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Soil Science Society of America Journal Abstract - DIVISION S-5—PEDOLOGY

Supervised Landform Classification to Enhance and Replace Photo-Interpretation in Semi-Detailed Soil Survey

 

This article in SSSAJ

  1. Vol. 67 No. 6, p. 1810-1822
     
    Received: May 18, 2002


    * Corresponding author(s): hengl@itc.nl
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doi:10.2136/sssaj2003.1810
  1. Tomislav Hengl *a and
  2. David G. Rossiterb
  1. a Dep. of Earth System Analysis, International Institute for Geo-Information Science & Earth Observation (ITC), P.O. Box 6, 7500 AA Enschede, The Netherlands
    b International Institute for Geo-Information Science & Earth Observation (ITC), P.O. Box 6, 7500 AA Enschede, The Netherlands

Abstract

A method to enhance manual landform delineation using photo-interpretation to map a larger area is described. Conventional aerial photo-interpretation (API) maps using a geo-pedological legend of 21 classes were prepared for six sample areas totaling 111 km2 in the Baranja region, eastern Croatia. Nine terrain parameters extracted from a digital elevation model (DEM) (ground water depth, slope, plan curvature, profile curvature, viewshed, accumulation flow, wetness index, sediment transport index, and the distance to nearest watercourse) were used to extrapolate photo-interpretation over the entire survey area (1062 km2). The classification accuracy was assessed using the error matrix, calculated by comparing both the whole API maps and point samples, with the results of classification. The first results, using a maximum-likelihood classifier, were 58.2% (hill land), 39.1% (plain), and 45.3% (entire area) reproducibility of the training set. Six classes in the plain were responsible for a large proportion of the misclassifications, due to an insufficiently detailed DEM and the complex nature of landforms (point bar complexes, levees, active channel banks), which cannot be explained with the terrain parameters only. Reproducibility for a simplified legend of 15 classes over the study area was improved to 65.8% (plain), 58.2% (hill land), and 63.4% (entire area) using the whole-API training set. After the simplification of legend (15) and with the iterative (3) selection of point-sample training set, classification was able to reproduce 97.6% (hill land), 86.7% (plain), and 90.2% (entire area) of the training set. The supervised classification showed fine details not achieved by photo-interpretation. The number of manual photo-interpretations that had to be prepared was reduced from 84 to 6. The methodology can be applied by soil survey teams to edit and update current maps and to enhance or replace API for new surveys.

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