Monitoring and modeling land use/cover changes in Arasbaran protected Area using and integrated Markov chain and artificial neural network

Shahi, Elham; Karimi, Saeed; Jafari, Hamid

doi:10.1007/s40808-020-00801-1

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…In addition, the advancement in computing technology couples with free access policies on datasets have made it possible to monitor changes at large spatial scales (WOODCOCK et al 2008;Turner et al 2015). The study of LULCC detection is important to urban planners, policy makers, natural resource managers, agriculture experts and environmentalist (Babalola and Shahi et al 2020). The Markov-CA approach has been applied in generating dynamics of LULCC modeling at watershed level in Indonesia (Yulianto et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, other researchers have predicted 2030 LULCs using the 2010-2020 calibration period (Fig.4). The largest changes were associated with conversion of forest lands into other land uses(Shahi et al 2020). Alongside with this rapid urbanization of the human society, it is estimated that, by 2030, cities will physically expand by 1.2 million km 2 , which is almost same size as the Republic of South Africa(Seto et al 2012;Tateishi 2015).…”

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confidence: 99%

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Multi-Decade Land Cover/Land Use Dynamics and Future Predictions for Zambia: 2000 - 2030

Chisanga,

Phiri,

Mubanga

2023

Preprint

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Accurate and up-to-date information on land use/land cover change (LULCC) is important in land use planning and natural resource management; however, in sub-Saharan Africa, detailed information on LULCC is still lacking. Therefore, this study assessed the dynamics of LULC change (2000–2020) and future projections (2020–2030) for Zambia. The 2000 and 2010 LULC maps were used to simulate the 2020 LULC scenario using Artificial Neural Network (Multi-layer Perception) algorithms in Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS 2.18.14. The 2010 and 2020 maps were used to predict the 2030 LULC classes. The reference 2020 and predicted 2020 LULC maps were used to validate the model. The validation between the predicted and observed 2020 LULC map, Kappa (loc) was 0.9869. The ANN-MLP simulated the 2020 LULC patterns successfully as indicated by the high accuracy level of more than 95%. LULC classes were predicted for 2030 using the 2010–2020 calibration period. The expected LULC types for 2030 revealed that built-up area will increase by 447.20 km2 (71.44%), while 327.80 km2 (0.73%) of cropland will be lost relative to 2020 LULC map. Dense forest (0.19%), grassland (0.85%) and bare land (1.37%) will reduce from 2020–2030. However, seasonally flooded, sparse forest, shrub land, wetland and water body will increase marginally. The largest LULC change is from forest into other LULC types. The insights from this study show that ANN-MLP can be used to predict LULCC, and that the generated information can be employed in land use planning at a national scale.

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