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Spatial and temporal dynamic change and influencing factors of ecological environment quality in Chaohu Lake basin based on GEE WANG Ying, LI Daiwei, ZHANG Fan, ZHU Huizi, LI Longwei, LI Nan Bulletin of Surveying and Mapping    2023, 0 (7): 7-13.   DOI: 10.13474/j.cnki.11-2246.2023.0193 Abstract （257）   HTML （19）    PDF（pc） （6645KB）（174）       Knowledge map    Save Taking Chaohu Lake basin as the research area, remote sensing ecological index (RSEI) is constructed through Google Earth Engine cloud computing platform, and large-scale and long-time dynamic monitoring analysis and evaluation of ecological environment quality in Chaohu Lake basin are carried out by means of spatial autocorrelation and geographic detectors based on Landsat TM/OLI series remote sensing data from 2000 to 2020. The results show that:①The average value of RSEI increased from 0.70 in 2000 to 0.74 in 2020, showing an overall improvement trend, and the ecological environment level is mainly excellent and good. ②The global Moran's I index of the study area is all greater than 0, and the ecological environment quality in Chaohu Lake basin presented a clustering trend on the global autocorrelation, with a significant spatial positive correlation. In the past 20 years, the low-low aggregation area had a trend of increasing firstly and then decreasing. ③The ecological environment is affected by many factors, among which human factors had a great impact on the ecological environment of Chaohu Lake basin in 2010, which leaded to the decline of ecological environment quality. Reference | Related Articles | Metrics

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Dual response of the Loess Plateau ecosystem to climate change and human activities JIANG Xiuwei, SHI Yun Bulletin of Surveying and Mapping    2023, 0 (7): 1-6.   DOI: 10.13474/j.cnki.11-2246.2023.0192 Abstract （256）            Knowledge map    Save In the context of climate change and increasingly intense human activities, to assess the spatial and temporal changes of ecological and environmental quality in the Loess Plateau region, this study takes the Loess Plateau as the study area based on the GEE platform, selects MODIS product data from 2000 to 2020 to construct the RSEI index, and uses the trend analysis method, standard deviation ellipse method, and residual analysis method to analyze the spatial and temporal changes of ecological and environmental quality in the Loess Plateau. The results show that: ①The RSEI of Loess Plateau has shown an increasing trend in the past 21 years, and its growth rate is 0.005/a ( p<0.01).②spatially, the RSEI is mainly increasing, accounting for 92.78% of the total area of the region, and keep improving trend in the future.③In terms of migration, the migration distance of poor grade is the largest, at 85.67 km, followed by good grade with 49.05 km; the smallest migration distance is medium grade with 30.18 km.④Both climate change and human activities have positive effects on the RSEI of Loess Plateau, and human activities are the main driving factor. Reference | Related Articles | Metrics

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Evaluation of regional landslide susceptibility based on convolutional neural network: a case study of Wanzhou district of Three Gorges Reservoir area YANG Yanchen, ZHOU Chao, SHI Jiamei Bulletin of Surveying and Mapping    2023, 0 (11): 1-6.   DOI: 10.13474/j.cnki.11-2246.2023.0318 Abstract （221）            Knowledge map    Save Carrying out regional landslide susceptibility assessment is the key to landslide meteorological early warning and risk assessment. Aiming at the fact that many current susceptibility studies do not consider the relationship between the occurrence of landslides and adjacent environments, a regional landslide susceptibility modeling framework based on convolutional neural network (CNN) is proposed. Taking Wanzhou district of the Three Gorges Reservoir area as an example, 12 factors such as slope and aspect are selected to construct an evaluation index system, and the influence of factors on landslide development is analyzed by information method. The local two-dimensional matrix is used to construct the dataset, CNN is used for susceptibility modeling. At the same time, the impact of the size of the local two-dimensional matrix to the accuracy when constructing samples is explored. The results show that landslides are more likely to occur the closer to the reservoir zone, and the water system and human engineering activities have a greater impact on the development of landslides. The accuracy of the CNN model is 0.925, which is significantly higher than that of the machine learning model, and the accuracy can be improved by increasing the local two-dimensional matrix size when constructing the sample. The CNN model has advantages in multidimensional spatial data processing, considering the influence of landslide location and its adjacent environment, and it is an accurate and reliable regional landslide susceptibility evaluation method. Reference | Related Articles | Metrics

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A deep learning method for nearshore bathymetry with ICESat-2 and Sentinel-2 datasets ZHONG Jing, SUN Jie, LAI Zulong, SHEN Yifu Bulletin of Surveying and Mapping    2023, 0 (7): 39-43.   DOI: 10.13474/j.cnki.11-2246.2023.0198 Abstract （216）            Knowledge map    Save Currently, satellite-derived bathymetry (SDB) is widely used for nearshore bathymetry. However, the commonly used empirical models are too simple to be applied to various complex shore environments. To break through the limitations of traditional methods, this paper proposes a deep learning method for nearshore bathymetry with ICESat-2 and Sentinel-2 datasets. Cat Islands (CI) and Buck Island (BI) are used as the study areas. ICESat-2 is used to extract a priori bathymetry points, and then a one dimensional convolutional neural network(1DCNN) is trained on the Sentinel-2 data to obtain a bathymetry map of the study area. band ratio (BR), random forest (RF) and multilayer perceptron (MLP) are also used as comparison methods. Through quantitative analysis of accuracy, the root mean square error and coefficient of determination of water depth measured by the proposed method in CI and BI are 0.20 m, 0.94 and 0.95 m, 0.95, respectively, which verify the accuracy better than other comparative methods and improved the accuracy of water depth inversion. Reference | Related Articles | Metrics

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Multi-source remote sensing landslide hazard identification method driven by knowledge graph LI Yongxin, WANG Defu, MA Zhigang, FAN Yajun, YANG Benyong, LIU Li, LUO Chao Bulletin of Surveying and Mapping    2024, 0 (1): 12-18.   DOI: 10.13474/j.cnki.11-2246.2024.0103 Abstract （209）      PDF（pc） （4718KB）（59）       Knowledge map    Save Remote sensing technology plays an important role in the field of geological disaster prevention and control. With the development of aerospace technology, more remote sensing data can be obtained and effectively applied to the identification of geological hazard bodies, especially in the identification of landslide hazards. Comprehensive use of InSAR and optical remote sensing data to identify geological hazards is a hot topic in recent research. The traditional recognition process relies entirely on the work experience of interpreters, with strong subjectivity and no fixed recognition logic to follow. Based on SBAS-InSAR and optical satellite imagery, this paper analyzes the process of landslide hazard identification, and constructs the Knowledge graph and identification extraction matrix model of landslide identification. Under the logic drive of the Knowledge graph, the regional spatial characteristics of landslide hazards identified by the combination of “optical remote sensing+InSAR” are analyzed, providing a reference implementation scheme with the significance of semi quantitative extraction of indicators for landslide wide area identification, and realizing the identification process of landslide hazards from completely subjective to semi quantitative. Experiments show that this method can provide reference for relevant research and practical engineering applications, and has certain application value. Reference | Related Articles | Metrics

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LiDAR point cloud registration with improved ICP algorithm XU Zhe, DONG Linxiao, WU Jiayue Bulletin of Surveying and Mapping    2024, 0 (4): 1-5.   DOI: 10.13474/j.cnki.11-2246.2024.0401 Abstract （197）      PDF（pc） （3266KB）（192）       Knowledge map    Save The traditional ICP algorithm has long matching time and is affected by initial values in LiDAR target point cloud matching, which leads to low positioning accuracy and poor robustness when applied to unmanned vehicle SLAM technology. Proposes an NDT-ICP algorithm that combines the KD-tree algorithm. Firstly, voxel grid filtering is used to preprocess the point cloud data obtained from LiDAR, and the method of plane fitting parameters is used to remove point cloud of ground. Secondly, use NDT algorithm for point cloud coarse matching to shorten the distance between the target point cloud and the point cloud to be matched. Finally, the KD-tree proximity search method is used to improve the speed of corresponding point search, and the precise matching of the ICP algorithm is completed by optimizing the convergence threshold. Through experiments, it has been shown that the improved algorithm proposed in this article has significantly improved speed and accuracy in point cloud matching compared to NDT and ICP algorithms, and has better accuracy and robustness in map construction. Reference | Related Articles | Metrics

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InSAR observations constrained coseismic slip distribution and Coulomb stress variation of Mw 6.7 Menyuan earthquake in 2022 WANG Xin, LI Shuiping, KANG Jing Bulletin of Surveying and Mapping    2023, 0 (7): 32-38.   DOI: 10.13474/j.cnki.11-2246.2023.0197 Abstract （188）            Knowledge map    Save In this paper, the line-of-sight (LOS) co-seismic deformation field of the Mw 6.7 Menyuan earthquake in Qinghai province on January 8, 2022 is obtained by using synthetic aperture radar differential interferometry (DInSAR) technology based on the Sentinel-1A satellite ascending and descending data. The non-negative least squares principle is used to retrieve the geometric parameters and co-seismic slip distribution of seismogenic faults. Finally, the Coulomb stress variation is calculated based on the fault slip distribution parameters and Coulomb fracture criterion. The results show that the Menyuan earthquake caused significant surface deformation, the coseismic deformation area is about 33 km×22 km, and the maximum LOS shape variables of ascending and descending data are -60 and 85 cm, respectively. Co-seismic sliding model display, the Menyuan earthquake is a left-lateral strike-slip event with a little thrust, and caused a co-seismic rupture about 36 km long (24 km for the main fault and 12 km for the branch fault) on the surface. The main rupture area is concentrated in 0～15 km depth, and the maximum slip of the main fault is 2.94 m, corresponding to 1.5 km depth.The maximum slip of the branch fault is 1.43 m, corresponding to 4.5 km depth. The seismic moment releases by inversion is 1.37×10 19 N·m, which is equivalent to a Mw 6.73 earthquake. Based on the results of field investigation and fault inversion, it is preliminarily determined that the co-seismogenic fault is the west end of Lenglongling fault and ruptures to the east end of Tuoleshan fault. The results of coseismic Coulomb stress variation and aftershock distribution show that the Coulomb stress at the east end of Lenglongling fault and the west end of Tuoleshan fault are obviously under loading condition, and the risk of strong earthquakes in the future is high. Reference | Related Articles | Metrics

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Optimization of land surface temperature inversion algorithm for Landsat 9 data WEN Yafei, LIU Yu, WANG Guanghui, ZHANG Qiuzhao Bulletin of Surveying and Mapping    2023, 0 (7): 74-79.   DOI: 10.13474/j.cnki.11-2246.2023.0204 Abstract （187）            Knowledge map    Save As a key parameter of energy exchange between surface and atmosphere, land surface temperature (LST) is widely used in many fields. Based on the Landsat 9 TIRS data, this paper optimized and updated the parameters of single window algorithm model and split window algorithm model to realize surface temperature inversion, and combined the measured data of SURFRAD site and the land surface temperature products for accuracy verification analysis. The results show that the determination coefficients of the two algorithms are both greater than 0.96. The split window algorithm model has higher accuracy and the error (RMSE) is about 1.45 K, while the single channel algorithm model has lower accuracy and the error (RMSE) is about 1.61 K. Compared with the single channel model, the split window model is less sensitive to the parameters. In the range of high water vapor content, the results of the split window model are better than those of the single channel model. The error (RMSE) values of the land surface temperature inversion method proposed in this paper and the official land surface temperature products are both within 2.5 K, which can meet the application requirements of producing land surface temperature products with thermal infrared remote sensing data. Reference | Related Articles | Metrics

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The filtering method of airborne LiDAR point cloud for tidal flat DEM construction FENG Xiaoke, Lü Peixian, ZHANG Ka, SHEN Huakang, YE Longjie, ZHAO Na, YANG Ying Bulletin of Surveying and Mapping    2023, 0 (7): 58-62.   DOI: 10.13474/j.cnki.11-2246.2023.0201 Abstract （169）            Knowledge map    Save In the process of constructing DEM based on laser point cloud, point cloud filtering is very important to distinguish ground points and non-ground points. Considering the requirement of high-precision DEM construction based on airborne LiDAR point cloud for coastal tidal flat regions, the paper proposes an improved slope filtering algorithm of airborne LiDAR point cloud. Firstly, a statistical outlier removal (SOR) method is used to remove noise from the original airborne LiDAR point cloud data. Secondly, utilizing the slope and elevation threshold of regular grids, the slope filtering method of ground points is designed for tidal flat point cloud data. Lastly, actual airborne LiDAR point cloud of the tidal flat in Changsha portof Rudong city is selected as experimental data to carry out the construction of tidal flat DEM and its accuracy is test, so as to verify the proposed method of point cloud filtering. Experimental results show that the accuracy of DEM constructed by LiDAR point cloud by the proposed methodmeets could meet the requirements of national and industrial standards. Reference | Related Articles | Metrics

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A deep neural network model for road extraction of MLS LiDAR point cloud LIU Jin, YANG Ronghao, WEN Wen, TAN Junxiang, LAN Qinglong, GAO Xiang, TANG Hong Bulletin of Surveying and Mapping    2023, 0 (12): 8-12,18.   DOI: 10.13474/j.cnki.11-2246.2023.0351 Abstract （162）            Knowledge map    Save PointNet++ has shown better performance than traditional methods in MLS LiDAR point cloud road extraction, but there are still the phenomena of over segmentation or under segmentation for road edge extraction.To address this issue, an improved neighborhood enhancement coding network E-PointNet++ is proposed. By introducing a neighborhood enhancement coding module before feature extraction, the connection between local neighborhood points is established to improve the network's road edge segmentation ability.Comparative experiments are conducted on two datasets, and E-PointNet++ shows significantly better performance than other methods, with accuracy, integrity and detection quality all exceeding 97%. This method performs robustly on different datasets and scenarios. Reference | Related Articles | Metrics

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Monitoring and analysis of landslide deformation based on SBAS-InSAR YANG Chunyu, WEN Yi, PAN Xing, YUAN Debao Bulletin of Surveying and Mapping    2023, 0 (11): 12-17.   DOI: 10.13474/j.cnki.11-2246.2023.0320 Abstract （156）            Knowledge map    Save Taking the mountain landslide occurred in Zaoling township in Shanxi province on March 15, 2019 as the research object, using Sentinel-1A SAR image data from July 5, 2018 to June 30, 2019 (a total of 30 scenes) before and after the landslide, this papere monitors the deformation data of landslides with the support of SBAS-InSAR technology. The results show that the deformation range of the study area is -52.03 to 33.77 mm/a, and the overall environment is relatively stable. The deformation rate and cumulative deformation variables of the long-term loess plateau are analyzed. The causes of landslides are analyzed based on relevant geological data from the research area. Using the standard deviation ellipse algorithm, it analyzes the spatio-temporal evolution characteristics of surface deformation in the loess plateau area where the landslide is located. The results show that the center of gravity of the standard deviation ellipse shifts to the northwest, the elliptical area decreases slightly, the deformation intensifies in the northwest southeast direction, and the deformation development in the northeast southwest direction is relatively gentle. The azimuth angle rotates counterclockwise, and the displacement is about 17.03 °. Reference | Related Articles | Metrics

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Monitoring and analysis of the subsidence in Changsha from 2015 to 2021 based on InSAR technique YANG Jiancheng, HE Yang, LIU Yiming, XIONG Xiong, YAO Haipeng, LI Xueyu, WEN Wufei, WANG Lingjue, HU Miaowen, XU Bing Bulletin of Surveying and Mapping    2023, 0 (7): 125-130.   DOI: 10.13474/j.cnki.11-2246.2023.0212 Abstract （150）            Knowledge map    Save Changsha city is an important central city in the middle reaches of Yangtze River. The geological condition is fragile and the soil is loose. Under the action of construction and rainfall, the soil is easy to consolidate and compact, resulting in ground deformation and threatening the stability of buildings and infrastructure. Land subsidence is one of the main causes of geological disasters in Changsha city. In order to monitor the land subsidence of Changsha city in recent years, we use the technology of MCTSB-InSAR and 151 Sentinel-1 data to obtain the land subsidence results of the main urban area of Changsha City from 2015 to 2021. The results show that the maximum accumulated settlement is about 250 mm, the maximum settlement rate is about 80 mm/a, and the average settlement rate in most areas is below 30 mm/a. The overall stability of Changsha city, local uneven settlement occurred, settlement area mainly distributed in the periphery of the main city, mainly caused by engineering construction. Reference | Related Articles | Metrics

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Construction technology of 3D real scene in whole area of city and county: taking Zhejiang province as an example WU Houqing, LIU Huadong, PAN Xiaojun, XIONG Chengli, WU Di Bulletin of Surveying and Mapping    2023, 0 (7): 154-159.   DOI: 10.13474/j.cnki.11-2246.2023.0217 Abstract （149）            Knowledge map    Save The construction of 3D real scene in China is an important measure to implement digital China and digital development, it is also a specific deployment to implement the construction of new national infrastructure. Based on the construction of 3D real scene project in whole area of city and county of Zhejiang province, this paper designs a product system that takes into account the geomorphologic characterization and application requirements, studies the production technology system, confirms the scientificity and applicability of the research results through the practice in Xiaoshan district, Hangzhou city, Zhejiang province. The research content gives consideration to the requirements of 3D real scene construction in China & the distinctive characteristics of Zhejiang province, it provides a reference for other regions to promote next work. Reference | Related Articles | Metrics

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Forest canopy height and biomass estimation based on LiDAR satellite (GEDI) in Guangdong province WU Zhenjiang, ZHANG Jiahua Bulletin of Surveying and Mapping    2023, 0 (12): 102-105.   DOI: 10.13474/j.cnki.11-2246.2023.0366 Abstract （149）            Knowledge map    Save Forest canopy height and biomass estimation play an important role in estimating forest carbon expenditure. In this study, the forest canopy height and biomass in Guangdong province use the global ecosystem dynamics survey (GEDI) LiDAR satellite as the data source, regression tree and Kerry kin interpolation algorithm, respectively. The results show that the height of trees in Guangdong province is generally between 10 and 20 m, accounting for more than 50%. The tree height high value occurs in Shaoguan, Zhaoqing and other cities in northern Guangdong province, and the tree height is generally 15~20 m, while the average tree height in Zhanjiang city is the lowest, generally less than 10 m. The maximum forest biomass in Guangdong province is 335.85 t/hm 2, the minimum value is 5.25 t/hm 2, and the average value is 98.27 t/hm 2.The areas with high value of forest biomass are mainly distributed in the eastern and western Guangdong province, while the forest biomass is lower in the plain and urbanized areas of Guangdong province. The results provide a scientific basis for estimating carbon absorption of forest ecosystem in Guangdong province. Reference | Related Articles | Metrics

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Review of the current research status of subway deformation monitoring HEI Junmiao, WANG Li, LI Ang Bulletin of Surveying and Mapping    2024, 0 (2): 39-44,79.   DOI: 10.13474/j.cnki.11-2246.2024.0207 Abstract （148）      PDF（pc） （1655KB）（58）       Knowledge map    Save This article includes the deformation monitoring techniques during subway construction and operation respectively, and then sorts out the current research status of subway deformation monitoring data processing methods. It summarizes the existing problems in the current research of subway deformation monitoring, and prospects the development direction of subway deformation monitoring research, providing some ideas for the systematization and automation of subway deformation monitoring research. Reference | Related Articles | Metrics

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Spatial and temporal changes of forest cover and its driving factors over the Yellow River basin MA Jingjing, GAO Meiling, LI Zhenhong, XU Huihui, PENG Jianbing Bulletin of Surveying and Mapping    2023, 0 (7): 51-57.   DOI: 10.13474/j.cnki.11-2246.2023.0200 Abstract （144）            Knowledge map    Save To investigate the spatio-temporal changes and driving factors of forest coverage from 1990 to 2020 in the Yellow River Basin, this study employs land use and land cover data, natural factors, and human activity data, and uses transfer matrix, Theil-Sen Median and Manna-Kendall trend analysis, and geographic detectors for research. The results show that: ①The forest coverage area has expanded in the Yellow River basin, with an increase of nearly 20 000 km 2. ②From 1990 to 2020, the forest coverage rate of 168 counties increased in the Yellow River basin, with only two counties showing a decrease in forest coverage. ③Based on geographic detectors, shortwave radiation has the greatest explanatory power for the distribution of forest coverage, while the interaction of rainfall and elevation has the strongest explanatory power in the analysis of factor interaction. This study can provide a scientific reference for the high-quality development of the ecological environment in the Yellow River basin. Reference | Related Articles | Metrics

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Exploring the spatio-temporal variation of the regional environment and driving factors based on ecological sensitivity: taking Ding'an county as an example SONG Peilin, XIE Jibo, YANG Tengfei, MOU Naixia, CHEN Mo, WANG Xiaoyu Bulletin of Surveying and Mapping    2023, 0 (7): 18-24.   DOI: 10.13474/j.cnki.11-2246.2023.0195 Abstract （141）            Knowledge map    Save Ecological sensitivity is one of the main methods for analyzing the stability of regional ecological environment, and its evaluation results are of great significance for regional ecological protection and decision-making. This article focuses on Ding'an county as the research area. It selects 9 sensitive indicators from four aspects of soil erosion, habitat, terrain, and water resources to construct an ecological sensitivity evaluation system. The combination weighting and analysis of the ecological sensitivity spatio-temporal distribution and changes in 2013 and 2021 are conducted using the AHP (analytic hierarchy process) and entropy weight method. Furthermore, it analyzes the spatial agglomeration, regional changes in ecological sensitivity, and the main driving factors behind them using geographic detector analysis. The results show: From 2013 to 2021, the ecological sensitivity of Ding'an county was the distribution pattern of high in the south and low in the north, and the overall ecological sensitivity showed a downward trend. The spatial agglomeration effect of ecological sensitivity is significant, but the spatial agglomeration decreases with time, and both high-value and low-value agglomeration areas tend to shrink. The transfer of ecologically sensitive areas mainly occurs in medium-sensitive and high-sensitive areas, and the level of change is mainly first-level increase and decrease. Compared with other sensitive indicators, the driving force of land use and vegetation coverage on ecological sensitivity is larger, and with the passage of time, the driving force of land use and vegetation coverage on ecological sensitivity tends to play a leading role. Reference | Related Articles | Metrics

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Shallow sea water depth inversion from WorldView-3 multispectral images based on seabed sediment classification YAO Chunjing, YU Zheng, WANG Jie, QIAN Chen, XU Junhao Bulletin of Surveying and Mapping    2023, 0 (7): 25-31.   DOI: 10.13474/j.cnki.11-2246.2023.0196 Abstract （140）   HTML （12）    PDF（pc） （1559KB）（143）       Knowledge map    Save In recent decades, sea water bathymetry inversion method based on remote sensing image has been a hot research topic. This paper uses WorldView-3 high-resolution satellite imagery, combined with satellite altimetry data, to focus on Wuzhizhou island which is near Hainan Island, China, and its adjacent waters as the main study area. After data preprocessing and substrate classification, multiple linear regression model, Stumpf logarithmic ratio model and BP neural network model are used to invert and analyze the water depth around the island. Results show that: for the three model, after the bottom sediment classification accuracy will be improved significantly. Among them, BP neural network model has the highest accuracy (root mean square error range of 0.2～0.7 m), followed by multiple linear regression model (root mean square error range of 0.3～0.8 m), and log ratio model has the lowest accuracy (root mean square error range of 0.6～1.1 m). Reference | Related Articles | Metrics

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Key technologies and applications of 3D terrain modeling for landslide simulations LÜ Yijie, YE Jian Bulletin of Surveying and Mapping    2023, 0 (11): 7-11,17.   DOI: 10.13474/j.cnki.11-2246.2023.0319 Abstract （138）            Knowledge map    Save High-resolution landslide terrain data is an important guarantee for the accuracy and visualization effect of landslide simulations. However, rendering all high-resolution landslide terrain data will cause the landslide simulation program to run slowly, managing terrain data hierarchically and loading terrain data blocks into memory in batches for combined rendering will lead to discontinuity and inconsistent resolution of terrain data participating in landslide simulation calculations. To solve the above problems, an improved quadtree LoD terrain modeling method for landslide simulations is proposed in this paper. Based on this method, terrain data is completely read into memory for numerical calculations, and terrain data blocks of different resolutions are dynamically constructed from the terrain data for combined rendering during the visualization stage, which not only provides complete and continuous terrain conditions for landslide simulations, but also ensures 3D visualization effect of landslide terrain, and effectively improves the running speed of simulation programs. More importantly, by using this method for modeling, the results of landslide simulation will not be affected when browsing the simulation scenes during the landslide simulation processes. The experimental results of landslide simulation show that the method proposed in this paper is effective and practical in 3D terrain optimization modeling for landslide simulations. Reference | Related Articles | Metrics

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Study on land use change and spatiotemporal variation of carbon storage in Beijing-Tianjin-Hebei based on InVEST model PENG Yunni, SANG Huiyong, ZHAI Liang, ZHANG Ziyi, DUAN Jinjiang Bulletin of Surveying and Mapping    2024, 0 (1): 19-24,31.   DOI: 10.13474/j.cnki.11-2246.2024.0104 Abstract （137）      PDF（pc） （3110KB）（53）       Knowledge map    Save The increase in atmospheric CO 2content is an environmental issue of widespread international concern, and human activities change land use patterns, and land-use/land-cover (LULC) changes further affect terrestrial ecosystem structure, function, and carbon cycling. With the support of global land cover data GlobeLand30, This paper analyzed the land use changes in Beijing-Tianjin-Hebei from 2000 to 2020, used InVEST model to imitate the Spatiotemporal changes of carbon stocks, and used the spatial autocorrelation analysis to study its zoning. The results show that:①From 2000 to 2020, the largest change area in Beijing-Tianjin-Hebei region is cultivated land and artificial surface, with an area decrease of 340 222.124 hm 2and an area increase of 246 333.493 hm 2respectively. ②The total carbon reserves of Beijing-Tianjin-Hebei in 2000, 2010 and 2020 are 1 666.47×10 6、1654.63×10 6、1632.88×10 6 t, the main reason for the decline in carbon storage are the loss of arable land and the expansion of artificial land surface. ③The high value of carbon storage is mainly distributed in mountain and forest areas with relatively high altitude, while the low value areas of carbon reserves are mainly concentrated in central Beijing, the coastal zone of Tianjin and Hebei and the eastern Cangzhou, southern Tangshan. ④The results of local autocorrelation show that the high value areas of carbon reserves are clustered in the north and west of the study area. Among the regions with low to low aggregation, Dongli district of tianjin city and Hanshan district of Handan city, Hebei province show a relatively obvious weakening trend. Reference | Related Articles | Metrics