Evaluation of Model Validation Techniques in Land Cover Dynamics

Ahmed, Bayes; Ahmed, Raquib; Zhu, Xuan

doi:10.3390/ijgi2030577

Cited by 78 publications

(48 citation statements)

References 45 publications

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“…In this study, owing to the CA-Markov model being a spatiotemporal model, kappa value alone cannot accurately reflect spatial changes [59]. The Map Comparison Kit 3 is a good alternative to present the different errors not only in quantity, but also in location [60]. By considering this reason, the Map Comparison Kit 3 was selected to validate our simulation results [61].…”

Section: Transition Probabilitymentioning

confidence: 99%

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

2018

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Abstract:Simulating future land use/cover changes is of great importance for urban planners and decision-makers, especially in metropolitan areas, to maintain a sustainable environment. This study examines the changes in land use/cover in the Tokyo metropolitan area (TMA) from 2007 to 2017 as a first step in using supervised classification. Second, based on the map results, we predicted the expected patterns of change in 2027 and 2037 by employing a hybrid model composed of cellular automata and the Markov model. The next step was to decide the model inputs consisting of the modeling variables affecting the distribution of land use/cover in the study area, for instance distance to central business district (CBD) and distance to railways, in addition to the classified maps of 2007 and 2017. Finally, we considered three scenarios for simulating land use/cover changes: spontaneous, sub-region development, and green space improvement. Simulation results show varied patterns of change according to the different scenarios. The sub-region development scenario is the most promising because it balances between urban areas, resources, and green spaces. This study provides significant insight for planners about change trends in the TMA and future challenges that might be encountered to maintain a sustainable region.

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Section: Transition Probabilitymentioning

confidence: 99%

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

2018

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“…Cohen's kappa measures the agreement between two raters. This approach is useful for identifying the degree that the cells are identical in two maps [30]. K is measured as follows:…”

Section: Model Validationmentioning

confidence: 99%

Change of Land Use/Cover in Tianjin City Based on the Markov and Cellular Automata Models

Wang

Murayama

2017

IJGI

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Abstract:In recent years, urban areas have been expanding rapidly in the world, especially in developing countries. With this rapid urban growth, several environmental and social problems have appeared. Better understanding of land use and land cover (LULC) change will facilitate urban planning and constrain these potential problems. As one of the four municipalities in China, Tianjin has experienced rapid urbanization and such trend is expected to continue. Relying on remote sensing (RS) and geographical information system (GIS) tools, this study investigates LULC change in Tianjin city. First, we used RS to generate classification maps for 1995, 2005, and 2015. Then, simulation models were applied to evaluate the LULC changes. Analysis of the 1995, 2005, and 2015 LULC maps shows that more than 10% of the cropland areas were transformed into built-up areas. Finally, by employing the Markov model and cellular automata (CA) model, the LULC in 2025 and 2035 were simulated and forecasted. Our analysis contributes to the understanding of the development process in the Tianjin area, which will facilitate future planning, as well as constraining the potential negative consequences brought by future LULC changes.

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“…The overall accuracies of the classified images (1989,1999, and 2009) were, respectively, found to be 86.48%, 90.69%, and 94.83%, with Kappa coefficients of 0.86, 0.91, and 0.95 ( Table 3). Note that the Kappa coefficient is a measure of the proportional (or percentage) improvement by the classifier over a purely random assignment to classes [56,57]. On the other hand, the user's accuracy measures the proportion of each land cover class, which is correct whereas producer's accuracy measures the proportion of the land base, which is correctly classified.…”

Section: Derivation Of Land Cover Mapsmentioning

confidence: 99%

Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh

et al. 2013

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Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST) in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP) area were used for analysis. This study first identified patterns of land cover changes between the periods and investigated their impacts on LST; second, applied artificial neural network to simulate land cover changes for 2019 and 2029; and finally, estimated their impacts on LST in respective periods. Simulation results show that if the current trend continues, 56% and 87% of the DMP area will likely to experience temperatures in the range of greater than or equal to 30 °C in 2019 and 2029, respectively. The findings possess a major challenge for urban planners working in similar contexts. However, the technique presented in this paper would help them to quantify the impacts of

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Evaluation of Model Validation Techniques in Land Cover Dynamics

Cited by 78 publications

References 45 publications

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

Change of Land Use/Cover in Tianjin City Based on the Markov and Cellular Automata Models

Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh

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