Table of Content

25 August 2023, Volume 0 Issue 8

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Analysis of temporal and spatial variation characteristics and driving factors of vegetation CUE in typical basin entering the sea in Beibu Gulf

ZHANG Yali, LIANG Xiaoyan, TIAN Yichao, LIN Junliang, WANG Donghua

2023, 0(8):  1-6.  doi:10.13474/j.cnki.11-2246.2023.0222

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Vegetation carbon use efficiency (CUE), which reflects the efficiency of vegetation ecosystem in carbon storage, is of important reference value for the research of vegetation carbon cycle under the background of "dual carbon" policy. Based on the MODIS data, meteorological data and DEM data, the trend analysis, coefficient of variation and other methods are used to investigate the termporal and spatial variation characteristics of vegetation CUE in the Beibu Gulf watershed over the 2001-2020 periods. As shown by the results: ① In terms of time, the average annual CUE presents a significant downward trend (P<0.01), with a downward slope of -0.003/a. The interannual CUE fluctuates slightly and is relatively stable. ② Spatially, the average CUE of the typical basin into the sea of the Beibu Gulf ranges from 0.08 to 0.60, with an overall average value of 0.465. The average annual CUE, in descending order, are Beilun River Basin (0.492), Qinjiang River Basin (0.487), Jiangping River Basin (0.483), Fangcheng River Basin (0.477), Maoling River Basin (0.474), Nanliu River Basin (0.458), Dafeng River Basin (0.450). ③ From the perspective of driving factors, the average temperature plays a major role in controlling the change of vegetation CUE in the Beibu Gulf Basin, followed by elevation and rainfall. Soil type and slope have limited driving force on vegetation CUE.

Research on the monitoring model of alpine wetlands in the northern Tibetan Plateau based on Gaofen satellite data:taking Mcdika Wetlands as an example

Pema Rigzin, Yeshe Dorji, Dhonyo Dorji, Bendor, Penpa Tsring

2023, 0(8):  7-13.  doi:10.13474/j.cnki.11-2246.2023.0223

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The unique alpine wetlands of the northern Tibetan Plateau provide advantageous resources and environment for Tibet and the whole country. Wetland degradation is becoming more and more serious due to human activities and natural causes. So far, this paper uses GF-1 remote sensing data to conduct multiple experiments to complete the establishment of a hierarchical classification decision tree, comprehensively considers the spectral characteristics and texture characteristics of wetlands, and combines appropriate scale segmentation images and various major wetland information identification methods. Finally, an alpine wetland monitoring model based on high-resolution satellite data is established for the northern Tibetan Plateau. This model can realize automatic extraction and classification of wetland information. The results lay a foundation for the research on wetland degradation and its ecological restoration. Using the model to monitor the wetlands of the Mcdika Nature Reserve in 2021, the total area of wetlands is 319.02 km², and the wetland area accounts for 36.26% of the total area of nature reserves. The areas of various types of wetlands from large to small are herbaceous swamps, lake wetlands, river wetlands, glacial snow, peat swamps, and floodplain wetlands. By randomly selecting test points and adopting the method of confusion matrix, the monitoring accuracy is evaluated. It is found that the total classification accuracy is 86.83%, and the classification accuracy Kappa coefficient is 0.827 5, so the model has achieved a good use effect.

Evaluation and spatial-temporal evolution analysis of ecological environment quality in Tianjin

FAN Xueshan, ZHENG Liquan, CHEN Zhigang, GONG Zhiwei

2023, 0(8):  14-18,66.  doi:10.13474/j.cnki.11-2246.2023.0224

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Ecological environment quality is a key index to measure the livable level of a region. Dynamic monitoring of regional ecological environment quality changes can provide scientific basis and decision support for ecological environment protection. To study the ecological quality changes in Tianjin in recent years, a modified remote sensing ecological index (MRSEI) was constructed by adding two indicators, population density (PD) and aerosol optical depth (AOD), based on the remote sensing ecological index (RSEI) index using principal component analysis. The change of eco-environmental quality in Tianjin was analyzed. The results showed that: ① MRSEI is reasonable and can be used to evaluate eco-environmental quality more comprehensively. ② Greenness (NDVI) and humidity (WET) were positively correlated with MRSEI value, while heat (LST), dryness (NDBSI), PD and AOD were negatively correlated with MRSEI value. ③ The MRSEI values in Tianjin in 2014, 2017 and 2020 were 0.59, 0.54 and 0.67, respectively. ④ The ecological quality of Tianjin expanded to the south, the ecological environment deviation in the south and the ecological environment preference in the north, but the gap was gradually narrowing. ⑤ The mean value of MRSEI of forest land is 0.75, shrub is 0.73, cultivated land is 0.68, grassland is 0.57, opaque water is 0.43, and wasteland is 0.28. In conclusion, based on the improved remote sensing ecological index, this paper evaluated the ecological environment quality of Tianjin from 2014 to 2020 in a more comprehensive way, providing reference for regional ecological governance.

Evolution of ecological network and identification of key restoration areas in the Loess Plateau during 2000-2020

ZHAO Juhua, YANG Yongchong, WANG Tao, YANG Meihuan, XIE Yanling, GUO Zhiwei, CHEN Jiawang

2023, 0(8):  19-23,77.  doi:10.13474/j.cnki.11-2246.2023.0225

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Ecological restoration projects play an important role in promoting national ecological protection. Ecological quality assessment and ecological network construction are important ways to identify ecological restoration areas. In this paper, remote sensing ecological index (RSEI), minimum cumulative resistance model and gravity model are used to construct the Loess Plateau ecological network in 2000, 2010 and 2020, and its evolution characteristics is analyzed, ecological key restoration areas in different periods are identified. The results show that: ① From 2000 to 2020, the overall ecological quality of the Loess Plateau was at a moderate and low level, with a spatial distribution feature of poor ecological quality in the northwest and better ecological quality in the southeast. From 2000 to 2010, the ecological quality improved significantly, accounting for 53.55% of the total area. ② From 2000 to 2020, the number of ecological corridors on the Loess Plateau increased, while the total length decreased. The main landscape types of ecological corridor are grassland, woodland and cultivated land. Most important corridors were distributed near the ecological source area in the southeast of the study area, and the number proportion increased year by year. The northwest corridor is small in number, long in distance, low in importance and poor in connectivity. ③ In the key ecological restoration areas of the Loess Plateau, the ecological pinch points were concentrated in the southwest and the east, and the obstacle points were scattered in the east and the south. The main land use types were grassland and cultivated land, and the area showed an increasing trend. Ecological fracture points are widely distributed and clustered around provincial capitals in the east of the Loess Plateau. This study can provide scientific reference for the implementation of territorial ecological restoration projects on the Loess Plateau.

Information extraction method of Enteromorpha prolifera based on UAV remote sensing image

MA Deming, ZHANG Xiulin, TIAN Ziwen, WANG Jiaxin

2023, 0(8):  24-28.  doi:10.13474/j.cnki.11-2246.2023.0226

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Enteromorpha prolifera, as a typical marine pollution, not only causes serious ecological and environmental problems, but also has a significant impact on coastal economic development. How to quickly and accurately obtain the location, boundary range and dynamic change information of Enteromorpha prolifera is the focus of natural resource authorities and researchers. In this paper, Daguan Island and its adjacent waters are selected as the study area, and a set of intelligent interpretation methods for the boundary range of Enteromorpha prolifera in multiple scenes is proposed based on UAV remote sensing images. At the same time, combined with the manual interpretation and verification, the application examples of Enteromorpha prolifera identification in coastal and marine areas are carried out respectively. The results show that the overall accuracy of the extraction of Enteromorpha prolifera from coastal and marine surfaces is 96.75% and 98.13%, respectively, and the Kappa coefficient is 0.72 and 0.71, respectively. The boundary range of Enteromorpha prolifera extracted matches well with the result of manual interpretation. The method proposed in this paper can quickly and effectively obtain the boundary range information of Enteromorpha prolifera, and its accuracy can meet the requirements of Enteromorpha prolifera information identification, and can provide data reference and technical support for high-precision tracking, refined disaster early warning and prevention and control.

Application of YOLOv7 in GPR B-Scan image interpretation

HU Rongming, LI Xin, JING Xia, WU Jianqiang, WEI Qingbo

2023, 0(8):  29-33.  doi:10.13474/j.cnki.11-2246.2023.0227

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Ground penetrating radar technology has been widely used in tunnel lining disease detection, for ground penetrating radar B-Scan image interpretation, many deep learning algorithms have emerged in recent years, but YOLOv7 algorithm has not been applied in this field. In this paper, the cavities and leakage water diseases of tunnel lining are simulated through FDTD forward performance, and 12 measuring lines in the tunnel are collected on the spot to analyze their disease distribution, so as to form the tunnel lining disease dataset, and then, based on the YOLOv7 algorithm, different image features of the two types of diseases are used to realize the automatic interpretation of tunnel lining diseases. The results show that the target detection algorithm of YOLOv7 is used to identify the overall disease with accuracy and recall rate of 97.87% and 90.61%, respectively. When the threshold IoU is 0.5, the identification accuracy of cavitation disease and leakage water disease is 97.2% and 96.4%, respectively. Finally, 100 images were randomly selected for testing, and the accuracy of the test results reached 94%. The final experimental identification results can be well used in production projects.

Detection of tunnel leakage based on gray scale image of laser point cloud

ZHOU Baoding, XIE Peiyao, GUO Wenhao, MAO Qingzhou

2023, 0(8):  34-39,90.  doi:10.13474/j.cnki.11-2246.2023.0228

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Water leakage detection of shield tunnel is the basis of tunnel maintenance. Aiming at the problem that traditional detection methods are difficult to obtain high-quality disease pictures under dim light conditions inside the tunnel, this paper uses lidar to detect internal water leakage in the tunnel. Firstly, based on the characteristics of water leakage disease in the grayscale image of laser point cloud, a data set of water leakage disease in the grayscale image of laser point cloud was established. Then, the Mask R-CNN (Region-CNN) model is used as the benchmark framework, and the Swin transformer network is used as the underlying feature extraction network to realize the rapid detection of tunnel water leakage diseases. Finally, the data collected by the railway mobile measurement system in Wuhan are used for experimental verification. The experimental results show that the detection accuracy of the improved Mask R-CNN model proposed in this paper is higher than that of the original algorithm by more than 12%, and it has good performance in the detection of subway tunnel water leakage diseases.

Micro geomorphic interpretation and extraction of quantitative topographic parameters with the help of tilt photogrammetry

LI Mengdi, ZHANG Rui, TANG Hongtao

2023, 0(8):  40-44,56.  doi:10.13474/j.cnki.11-2246.2023.0229

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The field work of traditional geological exploration is not only time-consuming, but also brings more cumbersome field workload to geologists. Using tilt photogrammetry and three-dimensional real scene modeling technology, the digital achievements such as 3D model, DOM and DSM with high resolution and covering an area of about 20 km² and 3.39 cm in the north foot of West Kunlun Mountain in Xinjiang are constructed, and their accuracy is evaluated. In addition, slope map of the study area are derived based on the DSM data. With the help of the high-precision digital results, the micro geomorphic interpretation of the geological structure of the study area is carried out, and the vertical fault quantities of the two large terrace scarps inside are accurately extracted. The successful application of this tilt Photogrammetry in geological exploration shows that this technology can greatly reduce the field workload of geologists and improve the efficiency. It has broad application prospects in the fields of geological survey, topographic micro geomorphic interpretation and quantitative parameter extraction, and provides a strong technical support for geologists.

Surface change identification of exposed slope based on UAV inclined photogrammetry

WANG Yuhao, LI Denghua, DING Yong

2023, 0(8):  45-50.  doi:10.13474/j.cnki.11-2246.2023.0230

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In order to overcome the defects of traditional manual slope investigation methods such as low efficiency, high risk and difficulty, the 3D reconstruction of slopes and disaster identification and classification method of UAV tilt photography is adopted for high and steep exposed slopes in this paper. The UAV multi-view sequence images are used to reconstruct the 3D realistic model of exposed slopes and unify the 3D models of slopes in different periods in the same coordinate system. The accuracy of the reconstructed model is better than 2 cm after experimental verification. The 3D point cloud data change detection algorithm based on point cloud and point cloud comparison algorithm analyzes the subtle differences between the point cloud models of two different periods and marks them in the 3D real-view model, and combines the PointNet++ classification neural network algorithm with the homemade point cloud data set to successfully realize the identification as well as classification of the marked areas, so as to realize the identification of slope landslides, The intelligent recognition of disaster scenes such as landslide, collapse and rockfall is successfully realized.

Extracting accurate building outlines from 3D point clouds considering local features

XU Jinfang, LUO Xiaolong, JIANG Weidong, ZHONG Kang, RAN Xingxing, LI Bingyang

2023, 0(8):  51-56.  doi:10.13474/j.cnki.11-2246.2023.0231

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Buildings are an important component of cities, and extracting building features from 3D point clouds data in a refined manner is currently a research hotspot. The paper proposes a methods for accurately extracting building outlines from 3D point clouds that considers local features. Firstly, a statistical-based filtering pre-processing method is used to separate ground points and non-ground points, remove outliers, and reduce the number of point clouds. Secondly, a building facade extraction method based on an improved 3D Hough transform is used to extract multiple facades of the point cloud data to improve the accuracy and efficiency of facade extraction. Finally, a point cloud outlines extraction method based on dimensionality reduction boundary indexing to obtain both local features and external outline features. The results demonstrate that our methods can effectively and accurately extract building outlines by fully considering both the overall external contours and local detailed features of the buildings. This methods provides technical support for various applications, such as urban planning and urban renewal.

Abandoned land identification based on Sentinel-2 and Landsat satellite time series images

OUYANG Xutong, ZHANG Xuan, LI Weiqing, LIU Juan, LIU Zhuosheng

2023, 0(8):  57-62.  doi:10.13474/j.cnki.11-2246.2023.0232

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Adequate food supply is vital to economic development and social stability. With the marginalization of cultivated land, the monitoring of abandoned land is essential to ensure the quantity and quality of cultivated land. This paper takes Yingshan county, Nanchong city, Sichuan province as the research area. GEE platform, Sentinel-2 and Landsat 7、8 data are applied to build a time series dataset, and indices such as NDVI, EVI, NDWI, BSI and MSI are calculated. Machine learning algorithms, support vector machine and random forest are separately used to extract abandoned land, and the overall accuracy is 73.76%, with Kappa coefficients 0.68. The highest F₁ score of abandoned land is 0.691 1. This paper develops an abandoned cultivated land identification method based on time series dataset, which can contribute to monitoring wasteland in hilly areas.

The methods for monitoring nighttime positions of northward migrating Asian elephant herds

ZHANG Junhua, LI Na, ZHANG Qinhui, MA Xiaowei

2023, 0(8):  63-66.  doi:10.13474/j.cnki.11-2246.2023.0233

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This paper mainly studies the design and practice of a night-time location monitoring program for the Asian elephant group migrating northward in the Xishuangbanna National Nature Reserve. Firstly, the monitoring requirements of the program are clarified, including night-time elephant group identification, real-time dynamic location update, migration route drawing, route and safety research and so on. Secondly, the software and hardware selection of the program is introduced, mainly relying on unmanned aerial vehicles combined with infrared cameras. Then, the scheme design of the program is discussed, including hovering height, flight design, monitoring point connection, recognition, measurement and handover operations. Finally, through practice and result analysis, this paper concludes that the night-time location monitoring program of unmanned aerial vehicles combined with infrared cameras can effectively identify the Asian elephant group in real time, update the position in time, draw the route, and predict the elephant group's route in advance, providing effective protection measures for the elephant group migration.

A method of urban road settlement monitoring combining Deep-ResUnet and PS-InSAR:a case study of Hefei city

ZOU Xin, WANG Lei, LI Jingyu, TENG Chaoqun, HUANG Jinzhong, LI Zhong, LI Shibao

2023, 0(8):  67-71.  doi:10.13474/j.cnki.11-2246.2023.0234

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In view of the problems of deformation monitoring of urban road network, such as difficulty in obtaining high-resolution images, low efficiency of manual road extraction, and heavy workload of traditional deformation monitoring, this paper proposes a deformation monitoring method of urban road network based on fusion of Deep-ResUnet and PS-InSAR. The main idea is to first perform pseudo color transformation on Sentinel-1A image data in the target area to establish a road dataset, then train a Deep-ResUnet model and extract the road network grid. Finally, the permanent scatterer interferometry (PS-InSAR) technique is used to obtain PS point deformation information and fuse it with the road network grid. The research results show that after the Sentinel-1A image is processed with pseudo color, the integrity of urban road network extraction can be improved, the intersection and merge ratio can be improved by 6%~9%, and the accuracy of road extraction can be improved by about 10% on average. The thematic map of urban road network deformation information obtained can provide scientific basis for urban road deformation monitoring and health assessment.

Design and implementation for BDS-based high-precision displacement monitoring inovation experimental platform

ZHOU Mingduan, XU Xiang, YANG Tianran, JI Xu, LI Pingping

2023, 0(8):  72-77.  doi:10.13474/j.cnki.11-2246.2023.0235

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In view of the disadvantages of the traditional displacement monitoring experimental mode, the BDS-based high-precision displacement monitoring innovation experimental is designed.Firstly, the mathematics model of BDS single epoch positioning is built, and the flowchart of fast integer ambiguity resolution applicable to the main and auxiliary correlation method(MAXCOM) of BDS multi-frequency signals is given.And then, the BDS-based high-precision displacement monitoring innovation experimental platform(LSMS) is developed based on Visual Studio 2019 development platform using C# programming language. The experimental testing and analysis results show that the BDS-based displacement monitoring accuracy of LSMS experimental platform can reach cm-level, and the monitoring accuracy is better than 0.9 cm in the north-direction, 1.2 cm in the east-direction, 3.0 cm in the vertical-direction, 1.5 cm in horizontal-direction and 3.3 cm in point-positioning for the observation epochs. The proposed method and experimental platform in this paper is feasible and effective applied for BDS-based high-precision displacement monitoring innovation experimental.

Construction and performance analysis of multisystem real time PPP/MEMS INS tight integration model in urban environment

LAI Luguang, LI Linyang, GUO Wenzhuo, CHENG Zhenhao, ZHANG Letian, ZHAO Dongqing

2023, 0(8):  78-83,129.  doi:10.13474/j.cnki.11-2246.2023.0236

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High-precision real-time position provided by sensors represented by GNSS and INS is an important foundation for autonomous driving, intelligent transportation and other fields. To improve the real-time positioning performance of GNSS and MEMS INS in urban environments, a multi-system real-time PPP/MEMS INS tight integration model is constructed using real-time precise orbit and clock correction numbers broadcasted by IGS, and the positioning performance of multi-system real-time PPP and PPP/MEMS INS tight integration is analyzed and evaluated. The positioning results obtained are compared with post precise products. The experimental results show that the multi-system PPP/MEMS INS tight combination can achieve continuous and reliable positioning in the case of short GNSS interruptions, the positioning accuracy of horizontal and vertical components reaches 0.374 and 0.339 m, respectively. But there is still a gap compared with the post precise products, and the difference of 3D positioning accuracy is about 0.15 m. The velocity and attitude accuracy of the PPP/INS tight integration mainly relies on INS, and the velocity measurement accuracy is about 1 cm/s, which is not significantly different from the results obtained using the IGS post product; the observability of heading angle is weak, and the error of heading angle increases significantly during static period.

Research on LiDAR Positioning method of fusion descriptor and particle filter when GNSS loss of lock

LI Ang, ZHONG Ruofei, LIU Zhengjun, XIE Donghai, WU Wei, ZHANG Yan

2023, 0(8):  84-90.  doi:10.13474/j.cnki.11-2246.2023.0237

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When GNSS loses lock, how to locate based on 3D LiDAR is a problem worthy of attention. Positioning by point cloud matching or Monte Carlo method has the problems of sensitivity to initial value, long calculation time and large search space. This paper proposes a two-step localization method, which first uses descriptor initialization, determines the rough pose, narrows the search space, and then uses Monte Carlo for fine localization. The use of descriptors in the method gives the initial value, reduces the search space, and pre-computes the three-dimensional grid probability, which reduces the computation time. Preliminary experimental results show that the positioning accuracy remains good in the case of GNSS loss of lock.

Helmert variance component estimation method for GPS+BDS and UWB integrated positioning

ZHAO Yiwen, YANG Tianran, MIAO Lili, MA Yuntao

2023, 0(8):  91-96.  doi:10.13474/j.cnki.11-2246.2023.0238

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In order to improve the positioning accuracy of indoor and outdoor transition areas, GPS/BDS and UWB integrated positioning is used to establish a integrated GPS/BDS and UWB positioning model, and for the different structures of GPS and BDS constellations, and the large difference between UWB and GNSS observation residuals, the Helmert variance component estimation method is used to determine GPS+BDS and UWB respectively. Finally, the accuracy is verified by the dynamic indoor and outdoor transition zone experimental integrated model, and the results show that based on the GNSS+UWB positioning system and the Helmert variance component method, the positioning accuracy in the north, east and zenith directions has been improved. The positioning accuracy can be improved by about 20% under static conditions and about 10% under dynamic conditions.

BDS/GPS maritime real-time PPP integrity monitoring and performance analysis

JIANG Aiguo, ZOU Fubing, LIU Xiaolin, ZHANG Jianzhou, ZHANG Jie, YANG Fuxin, HE Dongxu, XU Yinglong

2023, 0(8):  97-101.  doi:10.13474/j.cnki.11-2246.2023.0239

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Real-time precise point positioning is a cost-effective high-precision marine positioning technology. With the development of offshore precision operations, in addition to positioning accuracy, the reliability requirement for offshore high-precision position services has increased. The integrity monitoring of precise point positioning can ensure the high-precision reliability of offshore precision operations. Therefore, by constructing the advanced receiver autonomous integrity monitoring algorithm, the error limit of PPP at sea with strict integrity guarantee is established, which ensures the reliability of high-precision positioning under the condition of multiple interference. The actual measurement results of offshore drilling operation relying on a drilling platform show that the convergence positioning accuracy of BDS/GPS PPP can reach 10 cm, the probability of hazardous misleading information can meet the 10^-7 level and the continuity risk can meet the 10^-6 level.

Analysis of astronomical measurement considering effects of earth roration parameter prediction error

WEI Fei, GAO Yuping, YIN Dongshan

2023, 0(8):  102-107,125.  doi:10.13474/j.cnki.11-2246.2023.0240

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Due to the hysteresis of earth rotation parameters (ERP), currently only the forecast values of Bulletin A (referred to as Bulletin A) provided by the IERS are used for calculation, however, the effect of ERP forecast errors on astronomical measurement is currently lacking systematic research.To this end, this paper selects the forecast errors of IERS 2015-2021 Bulletin A in the past 7 years to analyze its long-term forecast and forecast errors in different time spans, and takes the observation results of digital zenith telescope at a station as an example to analyze the effect of the Bulletin A forecast error on astronomical measurement. It shows that with the increase of time, the forecast accuracy is getting worse and worse. The forecast error for one-year span of the polar shift reaches 0.021 4 as, The effect of polar shift prediction error on astronomical longitude、latitude and astronomical azimuth is 0.045 as、0.041 as and 0.042 as, which fully meets the accuracy requirements of first-class astronomical measurements.The UT1-UTC prediction accuracy is the major factor limiting the accuracy of the Bulletin A. The prediction error of UT1-UTC in a 60-day span reaches 0.007 s, and the effect on astronomical longitude reaches 0.379 as, which has exceeded the accuracy requirements of first-class astronomical measurements.In order to meet the requirements of first-class astronomical measurements, when the UT1-UTC predicted value is selected, the maximum time span is 40 d.

Monitoring and evaluation of environment effect of photovoltaic power station construction using MODIS satellite data

WANG Yiting, WANG Xinyue, ZHAN Yinggang, ZOU Rui, YANG Lixiang

2023, 0(8):  108-112.  doi:10.13474/j.cnki.11-2246.2023.0241

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This paper selects Gonghe Photovoltaic Industrial Park in Qinghai province, the largest photovoltaic industrial park in China as the study area, uses the MODIS medium resolution satellite data and meteorological data, and investigates the ecological and environmental effects of photovoltaic power station construction from 2010 to 2020. The M-K spatio-temporal trend analysis method was used to analyze the spatial and temporal variations of land surface biophysical parameters after the solar panels were built. Then the Granger causality test method was used to investigate the causal relationship between the changes in land surface parameters and meteorological factors. The results showed that the vegetation in the study area are increasing, while the surface temperature and albedo decreasing. Vegetation growth under photovoltaic panels is slightly better than that outside the panels. The increase of construction area is the Granger cause of the increase of vegetation in the study area. The study indicates that the establishment of photovoltaic can promote the growth of vegetation, reduce albedo and cool the surface temperature.

Multi-feature CNN-BiLSTM prediction method for urban bike-sharing demand

YANG Fan, CHE Xianghong, WANG Yong, DU Kaixuan, XU Shenghua, ZHU Jun

2023, 0(8):  113-119,150.  doi:10.13474/j.cnki.11-2246.2023.0242

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The demand prediction of shared bicycles is important for the refined operation of shared bicycles. This serves as the premise of solving the balance of supply and demand in the bicycle area. Aiming at accurately predicting the demand for bike-sharing, this study firstly analyzes the spatiotemporal characteristics of the bike-sharing data of Citi Bike in New York from 2017 to 2019 and explores the distribution patterns of bike-sharing trips. Subsequently, the study integrates the feature extraction ability of convolutional neural networks and the bidirectional temporal data processing ability of bi-directional long-short term memory to construct a convolutional bidirectional long-short-term memory network CNN-BiLSTM model. The input features of the CNN-BiLSTM model are determined using correlation analysis from travel data, meteorological data, and space-time travel characteristics of bicycles, and then a well-built bicycle prediction model is generated. Finally, CNN, LSTM, BiLSTM, and CNN-LSTM are selected as benchmarks to evaluate the performance of the CNN-BiLSTM prediction. Results show that the CNN-BiLSTM model has the smallest evaluation indicators of MAE and RMSE with 0.035 and 0.058, and the largest R² with 0.922, and achieves the best prediction performance. This research provides a reference for the actual scheduling and redistribution of shared bicycles.

A lossless compression method and application of hyperspectral images

WANG Lei, ZHANG Hengjing, GAO Xiaoming, XING Chen, MA Haichao

2023, 0(8):  120-125.  doi:10.13474/j.cnki.11-2246.2023.0243

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Aiming at the problem that the huge amount of data of remote sensing images causes great pressure and low compression ratio to transmission and storage, an efficient lossless compression method with improved adaptive band rearrangement and minimum mean squared error prediction is proposed. This method can adaptively determine the optimal order of the bands, and can make full use of this sort correlation to eliminate image redundancy based on the minimum mean squared error prediction. Firstly, the method adaptively groups the hyperspectral image bands and uses the minimum spanning tree algorithm to sort within each group to improve the inter-spectral correlation of adjacent bands. The intra-band bands are then adaptively selected for inter-spectral and intra-spectral predictions, removing redundancy from hyperspectral images. Finally, the binary arithmetic encoding of the prediction residuals removes statistical redundancy and completes the lossless compression of hyperspectral images. Experimental results based on ZY1-02D hyperspectral images show that this method effectively utilizes the correlation of intra-spectral and inter-spectral, improves the prediction performance, superior to common compression methods.

3D laser scanner in buildings application in fine reconstruction

LI Jiebin, WANG Ning, ZHAO Chunchen

2023, 0(8):  126-129.  doi:10.13474/j.cnki.11-2246.2023.0244

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With the continuous promotion of digital city construction and the development of 3D data acquisition technology, the precision of architectural model in key areas of cities has put forward higher requirements. At present, in large-scale urban 3D modeling, the method of ground laser scanning and tilt photogrammetry combined modeling is widely used because of its high modeling efficiency, but its model precision can not meet the requirements of building modeling precision in key areas. Therefore, multi-source point cloud fusion modeling is adopted for small-scale high-precision 3D modeling. Although fusion modeling has a relatively high level of model precision, the workload of point cloud data processing in the early stage is significant, which affects modeling efficiency. This article proposes a method for fine modeling of buildings and compares it with actual data. This method not only achieves fine modeling of buildings, but also improves the automation of dense point clouds and improves modeling efficiency.

Adaptive projection algorithm of multi-tilt angle digital sand table

WANG Xiangfei, LUO Yadan, LUO Qisi

2023, 0(8):  130-135.  doi:10.13474/j.cnki.11-2246.2023.0245

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Raising the digital sand table at a certain tilt angle according to the site environment helps to enhance the viewing experience of the physical sand table and projection content. However, the projection content is prone to offset after the sand table is tilted, resulting in the original content can not be projected to the corresponding position. In order to address this problem, this paper proposes an adaptive projection algorithm. Firstly, the original pixel matrix is established by the projected content and the environment parameters. And coordinate corrected models are established for different projected positions. Then the new coordinate positions after tilting are calculated by traversing the pixel points. Finally, the corrected pixel matrix is obtained by assigning the color value of each point to the new point, achieving adaptive projection. The experimental results show that the algorithm can ensure that the original projection content is accurately projected onto the sand table.

Remote sensing of “space-sky-terrestrial” using spectral and geometry for small slope hidden danger

WANG Bin, XU Jiayuan, TANG Liming, ZHOU Lipeng

2023, 0(8):  136-141.  doi:10.13474/j.cnki.11-2246.2023.0246

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The "space-sky-terrestrial" integrated three-check technology system was verified and applied to the large/medium-sized slope hidden danger identification early in Southwest China,but this method has a low recall rate for small disasters induced by human engineering activities.It is extremely urgent to quickly and accurately identify small hidden danger points. In response to the small、wide、frequent and hidden slope deformation,and the landslide disasters induced by artificially cutting the slope and building houses,this paper takes Guangdong Longchuan as an example,which researchs and experiments of "full inspection-belt sweep-point screening-surface core-Group defense-joint trial" early identification spectral and geometry remote sensing method,using the SBAS-InSAR time series inversion of Sentinel-1 radar images from January 8,2019 to June 29,2022 to extract abnormal deformation areas,then combining LiDAR and geological disaster background information to identify the slope hidden danger points from the abnormal deformation areas,and defines the types of hidden danger through verification in the field. The results show that 323 abnormal deformation areas are found by time series InSAR inversion,82 suspected hidden danger spots are identified by screening,55 spots are verified in the field,and 7 are defined as hidden danger points.It is proved that the method has application value,which can provide scientific basis for disaster prevention and reduction at geological hidden danger.

Application of deep learning remote sensing image interpretation technology in cultivated land protection

ZHANG Jian, GAO Ya

2023, 0(8):  142-145.  doi:10.13474/j.cnki.11-2246.2023.0247

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Cultivated land protection is related to national food security, ecological security and social stability. The wide application of deep learning technology in the field of massive data analysis provides a technical foundation for efficient and accurate remote sensing image interpretation. In this paper, the remote sensing image interpretation technology based on deep learning is studied, and a sample library of interpretation samples for training is constructed by using remote sensing image data and corresponding vector data, and an interpretation model based on deep residual network structure is proposed. The practicality of the model has been proved by experiments. In this paper, the change of the main land area in the experimental area is monitored, and the implementation of cultivated land reclamation is verified by comparing the multi-phase image interpretation results and the business data such as the increase and decrease linkage. The results show that deep learning remote sensing image interpretation technology has a wide application prospect in the field of cultivated land protection.

Indoor and outdoor integrated point cloud construction technology of buildings based on space ground fusion

YU Qian, LING Xiaochun, ZHAO Mingjin, WEI Wei, LIU Yang

2023, 0(8):  146-150.  doi:10.13474/j.cnki.11-2246.2023.0248

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Indoor and outdoor integrated building model is the basic data of smart city management. Tilt photography and ground 3D laser scanning are the two main ways to obtain building model data. In view of the problem that a single data acquisition method is restricted by many factors and it is difficult to obtain complete indoor and outdoor 3D data of buildings, this paper puts forward the technical route of the integration of tilt dense matching point cloud and ground laser scanning point cloud. This route uses ground laser scanning to obtain the building facade and indoor point cloud, and tilt photography to obtain the building top point cloud. The two combine to build a complete indoor and outdoor integrated building point cloud. Taking the Gaomi clay sculpture exhibition hall as the research object, this paper tests the feasibility of the above technical route and verifies the fusion accuracy, which can provide reference for similar projects.

Analysis of feasibility on re-refinement of provincial quasi-geoid

JIN Xinyang, WANG Bin, LIU Xiaoyun, GUO Xinwei, TIAN Jie

2023, 0(8):  151-154.  doi:10.13474/j.cnki.11-2246.2023.0249

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GNSS and leveling results are important control data for studying the refinement of quasi-geoid. Giving full play to the advantages of high-precision of GNSS/leveling is conducive to improving the precision of quasi-geoid model. Aiming at the problems that the existing provincial quasi-geoid models are insufficiently currency and their precision is low, a provincial quasi-geoid re-refinement scheme is proposed. Firstly, using the new GNSS/leveling results, the inverse distance weighting method is used to fit and correct the existing quasi-geoid. Secondly, compared with the conventional complete refinement scheme to study its feasibility. Results show that the provincial quasi-geoid re-refinement scheme can improve the precision of the quasi-geoid models to a certain extent, and the improvement ratios in the three experimental areas are 24%, 38% and 41%,but the improvement is not as good as the conventional refinement scheme. The conclusions have certain reference significance for the future provincial quasi-geoid refinement.

Geographical named entity recognition based on human-in-the-loop learning enhancement

YANG Ying, QIU Qinjun, XIE Zhong, TIAN Miao, ZHENG Shiyu, ZHENG Shuai

2023, 0(8):  155-160,177.  doi:10.13474/j.cnki.11-2246.2023.0250

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Geographical named entity recognition is an important part of high-quality geographic knowledge graph construction, which is widely used in geographic coding, semantic retrieval and geographic knowledge inference. The mainstream deep learning models suffer from the problems of time-consuming and laborious annotation corpus and poor model interpretability. In order to take advantage of the human-in-the-loop mechanism to promote learning models using a small number of samples, a geographical named entity recognition method based on human-in-the-loop learning enhancement is proposed: partially labeled and unlabeled geographic corpus is used as input, trained based on BERT-BiLSTM-CRF model and recognized to the labeled corpus, and the sentences that are incorrectly recognized by the model are provided with human intervention in the form of the corrected sentences are re-transported to the learning model for training again; after several iterations, the standard geographic named entity dataset and the human extraction model after loop reinforcement are finally formed. The performance of the model is evaluated using the geographic encyclopedia data as an example, and the accuracy of the method is over 90% for most of the geographical named entity recognition parses.

Research and prospects of HD and SD fusion map problem for in auto-driving industry

LI Yuehua

2023, 0(8):  161-166.  doi:10.13474/j.cnki.11-2246.2023.0251

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With the widespread application of high-definition map data in auto-driving industry,the application of laboratory simulation and real vehicle testing analysis based on HD and SD fusion maps in the industry has also become frequent.while it is convenient for application,there are also many problems based on HD and SD map fusion in the process of application of high-definition map manufacturers.This article analyzes and summarizes the components,main characteristics and existing problems of the HD and SD fusion map in the current auto-driving industry,and discusses the solutions and industry prospects of the problems related to the fusion map.

Application and analysis of real 3D model in topographic map quality inspection

WEN Zhengbing, LI Bingfeng

2023, 0(8):  167-171,177.  doi:10.13474/j.cnki.11-2246.2023.0252

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To solve the problem that the traditional quality inspection method of surveying and mapping geographic information can not accurately survey difficult terrain, a quality inspection method of topographic map based on real 3D is proposed. The terrain inspection is carried out by using the 3D technology of real scene, and the relevant survey data of complex terrain are obtained by using the tilt photography, and the accuracy is compared with the traditional surveying and mapping results. Therefore, the accuracy of the topographic map quality inspection method based on the 3D technology model of real scene is judged. Through analysis, it is found that in order to improve the efficiency and accuracy of topographic map quality inspection, it is necessary to optimize the image control layout parameters, tilt photogrammetry related parameters, control triangulation and beam method regional adjustment and other ways to carry out topographic map measurement and quality inspection in difficult mapping areas. For the quality inspection of some large-scale topographic maps, it is difficult to extract data and test coverage by traditional quality inspection methods. The topographic map quality inspection method based on 3D model of real scene can improve the accuracy of measurement results and reduce the difficulty of topographic map quality inspection work, which is worth popularizing and applying.

A narrow lane FCB estimation method considering prior constraints

LI Xiaona, CHEN Liang, ZHAN Xin, ZHANG Zhixin

2023, 0(8):  172-177.  doi:10.13474/j.cnki.11-2246.2023.0253

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Navigation precise point positioning (PPP) technology has been widely used in many fields because of its flexible operation, and the fix of ambiguity is the core problem of PPP. As the BeiDou-3 navigation satellite system (BDS-3) stations are mostly concentrated in the asia-pacific region, therefore, the narrow lane Fractional Cycle Bias (FCB) of the new system signal broadcast by BDS-3 is affected by the observation environment and observation conditions, and the estimated values of each time period are quite different, therefore, the estimation accuracy of FCB in narrow roadways seriously restricts the whole-cycle ambiguity of BDS-3 precise point positioning. In order to solve this problem, a new FCB estimation method based on minimum variance estimation and prior information is proposed. Firstly, gross error detection and isolation are performed on the observed data of each station, and minimum variance estimation is made according to prior variance and mean on the basis of processing"Clean" data. Finally, the data from 38 observatories in asia-pacific region are used for verification. The experimental results show that the monthly stability of FCB in the new system is less than 0.2 weeks, and the fixed rate of FCB in the new system can reach 95.1% under static condition when BDS-3 precise point positioning is performed, the root mean square errors (root mean square rms) of the positioning accuracy in the east, north and sky directions were 1.1, 1.3 and 3.9 cm.

Exploration and practice on the cultivation of undergraduate talents in Geographic Information Science in the era of “big knowledge”

DENG Min, LIU Qiliang

2023, 0(8):  178-181.  doi:10.13474/j.cnki.11-2246.2023.0254

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The transformation from "big data" to "big knowledge" provides new opportunity and challenge for geographic information science. Correspondingly, we need to explore a new mode of training undergraduate talents in geographic information science. In this paper, we first analyze the new characteristics of geographic information science education in the era of "big knowledge". Then, we introduce the practice of undergraduate talent training in geographic information science from the aspects of top-level design of talent training, construction of curriculum system, construction of teaching content, and reform of teaching philosophy. This paper may provide some reference for promoting the cultivation of undergraduate talents in geographic information science.