with in-situ soil moisture measurements than SMAP and ASCAT SM product, minimum values of MAE, RMSE, ubRMSE and Bias, and maximum of R were 0.024 cm³/cm³, 0.032 cm³/cm³, 0.030 cm3/cm3 and 0.011 cm³/cm³, and 0.888 respectively.

This paper is based on the rational function model, using a small number of control points to calculate the initial attitude angle correction value of a large area multi satellite image and correct it, establish a mapping relationship between satellite images and the ground, analyze the process flow, operation methods, accuracy indicators ,adjustment for high-resolution single satellite remote sensing images based on rational function models, explore fast orientation methods for large area images.

With the rapid development of computer software, hardware, and data processing, the methods for obtaining images are becoming increasingly diverse, data processing is becoming faster and faster, compared to other aerial images, pushbroom images orientation requires fewer control points and is more efficient in orientation. This paper is based on pushbroom images combined with aerial triangulation, we studied the directional accuracy of pushbroom images under different ground resolutions and control points.

With the development trend of the information society, various scenarios are being digitized and informatized. At the same time, people's requirements for the visibility and convenience of travel navigation are constantly increasing. Combining AR (Augmented Reality) technology with navigation has become a hot topic. However, currently, the production of most 3D (three-dimensional) scenes is complex and time-consuming, and lacks indoor scenes. Mainstream AR navigation is costly and cannot be widely used. This paper uses quickly established lightweight 3D scene models, which, after secondary development, form a B/S architecture for indoor and outdoor 3D scene applications. Simultaneously, it combines Three.js 3D rendering library technology with device sensors to form a mobile AR navigation application. This system and application provide scene browsing, building query positioning, path planning, and AR path navigation functions. It has the advantages of low development cost, short cycle, and low hardware requirements for devices.

Deep learning is increasingly being applied in the field of remote sensing image processing. However, researchers often face limitations in establishing high-quality datasets for target recognition in high-resolution imagery due to resource constraints. Moreover, traditional target recognition methods cannot leverage the extensive coverage of remote sensing images for broad-scope searches of specific targets. To address the scarcity of high-resolution remote sensing image sources for target detection tasks, achieve broad-scope automatic searching for areas of interest, and enhance the level of task automation, a system with the capability to acquire data from multiple network sources and recognize nuclear power plant targets was developed. The system initially integrates data from multiple network map servers, enabling the rapid and cost-effective acquisition of high-resolution, large-area remote sensing imagery. Deep learning samples are created based on multi-source image data, and multiple target detection models are trained. The software's user interface was developed and tested, facilitating broad-scope searches for key components of nuclear power stations based on extensive network imagery.

In response to the existing issues of building edge adhesion, low extraction completeness, and precision when using high-resolution remote sensing images for building extraction, this paper proposes an improved neural network that combines residual connection modules and parallel processing of different-level resolution remote sensing images. This network effectively extracts buildings from high-resolution remote sensing images. On the WHU high-resolution remote sensing image dataset, the proposed method achieves optimal accuracy across all metrics. The precision, recall, F1 score, IoU metric, and mIoU metric of this method are 91.29%, 89.66%, 90.48%, 85.59%, and 88.06%, respectively.

Icing can cause problems such as line overload and disconnection, posing a threat to the stability and safety of the power system. The traditional ice thickness detection method mainly relies on manual inspection and simple measurement tools, which is not only inefficient but also difficult to ensure accuracy. Therefore, this article proposes a method for detecting the ice thickness of overhead transmission lines based on remote sensing satellite data and SVM (Support Vector Machine) algorithm. This method utilizes remote sensing satellite data to monitor overhead transmission lines, obtain image information of the line surface, and then use SVM algorithm to process and analyze the image to predict the ice thickness. The experimental results show that this method can effectively detect the icing thickness of overhead transmission lines and improve prediction accuracy. This method has important practical value for ensuring the stability and safety of the power system.

Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) technology is widely used in different fields. In order to realize the dynamic change monitoring of water surface height and snow depth based on GNSS-IR, it is very important to study the Global Navigation Satellite System (GNSS) signal reflection characteristics of the base surface when the height/depth is zero. At the same time, exploring the differences in the inversion accuracy of different materials on the ground can also provide a basis for the extended application of GNSS-IR technology. In this paper, a smart phone is used to conduct GNSS-IR altitude inversion accuracy experiments on the plastic surface, water surface, cement surface and soil surface respectively. The root mean square error is the largest, which is 4.15cm, which proves that different reflectors have an impact on the accuracy of GNSS-IR height retrieval, and experiments have proved that the accuracy of the retrieval results is the highest when the observation value is in the range of 10°-60° elevation angle.

To improve the speed and accuracy of detecting track foreign object intrusions, the OneFormer image segmentation technology is introduced. This technology aids in the identification of track foreign object intrusions, thereby extending the use of image segmentation technology in the field of track foreign object intrusion detection. This paper involves the collection of a substantial volume of images depicting track foreign object intrusions in real-world engineering settings. Following preprocessing, a dataset comprising 10,060 images across five categories of track images was constructed. Subsequently, the experimental environment configuration and training parameters were specified. Finally, a comparative experiment was conducted with other segmentation methods, validating the method's feasibility based on the respective panoramic segmentation evaluation metrics, including PQ, SQ, and RQ. The method presented in this paper exhibits improvements in each evaluation index when compared to other mainstream segmentation methods, such as Panoptic FCN, UPSNet, and OANet. The method effectively addresses the issues of low detection efficiency and limited accuracy encountered in graphical segmentation methods within the domain of track foreign object intrusion detection. Moreover, it successfully overcomes the limitations associated with traditional methods and manual detection processes. As a result, the OneFormer method introduced in this study is better suited for the task of recognizing track foreign object intrusions in real operational environments. It not only offers enhanced performance in terms of detection efficiency and accuracy but also serves as a valuable reference and driver for the application of image segmentation in actual railroad engineering scenarios.

Polarization calibration (PolCal) is necessary for the quantitative application of polarimetric SAR data. The classic PolCal methods rely on corner reflectors (CRs) to calculate parameters, but the deployment of CRs is extremely expensive and they cannot even be deployed in complex terrains. Therefore, currently advanced methods can achieve calibration without relying on CRs. This method is based on the Bragg-like targets and uses the unitary zero helix (UZH) constraint to estimate the co-pol channel imbalance k for the segmented image by Gaussian Newton method. In this process, it is necessary to select appropriate range of k samples for fitting to obtain the final estimate of k. A fixed threshold may lead to improper sample selection, thereby reducing the accuracy of calibration. Therefore, this paper proposes an adaptive PolCal method, which introduces a coefficient of variation to adaptively select samples, and then ultimately estimates k using the best fit line. This paper conducts PolCal experiments using the image of GF3 02 satellite in the calibration field of Etuoke Banner, Inner Mongolia on January 1, 2022. The experiments demonstrate that the proposed method can enhance the stability of the PolCal method independent of CRs and further improve calibration accuracy.

In order to solve the problem that it is time-consuming, laborious and easy to make mistakes when using software such as Global Mapper to manually correct a large number of topographic maps during the customization of network map service products, this paper proposes a fast and accurate topographic map correction method based on OpenCV. Through contour detection, line simplification, corner detection, binarization and sub-pixel corner detection on topographic map and its region of interest, the corner of the inner neat line is finally accurately located, and the control point file in *.gcp format is directly generated. The experimental results show that the mean error of correcting a 1:50 000 or 1: 10 000 topographic map is about 0.2 pixels, and the average time cost is less than 8 seconds. Compared with the traditional manual method, it can ensure the accuracy of topographic map correction and greatly improve the working efficiency.

Airport airspace is an important environmental element among the three major elements of the "human -aircraft - environment" concept, and is of great significance in ensuring the safe and stable operation of airports. With the acceleration of urbanization, the contradiction between urban development and airspace protection has become more and more prominent, and the airspace environment of airports has been continuously damaged, and airspace insecurity incidents have been reported frequently. Therefore, this paper addresses the above pain point problems, based on 3D geographic information technology, using Cesium map engine, carries out the calculation of key points in the airport headroom area and realizes the high-precision visualization of the model in the system, and researches the analysis of obstacle over-height and height limit audit algorithms. The airport headroom over-height analysis system was developed, which has the functions of three-dimensional visualization of the headroom zone, loading and management of obstacles, and detection of obstacle over-height. It can effectively improve the information-ization level and work efficiency of building height limit audit and old obstacle over-height evaluation and management.

The severe impact of the COVID-19 pandemic on socio-economic development has prompted an exploration of its effects on urban activities. Using December 2021 as a case study during the pandemic in the Guanzhong region of China, this paper conducts a multiscale analysis of the area using NPP-VIIRS monthly composite data. The results indicate that during the pandemic, more than 80% of areas in Xi'an, Weinan, and Baoji showed a downward trend in nighttime lights, with Xi'an being the most severely affected but also recovering the fastest, while Tongchuan was the least affected and had the slowest recovery. The spatiotemporal evolution pattern in the Guanzhong region exhibited a northwest-to-southeast directionality, with changes in the standard deviation ellipse area primarily driven by changes in urban lighting in central cities. This study further verified that the Remote sensing of nighttime lights effectively reflects changes in urban lighting and indicated the significance in assessing the impact of policy implementations during major emergencies.

Cyberspace mapping has emerged as a new and compelling area of research within the field of cartography. This paper addresses the challenge of overlapping points in cyberspace visualization, which poses difficulties for visual analysis. To tackle this issue, we propose a cyberspace metaphorical cartogram map that combines metaphorical mapping and continuous cartogram technology. Additionally, based on our method, we use our method to construct a cyberspace map using global IPv6 data from the Asia-Pacific Network Information Center (APNIC) to analyze the current development pattern of the global IPv6 network. Experimental results demonstrate the effectiveness of our method in avoiding node duplication, aligning with the reading logic of geospatial maps, and providing an efficient visualization approach for data comprehension.

To address the issue of inaccurate migration of rocky desertification evaluation results from a large scale to the micro scale of parcels, this study introduces a series of methods. Firstly, it employs deep learning techniques to assess the level of rocky desertification in arable land units and to accurately identify the boundaries of these arable lands. Subsequently, the study conducts a time-series reconstruction of indexes used to discriminate rocky desertification, all while adhering to the constraints of these boundaries. Finally, it calculates the degree of rocky desertification within each parcel unit using an entropy weighting method. This approach is well-suited for the complex arable land scenarios typically found in mountainous areas. In the study area, a total of 49,961 cultivated lands have been identified. Notably, rocky desertified cultivated land accounts for 58% of the total, with a predominant distribution of mildly rocky desertified cultivated land.

Remote sensing technology provides a versatile means to detect surface water information without constraints of time and space. However, challenges in surface water extraction persist, including spectral interference and noise interference. In response, this study introduces a fusion marker-controlled watershed algorithm and an improved normalized water index method to extract surface water bodies. The segmentation of remote sensing images is achieved through morphological opening and closing operations, followed by the application of the improved normalized water index method to reduce spectral interference and minimize extracted noise. Comparative analysis with four classical water extraction methods, namely the single-band threshold method, normalized water index method, improved normalized water index method, and object-oriented method, was conducted. The study concludes with a consistency test using Kappa coefficients and accuracy rates, providing qualitative and quantitative insights into the various extraction methods. The results demonstrate that the method presented in this paper achieves higher extraction accuracy and holds significant potential for water extraction research.

The comprehensive positioning and seismic hazard assessment of fault zones are significant challenges faced by many countries worldwide. The author employs SBAS-InSAR technology to conduct spaceborne interferometry radar timeseries dynamic monitoring of the Handan area, with a focus on the Handong fault as the main subject of investigation. The monitoring period spans from the beginning of 2017 to 2021, comprising a total of 48 monitoring phases. The comprehensive deformation velocity fields and monthly cumulative deformation data were obtained and primarily used to analyze the Handong fault in conjunction with multiple data sources. The results indicate that the Handong fault is the primary structural feature in this area, acting as the demarcation line between regional uplift and subsidence deformations. On the western side of the Handong fault, there is evidence of uplift, while the eastern side experiences subsidence. The average uplift rate on the western side is approximately 0.51 mm/a, which is generally consistent with the overall uplift rate of the Taihang Mountain. On the eastern side, the average subsidence rate is -29.8 mm/a, which is mainly attributed to the over-extraction of groundwater, with a relatively lower likelihood of being influenced by tectonic activity.

In response to the existing beacon positioning methods in medium Earth orbit (MEO) search and rescue operations, an improved particle swarm optimization algorithm based on Time Difference of Arrival (TDOA) localization is proposed. The study analyzes and simulates the Geometric Dilution of Precision (GDOP) for TDOA localization, demonstrating that GDOP is directly proportional to time measurement errors and satellite position errors. Furthermore, a modified particle swarm optimization algorithm is presented, which incorporates an adaptive fitness function and adaptive parameter adjustments using a classic particle swarm optimization approach. The proposed method improves the inertial weight and introduces adaptive algorithms for fitness functions and inertial parameters. This improved particle swarm optimization algorithm not only effectively addresses issues such as poor convergence and susceptibility to local optima but also accurately determines the position of the beacon. Simulation results indicate that When the time error is 1μs, the improved Particle Swarm Optimization (PSO) algorithm achieves a positioning accuracy of 0.57km. This represents a 5% improvement over the classical PSO algorithm and a significant 15% improvement compared to the Weighted Least Squares (WLS) algorithm.

Traditional full-field surveying methods have problems such as large acquisition difficulty and low efficiency in earthwork surveying and mapping of complex terrain. This paper takes the solution of the earthwork of complex terrain as the starting point, applies the drone oblique photography technology to the calculation of the earthwork volume, and forms a real 3D model through the processes of multi-angle texture data collection, aerial triangulation, dense matching, and triangulation construction. Take the real 3D model as the basic data, and manually intervene to extract the elevation points semiautomatically as the important raw data for earthwork calculation. The square grid method is used as the earthwork volume calculation method, and compared with the traditional surveying and mapping method, the experiment shows that the oblique photogrammetry technology can meet the relevant accuracy requirements of earthwork survey, and has the advantages of high accuracy and fast efficiency.

WMTS map service has been widely used in our works and lives. In order to improve the management efficiency and service capability of geological spatial data, meet the requirements of convenient management and high-performance access of tens of millions of tiles, the characteristics of WMTS are analyzed, the management mode of massive tile is summarized, the GeoServer framework and GeoWebCache mechanism are studied. A system implementation is proposed which relies on GeoServer to provide WMTS service, based on GeoWebCache mechanism, uses MongoDB to realize tile storage management, and a technology system for the management and service of massive tiles is constructed.