小城镇基准地价更新方法研究

基准地价评估及更新是目前土地管理的一项重要和经常性的工作。针对小城镇的土地利用特点，对小城镇土地定级估价更新中的地价样本祷点、土地级别调整、基准地价更新评估等方面进行了研究，选择级别界线修订法更新小城镇土地级别，并在此基础上探索出适合小城镇特点的基准地价更新方法。     

土地市场景气指标体系及评价模型的建立

以徐州市为例建立了土地市场景气指标体系及评价模型。首先在建立指标体系时运用定性的方法对大量指标进行了初选,然后用主成分分析法确定土地市场景气指标体系,并运用层次分析法确定各指标的权重。在建立景气评价模型时,分析了传统扩散指数模型的缺陷,提出了合理的改进模型。同时,针对扩散指数与合成指数反映的土地市场运行状况之间的差异,引入了精度比较概念,在此基础上选择精度较高的指数进行土地市场景气状况评价。最后运用建立的评价模型对徐州市2001-2006年土地市场景气状况进行了评价,并对2007年徐州市土地市场运行状况进行了预测。分析表明,建立的景气指标体系及其评价模型能够较准确地评价和预测土地市场景气状况．     

土地定级中的数据检验

本文根据土地定级工作中土地级别划分合理性的检验指标,分析了采样数据的分布情况,提出了多种样本数据的检验方法,并给出了检验步骤和相应的数学模型,保证了土地级别划分的合理性。     

城市规划与城镇土地定级相互作用机理研究

城市规划与城镇土地定级之间的相互影响是多方面的。一是城市规划纲要、城市总体规划以及控制性详细规划的编制,分别从宏观和微观两方面影响着城镇土地定级工作;而城市规划的不同实施方式和不同实施程度对具体地块土地级别以及城镇土地级别空间分布格局有着直接和较大的影响。二是城镇土地级别现状和城镇土地级别变化趋势分析,潜在影响着城市规划方案设计、城市规划方案决策、城市规划实施和管理过程,并为其提供依据和指导。在此基础上,如何量化城市规划对城镇土地定级的影响值得进一步具体研究;发挥城镇土地定级工作的作用,更好地为城市规划服务,解决人们的住房问题,更值得深入思考。     

灰关联分析在土地可持续利用评价中的应用

土地既是一个社会经济综合体,又是一个自然历史综合体.土地可持续利用评价必然涉及社会、经济、资源、环境等方面.鉴于土地可持续利用评价的灰色性和不确定性,建立基于灰关联分析模型,将灰关联分析应用于土地可持续利用评价中.该模型以一种新的方法进行土地可持续利用评价,并结合实例进行应用.通过与综合指标法相比较,进一步验证该模型在土地可持续利用评价中的合理性和可行性,在实践上具有一定的应用价值.     

土地利用变化的数学模型解析

归纳了目前在研究土地利用变化中广泛采用的一些数学模型，解析了每种模型的涵义和意义。按照模型反映的内容不同，将其分为三大类：土地资源数量变化模型、土地资源质量变化模型和土地资源空间变化模型，总结了每类模型的特点和不足之处。     

巢湖流域SRTM高程误差的空间分异研究

数字高程模型是进行地学研究的重要基础数据,SRTM、DEM是公众可以免费获取的精度达90m的DEM数据.以我国1:5万DEM为参考数据,以巢湖流域为研究区,利用高程中误差模型和单位面积土地填挖方对SRTM、DEM进行高程精度研究,并分析了高程误差的空间分异规律.结果表明,SRTM、DEM的高程中误差和单位面积土地填挖方随着地貌类型的变化呈现明显的空间分异性,这主要由于SRTM像元精度不高所致.在研究区内的33块样区中,绝大部分都是在二级精度评价标准之内,可见SRTM、DEM整体精度较高.     

黄河流域城市土地利用效率与土地财政、经济增长影响研究基于面板联立方程和门槛模型北大核心

为了探索土地利用效率与土地财政、经济增长三者之间的影响关系,选取黄河流域2009—2020年的面板数据进行实证分析,使用包含非期望产出的三阶段Super SBM-SFA模型测度城市土地利用效率,构建面板联立方程模型和门槛模型分析土地财政、经济增长与城市土地利用效率之间的影响机制。结果表明:①土地财政对黄河流域城市土地利用效率产生显著抑制作用,经济增长对土地利用效率产生显著促进作用,且经济增长还可以通过促进土地财政影响城市土地利用效率,但土地财政却不能通过经济增长影响城市土地利用效率。②土地财政在影响经济增长时存在土地利用效率的双门槛效应,证实土地财政与经济增长之间存在非线性关系和区域差异性。③黄河流域上下游的经济增长与土地财政具有互相促进作用,但中游的土地财政却不能有效促进经济增长。黄河流域内各城市要认清土地财政的负面影响,改革土地市场,借助市场手段实现土地要素对经济增长和土地利用效率的促进作用,建立以生态文明为主的土地财政模式。     

基于决策树技术的城市建设用地信息提取方法研究

以2006年中巴多波段遥感影像为数据源,采用决策树分类技术对济南市城市建设用地信息进行提取.在建立研究区先验知识的基础上,对城市建设用地及不同类型背景地物的光谱响应特征进行了统计,建立了基于地物光谱特征的决策树分类模型与分类知识库,然后进行图像分类,最后经过精度评价,发现该方法能有效地将城市建设用地信息提取出来并具有较高的分类精度与效率.     

成都市可持续发展测度与分析

生态足迹是一种定量测量人类对自然利用程度的方法，已经被广泛应用于定量测度区域或城市的可持续发展。成都地处我国西部，是西部大开发的战略高地和“桥头堡”，对西部地区的发展起着示范、先导、辐射和带动作用。采用生态足迹理论和计算模型对成都市各类土地面积进行量化，具体计算出成都市的生态承载力、供需结构。计算结果表明，2003年其人均生态足迹超过了它的人均生态承载力，生态经济系统存在生态赤字，必须采取措施提高生态承载力，减少生态压力，探讨可持续发展的道路。     

PLANNING METHODOLOGY FOR ANALYSIS AND MANAGEMENT ON LAKE EUTROPHICATION1

ABSTRACT: The effects of changing nutrient inputs through land use management, waste water treatment, or effluent diversion are not clear, and managers are discovering that decisions which were effective in reversing eutrophication for one lake are often unsuccessful when applied to another. Simple empirical relationships are often used to predict the impact of management decisions. Errors in estimation could result in either substantial costs for overdesign or failure to meet desired eutrophication levels. This paper presents and illustrates a methodology to evaluate the impact of land use and water resource management decisions on lake eutrophication. The problems of worth of additional information, and uncertainty of estimates were handled within a cost-effectiveness framework. The probability of exceeding a critical level of eutrophication was considered as a measure of effectiveness. The cost criterion is the expected value of opportunity costs, costs of analysis and costs of additional information. Uncertainty analysis techniques were used to estimate the effectiveness of various management alternatives. Bayesian methods can be utilized to determine the worth of additional information. The methodology was applied to Beseck Lake, Connecticut, and the cost and effectiveness measures estimated for a number of land management alternatives. Worth of additional information was not determined in this initial effort in uncertainty analysis for lake eutrophication management.     

Roles of land use resolution and unit-area load rates in assessment of diffuse nutrient emissions

In contrast to its counterparts in Europe and North America, the Australian National Pollutant Inventory (NPI) includes estimates of aggregated emissions of nutrients (total nitrogen and total phosphorus) from catchments and facilities. Sparse or inadequate data limit the extent to which nutrient exports may be estimated from direct observations. The paucity of data for calibration and simulation limits the use of sophisticated models in most Australian catchments. Therefore, a simple unit-area load model-Catchment Management Support System (CMSS)-was selected to estimate aggregated catchment emissions for the NPI. Estimates from models like CMSS are sensitive to spatial and categorical resolution of land uses identified within the catchment and to nutrient generation rates selected for each land use category. Using three Hawkesbury-Nepean subcatchments, we show that while high spatial resolution of land use mapping is useful, only four or five major land use categories with carefully selected generation rates were required to estimate potential nutrient exports sufficiently well and to determine subcatchments contributing most. Nutrient emission estimates proved to be highly dependent on selection of generation rates so a bootstrap technique was adopted to reduce subjectivity and to improve estimates of confidence limits. This led to a specification of new generation rates for Natural, Unimproved pasture, Rural and Urban land uses and to establishment of uncertainty limits.     

Improving National-Scale Carbon Stock Inventories Using Knowledge on Land Use History

National-scale inventories of soil organic carbon (SOC) and forest floor carbon (FFC) stocks have a high uncertainty. Inventories are often based on the interpolation of sampled information, often using a number of covariables to help such interpolation. The rationale for the choice of these covariables is not always documented, despite the fact that many local-scale studies have identified the factors explaining spatial variability of SOC and FFC stocks. These studies indicate, among others the importance of long-term land use history. Despite this, information on the effects of land use history has never been used to explain variability of carbon stocks in national-scale inventories. We designed an alternative method to improve national-scale inventories of SOC and FCC for the Dutch sand area that takes stock of the findings of detailed case studies. Determinants for SOC and FFC stocks derived from landscape-scale case studies were used to map national-scale spatial variability and to calculate national totals. The resulting national-scale spatial distribution was compared with the SOC stock map from the current Dutch greenhouse gas inventory. Using land use history to explain SOC variability decreased the error of the SOC stock estimate in 60 % of the area. The error in FFC stocks decreased in half of the forest area after including soil fertility, tree species, and forest age as explanatory factors. Estimates with reduced uncertainty will make land use and land management a more attractive and acceptable mitigation option to reduce emissions of greenhouse gases for the LULUCF sector.     

PROBABILISTIC METHODS IN STREAM QUALITY MANAGEMENT1

ABSTRACT. Recent advances in water quality modelling have pointed out the need for stochastic models to simulate the probabilistic nature of water quality. However, often all that is needed is an estimate of the uncertainty in predicting water quality variables. First order analysis is a simple method of providing an estimate in the uncertainty in a deterministic model due to uncertain parameters. The method is applied to the simplified Streeter-Phelps equations for DO and BOD; a more complete Monte Carlo simulation is used to check the accuracy of the results. The first order analysis is found to give accurate estimates of means and variances of DO and BOD up to travel times exceeding the critical time. Uncertainty in travel time and the BOD decay constant are found to be most important for small travel times; uncertainty in the reaeration coefficient dominates near the critical time. Uncertainty in temperature was found to be a negligible source of uncertainty in DO for all travel times.     

A framework for assessing the impact of land use policy on community exposure to air toxics

Our research focuses on the linkage between land use planning policy and the spatial pattern of exposure to air toxics emissions. Our objective is to develop a modeling framework for assessment of the community health risk implications of land use policy. The modeling framework is not intended to be a regulatory tool for small-scale land use decisions, but a long-range planning tool to assess the community health risk implications of alternative land use scenarios at a regional or subregional scale. This paper describes the development and application of an air toxic source model for generating aggregate emission factors for industrial and commercial zoning districts as a function of permitted uses. To address the uncertainty of estimating air toxics emission rates for planned general land use or zoning districts, the source model uses an emissions probability mass function that weights each incremental permitted land use activity by the likelihood of occurrence. We thus reduce the uncertainty involved in planning for development with no prior knowledge of the specific industries that may locate within the land use district. These air toxics emission factors can then be used to estimate pollutant atmospheric mass flux from land use zoning districts, which can then be input to air dispersion and human health risk assessment models to simulate the spatial pattern of air toxics exposure risk. The model database was constructed using the California Air Toxics Inventory, 1997 US Economic Census, and land assessment records from several California counties. The database contains information on more than 200 air toxics at the 2-digit Standard Industrial Classification (SIC) level. We present a case study to illustrate application of the model. LUAIRTOX, the interactive spreadsheet model that applies our methodology to the California data, is available at http://www2.bren.ucsb.edu/~mwillis/LUAIRTOX.htm.     

Forecasting the number of soil samples required to reduce remediation cost uncertainty

Sampling scheme design is an important step in the management of polluted sites. It largely controls the accuracy of remediation cost estimates. In practice, however, sampling is seldom designed to comply with a given level of remediation cost uncertainty. In this paper, we present a new technique that allows one to estimate of the number of samples that should be taken at a given stage of investigation to reach a forecasted level of accuracy. The uncertainty is expressed both in terms of volume of polluted soil and overall cost of remediation. This technique provides a flexible tool for decision makers to define the amount of investigation worth conducting from an environmental and financial perspective. The technique is based on nonlinear geostatistics (conditional simulations) to estimate the volume of soil that requires remediation and excavation and on a function allowing estimation of the total cost of remediation (including investigations). The geostatistical estimation accounts for support effect, information effect, and sampling errors. The cost calculation includes mainly investigation, excavation, remediation, and transportation. The application of the technique on a former smelting work site (lead pollution) demonstrates how the tool can be used. In this example, the forecasted volumetric uncertainty decreases rapidly for a relatively small number of samples (20-50) and then reaches a plateau (after 100 samples). The uncertainty related to the total remediation cost decreases while the expected total cost increases. Based on these forecasts, we show how a risk-prone decision maker would probably decide to take 50 additional samples while a risk-averse decision maker would take 100 samples.     

Interlaboratory comparisons in EMF survey measurements

This article presents methods and results of interlaboratory comparison in the electromagnetic field (EMF) survey measurements performed by Electromagnetic Environment Protection Laboratory. Based on the results, the author analyzed factors affecting the precision of EMF measurements, in particular—difficult to estimate—the “human factor”. In practice, the human factor has never been taken into account in the EMF measurements accuracy estimations budget, and the author estimate the importance of this factor that may include even up to half of the total uncertainty of measurements. Taking into account all factors depredating the accuracy of measurements, the uncertainty of the survey EMF measurements was estimated at the level of 2–4 dB.     

Estimating the Variability and Confidence of Land Use and Imperviousness Relationships at a Regional Scale1

Abstract: Impervious cover is a commonly used metric to help explain or predict anthropogenic impacts on aquatic resources; often it is used as a surrogate for intensity of human impacts when evaluating effects on aquatic resources. The most common way to estimate imperviousness is based on relationships with land use. Few studies have evaluated how the relationship between impervious surface and land use varies among geographies with different levels of development and between types of imagery used to assign land use type. In this study, we assess variability in estimates of imperviousness based on two locally available land use datasets: one based on aerial imagery (2‐m resolution) and another based on satellite imagery (30‐m resolution). The ranges and variability in imperviousness within land use categories were assessed at several spatial scales, including within counties, between counties, and between watersheds. Results indicate that there was considerable variability for all developed land use types. Estimated impervious cover often varied over a range of 20‐40% points within a land use category. Furthermore, there were clear spatial patterns both between and within counties, with impervious cover for a given land use type being higher near the urban centers and lower at the margins of development. Estimates of imperviousness for 12 study watersheds indicated that variability increased with increasing watershed development, making it difficult to confidently set management or regulatory targets based on impervious cover. This study suggests that locally derived, high resolution satellite or aerial imagery should be used to estimate imperviousness when a high level of accuracy and precision is required for regulatory or management decisions. Furthermore, the error associated with impervious land use relationships should be accounted for when using impervious cover in runoff or water quality models, or when making management decisions regarding stream health.     

Land Resources Allocation Strategies in an Urban Area Involving Uncertainty: A Case Study of Suzhou,in the Yangtze River Delta of China

A large number of mathematical models have been developed to support land resource allocation decisions and land management needs; however, few of them can address various uncertainties that exist in relation to many factors presented in such decisions (e.g., land resource availabilities, land demands, land-use patterns, and social demands, as well as ecological requirements). In this study, a multi-objective interval-stochastic land resource allocation model (MOISLAM) was developed for tackling uncertainty that presents as discrete intervals and/or probability distributions. The developed model improves upon the existing multi-objective programming and inexact optimization approaches. The MOISLAM not only considers economic factors, but also involves food security and eco-environmental constraints; it can, therefore, effectively reflect various interrelations among different aspects in a land resource management system. Moreover, the model can also help examine the reliability of satisfying (or the risk of violating) system constraints under uncertainty. In this study, the MOISLAM was applied to a real case of long-term urban land resource allocation planning in Suzhou, in the Yangtze River Delta of China. Interval solutions associated with different risk levels of constraint violation were obtained. The results are considered useful for generating a range of decision alternatives under various system conditions, and thus helping decision makers to identify a desirable land resource allocation strategy under uncertainty.