Experiences with applied DSM: Protocol, availability, quality and capacity building

Abstract

This chapter considers both opportunities and constraints to applied, operational digital soil mapping (DSM) from the points of view of a) availability of suitable input data layers, b) protocols available for DSM, c) quality of input data layers and resultant output maps and d) other efforts required to build predictive mapping capacity and apply it effectively. Many potential DSM practitioners are discouraged by the real or perceived lack of availability of suitable input data layers to support DSM, particularly in regions with weakly developed spatial data infrastructures. Solutions to addressing problems of limited or sparse spatial data sets are identified for input layers derived from digital elevationmodels (DEM's), remotely sensed imagery and available secondary source maps. A variety of protocols for producing predictive soil maps are discussed under the general headings of unsupervised, supervised and knowledge-based (or heuristic) approaches. These key protocol activities support the ability to make maps of the spatial distribution of soil classes or attributes by developing predictive relations between spatially distributed input variables or classes and the desired output classes. Different strategies are reviewed to acquire and formalize tacit knowledge embodied in soil-landform conceptual models and to capture this tacit knowledge as quantitative rules. A considerable amount of resistance to DSM arises from real or perceived concerns about the quality of the resulting maps in comparison to existing maps produced using traditional mapping methods. Quality, defined as the ability of a map or product to correctly predict the characteristics of the landscape at particular points or within particular small areas, is discussed as are suitable approaches for evaluating and reporting it. The capacity to applyDSMroutinely and operationally requires additional support in the form of training, access to suitable tools and software and access to suitable input data. Approaches to developing support for DSM from decision makers and funding agencies in the face of institutional and discipline resistance to embracing new technologies are identified, specifically incremental projects with clearly defined goals and testable measures of success. Finally, it is noted that perhaps the biggest hurdle to building capacity is our own hesitancy to believe in ourselves and to dream big and try big. It is hoped that this chapter will encourage individualswith an interest in applying new predictive mapping techniques to embrace change and to try to create useful, operationalmaps for large areas in their own regions of interest. © 2008 Springer Science+Business Media B.V.