Components of uncertainty in primary production model: the study of DEM, classification and location error

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. The use of geographic information system (GIS)-based ecological models is increasing and input datasets of these models are improving daily. Still, there is a notable gap in quantifying the uncertainty related to these models. Quantifying uncertainty in spatial ecology is indeed crucial because it may improve the support that GIS provides for decision support systems. This article aims to quantify uncertainty and error propagation in a dynamic GIS model that predicts ecosystem productivity in dry environments. This was done through the following operative objectives: (1) comparing the contribution to model uncertainty of topographic error with classification error; (2) testing whether the uncertainty contributed by the secondary topographic index (radiation layer) is greater than the uncertainty contributed by the primary topographic indices (aspect or slope); and (3) quantifying the contribution of the location error to model uncertainty. The research was applied in four steps: (1) spatial database design and collection of validation data; (2) standard error determination, based on statistical indices for simulation; (3) development of simulation codes to assess the uncertainty and error propagation of the environmental variables; and (4) determination of the hierarchy of uncertainty factors. The results show that the contribution of the DEM layer to the model uncertainty is substantial, as opposed to the negligible uncertainty contributed by the rock map. The error simulation results were found to be different among subregions and were dependent on slope gradient and error magnitude. Error propagation from the secondary topographic index (radiation layer) was occasionally found to contribute less to the model uncertainty than the primary topographic index (aspect). It was also found that location error correction has only a small positive effect on the model's predictability. The reason is related to the limited ability …