生态干扰度:一种评价植被天然性程度的方法

地球上现存的植物或森林群落都是植被与其环境长期适应及人类长期干扰影响的结果 ,因此 ,现实的植被反映着历史上人类对它们不同程度的影响。‘生态干扰’的概念常常被用来描述历史上人类对植被或森林影响的大小 ,即‘生态干扰度’;或用来表示现实植被离开它的‘天然植被’的距离 ,即现实植被的‘天然性程度’。如此的‘天然植被’可以是 :( 1 )历史上从未受过人类任何干扰的‘原始植被’;( 2 )基于当前立地的‘潜在的自然植被’。然而 ,成百上千年以来人类对自然的利用干扰 ,致使真正的‘原始植被’现在几乎无处可寻。因此 ,‘潜在的自然植被’被用来作为生态干扰度估计或植被天然性程度评价的参照系。作为一种很实用的生态分析手段 ,生态干扰度的方法已被广泛地应用在农、林、水、景观、城市及自然保护等诸多领域的生态评价上。为此 ,本文论述了生态干扰度的概念、原理、方法和应用实例。     

大连市城市内部居住环境评价的空间结构——基于面源模型的分析

以大连市为例 ,首先利用ARC/INFO软件将研究地区划分成 162个元胞作为评价的基本空间单元 ,建立了居住环境评价的面源模型。其次利用MDS方法确定了评价因子、列联表法验证了居民属性与居住环境评价之间的相互独立程度、因子分析方法探讨了居住环境评价结构的平行性。最后应用GIS对居住环境评价空间分异现象进行了分析。     

GIS与土壤溶质运移模型结合研究进展

地理信息系统对土壤溶质模型研究而言，使用者的要求与GIS所能提供的功能之间，还存在巨大的差距。出于对环境问题的关注，土壤中水分及溶质运移的规律及其对环境的影响，成为当前研究的热点。随着对土壤溶质运移模型研究的深入，一方面由于田间土壤特性具有很大的空间变异；另一方面，在实际应用中，往往溶质运移的宏观特征而非微观特征，具有更重要的意义。将溶质运移模型与GIS技术结合，定量研究空间尺度的溶质运移，成为溶质运移研究的必然发展趋势。由于计算机软硬件技术的发展，GIS正处于一个高速发展的时期。将GIS与溶质运移模型相结合，或者进一步，以GIS理论和技术为基础，建立基于GIS的溶质运移模型，将对溶质运移的规律及其对环境的影响，做出更深入和准确的描述。     

基于GIS的滑坡CF多元回归模型及其应用

目前基于GIS滑坡变形失稳危险性评价方法均有各自的局限性。本文在基于数据的数学统计模型的基础上提出CF多元回归模型。通过将滑坡确定性稳定系数与回归模型的融合，在一定程度以解决了滑坡评价过程中影响因子的选择和量化的问题，有利于建立准确的滑坡分析模型。最后，将模型应用于云南小江流域，进行了该区的滑坡空间分布及稳定性分析。     

A Reconnaissance Method for Delineation of Tracts for Regional-Scale Mineral-Resource Assessment Based on Geologic-Map Data

The U.S. Geological Survey (USGS) is proposing to conduct a global mineral-resource assessment using geologic maps, significant deposits, and exploration history as minimal data requirements. Using a geologic map and locations of significant pluton-related deposits, the pluton-related-deposit tract maps from the USGS national mineral-resource assessment have been reproduced with GIS-based analysis and modeling techniques. Agreement, kappa, and Jaccard's C correlation statistics between the expert USGS and calculated tract maps of 87%, 40%, and 28%, respectively, have been achieved using a combination of weights-of-evidence and weighted logistic regression methods. Between the experts' and calculated maps, the ranking of states measured by total permissive area correlates at 84%. The disagreement between the experts and calculated results can be explained primarily by tracts defined by geophysical evidence not considered in the calculations, generalization of tracts by the experts, differences in map scales, and the experts' inclusion of large tracts that are arguably not permissive. This analysis shows that tracts for regional mineral-resource assessment approximating those delineated by USGS experts can be calculated using weights of evidence and weighted logistic regression, a geologic map, and the location of significant deposits. Weights of evidence and weighted logistic regression applied to a global geologic map could provide quickly a useful reconnaissance definition of tracts for mineral assessment that is tied to the data and is reproducible.     

Size Scaling of Cross Correlation Between Multiple Variables

Reservoir models have large uncertainty because of spatial variability and limited sample data. The ultimate aim is to use simultaneously all available data sources to reduce uncertainty and provide reliable reservoir models for resource assessment and flow simulation. Seismic impedance or some other attribute provides a key source of data for reservoir modeling. These seismic data are at a coarser scale than the hard well data and it not an exact measurement of facies proportions or porosity. A requirement for data integration is the cross-covariance between the well and seismic data.The size-scaling behavior of the cross correlation for different measurement scales was nvestigated. The size-scaling relationship is derived theoretically and validated by numerical studies (including an example with real data). The limit properties of the cross-correlation coefficient when the averaging volume becomes large is shown. After some averaging volume, the volume-dependent cross-correlation coefficient reaches a limit value. This plateau value is controlled mainly by the large-scale behavior of the cross and direct variograms.The cross correlation can increase or decrease with volume support depending on the relative importance of long- and short-scale covariance structures. If the direct and cross variograms are proportional, there is no change in the cross correlation as the averaging volume changes. Our study shows that the volume-dependent cross-correlation coefficient is sensitive to the shape of the cross variogram and differences between the direct variograms of the well data and seismic data.     

Water use, growth and yield of green gram under rainfed upland crop sequences in Gangetic Plains of Indian sub-tropics

A field experiment was conducted to study the effect of different crop sequences on water use, growth and yield of green gram (Vigna radiata (L) Wilezek) during 1992-93 and 1993-94 under rainfed upland condition in Indo-Gangetic plains of West Bengal. Green gram sown in the month of March as pre-rainy (summer) season crop, as and when the winter crops vacated the land, produced highest dry matter of 372 gm-2 which was significantly highest in black gram-yellow sarson sequence.The results of the field experimentation revealed that green gram gave highest grain yield to the extent of 10.80 q/ha when sown after black gram (rainy season) followed by yellow sarson (winter season) while the crop produced 10.63 q/ha under sesame-yellow sarson sequence. Highest water use of 267mm was achieved in green gram under black gram-yellow sarson sequence and the crop gave water use efficiency of 4.07 kg ha-1mm-1 under black gram-yellow sarson sequences.     

Water use, growth and yield of green gram under rainfed upland crop sequences in Gangetic Plains of Indian sub-tropics

A field experiment was conducted to study the effect of different crop sequences on water use, growth and yield of green gram (Vigna radiata (L) Wilezek) during 1992-93 and 1993-94 under rainfed upland condition in Indo-Gangetic plains of West Bengal. Green gram sown in the month of March as pre-rainy (summer) season crop, as and when the winter crops vacated the land, produced highest dry matter of 372 gm-2 which was significantly highest in black gram-yellow sarson sequence. The results of the field experimentation revealed that green gram gave highest grain yield to the extent of 10.80 q/ha when sown after black gram (rainy season) followed by yellow sarson (winter season) while the crop produced 10.63 q/ha under sesame-yellow sarson sequence. Highest water use of 267 mm was achieved in green gram under black gram-yellow sarson sequence and the crop gave water use efficiency of 4.07 kg ha-1mm-1 under black gram-yellow sarson sequences.