土地利用生态服务价值指标体系评估结果比较研究

生态系统服务价值评估越来越受到学术界关注。针对当前生态服务价值评估方法、指标体系和评估效果较多的实践,通过文献归纳,对国内外土地利用生态服务价值评价方法、指标体系及其系数进行了梳理、分类和评价,将指标体系分为Costanza和谢高地两大类,并将谢高地指标体系分为价值当量、价值系数和区域修正。以辽宁省2005、2010年土地利用数据为案例,运用Costanza价值系数、谢高地两个版本价值当量、两个版本价值系数的区域修正,分别核算了案例区生态系统服务价值及变化,并比较评价结果。研究得出Costanza价值系数应用于中国实践时评估结果显得偏低,谢高地2002版的价值当量和2002版的价值系数区域修正结果最接近平均值,相对较为可取,2007版的价值系数区域修正评估结果也较为接近均值但略低,2007版的价值当量评估结果总体偏高。研究结果有利于指导土地利用生态服务价值评估指标体系选取。     

基于SD和CLUE-S模型的区域土地利用变化对土壤有机碳储量影响研究

土地利用/土地覆被变化改变土壤呼吸条件,进而对土壤有机碳储量变化产生影响,而土壤有机碳储量则是影响农业可持续发展和全球碳平衡领域的重要因素。以上海市崇明岛为例,运用系统动力学模型(System Dynamics Model)预测2020、2030年土地利用需求变化,结合CLUE-S模型(Conversion of Land Use and its Effects at Small region extent Model)得出各种用地类型的空间分布,并引用碳密度法估算三种发展幕景下土地利用变化对土壤有机碳储量的影响。结果表明:2030年三种发展幕景土壤有机碳储量分别为:低速发展幕景为3 093.03×106kg,惯性发展幕景为3 079.47×106kg,高速发展幕景为3 059.81×106kg;研究期内土壤有机碳储量呈现缓慢下降趋势,但人类活动对其扰动较小;SD和CLUE-S耦合模型可以从时间和空间两方面对土壤有机碳储量进行模拟,具有可行性;建议通过加强城镇用地集约利用、农田保护、林地建设来减少人为活动对土壤有机碳储量的影响。     

环境约束下大都市城郊土地利用结构评价——基于信息熵和非期望产出模型分析

土地利用过程中会产生期望和非期望产出,针对以往土地利用结构评价中忽略环境产生的非期望产出而影响土地利用结构效率真实性问题,以大都市城郊的上海青浦区为例,宏观上,运用信息熵模型对该区2005~2014年土地利用结构有序性进行评价;微观上,运用非期望产出模型测算青浦区研究期间土地利用结构效率,为寻求区域土地利用结构效率损失的原因及改善途径提供参考。结果表明:(1)2005~2014年青浦区土地利用结构信息熵值时序上呈现"M"形变化趋势,空间上由中部向东西两翼递减;(2)2005~2014年青浦区土地利用结构效率变化趋势在时间序列上呈现"W"形变化趋势,空间上由中部的中心城区向东西两翼递减格局;(3)将环境的负产出纳入非期望产出模型进行土地利用结构效率评价,能更加准确的反映土地利用结构效率的真实性;(4)影响青浦区土地利用结构效率水平变化的主要因素为纯技术效率的变化,资源过度消耗和环境污染物排放过量是制约青浦区土地利用结构效率提升的主要原因。针对土地利用结构效率损失的原因及区域差异提出效率提升途径。研究结果可为青浦区土地利用结构优化和可持续发展提供科学借鉴。     

青藏高原生活污水处理及回用的工艺研究

在高寒缺氧的青藏高原如何对生活污水进行有效的处理,目前仍无好的解决方案。实验采用的生态大棚系统可在青藏高原的恶劣环境下提供适宜的人工小环境,保证生物处理正常运行所必需的热平衡、O2-CO2的循环平衡和水循环的平衡。采用的好氧生物-土地处理工艺不仅能保证污水处理达到国家一级排放标准,同时还解决了长期以来高原蔬菜种植的难题。     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?

Seventy-two squares of 100 ha were selected by stratified random sampling with probabilities proportional to size (pps) to survey landscape changes in the period 1996–2003. The area of the plots times the urbanization pressure was used as a size measure. The central question of this study is whether the sampling with probabilities proportional to size leads to gain in precision compared to equal probability sampling. On average 1.03 isolated buildings per 100 ha have been built, while 0.90 buildings per 100 ha have been removed, leading to a net change of 0.13 building per 100 ha. The area with unspoiled natural relief has been reduced by 2.3 ha per 100 ha, and the length of linear relicts by 137 m per 100 ha. On average 74 m of linear green elements have been planted per 100 ha, while 106 m have been removed, leading to a net change of −31 m per 100 ha. For the state variables ‘unspoiled natural relief', ‘ linear relicts', ‘removed linear green elements', and ‘new – removed linear green elements' there is a gain in precision due to the pps-sampling. For the remaining state variables there is no gain or even a loss of precision (`new buildings', ‘removed buildings', ‘new – removed buildings', ‘new linear green elements'). Therefore, if many state variables must be monitored or when interest is not only in the change but also in the current totals, we recommend to keep things simple, and to select plots with equal probability.     

Estimation of Land Use Specific Runoff and Pollutant Concentration for Tapi River Basin in India

Non-point source (NPS) pollution is the result of various land use practices such as agriculture, sites of construction and waste disposal, urban development and so on. The control of NPS pollution is possible by regular monitoring and assessment on watershed basis to educate people for implementing well-known structural and non-structural measures. Recent trend is to use GIS based modelling tool for assessment of rainfall-runoff and non-point loading. The approach requires generation and analysis of basin wide data on various features of land and estimates of Event Mean Concentrations (EMCs) of pollutants in the runoff. In the present paper, basin wide data in different districts of Tapi basin has been analysed for land use distribution; fertilizer application; low, medium and high-density habitation; and annual rainfall. Coefficients of runoff have been estimated considering pervious and impervious area for different land use types, and compared with the reported values for Indian conditions. The estimated mean annual runoff flow indicated that two districts Jalgaon and Dhule contribute maximum runoff to the Tapi River. Estimates of EMCs for BOD and nutrients (N and P) in the runoff from various districts are useful in GIS-based modelling study for NPS pollution assessment.     

中国城镇建设用地经济密度的区位差异及影响因素——基于273个地级及以上城市的分析

以全国地级及以上城市中的273个样本城市为研究对象,分别从全国、沿海地区、内陆地区三个层次考察了样本城市城镇建设用地经济密度的区位差异。根据柯布-道格拉斯生产函数的理论与分析方法建立模型,分析了资本、劳动力、土地三大投入要素和城市全要素生产率等对城镇建设用地的影响,并在控制以上变量的条件下侧重考察了城市区位差异的影响。发现我国城镇建设用地经济密度的区位差异明显,具有区位优势的城市土地效益平均高于缺乏区位优势的城市;沿海水陆口岸的区位优势十分显著,建设用地经济密度相对较高,而内陆地区水陆口岸的区位优势有待发掘;航空口岸对提高城镇建设用地经济密度未发挥积极作用。分析结果表明,生产要素投入、科技水平、市场环境和城市发展阶段是影响土地效益的主要因素,但不同区位条件下城镇建设用地经济密度的影响因素有所不同,为提高城镇建设用地经济密度,不同区位条件的城市着力点应各有侧重。     

Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery1

Claggett, Peter R., Judy A. Okay, and Stephen V. Stehman, 2010. Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery. Journal of the American Water Resources Association (JAWRA) 46(2):334-343. DOI: 10.1111/j.1752-1688.2010.00424.x Abstract: The Chesapeake Bay watershed encompasses 165,760 km² of land area with 464,098 km of rivers and streams. As part of the Chesapeake Bay restoration effort, state and federal partners have committed to restoring 26,000 miles (41,843 km) of riparian forest buffers. Monitoring trends in riparian forest buffers over large areas is necessary to evaluate the efficacy of these restoration efforts. A sampling approach for estimating change in riparian forest cover from 1993/1994 to 2005 was developed and implemented in Anne Arundel County, Maryland, to exemplify a method that could be applied throughout the Bay watershed. All stream reaches in the county were stratified using forest cover change derived from Landsat imagery. A stratified random sample of 219 reaches was selected and forest cover change within the riparian buffer of each sampled reach was interpreted from high-resolution aerial photography. The estimated footprint of gross change in riparian forest cover (i.e., the sum of gross gain and gross loss) for the county was 1.83% (SE = 0.22%). Stratified sampling taking advantage of a priori knowledge of locations of change proved to be a practical and efficient protocol for estimating riparian forest buffer change at the county scale and the protocol would readily extend to much broader scale monitoring.     

Evaluation of the National Land Cover Database for Hydrologic Applications in Urban and Suburban Baltimore,Maryland1

Smith, Monica Lipscomb, Weiqi Zhou, Mary Cadenasso, Morgan Grove, and Lawrence E. Band, 2010. Evaluation of the National Land Cover Database for Hydrologic Applications in Urban and Suburban Baltimore, Maryland. Journal of the American Water Resources Association (JAWRA) 46(2):429-442. DOI: 10.1111/j.1752-1688.2009.00412.x Abstract: We compared the National Land Cover Database (NLCD) 2001 land cover, impervious, and canopy data products to land cover data derived from 0.6-m resolution three-band digital imagery and ancillary data. We conducted this comparison at the 1 km², 9 km², and gauged watershed scales within the Baltimore Ecosystem Study to determine the usefulness and limitations of the NLCD in heterogeneous urban to exurban environments for the determination of land-cover information for hydrological applications. Although the NLCD canopy and impervious data are significantly correlated with the high-resolution land-cover dataset, both layers exhibit bias at <10 and >70% cover. The ratio of total impervious area and connected impervious area differs along the range of percent imperviousness – at low percent imperviousness, the NLCD is a better predictor of pavement alone, whereas at higher percent imperviousness, buildings and pavement together more resemble NLCD impervious estimates. The land-cover composition and range for each NLCD urban land category (developed open space, low-intensity, medium-intensity, and high-intensity developed) is more variable in areas of low-intensity development. Fine-vegetation land-cover/lawn area is incorporated in a large number of land use categories with no ability to extract this land cover from the NLCD. These findings reveal that the NLCD may yield important biases in urban, suburban, and exurban hydrologic analyses where land cover is characterized by fine-scale spatial heterogeneity.