Evaluation of Model Validation Techniques in Land Cover Dynamics

Bayes Ahmed,Raquib Ahmed,Xuan Zhu

doi:10.3390/ijgi2030577

Bayes Ahmed, Raquib Ahmed + Show 1 more

Open Access

https://doi.org/10.3390/ijgi2030577

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Abstract

This paper applies different methods of map comparison to quantify the characteristics of three different land change models. The land change models used for simulation are termed as “Stochastic Markov (St_Markov)”, “Cellular Automata Markov (CA_Markov)” and “Multi Layer Perceptron Markov (MLP_Markov)” models. Various model validation techniques such as per category method, kappa statistics, components of agreement and disagreement, three map comparison and fuzzy methods have then been applied. A comparative analysis of the validation techniques has also been discussed. In all cases, it is found that “MLP_Markov” gives the best results among the three modeling techniques. Fuzzy set theory is the method that seems best able to distinguish areas of minor spatial errors from major spatial errors. Based on the outcome of this paper, it is recommended that scientists should try to use the Kappa, three map comparison and fuzzy methods for model validation. This paper facilitates communication among land change modelers, because it illustrates the range of results for a variety of model validation techniques and articulates priorities for future research.

Highlights

A typical approach to land-use and land-cover change (LUCC) modeling is to investigate how different variables relate to historic land transitions, and to use those relationships to build models to project future land transitions [1,2]
With the growth of high-resolution spatial modeling, geographic information systems (GIS) and remote sensing the need for map comparison methods increases
Map comparison may be seen as finding a goodness-of-fit measure [8]

Summary

Introduction

A typical approach to land-use and land-cover change (LUCC) modeling is to investigate how different variables relate to historic land transitions, and to use those relationships to build models to project future land transitions [1,2]. Upon seeing the prediction results, questions may arise about the accuracy of the base maps, the performance of the model and whether this predicted map represents the real scenario [4]. In this regard, it is necessary to quantify the map errors, the amount of differences among the maps and to validate the models used for prediction. Good comparison methods are needed to perform calibration and validation of spatial results in a structured manner [5]. Map comparison may be seen as finding a goodness-of-fit measure [8]

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