Multi‐scale correlations between topography and vegetation in a hillside catchment of Honduras

All systems have causes and effects that can be appreciated at different spatial scales. Understanding and representing the complexity of multi‐scale patterns in maps and spatial models are key research objectives. We describe the use of three types of correlation analyses: (1) a standard Pearson correlation coefficient, (2) a ‘global’ multi‐scale correlation, and (3) local geographically weighted correlation. These methods were applied to topographic and vegetation indices in a small catchment in Honduras that is representative of the country's hillsides agro‐ecosystem which suffers from severe environmental degradation due to land‐use decisions that lead to deforestation, overgrazing, and unsustainable agricultural. If the geographical scale at which topography matters for land‐use allocation can be determined, then integration of knowledge systems can be focused. Our preliminary results show that: (1) single‐scale correlations do not adequately represent the relationship between NDVI and topographic indices; (2) peaks in the global multi‐scale correlations in agricultural areas coincided with the median farm size, but there was no evidence of any community or larger‐scale land‐use planning or optimization; and (3) local multi‐scale correlations varied considerably from the global results at all scales, and these variations have a strong spatial structure which may indicate local optimization of land use.     

GLOBAL sensitivity analysis,GIS and multi‐criteria evaluation for a sustainable planning of a hazardous waste disposal site in Spain

A novel application of Sensitivity Analysis is presented. Useful applications of Global SA (GSA) already exist in the field of numerical modelling. In this paper, we explore the joint use of GSA, Geographical Information Systems (GIS) and Multi‐Criteria Evaluation. In this preliminary case study, 11 factors have been used to find the best place to locate a hazardous waste landfill. Two variance‐based methods (Sobol' and E‐FAST) are used to compute sensitivity indices in order to identify the factors that determine the variance of the model output. The results show that only three factors jointly account for 97% of the output variance. This information is employed to make a simplification of the original model, retaining only these three influential factors. In addition, if the SA is carried out in a pilot area where the spatial properties are similar to those of the whole region, we can infer the results to the whole area. This procedure achieves the goal of the study with an optimized allocation of resources for GIS data acquisition.     

GIS,multi‐criteria and multi‐factor spatial analysis for the probability assessment of the existence of illegal landfills

This work deals with the identification of potentially contaminated areas using remote sensing, geographic information systems (GIS) and multi‐criteria spatial analysis. The identification of unknown illegal landfills is a crucial environmental problem in all developed and developing countries, where a large number of illegal waste deposits exist as a result of fast, and relatively unregulated, industrial growth over the past century. The criteria used to perform the spatial analysis are here selected by considering the characteristics which are ‘desirable’ for an illegal waste disposal site, chiefly related to the existence of roads for easy access and to a low population density which facilitates unnoticed dumping of illegal waste materials. A large dataset describing known legal and illegal landfills and the context of their location (population, road network, etc.) was used to perform a spatial statistical analysis to select factors and criteria allowing for the identification of the known waste deposits. The final result is a map describing the likelihood of an illegal waste deposit to be located at any arbitrary location. Such a probability map is then used together with remote sensing techniques to narrow down the set of possibly contaminated sites (Silvestri and Omri, 2008 Silvestri, S. and Omri, M. 2008. A method for the remote sensing identification of uncontrolled landfills: formulation and validation.. International Journal of Remote Sensing, 29(4): 975–989. [Taylor & Francis Online] , [Google Scholar]), which are candidates for further analyses and field investigations. The importance of the integration of GIS and remote sensing is highlighted and represents a key instrument for environmental management and for the spatially‐distributed characterization of possible uncontrolled landfill sites.