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Simulating and monitoring future land-use trends is one of the major challenges for researchers, decision makers, and local authorities in terms of data, methods, and models that should be used to create a realistic and sustainable land-use planning process. This study aims to use spatio-temporal data and models to generate realistic simulation and...
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Modeling urban growth trends has become one of the critical issues in the last decades. This study aims to evaluate spatio-temporal urban growth trends using spatial modeling. For this purpose, four land-use maps were used to visualize historical urban growth trends in Seremban, Malaysia. Land Change Modeller (LCM) was used to evaluate the spatial...
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... One tool that stands out in predicting changes in the landscape is the Land Change Modeler (LCM), which allows for the design and evaluation of modifications in the landscape structure, as well as planning of LULC when considering a before and after image (EASTMAN & TOLEDANO, 2018;LCM, 2021). This tool has been widely utilized and deployed to predict changes both in natural areas (ABURAS et al., 2018;AIRES et al. 2018;ANSARI & GOLABI, 2019;LI et al., 2020;KHOSHNOOD MOTLAGH et al., 2021), and urban areas (JAIN; JAIN, ALI, 2017; KIM; NEWMAN; GÜNERALP, 2020). ...
Deforestation is a complex problem worldwide caused mostly by the increase in farming activities. Despite the Brazilian Cerrado being the most biodiverse savanna in the world, this biome is threatened by high deforestation rates. The present study aimed to evaluate deforestation in in São Francisco municipality – Minas Gerais state in Brazil's southwestern region, analyzing changes in land cover between 2012 and 2020 focusing on forest, savanna, and grassland physiognomies, and predicting changes in the land cover for the year 2030. It was evaluated metrics of density and size, edge, shape, core area, and proximity. The forest and savanna fragments suffered a reduction in area (0.92% and 12.60%, respectively) and core area, and also increased its proximity. On the other hand, the grassland physiognomy increased its total area (12.72%), nuclear area, and distance from the other fragments. In general, the forest fragment and savanna fragments tended to be elongated and irregular. For 2030, a scenario is predicted with increasing grassland and decreasing forest and savanna. Those changes in the landscape can threaten the region's biodiversity, so public policy attention is needed to the conservation of the fragmented areas.
... This model is effective because it can simulate various types of land cover with adequate calibration and dynamic projection skills [80,81]. Various studies have also demonstrated that LCM is effective at predicting LULC change [82][83][84][85][86][87]. Therefore, the findings would be useful as inputs for planners and other stakeholders regarding LULC trends in the study area. ...
Land use and land cover change are among the drivers of environmental change. The Suluh River Basin’s land use and land cover are modeled in this study using a land change modeler. To accomplish the goals of this study, Landsat images and ancillary data sources were utilized. In eCognition Developer 9.2 software, nearest neighbor fuzzy classification was used to classify Landsat images. With the IDRISI Selva 17.3 software, change detection and modeling were carried out. Both qualitative and quantitative analyses of the data were conducted. The results showed that, despite a drop in forest land of 97.2%, grazing land of 89.8%, plantation land of 89.1%, shrub-bush land of 1.5%, and water bodies of 84.8% from 1990 to 2002, bare land increased by 10.6%, built-up land by 29.4%, and cultivated land by 65.4%. The model projects, bare, built-up, and cultivated land will increase at the cost of water bodies, grazing, forest, shrub-bush, and plantation land between the years 2028 and 2048. Rainfall, slope, height, distance to rivers, distance to highways, distance from towns, and population density were the main determinants of LULC change in the study area. Therefore, in order to promote sustainable development, safeguard the river basin, and lessen the severity of the changes, appropriate management and timely action must be taken by policymakers and decision makers.
... This model is effective because it can simulate various types of land cover with adequate calibration and dynamic projection skills [80,81]. Various studies have also demonstrated that LCM is effective at predicting LULC change [82][83][84][85][86][87]. Therefore, the findings would be useful as inputs for planners and other stakeholders regarding LULC trends in the study area. ...
... This model is effective because it can simulate various types of land cover with adequate calibration and dynamic projection skills [80,81]. Various studies have also demonstrated that LCM is effective at predicting LULC change [82][83][84][85][86][87]. Therefore, the findings would be useful as inputs for planners and other stakeholders regarding LULC trends in the study area. ...
Land use and land cover change are among the drivers of environmental change. The Suluh River Basin’s land use and land cover are modeled in this study using a land change modeler. To accomplish the goals of this study, Landsat images and ancillary data sources were utilized. In eCognition Developer 9.2 software, nearest neighbor fuzzy classification was used to classify Landsat images. With the IDRISI Selva 17.3 software, change detection and modeling were carried out. Both qualitative and quantitative analyses of the data were conducted. The results showed that, despite a drop in forest land of 97.2%, grazing land of 89.8%, plantation land of 89.1%, shrub-bush land of 1.5%, and water bodies of 84.8% from 1990 to 2002, bare land increased by 10.6%, built-up land by 29.4%, and cultivated land by 65.4%. The model projects, bare, built-up, and cultivated land will increase at the cost of water bodies, grazing, forest, shrub-bush, and plantation land between the years 2028 and 2048. Rainfall, slope, height, distance to rivers, distance to highways, distance from towns, and population density were the main determinants of LULC change in the study area. Therefore, in order to promote sustainable development, safeguard the river basin, and lessen the severity of the changes, appropriate management and timely action must be taken by policymakers and decision makers.
Keywords: nearest neighbor fuzzy classification; change detection; land change modeler; Suluh River Basin
... Several studies have demonstrated the utility of multilayer perceptron (MLP) and Markov chain methods using the Land Change Modeller (LCM) in the TerrSet for analysing future LULC change, urban growth and the validation of these results (Sundara Kumar et al., 2015). In Southeast Asia, there are a number of examples where LULC change modelling has been applied to modelling rapid urbanisation and expanding agricultural areas (Aburas et al., 2018;Hasan et al., 2020;Mahamud et al., 2019;Rendana et al., 2015;Saputra & Han, 2019), however, there are only a few examples of their application to regional development with multiple competing forms of land-use change drivers. ...
Rapidly developing regions in Southeast Asia, such as Kuantan, Malaysia, require robust spatial analysis to understand changing landscape patterns and their socioenvironmental impacts to guide sustainable development and conservation planning. This study aims to characterise and evaluate the historic and future projections of land‐use and land‐cover (LULC) change patterns to understand the dynamics of the regional development process and identify potential future land‐use conflicts. We first map coarse‐scale land‐cover classes using Landsat 5 TM and Landsat 8 OLI data and a Random Forest classifier in the Google Earth Engine platform, and then use auxiliary reference data to manually construct fine‐scale LULC for 3 years: 2010, 2015 and 2020. Subsequently, we modelled future LULC change patterns in 2030 using Land Change Modeller, which applies a multilayer perceptron neural network and Markov chain analysis. The study showed that the region's land cover in the last 10 years has been largely altered by human intervention, driven by an increase in oil palm plantations, followed by mining, residential and industrial site expansion, with a consequent decline in forest and vegetation cover. The 2030 land‐use projections revealed a continuation of these land‐use development patterns. The modelling showed that industry, mining and residential LULC are clustered and growing closer in proximity while expanding extensively, likely causing future land‐use conflict and lead to further environmental degradation. Furthermore, our analysis showed extensive decline in forest cover within reserves. Our modelling demonstrated that natural resource management needs to take an integrated approach as the drivers of land‐use changes are complex, competing and dynamic.
... Using historical maps of LULC change, it is possible to model future LULC changes based on historical patterns of change to support spatial land use planning. Future LULC modelling can predict key LULC change patterns by discerning the potential socioeconomic and biophysical forces that influence the rate and spatial patterns of LULC change and urbanisation processes (Aburas et al., 2018;Al-sharif & Pradhan, 2014;Shivamurthy et al., 2013;Yang et al., 2012). Common methods of land change modelling include: (1) machine learning, (2) cellular-based, (3) spatial modelling, (4) agent-based approaches (Han et al., 2015;Yang et al., 2012), and (5) hybrid approaches (Wang & Maduko, 2018). ...
Kuantan is emerging as a dynamically developing region supported by the megaeconomic development projects such as the East Coast Economic Development Plan in conjunction with the extension of China-Malaysia bilateral industrial parks and establishment of East Coast Rail Link (ECRL), a part of the great Belt and Road Initiative (BRI). Such a rapidly developing region requires a robust spatial analysis to understand the changing landscape pattern and its socio-environmental impacts to guide sustainable development. Addressing the lack of research focused on this key economic development region, this study aims to characterise and evaluate the historic and future projection of land use land cover (LULC) change patterns to understand the dynamics of the regional development process and to identify potential future land use conflicts. The methodology for this research includes construction of coarse-scale land cover classes by using Landsat 5 TM and Landsat 8 OLI data based on a combination of Random Forest classifier on Google Earth Engine (GEE) platform and manual refinement to construct fine-scale LULC maps by using auxiliary reference data. The produced timeseries imageries’ overall accuracy assessment scored at an average of 83%. Subsequently, to further assess and model the future LULC change pattern, the Land Change Modeler (LCM) in TerrSet was utilized by training the multilayer perceptron (MLP) neural network and using the Markov chain analysis. The study shows that the region’s land cover will be largely altered by human intervention driven by urbanisation and the region’s evolving economic vision. Overall, the LULC timeseries for the years 2010 to 2020 revealed a prominent increase in oil palm plantation, followed by mining, residential, and industrial site expansion, with a consequent decline in forest and disturbed vegetation cover. The future land use projection for the year 2030 also revealed similar land use development patterns. Both the historical remote sensing data and future projections showed that industry, mining, and residential are clustered and growing in close proximity while expanding extensively, which may likely be a cause of future land use conflict. Although modelled future projections may contain many uncertainties, having the ability to envision future possible scenarios provide key insights into the current and evolving future patterns of land use changes and predicting their impacts on people and the environment. This will assist government bodies, stakeholders, and policy makers by providing information essential for future planning and sustainable development decisions.
... As the demand for land increases, it eventually increases urban. This further increases urban areas on LULC, causing disturbance in the ecosystem affecting sustainability (Aburas et al. 2018). Thus, cellular automata (CA)-Markov chain model is used to understand the factors affecting the spatio-temporal distribution of LULC and to predict the future LULC changes. ...
... This model was created using Idrisi TerrSet's Land Change Modeler (LCM). To forecast the change in LULC, this hybrid model combines the CA with the Markov chain model (Aburas et al. 2018). ...
... To summarize, it was found that GEE aids in the study of LULC shift in a cost-effective and time-consuming manner, and it is commonly used in the literature (Agarwal and Nagendra 2019; Gomes et al. 2020;Noi Phan et al. 2020;Sidhu et al. 2018;Tamiminia et al. 2020;Tassi and Vizzari 2020;Xing et al. 2021). The CA-Markov chain model helps in determining the future land cover change and their land use patterns for decision-makers to provide sustainable development (Aburas et al. 2018;Ansari and Golabi 2019;Bose and Chowdhury 2020;Faichia et al. 2020;Fu et al. 2018;Ghosh et al. 2017;Gidey et al. 2017;Halmy et al. 2015;Hamad et al. 2018;Leta et al. 2021;Ozturk 2015). ...
Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake system (VLS), Kerala, in the short term, i.e., within a decade, utilizing three standard machine learning approaches, random forest (RF), classification and regression trees (CART), and support vector machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the three techniques, SVM performed poor at an average accuracy of around 82.5%, CART being the next at accuracy of 87.5%, and the RF model being good at the average of 89.5%. The RF outperformed the SVM and CART in almost identical spectral classes such as barren land and built-up areas. As a result, RF-classified LULC is considered to predict the spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the cellular automata (CA)–Markov chain analysis. The model’s efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94.5% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area.
... Prediction of LC changes is one of the approaches used by planners to facilitate the evaluation of any development effects. Today, remote sensing (RS) and geographic information system (GIS) techniques are commonly used to monitor and understand the spatial process of urban movement within a specific time (Al-shalabi et al. 2013;Aburas et al. 2018). Based on the advantages and disadvantages of some useful models, as described in Table 1, the CA-Markov is an effective and the most accepted model used in spatial and temporal simulation studies (Falahatkar et al. 2011). ...
Considering the level of carbon stored in green lands, vast forest ecosystems with large volumes of biomass can play an important role in carbon sequestration and reduction of man-made CO2 emissions. Accordingly, the present study aims to determine the amount of carbon stored in Qaem Shahr County, north of Iran, as part of the remaining Hyrcanian forests. To achieve the research objective, land cover (LC) classification is performed by object-oriented classification. Also, LC changes are modeled for 2027 using CA–Markov model. Then, the carbon storage sub-model of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model is used to estimate carbon storage level and distribution in the area under study. The results show that the highest amount of stored carbon is in forest areas, which is estimated to be 18.228 megagram (Mg) in 2014. Also, results show that in case of continuation of the current trends, the amount of stored carbon will be reduced by 321,216.68 Mg by 2027. The main reason of this reduction is attributed to the decrease in plant biomass and soil affected by this ecosystem, which has been caused by increased built area in the region, destruction of forest, and range of LCs in recent years. Finally, the results show that urban development and the built lands, in general, have a considerable effect on reducing carbon storage in a nearby town with broad-leaved forests as expected. Therefore, proper planning and management of the development process is necessary in the region.
... Land change models are simplifications of reality that offers an important means of predicting future land use land cover change pressure points [18,19] and develop ex-ante visions of urbanization process implications [1]. Models usefully simplify the complex suite of socioeconomic and biophysical forces that influence the rate and spatial patterns of land use land cover change and enable the estimation of the impacts of changes in land use land cover [17,[20][21][22][23][24]. To date, a variety of models have been developed, and are classified into the following types: (1) machine learning model, (2) cellular based model, (3) spatial based model, (4) agent based approaches [10,18,20,25,26], and (5) hybrid approaches [27]. ...
... ROC is defined as a graph between the rate of true positives on the vertical axis and the rate of false positives on the horizontal axis. Its value ranges between 0 and 1, where, 1 shows a perfect fit and 0.5 shows a random fit [24,26,29,53,68]. The threshold value for the relative operating characteristics used in this study is 100. ...
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.
... Morocco has implemented reforms to develop this sector [2]. Land use change is considered an important consequence of population growth on a national and global scale [3]. Impact of agricultural development has a negative effect on environment, natural resources, and also quality of ground and surface water [4]. ...
In recent decades, Morocco has experienced an expansion of agriculture, industrialization and urbanization, the quality of several natural resources has deteriorated by the inconveniences of this expansion. Groundwater is among the natural resources affected by this development. This study aims to prevent groundwater pollution in one of largest degraded aquifers in Morocco, which is coastal Chaouia aquifer. The purpose of this study is to mitigate the effects of agricultural expansion and pollution from agricultural activities. Indeed, Markov process for stochastic modeling and cellular automata are integrated into GIS. As a result, forecast maps for 2011 and 2019 were developed. compatibility with projected land use maps gave a similarity rate of 89.22% for 2011 map and 82.12% for 2019 map. this great success made it possible to create a forecast map for 2027and 2035. Analysis of land use maps classified amongst them showed that agricultural area class dominated other classes used. agricultural expansion was explained by population growth in Morocco, and in world, which requires an increase in food needs. forests were deteriorated by several causes, on one hand climate change, and on another hand agricultural practice.
