Predicting Land Cover Change in a Mediterranean Catchment at Different Time Scales

Abstract

Land cover has been changing rapidly throughout the world, and this issue is important to researchers, urban planners, and ecologists for sustainable land cover planning for the future. Many modeling tools have been developed to explore and evaluate possible land cover scenarios in future and time scales vary greatly from one study to another. The main objective of this study is to test land cover change prediction at different time scales in a Mediterranean catchment in SE France. Land cover maps were created from aerial photographs (1950, 1982, 2003, 2008, and 2011) of the Giscle catchment (235 Km 2 ) and surfaces were classified into four land cover categories: forest, vineyard, grassland, and built area. Explanatory variables were selected through Cramer's coefficient. Different time scales were tested in the study: short (2003-2008), intermediate (1982-2003), and long (1950-1982). To test the model's accuracy, Land Change Modeler (LCM) of IDRISI was used to predict land cover in 2011 and predicted images were compared to a real 2011 map. Kappa index and confusion matrix were used to evaluate the model's accuracy. Altitude, slope, and distance from roads had the greatest impact on land cover changes among all variables tested. Good to perfect level of spatial and perfect level of quantitative agreement were observed in long to short time scale simulations. Kappa indices (Kquantity = 0.99 and Klocation = 0.90) and confusion matrices were good for intermediate and best for short time scale. The results indicate that shorter time scales produce better predictions. Time scale effects have strong interactions with specific land cover dynamics, in which stable land covers are easier to predict than cases of rapid change and quantity is easier to predict than location for longer time periods.

Modeling Future Land Cover Changes and Their Effects on the Land Surface Temperatures in the Saudi Arabian Eastern Coastal City of Dammam

“…The transition potential map for each transition was produced and the LULCs for 2014 were modeled. Previous studies have found MLPM to produce better prediction accuracies in areas featuring stable and slow LULC changes [48]. Finally, in the SM model, Markovian conditional probability images from the years 1990 to 2002 were produced using the Markov module of IDRISI.…”

Land Cover Mapping Analysis and Urban Growth Modelling Using Remote Sensing Techniques in Greater Cairo Region—Egypt

“…It is embedded in the IDRISI software [7], where only thematic raster images with the same land cover categories listed in the same sequential order can be input for LULCC analysis [15]. LCM evaluates land cover changes between two different times, calculates the changes, and displays the results with various graphs and maps.…”

“…LCM evaluates land cover changes between two different times, calculates the changes, and displays the results with various graphs and maps. Then, it predicts future LULC maps on the basis of relative transition potential maps [15] relying upon Multi-Layer Perceptron (MLP) neural networks [16]. LCM was found to produce better prediction accuracy in short time scales, especially in the case of stable land covers rather than cases of rapid change [15].…”

“…Then, it predicts future LULC maps on the basis of relative transition potential maps [15] relying upon Multi-Layer Perceptron (MLP) neural networks [16]. LCM was found to produce better prediction accuracy in short time scales, especially in the case of stable land covers rather than cases of rapid change [15]. When compared to models that predict LULCC based on supervised approaches (e.g., the Weights of Evidence method, where the weights can be selected and edited by a user), LCM generates more accurate overall change potential maps because neural network outputs are able to express the change of various land cover types more adequately than individual probabilities obtained through the Weights of Evidence method [16].…”

Assessing and Predicting Changes in the Status of Gambari Forest Reserve, Nigeria Using Remote Sensing and GIS Techniques

“…Change detection is the concept of identifying contrasts or discrepancies in the state of an object or phenomenon by observing it at different times [32]. Change detection studies are centred on identifying the biotic and abiotic components of the spectral and temporal changes that are occurring within ecosystems [33]. It involves the ability to quantify temporal alteration and transformation using multi-temporal data sets.…”