The Impact of DEM Error on Predictive Vegetation Mapping
Digital elevation models (DEM) are one of the most important data sources for Land Use-Land Cover (LULC) and Predictive Vegetation Mapping (PVM). A number of indices are derived from DEMs and their use depends on the nature of the classification problem and the tool being employed. In some cases it is the practice to pre-classify these prior to modelling. This chapter examines the impact of doing this on the production of a LULC classification, and on the production of a surface, or field, prediction of a single species. Secondly, the error in classification resulting from error in the original DEM is examined to give some comparison. We show that, contrary to widespread practice, leaving the input variables in an unprocessed form is clearly better than almost any of the ‘improvements’ usually made. This applied to both classification of LULC and to the prediction of a surface, or field, representing a single species. As expected, forest type mapping is likely to be quite sensitive to some level of DEM error. We can see that the DEM error has an uneven impact on the different forest types. Importantly, when increasing the level of DEM error, we found a non-linear decrease in classification performance.
KeywordsForest Type Digital Elevation Model Generalize Additive Model Land Cover Classification Uncertainty Level
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