Fuzzy classification methods for determining land suitability from soil profile observations and topography

Abstract

Because conventional Boolean retrieval of soil survey data and logical models for assessing land suitability treat both spatial units and attribute value ranges as exactly specifiable quantities, they ignore the continuous nature of soil and landscape variation and uncertainties in measurement which can result in the misclassification of sites that just fail to match strictly defined requirements. This paper uses fuzzy classification to determine land suitability from (i) multivariate point observations of soil attributes, (ii) topographically controlled site drainage conditions, and (iii) minimum contiguous areas, and compares the results obtained with conventional Boolean methods. The methods are illustrated using data from the Alberta Agricultural Department experimental farm at Lacombe in Alberta, Canada. Data on site elevation and soil chemical and physical properties measured at 154 soil profiles were interpolated by ordinary block kriging to 15 m x 15 m cells on a 50 x 90 grid. The soil property data for each cell were classified by Boolean and fuzzy methods. The digital elevation model created by interpolating the elevation data was used to determine the surface drainage network and map it in terms of the numbers of cells draining through each cell on the grid. This map was reclassified to yield Boolean and fuzzy maps of surface wetness which were then intersected with the soil profile classes. The resulting classification maps were examined for contiguity to locate areas where a block of minimum size (45 m x 45 m) could be located successfully. In this study Boolean methods reject larger numbers of cells than fuzzy classification, and select cells that are insufficiently contiguous to meet the aims of the land classification. Fuzzy methods produce contiguous areas and reject less information at all stages of the analyses than Boolean methods. They are much better than Boolean methods for classification of continuous variation, such as the results of the drainage analysis.

Fire risk evaluation using multicriteria analysis—a case study

“…Integration of fuzzy logic with GIS in a decision-making framework has been used for different purposes, including land suitability based upon soil profiles (Burrough et al 1992), soil classification (Lark and Bolam 1997), landfill site screening (Charnpratheep et al 1997), soil erosion (Mitra et al 1998), crop land suitability analysis (Ahmed et al 2000), ranking burned forests to evaluate the risk of desertification (Sasikala and Petrou 2001), seeking optimum locations for real estate (Zeng and Zhou 2001), assessing vulnerability to natural hazards (Rashed and Weeks 2003;Tangestani 2004;Dixon 2005), estimating risk (Sadiq and Husain 2005), incorporating farmer's knowledge for land suitability classification (Sicat et al 2005), fuel type mapping (Nadeau and Englefield 2006), assessing spatial extent of dry land salinity (Malins and Metternicht 2006), etc. Therefore, many studies have been performed using fuzzy logic integrated with GIS in a MCDM framework demonstrating that the methods are robust and valid.…”

The Potential of P Loss from Livestock Farms to Watercourses in Wensleydale; A Risk Mapping Approach

“…However, the use of Fuzzy methods was crucial to represent the continuous properties of soil as well as the geomorphology in order to add as much detail and information as possible [57]. On the other hand, the maps are also the result of using Boolean methods in order to include values associated for example with sub-surface drainage.…”

A Comparison of Using Dominant Soil and Weighted Average of the Component Soils in Determining Global Crop Growth Suitability

“…Fuzzy classification methods define growth through membership functions and likelihoods (Burrough, MacMillan, & Deursen, 1992). The rationale behind this is that most soil parameters have a large error rate per se, due to sampling and handling errors, and crops are able to grow at various levels of these parameters (Rossiter, 1996).…”