This PDF file contains the front matter associated with SPIE Proceedings Volume 12980, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The updating of small and medium scale vector terrain database in china has been carried out regularly for many years. But in difficult areas such as depopulated zone, the surveying and mapping work is limited to the impact of many objective factors such as traffic, climate and environment. In particular, the research on the production technology of geomorphic data relying on photogrammetric technology is of great significance. In order to improve the efficiency and applicability of basic surveying and mapping, the technology of geomorphic elements production based on existing geographic information data was discussed and studied, and the manufacturing flow was designed. For different data source conditions, several test areas were selected for production, accuracy verification and results analysis. Forming a complete production solution which can provide implementation suggestions for geomorphic data production under similar conditions.

Using seismic sedimentology theory to study tight reservoirs is significant for predicting the distribution of unconventional reservoirs. Based on 90 °phase seismic data volume, the sand layer seismic reflection frequency is analyzed using time-frequency analysis technology. Combined with seismic filtering, the seismic interference of the sand layer is suppressed. The high-frequency isochronous stratigraphic framework is established based on the high-precision sequence stratigraphy analysis and seismic sedimentology research technology. The sedimentological interpretation of stratigraphic slices with different dominant frequencies is carried out by combining geology, logging, and seismic data. The study shows that the physical properties of rocks are the basis of sedimentological interpretation of seismic data. The delta front subfacies are the main sedimentary subfacies of the eighth member of the Shihezi formation. The main sedimentary microfacies are two distributary channels on a large scale. The natural gamma ray curves of the fillings and bodies of the underwater distributary channels are bell-shaped, toothed bell-shaped, or box-shaped. They show medium and strong energy zonal reflection patterns on the stratigraphic slices. This method helps improve the accuracy of sedimentary system prediction.

The author collected, organized, and analyzed data from 2,525 representative boreholes in the Handan region, conducted lithological stratification and established a database. Via the software platforms Petrel and ArcGIS, a 3D stratigraphic sequence-structural framework model was developed based on the typical borehole database and stratigraphic sequence-structural framework. The model provides valuable insights into the structural evolution in the Handan region, presenting structural maps, thickness maps, and Quaternary structural profile maps since the Late Quaternary. Through a detailed analysis of the deformation within the Qp ² , Qp ³ , and Qh stratum groups, this study unveils the distribution patterns and activity characteristics of concealed faults, including the Taihang Mountain piedmont fault (F₁), the Handan concealed fault (F₂), and the Lianfanglu fault (F₃).

To address the issue of widespread missing chlorophyll-a (Chl-a) concentration data in ocean water caused by environmental factors such as solar radiation and cloud cover during satellite remote sensing monitoring, this study focused on the Beibu Gulf area and used the empirical orthogonal function data interpolation method (DINEOF) based on Himawari-8 chlorophyll-a daily product data to interpolate the missing data. The study made adjustments to the original interpolation method by modifying the number of time sequences in the reconstructed dataset to improve the accuracy and efficiency of the interpolation reconstruction. In testing the accuracy of the adjusted DINEOF reconstruction method, the study replaced the existing data in the research area with null values to represent missing data for interpolation reconstruction. The results showed that the adjustments made to the research method improved the accuracy and efficiency of the interpolation reconstruction, with MAE, RMSE and R² values of 0.372 mg/m³ , 0.522 mg/m³ , and 0.917, respectively.

This study investigates the relationship between seismic activity and thermospheric anomalies related to the 2021Mw7.5 Peru earthquake. Utilizing data from the National Center Environmental Prediction (NCEP) reanalysis and the Ionospheric Connection Explorer (ICON) satellite with the Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI), temperature and vector wind anomalies were analyzed. Seismic thermal anomalies in the lower atmosphere were found to precede significant temperature fluctuations in the thermosphere, displaying a bottom-up propagation pattern. Additionally, vector wind analysis revealed a strengthening radial component originating from the epicenter, indicating energy propagation from the earthquake source. The observed vertical perturbations and horizontal disturbances in the thermosphere provide evidence of the link between seismic activity and thermospheric disturbances, advancing our understanding of the mechanisms underlying Lithosphere Atmosphere-Ionosphere Coupling (LAIC). These findings contribute to LAIC research and provide valuable insights into the processes involved in seismic-related ionospheric disturbances.

Cultivated land serves as the fundamental resource for agricultural production. As an essential component in precision agriculture, the rapid and accurate extraction of cultivated land plays a vital role in crop type identification, crop classification, and yield estimation. This study proposes a novel approach to improve the extraction method of cultivated land plots by leveraging the ResNet50 model as the backbone for feature extraction. Through integrating transfer learning and incorporating attention mechanisms, the fully connected layer of ResNet50 is replaced with the comprehensive Unet architecture. Experimental validation demonstrates that the proposed ResNet optimization model achieves significant enhancements in precision rate, recall rate, and F1-score for cultivated land plots extraction, with respective improvements of 6.25%, 5.63%, and 7.38% compared to the traditional Unet model. Thus, this research holds practical significance and provides valuable insights for the application and promotion of deep learning techniques in cultivated land parcel extraction.

At present, geological engineering construction has become a fundamental application in existing building projects. Additionally, three-dimensional (3D) reconstruction for geological structure can display the digital information and assist developers to clearly observe the geological tectonic. However, existing reconstruction methods utilize sensors to detect the information and visualize the collected data with directly processing, which cannot guarantee there construction integrity and loss undetected information. In this work, we utilize an implicit neural network (INN) to process the collected geological data, which can concentrate the unnoticed changes in the geological data and generate the visualization reconstruction structure of geological structures. Additionally, we utilize the minimum cross-entropy loss for the undetected areas and generate the whole geological information with incomplete geological data. From our extensive simulation results, we can significantly observe that the proposed method can present the digital reconstruction information of real geological structures through comparing with existing reconstruction modeling methods.

Geothermal resource is considered as a clean and renewable energy, which has the advantages of stable heat supply, low cost and high efficiency. As one of the most significant surface environmental parameters, Land Surface Temperature (LST) can be derived from satellite information or direct measurements. The fault structure is also another important indicators of geothermal resources. In order to study the spatial distribution of these two, based on the Landsat8 OLI data in Nimu area, this article superposed the relationship between LST and main faults. The single-window algorithm method was used to invert the LST, coupled with the interpretation of a combination of an active fault zone characteristics. The results showed that the intersection area of the Yadong-Gulu North-South fault zone and the Yarlung Zangbo suture zone is rich in geothermal resources. Rock fracture along the fault zone, joints and fractures, providing a good place for geothermal activities. In addition, the hidden fault structure in the region has a strong influence on hydrothermal activity, causing a large area of surface temperature anomalies. Mainly through structural and thermal analysis of surface temperature anomalies, the background of regional geothermal identified anomaly geothermal focus areas. Also shows thermal image data of its obtained has visual, image, speed, free from traffic conditions, and many other advantages, in particular, depopulated zone on the plateau geothermal anomaly detection, has an important significance.

In order to discuss several representative methods of the gravity anomaly separation and compare their separation effect, the article firstly introduces the principles of those methods, then uses them to test the same set of theoretical model and adopts the preferential regularized filtering methods to separate the Bouguer gravity anomaly in Jiuquan to Wuwei region. Through the analysis of its gravity anomaly characteristics, the regional tectonic background of the work region is preliminarily recognized and following conclusions are summarized: The Bouguer gravity anomaly in the working region is negative; the fault of Longshou Mountains’ southern margin is the most striking gravity gradient zone in the working region and its southwest side is the extension of the Qilian Mountains’ northern margin whose abnormal value changes sharply; the fault structures in the southwest of the work region tend to strike in a nearly east-west or northwest direction, with the southern edge of the Longshoushan fault being the representative; the central and northeastern fault structures are mostly northeast oriented, represented by the Bayanhot Fault and the Yabulai Mountain Front Fault.

This paper selects Zhifu District of Yantai City as the study area, takes the traffic community formed by OSM data (road network) as the identification unit of functional areas, constructs the weight model of "influence-space area", and uses POI (point of interest) data and Sentinel-2A image data to identify functional areas. Based on the theory of space syntax, this paper analyzes the spatial accessibility of urban roads through the variables of integration degree and choice degree. The results showed that the northern Zhifu core street space accessibility is higher, with more perfect function types, south street space accessibility is poorer, and function type is relatively single, corresponding improving measures are put forward accordingly.

With the rapid economic development of Beijing, the problem of ground subsidence has become an important focus of attention in the region. The purpose of this paper is to discuss the causes of ground subsidence in Beijing in order to provide valuable references for disaster warning in the region. In this study, the small baseline set synthetic aperture radar interferometry (StaMPS-SBAS) technique is used to process the 29-view Sentinel-1A dataset from January 2020toMay 2022 in the plain area of Beijing, to obtain the basic deformation information of the ground subsidence, and to verify the reliability of the InSAR data by using the GPS high-precision station data. Through the EOF empirical orthogonal function decomposition of the ground subsidence data in the plain area of Beijing, it is found that the variance contribution rate of mode 1 is 52.16%, which coincides with the spatial distribution of the groundwater leakage and the time coefficient corresponds to it, which indicates that the over-exploitation of groundwater is the main cause of ground subsidence in Beijing. The variance contribution of mode 2 is 16.14%. Through the sampling study of Daxing District, it is found that it is temporally and spatially correlated with the dynamic and static loads of transportation buildings, indicating that the dynamic and static loads of transportation buildings are the secondary causes of ground settlement in Beijing.

Leaf area index and plant height can be used as important indicators to measure crop growth and yield. Accurately, quickly, and non-destructive acquisition of soybean leaf area index LAI and plant height H is of great significance for soybean production management. This article utilizes a multi rotor drone equipped with multispectral sensors to obtain multispectral images of soybean flowering, pod setting, and bulging stages, and simultaneously collects ground data. Based on DSM extraction of soybean plant height H in the study area, the results showed that the measured plant height H was fitted with the height extracted based on DSM (R2=0.8246; RMSE=0.047). Then, Pearson correlation analysis was performed on the extracted multispectral vegetation index, plant height, and texture features. A soybean LAI estimation model was constructed using univariate linear regression, support vector machine regression (SVR), random forest regression (RF), and BP neural network regression models, respectively. The results show that after adding texture features, the accuracy of all three algorithm models can be improved, with R2 increased by 0.222, 0.202, and 0.178, respectively; In addition, the accuracy of the BP neural network model in inversion modeling at various growth stages is superior to the SVR model and RF model. The BP neural network model has the best accuracy at the soybean bulging stage, with R2 of 0.856 and RMSE of 0.143. Texture features can effectively improve the saturation problem of single use vegetation index estimation under high-density canopy during soybean podding, and can extract more information for estimating soybean LAI, thereby improving the accuracy of the estimation model and providing guidance for soybean field management.

To study and analyze the weight of atmospheric radiative bright temperature with the whole tropospheric atmospheric radiative bright temperature at different integration heights, this paper uses sounding data from Shanghai station for four seasons from March 2018 to February 2019 and uses the MonoRTM atmospheric radiative transfer model to simulate the brightness temperature of domestic QFW-6000 microwave radiometer water vapor and oxygen channels at different heights in all seasons. The experimental results show that the brightness temperature weights of the water vapor and oxygen channel frequencies in the four seasons are slightly different. The individual differences in the brightness temperature weights of the eight-channel frequencies of water vapor are small, and they all contribute to the brightness temperature at the height of 0-10 km, which shows that the inversion of the humidity profile using the frequency of the water vapor channel needs to calculate the brightness temperature to the height of 10 km. The individual differences in the bright temperature weights of the eight-channel frequencies of oxygen are large, and the three-channel frequencies have little contribution to the brightness temperature at altitudes above 3 km, indicating that using the three oxygen channel frequencies to invert the atmospheric temperature profile needs to calculate the brightness temperature to the height of 3 km.

Landslide is a very important geological disaster, which causes a large number of people's lives and property losses every year. Landslide susceptibility map is one of the effective methods to prevent and control disaster losses. However, the application of machine learning in susceptibility map under the high altitude localities is relatively lacking. Here we show that the RF model which is one of the machine learning methods can acquire a reliable susceptibility map. On this paper, we chose aspect, slope, lithology, fault, elevation, river and road as the factors to find the susceptibility map. TheIVM was applied as the first model to acquire the information of the factors. The coefficients of the factors were got by the RF model finally, the result was divided into five grades from very high to by very low by natural breakpoint method. Through the ROC curves, the credibility level is 86.3%. Therefore, this result can provide data support for local disaster prevention and mitigation work. At the same time, this study at the area revealed that the machine learning can be applied on the susceptibility maps and it were credible.

In this paper, a new method for SAR image classification based on decision tree and Wishart classifier was proposed. Firstly, feature parameters were extracted from the fully polarimetric SAR data. Next, the extracted feature parameters were used as input of the decision tree. Then the classification result was classified as the initial class of the Wishart classifier. Compared with the H-α decomposition, this method not only retains the physical scattering mechanism of classification results, but also can effectively classify sandy land features. The effectiveness of this method is demonstrated using C-band fully polarimetric SAR data of Hunshandake Sandy Land, acquired by the RADARSAT-2 sensor.

Shanxi Province is a large coal province, and its frequent subsidence disasters have seriously affected people's normal production and life. Therefore, it is crucial to monitor surface subsidence on a large scale and explore its causes. In this study, the Sentinel-1A data supported by StaMPS-SBAS technology was used to monitor surface deformation in central Shanxi Province from March 2017 to December 2021 and to obtain the surface deformation rate as well as time-series cumulative settlement, then to check the accuracy of the results. And then, the groundwater changes in the study area were inversed through the analysis of GRACE-FO satellite observations. Finally, the spatial and temporal characterization of surface InSAR time-series cumulative subsidence in central Shanxi Province from2017 to 2021wascarried out using REOF in order to dig deeper into the spatial and temporal evolution process of surface subsidence in this study. According to the analysis results of land subsidence characteristics in this study, the influence of ground water fluctuation on land subsidence can be more accurately explored, and valuable scientific insights can be provided in the field of land subsidence control and management.

Extraction of land use information from very high resolution (VHR) images plays a crucial role in urban planning and management. The study aims to extract urban land use information using VHR images and open geographic data using graph neural networks. We first obtained land cover objects using a semantic segmentation model. The spatial topological relationships between land cover objects were then modeled using graph theory and represented as graph-structured data, in which the attributes of graph nodes were computed based upon points of interest (POI) data and classified land cover map. Last, we used graph neural network to learn high-level structural features for urban land use classification. The proposed method was applied to the core urban area of Fuzhou city, China. Results showed that graph neural networks are effective for urban land use classification from VHR images, and integrating open geographic data further improves the accuracy of urban land use classification to 87% compared to the 84%accuracy obtained by using only VHR images. Our method exhibits high potential for extracting fine-grained urban land use in various urban areas.

The stability of the near-dam high slopes is critical to the safety of the reservoir and dam, and severe deformation may lead to slope sliding or even collapse. Monitoring and evaluating the deformation of high slopes in the dam area can help to identify potential safety hazards and take timely measures to protect the dam structure and the near-dam area. Groundbased synthetic aperture interferometric radar (GB-InSAR) technology is particularly suitable for surface deformation monitoring and stability assessment of various types of slopes due to its high spatial and temporal resolution. However, the GB-InSAR near-dam area monitoring data are affected by the atmospheric changes in the measurement area, resulting in less accurate deformation solution results. To address this problem, this paper proposes a polynomial atmospheric correction method based on high-quality pixels on the basis of the reference point method. The method is applied to the deformation monitoring of high slopes in the dam area of Huangdeng Hydropower Station, and the univariate polynomial model of distance and the binary polynomial model of coordinates are established respectively. The analysis results show that the method can effectively remove the atmospheric interference part of the data when applied to the atmospheric correction of the near-dam high slopes, and the accuracy of the binary polynomial model is better than that of the univariate model as a whole.

Rapid and accurate extraction of disaster-affected patches is of great importance. This study focuses on the extraction of flooded road patches and proposes a method based on the Segment Anything Model, which utilizes the semantic segmentation results obtained from the BiSeNet V2 model as prompt cues for the model. To better integrate the two models, a Prompt Conversion Module is designed to convert the semantic segmentation results into prompt cues, and the SAM model is fine-tuned accordingly. Additionally, a Semantic Fusion Module is introduced to incorporate semantic information from the segmentation results and BiSeNet V2. To validate the effectiveness of the proposed approach, experiments are conducted on the publicly available FloodNet dataset. The results demonstrate that the proposed method not only achieves faster and more accurate extraction of flood-affected patches but also provides corresponding semantic classification information for the extracted patches.

The invasion of Spartina alterniflora into China, especially the coastal wetland of Yancheng, is particularly serious. We extracted the spatial distribution, center of mass displacement, and other inversion parameters from S. alterniflora using GF satellite images, and analyzed the spatio-temporal change pattern of S. alterniflora and the trend of evolution of wetland landscape. The results showed that: (i) S. alterniflora in the study area has the characteristics of aggregated distribution with areas of 52.22 km² , 46.52 km² , and 51.66 km² in 2013,2017 and 2021, respectively. (ii) North of the33° latitude line changes from band distribution of S. alterniflora to cluster distribution, and south of the line spreads to the sea; the 32.8° latitude line has a vacancy of S. alterniflora in 2017, and the natural diffusion invades the area again. (iii) The number and average area of patches, the amount of annual change, and the annual rate of change varied considerably, and the weighted center of mass point showed a southeastward movement. This study has important data support and ecological significance for carrying out conservation and restoration of natural heritage sites.

This study applies the ENVI deep learning 1.2 version, which is a module based on TensorFlow for training deep learning models and implementing object detection model training and classification. TensorFlow is Google's secondgeneration open-source artificial intelligence learning system, which is a software library for implementing built-in framework learning of neural networks. The object detection tool uses RetinaNet convolutional neural network (CNN) to identify features in the image and extract elements based on the image's spatial and spectral characteristics through trained models. In this paper, aerial images of a graveyard area in Gutianxi Reservoir, Gutian County, Ningde City, Fujian Province were selected and used for training the model through multiple iterations to improve the relevance of model recognition of target elements, in order to achieve automatic extraction and analysis of the target area. This study is helpful in achieving precise management of forest graveyards and targeted management of scattered graves.

Aiming at the problem that the classical PolInsar height three-stage geometric inversion algorithm is susceptible to the assumption of terrain amplitude ratio and surface phase when inverting vegetation height, a PolInsar vegetation height joint inversion algorithm based on RVoG model is proposed. The algorithm does not need to assume that the terrain amplitude ratio is zero. The parameters obtained by Esprit algorithm and Freeman two-component decomposition are used to optimize the model. In order to verify the algorithm, this paper uses the simulation data generated by the software PolSARpro of the European Space Agency ( ESA ) to perform vegetation height inversion experiments. The results show that the PolInSAR vegetation height joint inversion algorithm based on RVoG model is better than the three-stage inversion algorithm.

The advancement of topography detection technologies over the whole ocean has led to a significant rise in the quantity of high-resolution and comprehensive data. This increase in data availability serves as a fundamental basis for the investigation of topography and geomorphology features in the deep sea. This study introduces the auxiliary classification using ruggedness and backscatter intensity data by considering the regional characteristics of the Caiwei seamounts. Through establishing a mapping relationship between the geomorphology types and geomorphology factors of the Caiwei seamounts, the semi-automatic geomorphology classification algorithm is enhanced. This algorithm enables batch calculation of geomorphology factors and succeeds in dividing the Caiwei seamounts into 10 distinct geomorphology units. Finally, a geomorphology classification map of the Caiwei seamounts is generated. The present methodology demonstrates a high level of accuracy in identifying various geomorphology types, including Summit edges and seamount ridges. This research is of great significance in studying the impact of seamount morphological alterations on the distribution features of biological organisms and the processes related to metallogenesis.

High-quality training samples are crucial to the accuracy of land cover classification, and traditional sample collection is mostly manual, which is not only time-consuming and labour-intensive, but also not adaptable to the study area with a large range, so this paper proposes an automatic sample collection method based on migration technology to screen out the training samples of Xiongan New Area with unchanged target years compared to the reference years for land cover mapping in the study area, which solves the problem of shortage of training samples for long time series land classification. The problem of shortage of training samples. In this paper, firstly, 10,000 sample points are randomly and uniformly selected based on the ESA_CCI dataset, and secondly, the spectral differences between the reference year2020 and the target years 2018, 2019,2020 and 2021 Sentinel-2 images are calculated, and the Euclidean distance (ED, Euclidean distance) and spectral angle (SAM. Spectral Angle Mapper) as the best magnitude and similarity metrics for bi-chronological variation monitoring, and samples with similar spectral distances and spectral angles tending to 0 are screened as training samples based on certain thresholds, and then the labelled values of the ESA_CCI dataset are assigned to the training samples, and spectral indices and texture analyses, such as NDVI, MDNWI, and NDBI, are added. Combined with the random forest (RF) classifier in GEE (Google Earth Engine) to complete the land cover mapping of Xiongan New Area, and an overall classification accuracy of 84% was obtained. Overall, the method proposed in this paper has high potential for land cover monitoring without sufficient training samples.

Lakes are an important component of national resources and an important driver of sustainable urban development. This paper selects lakes within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the research object, uses Landsat remote sensing data as the data source, and extracts lake data for a total of eight periods from 1986 to 2021 by combining the Modified Normalized Difference Water Index (MNDWI) model with manual visual interpretation. Conclusions are as follows: 1) The overall scale of the lakes in GBA during the study period shows a process of "Decline-Increase-Rise Fluctuating-Decline rapidly"; 2) The morphology of the lakes is generally stable, but there is a trend of gradual regularisation; 3) The centroid of the centre of mass of the lakes shows an obvious trend of shifting towards the northeast.

Soil erosion constitutes a critical environmental issue with far-reaching ramifications. Vegetation cover has been identified as a key factor in mitigating soil erosion. This study utilized remote sensing data and cloud computing resources provided by Google Earth Engine (GEE) to compute the land cover classification and Fractional Vegetation Cover (FVC) of the Minjiang River Basin in 2020. Subsequent to the utilization of the CSLE model incorporating precipitation data, Digital Elevation Model (DEM) data, and other relevant data, an assessment of soil erosion in the Minjiang River basin during 2020 was conducted. Furthermore, the correlation between FVC and soil erosion modulus was quantitatively examined. Our findings demonstrate that the predominant land cover type in the Minjiang River basin is forest and grassland, followed by arable land, with water having the smallest coverage. Over 65%of the study are a exhibits a FVC exceeding 0.8, indicative of a generally high level of vegetation coverage. The soil erosion modulus exhibited a marked decline with increasing FVC within the FVC intervals of 0-0.15 and 0.45-0.8. While the change of soil erosion modulus with FVC under other ranges was relatively flat. As such, erosion control measures may be more effective when implemented in the FVC ranges of 0-0.15 and 0.45-0.80. These findings provide decision-making references for governmental departments to formulate targeted soil and water conservation measures.

This article uses the post-mission method to evaluate the accuracy of Real-time Precise Point Positioning in the marine environment. For this purpose, the GNSS raw data and the Starfix G2 RT-PPP solution output by the Seastar Fugro9205receiver are collected in the vessel. The positioning accuracy of the post-mission solution and the real-time solution is compared by RTKLIB software. The results indicate that when using the GPS-only constellation as a reference, the real time two-dimensional positioning accuracy of Starfix G2 is about 4.77 cm, and the three-dimensional positioning accuracy is about 10.52 cm. In the marine environment without a reference station and cellular network, this experiment can achieve centimeter-level high-precision positioning with a single GNSS receiver, which reflects the availability and ease of use of Starfix G2 RT-PPP technology and can meet the requirements of centimeter-level precision applications in the marine environment.

Monitoring the wakes of ships at sea is one of the important applications of ocean remote sensing. Extraction of ship wake can be done using either synthetic aperture radar (SAR) images or optical images containing ship wake. However, wake detection based on SAR images or optical images can only obtain two-dimensional features of wake, although they are indeed three-dimensional (3D). In this paper, we demonstrate for the first time that 3D Kelvin wake can be retrieved by using the observation data of Tiangong-2 interference imaging radar altimeter (TG2-InIRA). The TG2-InIRAadoptsnear-nadir incidence (1° 8°) and a short interferometric baseline, the acquired images exhibit quite different features from any other SAR images (20°  60°), and the obtained high-quality interferometric phases can be applied to reconstruct the 3D ship wake. The reconstruction method is described in detail along with intermediate and final results presented.

The AFC-900 aerial camera developed by the Beijing Institute of Space Mechanical and Electrical Engineering has a total pixel of more than 900 million. It is the world's leading ultra-large format plane array aerial camera. It has large format, high precision, high operating efficiency, etc., and meets the requirements of 1:500 large scale drawing accuracy [1]. In order to verify the application effect and accuracy of the camera, the camera has completed a large number of flight tests. This paper has organized the verification test according to the requirement of 1:500 large scale drawing accuracy. It has been verified that the AFC-900 aerial camera DLG conforms to the 1:500 digital line drawing plane accuracy and elevation accuracy, the DEM basically conforms to the first-level accuracy of the 1:500 digital elevation model accuracy index, and the DOM conforms to the 1:500 digital orthographic accuracy. It can be widely used in many fields of aerial photogrammetry, such as topographic mapping, updated GIS database, aerial surveying and mapping, topographic surveying, land resources survey, environmental monitoring, ecological environment survey, urban fine management and disaster emergency monitoring, evaluation, etc.

This study examines the creation of an advanced model for Low Earth Orbit (LEO) satellites within the framework of Global Navigation Satellite Systems (GNSSs). The growing interest in LEO satellites, due to their potential navigation and communication benefits, has necessitated a standardized model for LEO broadcast ephemeris. Current models often overlook key factors like orbital altitude, eccentricity, and inclination, compromising reliability. We offer a refined19-parameter model considering these factors. Our methodology for parameter selection is rigorous and validated using real LEO satellite data. Despite having fewer parameters, our model offers improved accuracy, reliability, and efficiency for LEO satellite navigation.

The Qinghai-Tibet Plateau's harsh natural environment poses a significant risk to life, making environmental risk assessment crucial for developing comprehensive risk prevention measures. In order to establish the Extreme Environment Index (EEI) zone for the Qinghai-Tibet Plateau, a geographically weighted regression model was utilized in this study. Various natural factors, such as altitude, relief degree of the land surface, land cover index, temperature-humidity index, river network density index, human comfort, wind-chill index, and absolute oxygen content, were carefully chosen for analysis. The research findings reveal a range of EEI scores between 0.37 and 4.39 across the Tibetan Plateau, signifying a gradual intensification of environmental extreme conditions from the southeast to the northwest regions. The Changtang plateau stands out as the most environmentally extreme region, covering the largest area on the Qinghai-Tibet Plateau. This area is characterized by numerous mountains, which contribute significantly to its proportion. On the other hand, the zone with relatively milder environmental extremes encompasses the river valleys along the plateau's periphery and the Hengduan Mountains. Among these areas, the Tibet-South Valley stands out as having the least extreme environmental conditions, occupying the smallest geographical area.

Marine remote sensing, as a representative interdisciplinary field, has garnered increasing attention in recent years, leading to a surge in relevant scientific literature. Focusing on "Marine remote sensing" as the study subject, this paper retrieved global scientific literature related to marine remote sensing published between 2010 and 2020 from Web of Science Core Collection database. Complex network analysis and centroid migration algorithms were adopted in the study to reveal the collaboration patterns and migration characteristics of research productivity in the field of global marine remote sensing. Additionally, the K-core analysis method was employed to identify core, bridge, and peripheral nodes within the national cooperation network, offering a comprehensive quantitative portrayal of the global marine remote sensing collaboration network. The findings reveal an upward trajectory in marine remote sensing paper publication and a concurrent increase in international collaboration. China's contribution to marine remote sensing is rapidly expanding, and there is an observable shift of the publication center from west to east. Analysis of centrality measurements and hierarchical structure underscores the consistent dominance of the United States as the central player in the collaboration network. Degree and betweenness centrality measurements indicate a strengthening trend in international collaboration for European countries and China, illustrate their pivotal participants role on the global scale. Changes in closeness centrality highlight the accelerated pace of information dissemination within the collaboration network.

Assessing the risk of epidemics is crucial for safeguarding public health. Current research on epidemic risk assessment mostly relies on administrative divisions, which fail to capture the spatial differences in risk within these divisions. Taking Shanghai as a case study, this research employs geocoding techniques to spatialize the distribution of cases within administrative regions. It combines this information with geospatial big data that exhibits a strong correlation with population exposure rates as risk factors. Using GIS technology, the data is spatialized, and a relationship between risk factors and the distribution of new cases is established through geographic detectors and geographically weighted regression models. This approach enables the assessment of epidemic infection risks in different regions within administrative divisions based on the spatiotemporal variation of case distribution. The results demonstrate that the assessment method developed in this study effectively reflects the infection risks in different areas within administrative divisions. The risk index generated by the model exhibits a strong Spearman correlation coefficient (p = 0.869, p < 0.001) and a high coefficient of determination (R2 = 0.938, p < 0.001) when compared to the actual distribution of new cases. This confirms the accuracy of assessing infection risks across different spatial areas. The methodology proposed in this study can be applied for epidemic risk assessment during public health emergencies and assist in formulating effective prevention and control policies.

The implementation of ecological engineering such as afforestation and reforestation in the Loess Plateau over the past two decades has brought significant changes to its land use and land cover (LULC). However, the differing spatial resolutions of land use data from various sources hinder an objective analysis of the effects of national ecological projects on vegetation growth in the Loess Plateau. Based on the commonly used MODIS LULC data and the latest CLCD LULC dataset from Wuhan University, the land use transition matrix calculation and change pixel detection were conducted for the period between 2001 and 2019 in the Loess Plateau. The analysis of the CLCD LULC data revealed that grass, cropland, and forest dominate the land use types in the Loess Plateau. Over the past two decades, approximately 15% (around 97000 km² ) of the Loess Plateau experienced land use changes. During this period, the forest and grass areas increased by 14025 km² and 7262 km² , respectively. These increases were mainly due to the conversion from cropland and shrub, which saw decreases in area by -18843 km² and -1671 km² , respectively. The patterns of land use changes were consistent across the seven provinces and regions within the Loess Plateau, with notable afforestation effects observed in Shanxi and Shaanxi provinces, where forest increased by 6303 km² and 4143 km² , respectively. Inner Mongolia primarily witnessed grassland restoration, resulting in a substantial growth of 5879 km² in grass. However, the results obtained from MODIS LULC data were contradictory. According to these results, the cropland area in the Loess Plateau increased by 20688 km² , while the grass area decreased by 26635 km² between 2001 and 2019, constituting 32.4% and 41.7% of the total changed area, respectively. The study outcomes provide valuable insights for scientifically analyzing the ecological benefits of afforestation and reforestation projects in the Loess Plateau, serving as a reference for improving the accuracy of carbon sink estimation in ecological engineering efforts.

Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Synthetic Aperture Radar (SAR) is a kind of microwave remote sensing equipment, which has the advantage of all-weather and all-day, and can realize large-scale and periodic crop phenological monitoring. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. A stacking ensemble learning algorithm using time series Sentinel-1A SAR images for winter wheat phenology classification was proposed in this paper based on multiple machine learning models, including Random Forest (RF), Support Vector Machine (SVM), K-nearest Neighbor(K-NN), Naive Bayes (NB) and BP Neural Network (BP) models. The experimental results showed that, comparing with each single model, the stacking ensemble learning algorithm proposed in this paper had the optimal performance, with the highest overall recognition accuracy of 81.40%, demonstrating its effectiveness and application potential for winter wheat phenology identification.

To explore the spatiotemporal variation characteristics of drought in Henan Province, this article uses GRACE satellite data from 2004 to 2015 and constructs a water storage drought index (GRACE-DSI) based on GRACE time-varying gravity field data to monitor drought events in Henan Province during the research period and explore the spatiotemporal evolution characteristics of drought. The results indicate that there were 4 drought events in Henan Province during the research period, with the most severe occurring from August 2012 to January 2015; The severe drought areas in Henan Province are mainly distributed in the central and western regions and some parts of the northern region, with the worsening of drought in the northern region being the most severe; The changes in groundwater reserves have a significant impact on the GRACE-DSI changes in Henan Province, followed by precipitation factors. GRACE-DSI has a good correlation with SCPDSI, which can better reflect the drought situation in Henan Province.

The southern part of Shenfu Coalfield is located in the hinterland of the Mu Us Desert. Due to long-term coal mining, the geological structure has been damaged, causing serious impacts on the ecological environment of the mining area. Under the comprehensive factors of global warming, northward movement of precipitation lines, and ecological restoration and protection within the mining area, the overall ecological environment change in the area is showing a positive trend, but there are still negative changes in some areas. In order to conduct a detailed analysis of the impact of underground coal mining in Shenfu Coalfield on the ecological environment changes in the sandy grassland area, Landsat 8 satellite remote sensing data from the research area since 2014 were selected. Through remote sensing image preprocessing and indicator parameter calculation, the normalized vegetation index (NDVI) and vegetation coverage (FVC) were compared and analyzed to analyze the ecological environment changes in the area, Overall, the vegetation coverage in the study area reflects a downward trend from 2014 to 2016, and an overall upward trend from 2016 to 2022. Comprehensive analysis shows that changes in vegetation ecological environment are influenced by coal mining subsidence, groundwater level decline, land occupation damage, and artificial surface engineering activities. Overall, the increase in natural precipitation and artificial ecological environment restoration measures since 2016 are the main factors for the gradual improvement of the ecological environment in mining areas.

Coal mining in loess plateau environment has caused serious damage to land resources and ecological environment, and it is imperative to construct a fine DEM of the mining area. In this paper, a series of point cloud construction DEM processes are investigated and discussed with respect to the various errors affecting the UAV airborne LiDAR survey system in constructing a fine digital elevation model (DEM) of the mining area under the complex terrain of the Loess Plateau. Firstly, the applicability of several mainstream point cloud filtering and point cloud interpolation algorithms in the Loess Plateau mining area is compared and analysed, and it is found that the progressive triangular mesh encrypted filtering algorithm and the inverse distance-weighted interpolation algorithm show better point cloud filter classification accuracy and interpolation accuracy for constructing DEMs in the study area. Finally, the constructed DEMs were denoised using a locally weighted regression algorithm to further reduce the noise error in the DEMs, and the feasibility of the DEM denoising method was verified by comparing the error of this process with that of the DEMs constructed using the inverse distance-weighted algorithm. This study has led to a significant improvement in the accuracy of airborne topographic survey results in loess plateau mining areas.

TOD(Transit-Oriented Development) is a public transportation-oriented development mode, and as a spatial activity managed by the city will inevitably have an impact on the planning area. How to represent this spatial activity by means of indicators and formulate corresponding rules based on the indicators to reflect the changes generated by urban planning has become the focus of research. This study aims to explore and utilize key indicators that represent the characteristics of TOD scenes in order to realize the restoration of TOD urban scenes at the urban planning level. In this study, the key features of the TOD scene are analyzed in depth, and the indicators that can accurately reflect the characteristics of the TOD city are selected by comprehensively considering factors such as land use, distribution of transportation facilities, and public quality. The corresponding building generation rules are formulated according to the calculation of these indicators. By applying these representative indicators at the level of urban 3D (Three dimensional) modeling, the conditions of TOD city scenes can be better restored and simulated. This approach is expected to provide valuable references for urban planning decision makers to help better understand and analyze the characteristics of TOD scenes so as to optimize the planning and development of cities. In addition, this study also provides a new idea and methodology for modeling TOD scenes, which provides useful reference for future related research and practice.

The selection of high-quality point targets, such as persistent scatterer (PS), is very important for GB-SAR interfero-metry deformation inversion. Considering the limitation of single-threshold selection method of persistent scatterer (PS) point target for ground-based synthetic aperture radar (GB-SAR), a combined threshold selection method is proposed in this paper, and a quality assessment method for point target selection is designed to evaluate the effectiveness of the point selection. The efficiency of both the single-threshold method and the combined-threshold method was compared using the real-case continuous GB-SAR images in the Geheyan dam area. The analysis results demonstrate that the proposed threshold approach successfully integrates each of the single-threshold method's advantages. It achieves a higher selection rate of high-quality PS with lenient thresholds while also enhancing overall computational efficiency. This approach ensures robust support for subsequent time series analysis of GB-SAR interferometry.

Owing to the interplay of natural forces and human-driven developmental endeavors, the existing continental coastline could no longer precisely depict the foundational state of Shandong Province’s coastline. Shandong Province embarked on a fresh round of continental coastline surveys. Based on remote sensing and field investigation, this survey obtained the location of the coastline and related data more efficiently and accurately. Based on the results of this survey, this paper studied the overall changes of the coastline in the past ten years, delves into the examination of typical regions like the Yellow River Delta, Laizhou Bay, employing them as case studies to dissect the principal changes and underlying causes behind the shifts in the province’s continental coastline.

Based on the current high-precision Global Navigation Satellite System (GNSS) velocity and strain rate field calculations, this article analyzes the crustal deformation characteristics of the Fenwei Graben System. The research results show that the Fenwei Graben System as a whole has weak tensile deformation characteristics, while the strain rate characteristics inside the Shanxi Graben are relatively complex. The tensile strain is mainly distributed in the northern region of the study area, with a variation value of 10 to 20 nanostrain/yr. The compressive strain is mainly concentrated in the central and southern regions of the Ordos block and the Taihang Mountain tectonic area, with a variation value of -20 to 5 nanostrain/yr. The maximum value of shear strain rate is located in the Datong Basin, Xinding Basin, and Taihang Mountain area in the northern part of the Fenwei Graben System, with a maximum variation value of 15 nanostrain/yr. The low value area of shear strain rate is mainly distributed in the Taiyuan Basin, Yuncheng Basin, and Yinchuan Basin on the northwest side of the Ordos Block, with a variation value of less than 5 nanostrain/yr. The distribution pattern of shear strain rate indicates that the boundary faults of faulted basins have different modes of movement. There is a compressive movement between the Datong Basin and the Hetao Basin in the northern part of the Fenwei Graben System, while the Weihe Basin and Yuncheng Basin in the southern part of the Fenwei Graben System are in a tensile state, indicating that the overall counterclockwise rotation of the Ordos block is not significant, and its motion characteristics are more in line with the "frame wobbling" model.

The comparison between the satellite orbit coordinates obtained through broadcasting ephemeris calculation by Beidou-3 satellites and precise ephemeris reveals a distinct periodic characteristic in the orbit error. To capture this periodicity, we employ the autocorrelation function to accurately determine the period value, followed by applying sum of sine functionality to fit an approximation curve with relevant parameters. Furthermore, we utilize the K-means algorithm to optimize these parameters, extracting hidden periodic functions from the overall error curve to distinguish signals from noise. Consequently, corresponding correction models are established for enhancing real-time navigation positioning accuracy. The results demonstrate that this method enables accurate prediction and compensation of Medium Earth Orbit (MEO) and Inclined Geosynchronous Satellite Orbit (IGSO) satellite positions, thereby significantly improving orbit prediction precision.

The classification of dominant tree species holds significant practical importance in forest resource management and ecological research. However, in vast mountainous regions, the identification of dominant tree species faces challenges, and machine learning methods often require manual feature selection. Hence, this study leverages the Google Earth Engine (GEE) cloud computing platform, combining transfer learning and deep learning. Using Sentinel-2 satellite imagery and forest resource data from the Second National Forest Inventory, three deep learning algorithms RESNET34, RESNET50, and RESNET101 are employed. Pretrained weights from the ImageNet dataset are fine-tuned using transfer learning to adapt to the specific task of dominant tree species classification. Principal Component Analysis (PCA), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are computed. After 100 epochs of training, the combination of ResNet50 and PCA+NDVI+EVI in RGB format achieves the highest validation accuracy among all algorithms, reaching 77.02%. The research results indicate:(1)Combining PCA, NDVI, and EVI improves classification accuracy.(2)The ResNet50 deep learning algorithm is suitable for remote sensing classification.(3)Combining transfer learning, deep learning, and the GEE cloud platform effectively enhances the classification accuracy of tree species, making it applicable for dominant tree species classification in extensive mountainous areas.

To achieve the objective of reducing carbon emissions, countries worldwide are actively advancing the development of new energy sources, including wind power. This article introduces an enhanced method that utilizes deep neural networks based on YOLOv5 to identify and extract wind turbines from remote-sensing imagery by analysing high resolution remote-sensing images captured by multispectral sensors on satellites.

Remote sensing is a detection technology that has gained increasing prominence due to the global climate change and the degradation of ecosystems. In an effort to examine the current state of research and key areas of interest regarding remote sensing in the domain of ecological services, the core collection database of WOS papers was employed for retrieval. This involved querying papers published between 1994 and 2023 using specific keywords. Utilizing the literature analysis tools and Histcite provided by WOS, an analysis was conducted on the publication and citation trends among fundamental knowledge papers over different years, countries, journals, and academic disciplines. Subsequently, the CiteSpace software was utilized to identify research hotspots and create a knowledge map. The study's findings indicate that China holds a leading position in global remote sensing research related to ecological services. Moreover, there has been a notable increase in research activities concerning remote sensing in the ecological services field, accompanied by shifts in research focus. More and more studies are now directed towards addressing challenges posed by climate change, conserving biodiversity, establishing patterns for ecological security, and promoting ecological protection and restoration. These areas have emerged as prominent frontiers in this field.

Currently, the frequency domain three-dimensional marine electromagnetic forward modeling predominantly focuses on high-resistivity and large-scale anomalies, lacking comprehensive analysis for low-resistivity and small-scale distributions characteristic of deep-sea hydrothermal sulfide deposits. To investigate the frequency parameters of the transmitter system under low-resistivity anomalies, as well as the excitation and propagation characteristics of electromagnetic fields in different geological environments, this paper, within the framework of marine frequency domain electromagnetic methods, employs the Finite Element Method (FEM) to simulate the three-dimensional electromagnetic field response in a multi-turn small-loop transmitter situated in a borehole domain. Initially, the horizontal transmitter coil is equivalently represented as a magnetic dipole source, and the frequency domain Helmholtz equations for vector and scalar potentials are established. Subsequently, the electromagnetic field calculation space is discretized using unstructured grids, and the solution of the equation system with large sparse coefficient matrices is achieved using the BiCGSTAB iterative method, known for its computational efficiency. Furthermore, a comparison with results obtained from the calculation of Bessel function integrals using a uniform half-space model and extrapolated orthogonal method is employed for accuracy verification, revealing good agreement with the nonstructured finite element three-dimensional forward modeling results. Numerical simulation results indicate that in deep-sea environments, lower frequencies perform well, with reasonable skin depths and high resolution achieved in the range of 500 to 800 Hz. By setting the transmission frequency to 700Hz, this study summarizes the differential electromagnetic field response patterns for various ore vein characteristics, there by laying the foundation for subsequent three-dimensional inversion studies.

Modern architecture plays an essential role in the fields of land surveying and mapping, urban planning and change detection. Aiming at the problems of large workload, long cycle and poor timeliness of traditional field surveying and mapping of modern architectures, this paper proposes the Mask-RCNN network model and introduce the ECA attention mechanism to reflect the self-made street view image data set of building facade information, to quickly and accurately identify urban modern architectures. And compared with SVM, U-net and Mask-RCNN building extraction algorithms. Experiments show that the proposed method can extract modern architectures efficiently and accurately. For the same data set, the extraction result is 2.6 % higher than the original Mask-RCNN algorithm and is better than the comparison algorithm.

The typical GB-SAR image coordinate transformation approach conveniently ignores the impact of radar sensor attitude, resulting in an imaging projection plane that is not horizontal and bias in the 3D laser point cloud's slant range projection (SRP). This research proposes a point cloud SRP calculation approach that incorporates the radar attitude inclination and provides a detailed formula. To achieve deformation information extraction of the building façade, the high-quality pixels and point cloud projected using the exact formula are matched on this basis. The experimental analysis demonstrate that the suggested approach can reduce the impact of sensor attitude inclination, enhance the coordinate transformation precision of GB-SAR pixels, and offer assurance for the following integration and analysis of GB-SAR monitoring results with other technologies

Long-term, extensive overexploitation of coal mining resources has negatively impacted the ecological environment while bringing about economic prosperity. This has resulted in frequent mining geological disasters, particularly coal mining collapses, endangering the lives and property of locals and necessitating an urgent need for ecological restoration and all-encompassing management. For ecological restoration and thorough management, it is necessary to map out information on coal mining collapse on a national scale, including the location, scale, and waterlogging situation. On a national level, however, relatively few academics have examined coal mining collapse until this point. This study uses domestic high-resolution remote sensing data to track coal mining collapse across the country. The findings reveal that by the end of 2018, there were 35,453 coal mining collapse areas with an area of 21,957 km² ; the area of collapse pits was 14,859 km² , the area of waterlogged collapse pits was 1,716 km² , the area of restoration and treatment of collapse pits was 2,712 km² , and the overall rate of restoration and treatment of collapses was 15.43%. The ecological restoration models of coal mining collapses may be essentially categorized into five categories: the reclamation models for agriculture and forestry, fisheries and ecological wetland, landscape management, urban development, and new energy industry. The findings of this study can, to some extent, be used as data support for the supervision and thorough prevention and control of coal mining collapse in China as well as a source of reference for the ecological restoration of coal mining collapse, both of which have research significance.

Mangrove is an important coastal wetland ecosystem found in tropical and subtropical regions and, as a natural barrier against marine dynamics along coastlines, help prevent and mitigate coastal erosion. In this study, remote sensing satellite imagery from ZY3, GF-1, and Landsat are employed to monitor changes in mangrove within the Phang Nga and Nakhon demonstration zones of Thailand from 2000 to 2020. The results show that over the past two decades, the man grove resources in the Phang Nga demonstration zone have maintained a relatively stable state, whereas that in the Nakhon demonstration zone has grown by 15.8% due to the effective implementation of restoration policies. It can be concluded that the establishment of mangrove natural reserves and the practice of artificial planting of mangrove trees have made substantial contributions to the conservation of mangrove resources within these areas.

In response to the problem of minimizing the batch requirements for imaging satellites, this paper proposes a requirement coordination method for imaging satellite that considers temporal and spatial similarity. Firstly, the similarity of imaging satellite requirements was defined from the perspectives of observation time, target location, and remote sensor type. On this basis, combined with the imaging characteristics of point targets and area targets, an integrated method for imaging satellite requirement merging, requirement integration, and requirement conflict processing of point targets and area targets was designed. Finally, based on the prototype system verification, it was found that requirements has been greatly reduced through requirement coordination, which helps to achieve optimal utilization of satellite resources.

Deep learning (DL) methods have achieved promising results in high resolution (HR) remote sensing image (RSI) segmentation. While DL approaches rely heavily on large-scale training datasets, and a method trained on a dataset generally cannot be used to segment classes that the dataset does not contain. Existing RSI datasets mainly cover buildings, roads, vegetation, water, moving objects, etc. In this paper, we focus on a class of objects that have been rarely involved in previous datasets: offshore farms. The monitoring and management of offshore farms is important for the sustainable development of the aquaculture industry, and in this work, we provide an HR remote sensing dataset of offshore farms, named HROF. This dataset uses two types of sensors, i.e., multispectral and synthetic aperture radar (SAR). The multispectral data consists of 30 images with a resolution of about 1 m, including both raft and long line farms. The SAR data contains 41 images with a resolution of 3 m, in which the objects are breeding ponds. Objects in each image are manually annotated at the pixel-precise level. Furthermore, we provide baseline segmentation method and results on HROF and compare with mainstream methods. The dataset is available at https://github.com/FrontierQ/HROF.

Mangrove grow low-energy in tropical and subtropical coast intertidal zone. Through periodic flood tide, mangrove plants as the main body of evergreen shrubs or trees of tidal flat wetland woody biological communities, including herbaceous species. Mangrove grow on land and sea borders tidal flats, and it is a special kind of terrestrial and Marine ecosystems. With the rapid development of sensor technology, get all sorts of rate of high resolution remote sensing image has become possible. With high spatial resolution of the colored light panchromatic images (PAN) to improve the spatial resolution multi spectrum image (MS) of the image fusion technology is one of research hotspots in the field of high resolution earth observation, in many remote sensing application plays an extremely important role. In this paper, the application of worldview3 multispectral and panchromatic image fusion with four methods to. And then I get image fusion results through fusion index for further analysis. Based on the analysis of the specific fusion algorithm fusion effect of each index in the mangrove differences influence. The classification results of each kind of fusion image analysis discussion, find out the most suitable for specific fusion method to each type of mangroves. we combine with experimental data for each kind of method for comprehensive analysis and conclusion.

The Yunnan Guizhou Sichuan region has numerous rivers and is one of the regions with the richest water resources in China. Utilizing GRACE gravity satellite data and GLDAS hydrological model to invert groundwater reserves in the Yunguichuan region from 2003 to 2016, and verifying them with precipitation data. Using the empirical orthogonal function method to decompose the changes in water reserves in the region, analyze the factors affecting the interannual changes in groundwater reserves in the Yunguichuan region, and verify the correlation with TRMM precipitation data. The results indicate that GRACE and GLDAS can effectively invert groundwater reserves, with consistent trends and obvious seasonal variations; The variance contribution rate of the first two modes of significant annual cycle of water reserves change is 82.07%, of which the variance contribution rate of the first mode reaches 73.12%, and there is a relatively obvious annual cycle, which is highly consistent with the precipitation data in the same period. It can be concluded that precipitation plays a vital role in the change of groundwater reserves in Yunnan Guizhou Sichuan region

Artificial microhabitats offer shelter for intertidal organisms, enhancing biodiversity and abundance. Research on the effects of artificial microhabitats on intertidal organisms is mainly field experiments, with fewer studies on physical modeling and numerical simulation. It leads to the mechanism of artificial microhabitat to improve habitat space is not clear. Hydrodynamic conditions have a significant impact on living space. There are few field experiments on hydrodynamic conditions since the experimental conditions are limited, and corresponding numerical simulation studies have not been developed. In this study, a numerical model under different hydrodynamic conditions was developed. The improved effect of artificial microhabitats on biological habitat space under different conditions was analyzed and its improvement mechanism was discussed. The experimental conclusions can quantify the effects of microhabitat structure on intertidal organisms and guide the technical design, which will contribute to the coordinated development of the economy and ecology.

With a variety of techniques applied on GNSSs, its open service’s precision performance has achieved to millimeter level, which is close to its precision theoretical extreme value. And people play much more interest to explore its safety and reliability ability. In another word, we concern well about the Global Navigation Satellite Systems’ (GNSS) integrity. With a prominent advantage, low earth orbit satellite system (LEO) attracts technology researchers’ much more attention. And now it is considered as an additional GNSS for positioning, velocity and timing (PVT) application. In this paper, we study about the performance improvement on BeiDou Navigation Satellite System (BDS) advanced receiver autonomous integrity monitoring (ARAIM) users with simulated LEO constellation. The relationship between the constellation construction parameters of different simulated LEO systems and their corresponding protection level improvements will be considered well.

The rock body of the slope of the open pit mine will be affected by water, wind, sunlight, chemistry and other influences for a long time, which will cause the rock body to produce weathering phenomenon, and the weathering of the rock body will increase the difficulty of the identification of the structural surface, and the data volume of the point cloud is large, and the efficiency of the identification of the structural surface of the rock body needs to be improved, this paper adopts two sets of data for the identification of structural surfaces, and adopts the sphere fitting algorithm for the estimation of the normal vectors of the rock structural surface, and adopts a parallel The octree algorithm is used to segment the point cloud data of the structural surface of the rock body, and the Euclidean clustering algorithm based on the normal vectoris used to identify the structural surface in each subspace in parallel, and the structural surface is identified by the Euclidean clustering algorithm, the regional growth algorithm, the K-mean algorithm, and the algorithm proposed in this paper, and it can be obtained that the efficiency of the structural surface identification using the algorithm of this paper is significantly improved by experimental analysis, and it provides methodological support for the subsequent slope deformation analysis and early warning. deformation analysis and early warning to provide methodological support.

In the wake of intensified global climate change and accelerated urbanisation, significant impacts on urban transportation, economic activities, and the well-being of residents are exerted by severe convective weather events, including thunderstorms, gales, hailstorms, and tornadoes. With the advent of the big data era and the evolution of computational technologies, deep learning has been identified as a transformative approach in the realm of short-term meteorological forecasting. In this study, a sophisticated deep learning model, designed for the extraction of microphysical features from dual-polarised radar data, is presented. Through this model, the temporal progression of microphysical features is adeptly captured. To address the regression-to-average challenge, often encountered in many data-driven models, a transformer-based architecture reinforced with residual networks is introduced, ensuring that heightened responsiveness to output variations is achieved. In the concluding experiments, the immense potential of dual-polarised radar datasets for enhancing the precision of imminent convective weather predictions is underscored.

In the process of underwater buried target detection, the underwater buried target is replaced by a magnetic dipole model. In view of the low signal-to-noise ratio of the target signal, the orthogonal basis decomposition detection algorithm is used to greatly improve the SNR ratio of the magnetic target signal in water and effectively detect the magnetic target. A cruciform detection method is used to determine the location of the magnetic target. The simulation results show that this method has certain engineering application value.

The concentration of suspended solids is a critical parameter for water bodies, impacting optical properties like transparency and color, as well as the overall ecological environment. In this study, the field measurement data of total suspended matter concentration obtained from the sea area near the Yellow River estuary is used to train the inversion model of total suspended matter concentration and carry out the accuracy verification of the model inversion. Firstly, the inherent optical properties are calculated using the QAA algorithm based on Sentinel-2 data; then a linear regression model is established with the field-measured suspended matter concentration data, thereby inverting the suspended matter concentration. The model's validation accuracy, with an R 2 of 0.5557, demonstrates its effectiveness. The suspended matter concentration in the Yellow River estuary has obvious spatiotemporal characteristics: the suspended matter concentration in the Yellow River estuary is relatively high, and the concentration gradually decreases from the estuary outward; seasonally, the distribution of suspended matter concentration changes is high in summer and winter, and low in spring and autumn.

In comparison to technologies like photogrammetry and LIDAR, InSAR systems can image ground targets regardless of day or night, weather, or other factors. InSAR technique with phase measurement provides a higher measurement accuracy than SAR stereo mapping technology. As a result, D-InSAR technology can supply both highly precise and real-time topographic data and data sources for research such as seismic monitoring. In this paper, taking the Gashi earthquake with a magnitude of 6.4 that occurred in Xinjiang, China, on January 19, 2020 as an example, the digital elevation model (DEM) within the earthquake zone was updated and the surface deformation dynamics before and after the earthquake were obtained using the three-track method in D-InSAR for the 3-view open-access Sentinel-1Asatelliteimages. The obtained DEM results are randomly selected at sampling points in areas far from the earthquake-prone areas in this work, and the SRTM3 DEM data transformed to the same elevation reference are utilized as the presumed true values for future analysis and comparison. The DEM extracted using the D-InSAR technique from two pre-earthquake images was used for topographic phase simulation, then differential interference with the post-earthquake images, and finally surface displacements in vertical directions were extracted to verify the effectiveness and potential of D-InSAR in measuring the size of the Coseismic deformation field of earthquake and its spatial distribution.

During the construction of Huaneng Caofeidian Wharf, large particle size stone scattered in the sea by cofferdam construction, which were discovered in specific construction zone ,impacted on working processes. To ensure completed the project smoothly, this time geomorphology and shallow strata were detected in the obstacle area by using the equipment and investigation methods of bathymeter, side scan sonar and shallow formation profiler, etc. The distribution range of obstacles below the seabed surface and the mud surface is determined, which provides a reference for wharf construction and saves cost for engineering construction.

During high dynamic gravity measurements conducted on unmanned surface vehicle, the presence of low-frequency noise caused by the vertical and horizontal motion disturbances of the carrier in conjunction with the low-frequency excitation noise from the sensor, results in a direct mixture within the frequency band of the gravity signal. Admittedly, conventional filtering techniques such as finite impulse response (FIR) or infinite impulse response (IIR) filtering prove insufficient in eliminating the measurement noise, ultimately leading to a decrease in gravity measurement accuracy.In this regard, this paper proposes the use of the kalman smoothing method as a replacement for the traditional frequency domain low-pass filtering technique. This method allows for the identification of gravity anomaly information even in the presence of noise by employing optimal estimation methods.Given that the gravity measurement data is processed offline, this paper further utilizes the optimal fixed interval smoothing algorithm to process the gravity measurement data obtained from unmanned surface vehicle. This algorithm enhances the accuracy beyond what is achievable with traditional frequency domain low-pass filtering techniques. To validate the effectiveness of our proposed algorithm, we have conducted processing on real sea test data, confirming its efficacy.

Rainfall thresholds are the most used tool for predicting rainfall-induced thresholds worldwide. Nevertheless, a limited landslide catalogue hinders the definition of rainfall thresholds. Moreover, rainfall thresholds solely consider the rainfall characteristics and overlook another widely recognized indicator for landslide initiation, antecedent soil moisture condition. Thus, this study aims at defining credible hydro-meteorological thresholds for Lueyang county, Shaanxi Province using a landslide catalogue that exhibits a great imbalance between landslide occurrences and non-occurrences. The cumulated rainfall (E) and rainfall duration (D) thresholds were defined at several exceedance probability levels, considering the rainfall events not associated with landslides. The Bayesian approach was exploited to estimate landslide occurrence probabilities within the conditions of rainfall severity and antecedent soil moisture, where rainfall severity is determined by defined E-D thresholds, and soil moisture information is retrieved from a remote sensing reanalysis dataset (ERA5-Land). The results show that the defined negative E-D thresholds exhibit good reliability and robustness. Furthermore, by considering antecedent soil moisture, the predictive capability of hydro-meteorological thresholds shows a significant improvement.

The preceding algorithms are troubled by the singular problem of some formulas and the azimuth multi-value problem in the solution of trigonometric equations. In this paper, sine cosine theorem for spherical triangle is applied in the Mathematica computer algebra system to address and resolve the geodetic problem at special positions such as equator, meridian circle, south and north poles. The algorithm is optimized to avoid tedious quadrant determination. On this basis, a formally simple method for solution is proposed to be applicable under all circumstances, and greatly improve the accuracy of solution. As revealed by data verification, the improved algorithm is greatly efficient in computation, symbolically simpler, and highly universal, proving its significance to the guarantee for surveying and mapping.

The development of electrical detection technology and quantitative interpretation of results, 3D geological modeling, and the search for hidden mines have made conducting electrical 3D inversion and 3D visualization expression an important research topic. The three-dimensional nature of underground space determines the limitations of using two- dimensional profile measurement results to interpret three-dimensional underground geological bodies. 3Dinversionand3D visual expression are carried out based on the high-density electrical method 2D profile survey data in GuiLest BarQin mining area, Inner Mongolia. The continuous distribution of near east-west low resistivity anomaly zone reflects that there is a low resistivity structural zone in the contact zone of Jurassic and Devonian strata, and the structural attitude is nearly vertical; the polarizability anomaly body is distributed on both sides of the corresponding low resistivity structural belt. The strength and scale of the anomaly indicate that the mineralization is mainly distributed in the Jurassic strata close to the structural belt, followed by continuous mineralization in the deep Devonian strata far away from the structural belt. The mineralization projection on the surface corresponds to the IP anomaly, and has certain specificity with the Jurassic Tuff. The three-dimensional inversion and visualization results of high-density electrical method 2D profile measurement data in the mining area directly reflect the spatial occurrence of the structure and the spatial position relationship with mineralization, the exclusive relationship between mineralization and rock strata, and the corresponding relationship between mineralization and IP anomalies, indicating the direction for further exploration work.

Geohazard is a serious problem in Minxian County of Gansu Province, northwest China. A mid-sized landslide was selected for universal monitoring construction. Crack-meter, GNSS monitoring, soil moisture monitoring, real-time rain-gauge and alarm apparatus were used in the landslide monitoring network. Power supply of landslide monitoring instruments is from Li-ion battery and solar panels, providing sufficient power for 24h-per-day working. Data transmission of real-time data is based on near field communication and 2/3/4G mobile communication, with increasing reliability and cost reduction. Monitoring data were transmitted to national platform and provincial platform simultaneously, providing real-time data for both platforms. The monitoring data were analyzed automatically, using multi-module for landslide prediction and warning. When abnormal data were discriminated, SMS messages were delivered automatically to designated personnels for further processing. Based on the monitoring network and intelligent data platform, the landslide was under the inspection of instruments. On Sept. 16, 2021, the active trend was identified before the main sliding. Yellow, orange, and red alerts were sent to the professional staff, government officers and residents in the threat zone. People under risk were evacuated before sliding. Through the implementation of landslide universal monitoring projects, on the one hand, real-time monitoring of landslide deformation and environmental factors is realized, and a reference solution is provided for regions with similar problems.

Metro is an important component of modern urban rail transit. Monitoring deformations of urban metro lines is vital for protecting people and property. Sentinel-1A data with 36 scenes covering Jinan Metro Line 1 area are used for small baseline set differential interferometry to obtain the deformation rate and cumulative deformation of the area along the line. The Long-Short-Term Memory neural network model based on wavelet noise reduction is used to train and predict the deformation of the subsidence area. The results show that there is an uneven surface subsidence in the monitoring area, with a maximum subsidence rate of -24.96 mm/y and a maximum cumulative subsidence of -54.31mm. Subsidence phenomena is found in the Yanma Station to Jinan West Station section and the Wangfuzhuang Station to Zhaoying Station section. The Long-Short-Term Memory neural network model based on wavelet denoising can reasonably predict the development trend of surface subsidence and provide reference for the construction, operation, and maintenance of metros.

Forest fires are extremely hazardous, and forest fire sensitivity mapping can provide managers and planners with spatial information on forest fire susceptibility, which can help improve mountain fire prevention systems. In this study, 620 historical fire point data and 12 influence factors were selected, and multiple covariance analysis was used to analyse the correlation between the influence factors, eliminate the influence factors with high correlation and unfavourable to modeling, and normalize the influence factors using the frequency ratio method. In this paper, four different machine learning models, random forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGboost) and Boosted regression tree (BRT), are constructed, and the grid search or Bayesian optimization algorithm is applied to each model for hyper-parameter optimization, and the best parameters are selected and applied to the model. Finally, the model accuracy is evaluated using ROC curve and AUC. The results show that among the four hybrid machine learning models, the FR-RF model (AUC=0.88) and the FR-BRT model (AUC=0.97) perform better in forest fire risk assessment in Hunan Province.

Advanced aerial photogrammetry cameras are generally equipped with high-precision POS equipment, stable platform and automatic mission management software to form an aerial photogrammetry system with high measurement accuracy and operation efficiency. This paper presents a new generation of domestic advanced aerial photography system task management software, which adopts modular and parallel design and is composed of three functional modules: aerial photography task control, system hardware equipment control and aerial photography on-line monitoring. The software takes the automatic selection and determination of flight route and exposure waypoint as the logic operation core, and highly integrate the fully automatic management function of aerial photography tasks, hardware devices monitoring and control functions with online aerial photography monitoring functions, realizing diversified photography modes such as timing and fixed point, and meeting the needs of variable baseline aerial photography of complex terrain such as hills and mountains. The software has completed the large area 1:500 automatic aerial photogrammetry task with the domestic area array aerial photographing system, and improved the operation efficiency, automation, accuracy and task visualization level of the domestic area array aerial photographing system.

The Ground-Based SAR Dynamic Measurement System is capable of acquiring the time-displacement sequence of ancient pagodas, enabling effective assessment of their structural health. To extract the instantaneous resonance frequency of the pagodas from signals containing noise, the conventional Hilbert-Huang Transform often encounters mode mixing issues, leading to the influence of false components on the measurement accuracy. Therefore, this study employs a non-interference method based on normal time-frequency transform Theory to monitor the pagodas.The essence of the normal time-frequency transform Theory lies in a kind of linear filter, which exhibits strong anti-interference capability in extracting periodic signal components. This approach yields time-varying, unbiased harmonic instantaneous amplitude, frequency, and phase. The monitoring and analysis of the ground-based SAR dynamic detection signals from the Rangdeng Pagoda in Tongzhou District, Beijing, were conducted in this study. Experimental results indicate that the amplitude and frequency derived from the normal time-frequency transform Theory better match the actual vibration characteristics of the pagoda. Additionally, this method involves lower computational complexity compared to conventional approaches and places greater emphasis on continuous time-frequency analysis. These findings provide scientifically reasonable foundational data for analyzing the architectural characteristics and safety assessment of ancient pagodas.

Tropical cyclone (TC) disasters have a serious threat to social and economic development, so it is necessary to retrieve atmospheric temperature and humidity profiles in the typhoon region to provide timely and accurate information on the initial field of atmospheric humidity and temperature for numerical weather prediction, and to enhance the early warning capability of catastrophic typhoon weather. This paper addresses the retrieval of tropical cyclone temperature and humidity profiles over the western Pacific Ocean from the data acquired by the Microwave Temperature Sounder(MWTS) and the Microwave Humidity Sounder (MWHS) on Fengyun 3E (FY-3E) using the batch normalization and robust neural network (BRNN) algorithm. To improve retrieval accuracy, the FY-3E MWTS and MWHS observations in the TC region are classified according to different scattering conditions (clear, stratiform, convective), and the atmospheric temperature and humidity profiles in the TC region are retrieved by a deep learning method. The results show that, the root mean square errors (RMSEs) of the retrieved temperature profiles are less than 1.4 K, while the RMSEs of the derived humidity profile are less than 1.1 g/kg. In general, the retrieval algorithm can invert a reasonable TC thermal structure.

Atmospheric temperature and humidity profiles are key parameters in weather forecasting and climate studies. This paper addresses the retrieval of atmospheric temperature and humidity profiles from the data acquired by the Microwave Temperature Sounder (MWTS) and the Microwave Humidity Sounder (MWHS) on Fengyun 3E (FY-3E) satellite using deep learning neural networks. The four-layer back-propagation neural network (BPNN) and the back-propagation dendritic neural network (BDNN) are firstly constructed, and then they are trained and validated using the matching samples between FY-3E MWTS/MWHS data and the fifth European Centre for Medium-Range Weather Forecasts(ECMWF) Re-analysis (ERA5) data. The results show that the BDNN method is better than the traditional linear regression method and the BPNN method. Over ocean, the root mean square errors (RMSEs) of the temperature and humidity profiles retrieval are less than 2.0 K and 1.0 g/kg, respectively, whereas they are, respectively, less than3.0Kand 2.0 g/kg over land.

Two campaigns were undertaken within the expanse of the China Sea to assess the accuracy of the Trimble RTX technology. In the first campaign, the accuracy was tested through both static and dynamic modes. The GAMIT/GLOBK software and TRACK module were used to solve the GNSS data and the results were compared with Trimble RTX. In the static mode, the differentials emerged between the Trimble RTX post-processing technology and GAMIT/GLOBK, manifested as 0.60 ± 0.22 cm, -0.22 ± 0.33 cm, and -0.86 ± 0.91 cm along the x, y, and h directions, respectively. Meanwhile, compared with the GAMIT/GLOBK results, real-time data obtained through Trimble RTX exhibited disparities of 1.82 ± 0.85 cm, 2.13 ± 0.86 cm, and 1.33 ± 3.12 cm in the x, y, and h directions, respectively. In the dynamic mode, the differences of the GNSS buoy results between the TRACK module and Trimble RTX technology in the x, y, and h directions are 0.12 ± 5.64 cm, 2.40 ± 1.98 cm, and 2.94 ± 3.27 cm, respectively. In the second campaign, a pioneering equipment, known as high accurate sea surface height measurement system based on mobile platform (HASMS), was harnessed to measure the undulations of the sea surface in southeast of the China Sea. The measured sea surface was compared with CLS_MSS2015 after tidal correction using the FES2014 global tide model. The mean bias between them was -10.99 cm with a standard deviation of 16.31 cm. The collective findings underscore the potential efficacy of the RTX technology in sea surface measurements, poised with an impressive centimeter-level accuracy. Notably, Trimble RTX technology can fulfill the needs of most marine surveying projects in offshore areas.

The vegetation coverage of reservoir slope in high- vegetation area is relatively dense, which leads to the serious spatial and temporal decoherence of D-InSAR technology, and it is difficult to achieve the required monitoring accuracy. The corner reflector InSAR (CR-InSAR) technology overcomes the influence of space-time decoherence by arranging a certain number of corner reflectors in the monitoring body, and has the ability to detect the micro-deformation of the surface in the complex terrain area. Therefore, in order to obtain the stability of reservoir slope in high-vegetation area, this study uses TerraSAR-X image to monitor the slope of Fengshan Reservoir project based on CR-InSAR technology. According to the terrain characteristics of the study area, the data processing process was optimized, and the reference point was connected to the CR points for subsequent related calculations. The results show that the mean square error (MSE) of CR point 1,3 and 4 is 1.9 mm, 2.0 mm and 2.4 mm respectively, and the total time-series MSE of CR point is 2.1 mm with CR point 2 as the reference point, which realizes the mm-level monitoring of reservoir slope in high-vegetation area. Simultaneously, it shows that CR-InSAR technology can be used for high-precision monitoring of reservoir slopes in highvegetation areas. The study results can provide reliable data support for the stability evaluation and management of reservoir slope in high-vegetation area, and also have important significance for ensuring the safety of surrounding ecological environment and people 's life and property.

Rapidly acquiring disturbed patches in production and construction projects through high-resolution remote sensing images holds significant importance for enhancing soil and water conservation supervision capabilities and controlling human-induced soil erosion. Traditional visual interpretation methods for identifying disturbed patches require substantial effort and time, leading to numerous limitations. To improve the efficiency of soil and water conservation supervision, this paper analyzes and summarizes the change characteristics of production and construction projects between two periods of remote sensing images. An intelligent extraction method for disturbed patches in these projects is proposed, based on deep learning and high-resolution remote sensing images. The U-Net++ architecture is employed as sub-network to construct a Siamese network model, with the integration of an attention mechanism module to enhance model performance. Experimental results in the validation area demonstrate that the proposed method achieves a detection rate of 91.52% for disturbed patches, with a false-negative rate of 8.48%. This outperforms the disturbance patch detection rate of 87.28% and a false-negative rate of 12.72% achieved by the dual-temporal early fusion strategy. The extracted boundaries of disturbed patches closely align with manually annotated patch boundaries, indicating the feasibility of utilizing deep learning for extracting disturbed patches in production and construction projects. This approach offers a novel perspective to enhance the efficiency of soil and water conservation supervision.

Based on the Google Earth Engine (GEE) platform, this study obtained Landsat5 TM and Landsat8 OLI/TIRS remote sensing images, and extracted Normalized Difference Vegetation Index (NDVI) across Jiawang coal mining subsidence areas, Xuzhou. Then, the dimidiate pixel model and unitary linear recursive analysis were used to estimate the vegetation coverage and recognize the characteristics of time series variation from 2010-2021 for Jiawang coal mining subsidence areas, thus evaluating the effect of vegetation restoration. The results showed: With the conducting of three steps of vegetation restoration in Jiawang coal mining subsidence areas, Xuzhou, the vegetation coverage generally showed an increased trend, and the significantly increased areas accounted for 39.21% of the total areas. The area of middle and high vegetation coverage accounted for 61.66% in 2021. The vegetation restoration has achieved good results, and the ecological environment has continuously improved. From a spatial perspective, high vegetation coverage area was mainly distributed in southwest and southeast coal mining subsidence areas. For the middle, northwest and northeast areas, vegetation restoration work is still needed.

With the continuous development of industry, the discharge of pollutants in marine areas has attracted widespread attention. As a rapidly developing city, Hong Kong faces significant challenges regarding the large-scale discharge of pollutants into its marine areas. Therefore, studying the inversion model of chlorophyll-a concentration in the coastal waters of Hong Kong is an important topic. Traditional BP neural networks are widely used for their ability. However, it is challenging to scientifically and effectively determine the parameters, and the stability of the model is insufficient. In this study, based on the measured data of chlorophyll-a concentration in the coastal waters of Hong Kong and Landsat 8 OLI data of the Hong Kong offshore area, the traditional BP neural network is optimized and improved. Three neural network models are established: BP neural network model, BP neural network optimized by genetic algorithm (GA-BP), and BP neural network model improved by whale optimization algorithm (WOA-BP). By comparing the prediction accuracy , the results show that the WOA-BP neural network model achieves high accuracy and good stability in the inversion of chlorophyll-a concentration, with an average relative error of 12.91%, which is lower than that of the traditional BP neural network model.

As one of the key earthquake early warning area, it has very important practical significance to study on spatial distribution and capability of earthquake early warning network in Shandong province. According to the spatial distribution characteristics of 90 base stations, 148 basic stations and 1230 general stations, which are affiliated with Shandong Sub-project of “National Seismic Intensity Rapid Reporting and Early Warning” Project , the capability is analyzed and studied base on historical earthquake events, active fault and seismic peak ground acceleration zonation. The results show that: 1) The blind area of earthquake early warning network in Shandong province is 21.55kmwhichis affected with the instrument delay time of the early warning system. 2) The density of earthquake early warning network near historical earthquake events and high ground motion of seismic peak ground acceleration is higher, which can provide effective earthquake early warning. However, the monitoring intensity of some active faults is insufficient and the blind area is large, which still needs further optimization. 3) The monitoring capability of the speedometer stations is ML0.6. which is ML2.3 of the accelerometer stations and ML3.7 of the intensity meter stations.

Aiming at the problems of low accuracy and efficiency and potential safety hazards of traditional wellbore safety monitoring methods, a method of wellbore deformation monitoring based on inertial navigation and three-dimensional laser scanning fusion technology is proposed. The internal information of the wellbore is perceived by of the wellbore is fitted and analyzed. Compared with the traditional monitoring method, the working time of wellbore monitoring by using three-dimensional laser scanner and inertial navigation fusion technology is shortened from 1d or even longer to1h, and the monitoring work can be completed within the maintenance time of the wellbore cage, which improves the working efficiency.

Oil and gas extraction can cause ground subsidence, posing a threat to human safety. Therefore, timely identification of subsidence areas and implementation of mitigation measures are of significant importance. This study employed SBAS-InSAR technology to process 29 scenes of Sentinel-1A images covering the Thamama C area of the Bab oilfield. It thoroughly analyzed the spatio-temporal distribution characteristics of surface subsidence in the research area from March 2022 to March 2023. Three distinct areas with prominent subsidence were selected as feature areas, and their stability was analyzed using the entropy method. The results indicate that: (1) As of March 6, 2023, the overall research area exhibited a subsidence trend, with the extent and magnitude of subsidence continuously expanding over time. (2) By calculating the entropy values of the three selected feature areas using the entropy method, it was observed that all three areas experienced a certain degree of deformation fluctuation. This highlights the need for attention in subsequent production and construction activities. The study demonstrates that combining the entropy method with SBAS-InSAR technology enables effective monitoring and analysis of large-scale and long-term deformation in mining areas, providing assurance for the construction and operation of relevant engineering projects.

To conduct an investigation on the deformation of the Shuangwangcheng Reservoir, this study employed the full-resolution time-series InSAR method to analyze the Sentinel-1 ascending and descending track data from July 2015 to July 2023. The investigation unveiled substantial subsidence in the reservoir dam, exhibiting deformation rates varying from -15 mm/year to -30 mm/year. Notably, a disparity in subsidence rates was observed between the eastern and western sides of the reservoir. Subsequently, the SBAS-InSAR method was employed to monitor the deformation of the surrounding area of the reservoir. It was revealed that the differential deformation rate of the dam may be attributed to the extraction of underground brine from the salt field located on the eastern side of the reservoir.

This paper addresses the current research gap regarding the morphological characteristics of urban agglomerations and the influence of multiple driving forces on building features at different scales. In the field of urban spatial analysis, there exists a significant scale dependency in the relationship between urban building changes and their driving factors. The driving forces identified at one scale may not necessarily apply to other scales in terms of urban spatial characteristic changes. To fill this research gap, a combined analysis of urban spatial characteristics and driving factors is proposed. By employing the geodetector model, this study investigates the patterns and mechanisms of multi-scale driving forces in urban spatial changes. The findings contribute to a better understanding of the process and mechanisms of urban spatial pattern changes, facilitating a more accurate comprehension of the patterns of regional urban spatial changes and enabling the rational and sustainable utilization of urban land resources.

The land cover type and river morphology changes are closely related and will have a profound impact on human life and ecosystems. This article is based on the 2-meter resolution ZY-3 satellite images to carry out land cover and river geomorphology monitoring in the typical demonstration area of the Ping River in Thailand. The results showed that the classification accuracy of land cover in 2013 and 2019 was 95.78% and 92.71%, respectively. The surface type with the greatest change was bare land, which decreased from 1352.33 km2 to 593.08 km2. The curve shape of the Ping River in the demonstration area has undergone significant changes, posing a high risk of erosion and sedimentation, especially in cultivated and bare land areas.

Semi-airborne frequency-domain electromagnetic method is an important method for rapid detection of deep mineral resources in complex terrain areas. The divergence of tipper imaging has the advantages of intuitive interpretation and strong identification of abnormal targets. There is currently limited research on the characteristics of divergence of tipper in the semi-airborne frequency domain systems excited by multiple sources. However, this is critical to the design of the observation scheme. Therefore, it is necessary to analyze the characteristics. In the paper, single source tipper and orthogonal source tipper are obtained by calculating the three- component magnetic field. The changes in the lateral identification ability of divergence of tipper under different transmitter-receiver distance and different source deployment methods are analyzed to develop a field source observation scheme. The divergence of tipper sounding method in the semi-airborne frequency domain electromagnetic system is proposed, which improves the longitudinal resolution of divergence of tipper. This paper verifies the theoretical feasibility and effectiveness of divergence of tipper sounding method and proposes a possible development direction for semi-airborne frequency domain electromagnetic detection. It also provides a theoretical basis for the field application of the divergence of tipper in multi-source semi-airborne frequency domain electromagnetic detection.

The deformation field of the Philippine earthquake is obtained by Sentinel-1A orbit descending data according to D-InSAR technology. Secondly, fault parameters and optimal sliding distribution model are inversed using the Okada uniform elastic semi-infinite dislocation model. Finally, the 3D deformation field is calculated based on the fault slip distribution model inversed, and the displacement component of ENU directions projected into the LOS direction is then obtained. Results show that the earthquake seismic fault was a thrust and left-handed slip type with a maximum sliding momentum of 1.3m. The maximum uplift deformation and the maximum subsidence deformation are 15 cmand20cmrespectively in the radar line direction, which are the combined effects of the main shock and aftershocks. The strike of the seismogenesis fault is 35°. The dip angle is 12°. The slip distribution is mainly concentrated in the underground16km. The maximum slip momentum is 1.6m. The location of the epicenter is (17.49°N, 120.76°E). The sliding angleis37°. The moment magnitude is Mw7.0 with a corresponding seismic moment of 3.97E+19N*m. Combined with the fault structure characteristics in the earthquake area and the distribution location of aftershocks, our work suggests that there is a hidden fault parallel to the Abra River fault near the earthquake area, which is the seismogenic fault of this earthquake.

Building and road detection are fundamental tasks in remote sensing and geospatial analysis, with applications ranging from urban planning to disaster management. Traditional methods for building and road detection often rely on handcrafted features and complex rule-based algorithms, which may struggle to handle the variability and complexity of real-world scenarios. In recent years, deep learning techniques have emerged as a powerful approach for automating and enhancing building detection tasks. which shows the potential to handle complex patterns and adapt to various imaging conditions., However, the state of art deep learning algorithm YOLOv8 exhibits limitations in achieving precise localization when it comes to detecting smaller objects. In light of this ,We propose an enhanced YOLOv8-based semantic segmentation algorithm, incorporating a Convolutional Block Attention Module (CBAM) into the network. Results demonstrate the algorithm's effectiveness in automating building and road recognition with minimal human intervention, significantly improving accuracy even with limited training rounds.

Eastern Indonesia is a very beautiful area that is supported by biodiversity and the development of the industry that is currently being developed. Eastern Indonesia has one of the Luwuk waters, with locations (0°50'37.5" S, 123°05'38.0" E) in Banggai Regency. Indonesia has become a place for the construction of many public ports and private ports. This requires study activities to find out the depth of the sea using underwater acoustic technology with a multibeam echosounder so that it can accurately find out the depth of the sea in these waters. A multibeam echosounder can produce data density information and high-resolution digital surface models. This result is supported by the correction of positions using a DGPS (Differential Global Positioning Systems) system with satellite correction and survey calibration using a patch test (the value correction is 2.4°, 3.2°, and -4.55°). The results of the depth value were generated by making corrections using tidal data and sound speed data. The tidal datum we use is mean sea level, and the tidal type is mixed tide, with dominated semi-diurnal. The calculated average depth is between 10 m to 13 m, the calculated minimum depth is 0.90 m, and the calculated maximum depth value is 27.72 m.

Ocean observations are crucial for reducing the uncertainty in oceanic numerical predictions. However, ocean observations are too costly to cover the whole areas of interest. Therefore, it is necessary to optimize the observational design to achieve more efficient and cost-effective observations. In this paper, we propose a method to design the optimal ocean observation path. Specifically, the optimal observation area is identified by the Conditional Nonlinear Optimal Perturbation (CNOP) approach through the oceanic targeted observation. The time-optimal path is further determined by solving the related traveling salesman problem (TSP). We apply our method to a hindcast case experiment by Regional Ocean Modeling System (ROMS) to show the optimal path of ocean observation.

Conventional vision-based methodologies encounter challenges when measuring the displacement of the internal point of the object. This paper proposes a practical videogrammetric method for measuring the internal point displacements of the suspendome structure node using a pair of high-speed cameras. Firstly, the intrinsic parameters of each camera are calibrated using a precise calibration board, and the extrinsic parameters are calculated by the bundle adjustment and the circular marks that are fixed in the common field of view. Then, the initial 3D position of internal point of suspendome structure node is calculated by the specially designed marks and the high-precision total station. The displacement of the suspendome structure node's internal point is derived by tracking the markers affixed to the surface of the structure node and combined with spatial coordinate transformation. In a suspendome structure disruption experiment, the internal point's measurement accuracy is about the submillimeter when compared to the total station, and the credibility of the measurements is further verified by comparison with numerical simulations.

The sensitivity to permeability changes in fracture zones caused by earthquakes remains unclear. In this study, we compare the changes in tidal parameters of water levels in four monitoring wells in the Huayingshan Fracture zone. The results demonstrate differing sensitivity to permeability changes caused by seismic waves in the fracture zone. Based on the mechanisms underlying permeability changes induced by earthquakes and geological information of the fracture zone, the influencing factors were discussed. The permeability changes in the fracture zone with gas are more sensitive to seismic waves than those without gas. The differences in sensitivity may be attributed to the types of clogged materials within the fractures. This study provides novel insights into the mechanisms underlying permeability changes that induce hydrological responses and the challenges faced by resources and the environment due to seismic waves

Crop area monitoring using remote sensing technology has played an important role in serving agricultural production, ensuring food security, and achieving sustainable water resources management. To obtain information on the distribution of major crops in the Yellow River Basin (YRB), this study utilized the MODIS time series remote sensing dataset from2001 to 2021, with wheat and maize as the main crops of interest. The study identified crop planting patterns and types using NDVI long-term time series data and a threshold method, and further analyzed and explored their spatiotemporal evolution patterns. Finally, the spatial characteristics of potential evapotranspiration in the YRB are analyzed. The results indicated that the overall classification error of wheat and maize planting areas in the YRB was small. There were significant spatiotemporal differences in crop planting structure in the YRB, with a gradual decline in wheat planting area and a continuous increase in maize planting area. The planting centers of both crops were shifting towards the northeast. The results will provide a rapid and robust method to be applied for wheat and maize planted area monitoring in other regions. This study also contributes to achieving multi-year dynamic monitoring of crop types and exploring the variation patterns of evapotranspiration in the YRB.

Yulin City serves as an important coal industrial base in the Yellow River Basin，also a typical ecological fragile area in China, and its eco-environmental quality changes have attracted much attention in recent years. In order to effectively monitor the changes of eco-environmental quality in Yulin City and further assess the effectiveness of ecological restoration in Yulin City, the research integrated multi sensors including Landsat TM, ETM, OLI and MODIS land surface temperature products to construct long time-series Remote Sensing Ecological Index (RSEI) based on Google Earth Engine (GEE) platform. Then, patterns and changes of spatial-temporal distribution from 2000—2020 in Yulin City were revealed and driving force was discussed. Results show that: (1) The eco-environmental quality of Yulin City improved to a large extent from 2000 to 2020, with a significant improvement exceeding 56% of the whole study area which mainly occurred in the southeast of Yulin; Notably, the potential risk of eco-environmental quality degradation exists in the north and southwest; (2) The eco-environmental quality of Yulin City shows obvious geomorphological differences: the eco-environmental quality of Loess Plateau areas in the southeast is much better than that of the Windblown Sand Grassland areas in the north; (3) Transformation of land use types caused by the implementation of policies such as returning farmland to forests and grasslands and mine ecological restoration plays a leading role in the process of restoring the quality of the ecological environment, while extreme meteorological disasters such as droughts can lead to rapid deterioration of the eco-environmental quality.

The implementation of power grid construction projects plays a pivotal role in driving economic growth. However, activities such as land excavation, building demolition, and power line installation during the construction process often have detrimental impacts on soil, water bodies, and the ecological environment. Consequently, enhancing the supervision of environmental protection and soil conservation (EPSC) in power grid construction projects is paramount for fostering sustainable development. A comprehensive approach to EPSC supervision, incorporating satellite and unmanned aerial vehicles (UAVs), has been proposed. Firstly, high-resolution images of the disturbance areas in power grid construction projects are obtained through satellite surveys. Subsequently, professional software is employed for data processing to determine the coordinates of the areas that require key supervision. Finally, UAV are deployed for refine inspections. The pilot application of this technology at the construction site of the 500 kV Shenyan Transmission Line in Shuozhou City, Shanxi Province has demonstrated the exceptional performance of the technology which significantly enhances data quality, improves data acquisition efficiency, and reduces manual workload. By embracing our technology, the intelligent level of EPSC supervision in power grid construction projects will be substantially elevated.

Low Earth orbit (LEO) satellites are a key focus in the research and development of next-generation navigation systems. When combined with existing Global Navigation Satellite Systems (GNSS), they can significantly enhance the accuracy, integrity, availability, and anti-jamming capabilities of satellite navigation and positioning services. Broadcast ephemeris, which is essential for providing navigation services, directly impacts user experience. Currently, main stream broadcast ephemeris fitting primarily employs orbital element-based ephemeris models. However, these models are intermittently affected by solar radiation pressure perturbations during satellite entry and exit from the Earth's shadow, which is more frequent due to the shorter orbital periods in LEO. Therefore, this paper investigates the feasibility of using Chebyshev polynomials to fit broadcast ephemeris in LEO. It also addresses issues related to setting Chebyshev polynomial fitting parameters. Experimental analyses are conducted on fitting order, fitting arc length, and selection of fitting point intervals. The results show that the Chebyshev polynomial fitting is more stable than the 22 parameter ephemeris model with orbital elements in the fitting arc segment containing the incoming and outgoing shadows. When the fitting order is25, the fitting time does not exceed 90 minutes, and the fitting accuracy can reach the level of 0.1 millimeters.

Ningxia is one of the most vulnerable regions in China with serious ecosystem atrophy and soil and water loss. A variational mode decomposition method along with the Mann-Kendall significance test was used to find the monotonic trend of vegetation in this area using the 16-day MODIS NDVI time-series data during 2011 to 2020. The trend was then contrasted with that produced by empirical mode decomposition. The findings demonstrated a trend toward greening in the Ningxia vegetation. The area with a propensity for greening and browning made up respectively 89.38%and6.75%, while the area with no alteration made up 3.87%. The fast ascend of vegetation in the southern of Ningxia, including the conversion of farmland to woodland, has been severely impacted by the country's large forestry projects, and the trend for browning is linked to urban growth.