Past and future prediction of land cover land use change based on earth observation data by the CA–Markov model: a case study from Duhok governorate, Iraq

Khwarahm, Nabaz R.; Najmaddin, Peshawa M.; Qader, Sarchil

doi:10.1007/s12517-021-07984-6

Cited by 25 publications

(18 citation statements)

References 67 publications

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“…It also fails to objectively reflect stochastic evolution and future development trends in land-use change [62]. The Markov model conducts superior simulation over long time periods but is unable to simulate spatial pattern change and needs to be combined with a spatial prediction model [63]. The CLUE-S model has a strong spatial allocation function but is limited in land quantity prediction [36].…”

Section: Discussion Of the Rf-markov-flus Modelmentioning

confidence: 99%

Multi-Scenario Simulation of Production-Living-Ecological Space in the Poyang Lake Area Based on Remote Sensing and RF-Markov-FLUS Model

Fang

Xia

et al. 2022

Remote Sensing

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With industrialization and urbanization, the competition among land production, living, and ecological (PLE) spaces has intensified. Particularly in ecological reserves, competition among various types of land use restricts the coordinated development of PLE space. To explore spatial sustainable development, this study starts from a PLE spatial perspective, based on Landsat long time series images. Object-based image analysis (OBIA) and landscape index analysis were selected to monitor the spatial and temporal land use and landscape pattern changes in the Poyang Lake region (PYL region) from 1989 to 2020. The RF-Markov-FLUS coupled model was used to simulate spatial changes in 2030 under four scenarios: production space priority (PSP), living space priority (LSP), ecological space priority (ESP), and an integrated development (ID). Finally, the goal-problem-principle was used to enhance PLE space. The results showed that: (1) production space and ecological spaces decreased in general from 1989 to 2020 by 3% and 7%, respectively; living space increased by 11%. (2) From 1989 to 2020, the overall landscape spread in the Poyang Lake (PYL) area decreased, connectivity decreased, fragmentation increased, landscape heterogeneity increased, and landscape geometry became more irregular. (3) Compared with the other three scenarios, the ID scenario maintained steady production space growth in 2030, the expansion rate of living space slowed, and the area of ecological space decreased the least. (4) Spatial pattern optimization should start with three aspects: the transformation of the agricultural industry, improving the efficiency of urban land use, and establishing communities of “mountains, water, forests, fields, lakes and grasses”. The results provide scientific planning and suggestions for the future ecological protection of Poyang Lake area with multiple scenarios and perspectives.

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Section: Discussion Of the Rf-markov-flus Modelmentioning

confidence: 99%

Multi-Scenario Simulation of Production-Living-Ecological Space in the Poyang Lake Area Based on Remote Sensing and RF-Markov-FLUS Model

Fang

Xia

et al. 2022

Remote Sensing

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“…Machine learning, especially hybrid machine learning models, has become a new technique in the field of land use change prediction. Abijith [30], Khwarahm [31], Fitawok [32], Nasiri [33], Nath [34], and Al-sharif [35] evaluated the uncertainty of cellular automata-Markov chain approach modeling, and they predicted and analyzed land use change in the northern coastal region of Tamil Nadu in India, Bahir Dar City in Ethiopia, Arasbaran region in Iran, and Tripoli Metropolitan City in Lebanon, depending on the approach. ZiaeeVafaeyan [36], Gounaridis [37], Levy [38], and Abdullahi [39] conducted a multi-scenario analysis of land use change by leveraging the cellular automata-random forest model, bee colony optimization, random forest-cellular automata modeling, and Bayes theory.…”

Section: Literature Review 21 Research Methods and Modelmentioning

confidence: 99%

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Zhang

Zhao

et al. 2022

Land

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Making efforts to promote rationalized urban construction land change, distribution, allocation, and its performance is the core task of territory spatial planning and a complex issue that the government must face and solve. Based on the Boston Consulting Group matrix, a decoupling model, and a GIS tool, this paper constructs a new tool that integrates “dynamic analysis + performance evaluation + policy design” for urban construction land. We reached the following findings from an empirical study of Gansu, China: (1) Urban construction land shows diversified changes, where expansion is dominant and shrink cannot be ignored. (2) Most cities are in the non-ideal state of LH (Low-High) and LL (Low-Low), with a small number in the state of HH (High-High) and HL (High-Low). (3) Urban construction land change and population growth, economic development, and income increase are in a discordant relationship, mostly in strong negative decoupling and expansive negative decoupling. (4) The spatial heterogeneity of urban construction land change and its performance are at a high level, and they show a slow upward trend. Additionally, the cold and the hot spots show obvious spatial clustering characteristics, and the spatial pattern of different indexes is different to some extent. (5) It is suggested that in territory spatial planning Gansu should divide the space into four policy areas—incremental, inventory, a reduction development policy area, and a transformation leading policy area—to implement differentiated management policies and to form a new spatial governance system of “control by zoning and management by class.” The change of urban construction land, characterized by dynamics and complexity, is a direct mapping of the urban growth process. The new tools constructed in this paper will help to reveal the laws of urban development and to improve the accuracy of territory spatial planning in the new era. They are of great theoretical significance and practical value for promoting high-quality and sustainable urban development.

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“…Through that period, the bare land displayed a constant reduction of about 11.9% of the entire area, while the built-up lands extent raised from 240 to 1118 km 2 , Table 4. The growth of the urban area denotes the growth of the population and expansion of organization (Khwarahm et al 2021a). Population development is considered one of the key factors that cause LU/LC alterations (Wang and Zheng 2022).…”

Section: Assessment Of the Classi Cationmentioning

confidence: 99%

“…Generally, there was an important degree of tting between the simulated and real images, Table 5. The total Kappa numerical differences of K no = 0.8635, K location = 0.8541, K location Strata = 0.8541, K standard = 0.7853 were accomplished, which are measured suitable to the extent that the model justi cation consistency is concerned for additional use (Khwarahm at al 2021a). The model is implemented reasonably in expecting the water bodies, bare land, urban areas, cultivated lands, and forest lands, Table 4.…”

Section: Model Validationmentioning

confidence: 99%

Predicting Land Use/Land Cover Changes in the Lesser Zab River Catchment/Iraq through CA-Markov Synergy Model

Mahdi

Mohammed

2022

Preprint

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Land use/land cover is measured as one of the utmost dynamic constituents of the atmosphere that has been altering abnormally from the time when after the industrial revolution at different measures. A well understanding of the drive and strength of environments needs regular monitoring and quantifying for land use/land cover alteration changing aspects. The current research targets to predict the prospect land use/land cover (LU/LC) alterations, for the Lesser Zab catchment in the Northern part of Iraq, applying the synergy Cellular Automata-Markov simulation. Three sequential year Landsat images (1999, 2010, and 2021) were categorized by the Maximum Likelihood method. Then, three LU/LC images with numerous class classifications were created and an alteration identification examination was performed. With the categorized (1999–2010) as well as (2010–2021) LU/LC maps in the hybrid model, the corresponded LU/LC maps for 2021 and 2041 were modeled, correspondingly. The classified 2021 LU/LC map was considered to validate model output 2021. The agreement accuracy between the categorised and the modeled images were Kno = 0.864, Klocation = 0.854, Kstandard = 0.785, in that order. Prospect likelihoods validate that between 2021 and 2041, the urban area would rise by 78% (from 1118 to 5200 km2). However, bare lands/light, agricultural lands, water bodies, bare lands/dark, and forest lands would decrease by 3% (from 6983 to 6736 km2), 12% (from 7992 to 7036 km2), 15% (from 141.03 to 119.86 km2), 30% (from 7 to 4 km2), and 76% (from 3810 to 904 km2), correspondingly. This study’s conclusions are priceless for policymakers, urban managers, and ecological researchers.

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Past and future prediction of land cover land use change based on earth observation data by the CA–Markov model: a case study from Duhok governorate, Iraq

Cited by 25 publications

References 67 publications

Multi-Scenario Simulation of Production-Living-Ecological Space in the Poyang Lake Area Based on Remote Sensing and RF-Markov-FLUS Model

Multi-Scenario Simulation of Production-Living-Ecological Space in the Poyang Lake Area Based on Remote Sensing and RF-Markov-FLUS Model

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Predicting Land Use/Land Cover Changes in the Lesser Zab River Catchment/Iraq through CA-Markov Synergy Model

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