Predicting and mapping land cover/land use changes in Erbil /Iraq using CA-Markov synergy model

Khwarahm, Nabaz R.; Qader, Sarchil; Ararat, Korsh; Al-Quraishi, Ayad M. Fadhil

doi:10.1007/s12145-020-00541-x

Cited by 63 publications

(21 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is vital to measure the degree of agreement between automatic cataloguing data with reference data (Li et al 2018). An autonomous data comprising 30 per cent of the exercise data per class, for example, 30 per cent of the 200 spectral autograph trials for every feature class equal to 60 points, was achieved by the ArcMap tool (Khwarahm et al 2021b). The accuracy of 1999, 2010, and 2021 LU/LC maps, which were obtained from the maximum likelihood method, were assessed by employing that dataset.…”

Section: Classi Cation Valuationmentioning

confidence: 99%

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

Mahdi

Mohammed

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Classi Cation Valuationmentioning

confidence: 99%

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

Mahdi

Mohammed

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It not only reflects the land use type structure at the beginning and end of the period but also shows the transfer and change of various land use types during the research period. It is used to describe the direction of regional land use change and the source and composition of various land use types at the end of the study period [26]. The transition matrix generates the transition probability matrix of land use change to predict the changing trend of regional land use in a specific scenario [27].…”

Section: Land Use Transfer Matrixmentioning

confidence: 99%

The Influence of Land Use Evolution on the Visitor Economy in Wuhan from the Perspective of Ecological Service Value

Qiao

Mao

Morrison

2021

Land

View full text Add to dashboard Cite

This research used transfer matrix, dynamic attitude, and a linear regression model to investigate the characteristics of land-use change and evolution of ecological service values and their impacts on Wuhan’s visitor economy. The results showed that: (1) the land-use scale in the Wuhan metropolitan area changed significantly from 1990 to 2018. The area of arable land, forest land, and grassland decreased at a faster rate, whereas that of water and construction land continued to increase; (2) there were differences in the dynamic attitudes of land-use at different stages. The dynamic attitude of construction land-use changed the most with cultivated land, water area, forest land, unused land, and grassland. From 1990 to 2005, land-use change exhibited a relatively gentle trend, whereas from 2005 to 2020, it accelerated; (3) although land-use regulation service, support service, and cultural service values positively responded to tourism economic growth, their influences were dissimilar. This study clarifies the effects of urban land-use on tourism economic development and provides a reference for its effective control.

show abstract

“…The standard Kappa (denoted as kstandard) reveals the ability of classification to attain perfect classification and uses for quantity (not location) assessment (Pontius Jr, 2000). As Eq.9 indicates, Kstandard signifies the ratio of the categories that correlated correctly to categories that correlated correctly by chance (Khwarahm et al, 2021).…”

Section: Ca-mc Model Validationmentioning

confidence: 99%

Urban Growth Modeling Considering Educational Institutions: An Investigation Into the Performance of Three Anfis Methods

Ghobadiha

Motieyan

2021

Preprint

View full text Add to dashboard Cite

Due to increasing urbanization, the rapid expansion of urban spaces has become a major environmental concern over the last few decades. Therefore, modeling the urban expansion as a complex system has been scrutinized in recent years; however, determining the rules that lead to the expansion of urban areas has always been a challenging factor in this field, especially for disaggregated models like cellular automata (CA). To overcome this issue, in this research, an Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to enhance the simulation of urban growth through the automatic production of transition rules. The ANFIS can be associated with several inputs division methods, such as ANFIS accompanied by grid partitioning (ANFIS-GP), subtractive clustering (ANFIS-SC), and fuzzy c-means clustering (ANFIS-FCM). Hence, twenty-two ANFIS models based on Landsat images for the time interval from 2000 to 2010 and using different division methods were trained to investigate their effect on the efficiency of ANFIS in urban growth modeling. To examine the efficiency, the Cellular Automata-based Markov Chain (CA-MC) as a popular method was developed, and the simulation accuracy of CA-MC and the most accurate ANFIS models were obtained through comparison with observed data. The most accurate ANFIS-SC model had a Kappa of 0.76 and an overall accuracy of 93.41% for the 2019 simulated map. The results from this study reveal that the ANFIS model is effective at simulating urban expansion and the ANFIS-SC is superior to CA-MC, ANFIS-GP, and ANFIS-FCM models in urban expansion modeling.

show abstract

Predicting and mapping land cover/land use changes in Erbil /Iraq using CA-Markov synergy model

Cited by 63 publications

References 53 publications

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

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

The Influence of Land Use Evolution on the Visitor Economy in Wuhan from the Perspective of Ecological Service Value

Urban Growth Modeling Considering Educational Institutions: An Investigation Into the Performance of Three Anfis Methods

Contact Info

Product

Resources

About