Revisiting Kappa to account for change in the accuracy assessment of land-use change models

Vliet, Jasper van; Bregt, A.K.; Hagen‐Zanker, Alex

doi:10.1016/j.ecolmodel.2011.01.017

Cited by 261 publications

(184 citation statements)

References 35 publications

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“…In order to check the quantification of map similarities and dissimilarities, the Map Comparison Kit (MCK) was used in this study [29]. The MCK is software used for map comparison.…”

Section: Model Validationmentioning

confidence: 99%

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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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“…In order to check the quantification of map similarities and dissimilarities, the Map Comparison Kit (MCK) was used in this study [29]. The MCK is software used for map comparison.…”

Section: Model Validationmentioning

confidence: 99%

“…In order to ensure that the Markov model and CA model were valid for simulation, Cohen's kappa coefficient was calculated [29]. Cohen's kappa measures the agreement between two raters.…”

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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“…To statistically quantify these errors, a random selection of pixels of the classified maps was performed to build a confusion matrix. The kappa coefficient K, a discrete multivariate technique used in accuracy assessments of thematic maps, is an efficient approach to derive information from an image via the confusion matrix [22][23][24][25]. K > 0.80 represents strong agreement and good accuracy, 0.40-0.80 is middle, and <0.40 is poor [26].…”

Section: Land Use Land Cover Change Analysismentioning

confidence: 99%

Land Use and Land Cover Changes under Climate Uncertainty: Modelling the Impacts on Hydropower Production in Western Africa

Obahoundje¹,

Ofosu

Akpoti³

et al. 2017

Hydrology

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Abstract:The Bui hydropower plant plays a vital role in the socio-economic development of Ghana. This paper attempt to explore the combined effects of climate-land use land cover change on power production using the (WEAP) model: Water Evaluation and Planning system. The historical analysis of rainfall and stream flow variability showed that the annual coefficient of variation of rainfall and stream flow are, respectively, 8.6% and 60.85%. The stream flow varied greatly than the rainfall, due to land use land cover changes (LULC). In fact, the LULC analysis revealed important changes in vegetative areas and water bodies. The WEAP model evaluation showed that combined effects of LULC and climate change reduce water availability for all of demand sectors, including hydropower generation at the Bui hydropower plant. However, it was projected that Bui power production will increase by 40.7% and 24.93%, respectively, under wet and adaptation conditions, and decrease by 46% and 2.5%, respectively, under dry and current conditions. The wet condition is defined as an increase in rainfall by 14%, the dry condition as the decrease in rainfall by 15%; current account is business as usual, and the adaptation is as the efficient use of water for the period 2012-2040.

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“…It was progressively noted that a full characterization of a fit between two maps should tackle not only quantity or location of changes of matching cells but also distances between locations of matching cells [44]. To address this and a number of other map comparison bottlenecks [46,47], alternative algorithms have been proposed over the years [48][49][50]. Pontius and Millones [51] suggested that summarizing cross-tabulation matrix of the simulated and the observed land-use map in terms of quantity and spatial allocation disagreements will sufficiently account for differences between two categorical maps in terms of the quantity (changes or persistence) and allocation of matching cells.…”

Section: Model Evaluationmentioning

confidence: 99%

Land-Use Change Modelling in the Upper Blue Nile Basin

et al. 2016

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Land-use and land-cover changes are driving unprecedented changes in ecosystems and environmental processes at different scales. This study was aimed at identifying the potential land-use drivers in the Jedeb catchment of the Abbay basin by combining statistical analysis, field investigation and remote sensing. To do so, a land-use change model was calibrated and evaluated using the SITE (SImulation of Terrestrial Environment) modelling framework. SITE is cellular automata based multi-criteria decision analysis framework for simulating land-use conversion based on socio-economic and environmental factors. Past land-use trajectories were evaluated using a reference Landsat-derived map (agreement of 84%). Results show that major land-use change drivers in the study area were population, slope, livestock and distances from various infrastructures (roads, markets and water). It was also found that farmers seem to increasingly prefer plantations of trees such as Eucalyptus by replacing croplands perhaps mainly due to declining crop yield, soil fertility and climate variability. Potential future trajectory of land-use change was also predicted under a business-as-usual scenario (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024)(2025). Results show that agricultural land will continue to expand from 69.5% in 2009 to 77.5% in 2025 in the catchment albeit at a declining rate when compared with the period from 1986 to 2009. Plantation forest will also increase at a much higher rate, mainly at the expense of natural vegetation, agricultural land and grasslands. This study provides critical information to land-use planners and policy makers for a more effective and proactive management in this highland catchment.

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Revisiting Kappa to account for change in the accuracy assessment of land-use change models

Cited by 261 publications

References 35 publications

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

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

Land Use and Land Cover Changes under Climate Uncertainty: Modelling the Impacts on Hydropower Production in Western Africa

Land-Use Change Modelling in the Upper Blue Nile Basin

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