Andrea G. Fabbri

Multivariate Regression Analysis for Landslide Hazard Zonation

Based on several layers of spatial map patterns, multivariate regression methods have been developed for the construction of landslide hazard maps. The method proposed in this paper assumes that future landslides can be predicted by the statistical relationships established between the past landslides and the spatial data set of map patterns. The application of multivariate regression techniques for delineating landslide hazard areas runs into two critical problems using GIS (geographic information systems): (i) the need to handle thematic data; and (ii) the sample unit for the observations. To overcome the first problem related to the thematic data, favourability function approaches or dummy variable techniques can be used.

The representation of geoscience information for data integration

In mineral exploration, resource assessment, or natural hazard assessment, many layers of geoscience maps such as lithology, structure, geophysics, geochemistry, hydrology, slope stability, mineral deposits, and preprocessed remotely sensed data can be used as evidence to delineate potential areas for further investigation. Todays PC-based data base management systems, statistical packages, spreadsheets, image processing systems, and geographical information systems provide almost unlimited capabilities of manipulating data. Generally such manipulations make a strategic separation of spatial and nonspatial attributes, which are conveniently linked in relational data bases. The first step in integration procedures usually consists of studying the individual charateristics of map features and interrelationships, and then representing them in numerical form (statistics) for finding the areas of high potential (or impact).Data representation is a transformation of our experience of the real world into a computational domain. As such, it must comply with models and rules to provide us with useful information. Quantitative representation of spatially distributed map patterns or phenomena plays a pivotal role in integration because it also determines the types of combination rules applied to them.Three representation methods—probability measures, Dempster-Shafer belief functions, and membership functions in fuzzy sets—and their corresponding estimation procedures are presented here with analyses of the implications and of the assumptions that are required in each approach to thematic mapping. Difficulties associated with the construction of probability measures, belief functions, and membership functions are also discussed; alternative procedures to overcome these difficulties are proposed. These proposed techniques are illustrated by using a simple, artificially constructed data set.

Is Prediction of Future Landslides Possible with a GIS

This contribution explores a strategy for landslide hazard zonation inwhich layers of spatial data are used to represent typical settings inwhich given dynamic types of landslides are likely to occur. Theconcepts of assessment and prediction are defined to focus on therepresentation of future hazardous events and in particular on themyths that often provide obstacles in the application of quantitativemethods. The prediction rate curves for different applications describethe support provided by the different data layers in experiments inwhich the typical setting of hazardous events is approximated bystatistically integrating the spatial information.

Integrating spatial statistics and remote sensing

This paper presents an integrated approach towards spatial statistics for remote sensing. Using the layer concept in Geographical Information Systems we treat successively elements of spatial statistics, scale, classification, sampling and decision support. The layer concept allows to combine continuous spatial properties with classified map units. The paper is illustrated with five case studies: one on heavy metals in groundwater at different scales, one on soil variability within seemingly homogeneous units, one on fuzzy classification for a soillandscape model, one on classification with geostatistical procedures and one on thermal images. The integrated approach offers a better understanding and quantification of uncertainties in remote sensing studies.

A Strategy for Sustainable Development of Nonrenewable Resources using Spatial Prediction Models

This contribution provides an analytical strategy applicable in mineral exploration to not only predicting the location of undiscovered mineral resources but also estimating the probability of the next discovery at that location. In addition, the strategy is applicable to the likely environmental impacts of developing the resources as a result of the exploration. General concepts of spatial prediction, of the likelihood ratio model, and of a two-stage approach to derive the probability of the next discovery in each prediction class are introduced.

Shape analysis and multispectral classification in geological remote sensing

This paper discusses the usage of mathematical morphology in image processing of remotely-sensed data for geologic interpretation. Particular attention is given to noise-reducing transformations of spectral bands before and after different methods of classification, and to the usage of textural context. The development of a viable processing strategy requires a multidisciplinary approach and expert knowledge in different areas: (a) geology, geomorphology, and vegetation in a study area, (b) properties of the sensor for imagery photointerpretation, (c) spectral/spatial properties of the digital data within an integrated dataset (remote sensing and ancillary data), and (d) data-processing tools including mathematical morphology theory. Examples of geometric characterization of Canadian LANDSAT scenes are described in which shape measurements are obtained using a PC-based hybrid image-processing and geographic information system, termed ILWIS, which was developed at ITC, in the Netherlands. Classes from supervised and unsupervised classification are compared to guide in geological mapping. Classes over individual occurrences of broad vegetation-landform units are studied to aid in environmental mapping. Field knowledge is the context necessary to construct expert procedures to drive sequences of data-processing steps toward a target result such as optimal classification, enhancement, or feature extraction. The interaction between expert rules and the image-processing steps can be based on synthetic measurements of shape to quantize the information either spatially or spectrally. Many useful geometrical transformations of spatially-distributed data are extensions or generalizations of spatial analysis functions typical of geographic information systems.

The Impact of Mining on The Environment

The Tharsis-Lagunazo area, in the Province of Huelva, southwestern Spain, has a long history of mining. For over three thousand years, the contour of the land has continuously been modified and re-modified by mining activities and unplanned mine wastes to provide the present landscape. This study, making use of Lands at TM imagery from 1984 and black and white aerial photographs from 1973, was able to assess the implication and impact of mining on this area. From these datasets, it was possible to detect the number of open-pit mines, waste rock dumps, tailings, slime dams, land use/cover changes and subsurface groundwater pollution.