Application of Remote Sensing and GIS Technology to the Study of Desertification of Arable Lands in North Shaanxi, China

The policy of the Chinese government concerning the horizontal expansion of the cultivated land through the reclamation of desert soils result in a total increase of 665. 985 km^2 during the period 1987-1999 in North Shaanxi. This increase is less than the loss in arable land by urbanization. The accelerated rate of change in agricultural areas calls for more rapid surveys of urbanization and loss of arable land. Remote sensing has a number of advantages over ground-based methods for such surveys. The multi-scale concept of remote sensing data help us study the problem in four towns. Several maps were produced to analyze the situation of urban coverage in different times. The evaluation of the status, rate and risk of urbanization are based on an accepted average of urban increase as 2% of population growth per year.     

Integration of neural networks and cellular automata for urban planning

This paper presents a new type of cellular automata (CA) model for the simulation of alternative land development using neural networks for urban planning. CA models can be regarded as a planning tool because they can generate alternative urban growth. Alternative development patterns can be formed by using different sets of parameter values in CA simulation. A critical issue is how to define parameter values for realistic and idealized simulation. This paper demonstrates that neural networks can simplify CA models but generate more plausible results. The simulation is based on a simple three-layer network with an output neuron to generate conversion probability. No transition rules are required for the simulation. Parameter values are automatically obtained from the training of network by using satellite remote sensing data. Original training data can be assessed and modified according to planning objectives. Alternative urban patterns can be easily formulated by using the modified training data sets rather than changing the model.     

Land-use spatial optimization based on PSO algorithm

The optimization of land-use spatio-structure is one of the most important areas of land use management;constructing a spatial optimization model that is based on the micro spatial unit in a bottom-up mode plays an important role in coupling the quan-tity structure and spatial structure effectively.The objective of this research is to develop a land use spatial optimization model based on particle swarm optimization to make spatial decision in land use management.The model is implemented using real data-sets to emulate the process of spatial structure optimization in order to get the best landscape pattern under the control of decision environments.Simulation results revealed that the particle swarm optimization model has the ability to utilize the quantity and spa-tial structure.Furthermore,the result demonstrated that it can be used to stimulate the landscape pattern in designing the appropriate optimization environment,which could land quantity target to the basic spatial units effectively and provide appropriate spa-tio-structure for regional land use space layout decision making.     

基于城市引力模型与随机扩散方程的城镇基准地价平衡(英文)

A balance of urban datum land prices is achieved to harmonize regional land prices and make the prices truly reflect different economic development levels and land prices among cities. The current piecewise linear interpolation balance method widely used has two drawbacks that always lead to an unsatisfactory balance among some cities. When the excess of land price in the central city to the surrounding zone reaches a certain degree, land price in the circumjacent city is not only consistent with the local land grade and land use level, but also influenced by the diffusion of land price in the central city. Thus, a new balanced scheme of datum land prices based on the city gravitation model and stochastic diffusion equation is brought forward. Finally, the new method is examined in the practice of datum land price balance in Hubei Province, China.     

A shoreline change analysis along the coast between Kanyakumari and Tuticorin,India, using digital shoreline analysis system

The shoreline is one of the rapidly changing landforms in coastal areas.They are the key element in coastal GIS and provide the most information on coastal land form dynamics.Therefore,accurate detection and frequent monitoring of shorelines is very essential to understand the coastal processes and dynamics of various coastal features.The present study is to investigate the shoreline changes along the coast between Kanyakumari and Tuticorin of south India(where hydrodynamic and morphologic changes occur continuously after the December 2004 tsunami)by using Digital Shoreline Analysis System(DSAS),an extension of ArcGIS.Multidate IRS and Landsat Satellite data(1999,2001,2003,2005,2007,and 2009)are used to extract the shorelines.The data is processed by using the ERDAS IMAGINE 9.1 software and analyzed by ArcGIS 9.2 workstation.The rates of shoreline changes are estimated by three statistical methods,namely,End Point Rate(EPR),Linear Regression Rate(LRR),and Least Me-dian of Squares(LMS)by using DSAS.The study reveals that most of the study area has undergoing erosion.Both natural and an-thropogenic processes along the coast modify the shoreline configuration and control the erosion and accretion of the coastal zones.The coastal zones along the estuary have experienced accretion due to the littoral processes.The zones with headlands have more eroded than other zones along the study area.The study also shows that the coastal zones where sand is mined have relatively more rate of erosion than that of the other zones.Improper and unsustainable sand mining may also lead to severe erosion problem along this area.The shoreline change rates are altered by various geological processes along the coast.Thus,the present study implies that proper beach filling and nourishment projects should be made in the study area to save from hazards.It also indicates the advantage and suitability of DSAS to assess the shoreline changes compared with the traditional manual shoreline change analysis and prom-ising its applications for coastal zone management in other regions.     

A CSCW system for building reviewing by integrating GIS with OA

1 lntroductionIt is the main task of urban development controlin China to review buiIding applications submittedby public agencies or private citizens, regardingnew buildings or concerning existing building withor without change in their original functions. Theprocess is a collaborative work by a group of urbanpIanning staffs and land managers who are workingwith different departments of the organization.There are three ingredients for improving the workefficiency, i. e., agile cornrnunicatio…     

Analysis on public transportation influences on real estate development in East Lansing

The purpose for this article is to research the relationship between public transportation and real estate based on the area of East Lansing.Two methods are mainly used to analyze the data:correlation analysis and means analysis with the help of Statistical Package for the Social Sciences(SPSS) data analyzing software.There are two analysis results:(1) the real estate,which is closer to public transportation,will have higher value than that is far from public transportation,and(2) the number of bus in one bus stop has no obvious relation with the value of real estate.Based on these results,real estate developers could build residential real estate close to public transportation or bring public transportation in their real estate community by contract with transportation corporations.In sum,they should reasonably use public transportation as attractive features to convince people to prefer to their houses.     

应用空间技术研究干旱和半干旱地区由盐化作用造成的土地退化(英文)

This study applied a computerized parametric methodology to monitor, map, and quantify land degradation by salinization risk detection techniques at a 1：250 000 mapping scale using geo-information technology. The northern part of the Shaanxi province in China was taken as a case. Multi-temporal remotely sensed materials of both Landsat TM and thematic maps （ETM＋） were used as the bases to provide comprehensive views of surface conditions such as vegetation cover and salinization detection. With ERDAS ver. 9.1 software, the Normalized Differential Salinity Index （NDSl） and Salinity Index （S.I.） were computed and then evaluated for land degradation by salinization. Arc/Info ver. 9.2 software was used along with field observation data （GPS） for analysis. Using spatial analysis methods, results showed that 19 973.1 km^2 （72%） of land had no risk of land degradation by salinization, 3 684.7 km^2 （13%） had slight land degradation by salinization risk, 2 797.9 km^2 （10%） had moderate land degradation by salinization risk, and 1 218.9 km^2 （4%） of the total land area was at a high risk of land degradation by salinization. The study area, in general, is exposed to a high risk of soil salinization.     

Urban growth pattern modeling using logistic regression

Transformation of land use/land cover change occurs due to the numbers and activities of people.Urban growth mod-eling has attracted substantial attention because it helps to comprehend the mechanisms of land use change and thus helps relevant policies made.This paper tends to apply logistic regression to model urban growth in the Jiayu county of Hubei province,China.It is applied in a GIS environment to calculate variables and,then,in SPSS to discover the relationships between urban growth and the driving forces.The relative operating characteristic(ROC) shows the modeling accuracy with the curve 0.891 with standard er-ror 0.001.A probability map is generated finally to predict where urban growth will occur as a result of the computation.The result shows the model simulates urban growth well in the county scale.     

Remote sensing image classification with GIS data based on spatial data mining techniques

Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification.Two learning granularities are proposed for inductive learning from spatial data,one is spatial object granularity,the other is pixel granularity.We also present an approach to combine inductive learning with conventional image classification methods,which selects class probability of Bayes classification as learning attributes.A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data.Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning.Comparing with the result produced only by Bayes classification,the overall accuracy increased by 11% and the accuracy of some classes,such as garden and forest,increased by about 30%.The results indicate that inductive learning can resolve spectral confusion to a great extent.Combining Bayes method with inductive learning not only improves classification accuracy greatly,but also extends the classification by subdividing some classes with the discovered knowledge.     

Urban residential land value analysis: Case Danyang,China

The research attempts to find out how the location of the CBD(central business district), the distance to the main roads, the distribution of the public facilities, and the urban land-use pattern influence the urban residential land value variations. The study begins by identifying the influences into two categories: general circumstance and micro/neighboring circumstances. Benchmark price and market land value are tested to be the results influenced by general circumstance and both the influential range and the influential force of individual land-use are investigated and compared. At last explicit case comparisons are also taken for testing the result. The finding of the research is not only useful for understanding the spatial patterns of land values, but also beneficial for the policy-makers concerning land administration and urban planning.     

新疆克拉玛依市商业地价空间分布规律研究

城市地价是一个具有时空性质的多维概念,在空间分布上具有较强的关联性和特殊性。但目前对城市地价空间分布规律方面的研究相对较少,还没有形成系统性的理论与方法依据,本文以克拉玛依市2007年土地调查商业用地数据和地价动态监测数据为依据,以ARCGIS软件为平台,采用地统计学的方法,对克拉玛依市商业用地价格的空间分布进行了研究。研究表明:地统计学的理论与方法对城市地价空间分布进行研究具有独特的优越性;城市商业用地地价的分布在空间上既有连续性,也存在变异性,受区域整体趋势的影响明显高于小范围的变异性,属于强空间自相关;商业用地地价分布整体呈现圈层结构,交通道路对地价的空间分布具有重要影响。     

基于移动趋势面分析法的城市基准地价评估研究

通过评价有限的、具有内在相互作用和代表性的样点，采用移动趋势面分析法测算城市基准地价，建立区域三维地价曲面模型，并以该技术路线对武汉市江汉区的基准地价进行评估，对其结果进行了验证分析，建立了地价结果趋势面图。     

基于RS与GIS的宁波市鄞州区土地利用变化分析

利用1990年土地详查变更调查数据和2006年SPOT遥感影像数据,借助GIS空间分析能力,通过计算土地利用类型动态度、转移矩阵、扩展强度指数分析了鄞州区的城镇土地利用变化的时空特征。研究结果表明:1)鄞州区城市、建制镇、交通运输用地、工矿用地扩展的大部分来源于耕地;2)区各街道城镇建设用地扩展强度指数大于建制镇;3)建设用地扩展以新城区及宁波城区周边的街道镇为主。     

基于遥感与GIS的城镇用地扩展研究

利用1990年土地详查变更调查数据和2006年的SPOT遥感影像,借助GIS空间分析能力,通过计算土地利用类型动态度、转移矩阵、扩展强度指数分析了慈溪市的城镇用地利用变化的时空特征.研究结果表明:1)慈溪市城市、建制镇、交通运输用地扩展的大部分来源于耕地;2)浒山街道城镇,交通建设用地扩展强度指数大于其他镇;3)建设用...     

基于人工蜂群CA模型的南京市土地利用变化模拟与预测

以南京市为例,构建人工蜂群元胞自动机（CA_ABC）模型,对2000—2007年的土地利用变化进行模拟以实现CA_ABC模型的校正,并以2007—2015年的土地利用变化为案例,验证该模型的有效性。模拟结果总体精度（OA）2007年为87.79%,2015年为80.61%;模拟结果的品质因数（FOM）2007年为21.23%,2015年为19.25%。基于CA_ABC模型和马尔可夫链预测未来城市土地总量,对南京市2025和2035年的土地利用格局进行了预测,对城市扩张和生态用地被侵占现象进行分析。模型预测结果表明,未来20年的城市扩张主要以牺牲耕地和林地为代价,2025和2035年80%的城市扩张面积来源于对耕地面积的侵占,17%的城市面积扩张是由2015年的林地转换得到的。研究表明,准确模拟、预测未来城市格局及评估城市扩张能够对生态用地侵占,以及为决策者合理规划城市、推动城市可持续发展提供帮助。     

基于ArcGIS的土地用途分区研究

土地用途分区是土地利用总体规划的重要组成部分,是确定土地用途和采取具体土地利用措施的主要依据之一。GIS的空间分析功能可以辅助土地利用变化分析,完成土地适宜性评价以及土地利用结构和布局调整等规划任务,并根据未来土地用途管制要求,划定和调整土地用途分区。文章以葫芦岛市龙港区土地资源作为研究对象,应用GIS的空间叠置统计分...     

Voronoi图在城镇地价指数监测中的应用探讨

城市地价指数是地价动态监测的重要手段。针对我国城市地价指数测算公式发表了自己的见解,并提出采用Voronoi图计算地价监测点的影响范围,作为监测样点的权重用于计算地价指数的方法,以广东省某市的地价指数测算为例证明了该方法的可行性。     

城市地价动态变化监测的空间分析方法研究

提出以土地交易样本作为监测样本进行城市地价变化监测与分析的新方法，通过时序监测样本的Voronoi图，分析城市土地市场的热点区域和发展趋势，采用主要交通干道作为特征剖面曲线对两个时点的地价变化值的等值线进行空间分析，确定地价突变区域作为城市基准地价更新资料调查的主要范围，并以武汉市2000～2003年的城市地价变化为例，分析验证了上述方法的可行性与合理性。     

土地利用动态遥感监测数据库标准探讨

为了将土地利用动态遥感监测技术和成果进一步普及和应用,规范土地利用动态遥感监测数据库建设,依据国土资源信息化标准建设的总体部署和要求,参考相关标准和规程,结合土地利用动态遥感监测业务特点,制定了土地利用动态遥感监测数据库标准草案。