THE GLOBAL BIFURCATION STRUCTURE OF A KIND OF DIGIT MAPPING

The global structure of the mapping is studied. The symmetric unconnected substructures of T2 is coincident with [1] by computer, but for n=3 the symmetry of these substructures vanishes. As n is increasing, the global bifurcation structure of Tn is shown. Finally, similar results for the mapping are also proved.

Recent progress and future prospect of digital soil mapping： A review

To deal with the global and regional issues including food security, climate change, land degradation, biodiversity loss, water resource management, and ecosystem health, detailed accurate spatial soil information is urgently needed. This drives the worldwide development of digital soil mapping. In recent years, significant progresses have been made in different aspects of digital soil mapping. The main purpose of this paper is to provide a review for the major progresses of digital soil mapping in the last decade. First, we briefly described the rise of digital soil mapping and outlined important milestones and their influence, and main paradigms in digital soil mapping. Then, we reviewed the progresses in legacy soil data, environmental covariates, soil sampling, predictive models and the applications of digital soil mapping products. Finally, we summarized the main trends and future prospect as revealed by studies up to now. We concluded that although the digital soil mapping is now moving towards mature to meet various demands of soil information, challenges including new theories, methodologies and applications of digital soil mapping, especially for highly heterogeneous and human-affected environments, still exist and need to be addressed in the future.

A case-based method of selecting covariates for digital soil mapping

Selecting a proper set of covariates is one of the most important factors that influence the accuracy of digital soil mapping(DSM).The statistical or machine learning methods for selecting DSM covariates are not available for those situations with limited samples.To solve the problem,this paper proposed a case-based method which could formalize the covariate selection knowledge contained in practical DSM applications.The proposed method trained Random Forest(RF)classifiers with DSM cases extracted from the practical DSM applications and then used the trained classifiers to determine whether each one potential covariate should be used in a new DSM application.In this study,we took topographic covariates as examples of covariates and extracted 191 DSM cases from 56 peer-reviewed journal articles to evaluate the performance of the proposed case-based method by Leave-One-Out cross validation.Compared with a novices’commonly-used way of selecting DSM covariates,the proposed case-based method improved more than 30%accuracy according to three quantitative evaluation indices(i.e.,recall,precision,and F1-score).The proposed method could be also applied to selecting the proper set of covariates for other similar geographical modeling domains,such as landslide susceptibility mapping,and species distribution modeling.

Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms

Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land management,agricultural activities,water quality,and sustainable development.The remote sensing images taken by the synthetic aperture radar(SAR)Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area;however,there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization.Therefore,in this study,we used topography indices derived from digital elevation model(DEM),SAR indices generated by Sentinel-1,and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China,and evaluated the potential of multi-source sensors to predict soil salinity.Using the soil electrical conductivity(EC)values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable,we constructed three soil salinity inversion models based on classification and regression tree(CART),random forest(RF),and extreme gradient boosting(XGBoost).Then,we evaluated the prediction ability of different models through the five-fold cross validation.The prediction accuracy of XGBoost model is better than those of CART and RF,and soil salinity predicted by the three models has similar spatial distribution characteristics.Compared with the combination of topography indices and vegetation indices,the addition of SAR indices effectively improves the prediction accuracy of the model.In general,the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor.In addition,SAR indices,vegetation indices,and topography indices are all effective variables for soil salinity prediction.Weighted Difference Vegetation Index(WDVI)is designated as the most important variable in these variables,followed by DEM.The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.

The digital mapping produced with satellite image of the Zhongshan Station area in Antarctica

Ice and snow domint the land features in Antarctica. The great brightness and poorcontrast of ice and snow and streaking noise in satellite image make the procedure of image processing difficult. On the other hand however, the contrast between bare rock land/sea water and ice/snow is so high that the details of image will be overcompressed.In the light of characteristics of satellite image in Antarctica, a filtering to remove streaking noise has adn discussed. Based on automatic identify classification to enhance the details of objects and the method and theory of digital rectification of satellite image with ground control points measured from field survey are also presented.

Application of RgMap system on digital regional geological survey

Digital geological mapping fundamentally broke through the traditional working pattern,successfully carried out the geological mapping digitalization.By using the RGMAP system to field digital geological mapping,the authors summarized the method of work and the work flow of the RGMAPGIS during the field geological survey.First,we prepared material,set up the PRB gallery,then put the geographic base map under the background maplayer and organizing the field hand map,forming the field factual datum map.At last,the geological space database is formed.

Optical Image Encryption Based on Mixed Chaotic Maps and Single-Shot Digital Holography

Random phase masks play a key role in optical image encryption schemes based on double random phase technique. In this paper, a mixed chaotic method is proposed, which can efficiently solve some weaknesses that one-dimensional (1-D) single chaotic maps encounter to generate random phase masks. Based on the chaotic random phase masks, optical image encryption and decryption are realized with a single-shot digital holographic technique. In the proposed encryption scheme, the initial value and parameters of mixed chaotic maps serve as secret keys, which is convenient for the key management and transmission. Moreover, it also possesses high resistance against statistical attack, brute-force attack, noise attack and shear attack. Simulation results and security analysis verify the validity and security of the proposed encryption scheme. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

The Effect of the Geomorphologic Type as Surrogate to the Time Factor on Digital Soil Mapping

Many environmental variables are frequently used to predict values of soil in locations where they are not measured. Digital soil mapping (DSM) has a long-standing convention to describe soils as a function of climate, organisms, topography, parent material, time and space. It is obvious that terrain, climate, parent material and organisms are used frequently in the prediction of soil properties while time and space factors are rarely used. Time is the indirect factor for the formation and development of soil. Moreover, it is very useful to explicit and implicit estimates of soil age for DSM. However, it is often difficult to obtain time factor. In the absence of explicit soil age data, geomorphologic data are commonly related to soil relative age. Consequently, this study adopts the geomorphologic types (genesis type of geomorphology) as surrogate to the time factor and analyzes its effect on DSM. To examine this idea, we selected the Ili region of northwestern China as the study area. This paper uses geomorphologic data from a new digital geomorphology map as the implicit soil age in predictive soil mapping. For this study, Soil-landscape inference model (SoLIM) was used to predict soil properties based on the individual representation of each sample. This model applies the terrain (topography), climate, parent material (geology) and time (geomorphologic type) to predict soil values in the study area where they are not measured. And the independent sample validation method was used to estimate the precision of results. The validation result shows that the use of geomorphologic data as surrogate to the time factor in the individual representation leads to a considerable and significant increase in the accuracy of results. In other words, implicit estimates of soil age by genesis type of geomorphology are very useful for DSM. This increase was due to the high purity of the geomorphologic data. This means that the geomorphologic variable, if used, can improve the quality of DSM. Predicted value through the proposed approach comes closer to the real value.

Efficient and reliable road modeling for digital maps based on cardinal spline

In order to realize an optimal balance between the efficiency and reliability requirements ofroad models,a road modeling method for digital maps based on cardinal spline is studied.First,the cardinal spline is chosen to establish an initial road model,which is specified by a series of control points and tension parameters.Then,in view of the initial road model,a gradual optimization algorithm,which can determine the reasonable control points and optimal tension parameters according to the degree of the change of road curvature,is proposed to determine the final road model.Finally,the proposed road modeling method is verified a d evaluated through experiments,and it is compared with the conventional method for digital maps based on the B-spline.The results show that the proposed method can resize a neaoptimal balance between the efficiency and reliability requirements.Compared with the conventional method based on the B-spline,this method occupies less data storage and achieves higher accuracy.

Design of Conceptual Model in Digital Map Database

The components of map information are analyzed theoretically in this paper,and the map information includes mainly the spatial information,attributive information and temporal characteristics information.Then the digital map entity is defined according to construction characteristics of the map information.Finally,on the basis of the analyses of the construction characteristics of digital map entity and present conceptual model of digital map database,an abstracted conceptual model of digital map database is presented.And the Normal Form theory of relational database is discussed particularly.

Modeling the special intersections for enhanced digital map

A new lane-level road modeling method based on cardinal spline is proposed for the special intersections which are covered by vegetation or artificial landscape in their central regions.First,cardinal spline curves are used to fit the virtual lanes inside special intersections,and an initial road model is established using a series of control points and tension parameters.Then,the progressive optimization algorithm is proposed to determine the final road model based on the initial model.The algorithm determines reasonable control points and optimal tension parameters according to the degree of road curvature changes,so as to achieve a balance between the efficiency and reliability of the road model.Finally,the proposed intersection model is verified and evaluated through experiments.The results show that this method can effectively describe the lane-level topological relationship and geometric details of this kind of special intersection where the central area is covered by vegetation or artificial landscape,and can achieve a good balance between the efficiency and reliability of the road model.

The digital mapping of satellite images under no ground control and the distribution of landform, blue ice and meteorites in the Grove Mountains, Antarctica

The colorful satellite image maps with the scale of 1∶100000 were made by processing the parameters-on-satellite under the condition of no data of field surveying. The purpose is to ensure the smooth performance of the choice of expedition route, navigation and research task before the Chinese National Antarctic Research Expedition (CHINARE) first made researches on the Grove Mountains. Moreover, on the basis of the visual interpretation of the satellite image, we preliminarily analyze and discuss the relief and landform, blue ice and meteorite distribution characteristics in the Grove Mountains.

Updated Digital Map of Mafic Dyke Swarms and Large Igneous Provinces in Western Australia

Since 1894,the Geological Survey of Western Australia(GSWA)has released 14 versions of the‘Geological Map of Western Australia’.The latest in this series,published in December 2015,is the first bedrock geology map

SEVERAL PROBLEMS OF DIGITAL MAP TECHNIQUE IN LOW ALTITUDE PENETRATION

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The Digital Mapping of Ukrainian Soils on the Base of High Resolution Space Images

The first Ukrainian using experience of multispectral space scanning for digital soil mapping is described in this paper. Methodical approaches for detailed soil observation of Ukrainian forest regions are elaborated based on modem mapping principles. For the first time in Ukraine, digital soil maps based on GIS （geographic information system） were obtained for individual farms. In GIS based on space images and digital relief models, the medium-scale and large-scale soil maps were created by geo-statistical methods. According to elaborated methods, modem digital soil mapping should provide all combined works： remote sensing and traditional soil observations. The modem digital soil mapping should be based just on quantitative principles： on remote sensing data, geomorphologic field parameters, and chemical analyses. The methodological approaches, which were used for the first time in Ukraine during digital soil mapping by remote sensing methods, are described in this paper.

The Comparison of Several 3D Digital Techniques Applied in Rockery Surveying and Mapping:A Case Study of North China University of Technology

By summarizing research status of traditional garden,the obstacles and difficulties of garden rockery research are analyzed,and the breakthroughs and advantages of 3D digital technique in garden rockery surveying and mapping are introduced.The rockery in North China University of Technology is taken as the research object.Using many kinds of digital 3D techniques,3D scanning and modeling analysis of the rockery are conducted.Modeling result and accuracy of each technique are analyzed,and application prospect of 3D digital technique in rockery research of classical garden is explored.

Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.

Mapping and understanding degradation of alpine wetlands in the northern Maloti-Drakensberg, southern Africa

The alpine terrestrials of the Maloti-Drakensberg in southern Africa play crucial roles in ecosystem functions and livelihoods,yet they face escalating degradation from various factors including overgrazing and climate change.This study employs advanced Digital Soil Mapping(DSM)techniques coupled with remote sensing to map and assess wetland coverage and degradation in the northern Maloti-Drakensberg.The model achieved high accuracies of 96%and 92%for training and validation data,respectively,with Kappa statistics of 0.91 and 0.83,marking a pioneering automated attempt at wetland mapping in this region.Terrain attributes such as terrain wetness index(TWI)and valley depth(VD)exhibit significant positive correlations with wetland coverage and erosion gully density,Channel Network Depth and slope were negative correlated.Gully density analysis revealed terrain attributes as dominant factors driving degradation,highlighting the need to consider catchment-specific susceptibility to erosion.This challenge traditional assumptions which mainly attribute wetland degradation to external forces such as livestock overgrazing,ice rate activity and climate change.The sensitivity map produced could serve as a basis for Integrated Catchment Management(ICM)projects,facilitating tailored conservation strategies.Future research should expand on this work to include other highland areas,explore additional covariates,and categorize wetlands based on hydroperiod and sensitivity to degradation.This comprehensive study underscores the potential of DSM and remote sensing in accurately assessing and managing wetland ecosystems,crucial for sustainable resource management in alpine regions.

Correction of Inertial Navigation System’s Errors by the Help of Video-Based Navigator Based on Digital Terrarium Map

This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a Digital Terrain (or Elevation) Map (DTM/DEM). More specifically, it has been shown that the optical flow derived from two consecutive camera frames can be used in combination with a DTM to estimate the position, orientation and ego-motion parameters of the moving camera. As opposed to previous works, the proposed approach does not require an intermediate explicit reconstruction of the 3D world. In the present work the sensitivity of the algorithm outlined above is studied. The main sources for errors are identified to be the optical-flow evaluation and computation, the quality of the information about the terrain, the structure of the observed terrain and the trajectory of the camera. By assuming appropriate characterization of these error sources, a closed form expression for the uncertainty of the pose and motion of the camera is first developed and then the influence of these factors is confirmed using extensive numerical simulations. The main conclusion of this paper is to establish that the proposed navigation algorithm generates accurate estimates for reasonable scenarios and error sources, and thus can be effectively used as part of a navigation system of autonomous vehicles.

数字填图系统RGMAP在野外实测地质剖面中的应用与方法探索

数字填图系统(RGMAP)是中国地质调查局开发的数字地质调查系统(DGSS)的移动端系统,能够实现野外实测地质剖面数字化采集,由于手机(掌上机)GPS定位精度易受硬件和环境因素的影响,致使数据精度不足。笔者结合工作实际,简述RGMAP实测剖面操作流程和逻辑,分析与传统剖面测量的区别,并进行3种方法测试对比,总结出了一套实测剖面测量方法,实测剖面测量主要通过掌上机进行野外数据采集,融入RTK进行坐标测量,再配合测绳对分层、照片、产状等要素进行控制,辅助确认要素在导线间的位置,该套方法能切实有效地提高工作效率和数据精度。