Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques

Mansour, Shawky; Al-Belushi, Mohammed Ali K.; Al‐Awadhi, Talal

doi:10.1016/j.landusepol.2019.104414

Cited by 250 publications

(122 citation statements)

References 65 publications

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“…The mountainous terrain in the watershed, which limits the land use expansion to a certain extent, leads to insignificant changes in simulated cultivated land and construction land. This conclusion is consistent with previous studies that suggest LUCC will not undergo huge structural changes [57,58].…”

Section: Land Use Changesupporting

confidence: 94%

Future Flood Risk Assessment under the Effects of Land Use and Climate Change in the Tiaoxi Basin

Yang

2020

Sensors

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Global warming and land-use change affects runoff in the regional basin. Affected by different factors, such as abundant rainfall and increased impervious surface, the Taihu basin becomes more vulnerable to floods. As a result, a future flood risk analysis is of great significance. This paper simulated the land-use expansion and analyzed the surface change from 2020 to 2050 using the neural network Cellular Automata Markov (CA-Markov) model. Moreover, the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) dataset was corrected for deviation and used to analyze the climate trend. Second, the verified SWAT model was applied to simulate future runoff and to analyze the future flood risk. The results show that (1) land use is dominated by cultivated land and forests. In the future, the area of cultivated land will decrease and construction land will expand to 1.5 times its present size. (2) The average annual precipitation and temperature will increase by 1.2% and 1.5 degrees from 2020 to 2050, respectively. During the verified period, the NSE and r-square values of the SWAT model are greater than 0.7. (3) Compared with the historical extreme runoff, the extreme runoff in the return period will increase 10%~25% under the eight climate models in 2050. In general, the flood risk will increase further under the climate scenarios.

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Section: Land Use Changesupporting

confidence: 94%

Future Flood Risk Assessment under the Effects of Land Use and Climate Change in the Tiaoxi Basin

Yang

2020

Sensors

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“…The CA–Markov model combines the respective advantages of the Markov model and the CA model [ 28 ]. Its working principle is to take the land use in the base period as the initial state based on the land-use transfer area in its previous stage and the land suitability atlas under the influence of multiple factors in the starting year [ 35 ]. The land-use type is then redistributed until the area predicted by the Markov chain is reached.…”

Section: Methodsmentioning

confidence: 99%

Dynamic evolution and scenario simulation of habitat quality under the impact of land-use change in the Huaihe River Economic Belt, China

Tang

Wang

et al. 2021

PLoS ONE

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Habitat quality is an important indicator for evaluating the biodiversity provided by ecosystem. Estimating and scenario-simulating the dynamic evolution and future development trends of habitat quality under the influence of land-use change is significant in regional biodiversity conservation, formulating land-use planning, and maintaining the ecological environmental sustainability. In this article, we included the Huaihe River Economic Belt as the area of study because of its vital location in China and applied the CA–Markov and InVEST models to analyze the spatio-temporal evolution of habitat quality and to simulate the future development trends of habitat quality under three different land-use scenarios: fast urban growth scenario, farmland conservation-oriented scenario, and ecological conservation-oriented scenario. The results showed that the land-use change in the Huaihe River Economic Belt was mostly represented by the continuous increase of the built-up area, whereas other land types all declined in area from 1995 to 2015. The land-use changes under these three abovementioned alternative future scenarios with different development orientations were considerably different. The built-up area has been shown to expand rapidly to occupy other land types on a large scale under the fast urban growth scenario. Urban land increased slightly and a large area of rural residential land would be converted into farmland under the farmland conservation-oriented scenario. The built-up area and farmland might decrease while woodland, grassland and water would increase in extent of areas under the ecological conservation-oriented scenario. Habitat quality has been shown to be generally poor, continuing to decline from 1995 to 2015, while its spatial distribution was higher in the southwest and northeast areas and lower in the central regions. The future habitat quality would display a downward trend under the fast urban growth and farmland conservation-oriented scenarios with a further deterioration of the ecological environment, while the ecological conservation-oriented scenario predicted the converse trend that the ecological environment would be improved productively. This study may be useful for understanding the impact of land-use dynamics on biodiversity. The research results can provide a scientific basis for the decision-makers to formulate biodiversity conservation and land management policies.

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“…Furthermore, uncertainty, class consistency, and mis-registration issues become more of a problem as more time periods are used in the SLEUTH model. There are many studies have been implemented the combination of CA-Markov for land use modelling (Gharaibeh et al, 2020;Halmy et al, 2015;Mansour et al, 2020;Okwuashi & Ndehedehe, 2020;Rahnama, 2021;Wang et al, 2021;Wu et al, 2019;Zhou et al, 2020). However very limited studies using neural network combination with CA for modelling land-use dynamic.…”

Section: Previous Researchmentioning

confidence: 99%

An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia

Ramdani¹,

Setiawan²,

Rusydi³

et al. 2021

Preprint

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Great Malang region is developing rapidly with the population increase and inhabitant`s activity, like migration and urbanization. Other activities like agricultural expansion as well as an uncontrolled residential development need to be monitored to avoid any negative impact in the future. The availability of free and open-source software, spatial high-resolution satellite imagery datasets, and powerful algorithms open the possibilities to map, monitor, and predict the future trend of land use land cover (LULC) changes. However, the accuracy and precision of this model is still in doubt, especially in the Great Malang region. Research is needed to provide a foundational basis and documentation on how the changes occur, where did the changes occur, and the accuracy of the predicted model. This study tries to answer those questions using the high spatial resolution of Sentinel-2 imageries. Combination of the fuzzy algorithm, artificial neural network, and cellular automata was utilized to process the datasets. We analysed four different scenarios of simulation and the result then compared. The different number of hidden layers and iteration was used and evaluated to understand the effect of different parameters in the prediction result. The best scenario was then used to predict future land use changes. This study has successfully produced the future LULC model of Great Malang region with high accuracy level (87%). The study also found that the land use transformation from agriculture to urban built-up area is relatively low, where changes of the built-up area over three periods of analysis are below than 5%. This is due to the physical condition of Great Malang region where mountainous areas are dominated.

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Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques

Cited by 250 publications

References 65 publications

Future Flood Risk Assessment under the Effects of Land Use and Climate Change in the Tiaoxi Basin

Future Flood Risk Assessment under the Effects of Land Use and Climate Change in the Tiaoxi Basin

Dynamic evolution and scenario simulation of habitat quality under the impact of land-use change in the Huaihe River Economic Belt, China

An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia

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