Reducing uncertainties in land cover change models using sensitivity analysis

Abstract

Land cover change (LCC) models aim to track spatiotemporal changes made in land cover. In most cases, LCC models contain uncertainties in their main components (i.e., input parameters and model structure). These uncertainties propagate through the modeling system, which generates uncertainties in the model outputs. The aim of this manuscript is to propose an approach to reduce uncertainty of LCC prediction models. The main objective of the proposed approach is to apply a sensitivity analysis method, based on belief function theory, to determine parameters and structures that have a high contribution in the variability of the predictions of the LCC model. Our approach is applied to four common LCC models (i.e., DINAMICA, SLEUTH, CA-MARKOV, and LCM). Results show that uncertainty of the model parameters and structure has meaningful impacts on the final decisions of LCC models. Ignoring this uncertainty can lead to erroneous decision about land changes. Therefore, the presented approach is very useful to identify the most relevant uncertainty sources that need to be processed to improve the accuracy of LCC models. The applicability and effectiveness of the proposed approach are demonstrated through a case study based on the Cairo region. Results show that 13% of the agriculture and 3.8% of the desert lands in 2014 would be converted to urban areas in 2025.