Future Land Use And Land Cover Change Research Articles

Urban explosion and hotspots of forest loss in western Himalaya: Mapping land use/cover change trends since 1975

The Western Himalayan Region (WHR) is under constant threat from Land Use and Land Cover Change (LULCC) brought on by various socioeconomic activities such as urbanization, agricultural expansion, and population growth. Detailed research into the scope of these changes is necessary to comprehend the challenges they represent. This study aimed at (1) examining and quantifying spatiotemporal patterns of LULCC from 1975 to 2015, (2) mapping the hotspots of forest loss, and (3) modelling future LULCC for the year 2055 using medium spatial resolution multispectral satellite imagery (MSS, TM, LISS-I, LISS-III, AWiFS), field data, visual interpretation and logistic regression model in the WHR. Logistic regression integrated various drivers of LULCC, including climatic, topographic, and socio-economic factors and past LULCC trends to forecast potential LULC in the WHR by 2055. The results highlight an outrageous 184% increase in regional urbanization. Expansion of urban and cropland (12%) led to deforestation which reduced the area of natural habitats, specifically forests (−11%), water bodies (−8%) and scrubland (−6%). A significant rise in barren land by 30% was noted, while there was a decrease of 20% in glacier/snow melt. The future projection for 2055 anticipates continuing current trends, emphasizing an intensified urban expansion of 63% and an approximate 9% reduction in overall forest coverage. The accuracy of the predicted 2015 LULC map was assessed using ROC/AUC (Receiver Operating Characteristic/Area under Curve) which was found to be 0.79.

Modeling future land use and land cover under different scenarios using patch-generating land use simulation model. A case study of Ndola district

Accurate predictions of changes in Land-use and Land-cover (LULC) are crucial in climate modeling, providing valuable insights into the possible effects of land-use alterations on Earth’s intricate system. This study focuses on forecasting and examining future LULC changes in the Ndola district from 2022 to 2042, considering three scenarios: Traditional mode (TM), Ecological protection (EP), and Economic Development (ED). TM reflects past land use changes, EP prioritizes environmental conservation, and ED emphasizes economic growth and urbanization. Using the patch-generating land use simulation (PLUS) model, we achieved precise predictions of LULC changes in Ndola district. The model, which combines LEAS rule-extraction with a CA model using CARS, addresses limitations of previous models like CLUE-S, CA-Markov, and FLUS by accurately simulating scattered LULC patterns and the mutual attraction and evolution of open space and urban land under different policies. Using LULC data from the livingatlas platform for the base period (2017–2022), the model demonstrated a Kappa coefficient of 78% and a FoM value of 0.34. Key findings indicate significant trends, such as reductions in forest and agricultural lands in the TM and ED scenarios, with rangeland expanding consistently across all scenarios, particularly in the ED scenario. The decline in agricultural and forest lands raises concerns about household food security, habitat fragmentation, biodiversity loss, and diminished ecosystem services. Urban sprawl onto other land uses could further strain urban infrastructure and public services. Future research should incorporate uncertainty analysis methods such as fuzzy logic or Bayesian methodologies to quantify and differentiate uncertainties related to modeling simulations.

Integrating spatiotemporal co-evolution patterns of land types with cellular automata to enhance the reliability of land use projections

Land use and land cover change (LUCC) simulation aids the interpretation of the causes and consequences of future landscape dynamics under various scenarios, which in turn supports policy decisions. The essence of LUCC simulation lies in representing complex spatiotemporal associations among land types, including competitions and interactions. Currently, analyses of complex spatiotemporal LUCC associations mainly focus on the spatial configuration of land use while ignoring the intricate spatiotemporal co-evolution patterns of land types. Therefore, by integrating spatiotemporal co-evolution pattern mining (STC) in a future land use simulation (FLUS) model, a land use change simulation model named STC-FLUS was developed in this study. The proposed model is innovative because it can accurately quantify the spatiotemporal co-evolution patterns of land types, which can be effectively incorporated into LUCC simulations. A set of simulations indicate that the STC-FLUS model is more accurate than the classical FLUS model, with a figure of merit score of 0.135 compared with 0.114. Simulation results under five localized shared socioeconomic pathway scenarios from 2020 to 2040 demonstrate that the proposed model is effective for future LUCC simulation under a set of development scenarios. We conclude that spatiotemporal co-evolution patterns of land types can enhance the reliability of land use projections. Moreover, the STC-FLUS model can serve as a useful tool to understand future land use dynamics.

Urban Growth Monitoring and Prediction Using Remote Sensing Urban Monitoring Indices Approach and Integrating CA-Markov Model: A Case Study of Lagos City, Nigeria

As one of the swiftly advancing megacities globally, Lagos faces significant challenges in managing its urban expansion. Mainly, this study focuses on monitoring and predicting urban growth using a comprehensive approach incorporating Global Land 30 (GL30), satellite-based nighttime light observations, and built-up and population density data. The application of remote sensing techniques, combined with utilizing the GL30 dataset, provides an effective means to monitor and predict urban growth trends and patterns. The major patterns occurred from 2000 to 2020, including increased cultivated land; reductions in grasslands, shrublands, and wetlands; and major urbanization. Predictive models indicate that urbanization will continue. Furthermore, employing the Cellular Automata (CA) Markov model in land-use and land-cover (LULC) change prediction. The findings revealed significant changes in LULC over the two decades. Particularly, the percentage of artificial terrain increased from 17.016% to 25.208%, and the area under cultivation increased significantly, rising from 46,771 km2 (1.238%) in 2000 to 75,283 km2 (1.993%) in 2020. Grasslands fell from 7.839% to 1.875%, while forest cover somewhat increased, climbing from 39.319% to 43.081%. Additionally, marshes fell from 9.788% to 5.646%, while shrublands decreased from 4.421% to 2.640%. Surprisingly, bare ground decreased sharply from 0.677% to 0.003%. To forecast future LULC changes, the study also used a Markov Chain Transition Matrix. According to the data, there is a 3.54% chance that agricultural land will become urban, converting it from being used for agriculture to urban development. On the other hand, just 1.05% of forested regions were likely to become municipal areas. This study offers foundations for the upcoming research to enhance urban growth models and sustainability strategies in the face of rising urbanization and environmental concerns in the region, as well as laying the groundwork for informed decision-making in the region.

Patterns of green space change and fragmentation in a rapidly expanding city of northern Ghana, West Africa

Green spaces such as forests, grasslands, and croplands (including gardens) can be found in urban environments. Although they benefit human and animal well-being, they have become threatened due to rapid urban growth and unplanned development. Yet, little attention has been given to studying the dynamics of urban green spaces in sub-Saharan Africa. In this study, we examined the dynamics of land use and land cover (LULC) change and fragmentation (especially, green spaces) within the second fastest urbanising city in Ghana, Tamale. In particular, we focused our analyses on its urban core (∼5 km radius around the city centre) due to its relevance to urban economy and society. Landsat data was used to estimate fragmentation metrics of past and future LULC changes in the study area from 1990 to 2052. We found clear patterns of green space decline and fragmentation within the urban core: i.e., green spaces became patchy over time and the pattern was expected to continue in the future. Additionally, we found the built-up class benefited from the decline of green spaces with the latter being significantly negatively correlated with human population size. Our investigation reveals that protected forests and tree plantations contributed to a significant proportion of available green spaces in the urban core. However, these areas were becoming increasingly threatened by forest reserve downsizing, indiscriminate activities (e.g., logging and encroachment), and sale of public lands to private developers, practices commonly associated with population growth. Hence, the enforcement of relevant local legislations (e.g., the 2016 Land Use and Spatial Planning Act [Act 925]) coupled with the integration of urban initiatives and policies that encourage green spaces are needed to ensure the sustainability of urban ecosystems for the well-being of humans and the environment.

LAND USE AND LAND COVER SIMULATION BASED ON INTEGRATION OF ARTIFICIAL NEURAL NETWORKS WITH CELLULAR AUTOMATA-MARKOV CHAIN MODELS APPLIED TO EL-FAYOUM GOVERNORATE

Abstract. The paper aims to evaluate the effectiveness of the multi-layer perceptron-Markov chain analysis (MLP-MCA) integrated method in predicting future Land Use and Land Cover (LULC) change scenarios in Fayoum due to rapid urbanization. The study employed machine learning algorithms for image classification using Google Earth Engine (GEE) for classification techniques to derive LULC maps from Landsat imagery taken in 2001, 2011, and 2021. The 2001 and 2011 LULC maps were used to predict the LULC scenario for 2021 using MLP-MCA, and the predicted result was validated against the observed 2021 LULC map using Area under the curve (AUC) that was derived from the Receiver Operating Characteristics (ROC). Subsequently, the study predicted future LULC changes for 2031 using two sub-models; sub- Agri and sub-built. The results show that a rapid growth in both built and agricultural area. The findings of this study highlight the potential of the MLP-MCA method in predicting future LULC changes due to urbanization.

EVALUATION AND PREDICTION OF LAND USE, AND LAND COVER CHANGES USING REMOTE SENSING AND CA-ANN MODEL IN HUONG HOA DISTRICT, QUANG TRI PROVINCE

Evaluation of land use and land cover change (LULC) is necessary for densely vegetated areas like Huong Hoa district, Quang Tri province. It is a basis for sustainable development strategies. Therefore, the study aims to evaluate the LULC change in the 10-year period of 2013–2023 by using Landsat 8 satellite image data with the Maximum Likelihood Classification method and to predict future LULC changes. The LULC maps for 2013, 2018, and 2023 are accurate, with Kappa coefficients of 0.82–0.85. In the period of 2013–2023, the dense vegetation area tended to decrease by 1.4%. The decrease was mainly due to the transition to sparse vegetation cover. Bare land increased by 0.5%, and the built-up area decreased by 0.6%. Meanwhile, the water bodies changed slightly. The prediction of LULC change with the CA-ANN model in the QGIS MOLUSCE plugin is based on the history of LULC change and two spatial variables: DEM and distance to the road. The accuracy of the CA-ANN model is satisfactory, with an overall accuracy of 86% and a Kappa coefficient of 0.76. In the simulated LULC of 2033, dense vegetation is predicted to keep a higher decrease (2%) in the area compared with the LULC of 2023. Sparse vegetation steadily increased by 1.3% over the subsequent 10 years. Similarly, the built-up area, water boddies, and bare land extended slightly by 0.5, 0.1, and 0.1%, respectively. The CA-ANN model in the QGIS MOLUSCE plugin is suitable for the simulated LULC changes for the studied area.

High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe

Abstract. Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM–LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1∘ resolution for Europe with annual LULC maps from 1950 to 2100 (https://doi.org/10.26050/WDCC/LUC_hist_EU_v1.1, Hoffmann et al., 2022b, https://doi.org/10.26050/WDCC/LUC_future_EU_v1.1, Hoffmann et al., 2022a), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE – LANDMATE – PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25∘ resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide.

Predicting the ecosystem service values and constructing ecological security patterns in future changing land use patterns

The land use and land cover change (LUCC) associated with climate change and human activities poses a persistent threat to ecosystems, profoundly impacting ecological security. Simulating spatiotemporal changes in ecosystem service values (ESVs) and ecological security patterns (ESPs) under different future land use scenarios is crucial for formulating sustainable development policies. This study introduces a comprehensive framework for multi-scenario analysis, which combines the system dynamics (SD) model, patch-generated land-use simulation (PLUS) model, and minimum cumulative resistance (MCR) model to simulate ESV and ESP changes under future LUCC scenarios using the SSP-RCP scenarios provided by CMIP6. The results show that under the three scenarios (SSP245, SSP585, and SSP126), the ESV for the year 2050 is predicted to be 10.285 billion yuan, 10.248 billion yuan, and 10.327 billion yuan, respectively. Additionally, the area of ecological sources is estimated to be 78.56 km2, 65.90 km2, and 141.38 km2, while the lengths of ecological corridors are predicted to be 428.05 km, 332.45 km, and 527.10 km, and the numbers of ecological nodes are 14, 9, and 15, respectively. Notably, the SSP126 scenario demonstrates a significant improvement in ESP compared to the SSP245 and SSP585 scenarios, indicating effective protection of ecological land. This study provides valuable insights and serves as a crucial reference for future decision-making concerning ecosystem conservation and land resource management at the county and municipal scales.

Grassland Carbon Change in Northern China under Historical and Future Land Use and Land Cover Change

Land use and land cover (LULC) change has greatly altered ecosystem carbon storage and exerted an enormous impact on terrestrial carbon cycling. Characterizing its impact on ecosystem carbon storage is critical to balance regional carbon budgets and make land use decisions. However, due to the availability of LULC data and the strong variability in LULC change, uncertainty remains high in quantifying the effect of LULC change on the historical and future carbon stock. Based on four historical LULC maps and one future LULC projection, this study combined the Land Use and Carbon Scenario Simulator (LUCAS) with a process-based CENTURY model to evaluate the historical and future LULC change and its impact on grassland carbon storage from 1991 to 2050 in northern China. Results showed that grassland experienced a drastic decrease of 16.10 × 103 km2 before 2005, while agriculture and barren land increased by 16.91 × 103 km2 and 3.73 × 103 km2, respectively. After that, grassland was projected to increase, agriculture kept steady, and barren land decreased. LULC change has resulted in enormous total ecosystem carbon loss, mainly in agro-pasture areas; the maximum 8.54% of carbon loss happened in 2000, which was primarily attributed to agriculture to grassland, forest to grassland, grassland to agriculture, and grassland to barren. Before 2000, the grassland net biome productivity was projected to be −15.54 Tg C/yr and −2.69 Tg C/yr with and without LULC change. After 2001, the LULC change showed a positive impact on the grassland carbon balance, and the region was projected to be a carbon sink. Ecological projects have made a significant contribution to grassland carbon storage. The paper provides a framework to account for the effects of LULC change on ecosystem carbon and highlights the importance of improving grassland management in balancing the grassland carbon budget, which is helpful to understand the regional carbon budget and better inform local land use strategies.

Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh

Anthropogenic activities have a significant influence on land use and land cover (LULC) changes, especially in rapidly growing areas. Among several models, the combination of a cellular automata–artificial neural network (CA-ANN) model is being widely used for assessing future LULC changes using satellite images. This study aimed to investigate LULC changes in Gazipur City Corporation (GCC), Bangladesh, and the changes in LULC patterns over the last two decades (2002 to 2022). In this study, the maximum likelihood supervised classification technique was used for processing the available satellite images. The results show that the urban area and vegetation coverage increased by 150% and 22.78%, whereas the bare land and waterbody decreased by 7.02% and 78.9%, respectively, from 2002 to 2022 inside the GCC area. For future LULC predictions, the CA-ANN model was developed, the accuracy percentage of which was 86.49%, and the kappa value was 0.83. The future LULC prediction model results show that the urban area will increase by 47.61%, whereas the bare land and waterbody are supposed to decrease by 24.17% and 67.23%, respectively, by 2042. The findings of this study could be useful for future sustainable urban planning and management, as well as enabling decision making by authorities for improvements in environmental and ecological conditions in the study area.

Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models

AbstractIncreasing temperature and water cycle changes due to warming climate may increase the frequency and intensity of wildfires. Fire emission projections are useful for informing strategies for adaptation and mitigation of fire impacts on societies and ecosystems. Here, we construct a neural network (NN) model explained by the Shapley Additive explanation to predict fire PM2.5 emissions change and understand their drivers over the contiguous US (CONUS) in the mid‐21st century under a high greenhouse gas emissions scenario (SSP5‐8.5). Using future meteorology and leaf area index (LAI) simulated by eight global climate models from the Coupled Model Intercomparison Project Phase 6, future population density, and present‐day land use and land cover (LULC) as input to the NN model, the total fire PM2.5 emissions over CONUS are projected to increase by 4%–75% (model spread). Among different regions, fire emissions in the western US are projected to increase more significantly in June‐July‐August than in other seasons and regions, with the median ratios of future to present‐day fire emissions ranging from 1.67 to 2.86. The increases in fire emissions are mainly driven by increasing normalized temperature (23%–29%) and decreasing soil moisture (2%–10%) in the future. When future LULC change is considered, the projected fire emissions further increase by 58%–83% over the western US compared to projections without LULC change because of future increases in vegetation fraction. The results highlight the important role of warmer temperature, decreasing soil moisture, and LULC change in increasing fire emissions in the future.

Urban sprawl at the expense of cultivated land: decadal land use and land cover changes and future projections in the upper Awash basin of central Ethiopia

IntroductionUrban sprawl in developing countries affects future sustainability concerns related to environment, ecology and cropland losses. This study aimed at identifying, classifying and quantifying the changes in land-use and land-cover (LULC) during the last two decades (2000–2020) and predict the decadal pattern for the next three decades (2030–2050), with particular emphasis on urban sprawl at the expense of cultivated land in the upper Awash basin of central Ethiopia.MethodsLandsat images were used to analyze the LULC change dynamics using hybrid image classification and maximum likelihood classifier techniques. The Cellular Automata-Markov Chain (CAMC) Model was used to predict its future LULC change dynamics. A detailed analysis of the urban sprawl in the cities of Addis Ababa, Adama, and Mojo was also conducted.ResultsIt was observed that during the last two decades, settlements, particularly urban sprawls, substantially expanded at the expense of cultivated land, whereas the latter modestly expanded at the expense of forestland, bare land, and shrubland. From its size in 2000, the urban sprawl of Addis Ababa has grown in all directions towards cultivated land by more than six times in 2020 and is expected to become double of that by 2050 in the urban and peri-urban areas of the basin. Adama city has expanded by the same rate of 9.7% in the past two decades, while Mojo town has sprawled 9 folds between 2010 and 2020. The predicted percentage change in settlement for Addis Ababa and its neighboring districts within 22 km radius from the centroid shows a 40% expansion in 2030, 77% in 2040, and 87% in 2050 compared with the reference year 2020. Similarly, the Mojo town will expand significantly in 2030 (54%), 2040 (119%), and 2050 (by a staggering magnitude of 244%), while Adama will expand within the 12 km radius by 18%, 42%, and 117% in the next three respective decades.DiscussionThe rapid cultivated land conversions in the basin may contribute to major shifts in urban and peri-urban ecological environments. Our predictive LULC change maps and urban sprawling could be useful for developing effective and sustainable land-use policies for environmental and ecological management in the upper Awash basin.

Impact of land use change on the urban-rural temperature disparity in Eastern China

Unprecedented land use and land cover change (LULCC) has occurred across China over the past two decades. While urban heat island effects have been well documented, few studies identified the LULCC-induced temperature disparity within cities and nearby rurual areas. Through an integrated analysis of satellite observations and regional model simulations, this study quantifies LULCC effect on summer temperature in old urban areas (existed in 2003), new urban areas (developed after 2003) and rural areas (non-urban areas) over Eastern China in 2003–2019. The results show that LULCC causes urban areas to become warmer, with the effect being more pronounced in new urban areas than in old urban areas. Air temperature (2 m, T2) and surface temperature (TSK) in new urban areas increase respectively by 1.69 °C and 3.40 °C, while in old urban areas, T2 (TSK) increases by 0.10 °C (0.13 °C). Significant vegetation degradation is observed in new urban areas, whereas vegetation restoration occurs in old urban areas in recent years as a result of urban renewal. In contrast, LULCC cools and greens rural areas, with a 0.02 °C (0.03 °C) decrease in T2 (TSK). Urbanization warming can spread to the suburbs, along with vegetation degradation. The analysis of surface energy budget reveals that the reduction in latent heat plays a dominant role in urban warming. Our study underscores that a much larger greenspace fraction and albedo management in urban areas are suggested as an inherent part of future LULCC policy to alleviate urban-rural temperature disparity.

Dynamics of past forest cover changes and future scenarios with implications for soil degradation in Misiones rainforest, Argentina

Misiones rainforest is one of the most threatened subtropical forests worldwide. Anthropogenic pressure by agriculture and forestry expansion continues transforming landscapes with negative consequences on ecosystem service provision, such as soil erosion control. Understanding how land use and land cover change (LUCC) management, policies, and social factors influenced in the past, allows decision-makers to anticipate potential effects on future land use and soil loss, contributing to the sustainable planning and management of productive activities. We developed three spatially explicit scenarios for Misiones province by 2030 using the Dinamica EGO modeling platform: 1) Business as Usual (BAU), 2) Low Deforestation (ALTlow), and 3) High Deforestation (ALThigh), based on different international and domestic socioeconomic contexts. We used land cover data from 2002 to 2015 as well as biophysical, social-infrastructure, political-administrative factors, and legal restrictions to estimate changes that may occur by 2030. We analyzed magnitude, intensity, and spatial pattern of future forest cover changes through transition rates and a cellular automata allocation model. Moreover, we used the Universal Soil Loss Equation (USLE) integrated into a Geographic Information System (GIS) to determine soil water erosion and soil loss tolerance in each scenario. Our results revealed that around 19% of the remaining native forest would be transformed into either agriculture or cultivated forest by 2030 for all scenarios. In addition, and contrary to that trend, the ALTlow scenario showed a recovery of 3% of native forest. Regarding soil erosion, our study indicated that the mean annual soil loss by 2030 would range from 12.03 to 19.15 t. ha−1.year−1 for ALTlow and ALThigh scenarios, respectively. Additionally, between 21% and 31% of Misiones province showed soil loss values higher than tolerance. Our work shows that a 10% decrease in the deforestation rate, compared to the current rate, would lead not only to a recovery of native forest cover, but also to a reduction in soil loss of about 4.5 Mt.yr−1 by 2030. This study demonstrates the suitability of the applied model to simulate future LUCC processes and provides inputs for decision-making involving natural resource management and the potential impacts of these decisions on ecosystem services. Finally, our results highlight the need for appropriate policies and regulations, especially, in terms of land use change restrictions in areas of high erosion risk.

Modelling past and future land‐use changes from mining, agriculture, industry and biodiversity in a rapidly developing Southeast Asian region

AbstractRapidly 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.

Sustainable agricultural lands management by analyzing and predicting urban growth—a case study of Mitidja Plain, Algeria

The suitability of agricultural lands is basic data for both sustainable agricultural planning and optimal orientation of future urbanization projects. Indeed, unplanned urban expansion leads to many social and ecological problems. The prediction of urban expansion will strongly help the decision makers and local authorities to create a suitable future urban planning while preserving the regional ecosystem and especially the agricultural lands. Using multitemporal satellite images acquired in 1997, 2001, and 2013 over the Mitidja plain in Algeria, the aim of this study is the analysis of the land use and land cover change (LULCC) spatio-temporal distributions from 1997 to 2013 to model and then predict the future LULCC in 2025. The cellular automata Markov chain model (CA-Markov) that we developed is used for LULCC prediction with a focus on urban sprawl and agricultural lands degradation over the Mitidja plain. Once calibrated and validated, the CA-Markov model generates land suitability maps that indicate the best locations for urban expansion while safekeeping the agricultural lands. The results are interesting and present valuable tools for urban planners and policymakers to ensure the sustainability management of agricultural areas over the study region.

Cell-level coupling of a mechanistic model to cellular automata for improving land simulation

ABSTRACT The land use and land cover change (LUCC) process is crucial for climate and environmental change studies. Cellular automata (CA) models based on machine learning techniques have commonly been used to simulate LUCC. However, conventional CA models can only capture the historical relationship between driving forces and LUCC through statistics. Climate change significantly affects LUCC, especially natural vegetation. The statistical relationship between driving forces and LUCC may change with the climate. Furthermore, the existing coupled models of CA and mechanistic models are only loosely coupled in terms of the quantity of land demand provided by mechanistic models, which denotes that mechanistic models do not directly guide CA models in simulating spatial land dynamics. Thus, herein, a novel model that couples Lund Potsdam Jena (LPJ), a mechanistic model, and CA at the cell level is proposed to consider the mechanisms and statistics in LUCC simulations. The proposed coupled model (LPJ-FLUS) is validated by comparison with two historical LUCC simulations in China from 2001 to 2010 and 2015. The results show that the proposed coupled model affords higher simulation accuracy, especially on natural vegetation, compared to the two conventional CA models. The overall Figure of Merit value of the LPJ-FLUS model in the historical LUCC simulations is about 5% higher than that of the conventional CA model, reaching 23.37%, and is even 8% higher for some natural vegetation types. The proposed model is employed to predict the future LUCC under two SSP-RCP scenarios in China from 2015 to 2100. The simulation results show that the proposed model effectively combines the strengths of LPJ and CA, retaining the high spatial resolution of CA while representing the spatial possibilities of LUCC under different future climate scenarios assessed by LPJ. The quantitative validation results show that the simulation results of the LPJ-FLUS model are more spatially correlated with the LPJ model than those of conventional CA models. The proposed LPJ-FLUS model is a valuable attempt toward tightly coupling mechanistic and CA models at the cell level. Additionally, it has promising potential for high spatial resolution LUCC simulations and environmental impact analysis under future extreme climate scenarios.

Exploring the future of Kafue National Park, Zambia: Scenario-based land use and land cover modelling to understand drivers and impacts of deforestation

Land use and land cover (LULC) dynamics in tropical forests of sub-Saharan Africa are often difficult to quantify and predict, despite rapid forest losses and increasing human population pressure. As deforestation threatens the biodiversity of both flora and fauna, we used LULC change assessment and scenario modelling to analyse whether policy measures can safeguard the multi-functionality of tropical dry forests in western Zambia from 2010 to 2040. Our data comprised information on deforestation and human encroachment due to i.e., agricultural expansion, charcoal production, infrastructure development in the Kafue National Park (NP) and adjacent Game Management Areas (GMAs) (total area: 7,102,147 ha), which is part of the first Reducing Emissions from Deforestation and Forest Degradation (REDD+) focus areas in Zambia. We modelled a business-as-usual scenario (BAU) and four REDD+ policy-screening scenarios with varying levels of protection enforcement and future annual deforestation rates. We quantified scenario impacts on forest cover using three indicators: cropland and forest-related LULC trajectory, forest connectivity, and long-term carbon stock changes in 2040. Scenario results suggested that only under strong enforcement and low demand for agricultural areas, deforestation in Kafue NP and GMAs could be avoided by 93% (40,457 ha) and 1% in carbon stocks could be gained by 2040 in comparison to BAU. Spatial analyses revealed that cropland expansion will continue to encroach protected areas. We highlight that variations in carbon stocks and forest fragmentation were small across scenarios which has implications for land use management and the expected future benefits of REDD+ projects. The combination of GIS, scenario development and LULC modelling helped to identify and locate potential future deforestation and LULC changes. This can support appropriate management pathways of REDD+ induced local and national leakage effects and related decision making.

Simulating Spatiotemporal Changes in Land Use and Land Cover of the North-Western Himalayan Region Using Markov Chain Analysis

Spatial variabilities and drivers of land use and land cover (LULC) change over time and are crucial for determining the region’s economic viability and ecological functionality. The North-Western Himalayan (NWH) regions have witnessed drastic changes in LULC over the last 50 years, as a result of which their ecological diversity has been under significant threat. There is a need to understand how LULC change has taken place so that appropriate conservation measures can be taken well in advance to understand the implications of the current trends of changing LULC. This study has been carried out in the Baramulla district of the North-Western Himalayas to assess its current and future LULC changes and determine the drivers responsible for future policy decisions. Using Landsat 2000, 2010, and 2020 satellite imagery, we performed LULC classification of the study area using the maximum likelihood supervised classification. The land-use transition matrix, Markov chain model, and CA-Markov model were used to determine the spatial patterns and temporal variation of LULC for 2030. The CA-Markov model was first used to predict the land cover for 2020, which was then verified by the actual land cover of 2020 (Kappa coefficient of 0.81) for the model’s validation. After calibration and validation of the model, LULC was predicted for the year 2030. Between the years 2000 and 2020, it was found that horticulture, urbanization, and built-up areas increased, while snow cover, forest cover, agricultural land, and water bodies all decreased. The significant drivers of LULC changes were economic compulsions, climate variability, and increased human population. The analysis finding of the study highlighted that technical, financial, policy, or legislative initiatives are required to restore fragile NWH regions experiencing comparable consequences.