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A prototype auto-human support system for spatial analysis

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Progress in natural Science:Materials International 2006年第9期16卷 954-966页

作者： LI Lianfa and WANG Jinfeng (state key {2. of resources & environmental information system, institute of geographical sciences & natural resources Research, Chinese Academy of sciences, Beijing 100101, China) state key {2. of resources and environmental information system Institute of Geographical Sciences and Natural Resources Re-search Chinese Academy of Sciences Beijing 100101 China

Spatial analysis is a multidisciplinary field that involves multiple influential factors, variation and uncertainty, and modeling of geospatial data is a complex procedure affected by spatial context, mechanism and assumptions. In order to make spatial modeling easier, some scholars have suggested a lot of knowledge from exploratory data analysis (EDA), specification of the model, fitness and diagnosis of the model, to interpretation of the model. Also an amount of software has improved some functionalities of spatial analysis, e. g. EDA by the dynamic link (GeoDa) and robust statistical calculation (R). However, there are few programs for spatial analysis that can automatically deal with unstructured declarative issues and uncertainty in machine modeling using the domain knowledge. Under this context, this paper suggests a prototype support system for spatial analysis that can automatically use experience and knowledge from the experts to deal with complexity and uncertainty in modeling. The knowledge base component, as the major contribution of the system, in support of the expert system shell, codes and stores declarative modeling knowledge, e.g. spatial context, mechanisms and prior knowledge to deal with declarative issues during the modeling procedure. With the open architecture, the system integrates functionalities of other components, e.g. GIS’ visualization, DBMS, and robust calculation in an interactive environment. An application case of spatial sampling, design and implementation of spatial modeling with such a system is demonstrated.

关键词： spatial analysis automatic support system modeling of spatial data artificial inference for modeling.

GIS and ANN model for landslide susceptibility mapping

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Journal of geographical sciences 2001年第3期11卷 374-381页

作者： XU Zeng-wang (state key {2. of resources and environment information system, institute of geographic sciences and natural resources Research, CAS, Beijing 100101, China) state key {2. of resources and environment information system Institute of Geographic Sciences and Natural Resources Research CAS

Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage Une are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2.GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.

关键词： GIS artificial neural network model landslide susceptibility mapping

Origin Distribution Patterns and Floating Population Modeling:Yiwu City as a Destination

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Chinese geographical Science 2012年第3期22卷 367-380页

作者： LI Hongsheng WANG Yingjie HAN Jiafu state key Laboratoty of {2. and environmental information system Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

Existing quantitative migration studies are mainly at the inter-region or inter-province level for lacking of detailed geo-referenced migration ***,few of them integrate explorative spatial data analysis and spatial regression model into migration *** on aggregated registered floating population data from 2.05 to 2.08,the phenomena that population floating to Yiwu City in Zhejiang Province is analyzed at the provincial and county *** spatial layout of Yiwu's pull forces is proved as a V-shaped pattern excluding Sichuan Province based on map visualization *** the migration ratio in 2.07 as an explanatory variable,two models are compared using ordinary least square,spatial error model and spatial lag model methods for county-level data in Jiangxi and Anhui *** model with migration stock provides an improved fitting over the model without migration stock according to the model fitting *** floating population flocking into Yiwu City from Jiangxi is determined mostly by migration stock while the determinant factors are migration stock and distance to Yiwu City for *** distance-decay effect is true for migration flow from Anhui to Yiwu City while the distance rule is not confirmed in Jiangxi with the best fitting *** correlation between per capita net income of rural labor forces and migration ratio is not significant in Jiangxi and significant but at the 0.1 level only in *** analysis shows that the distance,income and man-land ratio are important factors to explain population floating at earlier ***,as the dynamic population floating process evolves,the determinant factor would be migration stock.

关键词： floating population origin distribution visualization spatial regression model Yiwu City GIS

Saline-alkali land in the Yellow River Delta: amelioration zonation based on GIS

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Journal of geographical sciences 2001年第3期11卷 313-320页

作者： GUAN Yuan-xiu, LIU Gao-huan, WANG Jin-feng (state key {2. of resources and environment information system, institute of geographic sciences and natural resources Research, CAS, Beijing 100101, China) state key {2. of resources and environment information system Institute of Geographic Sciences and Natural Resources Research CAS

Soil salinization is one of the major land degradation types and has greatly influenced sustainable agricultural development. Zonation of saline-alkali land is the precondition for effective amelioration. The present situation of saline-alkali land is monitored by remote sensing image processing. Causes for land salinization are analyzed, especially the two key factors, ground water depth and its mineralization degree, are analyzed by using long-term observation data. Previously, zonation of saline-alkali soil was made descriptively and artificially. Based on the present situation of saline-alkali land, ground water depth and ground water mineralization degree, the zonation of saline-alkali land for amelioration in the Yellow River Delta was completed quantitatively. Four different types of saline-alkali land amelioration zones are delineated, namely, easy ameliorated zone, relatively difficult ameliorated zone, difficult ameliorated zone and unfavorable ameliorated zone. Countermeasures for ameliorating saline-alkali soils are put forward according to ecological conditions of different saline-alkali land zones.

关键词： GIS the Yellow River Delta saline-alkali land zonation

Modeling of Hillslope Runoff and Soil Erosion at Rainfall Events Using Cellular Automata Approach

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Pedosphere 2009年第6期19卷 711-718页

作者： MA Ting ZHOU Cheng-Hu CAI Qiang-Guo state key {2. of resources and environmental information system Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 (China) Graduate University of the Chinese Academy of sciences Beijing 100049 (China)

A novel quantitative cellular automata (CA) model that simulates and predicts hillslope runoff and soil erosion caused by rainfall events was developed by integrating the local interaction rules and the hillslope surface hydraulic processes. In this CA model, the hillslope surface was subdivided into a series of discrete spatial cells with the same geometric features. At each time step, water and sediment were transported between two adjacent spatial cells. The flow direction was determined by a combination of water surface slope and stochastic assignment. The amounts of interchanged water and sediment were computed using the Chezy-Manning formula and the empirical sediment transport equation. The water and sediment discharged from the open boundary cells were considered as the runoff and the sediment yields over the entire hillslope surface. Two hillslope soil erosion experiments under simulated rainfall events were carried out. Cumulative runoff and sediment yields were measured, respectively. Then, the CA model was applied to simulate the water and soil erosion for these two experiments. Analysis of simulation results indicated that the size of the spatial cell, hydraulic parameters, and the setting of time step and iteration times had a large impact on the model accuracy. The comparison of the simulated and measured data suggested that the CA model was an applicable alternate for simulating the hillslope water flow and soil erosion.

关键词： hillslope surface hydraulic parameters sediment simulation water

Characterizing the Mass Elevation Effect across the Tibetan Plateau

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Journal of Mountain Science 2018年第12期15卷 2651-2665页

作者： HAN Fang ZHANG Bai-ping ZHAO Fang WAN Li TAN Jing LIANG Tian School of Civil and Architectural Engineering, Shandong University of Technology institute of geographic {2. and natural resources Research, state key laboratory of resources and environmental information system, Chinese Academy of {2. College of environment and Planning, Henan University Chinese Research Academy of environmental {2. Beijing Aerospace TITAN Technology Co., Ltd School of Civil and Architectural Engineering Shandong University of TechnologyZibo 255000China institute of geographic {2. and natural resources Research State Key Laboratory of Resources and Environmental Information SystemChinese Academy of SciencesBeijing 100101China College of environment and Planning Henan UniversityKaifeng 455000China Chinese Research Academy of environmental sciences Beijing 100012China Beijing Aerospace TITAN Technology Co. LtdBelling 100083China

It is over 110 years since the term Mass Elevation Effect(MEE) was proposed by A. D. Quervain in 1904. The quantitative study of MEE has been explored in the Tibetan Plateau in recent years; however, the spatial distribution of MEE and its impact on the ecological pattern of the plateau are seldom known. In this study, we used a new method to estimate MEE in different regions of the plateau, and, then analyzed the distribution pattern of MEE, and the relationships among MEE, climate, and the altitudinal distribution of timberlines and snowlines in the Plateau. The main results are as follows:(1) The spatial distribution of MEE in the Tibetan Plateau roughly takes on an eccentric ellipse in northwestsoutheast trend. The Chang Tang Plateau and the middle part of the Kunlun Mountains are the core area of MEE, where occurs the highest MEE of above 11℃; and MEE tends to decreases from this core area northwestward, northeastward and southward;(2. The distance away from the core zone of the plateau is also a very important factor for MEE magnitude, because MEE is obviously higher in the interior than in the exterior of the plateau even with similar mountain base elevation(MBE).(3) The impacts of MEE on the altitudinal distribution of timberlines and snowlines are similar, i.e., the higher the MEE, the higher timberlines and snowlines. The highest timberline(4600–4800 m) appears in the lakes and basins north of the Himalayas and in the upper and middle reach valleys of the Yarlung Zangbo River, where the estimated MEE is 10.2.2.℃–10.6904℃. The highest snowline(6000–62.0 m) occurs in the southwest of the Chang Tang Plateau, where the estimated MEE is 11.2.59°C–11.5488℃.

关键词： Mass Elevation Effect (MEE) Distribution Pattern Timberline Snowline Tibetan Plateau

Estimation of soil organic carbon reservoir in China

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Journal of geographical sciences 2001年第1期11卷 3-13页

作者： WANG Shao-qiang1, ZHOU Cheng-hu1, LI Ke-rang1, ZHU Song-li2. HUANG Fang-hong1 (1.The state key {2. of resources and environment information system, institute of geographic sciences and natural resources Research, CAS, Beijing 100101, China The state key {2. of resources and environment information system Institute of Geographic Sciences and Natural Resources Research CAS Beijing China institute of environment Science Beijing Normal University Beijing China

The paper respectively adopted physio-chemical properties of every soil stratum from 2.73 soil profiles of the second national soil survey. The corresponding carbon content of soils is estimated by utilizing conversion coefficient 0.58. In the second soil survey, the total amount of soil organic carbon is about 92..18xl08t and carbon density is about 10.53 kgC/m2.in China according to the area of 877.63x106hm2.surveyed throughout the country. The spatial distribution characteristics of soil organic carbon in China is that the carbon storage increases when latitude increases in eastern China and the carbon storage decreases when longitude reduces in northern China. A transitional zone with great variation in carbon storage exists. Moreover, there is an increasing tendency of carbon density with decrease of latitude in western China. Soil circle is of great significance to global change, but with substantial difference in soil spatial distribution throughout the country. Because the structure of soil is inhomogeneous, it could bring some mistakes in estimating soil carbon reservoirs. It is necessary to farther resolve soil respiration and organic matter conversion and other questions by developing uniform and normal methods of measurement and sampling.

关键词： terrestrial ecosystem global change soil carbon reservoir carbon cycle

Differential Hydrological Grey Model (DHGM) with self-memory function and its application to flood forecasting

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Science China(Technological sciences) 2009年第4期52卷 1039-1049页

作者： CHEN XiangDong1, XIA Jun1,2.& XU Qian3 1 key laboratory of Water Cycle and Related Land Surface Processes,{2. Research, Chinese Academy of sciences, Beijing 100101, China 2.state key laboratory of Water resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072. China 3 School of Hydrology and Water resources, Hohai University, Nanjing 2.0098, China key laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China state key {2. of Water resources and Hydropower Engineering Science Wuhan University Wuhan China School of Hydrology and Water resources Hohai University Nanjing China

This paper addresses a problem of flood forecasting with the self-memory function. Considering flood forecasting’s uncertainty and updating demand, a hybrid hydrological model, namely Differential Hy- drological Grey Model with self-memory function (DHGM-SM), is developed. The model has two fold features. One is to establish a self-memorization equation linked with DHGM, that could extract useful information from past data series and realize updating of hydrological dynamic process. The other is that this model has higher efficiency relative to original hydrological model without self-memory func- tion. This approach was applied to river flow forecasting of two representative basins in Tunxi of South China and Daqinggou of North China. It is shown that this hybrid method has satisfactory forecasting accuracy by examination of both calibration and validation.

关键词： differential grey model the self-memorization principle real-time forecasting faded-memory recursive least square method

Spatial pattern of land cover change in China's semiarid environment

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Journal of Arid Land 2009年第1期1卷 16-25页

作者： BaoLin LI1 and QiMing ZHOU2.1 state key {2. of environment and resources information system, institute of geographical sciences and resources Research, Chinese Academy of sciences, Beijing 100101, China 2.Department of Geography, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China state key {2. of environment and resources information system Institute of Geographical Sciences and Resources Research Chinese Academy of Sciences Beijing 100101 China Department of Geography Hong Kong Baptist University Kowloon Tong Hong Kong

This study seeks a routine to quantify spatial pattern of land cover changes in semiarid environment of China based on post-classification comparison method. The method consists of three major steps: (1) the image classification and unification of classified results based on two-level land cover classification themes, (2. the establishment of land cover change classes based on an unification land cover classification theme, (3) the reclassification and mapping of land cover change classes with three overall classes including no-change, gain and loss based on the unification land cover class. This method was applied to detect the spatial pattern of land cover changes in Yinchuan Plain, one of famous irrigation agricultural zones of the Yellow River, China. The results showed the land cover had undergone a remarkable change from 1991 to 2.02.in the study area (the changed area was over 30%). Rapid increase of cropland (12.5%), built-up area (131.4%) and rapid decrease of bare ground (51.7%) were alarming. The spatial pattern of land cover changes showed clear regional difference in the study area and was clearly related to human activities or natural factors. Thus, it obtained a better understanding of the human impact on the fragile eco{2. of China’s semiarid environment.

关键词： change detection spatial pattern land cover semiarid zone of China remote sensing human activities