云南省热区农业气候、土地资源图的编制

本文采用农业气候分类与土地特征(地貌、坡度)分级相结合的方法编制云南省热区图。这是用一张图,在同一块图斑上同时表示出农业气候和土地两种专业内容特征;按土地和气候的本来面目分型划类,并量算出各类型分区域的土地面积。这是一次新的尝试,以期使科学性和实用性能较好地结合起来。     

试论根据土壤生产潜力及土地利用强度评价土地利用合理性问题——以加拿大渥太华市郊为例

土地利用的合理性取决于土地利用强度是否与土壤生产潜力相适宜。土地利用强度等级与土壤生产潜力级别的一致性是一地区土地利用合理性的标志。依据作物系统编制的农业土地利用系统图较传统的土地覆盖图有更大的实用性。可利用该图与土壤生产潜力图迭加制成的农业资源分区图来分析1978—1987年渥太华市郊土地利用的合理性。结果指出,渥太华市郊土地利用方式基本上是合理的,变化趋势也是适宜的,但同时有不足之处,建议当地市政当局对某些不合理的土地利用方式做相应调整。     

Environmental Assessment of Water, Sediment and Plants in the Mankyeong River, ROK

This study was carried out to evaluate water quality, sediment and plant vegetation in eight tributaries of the Mankyeong River for enhancement of natural purification. Among the tributaries, the Iksancheon water had the highest concentration of BOD, T–N and NH₄–N due to inflow of swine wastes from the livestock district. The Yucheon water had the highest level of electrical conductivity and SO ₄ ^2– due to inflow of mis-treated wastewater from industrial districts. The Tabcheon had generally similar concentrations of nitrogen and phosphate to that of the upstream of the Mankyeong River: agricultural activity along the Tabcheon appeared to have little negative influence to the water quality. Among various sediments, concentration of organic matter, nitrogen and phosphate were high in the Iksancheon and the Yucheon due to the livestock wastes and industrial wastes. There were 282 species of plants during summer with 43 aquatic plants, 57 hydrophytes, 178 waterside plants and 4 terrestrial plants. Some plant resources were recommended due to much absorption of nitrogen and phosphate for enhancement of natural purification. C. demersum and H. verticillata were recommended in the submerged aquatic plants, H. dubia, N. indica and N. subinteperrimum in the floating leaf aquatic plants, P. communis, Z. latifolia and T. orientalis in the emerged aquatic plants, C. scutata and P. distichum in the waterside plants.     

Modelling Replicated Weed Growth Data using Spatially-varying Growth Curves

Weed growth in agricultural fields constitutes a major deterrent to the growth of crops, often resulting in low productivity and huge losses for the farmers. Therefore, proper understanding of patterns in weed growth is vital to agricultural research. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data, which enable more sophisticated spatial analysis. Our current application concerns the development of statistical models for conducting spatial analysis of growth patterns in weeds. Our data comes from an experiment conducted in Waseca, Minnesota, that recorded growth of the weed Setariaspp. We capture the spatial variation in Setaria spp. growth using spatially-varying growth curves. An added challenge is that these designs are spatially replicated, with each plot being a lattice of sub-plots. Therefore, spatial variation may exist at different resolutions – a macro level variation between the plots and micro level variation between the sub-plots nested within each plot. We develop a Bayesian hierarchical framework for this setting. Flexible classes of models result which are fitted using simulation-based methods.     

Twenty years of salt marsh succession on a Dutch coastal barrier island

After a formerly grazed salt marsh was released from cattle grazing, changes in plant species composition were monitored for 20 yr, using vegetation maps and permanent plots. Three areas, differing in age and nutrient status were compared. The number of plant species and plant communities decreased.Elymus athericus (Elytrigia pungens) became dominant in most plant communities after 5–20 yr on the oldest and most productive salt marsh. In younger areas it took more time forE. athericus to become dominant. At least 7 cm of clay seemed to be a prerequisite for this plant species to increase in dominance. The results from monitoring over decades are discussed in view of the knowledge on succession over centuries as derived from a chronosequence.     

GIS and geostatistics: Essential partners for spatial analysis

Initially, geographical information systems (GIS) concentrated on two issues: automated map making, and facilitating the comparison of data on thematic maps. The first required high quality graphics, vector data models and powerful data bases, the second is based on grid cells that can be manipulated by suites of mathematical operators collectively termed map algebra. Both kinds of GIS are widely available and are taught in many universities and technical colleges. After more than 20 years of development, most standard GIS provide both kinds of functionality and good quality graphic display, but until recently they have not included the methods of statistics and geostatistics as tools for spatial analysis. Recently, standard statistical packages have been linked to GIS for both exploratory data analysis and statistical analysis and hypothesis testing. Standard statistical packages include methods for the analysis of random samples of cases or objects that are not necessarily co-located in space—if the results of statistical analysis display a spatial pattern then that is because the underlying data also share that pattern. Geostatistics addresses the need to make predictions of sampled attributes (i.e., maps) at unsampled locations from sparse, often expensive data. To make up for lack of hard data geostatistics has concentrated on the development of powerful methods based on stochastic theory. Though there have been recent moves to incorporate ancillary data in geostatistical analyses, insufficient attention has been paid to using modern methods of data display for the visualization of results. GIS can serve geostatistics by aiding geo-registration of data, facilitating spatial exploratory data analysis, providing a spatial context for interpolation and conditional simulation, as well as providing easy-to-use and effective tools for data display and visualization. The value of geostatistics for GIS lies in the provision of reliable interpolation methods with known errors, methods of upscaling and generalization, and for supplying multiple realizations of spatial patterns that can be used in environmental modeling. These stochastic methods are improving understanding of how errors in models of spatial processes accrue from errors in data or incompleteness in the structure of the models. New developments in GIS, based on ideas taken from map algebra, cellular automata and image analysis are providing high level programming languages for modeling dynamic processes such as erosion or the development of alluvial fans and deltas. Research has demonstrated that these models need stochastic inputs to yield realistic results. Non-stochastic tools such as fuzzy subsets have been shown to be useful for spatial analysis when probabilistic approaches are inappropriate or impossible. The conclusion is that in spite of differences in history and approach, the linkage of GIS, statistics and geostatistics provides a powerful, and complementary suite of tools for spatial analysis in the agricultural, earth and environmental sciences.     

城市安全生产画像构建与应用探讨

为使城市安全生产监管部门及时掌控安全生产状态,实现监管资源的合理调配与工作部署,本文针对城市安全生产领域监管系统多、数据量庞大、数据分析不深入等问题,利用城市安全生产画像的表现形式,对城市安全生产画像构建的各个环节进行针对性探讨;以某地安全监管部门为例,整合该地下属8个区域的执法检查和处罚数据,构建辅助当地安全生产监管力量部署的多维画像,并通过脸谱聚类图这种可视化形式来表征此地区监管力量的局部与总体特征。结果表明:城市安全生产画像,是一种有效的大数据分析手段,有助于监管部门迅速掌握城市整体及局部安全状况,预判安全趋势,合理部署监管资源,降低事故风险概率,显著提高城市安全生产管理水平。     

Stakeholder Management for Conservation Projects: A Case Study of Ream National Park, Cambodia1

The paper gives an account of the development and implementation of a stakeholder management framework at Ream National Park, Cambodia. Firstly, the concept of stakeholder is reviewed in management and in conservation literatures. Secondly, the context in which the stakeholder framework was implemented is described. Thirdly, a five-step methodological framework is suggested: (1) stakeholder analysis, (2) stakeholder mapping, (3) development of generic strategies and workplan, (4) presentation of the workplan to stakeholders, and (5) implementation of the workplan. This framework classifies stakeholders according to their level of influence on the project and their potential for the conservation of natural resources. In a situation characterized by conflicting claims on natural resources, park authorities were able to successfully develop specific strategies for the management of stakeholders. The conclusion discusses the implications of the Ream experience and the generalization of the framework to other protected areas.     

Assessing the efficiency gains of improved spatial targeting of policy interventions; the example of an agri-environmental scheme

GIS-based spatial targeting is increasingly recognised as a potentially useful tool to design more efficient policy interventions. The use of this tool has also been advocated in the context of incentive-based agri-environmental schemes, but there has been little work to date to estimate the level of efficiency gains which it may help to achieve. This paper investigates the requirements to arrive at such estimates, using a Scottish farm woodland scheme as a case study. This agri-environmental scheme aims to provide visual amenity and biodiversity. Maps of these two benefits are used to develop improved spatial targeting scenarios that deliver significant efficiency gains in comparison to the existing scheme design. The paper discusses the nature of the spatial distribution of the relevant benefits at the landscape scale and the data requirements for the realistic estimation of efficiency gains. It concludes that although much work needs to be done, the methods available today could and should play a much greater role in improving the landscape-scale design of existing land use schemes focused on the delivery of non-market benefits.     

Using hidden Markov chains and empirical Bayes change-point estimation for transect data

Consider a lattice of locations in one dimension at which data are observed. We model the data as a random hierarchical process. The hidden process is assumed to have a (prior) distribution that is derived from a two-state Markov chain. The states correspond to the mean values (high and low) of the observed data. Conditional on the states, the observations are modelled, for example, as independent Gaussian random variables with identical variances. In this model, there are four free parameters: the Gaussian variance, the high and low mean values, and the transition probability in the Markov chain. A parametric empirical Bayes approach requires estimation of these four parameters from the marginal (unconditional) distribution of the data and we use the EM-algorithm to do this. From the posterior of the hidden process, we use simulated annealing to find the maximum a posteriori (MAP) estimate. Using a Gibbs sampler, we also obtain the maximum marginal posterior probability (MMPP) estimate of the hidden process. We use these methods to determine where change-points occur in spatial transects through grassland vegetation, a problem of considerable interest to plant ecologists.