Detection of Coastal Saline Land Uses with Multi-Temporal Landsat Images in Shangyu City,China

Many coastal regions in China are confronted with pressing problems of scarce land resources and heavy population. Over the past 30 years, considerable parts of coastal tidelands have been enclosed and reclaimed for agricultural land uses. To assess, plan, and implement large-scale reclamation programs, up-to-date and reliable information concerning the nature, areal extent, and physical and chemical characteristics of coastal saline lands is essential. This paper reports a remote sensing approach to detecting coastal saline land uses in Shangyu City, China, by using multi-temporal Landsat images. First, with the aid of resolution-sharpened Landsat-7 ETM+ images and their enhanced linear features, a visual interpretation is applied to extract individual dikes. Based on time series images and local government records, a spatial zoning procedure is then used to define six sub-zones with different historical years of reclamation. It shows that a total of 15,668 ha of coastal saline lands were enclosed and reclaimed from 1969 to 1996. Second, a modified land-use classification system for the study area is prescribed, and both unsupervised and supervised classifiers are performed for land-use classifications of grouped sub-zones. Information obtained from the spatial zoning, Tasseled Cap transformation and Normalized Difference Vegetation Index, is also utilized to facilitate the supervised classification process. Finally, a detailed land-use map is produced, with an overall classification accuracy of 77.8%. Results show that dominant agricultural land uses of sub-zones are changed with historical reclamation years, from saline lands with wildgrass (very recently reclaimed) to aqua-farm ponds, to cotton fields, and to paddy fields and orchards (very early reclaimed). This transform process is primarily affected by soil salinities, and according to a soil survey an electrical conductivity of saturation extract decreased from 7.3 ds/m in the saline land reclaimed in 1996 to below 2 ds/m in the land reclaimed before 1969. The study concludes that multi-temporal remotely sensed images are important and effective data sources for monitoring the rapid changes of coastal land uses.     

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.     

APPLICATION OF A LOW-FLOW ASSESSMENT MODEL FOR THE MONONGAHELA RIVER BASIN1

ABSTRACT: The application of a low-flow assessment model is illustrated for the Monogahela River Basin. The model simulates the impact of reservoir operating rules and consumptive use limitation policies on low-flow frequency at downstream locations in the basin. Policies are evaluated using an observed flow sequence and synthetic flow inputs. The paper reviews the historical development of flow management on the Monogahela to provide background for the current study.     

荒漠化类型划分及其数量化评价体系

从我国荒漠化监测的实际需要出发，提出了包括气候区、外营力、土地利用类型、地表特征和荒漠化程度在内的多因素复叠式荒漠化分类体系；既可适用于小区域大比例尺的重点监测，又可适用于大区域小比例尺的宏观监测的各荒漠化类型数量化程度判定指标体系和判定方法；适用于各级行政区的荒漠化水平评价方法．     

蓄热式加热炉安全技术操作规程优化初探

唐钢中型厂2#加热炉技术改造后，在加热炉设备检修、维修、维护过程中，如何在保证操作安全的同时，保证操作的准确到位，为安全管理工作提出了研究课题。     

我国安全生产基本特征规律研究

从经济社会发展视角,分析安全生产基本特征规律,指出安全生产随产业结构呈现不均衡分布特点,劳动力结构制约安全生产,城市化进程增加安全生产不稳定因素.安全生产与GDP对比分析表明,党和国家采取的一系列重大措施有效抑制了经济快速增长给安全生产带来的负面作用,我国安全生产提前进入事故波动期.通过研究区域经济与安全生产关系,得出我国4大经济区域社会经济发展水平与安全生产之间存在规律性分布特征.该研究的成果为建立安全生产长效机制,实现我国经济社会与生产安全协调发展探讨新的途径.     

基于模糊聚类的生态功能区若干指标定量划分的研究

以江苏省沿东陇海线地区为研究区域,利用遥感与G IS工具,研究基于模糊聚类与若干定量指标的生态功能区划方法。该方法选取了5个指标用于区划过程,首先将研究区域分成5×5km大小的空间单元,并提取各单元属性数据生成属性矩阵,利用M atlab模糊逻辑工具箱对单元进行模糊聚类。参照聚类结果与其他资料进行了生态功能区划,并根据区划结果对各分区的产业发展与生态保护提出了建议。     

危险化学品分类体系现状及在海洋环境应急监测中的应用

综述了国内外危险化学品分类体系,以苯乙烯为例,参考欧盟SEBC体系,建立了危险化学品海洋环境应急监测信息卡,内容包括分类、索引号及纳入名录,理化性质及行为特征判断,健康危害及防护要求,检测方法及评价标准等4类信息。建议进一步筛选海洋污染事故涉及的典型或高危化学品,细化SEBC体系,加强快速检测技术研究,构建适应我国海洋环境应急监测需求的危险化学品分类体系和数据库。     

日本安全监督管理启示录

通过对日本安全监督管理机构、研究机构、社团组织和企业的亲身考察 ,对日本安全生产法规建设、监督管理体系、工作机制、主要措施进行了分析、思考和总结 ,认为对如何加强我国安全生产监督管理、改进工作有很大的启示和有益的借鉴。希望文中介绍的内容和观点能在安全生产专家、学者中 ,引起广泛讨论 ,从而建立适合我国国情的安全生产法规体系、安全生产监督管理体系和符合时代发展的创新的安全生产工作思路。     

Nest selection by snow petrels Pagodroma nivea in East Antarctica: Validating predictive habitat selection models at the continental scale

Little is known on the factors controlling distribution and abundance of snow petrels in Antarctica. Studying habitat selection through modeling may provide useful information on the relationships between this species and its environment, especially relevant in a climate change context, where habitat availability may change. Validating the predictive capability of habitat selection models with independent data is a vital step in assessing the performance of such models and their potential for predicting species’ distribution in poorly documented areas.From the results of ground surveys conducted in the Casey region (2002–2003, Wilkes Land, East Antarctica), habitat selection models based on a dataset of 4000 nests were created to predict the nesting distribution of snow petrels as a function of topography and substrate. In this study, the Casey models were tested at Mawson, 3800 km away from Casey. The location and characteristics of approximately 7700 snow petrel nests were collected during ground surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced for the Mawson region with the models derived from the Casey datasets and predictions were compared to the observed data. Models performance was assessed using classification matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification rates for the Casey models varied from 57% to 90%. However, two geomorphologically different sub-regions (coastal islands and inland mountains) were clearly distinguished in terms of habitat selection by Casey model predictions but also by the specific variations in coefficients of terms in new models, derived from the Mawson data sets. Observed variations in the snow petrel aggregations were found to be related to local habitat availability.We discuss the applicability of various types of models (GLM, CT) and investigate the effect of scale on the prediction of snow petrel habitats. While the Casey models created with data collected at the nest scale did not perform well at Mawson due to regional variations in nest micro-characteristics, the predictive performance of models created with data compiled at a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor of nest presence between Casey and Mawson. This study demonstrate that it is possible to predict at the large scale the presence of snow petrel nests based on simple predictors such as topography and substrate, which can be obtained from aerial photography. Such methodologies have valuable applications in the management and conservation of this top predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and lower latitudes species and in a variety of habitats.