Table of Content

25 March 2022, Volume 0 Issue 3

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Analysis of spatiotemporal variation of sparse vegetation net primary productivity and meteorological factors

YAN Linan, WANG Xinjun, CHEN Bei, CHANG Mengdi, LI Na

2022, 0(3):  1-6.  doi:10.13474/j.cnki.11-2246.2022.0067

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Based on the improved CASA model,this paper uses methods containing spatial analysis,correlation analysis,and geographical detector model to explore the spatiotemporal pattern of the vegetation net primary production (NPP) in the Gurbantunggut desert from 2001 to 2018,and reveals the climate forcing factors of NPP and their impacts in the study area.The results of this paper show that:①In the past 18 years,the change of vegetation NPP in the Gurbantunggut desert showes a fluctuating increasing trend,with the growth rate of 0.56 gC· a^-1,and the average NPP is 46.90 gC· m^-2· a^-1.②From 2001 to 2018,the average annual NPP showes a spatial distribution pattern of increasing from northwest to southeast,and it is stable in the hinterland of the desert and more active around.③NPP in the Gurbantunggut desert is mainly affected by precipitation and is positively correlated with precipitation and temperature.In terms of the forcing analysis of each factor,precipitation (0.614 4) is the dominant factor restricting the growth of desert vegetation.

Sensitivity analysis of ecological environment in Dongchuan district based on GIS

YANG Yiyuan, YANG Cunjian

2022, 0(3):  7-12.  doi:10.13474/j.cnki.11-2246.2022.0068

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The sensitivity of ecological environment in Dongchuan district is analyzed by using GIS technology and analytic hierarchy process (AHP).Five evaluation indexes are selected in this study.Firstly,the single factor evaluation is carried out,and then the comprehensive ecological sensitivity of Dongchuan district is divided into five grades based on GIS spatial analysis function.Finally,the comprehensive ecological sensitivity distribution map of Dongchuan district is constructed.The results show that:① Among the five ecological evaluation factors,the slope factor has the greatest impact on the eco-environmental sensitivity of Dongchuan district,with the weight value of 0.36.According to the degree of impact on ecological sensitivity,the order is that it is:slope,elevation,land use,NDVI and water buffer zone.② The eco-environmental sensitivity of Dongchuan district is on the high side,and the extremely sensitive area and the highly sensitive area account for 44.17% of the total area,the medium sensitive area account for 26.1% of the total area,and the sum of the low sensitive area and the extremely low sensitive area account for 29.73%.③ In Dongchuan district,the most sensitive and highly sensitive areas are mainly distributed in the northwest,while the most low sensitive and low sensitive areas are mainly distributed in the northeast and the central valley.

Study on the clustering of surface cover changes within the red line of Beijing's ecological protection

ZHANG Yi, CHEN Pinxiang, LIU Yu, YU Yongxin, WU Runze, XU Tianhao, YANG Xudong, GONG Yun

2022, 0(3):  13-17,22.  doi:10.13474/j.cnki.11-2246.2022.0069

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This study uses Beijing’s 2015,2017,and 2019 three phases of the geographic national survey and monitoring data to analyze the spatial change characteristics of the land surface cover within the red line of Beijing’s ecological protection,and on the basis of the global and local Moran’s I spatial autocorrelation analysis,spatially identify areas with significant ecological restoration and significant ecological damage within the red line of ecological protection.Studies results have shown that the ecological land within the ecological protection red line from 2015 to 2019 is in an outflow state as a whole,and various change patches have obvious spatial autocorrelation effects.15 areas are densely human activities,including Wuling Mountain and surrounding mountains,Songshan and Yudu Mountain Reserve,Nanhaizi Park,Miyun Reservoir,HuaishaHuaijiu River,and Pinggu Section (southwest section) of the Yanghe River are areas with significant ecological damage.It is recommended that the above areas be increased supervision;areas with significant ecological restoration are in the Miyun section of the Jingmi Water Diversion Canal,the other 8 areas all coexist with manmade destruction and repair activities.The research provides a decision-making basis for strictly observing the red line of ecological protection,accelerating the construction of beautiful Beijing and the construction of ecological civilization.The research method is also applicable to the monitoring of changes in surface cover within the red line of ecological protection in other regions of the country.

Analysis of forest continuous changes in Jiangxi province during 1990-2019 under the Google Earth Engine

XIAO Zhen, DING Mingjun, LIU Yiyuan, HUANG Keyi, ZHANG Zhen

2022, 0(3):  18-22.  doi:10.13474/j.cnki.11-2246.2022.0070

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Forests have an irreplaceable role in national economic construction and sustainable development,and it is very important to monitor and analyze changes in their spatial and temporal patterns.The continuous change detection and classification method are used to analyze the continuous change in the area and greenness of Jiangxi forests based on the Google Earth Engine platform.The results have certain reference value for monitoring and analyzing the spatio-temporal pattern of forest.

Research progress of urban green space landscape pattern change based on satellite remote sensing

YE Jun, KANG Siqi, FU Genshen, LÜ Haiyan, QIAN Wenqi, TANG Xuehai

2022, 0(3):  23-27.  doi:10.13474/j.cnki.11-2246.2022.0071

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Urban green space landscape is the ecological basis for the natural elements of urban landscape and the sustainable development of social economy,which plays an important role in the structure,function and change of urban landscape.Using satellite remote sensing technology to study urban green space pattern has become a hot spot.By reading and organizing the relevant literatures,this study expounds the remote sensing classification method of urban green space landscape,the selection of landscape pattern index,and the specific content of driving force analysis of urban green space landscape pattern dynamic change.The results shows that there are some deficiencies in the data source and landscape pattern index selection of urban green space landscape pattern research based on remote sensing technology.This study puts forward to the prospect of urban green space landscape pattern analysis from the aspects of remote sensing data sources,classification methods,landscape index selection,and multidisciplinary and multi-angle cross-synthesis research method

Quality evaluation method of wetland resources:a case study the international important wetlands of East Dongting Lake in Hunan province

CHEN Genliang

2022, 0(3):  28-31,82.  doi:10.13474/j.cnki.11-2246.2022.0072

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Wetland is one of the most important ecosystems in the world,due to the adverse effects of environment pollution,excessive reclamation and illegal enclosure,wetland resources are confronted with extreme threats.This paper preliminarily inquiries into the general approach and technical method of international important wetlands quality evaluation through constructing evaluation index system based on natural elements and human activities and calculating evaluation index weight by utilizing analytic hierarchy process and Delphi method.Taking East Dongting Lake as an example to verify the quality evaluation method,the result indicates that it is feasible and it can provide scientific references to the protection,management and capacity-building of fulfilling agreements of international important wetlands.

Application progress of BDS in monitoring crustal deformation

GAO Zhiyu, GUO Jinyi, LIU Jie

2022, 0(3):  32-35.  doi:10.13474/j.cnki.11-2246.2022.0073

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BDS has been completed in 2020 and begins to provide services to the world.Here,we focus on its application in disaster prevention and mitigation.In this study,we present the application of BDS data in crustal deformation research,including monitoring coseismic and interseismic crustal deformation.We also summarize the positioning accuracy of BDS data.Besides,we summarize several key issues,which need to be further solved and expanded,regarding the application of BDS data in crustal deformation.First,in terms of expanding the connotation of GNSS in crustal deformation monitoring,a dual-mode or multi-mode algorithm incorporating both GPS and BeiDou is needed,to improve the positioning accuracy.Second,we need to carry out the application of BDS real-time deformation monitoring in the early identification of geological disasters.Third,we need to develop and publish data processing algorithms and software in order to promote the application of BDS in high-precision crustal deformation monitoring.

Improvement of indoor positioning method combining UWB and PDR

LI Jingwen, WEI Jingshan, ZHOU Junfen, MAO Jiaying, LU Yanling

2022, 0(3):  36-40.  doi:10.13474/j.cnki.11-2246.2022.0074

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In recent years,with the progress and innovation of science and technology,the research of indoor positioning is developing toward the direction of complementary integration of multiple technologies,and the integration of navigation technology with indoor positioning has become a current research hotspot.Pedestrian dead reckoning (PDR) and ultra wide band (UWB) technologies have become mainstream research techniques for indoor positioning with their unique positioning advantages and accuracy among many other advantages.However,PDR is only suitable for high-precision indoor navigation needs in short time due to its cumulative error,while UWB may seriously distort the time information in complex environments,resulting in missing positioning information.Therefore,in this paper,the extended kalman filter (EKF) is used to improve the fusion of the two in order to take advantage of the advantages of each technique.The experimental results show that the maximum endpoint error of the positioning solution is 0.819 5 m,the minimum endpoint error is 0.144 3 m,the average endpoint error is 0.347 8 m,and the average position error is 0.475 0 m,which effectively improves the accuracy of indoor positioning.

Detection of new construction land change based on attention intensive connection pyramid network

PAN Jianping, LI Xin, SUN Bowen, HU Yong, LI Mingming

2022, 0(3):  41-46,59.  doi:10.13474/j.cnki.11-2246.2022.0075

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To settle the problems of frequent and rapid changes of new urban construction sites and complex scenarios which lead to under-segmentation or over-segmentation of change detection results,this paper proposes a densely connected pyramid network with a fused attention mechanism for urban new construction site change detection.In the coding stage,a convolutional attention model is applied to enhance the attention to change information and highlight important features;then a densely connected null convolutional spatial pyramid pooling module is used to realize the extraction and fusion of multi-scale features and improves the feature utilization and propagation efficiency;in the decoding stage,the spatial scale features of the image are restored by upsampling the extracted feature maps.The experimental results show that the method in this paper effectively improves the under-segmentation and oversegmentation problems,and the change detection effect is better.

Automatic monitoring of farmland occupation by farm house based on deep learning network

GAO Ming, ZHOU Xinxin, LIU Qi, YANG Guangdi, WU Changbin

2022, 0(3):  47-53.  doi:10.13474/j.cnki.11-2246.2022.0076

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In recent years,illegal occupation of arable land has been repeatedly prohibited.How to use artificial intelligence and other new-generation information technology to quickly figure out the number of illegal occupation of arable land and build houses in rural areas,and achieve"early detection,early stop,strict investigation and punishment ",is one of the current research difficulties in the work of rectifying the illegal occupation of farmland in rural areas.This paper preprocesses high-resolution natural resource image data,and then builds an automated monitoring model based on a deep learning network.Thirdly,it applies model to predict and GIS optimization and spatial overlay of output results.Experimental results show that this method can quickly detect illegal houses that are suspected of occupying cultivated land,and provides intelligent technology options for sticking to the bottom line of" not breaking through the red line of cultivated land",and can serve the work of rectifying building houses on the cultivated land in rural areas.

Jujube garden detection and recognition in GF-6 image using deep learning

DUAN Chenyang, FENG Jianzhong, QUAN Bin, BAI Linyan, WANG Panpan

2022, 0(3):  54-59.  doi:10.13474/j.cnki.11-2246.2022.0077

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Focusing on the large-scale jujube fields in the southern Xinjiang,this paper proposes a jujube orchard detection method based on a generalized deep transfer learning principle.From GF-6 satellite imagery,a jujube field dataset is made,and then it is augmented effectively.Grounded on a Faster R-CNN system,a multi-modally cooperative mode is used to realize the effective correlation and optimization reconstruction of the expanded dataset,and a transfer deep learning of detection and recognition model is thus carried out to improve the generalization ability of the detection and recognition of target object on jujube fields.The results show that the precision,recall and F1-score of the model algorithm reached 0.979,0.952 and 0.965,respectively.In the application tests,the average values of the three indexes are all more than 0.929,which could better than traditional detection method,and the overall classification accuracy and Kappa coefficient of this model method are 0.97 and 0.93,which are higher than the object-oriented nearest neighbor method,and effectively meet the requirements of high-efficient and accurate large-scale jujube orchard detection in the study area.Then it provides the basis for fine jujube orchard field management.

Geometric constraints and local description for aerial images line matching algorithm

ZHUO Guangping, ZHANG Junhua

2022, 0(3):  60-64.  doi:10.13474/j.cnki.11-2246.2022.0078

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Due to the complexity of aerial image scene and many uncertain factors that interfere with line matching,we propose a line matching algorithm based on geometric constraint and MSLD in this paper.Firstly,the linear detector LSD is employed to obtain the line feature information of the image.Secondly,according to the geometric features between the different straight lines as the constraint condition,the straight line pair of the two aerial images is obtained by grouping.Then,the candidate line pairs are determined by using the kernel constraints,and the support regions of the reference and candidate line pairs are constructed in turn.The affine transformation is used to unify the size of the support regions MSLD describing the local appearance of lines.By calculating the Euclidean distance between line descriptors in aerial images,candidate lines satisfying the nearest neighbor distance ratio criterion are determined as matching results.Finally,the matching results are checked to remove redundant matching lines.The experiment uses aerial image data of different scene types.The experimental results show that the proposed algorithm can deal with the problem of the poor line matching effect of aerial images.

LiDAR point cloud segmentation algorithm based on supervoxel and pairwise linkage clustering

PU Dongdong, DING Haiyong

2022, 0(3):  65-69.  doi:10.13474/j.cnki.11-2246.2022.0079

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Aiming at the problems of poor robustness and low efficiency of existing LiDAR point cloud segmentation algorithms,this paper proposes a new hierarchical clustering segmentation algorithm.Firstly,a supervoxel with adaptive resolution is generated from the LiDAR point clouds.Then an improved pairwise linkage segmentation algorithm is used to the supervoxel to get the segmentation results.Experimental results show that the proposed segmentation algorithm has better robustness and higher computational efficiency compared with that of the existing segmentation methods.The issues of over segmentation and insufficient segmentation of the point clouds have been solved.The proposed algorithm is more prominent in segmentation details,and the segmentation results can effectively ensure the accuracy of subsequent data processing.

Winter wheat classification method based on feature optimization of random forest

FENG Zhili, XIAO Feng, LU Xiaoping, HAO Bo, WANG Ruyi, ZHU Rui

2022, 0(3):  70-75.  doi:10.13474/j.cnki.11-2246.2022.0080

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Based on the multi-temporal Landsat 8 OLI data,this paper conducts research on the feature extraction and feature selection methods of comprehensive spectral features and vegetation index features.By analyzing the temporal changes of the spectral and vegetation index features,the optimal time-phase spectrum is extracted,and the wheat extraction features are constructed.A random forest feature selection algorithm based on importance and Pearson correlation is used to select features and classify them.The results show that:when using the selected features to classify,the overall accuracy of classification is 89.78%,and the classification accuracy of wheat is 98.33%.Compared with the classification results of the features before optimization,the classification accuracy is increased by 2.96% and 2.55%,respectively.Random forest feature selection based on importance and relevance not only improves the classification accuracy,but also improves the efficiency of the classifier.

Recognition of vegetation types in Leizhou Peninsula based on Sentinel-2A data

WANG Gang, DING Huaxiang

2022, 0(3):  76-82.  doi:10.13474/j.cnki.11-2246.2022.0081

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Using Sentinel-2A image data and real vegetation sample data from Leizhou Peninsula as the research area,this paper comprehensively discusses the classification effects of random forest and support vector machine in machine learning,and compares them with the traditional maximum likelihood method.Firstly,9 bands,7 vegetation indexes and 72 texture features of Sentinel-2A image are extracted successfully,then the feature combination of 10 features is selected by recursive feature elimination method and applies to three classification methods,and the classification effect is compared.The results show that:①Effectively using a variety of characteristic variables is the key to improve the vegetation type recognition accuracy,in terms of the importance of the different characteristics of vegetation type recognition,the spectral features are the same to the texture features and greater than vegetation index,three importance are similar.②Random forest classification has the best effect,which can not only select features effectively,but also ensure the precision of vegetation type extraction and improve the operation efficiency.③The feature combination based on the recursive feature elimination method of random forest feature selection can not optimize the performance of other classifiers,and the optimization effect of the random forest model itself is limited.

Improved HRNet applied to segmentation and detection of pavement cracks

ZHANG Boshu, ZHANG Zhihua, ZHANG Yang

2022, 0(3):  83-89.  doi:10.13474/j.cnki.11-2246.2022.0082

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Aiming at the problems of low accuracy,loss of information and blurred edges in the traditional convolutional neural network for pavement crack segmentation,a pavement crack segmentation algorithm based on the improved HRNet model is proposed.The model is improved on the basis of the original HRNet,the backbone network part uses DUC module instead of bilinear interpolation;downsampling is changed to passthrough layer to replace the original convolution,SE-Block is introduced while performing step-by-step upsampling to re-calibrate the fusion of different feature layers.Comparing with the original HRNet and the other traditional convolutional neural networks U-Net,it can be concluded that the segmentation accuracy of this algorithm is the best on public data and self-made data sets,with F1 score reaching 91.31% and 78.69% respectively,proving that the algorithm can be very good to meet the needs of actual engineering.

Study on the spatial and temporal characteristics of the expansion of the built-up area in Guangdong-Hong Kong-Macao Greater Bay Area

KUANG Xu, LIU Chuanli, LI Minghai

2022, 0(3):  90-95,110.  doi:10.13474/j.cnki.11-2246.2022.0083

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Based on the nighttime light data (DMSP/OLS,NPP/VIIRS) from 1992 to 2019,this paper refers to the built-up area data of the Guangdong-Hong Kong-Macao Greater Bay Area and compares the light threshold,and finally obtains the built-up area boundary of the Greater Bay Area.It makes a quantitative analysis of the spatiotemporal characteristics of the expansion of the built-up area of the Greater Bay Area from three aspects:expansion mode,expansion speed and expansion degree.At the same time,the spatial and temporal pattern characteristics of the expansion of the built-up area in the Greater Bay Area are also analyzed with the method of landscape ecology.The results show that:① The center of gravity of the Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration was located in Dongguan in 1992,and moved to Guangzhou in 2019,forming the development core area of the pearl river delta region with Guangzhou as the center.②The expansion of the whole region presents a trend of regional integration,with the speed of spread decreasing and the degree of spread increasing,and the radiation capacity of the Pearl River Delta region to the surrounding cities increasing.③The urban expansion mainly centers on the Pearl River Delta region and develops in an inverted "V" shape along the transportation lines.

Combination prediction model of optimized short-term residual water level

FENG Junjun, ZHOU Li, OUYANG Quanping, ZHOU Zhen

2022, 0(3):  96-100.  doi:10.13474/j.cnki.11-2246.2022.0084

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In order to solve the problem that the existing non-stable and non-linear residual water level prediction models are less and low accuracy,a combined residual water level prediction model based on MEEMD algorithm and genetic optimization BP neural network is studied.Based on the time series data of residual water level obtained from four long-term tidal stations in Hawaii island,the genetic algorithm MEEMD is firstly used to process and analyze the time series data of residual water level,and a relatively stable IMF component of residual water level is obtained.Then,the stable IMF components decomposed by genetic algorithm optimization are taken as the input variables of BP neural network prediction model,and the prediction models of BP neural network optimized by MEEMD genetic algorithm for 12,24 and 48 h short-term residual water levels are established respectively.By comparing with the results of the non-optimal BP neural network prediction model,the results show that the deviation of the root mean square error before and after optimization is up to 2.03 cm,which verifies that the short-term residual water level within 24 h is still maintained its relevant characteristics.The combined prediction model is of great significance to the analysis of the variation law of residual water level,the accuracy of tide prediction and the correction of water level.

A Chinese addresses matching method based on the pseudo-semantic model

YU Ting, WANG Duo, CHEN Qin

2022, 0(3):  101-106.  doi:10.13474/j.cnki.11-2246.2022.0085

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Due to various ways to express the address element such as abbreviation and logogram,address matching is a difficult task specially in Chinese address matching.One important address matching method is relying on similarity.However,these traditional similarity methods focused on the overlap characters,and could not deal with the situation.The other crucial and useful method is based on deep learning technology,but it is difficult to generate a large amount of learning samples.In this paper,Bi-directional long short-term memory conditional random field is applied to achieve the goal of Chinese address segmentation.Then,a new similarity named pseudo-semantic is constructed to solve the problem of abbreviation and logogram.According to current results,the pseudosemantic similarity can provide better performance than other similarity models in the matching process and its recall and precision are both reaching 0.9 on the test set.The samples proved that the pseudo-semantic can recognize the abbreviation and logogram of address elements.

Kunming zenith wet delay model based on a backpropagation neural network

DING Renjun, WANG Youkun, ZHANG Junhua, LIU Chen

2022, 0(3):  107-110.  doi:10.13474/j.cnki.11-2246.2022.0086

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For the high-precision zenith wet delay (ZWD) used in Kunming continuously operating reference stations (KMCORS),this paper developes the Kunming model (KM) suitable for the KM area.According to the sounding data of the KM sounding station from 2015 to 2018,the KM model is generated based on a backpropagation (BP) neural network.This study then validates the prediction performance of the KM model using the sounding data during 2019.Test results show that the RMSE of the KM model decreases from 4.0 cm to 2.2 cm compared with the widely used Saastamoninen (SA) model,indicating its 45% accuracy improvement.Additionally,the Bias of the KM and SA models are 0 and-3.1 cm,respectively,suggesting that the ZWD estimation of the KM model is unbiased,while the SA model has the problem of overestimation in the plateau area.In summary,the KM model has better prediction performance than the SA empirical model,and the application of the KM model will help to improve the service quality of KMCORS.

Extraction of multi-feature winter wheat area based on Sentinel-2 and Landsat 8 data

WANG Xiaoxiao, HAN Liusheng, YANG Ji, LI Yong, ZHANG Dafu, SUN Guangwei, FAN Junfu

2022, 0(3):  111-115.  doi:10.13474/j.cnki.11-2246.2022.0087

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Remote sensing satellite band setting,signal to noise ratio and sensor observation angle will affect the accuracy of crop extraction.In order to fully tap the advantages of Sentinel-2 satellite multispectral instrument and Landsat8 land imager in winter wheat information extraction.this study takes Shanghe County as the research area.Based on the combination data of spectral characteristics,texture characteristics and vegetation index characteristics of the two data sources,random forest classification and support vector machine are used to extract winter wheat.Experiments show that the optimal Kappa coefficient and optimal OA based on a single image are 0.89 and 95.13%,respectively.The optimal Kappa coefficient based on the combined data source is 0.92 and the optimal OA is 95.28%.The accuracy of the combination of two data sources is better than that of the single data source.The data combination effect is related to the performance of the classifier.The kappa coefficient of RFC is increased by 0.04,0.20 and 0.11 compared with SVM,and OA is increased by 2.41%,11.31% and 6%,respectively.The extraction accuracy of RF for winter wheat is better than that of SVM.This study is of great significance for constructing a typical crop classification and extraction system based on medium-high resolution image combination.

Application of UAV tilt photogrammetry in building area measurement of housing construction area

REN Xubin, KANG Jianfeng, YU Donghai

2022, 0(3):  116-120,126.  doi:10.13474/j.cnki.11-2246.2022.0088

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In view of the existing limitations (high field work intensity,low measurement accuracy,and low mapping efficiency) in current measurement of housing construction areas,this paper proposed a more advanced method based on UAV tilt photogrammetry for measuring housing construction areas.Meanwhile,we use this approach to study UAV tilt photogrammetry,real scene 3D modelling,digital orthophoto production,3D digital mapping,map production,calculation of area index,and so on,then the accuracies of test results are assessed.The results show that the errors of topographic map in the plane position,height,the point position of housing corner,the building side length,and the building area are 4.6 cm,4.2 cm,4.6 cm,4.5 cm,and the area difference of the house building is less than the area limit of the secondary precision of the urban commercial house,respectively,these measurement accuracies are up to the national standard.Therefore,we conclude that this method is feasible for housing areas measurements.

Groundwater storage data inversion and spatiotemporal evolution analysis in Henan province using GRACE and GLDAS data

LI Mingyu, CHEN Lijun, LIU Guoxiang, MAO Wenfei, XIANG Wei, CAI Jialun, ZHANG Bo, ZHANG Rui

2022, 0(3):  121-126.  doi:10.13474/j.cnki.11-2246.2022.0089

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Traditional monitoring methods are difficult to acquire groundwater storage observation in large-scale and long-term.Thus,the groundwater storage data inversion has become a hot topic based on the GRACE gravity satellite.This paper uses the GRACE RL06 monthly data released by CSR during the period of 2012 to 2016 to produce terrestrial water storage changes.Later,subtract surface water storages changes calculated by the GLDAS hydrological model over the same period.Finally,the time series results of groundwater storage changes in Henan province are obtained.Considering the verification between the produced results and the measured groundwater level data,the calculated correlation coefficients are all at the significance level of 0.01,which indicates that the groundwater storage change monitoring method in this paper is highly reliable.From the least squares linear fitting change rate results,it can be seen that the main loss area of groundwater in the province is the northern region with the maximum rate exceeding 26 mm/a,and the main surplus area is located in the central and eastern regions with the maximum rate exceeding 16 mm/a.These mentioned results are basically consistent with existed researches as well as the main groundwater overexploitation areas announced by the Henan provincial Water Resources Bureau.The research results in this paper are aimed at using GRACE gravity satellite data and GLDAS hydrological model to obtain the spatial distribution difference and evolution trend of groundwater storage changes in Henan province.While providing data support for the rational use and protection of groundwater resources,it also offers reference for the protection and reasonable use of groundwater resources in the area.

Recognition and monitoring of buildings in aerodrome obstacle free space

TONG Kuang, SONG Yang, KONG Xiangfen

2022, 0(3):  127-131.  doi:10.13474/j.cnki.11-2246.2022.0090

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In view of the current building recognition and monitoring,the shadow height measurement method based on single highresolution remote sensing image and the stereo image pair method based on multi-view image are independent of the recognition of the contour or name of the building and the height monitoring,resulting in low automation level,high data redundancy and cost.Based on the data of digital surface model (DSM) and point of interest (POI),this paper proposes an integrated method for building recognition and monitoring in airport clearance area,and verifies the feasibility of this method by taking Shenzhen Baoan International Airport as a test area.The experimental results show that:① The accuracy of this method in building recognition and monitoring only depends on the accuracy of DSM,but there is no limitation on the data source of DSM.② Based on the height change rate of potentially dangerous buildings,the irregular dynamic monitoring can ensure the safety of airport clearance area during the monitoring interval and reduce the redundancy and cost of data problems caused by repeated identification and monitoring of all buildings.

Construction of building automatic extraction process based on image-aided nDSM of BJ-2

MAO Bin, HAN Wenquan, XIE Hongquan, LÜ Haiyang

2022, 0(3):  132-137.  doi:10.13474/j.cnki.11-2246.2022.0091

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Aiming at the problem of lack of generalization of the building method of extraction,seven-channel images of nDSM,BJ-2 image,NDVI,and BAI are combined as the data source extraction method in this paper.Random forest,gradient boosting machine,support vector machine,BP neural network classifiers are applied to extract buildings to obtain the best classifier model;Binarization,opening and closing operations are applied,using the ratio of the area of the building to the area of the smallest enclosing rectangle is used as the threshold,and the smallest enclosing rectangle and DP algorithm are used to fit the buildings respectively to optimize the building extraction results.The experimental results show that the gradient booster (GBDT) has higher F-score accuracy when extracting buildings in different scenarios.

Monitoring of surface deformation along the Qinghai-Tibet Railway with the time series InSAR technology

LU Zhongxiang, FAN Yanguo, LI Guosheng

2022, 0(3):  138-142,156.  doi:10.13474/j.cnki.11-2246.2022.0092

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The ground surface along the Qinghai-Tibet Railway is affected by permafrost,which will cause uplift and subsidence,so deformation monitoring is very important for its safe operation.The 41 scenes of C-band Sentinel-1A ascending data are used,combined with the relatively uniformly distributed permanent scatterers detected by the sub-region division method,as the ground control point of the SBAS InSAR technology to monitor the surface deformation of the railway from Yangbajing Station to Wumatang Station.The experimental results show that the annual deformation rate of this section of the railway is between-8 mm/a and 2 mm/a.The deformation of this area is affected by the surrounding frozen soil,which changes periodically with the seasons.The SBAS InSAR technology and the PS-SBAS InSAR technology used in this paper are compared in terms of the deformation trend and degree of deformation at the homologous point.The results are consistent,which shows the reliability of the method in this paper.

The production of 1:10 000 DOM using rare satellite image control points:taking the survey area of Alxa League as an example

LI Zhijuan, LI Hui, LIU Jianjun, ZHANG Donghua

2022, 0(3):  143-147.  doi:10.13474/j.cnki.11-2246.2022.0093

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Aiming at the problem of using control points to correct high-resolution satellite remote sensing images in areas with difficult image control,a block adjustment method based on rare image control points is proposed,which uses high-precision DEM for constraints,and uses satellite images to quickly produce 1:10 000 DOM.In this study,the survey area of Alxa League is selected as the test area and the accuracy evaluation is performed.The results show that the accuracy of 1:10 000 DOM can be met by using rare image control points,which can realize the rapid production of remote sensing images based on rare control points,and can effectively solve the difficulty of obtaining control points.This method can greatly improve the production schedule due to DOM accuracy and schedule problems caused by the problem,and can provide reference for deserts,forest areas and other areas lacking control points.

Point cloud extraction of airborne buildings using bidirectional cloth simulation method

LI Shaoxian

2022, 0(3):  148-151.  doi:10.13474/j.cnki.11-2246.2022.0094

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Aiming at the problem that the existing building extraction methods of airborne laser scanning data are complicated and easily disturbed by vegetation,a building extraction method using a two-way cloth simulation strategy is proposed.First,a regular grid is established for the original point cloud,and on the basis of forward cloth simulation filtering,the over-high buildings are first extracted through the grid elevation difference statistics,and then the reverse cloth simulation is used to roughly extract the top of the building from the remaining feature points.Surface point cloud;then conduct penetration analysis and combine the morphological opening operation to further eliminate the wrongly mentioned vegetation points;finally,the three-dimensional grid containing the top surface point cloud is used as the seed grid,according to the adjacency relationship between the grids and The geometric features of the internal point cloud are used for regional growth to obtain a complete building point cloud.Experimental results show that in complex scenes,this method can effectively avoid the interference of vegetation,quickly extract building point clouds,and has the advantages of high extraction accuracy and less calculation time.

Application of multi-filter algorithm based on ICEEMDAN in dynamic deformation monitoring of super high-rise structures

XIONG Chunbao, PANG Hongxing, WANG Meng, SHI Qingfa

2022, 0(3):  152-156.  doi:10.13474/j.cnki.11-2246.2022.0095

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Aiming at the multi-path errors and random noise in the monitoring data,a combined algorithm (IWPR) based on improved complete ensemble empirical mode decomposition with adaptive noise,wavelet packet decomposition and recursive least square algorithm is proposed.Firstly,the original signal is decomposed by ICEEMDAN to obtain a series of intrinsic mode function (IMF) components.Then,IMFs are divided into high-frequency IMF and low-frequency IMF on the basis of the mean of standardized accumulated modes (MSAM).Finally,considering the correlation coefficient,WP and RLS are employed to denoise the high-frequency and low-frequency IMF respectively.In order to acquire the dynamic displacement response of structures,both signals denoised by means of WP and RLS will be reconstructed.The results indicate that,compared with single algorithm EMD,CEEMDAN and ICEEMDAN,IWPR algorithm can eliminate the multi-path errors and random noise more effectively.And this algorithm can effectively improve the monitoring data accuracy of GNSS RTK.

Cohesion of forestry investigation data and grassland data from the view of unified investigation of natural resources

WEI Zhongyang, YE Kefeng, LI Xindong, WU Qiujing, YANG Zhengbei

2022, 0(3):  157-160,174.  doi:10.13474/j.cnki.11-2246.2022.0096

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Grassland investigation is an important part of natural resources investigation,the investigation results play an important role in grassland resource management.For a long time,there is conflicts between the land investigation result grassland data and forestry investigation result grassland data.This paper analyzes the difference of classification standards and investigation grassland data in land,grassland,forest departments.In combination with Yangshuo’s third national land investigation and unified investigation and monitoring of natural resources,the cohesive approach to grassland data has been discussed under the work classification of the third land investigation.Finally,some suggestions about grassland investigation in the new period are put forward,hoping to promote the integration of forest investigation and grassland investigation and the construction of unified investigation and monitoring system of natural resources.

A geometric accuracy and currency evaluating method for areal water of multi-source vector spatial data

CHEN Huanxin, WEN Bowei, ZHU Rui, CHANG Zhengyang, CHENG Mianmian, WANG Junchao, CHANG Lijun

2022, 0(3):  161-165.  doi:10.13474/j.cnki.11-2246.2022.0097

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Evaluating methods for multi-source vector spatial data have been few and defective until now.Expert decision is a frequently-used method,but it relies on artificial participation,which means high-subjectivity and low-efficiency.And expert decision method is not convenient for common users.The paper puts forward an evaluating method for areal water of multi-source vector spatial data,which can evaluate geometric accuracy and currency quantitatively.And the visualization expression of evaluate result is intuitive and vivid for common users.The experiments indicate that this method holds higher scientificity,efficiency and reliability.

Assessment of road flood disasters risk based on analytic hierarchy process:taking the Wuyi Mountain region as an example

LI Yanping, WANG Tao, WANG Xiwei, LIU Dongge, JIN Zhao

2022, 0(3):  166-170.  doi:10.13474/j.cnki.11-2246.2022.0098

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In this study,the AHP evaluation method based on multi-criteria decision-making is used to build a flood disaster assessment model.Two categories of factors,hazard and vulnerability,are presented based on natural disaster risk theory.There are seven sub-factors including precipitation,accumulation,slope,altitude,land cover,road category and surface runoff capability.The AHP is used to determine reasonable weights for all factors.The flood risk assessment model was established and the risk maps were produced afterwards.Wuyi Mountain region of Fujian province is selected as the study area in this work.The assessment results indicated that the roads with medium and high risks of flooding are mainly in the eastern,western and central south regions.The research results can be used as a guidance for road maintenance,emergency rescue,and early warning of road floods.This paper evaluates the road flood disasters risk,which can be used for flood disaters risk warning and emergency rescue planning.

The design and practice of open-style end-of-term online assessment of remote sensing image interpretation and applications

ZHAO Hengqian, CUI Ximin, YUAN Debao, LI Jing, LI Jun, CHEN Wei

2022, 0(3):  171-174.  doi:10.13474/j.cnki.11-2246.2022.0099

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In the context of COVID-19,it is an urgent task to adjust the end-of-term assessment with the online studying in the field of education reform of university.The design requirements of the solution were thoroughly explained from four aspects,including comprehensiveness,openness,individualization,and rationality.Taking remote sensing image interpretation and applications as an example,the content design and scoring standard of the open-style end-of-term assessment are given.Practical results show that the open-style end-of-term assessment can not only examine the students’ mastery of the professional knowledge learned in the course,but also fully mobilizes the students’ enthusiasm for independent learning and thinking.The solution proposed in this paper has a good demonstration effect on the reform of the end-of-term assessment method of professional courses in colleges and universities,but also provides a useful reference for online and offline blended teaching.