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.     

矿区生态环境遥感监测数据库系统设计与实现

以肥城矿区为背景,基于Personal Geodatabase模型构建了空间数据库,采用C#作为开发语言,借助ArcGIS Engine的控件开发了矿区生态环境遥感监测数据库系统.根据数据获取方式不同,将空间数据库分为基础地理数据库、专题数据库、实地监测数据库和表格数据库4部分,系统实现了矿区空间数据和属性数据的统一存储和管理,多时相遥感数据的对比分析,水质、土壤湿度的动态监测,矿区各类地物面积的统计分析等功能.     

Projecting Climate Change Effects on Forest Net Primary Productivity in Subtropical Louisiana,USA

This study projected responses of forest net primary productivity (NPP) to three climate change scenarios at a resolution of 5 km × 5 km across the state of Louisiana, USA. In addition, we assessed uncertainties associated with the NPP projection at the grid and state levels. Climate data of the scenarios were derived from Community Climate System Model outputs. Changes in annual NPP between 2000 and 2050 were projected with the forest ecosystem model PnET-II. Results showed that forest productivity would increase under climate change scenarios A1B and A2, but with scenario B1, it would peak during 2011–2020 and then decline. The projected average NPP under B1 over the years from 2000 to 2050 was significantly different from those under A1B and A2. Forest NPP appeared to be primarily a function of temperature, not precipitation. Uncertainties of the NPP projection were due to large spatial resolution of the climate variables. Overall, this study suggested that in order to project effects of climate change on forest ecosystem at regional level, modeling uncertainties could be reduced by increasing the spatial resolution of the climate projections.     

农村土地流转影响因素的理论与实证研究——基于农民阶层分化与产权偏好的视角

随着我国经济的发展和社会转型,农民群体逐渐分化为不同职业的农民阶层,由于职业选择的不同以及生活环境的变化,农民在经济收入、产权偏好等方面都出现了巨大的差异,这导致他们对土地流转意愿与决策行为的选择也出现分化,因此,研究由于农民职业分化导致的产权偏好不同及对土地流转的影响就具有重要现实意义.本文利用笔者对江苏省实地问卷调查所得的数据,在理论分析与研究假说的基础上,运用Lagistic模型对农民职业分化、产权偏好与农村土地流转的关系进行了实证分析.研究结果表明:文化程度、职业类别、非农收入比重、是否具有非农就业技能、是否拥有转让权、地权稳定性、地区虚拟变量对土地流转有正向影响,年龄则对土地流转有负向影响,而农业劳动力人数、土地的养老保障作用和是否具有抵押权则对土地流转决策没有显著影响.基于上述研究结论,本文提出了应强化土地产权、完善产权结构、大力发展非农产业和提供非农就业机会、提高农村劳动力职业技术素质、逐步建立和完善农村社会保障体系等政策建议.     

Contaminants in the coastal karst aquifer system along the Caribbean coast of the Yucatan Peninsula, Mexico

Intensive land development as a result of the rapidly growing tourism industry in the “Riviera Maya” region of the Yucatan Peninsula, Mexico may result in contamination of groundwater resources that eventually discharge into Caribbean coastal ecosystems. We deployed two types of passive sampling devices into groundwater flowing through cave systems below two communities to evaluate concentrations of contaminants and to indicate the possible sources. Pharmaceuticals and personal care products accumulated in the samplers could only have originated from domestic sewage. PAHs indicated contamination by runoff from highways and other impermeable surfaces and chlorophenoxy herbicides accumulated in samplers deployed near a golf course indicated that pesticide applications to turf are a source of contamination. Prevention and mitigation measures are needed to ensure that expanding development does not impact the marine environment and human health, thus damaging the tourism-based economy of the region.     

A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays

The power-voltage (P-V) characteristic curves of a PV array are nonlinear and have multiple peaks under partially shaded conditions (PSCs). This paper proposes a novel maximum power point tracking (MPPT) method for a PV system with reduced steady-state oscillation based on a two-stage particle swarm optimization (PSO) algorithm. The grouping method of the shuffled frog leaping algorithm (SFLA) is incorporated in the basic PSO algorithm (PSO-SFLA), ensuring fast and accurate searching of the global extremum. An adaptive speed factor is also introduced into the improved PSO to further enhance its convergence speed. Test results show that the proposed method converges in less than half the time taken by the conventional PSO method, and the power is improved by 33% under the worst PSCs, which confirms the superiority of the proposed method over the standard PSO algorithm in terms of tracking speed and steady-state oscillations under different PSCs.     

From land cover change to land function dynamics: a major challenge to improve land characterization

Land cover change has always had a central role in land change science. This central role is largely the result of the possibilities to map and characterize land cover based on observations and remote sensing. This paper argues that more attention should be given to land use and land functions and linkages between these. Consideration of land functions that provide a wide range of goods and services makes more integrated assessments of land change possible. The increasing attention to multifunctional land use is another incentive to develop methods to assess changes in land functions. A number of methods to quantify and map the spatial extent of land use and land functions are discussed and the implications for modeling are identified based on recent model approaches in land change science. The mixed use of land cover, land use and land function in maps and models leads to inconsistencies in land change assessments. Explicit attention to the non-linear relations between land cover, land use and land function is essential to consistently address land change. New methods to map and quantify land function dynamics will enhance our ability to understand and model land system change and adequately inform policies and planning.     

Woodland restoration in Scotland: ecology, history, culture, economics, politics and change

In the latter half of the 20th century, native pine woodlands in Scotland were restricted to small remnant areas within which there was little regeneration. These woodlands are important from a conservation perspective and are habitat for numerous species of conservation concern. Recent developments have seen a large increase in interest in woodland restoration and a dramatic increase in regeneration and woodland spread. The proximate factor enabling this regeneration is a reduction in grazing pressure from sheep and, particularly, deer. However, this has only been possible as a result of a complex interplay between ecological, political and socio-economic factors. We are currently seeing the decline of land management practices instituted 150-200 years ago, changes in land ownership patterns, cultural revival, and changes in societal perceptions of the Scottish landscape. These all feed into the current move to return large areas of the Scottish Highlands to tree cover. I emphasize the need to consider restoration in a multidisciplinary framework which accounts not just for the ecology involved but also the historical and cultural context.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

How good is GLASOD?

The Global Assessment of Soil Degradation (GLASOD) has been the most influential global appraisal of land quality in terms of environmental policy. However, its expert judgments were never tested for their consistency and could not be reproduced at unvisited sites, while the relationship between the GLASOD assessments of land degradation and the social and economic impact of that degradation remains unclear. Yet, other methodologies that could respond to urgent calls for an updated assessment of the global environmental quality are not operational or, at best, in progress. Therefore, we evaluate the reliability and social relevance of the GLASOD approach and assess its candidacy for new global environmental assessments. The study concentrates on the African continent, capitalizing on new GIS data to delineate and define the characteristics of GLASOD map units. Consistency is tested by comparing expert judgments on soil degradation hazard for similar combinations of biophysical conditions and land use. Reproducibility is evaluated by estimating an ordered logit model that relates the qualitative land degradation classes to easily available information on explanatory variables, the results of which can be used to assess the land degradation at unvisited sites. Finally, a cross-sectional analysis investigates the relation between GLASOD assessments and crop production data at sub-national scale and its association with the prevalence of malnutrition. The GLASOD assessments prove to be only moderately consistent and hardly reproducible, while the counter-intuitive trend with crop production reveals the complexity of the production-degradation relationship. It appears that increasing prevalence of malnutrition coincides with poor agro-productive conditions and highly degraded land. The GLASOD approach can be improved by resolving the differences in conceptualization among experts and by defining the boundaries of the ordered classes in the same units as independent, quantitative land degradation data.