Table of Content

25 June 2024, Volume 0 Issue 6

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Remote sensing monitoring and influencing factors analysis of grassland degradation in Xinjiang from 2001 to 2020

MA Lisha, ZHENG Jianghua, PENG Jian, LI Gangyong, HAN Wanqiang, LIU Liang

2024, 0(6):  1-7.  doi:10.13474/j.cnki.11-2246.2024.0601

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Due to climate change, overgrazing and overcultivation, grassland degradation and other ecological problems in Xinjiang have attracted increasing attention. In this study, MODIS NDVI remote sensing data products are used to monitor and analyze the grassland status in Xinjiang from 2001 to 2020. The pixel binary model, grassland degradation index based on coverage change, cold/hot spot analysis and other methods are used to obtain the temporal and spatial characteristics of grassland degradation in Xinjiang according to the national standard of grassland degradation grade classification, and then the influencing factors are analyzed. The results show that:①the coverage of grassland in Xinjiang is increasing with a main variation degree of stability (55.4%), and the distribution shows a gradually decreasing trend from north to south.②In the past 20 years, the degradation level of grassland coverage in Xinjiang was in a state of moderate to mild degradation, with the north mainly in an undegraded or mildly degraded state, while the east and south were mainly in a state of mild to moderate degradation, and the grassland was in a recovering trend during different study periods.③The grassland degradation index in Xinjiang shows an overall downward trend, increasing first and then decreasing in northern Xinjiang, decreasing continuously in eastern Xinjiang, and slightly increasing in southern Xinjiang. The cold/hot pattern shows that the cold spot increases and the hot spot decreases. It means, grassland degradation has weakened and is gradually recovering.④The grassland type caused by human activities is mainly transformed into bare land and farmland, and the grassland area decreases by 1.676 million hm². The temperature shows a mean temperature inhibition, a high-temperature promotion, and a low-temperature inhibition effect on the grass coverage.Precipitation has a promoting effect on grassland coverage.The results of this study can provide targeted guidance for the degradation of grassland in Xinjiang at the regional scale and provide a decision-making basis for the protection and restoration of the ecological environment.

Application of multi-temporal satellite remote sensing data in the analysis of wind and sand control effectiveness in Maigaiti county

FENG Wei, WANG Xiaoguang, XIE Sanwu, SU Linge

2024, 0(6):  8-12.  doi:10.13474/j.cnki.11-2246.2024.0602

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In order to protect our homeland and create a pleasant,livable,business friendly,and tourist friendly social development environment,people of all ethnic groups in Maigaiti county have been fighting against the sand for many years,enabled the yellow sand to recede and the green to be rebuilt. This article used domestic satellite images as a data source to extract information on the changes in the million acre ecological forest construction project of windbreak and sand fixation in Maigaiti county over the years. The digital reconstruction process analyzed the change characteristics from the aspects of distribution,area,health status,and other indicators. Remote sensing monitoring of windbreak and sand fixation projects since 2013 was carried out,and the actual effectiveness of windbreak and sand fixation has been analyzed and displayed through satellite images,provided scientific basis for the economic,social development and ecological environment protection of Maigaiti county.

Oil spill detection method of compact polarization SAR based on convolution neural network

LUO Qingli, CHEN Zhiyuan, LIU Yuting, ZHANG Jin, LI Yu

2024, 0(6):  13-18.  doi:10.13474/j.cnki.11-2246.2024.0603

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To investigate the feasibility of using compact polarimetric synthetic aperture radar (SAR) as an alternative to fully polarimetric SAR for oil spill detection and to determine the impact of different polarization parameters on the accuracy of oil spill detection. To this end,a SAR oil spill detection algorithm based on convolutional neural networks (CNN) is employed. This algorithm extracts polarization parameters from both fully polarimetric and derived compact polarimetric SAR data to study their impact on the classification accuracy of oil spills. Furthermore,the impact of different SAR data preprocessing steps on the accuracy of oil spill detection is evaluated. The results demonstrate that the linear stretching method can effectively enhance the accuracy of oil spill detection. Concerning the selection of polarization parameters,the polarization entropy H achieved the highest classification accuracy in both fully polarimetric and compact polarimetric modes,with a classification accuracy of 0.972 for fully polarimetric and 0.978 for compact polarimetric. This demonstrates the potential of using compact polarimetric SAR for oil spill detection and its promising application prospects.

Distillation method based on global background strengthen for Enteromorpha prolifera target detection

LIU Bing, LIU Yu, JIN Fengxue, ZOU Yibo, GE Yan, ZHAO Linlin

2024, 0(6):  19-23.  doi:10.13474/j.cnki.11-2246.2024.0604

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The detection of Enteromorpha prolifera stands as a pivotal research area within the realm of intelligent marine environment monitoring.Addressing the challenge posed by the substantial training sample requirements inherent in conventional methods of Enteromorpha prolifera detection,this paper proposes GBS-LD model.By introducing a global context module and background distillation loss branch,the shortcomings of the original position distillation method in modeling background features are solved,effectively improving the robustness of detection system in complex marine environments.Our proposed model has achieved high accuracy and real-time performance in the dataset of Enteromorpha prolifera,providing important reference for intelligent monitoring of marine.

Research on resource-exhausted city spatio-temporal patterns and drives of ecological quality:take central urban area of Fuxin for example

FAN Qiang, SUN Shuang, SUN Huhu

2024, 0(6):  24-29.  doi:10.13474/j.cnki.11-2246.2024.0605

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In order to evaluate the spatio-temporal patterns of ecological quality in resource-depleted cities during the transition period.This article takes the central urban area of Fuxin as the research area, constructs a remote sensing ecological index(RSEI) based on Landsat images, and identifies urban functional areas based on OSM data and POI data. This article uses optimal parameters-based geographical detector to explore the complex relationship between urban ecological quality and urban functional areas. The results show:①From 2004 to 2022, the ecological quality showed a slight downward trend, with relatively stable fluctuations, forming a basin-like pattern with good surroundings and poor middle.②The average value of the Hurst index is 0.67, with a strong persistence nearest neighbor index value of 146.53.This indicates the future change trend of ecological quality shows uniform sustainability, the change trend of RSEI in most areas is the same as before, but there is an anti-sustainable trend in some areas.③“31+1” types of urban functional areas are identified, and the ecological quality of functional areas from high to low. They are green space, public services, industry, transportation, residential, commercial, and mining areas. Interactions among different factors enhance the explanatory power of ecological quality. The research results can provide scientific reference for ecological protection and restoration work in different functional areas during the transformation period of resource-depleted cities.

Application of improved 3D-BoNet to segmentation and 3D reconstruction of point cloud instances

GUO Baoyun, YAO Yukai, LI Cailin, WANG Yue, SUN Na, LU Yihui

2024, 0(6):  30-35.  doi:10.13474/j.cnki.11-2246.2024.0606

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In order to better utilize point cloud data to reconstruct indoor 3D models, this paper proposes a 3D reconstruction method for indoor scenes based on 3D-BoNet-IAM algorithm. The method improves the instance segmentation accuracy of the point cloud data by improving the 3D-BoNet algorithm.For the problem of missing point cloud data, a method based on plane primitive merging optimization is proposed to fit the plane, and the new plane obtained from the fitting is used to reconstruct the building surface model. The improved effect of 3D-BoNet algorithm is verified on S3DIS and ScanNet V2 dataset, and it is proved through experiments that the algorithm of 3D-BoNet-IAM proposed in this paper improves the segmentation accuracy by 3.3% compared with the original algorithm; the modeling effect of this paper is compared with other modeling effects, and it is proved through comparisons that this paper’s modeling effect is more accurate. The method in this paper can improve the instance segmentation accuracy of indoor point cloud data, and at the same time obtain high-quality indoor 3D models.

Multi-resolution adaptive seamless fusion method for UAV image stitching

SHI Yaorong, XIAO Jingda

2024, 0(6):  36-40.  doi:10.13474/j.cnki.11-2246.2024.0607

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Aiming at the problems of geometric misalignment and stitching seams in UAV image stitching, a joint application of the optimal stitching seam algorithm and image fusion is proposed to repair and seamlessly fuse image geometric misalignment and stitching seam regions. Firstly, the graph cut algorithm is used to search for the minimum difference line of interframe splicing to repair the geometric misalignment between frames. Then, the region of interest (ROI) of strip fusion is established based on the difference line, and the size of the fusion ROI range changes adaptively with the size of the tonal exposure difference between frames. Finally, the fusion ROI mask is introduced to improve the Laplace multi-resolution image fusion algorithm, and the tonal exposure difference between frames is smoothed,so as to achieve seamless fusion of UAV. And the seamless fusion of image stitching. The experimental results show that compared with the traditional Laplace fusion algorithm,the image fusion results in this paper improve the mutual information by an average of 12.79%,the peak signal-to-noise ratio by an average of 21.48%,and achieve a natural and seamless visual effect,which is of certain application value.

Landslide displacement monitoring method by optical satellite images

LIU Weinan, SONG Hongke, LI Qingbo, XIE Mowen

2024, 0(6):  41-45,64.  doi:10.13474/j.cnki.11-2246.2024.0608

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In order to fully explore the application potential of optical remote sensing images in landslide monitoring, the article derives the landslide displacement optical image calculation formula based on the digital image correlation method. The formula adapts to the non-uniform deformation of landslides and the influence of natural illumination. Based on this formula, the article establishes a landslide displacement monitoring method based on optical images. The experiment analyzes the influence of the surface deformation mode and the amount of deformation on the calculation formula of landslide displacement optical image, and gives the change curve of the probability cumulative value of the calculation error under three different deformation modes of surface translation, rotation and distortion. The experimental results prove that 95% of the calculation results of the formula are within 0.04 relative error. The article calculates the planar displacement field of a landslide using two-phase SkySat satellite optical images and analyzes the surface deformation characteristics of the landslide. The study provides a new technical reference for landslide disaster monitoring and early warning.

Research on groundwater storage and surface subsidence in Huangshui Valley based on GRACE and Sentinel-1A

HU Xiangxiang, KE Fuyang, SHI Yaya, WU Tao, LIU Baokang, PANG Dongdong, ZHANG Lili, SONG Bao

2024, 0(6):  46-52.  doi:10.13474/j.cnki.11-2246.2024.0609

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GRACE/GRACE-FO and GLDAS data are used to invert the groundwater changes in 2019—2022 in Huangshuang Valley area. And SBAS-InSAR technology is used to obtain the simultaneous rate of surface subsidence in the region, which is combined with the precipitation data to study the correlation between surface subsidence and groundwater changes in Huangshuang Valley area. The results show that: ① The overall direction of groundwater loss in Huangshui Valley is from northwest to southeast. ②Groundwater changes have a greater impact on the more serious surface deformation in the region. ③The greater the surface deformation (uplift), the more groundwater reserves are lost. The upper reaches of the Yellow River have the greatest uplift, and the loss of groundwater reserves is the greatest. ④ The surface deformation in the northern part of the Huanghe Valley is not sensitive to changes in groundwater reserves, while the surface deformation in the southern part is more sensitive to changes in groundwater reserves. The conclusions of this paper can provide important scientific reference for local geological disaster warning, sustainable utilization of water resources, ecological protection and high-quality green development.

LSTM goaf surface subsidence prediction method combining convolutional neural network and attention mechanism

GAO Motong, YANG Weifang, LIU Zuyu, CAO Xiaoshuang, ZHANG Ruiqi, HOU Yuhao

2024, 0(6):  53-58,170.  doi:10.13474/j.cnki.11-2246.2024.0610

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In order to solve the problem of difficult extraction of spatial features of monitoring points in the time series prediction of surface collapse areas in the mining zone, a CNN-Attention-LSTM combined neural network model that can extract key spatial features of monitoring points is proposed. Firstly, the number of neighbouring monitoring points as feature input is increased, and the spatial features of the multidimensional time series composed of multiple monitoring points are extracted using convolutional neural network (CNN). Secondly, the extracted multidimensional feature time series are input into the multilayer perceptron (MLP) to calculate the attention weights and make Hadamard product with the feature inputs to achieve the allocation of the attention weights of the feature inputs. After that regression prediction is performed using long short term memory neural network (LSTM). Finally, through the fully connected layer, the predicted values of the target monitoring points are integrated and output. In this paper, we take the surface collapse area in the west second mining area of Longshou mine as an example to give the prediction results of its surface subsidence monitoring data and compare them with the actual collected data. The results show that the combined CNN-Attention-LSTM model with the introduction of the attention mechanism is more accurate than the CNN-LSTM model and the LSTM model respectively, and the addition of effective feature inputs can significantly improve the prediction accuracy of the CNN-Attention-LSTM model.

Construction waste classification method based on multiple feature combination

ZHANG Daixinyue, LIU Yang, GAO Siyan

2024, 0(6):  59-64.  doi:10.13474/j.cnki.11-2246.2024.0611

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The rapid growth of urban areas has led to a significant rise in construction waste, causing urban pollution and waste accumulation issues. This study aims to investigate the impact of multi-feature combination methods on the accuracy of construction waste classification in hyperspectral images, focusing on a specific region in Beijing. Construction waste classification experiments are conducted using on-site spectral data collected with the ASD QualitySpec Trek handheld spectrometer and Zhuhai-1 hyperspectral images. A total of 90 feature variables, including spectral features, vegetation index, water index, and texture features, are extracted and ranked based on their importance using the mean decrease impurity. Feature variables with relatively high importance scores are selected to create a multi-feature combination vector for classification. The random forest algorithm is then employed to perform construction waste classification experiments on this vector.The experimental results reveal that the random forest classification method using multi-feature combination outperforms the traditional random forest method, achieving an overall classification accuracy of 85.86% and a Kappa coefficient of 0.80. In comparison, the original random forest method achieves an overall classification accuracy of 83.48% and a Kappa coefficient of 0.75,indicating the effectiveness of the random forest algorithm using multiple feature combinations.

Change detection of buildings in high-resolution satellite images based on quantum multi-scale fusion

ZHANG Yanping, ZHANG Ka, ZHAO Like, TAO Xia, ZHANG Bang, WANG Yujun, GU Zhen, LIU Haolin

2024, 0(6):  65-70,126.  doi:10.13474/j.cnki.11-2246.2024.0612

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In order to improve the accuracy of the traditional high-resolution satellite image change detection method based on pixels, this paper proposes a building change detection algorithm based on quantum multi-scale fusion for high-resolution satellite images. Firstly, multi-scale segmentation of dual temporal high-resolution satellite images is carried out to form a multi-scale image dataset.Secondly, the multi-scale image dataset is transformed by iterative slow feature transformation to obtain the change intensity map of different scales, and then the multi-scale change intensity map is fused by quantum theory to obtain the fused change intensity map.Finally, the threshold segmentation of the change intensity map is completed by the maximum variance between classes method, and the binary change detection results are obtained. Two groups of real high-resolution satellite images with different time phases are used to verify the algorithm in this paper. The experimental results show that compared with the single-scale object-oriented change detection method and the multi-scale fusion method of entropy weight method, the algorithm in this paper can achieve higher accuracy in building change detection.

Urban water extraction based on spectral features

MA Shanshan, YANG Jiawei, WANG Jiyan, XIONG Junnan

2024, 0(6):  71-76.  doi:10.13474/j.cnki.11-2246.2024.0613

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Most of the existing water extraction methods are based on multi-spectral remote sensing data, and when they are applied to hyperspectral data, the spectral information is not fully utilized and it is difficult to select appropriate bands, and the shadow effect of buildings in the city is not considered. Therefore, in this study, the modified hyperspectral difference water index (MHDWI) is constructed based on hyperspectral remote sensing image data, using the near-infrared band thresholds and the integral difference in the fixed band range. Four commonly used water indices are selected for experiments and accuracy comparison analysis in Jiaxing, Changsha and Zhoushan areas under different sensors, and the experimental results show that the overall accuracy and Kappa coefficient of the method are 98.50%, 98.76%, 97.35%, and 0.88, 0.85, 0.86, respectively, which are higher than the other four methods. And the misclassification error is significantly reduced compared with the other methods, which shows that the method can effectively suppress building shadows and is applicable to water extraction in different areas with different sensors.

Road extraction of UAV remote sensing image based on deep learning

ZHANG Wei, ZHANG Chaolong, WANG Benlin, CAI Anning

2024, 0(6):  77-81.  doi:10.13474/j.cnki.11-2246.2024.0614

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Aiming at the problems of high-resolution remote sensing images and road image datasets in the target scene in terms of difficulty in acquiring, high cost, etc., we explore the optimal image resolution of the network models to perform the extraction task at different scales, evaluate the applicability and reliability of each model on road extraction, and provide methodological reference and case study for the road recognition project. In this paper, three classical network models in the field of image segmentation are introduced, the models are trained using public datasets, and the unmanned aerial images of Chuzhou city, Anhui province are used as the test data to perform the road extraction work at different scales, to find out the optimal resolution and model applicability of each model in the new scene, and to evaluate the reliability. The experimental results show that the applicability of the D-LinkNet network model is more prominent in the road extraction task at different scales, the reliability of the DeepLabV3+ network model is poorer, and the optimal resolutions of the road extraction input images for the U-Net and D-LinkNet network models are 1.0 and 0.5 m, respectively.

Spatial and temporal distribution and influencing factors of PM_2.5 in 3km Beijing-Tianjin-Hebei region based on GTWR model

WANG Yan, LIU Jiping, ZHAO Yangyang, XU Jing

2024, 0(6):  82-89.  doi:10.13474/j.cnki.11-2246.2024.0615

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PM_2.5 is closely related to air quality and public health, and many studies use remote sensing combined with other auxiliary data models to invert PM_2.5 concentration to capture the spatial and temporal distribution of PM_2.5 in various regions. Aiming at the problem of low data resolution in the Beijing-Tianjin-Hebei region, this study adopts 3 km resolution aerosol optical depth(AOD) data and 12 auxiliary variables to establish a geographically and temporally weighted regression model (GTWR) to estimate the PM_2.5 concentration distribution in the 3 km Beijing-Tianjin-Hebei region during 2020—2022. The results show that: ①the R² of GTWR model data (0.86) is better than that of OLS model data (0.66) and GWR model data (0.78).②The spatial and temporal distribution of PM_2.5 concentration in the Beijing-Tianjin-Hebei region during 2020—2022 is negatively correlated with the terrain. The low-value area is mainly distributed in the high-lying mountainous area, and the high-value area is mainly distributed in the low-lying plain.③The seasonal mean concentration of PM_2.5 in Beijing-Tianjin-Hebei from 2020 to 2022 was significantly different as follows: winter (60.88 μg/m³), autumn (37.78 μg/m³), spring (31.75 μg/m³), summer (22.16 μg/m³).④The correlation between PM_2.5 concentration and AOD is the strongest. It is concluds that the combination of 3 km resolution AOD data and GTWR model has good applicability in retrieving PM_2.5 concentration.

Land use evolution and multi-scenario prediction of 2000—2020 “One River and Two Rivers” basin in Tibet

SU Wan, QIU Chunxia, ZHAO Liu

2024, 0(6):  90-95.  doi:10.13474/j.cnki.11-2246.2024.0616

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Aiming at the lack of research on land use evolution,change drivers and land use prediction in large-scale remote watersheds,the paper analyses the land use evolution of the watersheds based on the data of land cover of “One River and Two Rivers” watersheds of Tibet for five periods from 2000 to 2020 by using GIS technology,geo-detector and PLUS model,and explores the driving mechanism affecting the change of cultivated land. Based on the five periods of land cover data from 2000 to 2020,we analysed the evolution of land use in the basin,explored the driving mechanisms affecting the change of cultivated land,and simulated the land use status of the basin in 2025 under multiple scenarios. The results show that: ①the spatial pattern of land use in the “One River and Two Rivers” watershed in Tibet will change significantly from 2000 to 2020,with a decreasing trend in the areas of arable land,watershed and unused land,and an increase in the areas of construction land and forest and grassland.②The degree of land use in the basin with wide vegetation cover is medium-low level,and its comprehensive land use dynamics shows the trend of “decreasing firstly and then increasing”. ③Distance from water,precipitation and elevation are the main factors affecting the change of arable land in the watershed.④The ecological protection scenario is more in line with the green and sustainable development of land use in the “One River and Two Rivers” watershed in the future,and can provide a reference for the rational planning of land resources in the watershed.

Deep learning-based map matching considering road network constraints

ZHONG Qingcen, WU Chenhao, XIANG Longgang, YAO Peng

2024, 0(6):  96-102,133.  doi:10.13474/j.cnki.11-2246.2024.0617

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In low-frequency or non-uniform sampling conditions, existing map matching algorithms have problems of low matching accuracy or low efficiency. In this paper, we propose a road network constrained map matching model based on deep learning (RNCMM). Firstly, Seq2Seq framework is used to map the low frequency track point sequence to the high frequency road segment sequence from end to end. Secondly, a fine-grained constraint mask layer is constructed according to the distance and azimuth difference between the road and the trajectory point, which is conducive to alleviating the limitations of the trajectory grid representation and improving the matching accuracy. Then, attention mechanism and multi-task learning mechanism are introduced to mine the spatiotemporal correlation between trajectory points and perform joint prediction of road segments and directions. Finally, experiments are conducted on the Porto taxi trajectory dataset and OSM road network. The results show that compared to traditional hidden Markov model(HMM), the proposed algorithm can effectively improve the matching accuracy and efficiency of low-frequency floating car trajectories.

Electronic fence out-of-bounds detection method based on 3D field superimposed video stream

YIN Zezhong, LI Gongquan

2024, 0(6):  103-108.  doi:10.13474/j.cnki.11-2246.2024.0618

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With the rapid development of real-life 3D city construction, electronic maps can now be presented in 3D real-life scenes, and use more intelligent image processing methods to integrate the real world and real-world model data to achieve the combination of virtual and reality. In this context, in view of some problems in the detection method of electronic fence in 2D scenes, this paper proposes a cross-border detection method of electronic fence in the case of the fusion of video and three-dimensional real scene. The underlying data is based on the real-world 3D model and the surveillance video stream, firstly, the virtual 3D space electronic fence projected by video surveillance in the geographical scene is established, and at the same time, the video is connected to the optimized deep learning neural network model and the human posture estimation is applied, and the estimated point coordinates and fence coordinates are converted into 3D local coordinates through coordinate conversion, and then the detection algorithm is compared with the planned 3D electronic fence in real time to realize the real-time and effective judgment of the object crossing the boundary. Through experimental verification in different video scenarios, the results show that the method is effective and feasible, without specific hardware support and scene constraints.

A distributed storage and index method of trajectory big data based on the Hilbert curve

CHEN Kai, SONG Weiwei, JIN Baoxuan, LI Yongning, PU Hongxun

2024, 0(6):  109-114,138.  doi:10.13474/j.cnki.11-2246.2024.0619

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In response to the rapid growth of trajectory big data with spatio-temporal characteristics and the urgent need for its fast query, traditional relational databases have certain limitations on the storage of massive trajectory data and specific query requirements, while non-relational databases are difficult to meet the efficient indexing requirements of massive data, and the efficiency of the storage and indexing of trajectory data is still in urgent need of improvement. In this paper, a framework for storage and retrieval based on HBase database is designed and implemented to cope with the efficient management of spatio-temporal trajectory data. Firstly, a novel Rowkey structure is designed, and the GeoMesa-HBase underlying storage model is constructed by combining spatio-temporal indexing tools. Secondly, a Hilbert curve-based coding technique is integrated to construct the spatial index, which improves the storage and retrieval efficiency of trajectory data. In order to evaluate the effectiveness of the proposed method, this paper compares its storage and query performance with traditional storage databases (HBase and MySQL) and Geohash index. The experimental results show that the scheme is able to achieve effective storage of trajectory data and improve the retrieval efficiency, which is of great practical significance in addressing the challenges associated with trajectory big data management.

Comparison of the visualization of typical planetary topography: the example of Mars and the Moon

BI Jiehao, YING Shen, CHEN Chi, DOU Xiaoying

2024, 0(6):  115-119.  doi:10.13474/j.cnki.11-2246.2024.0620

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A number of exploration missions to Mars and the Moon have led to a certain understanding of the commonalities and differences between the topography of the planets. Since the topography of the two is different from that of the Earth’s surface, a targeted visualization method should be adopted for their visualization to match their respective landform types and surface color systems, which will facilitate planet mapping and planet exploration. The paper compares the similar landforms about surface topography of the Moon and Mars, especially impact craters, ridges and grooves, which are different from the Earth’s landforms, and visualize them through human-eye suitable visible light color and highlight landform characters to realize the enhanced visualization in accordance with the surface topography of the Moon and Mars, which will enable map users to quickly grasp topographic and geomorphological information, and facilitate further deep space exploration research and public science popularization.

Development and application of a eutrophic lake water environment monitoring and simulation platform

WANG Jindi, QIAN Jianguo, QIU Yinguo, WANG Zhiyong, LIU Fuzhang, LIU Jiaxin

2024, 0(6):  120-126.  doi:10.13474/j.cnki.11-2246.2024.0621

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The frequent outbreaks of algal blooms caused by lake eutrophication have become a significant constraint on the sustainable development of regional economies. Timely and comprehensive acquisition of information on water quality and algal blooms in eutrophic lakes is of paramount importance for the scientific prevention and control of algal blooms. Traditional methods for monitoring water quality and algal blooms in lakes suffer from issues such as weak timeliness, low spatial coverage, and a lack of diverse data analysis tools. This paper utilizes technologies such as satellite remote sensing, spatial databases, and WebGIS to develop an automatic monitoring and simulation analysis platform for water quality and algal blooms in eutrophic lakes. The platform integrates core technologies such as automatic monitoring of satellite remote sensing, dynamic virtual buoys, and dynamic spatio-temporal analysis of target aquatic environments. It achieves high spatio-temporal resolution monitoring of water quality and algal blooms in lakes, providing fine-grained representation and reanalysis capabilities for lake water quality and algal blooms at multiple scales. The platform has been demonstrated in pilot applications in Lake Taihu, Lake Chaohu, and Lake Dianchi, playing a crucial role in supporting monitoring, early warning, and management of water quality and algal blooms in lakes.

Pond water quality analysis and visualization design utilizing unmanned aerial vehicle multi-spectral technology

LU Yuting, DUAN Jinrong

2024, 0(6):  127-133.  doi:10.13474/j.cnki.11-2246.2024.0622

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As one of the crucial water resources, pond water quality management is of paramount importance for ecological preservation and the sustainable development of aquaculture. Nonetheless, conventional water quality monitoring methods are constrained by sparse sampling points and periodic measurements,rendering them incapable of delivering real-time, high-resolution water quality information.The aim of this study is to develop a water quality monitoring visualization system utilizing UAV multi-spectral technology.UAVs equipped with multi-spectral sensors are deployed to routinely fly over designated ponds,gather multi-spectral data from water bodies for water quality analysis, and subsequently facilitate water environment data management and efficient real-time water quality inversion through WebGIS technology.This system is designed to provide robust technical support for aquaculture and environmental protection.The findings reveal:①UAV multi-spectral technology can be effectively employed for pond water quality monitoring,facilitating swift responses to water quality fluctuations,thereby enhancing pond aquaculture management efficiency.②Nanquan pond exhibits minimal spatial variations in water quality but distinct temporal fluctuations,with water quality generally being superior from March to April and displaying a tendency to deteriorate from May to August.③It is advisable to intensify pond management during the summer months to mitigate aquaculture-related water quality pollution.This research introduces novel tools and methodologies for water quality management and ecological conservation,with the potential for widespread adoption and demonstration within the aquaculture industry.

Ocean current direction estimation based on improved MUSIC method

PAN Bozhi, HE Hongchang, FAN Donglin, GONG Ziyi, LIU Zhenhao

2024, 0(6):  134-138.  doi:10.13474/j.cnki.11-2246.2024.0623

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Ocean currents can regulate global heat distribution and reduce shipping costs. The previous methods for estimating the direction of current have not completely eliminated the correlation between current signal sources,resulting in poor estimation performance. Therefore,a method for estimating the current direction based on the improved MUSIC method was designed. Firstly,based on the collected current signals,the current array signal model is constructed,and the current signals are denoised to improve the quality of signal data. Then to reduce the impact of the correlation between current signal sources on the estimation results,the correlation of current signal sources is eliminated using the improved MUSIC method and the covariance matrix. The current bearing estimation model is constructed by calculating the correlation parameters of the signal sources. Finally,by converting the ocean current signal source,the estimation of the ocean current direction is achieved. In the simulation experiment,a portion of the South China Sea is used as the experimental object to evaluate the estimation performance of estimation methods under different spectral points. Compared with previous ocean current direction estimation methods,the designed ocean current direction estimation method based on the improved MUSIC method has an estimation accuracy of 97.2% and a better application effect.

Satellite remote sensing monitoring of canopy cover of conversion of farmland to forestland project area in the Inner Mongolia

WANG Tiancan, WANG Jianhe, SHEN Tong, YUE Lianggaoke

2024, 0(6):  139-145.  doi:10.13474/j.cnki.11-2246.2024.0624

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The new period of returning farmland to forestland and grassland project is launched in 2015, which is amajor measure to restore the ecological environment of the Inner Mongolia. Using the Sentinel-2A images and terrain data in the 2022 growing season, combined with field measurement data, and based on the GEE platform, the gradient boostingreg ression tree(GBRT) is used to invert the canopy cover of the new period of farmland to forestland project area in the Inner Mongolia. The results show that the model verification coefficient of determination (R²) is 0.87,the root mean square error (RMSE) is 0.079 and the mean absolute error (MAE) is 0.062. The average canopy cover in the new period of returning farmland to forestland project areas in Inner Mongolia is 0.147. There are obvious spatial differences in its distribution, which is affected by factors such as vegetation configuration type, planting year, and geographical environment. It generally increases gradually from west to east. Sentinel-2A images and terrain data can be used to effectively estimate canopy cover in the conversion of farmland to forestland project area, providing a reference for the inversion of low canopy cover areas.

Multi-modal point cloud data registration technology in complex scenarios

FU Chao, XIA Jiayi, XIE Kun, WU Dapeng, FU Qincheng

2024, 0(6):  146-150.  doi:10.13474/j.cnki.11-2246.2024.0625

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Due to the difficulty in obtaining multi-modal point cloud data in complex environments, the requirements for point cloud data registration and 3D model construction accuracy are becoming increasingly high, this article takes the real-life 3D modeling of Nantong Grand Theater as an example. When the position difference between the initial point cloud and the calibration point cloud is significant, using the ICP algorithm for point cloud registration can easily lead to local optimization problems. The proposed nearest point iteration (CPA-ICP) algorithm based on control point auxiliary constraints is used to register point cloud data, and the experimental comparison of the other three point cloud registration algorithms fully demonstrates, This method has high registration accuracy and efficiency, and has good reference significance for multimodal point cloud data fusion in complex scenes.

Spatial characteristics analysis of urban thermal diurnal environment based on ECOSTRESS

PENG Min, YAO Na, SUN Peilei, ZOU Bowen, WANG Wenshuo

2024, 0(6):  151-156,181.  doi:10.13474/j.cnki.11-2246.2024.0626

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Land surface temperature (LST) is an important index to characterize the change of urban thermal environment, and its distribution information is of great significance for monitoring urban thermal environment. Based on ECOSTRESS data from June to August from 2018 to 2023, diurnal LST in the fourth ring road of Shenyang is obtained through the correction of LST. Mean-standard deviation method and spatial autocorrelation analysis are used to extract diurnal spatial characteristics of LST, and combined with land use data, the contribution degree of different land types to the spatial distribution of land surface temperature is analyzed. As indicated by the results, the LST in the fourth ring road of Shenyang is high in the north and low in the south, and high in the west and low in the east. There is a large difference in LST between day and night. The high temperature area is mainly concentrated in Huanggu district, Dadong district, western Shenhe district and eastern Tiexi district, while the low temperature area and the middle temperature area are mainly concentrated at the edge of the fourth ring road, the artificial LST is mostly higher than the natural LST, and the high temperature area of building land category accounted for the largest proportion, which is the main factor for the warming of LST, while the natural LST is the main factor for the cooling of LST. There are significant clustering and hot spots in the fourth ring road of Shenyang, and the diurnal variation of LST is consistent with the aggregation distribution characteristics of LST.

Surface deformation monitoring of industrial parks based on temporal InSAR technology

HUANG Biao, ZHANG Hui, YIN Jianhui

2024, 0(6):  157-163.  doi:10.13474/j.cnki.11-2246.2024.0627

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Industrial parks, as the core areas of urban economic development, are particularly important for monitoring surface deformation. Currently, there is limited research on the deformation mechanism of industrial parks, and traditional monitoring methods are costly and inefficient. Therefore, this study proposes the use of time-series interferometric synthetic aperture radar (InSAR) technology to construct a comprehensive monitoring model for industrial parks, which improves monitoring efficiency while reducing costs. Taking the Yinxi industrial park in Baiyin district as an example, based on 34 scenes of Sentinel-1A data from June 2018 to April 2021, the deformation information of the park’s surface was obtained using the StaMPS-PS (stanford method for persistent scatterers-permanent scatterers) and SBAS-InSAR (small baseline subsets-interferometry synthetic aperture radar) techniques. The deformation information obtained from the two techniques was cross-validated from a spatio-temporal distribution perspective. The results show that the deformation features obtained by both techniques correspond to the deformation locations in field survey photos. Additionally, using 585 identical latitude and longitude points for accuracy verification, a good correlation between the two techniques is found, with a coefficient of determination (R²) of 0.82 and a root mean square error (RMSE) of 2.20 mm/a. The deformation rates are highly consistent as well. Since the StaMPS-PS technique identifies 47% more deformation points than the SBAS-InSAR technique, it is more applicable for the industrial park. Finally, the geological conditions and factors inducing surface deformation in the industrial park are analyzed and discussed, providing reference for better understanding the deformation mechanism and early warning of disasters in the park.

Subway tunnel safety risk monitoring technology and application based on distributed optical fiber

WEI Shaojun, LIAO Mengguang, QIU Bingshui

2024, 0(6):  164-170.  doi:10.13474/j.cnki.11-2246.2024.0628

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The total station robot monitoring system is one of the important technical means of subway tunnel safety risk monitoring, but the monitoring points are relatively limited. Based on distributed optical fiber measurement data, the paper proposes a subway tunnel safety risk monitoring technology based on distributed optical fiber. Firstly, the strain data measured by distributed optical fiber monitoring system is mapped to more intuitive deformation and displacement data. Secondly, a strain-displacement transformation model based on BP neural network is constructed by determining 3 input parameters, 2 hidden layers and 1 output parameter. The verification results show that the error between the predicted value and the real value is within 0.2 mm, indicating that the established model can better fit the change law of the data. Finally, it was applied to a certain section of Qingdao metro Line 1, and 401.66～699.45 με is determined as the safe interval of the early warning value of each measurement point, and 202.49～899.98 με is determined as the safe interval of the control value. The calculated results are consistent with the field measured results, and the errors meet the engineering needs.

Submarine pipe cable detection using linear frequency modulation sonar and beam angle adjustment technology

MA Haiwei, LUAN Kunxiang, CHEN Liang

2024, 0(6):  171-175.  doi:10.13474/j.cnki.11-2246.2024.0629

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For the laying and burial of submarine tube cables are complicated,the difficulty of positioning and deep detection of small buried pipe cable.This paper introduces the linear frequency modulation sonar and beam angle adjustment technology in the advantages of submarine tube cable detection,combined with EdgeTech3400 OTS shallow diameter cable detection in offshore wind farms and coastal channel buried pipeline detection application cases,shallow stratum profile instrument in the deep buried cable detection identification technical difficulties,analysis of the cable detection influence factors and optimization measures,the navigation safety management,port and navigation project construction and offshore wind farm construction operations has positive significance.

A large-scale linear element map synthesis method supported by geometric feature importance algorithm

LU Xiangzhen, KANG Ermei

2024, 0(6):  176-181.  doi:10.13474/j.cnki.11-2246.2024.0630

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Aiming at the problem of low rendering efficiency of large-scale linear element map when the scale level of single vector tile data of geographic information is low, this paper proposes a large-scale linear element map synthesis method supported by geometric feature importance algorithm. In this method, the importance index of geometric features of line elements is proposed as the ranking basis when line elements are deleted, so as to realize the automatic synthesis of line elements taking into account the global characteristics of data, which not only retains the main geometric information on a single vector tile, but also effectively improves the efficiency of map rendering. The experimental results show that the rendering speed of line elements is increased by 3 times by applying this method to the vector data of “MapWorld Gansu”.