Indigenous Systems of Forest Classification: Understanding Land Use Patterns and the Role of NTFPs in Shifting Cultivators’ Subsistence Economies

This article discusses the system of classification of forest types used by the Pwo Karen in Thung Yai Naresuan Wildlife Sanctuary in western Thailand and the role of nontimber forest products (NTFPs), focusing on wild food plants, in Karen livelihoods. The article argues that the Pwo Karen have two methods of forest classification, closely related to their swidden farming practices. The first is used for forest land that has been, or can be, swiddened, and classifies forest types according to growth conditions. The second system is used for land that is not suitable for cultivation and looks at soil properties and slope. The article estimates the relative importance of each forest type in what concerns the collection of wild food plants. A total of 134 wild food plant species were recorded in December 2004. They account for some 80–90% of the amount of edible plants consumed by the Pwo Karen, and have a base value of Baht 11,505 per year, comparable to the cash incomes of many households. The article argues that the Pwo Karen reliance on NTFPs has influenced their land-use and forest management practices. However, by restricting the length of the fallow period, the Thai government has caused ecological changes that are challenging the ability of the Karen to remain subsistence oriented. By ignoring shifting cultivators’ dependence on such products, the involvement of governments in forest management, especially through restrictions imposed on swidden farming practices, is likely to have a considerable impact on the livelihood strategies of these communities.     

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.     

Soil erosion and non-point source pollution impacts assessment with the aid of multi-temporal remote sensing images

Soil erosion associated with non-point source pollution is viewed as a process of land degradation in many terrestrial environments. Careful monitoring and assessment of land use variations with different temporal and spatial scales would reveal a fluctuating interface, punctuated by changes in rainfall and runoff, movement of people, perturbation from environmental disasters, and shifts in agricultural activities and cropping patterns. The use of multi-temporal remote sensing images in support of environmental modeling analysis in a geographic information system (GIS) environment leading to identification of a variety of long-term interactions between land, resources, and the built environment has been a highly promising approach in recent years. This paper started with a series of supervised land use classifications, using SPOT satellite imagery as a means, in the Kao-Ping River Basin, South Taiwan. Then, it was designed to differentiate the variations of eight land use patterns in the past decade, including orchard, farmland, sugarcane field, forest, grassland, barren, community, and water body. Final accuracy was confirmed based on interpretation of available aerial photographs and global positioning system (GPS) measurements. Finally, a numerical simulation model (General Watershed Loading Function, GWLF) was used to relate soil erosion to non-point source pollution impacts in the coupled land and river water systems. Research findings indicate that while the decadal increase in orchards poses a significant threat to water quality, the continual decrease in forested land exhibits a potential impact on water quality management. Non-point source pollution, contributing to part of the downstream water quality deterioration of the Kao-Ping River system in the last decade, has resulted in an irreversible impact on land integrity from a long-term perspective.     

香港灌丛植被的数量分类与环境关系分析

灌丛是香港植被的主要组成部分,2005年1—3月在香港地区(包括香港岛、九龙、新界和大屿山岛)设置样地,利用组平均法(UPGMA)和除趋势对应分析法(DCA)对香港灌丛群落进行聚类和排序分析. 结果表明,灌丛植被可划分为7个群落类型:桃金娘、米碎花、岗松、苏铁蕨、大头茶、山油柑和细齿柃. DCA排序揭示了灌丛群落的分布格局与环境因子的生态关系,DCA排序第1轴反映了灌丛群落所在环境的湿度梯度,排序图的对角线指示出海拔梯度,也反映了温度的变化. 结合物种多样性分析发现,物种多样性指数随海拔的升高而减少,说明水热条件和海拔是制约灌丛植被分布格局的主要因素.      

基于层次分析法的有毒作业场所职业危害程度分级指标研究

我国目前职业危害形势依然严峻,急、慢性职业中毒案例时有发生。对可能引起急、慢性职业中毒的有毒作业场所,按照其职业危害严重程度实施分级、分重点监管,成为我国职业危害监管工作需要解决的迫切需求。实施有毒作业场所分级监管的关键,是确定合理的分级指标,从而将有毒作业场所区分开。本文应用层次分析法,对有毒作业场所的各项评价指标进行分析,并最终确定毒物浓度超标倍数作为最终的分级指标,从而为制定有毒作业场所分级标准,开展有毒作业场所分级监管提供科学的依据。     

乡镇垃圾分类循环利用现状——以闽侯县上街镇为例

由于我国乡镇经济的迅速发展以及乡镇村民生活水平加速提高,乡镇的垃圾种类及数量趋向于城市.乡镇村民居住较为分散,垃圾分类、收集、循环利用相较于城市困难,因此推进乡镇垃圾分类循环利用迫在眉睫.本文从闽侯县上街镇展开调查,以乡镇垃圾分类、回收循环利用为关键词展开分析,并从上街镇垃圾分类循环利用模式存在的问题进行分析、总结和建...     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Community participatory landscape classification and biodiversity assessment and monitoring of grazing lands in northern Kenya

In this study, we asked the Ariaal herders of northern Kenya to answer "why, what and how" they classified landscape, and assessed and monitored the biodiversity of 10 km(2) of grazing land. To answer the "why question" the herders classified grazing resources into 39 landscape patches grouped into six landscape types and classified soil as 'warm', 'intermediate' or 'cold' for the purpose of land use. For the "what question" the herders used soil conditions and vegetation characteristics to assess biodiversity. Plant species were described as 'increasers', 'decreasers' or 'stable'. The decreaser species were mostly grasses and forbs preferred for cattle and sheep grazing and the increasers were mostly woody species preferred by goats. The herders evaluated biodiversity in terms of key forage species and used absence or presence of the preferred species from individual landscapes for monitoring change in biodiversity. For the "how question" the herders used anthropogenic indicators concerned with livestock management for assessing landscape potential and suitability for grazing. The anthropogenic indicators were related to soils and biodiversity. The herders used plant species grazing preferences to determine the links between livestock production and biodiversity. By addressing these three questions, the study shows the value of incorporating the indigenous knowledge of herders into classification of landscape and assessment and monitoring of biodiversity in the grazing lands. We conclude that herder knowledge of biodiversity is related to the use as opposed to exclusive conservation practices. This type of knowledge is extremely valuable to conservation agencies for establishing a baseline for monitoring changes in biodiversity in the future.     

上海郊区小型沼气发展的前景与方向

文章介绍了上海郊区沼气开发利用的发展历程和现状,分析了上海沼气发展萎缩的原因,指出上海在节能减排的大形势、寻找合理的农村生活垃圾处理出路和解决小型禽畜养殖场的环境问题等压力下,对郊区的沼气发展产生了新的需求。而以小型养殖场为中心、吸纳农村有机生活垃圾的小型沼气系统则是上海郊区未来沼气发展的方向,也是新农村环境整治的可行途径。     

化学品危险性鉴别分类研究的现状与展望

从对GHS(化学品分类及标记全球协调制度)化学品危险性鉴别分类的分析出发,归纳和总结了联合国、欧盟、美国以及中国的化学品危险性鉴别分类研究现状;提出化学品危险性鉴别分类是获取化学品安全信息、评估化学品的固有危险性的重要手段;给出化学品危险性鉴别分类试验必须满足OECD(经济合作与发展组织)的各项标准和准则;对化学品危险性鉴别分类的应用前景及未来工作进行评述和展望;指出为化学品安全管理、事故应急救援、事故调查及做好基础数据的积累是今后研究工作的努力方向;提出了借鉴GHS,REACH(化学品注册、评估、许可和限制)等先进化学品安全监管法规,对完善我国的化学品安全监管制度,化学品鉴别分类标准的重要现实意义。