Sensitivity analysis of logistic regression parameterization for land use and land cover probability estimation

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan, 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 recent availability of regional-scale land use and land cover data made it possible to apply land use and land cover models at regional-, national-and even continental-scale levels. A commonly used modelling approach is based on the assessment of land cover probabilities by means of logistic regression equations. In most cases, however, the error involved in the parameterization of logistic regression equations is not known. In this article, the sensitivity of logistic regression parameterization for land use and land cover probability assessments is analysed by comparing the results using input maps from different sources. Land cover maps with a range of accuracy levels were collected for a sub-catchment of the Lake Balaton watershed in Hungary. The results show that the parameterization of the logistic regression coefficients is highly dependent on the quality of the input maps. Both the spatial pattern and the area covered by a specific land cover type have a direct influence on the error propagation in the land cover probability maps. These findings should be taken into account when interpreting the results of land use and land cover change models at regional-and national-scale levels. 1. Introduction Human transformations of ecosystems and landscapes are the largest source of change on earth affecting the ability of the biosphere to sustain life. The intensification and diversification of land use has led to rapid changes in biogeochemical cycles, hydrological processes and landscape dynamics. Not surprisingly, the understanding of the causes and consequences of land use and land cover (LULC) change became an important point on the agenda of the geosciences community. The increased availability of digital LULC maps at various spatial scales allowed describing and analysing LULC patterns in more detail than ever before. This new information …