Monitoring and Predicting Land Use Changes in Kurdistan Province by GIS and CA-Markov Model

Document Type : Research Paper

Authors

Departmen of Agricultural Management of Development, Faculty of Agricultural Economics and Development, University of Tehran, Karaj, Iran

Abstract

Land use changes are one of nature's most dynamic components, evolving dramatically at various sizes since the Industrial Revolution. Ongoing monitoring and quantification of such changes allow us to have a better knowledge of the ecosystem's function and health. The present study aims to apply the Markov hybrid Cellular Automata (CA) model to model land use changes in Kurdistan province. Land use maps were provided using Landsat data and the maximum probability classification approach in six groups for the years 1980, 1990, and 2000. It was then analyzed using the simulation map of 2000 and the real map of the model, and finally, the user map for the years 2030 and 2040 was forecasted using six user classifications. The utilization of agricultural lands, barren lands with little vegetation, urban and built-up regions, and irrigated beds rose by 19.05, 12.36, 0.30, and 0.27 percent, respectively, between 2000 and 2011. Forest and rangeland land use, on the other hand, have declined by 14.75 percent and 17.22 percent, respectively. The kappa coefficient of the model in all coefficients was greater than 0.8, indicating that the model is very efficient. The assessment of future changes in land use compared to 1980 found that forest and rangeland land uses dropped by 17.47 and 28.34 percent between 1980 and 2030, and by 21.98 and 29.78 percent between 1980 and 2040, respectively. Agricultural lands, barren lands with vegetation, residential and built-up regions, and water beds have grown 32.54%, 12.55%, 0.46%, and 0.26%, respectively, from 1980 to 2030, while these uses have increased 36.96%, 12.88%, 1.58%, and 0.33%, respectively, from 1980 to 2040. The findings of this study demonstrate the present and future trends in land use change, which is extremely necessary and beneficial to natural science researchers, environmentalists, non-governmental organizations (NGOs), politicians, and urban planners.

Keywords

Main Subjects

Extended Abstract

Objectives     

Land use changes are one of nature's most dynamic components, evolving dramatically at various sizes since the Industrial Revolution. Ongoing monitoring and quantification of such changes allow us to have a better knowledge of the ecosystem's function and health. To avoid such massive losses, we must study and monitor land use changes over time in these places, which may be done through remote sensing techniques, so that researchers can get up-to-date information on land cover and land use. In addition, it is beneficial for spatial analysis and modeling of land changes over time, as well as cost-effective on a regional scale in terms of time and expense. The present study aims to apply the Markov hybrid Cellular Automata (CA) model to model land use changes in Kurdistan province. The Markov hybrid Cellular Automata (CA) approach may be listed among the remote sensing models and methodologies that are used to examine the trend of land changes and their forecasting.

 

Methods

    The satellite images and topographic maps were included in all of the data utilized in this investigation. The US Geological Survey provided Landsat satellite images (TM, ETM +, OLI) from June through August during the vegetation growing season. The images and their categorization were processed and analyzed using ENVI 5.3 software, and the instructive maps of terrestrial reality were created using Arc GIS 10.4.

The preprocessing procedure, which involves two phases of geometric and radiometric adjustments, was completed before utilizing satellite images. In the present method, the land use map generated for the years 2000, 2010, and 2020 were chosen as the input for Land Change Modeler (LCM) to analyze the regional changes and predict the land use changes. To evaluate the model's accuracy, kappa statistics relating to total kappa values, location-based kappa, and value-based kappa were used. The CA-Markov model, which is a mix of cellular automata and a Markov chain, was used to determine the transfer probability from one user to another. This model incorporates hybrid cellular automata and the Markov chain, as well as the inclusion of spatial structure and geographic distribution to the Markov chain for pixel position and land use prediction

 

Results

Analyzing the change patterns in Kurdistan province revealed that some substantial changes in the level of land use in this area occurred during the statistical period (2000-2020). In doing so, among the six land uses, the area of agricultural land use, barren land with rare vegetation, residential and built-up areas, and water bodies all were increasing since 2000, while pastures and forests were decreased (Figure 3). During this period, agricultural fields, forests, pastures, barren lands with low vegetation, residential and built-up places, and water beds have changes, respectively, from 12.65%, 27.16%, 47.70%, 11.96%, 0.34%, and 19% In 2020, to 31.69, 12.41, 30.48, 24.31, 0.64, and 0.46 in 2020. Based on the results of CA-Markov model, forest and pasture land uses dropped by 17.47 and 28.34 percent respectively, between 2000 and 2030, and these amounts are 21.98 and 29.78 percent between 2000 and 2040, respectively. Agricultural lands, barren lands with low vegetation, residential and built-up areas, and water beds have grown by 32.54, 12.55, 0.46, and 0.26 percent, respectively, from 2000 to 2030. These land uses have increased by 36.96, 12.88, 1.58, and 0.33 percent, respectively, from 2000 to 2040.

 

Discussion

Based on the results, agricultural lands have increased and will increase until the timeline of this research, and it is mainly because of population growth in the district, which, consequently, people have been obliged to cultivate additional land due to the growth in population over the past several decades, as well as the demand for food, shelter, and welfare of this rising population (pastures and forests). Raising agricultural areas and population, on the other hand, need water, which may be obtained through a variety of techniques, including surface water control, such as dam building, which will be the primary factor in increasing water beds in this region in the next years. Generally, one of the major components of innovation in this work is the use of a Markov model to forecast changes and the preparation of a map to anticipate them in various usage. Furthermore, the simulated maps might serve as a useful tool for natural resource managers and planners. Besides, simulated user maps can be utilized as a warning system for the repercussions and long-term effects of land use change.

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Iranian Journal of Agricultural Economics and Development Research
Volume 55, Issue 2
August 2024
Pages 379-395

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