机助地图叠置分析及其在自然资源研究中的应用

机助地图叠置分析包括多边形叠置分析和网格叠置分析两种方法。本文论述了多边形叠置分析的原理和方法,以及多边形叠置分析软件设计的基本思想,探讨了机助地图叠置分析在自然资源研究中的应用领域,并以实例说明了机助地图叠置分析应用于实践的方法和过程。机助地图叠置分析可应用于自然资源数据的统计计算、自然资源的分类和评价,以及动态、变化分析等方面。它对于自然资源尤其是农业自然资源的研究有着一定的意义。     

家用综合防盗系统

家用综合防盗系统从实际防护入手,根据门、窗(阳台)所处位置特点,独立防护,防护系统与控制系统用无线联系,便于隐蔽、安装灵活,根据防护实际需要可随意增、减独立防护装置;采用交直流电源转换电路,增强了抗破坏能力,是工薪族家庭防非法侵入的理想防护装置.本系统由雷达扫描、护窗口(阳台)无线电发射电路、微振动护门无线电发射电路、无线电接收中央控制、功放报警电路等五部分组成.     

The Threat of Transformation: Quantifying the Vulnerability of Grasslands in South Africa

Abstract:  The ability to predict which areas of conservation importance are most vulnerable to transformation and to rank the relative damage that transforming land uses could cause to biodiversity are important components of an effective and realistic conservation planning process. We used the South African grassland biome as a case study to illustrate the assessment of vulnerability to land-use transformation through the construction of a "threat map." We identified the dominant transforming land uses and created suitability models based on area appropriateness for each. Land uses were scored according to their expected relative impacts on biodiversity, with a framework that included compositional, structural, and functional components. This information, once combined, resulted in a map that highlighted the areas most vulnerable to transformation in terms of the likelihood of their transformation and the impact on their biodiversity. We propose that such an analysis of the threat of transformation, in combination with species-representation approaches, will aid the integration of conservation planning theory and decision making. This approach can be applied at any scale and in any region with a robustness and accuracy dependent on data quality, resultant suitability models, and comprehension of how land uses affect an area's biodiversity.     

六安—蔡河地学剖面构造岩性单元的划分

叙述了大别造山带主要岩石类型及热源体周围微量元素的分布、分配特征 ,研究了它们的影响因素和地质意义 ,并根据六安—蔡河地学剖面微量元素和收集的地球物理、同位素年龄等资料 ,对该剖面构造岩性单元进行了划分。     

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.     

Statistical selection of perimeter-area models for patch mosaics in multiscale landscape analysis

This paper presents a statistical method for detecting distinct scales of pattern for mosaics of irregular patches, by means of perimeter–area relationships. Krummel et al. (1987) were the first to develop a method for detecting different scaling domains in a landscape of irregular patches, but this method requires investigator judgment and is not completely satisfying. Grossi et al. (2001) suggested a modification of Krummel's method in order to detect objectively the change points between different scaling domains. Their procedure is based on the selection of the best piecewise linear regression model using a set of statistical tests. Even though the change points were estimated, the null distributions used for testing purposes were those appropriate for known change points. The present paper investigates the effect that estimating the change points has on the underlying distribution theory. The procedure we suggest is based on the selection of the best piecewise linear regression model using a likelihood ratio (LR) test. Each segment of the piecewise linear model corresponds to a fractal domain. Breakpoints between different segments are unknown, so the piecewise linear models are non-linear. In this case, the frequency distribution of the LR statistic cannot be approximated by a chi-squared distribution. Instead, Monte Carlo simulation is used to obtain an empirical null distribution of the LR statistic. The suggested method is applied to three patch types (CORINE biotopes) located in the Val Baganza watershed of Italy.     

DEFINING REGIONAL POPULATIONS OF LAKES FOR THE ASSESSMENT OF SURFACE WATER QUALITY1

ABSTRACT: Topographic maps are commonly used to define populations of lakes in regional surveys of surface water quality. To illustrate the effect of different maps on that process, we compared the lakes represented on the 1:250,000-scale maps used for the Northeast Region of the Eastern Lake Survey—Phase I (ELS-I) to the lakes on a sample of large-scale maps (1:24,000 or 1:62,500). Lake areas at or near the lower limit of representation delimited “smallest-lake” values for the compared 1:250,000-scale maps. The regional median for these values was 4.5 hectares (ha) and ranged from 0.6 to 24.8 ha. Lake representation is influenced by cartographic limitations such as map scale, age, and complexity as well as the inherent variability of waterbodies (e.g., water level fluctuations or the creation of reservoirs, beaver impoundments, and oxbows). The total number of lakes on large-scale maps increased markedly as lake area decreased. Approximately 15,700 of the estimated 29,000 lakes in the EPA's Northeast Region were 1 to 4 ha in area. Because maps affect the size distribution of lakes included in a regional survey and because lake areas are thought to modify lake chemistry, maps ultimately affect the estimates of regional surface water quality.     

Estimating the risk of insect species invasion: Kohonen self-organising maps versus k-means clustering

Previous work on the estimation of the invasiveness of insect pest species used a single Kohonen self-organising map (SOM) to quantify the invasion potential of each member of a set of species in relation to a particular geographic region. In this paper that method is critically compared to an alternative approach of calculating the invasive potential of insect pest species as an outcome of clustering of regional species assemblages. Data clustering was performed using SOM and k-means optimisation clustering and multiple trials were performed with each algorithm. The outcomes of these two approaches were evaluated and compared to the previously published results obtained from a single SOM. The results show firstly, due to the inherent variation between trials of the algorithms used, that multiple trials are necessary to determine reliable risk ratings, and secondly, that k-means clustering can be considered a more appropriate algorithm for this particular application, as it produces clusters of higher quality, as determined by objective cluster measures, and is far more computationally efficient than SOM.     

尾矿坝浸润线时空混沌及其安全机理

在单向耦合映象格子模型基础上,考虑尾矿坝坡角因素,建立了浸润线耦合映象格子模型vn 1(i)=(1-ε)f(vn(i)) ε(1 sinθ)f(vn(i-1)).根据浸润线的初值条件和边界条件,对该模型进行了非线性动力学分析.结果表明,浸润线演化存在复杂时空混沌行为,可分为随机图样流、选择图样流、缺陷传播行为及完全发展湍流4种.非线性强度α和耦合强度ε的取值范围较小, 且ε较小时就可以得到非常丰富的动力学图案.浸润线演化受坡角影响比较明显,坡角越大越容易出现混沌状态.浸润线的各种时空行为反映了其相应的客观状态,从而揭示了浸润线的安全机理.浸润线耦合映象格子模型也可以用来解释其他各类边坡的复杂渗流现象.     

2000年北京沙尘暴源地解析

2000年我国北方春季干旱少雨,气温回升快,低涡频繁侵入,4月3～9日连续3次冷空气活动,造成了北京地区严重的沙尘暴天气。笔者利用卫星云图和中尺度数值模式MM5,分析了该次沙尘暴源地及沙尘输送机制。结果证明,北京沙尘暴初始源地为蒙古高原及冷涡移动路径上的沙漠戈壁地区;北京周边地区裸露地的沙尘在气旋尾部上升气流的扰动下,亦形成了边界层的近距离输送。