Future Simulation of Land Use Changes in Rapidly Urbanizing South China Based on Land Change Modeler and Remote Sensing Data

Abstract

Landscape transformations in rapidly urbanizing Guangdong, Hong Kong, and Macao (GHKM) regions of South China represent the most complex and dynamic processes altering the local ecology and environment. In this study, Land Change Modeler (LCM) is applied to land use land cover (LULC) maps for the years 2005, 2010, and 2017, derived from Landsat images, with the aim of understanding land use land cover change patterns during 2005–2017 and, further, to predict the future scenario of the years 2024 and 2031. Furthermore, the changes in spatial structural patterns are quantified and analyzed using selected landscape morphological metrics. The results show that the urban area has increased at an annual rate of 4.72% during 2005–2017 and will continue to rise from 10.31% (20,228.95 km2) in 2017 to 16.30% (31,994.55 km2) in 2031. This increase in urban area will encroach further into farmland and fishponds. However, forest cover will continue to increase from 45.02% (88,391.98 km2) in 2017 to 46.88% (92,049.62 km2) in 2031. This implies a decrease in the mean Euclidian nearest neighbor distance (ENN_MN) of forest patches (from 217.57 m to 206.46 m) and urban clusters (from 285.55 m to 245.06 m) during 2017–2031, indicating an accelerated landscape transformation if the current patterns of the change continues over the next decade. Thus, knowledge of the current and predicted LULC changes will help policy and decision makers to reconsider and develop new policies for the sustainable development and protection of natural resources.

Modeling and Prediction of Land Use Land Cover Change Dynamics Based on Land Change Modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin, Ethiopia

“…The Cramer's V does not assure a strong performance of the variables, since it cannot represent the scientific prerequisites and the multifaceted nature of the relationships. It simply helps to determine whether or not to include the particular variable as a driving factor of LULC change [50].…”

Complex Socio-Ecological Systems: Translating Narratives into Future Land Use and Land Cover Scenarios in the Kilombero Catchment, Tanzania

“…The simulation of the LULC scenarios was performed using the Land Change Modeler (LCM) [66][67][68] integrated into the IDRISI GIS-Software TerrSet (version 18.31) [67]. The LCM is widely applied in LULC scenario modeling such as deforestation [69], urban growth [70], conservation [71,72], and water resources modeling [5].…”

“…Given the available data, those years represent the current changes in the Kilombero catchment most appropriately [5]. Second, two transition submodels representing the identified major LULC transformations were calibrated deploying the multi-layer perceptron (MLP) neural network [68,70,71]. The submodel 'cropland' included all LULC class transitions from natural vegetation to cropland while the submodel 'rice' included all transitions from natural vegetation to rice.…”

“…As the CLD was developed based on causal loops identified within the stakeholder workshop, the participatory insights were thereby indirectly incorporated into the model [13,49]. Second, literature on driver variables frequently used in the LCM modeling process was reviewed [5,61,[69][70][71][72]. Consequently, eight explanatory variables were included for calibrating both submodels: (i) A digital elevation model, (ii) slope gradients, (iii) soil types, and (iv) the evidence likelihood of change for each LULC category as well as distance variables such as (v) the proximity to rivers, (vi) the proximity to roads, (vii) to settlement structures and (viii) to already established rice fields (submodel 'rice') or cropland (submodel 'cropland') as of 2004.…”

Future land use change simulations for the Lepelle River Basin using Cellular Automata Markov model with Land Change Modeller-generated transition areas

“…This study also uses the CAM model. Both the Cellular Automata (CA) and Markov model have great advantages in the study of LUCs, albeit both have respective disadvantages (see Hasan et al 2020). The Markov chain model has been widely used to determine LUCs, however, the traditional Markov chain model has slight difficulty in predicting the spatial pattern of LUCs.…”