A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions

Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparse higher accuracy (hard) class labels is presented. Such assessment leads to local indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematic map. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responses provides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps for a Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).     

一种系统化的生产安全事故分级新方法研究

为了强化《生产安全事故报告和调查处理条例》(493号令)对事故管理及其影响评价的重要作用。笔者在论证了关于提出事故分级新方法的必要性后,在前人已有研究成果的基础上,结合生产安全事故特点,初步提出了一种基于"人员伤亡、财产损失、社会影响和环境污染"四个指标的系统化事故分级新方法。该事故分级理念及方法能更好地切合生产安全事故定义,对新时期下事故分级理念中事故影响多样性内涵的引入是一种积极和有效的探索,具有一定意义。     

基于MMEM的农村社区小微企业事故隐患研究

为了科学有效地提升小微企业事故隐患(AP)排查治理效果,以河北省28个农村社区为例,结合MMEM理论分析和实地调查,揭示了农村社区小微企业存在的事故隐患及其分布特征,进而建立了小微企业事故隐患分类方法。研究结果表明,在农村社区小微企业中存在未进行安全培训、未设置机器设备防护罩、未及时更新设备以及未配备消防设备四类典型的事故隐患,所建立的小微企业事故隐患分类框架是促进农村社区事故风险管控的基础。     

化学合成类制药工业污染排放自行监测方案设计要点

阐述了化学合成类制药工业污染排放自行监测方案的编制依据,分析了化学合成制药的生产工艺与污染排放特征,在此基础上提出了自行监测方案的主要内容,以及监测点位、监测指标、监测频次设定的注意事项,并明确了特殊情况监测方案的设计原则及自行监测信息记录的相关内容。     

化学品混合物的安全监管技术研究

从分析我国化学品混合物安全监管技术出发,研究对比我国和国外在化学品混合物的危险性鉴别与分类、命名以及名录收入形式等安全监管技术方面的不同做法;指出我国在混合物危险性鉴别与分类、命名以及名录收入形式方面的缺陷和不足;提出完善化学品混合物安全监管技术是提高我国化学品混合物安全监管水平的重要手段;认为借鉴欧盟RAECH、联合国《关于危险货物运输的建议书-规章范本》及GHS的先进做法制定化学品混合物技术名称命名规则、修订现行危险化学品名录中混合物条目、引进混合物健康及环境危害估算方法,对提高我国化学品混合物的安全监管技术水平,促进化学品混合物的安全监管具有重要现实意义。     

应用物种DNA条形码识别太湖流域部分底栖无脊椎动物种类

将物种DNA条形码实际应用于太湖流域底栖无脊椎动物分类,并与形态学分类结果比对,结果表明,DNA条形码技术可应用于本流域底栖无脊椎动物分类,但现阶段无法替代形态学鉴定,主要原因是太湖流域底栖无脊椎动物的绝大多数物种COⅠ基因特征序列是未知的或是BLAST所拥有的分类程度不够,提出在今后的研究中应探索建立太湖流域底栖无脊椎动物COⅠ基因数据库。     

Comparing the Use of Indigenous Knowledge with Classification and Ordination Techniques for Assessing the Species Composition and Structure of Vegetation in a Tropical Forest

dentification of groups that are similar in their floristic composition and structure (habitat types) is essential for conservation and forest managers to allocate high priority areas and to designate areas for reserves, refuges, and other protected areas. In this study, the use of indigenous knowledge for the identification of habitat types in the field was compared against an ecological characterization of habitat types, including their species composition obtained by using classification and ordination techniques for a tropical landscape mosaic in a rural Mayan area of Quintana Roo, Mexico. Plant diversity data calculated from 141 sampled sites chosen randomly on a vegetation class’s thematic map obtained by multispectral satellite image classification were used for this propose. Results indicated high similarity in the categorization of vegetation types between the Mayan classification and those obtained by cluster and detrended correspondence analysis. This suggests that indigenous knowledge has a practical use and can be comparable to that obtained by using science-based methods. Finally, identification and mapping of vegetation classes (habitat types) using satellite image classification allowed us to discriminate significantly different species compositions, in such a way that they can provide a useful mechanism for interpolating diversity values over the entire landscape.     

Spatial Relations Between Floodplain Environments and Land Use – Land Cover of a Large Lowland Tropical River Valley: Pánuco Basin, México

Large lowland river valleys include a variety of floodplain environments that represent opportunities and constraints for human activities. This study integrates extensive field observations and geomorphic data with analysis of satellite remote sensing data to examine spatial relations between land use/land cover (LULC) and floodplain environments in the lower Pánuco basin of eastern Mexico. The floodplain of the lower Pánuco basin was delineated by combining a digital elevation model with a satellite image of a large flood event. The LULC was classified by combining a hybrid classification strategy with image stratification, applied to 15-m-resolution ASTER data. A geomorphic classification of floodplain environments was performed using a dry-stage image (ASTER data) and a 1993 Landsat image acquired during a large flood event. Accuracy assessment was based on aerial photographs (1:38,000), global positioning satellite ground-truthing, and a Landsat 7ETM⁺ image from 2000, which resulted in an overall accuracy of 82.9% and a KHAT of 79.8% for the LULC classification. The geomorphic classification yielded 83.5% overall accuracy, whereas the KHAT was 81.5%. LULC analysis was performed for the entire floodplain and individually within four valley segments. The analysis indicates that the study area is primarily utilized for grazing and farming. Agriculture is primarily associated with coarse-grained (sandy/silty) natural levee and point bar units close to the river channel, whereas cattle grazing occurs in distal and lower-lying reaches dominated by cohesive fine-grained (clayey) deposits, such as backswamps. In the Pánuco valley, wetlands and lakes occur within backswamp environments, whereas in the Moctezuma segments, wetlands and lakes are associated with relict channels. This study reveals considerable variation in LULC related to spatial differences in floodplain environments and illustrates the importance of considering older anthropogenic influences on the landscape. The research design should be applicable for other large lowland coastal plain river valleys where agriculture is a major component of the floodplain landscape.     

Human Influences on Water Quality in Great Lakes Coastal Wetlands

A better understanding of relationships between human activities and water chemistry is needed to identify and manage sources of anthropogenic stress in Great Lakes coastal wetlands. The objective of the study described in this article was to characterize relationships between water chemistry and multiple classes of human activity (agriculture, population and development, point source pollution, and atmospheric deposition). We also evaluated the influence of geomorphology and biogeographic factors on stressor-water quality relationships. We collected water chemistry data from 98 coastal wetlands distributed along the United States shoreline of the Laurentian Great Lakes and GIS-based stressor data from the associated drainage basin to examine stressor-water quality relationships. The sampling captured broad ranges (1.5–2 orders of magnitude) in total phosphorus (TP), total nitrogen (TN), dissolved inorganic nitrogen (DIN), total suspended solids (TSS), chlorophyll a (Chl a), and chloride; concentrations were strongly correlated with stressor metrics. Hierarchical partitioning and all-subsets regression analyses were used to evaluate the independent influence of different stressor classes on water quality and to identify best predictive models. Results showed that all categories of stress influenced water quality and that the relative influence of different classes of disturbance varied among water quality parameters. Chloride exhibited the strongest relationships with stressors followed in order by TN, Chl a, TP, TSS, and DIN. In general, coarse scale classification of wetlands by morphology (three wetland classes: riverine, protected, open coastal) and biogeography (two ecoprovinces: Eastern Broadleaf Forest [EBF] and Laurentian Mixed Forest [LMF]) did not improve predictive models. This study provides strong evidence of the link between water chemistry and human stress in Great Lakes coastal wetlands and can be used to inform management efforts to improve water quality in Great Lakes coastal ecosystems.     

农村生活污水治理技术方案的合理性评估——以上海和苏南地区为例

以上海和苏南地区农村生活污水治理的10个示范工程为例,评估研究了农村生活污水治理技术方案的合理性。文章认为,在技术合理性方面,现有示范工程的处理模式选择和工艺设计基本实现了因地制宜,但在成本的有效性方面,现有示范工程还存在较大的成本下降空间。