Spatio-temporal Dynamics Of Land Use Research Articles

Assessing and predicting the dynamics of land use/land cover in northern Bangladesh using cellular Automata-Markov chain model

ABSTRACT Rapid urbanization and land use changes significantly impact environmental sustainability and resource management, particularly in developing regions. Therefore, this study examines the spatiotemporal dynamics of land use and land cover (LULC) in Rangpur, Bangladesh, from 1991 to 2021 and projects future trends to 2041. Using supervised and unsupervised classification techniques, along with cellular automata and Markov-chain models, we assessed historical LULC changes and predicted future scenarios. Results show a 38.86% increase in built-up areas (BAs) and a 49.86% decrease in vegetation land (VL) during the study period, with classification accuracy above 87%. Projections indicate a further loss of over 210 km² of VL and an increase of more than 123 km² in urban areas by 2041. Notably, urban expansion is linked to the development of road networks, with significant growth from 115.06 km2 in 2021 to 124.33 km2 by 2041 within a 15-kilometer radius around the city center. These findings offer crucial insights for urban planning, emphasizing the need for sustainable strategies to manage urban expansion and protect environmental and socio-economic resilience in Rangpur.

Spatiotemporal dynamics of LULC change and implications for sustainable urban growth in Saudi Arabia

This study investigates the spatiotemporal dynamics of land use and land cover (LULC) change in Saudi cities between 2013 and 2023. Using Landsat satellite imagery and a Maximum Likelihood Classifier, LULC changes are analyzed in nine cities to understand the impact of Saudi Arabia’s rapid urbanization following the oil crisis. The objectives are: (1) to evaluate the LULC changes in nine cities between 2013 and 2023; (2) to explore the spatiotemporal patterns of LULC changes in this period; and (3) to interpret the urban expansion that occurs in Saudi Arabia in the light of the kingdom sustainable development plan. The results identifiy four primary LULC change models: Urbanization, Vegetation Recovery, Land Degradation, and Ecological Transition Models. The Urbanization and Land Degradation Models determine Saudi Arabia’s urban growth trend, posing significant challenges to sustainable development. Most cities experienced substantial increases in impermeable surfaces and developed lands, while concurrently losing high terrain and barren areas. However, the small cities showed lower urbanization and land degradation rates than major cities, showing an opportunity to sustainably grow by adopting sustainable planning approaches. The study’s findings show that more targeted and comprehensive policies are needed to address the challenges of urbanization and land degradation, mitigate environmental impacts, and ensure long-term resilience. It underscores the importance of considering Ecological Urbanism in urban development plans.

Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

Spatiotemporal Dynamics of Land Use and Land Cover Change in the Ebo Forest Reserve, Littoral Region of Cameroon

Spatiotemporal Dynamics of Land Use and Land Cover Change in the Ebo Forest Reserve, Littoral Region of Cameroon

Spatiotemporal Dynamics of Land Use and Community Perception in Peri-Urban Environments: The Case of the Intermediate City in Indonesia

Spatiotemporal Dynamics of Land Use and Community Perception in Peri-Urban Environments: The Case of the Intermediate City in Indonesia

Spatio-Temporal Dynamics of Land Use in Mato Grosso do Sul from 1988 to 2018 and Payments for Ecosystem Services

Spatio-Temporal Dynamics of Land Use in Mato Grosso do Sul from 1988 to 2018 and Payments for Ecosystem Services

Spatiotemporal dynamics of land use and land cover change around Volcanoes National Park and their implications for biodiversity conservation

ABSTRACT This study comprehensively analyses spatiotemporal land use/land cover (LULC) dynamics within a 10-km buffer zone surrounding Volcanoes National Park (VNP), Rwanda, from 2000 to 2019, integrating biophysical and socio-economic drivers. It aims to quantify LULC change extent, rate, and type around VNP and inside the park; identify socio-economic, demographic, and policy drivers of LULC change; and evaluate the impact of LULC changes on VNP’s biodiversity via habitat fragmentation, connectivity, and species diversity. Employing remote sensing, spatial modelling, and field data, we reveal profound transformations within the park’s periphery. Forests and woodlots have significantly declined, replaced by agriculture, grasslands, and built-up areas, particularly in the 5.1–10 km belt (92.29% forest loss). While reforestation efforts exist, they are outpaced by deforestation, creating a stark deforestation-to-reforestation ratio of 12:1 in the 5.1 10 km belt and 6:1 in the 0–5 km belt. This alarming trend threatens VNP’s biodiversity as resource dependent communities continue to extract firewood, construction materials, and beanpoles from the park. To mitigate these impacts, we propose integrated strategies encompassing strengthened law enforcement, sustainable land management practices, community-based conservation initiatives, and alternative livelihood development.

A comprehensive analysis and future projection of land use and land cover dynamics in a fast-growing city: A case study of Sekondi-Takoradi metropolis, Ghana

Sekondi-Takoradi is the most urbanized metropolis in the Western region of Ghana. Over the past two decades, it has experienced rapid shifts in both population density and land cover. Unfortunately, these transformations have significantly altered the natural landscape and expanded urban coverage. Previous studies have not established enough evidence on land use and land cover changes in the metropolis. For this reason, this study examined the existence, rate, and spatial distribution of land use and land cover as well as the future changes in land cover in the metropolis. Landsat imagery spanning from 1991, 2009, 2016, to 2023 provided the basis for evaluating the spatiotemporal dynamics of land use and cover within the metropolis. The Random Forest Classification algorithm was employed to categorize the images into three distinct classes: water, vegetation, and built-up areas. Analysis revealed a rapid growth in built-up areas by 63.08 km² (32.91 %), accompanied by a decrease in vegetation and water coverage by 60.99 km² (31.82 %) and 2.08 km² (1.09 %) respectively. This highlights the accelerating urbanization trend, emphasizing the critical need for vigilant monitoring and controlled urban expansion to mitigate potential adverse effects. Projections for 2030 indicate further changes with water areas decreasing to 1.21 km² (0.63 %), vegetation diminishing to 95.31 km² (49.73 %), and built-up areas expanding to 95.14 km² (49.64 %). This signifies a significant shift towards built-up areas, altering the metropolis' land cover composition and potentially impeding the achievement of Sustainable Development Goal 11. Understanding these changes in land use and land cover holds significant value for policymakers, enabling them to effectively monitor and safeguard natural resources.

The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory

IntroductionThis study delves into the spatiotemporal dynamics of land use and land cover (LULC) in a Metropolitan area over three decades (1991–2021) and extends its scope to forecast future scenarios from 2031 to 2051. The intent is to aid sustainable land management and urban planning by enabling precise predictions of urban growth, leveraging the integration of remote sensing, GIS data, and observations from Landsat satellites 5, 7, and 8.MethodsThe research employed a machine learning-based approach, specifically utilizing the random forest (RF) algorithm, for LULC classification. Advanced modeling techniques, including CA–Markov chains and the Land Change Modeler (LCM), were harnessed to project future LULC alterations, which facilitated the development of transition probability matrices among different LULC classes.ResultsThe investigation uncovered significant shifts in LULC, influenced largely by socio-economic factors. Notably, vegetation cover decreased substantially from 49.21% to 25.81%, while forest cover saw an increase from 31.89% to 40.05%. Urban areas expanded significantly, from 7.55% to 25.59% of the total area, translating into an increase from 76.31 km2 in 1991 to 258.61 km2 in 2021. Forest area also expanded from 322.25 km2 to 409.21 km2. Projections indicate a further decline in vegetation cover and an increase in built-up areas to 371.44 km2 by 2051, with a decrease in forest cover compared to its 2021 levels. The predictive accuracy of the model was confirmed with an overall accuracy exceeding 90% and a kappa coefficient around 0.88.DiscussionThe findings underscore the model’s reliability and provide a significant theoretical framework that integrates socio-economic development with environmental conservation. The results emphasize the need for a balanced approach towards urban growth in the Islamabad metropolitan area, underlining the essential equilibrium between development and conservation for future urban planning and management. This study underscores the importance of using advanced predictive models in guiding sustainable urban development strategies.

Spatio-Temporal Dynamics of Land Use and Land Cover, and Small-Scale Forest Expansion in Northwestern Ethiopian Highlands

Spatio-Temporal Dynamics of Land Use and Land Cover, and Small-Scale Forest Expansion in Northwestern Ethiopian Highlands

Groundwater Recharge and Surface Runoff Modeling Response to Land Use and Land Cover Dynamics in a Mae Wong Watershed of Thailand

This study aims to develop efficient management strategies and assess the spatiotemporal dynamics of land use and land cover (LULC) on surface runoff modeling response dynamics for the long-term sustainability of watersheds. The soil and water assessment tool (SWAT) model was used to evaluate the LULC dynamics on GRSR in the Mae Wong Watershed (MWW) of Thailand. Using Landsat images, three different LULC maps (2011, 2021, and 2031) were created using the cellular automata markov chain (CA-Markov) model, and TerrSet 2020 geospatial monitoring and modeling software. In the overall MWW, the forestland has undergone deforestation and decreased by 2.10% of the total area and 2.72% of the total area has been transformed into agricultural lands due to human activity and population growth. The soil, LULC, weather, and the digital elevation model (DEM) were all used in the SWAT simulation procedure. To understand the groundwater recharge and surface runoff (GRSR) responses of each hydrologic response units (HRUs), the SWAT model was calibrated and verified using streamflow and the sequential uncertainty fitting (SUFI-2) technique from the SWAT calibration and uncertainty program (SWAT-CUP). The results indicate that there is a good agreement for both the calibration and validation phases of all LULC simulations. The study indicated that groundwater recharge has decreased over the last two decades while surface runoff has increased due to the forest area being converted to agricultural land. Thus, the study can support maximizing water management and strategies for systematically attaining sustainability.

Spatio-temporal dynamics of land use and land cover in the Alibori basin in Northern Benin Republic (West Africa)

Forest ecosystems of the Alibori basin are subject to multiple anthropogenic pressures witch therefore modify their land use and their land cover. This research aims at analyzing the spatio-temporal dynamics of land use and land cover in the Alibori basin in Northern Benin. The methodological approach used is based on the diachronic analysis of land cover from Landsat 2, 7, and 8 satellite images acquired respectively in 1980, 2000, and 2020, and the evaluation of land cover change parameters (conversion rate, level of deforestation, intensity and speed of change of land cover units). The results obtained reveal that the number of classes has increased from 8 to 9 with the appearance of plantations between 1980 and 2000. Between 1980 and 2020 the basin recorded a degradation of forest formations and an anthrogenization of savannah formations. The intensity and speed of loss of area are quite rapid in dense dry forests, open forests, and wooded savannahs between 1980 and 2020. The average rate of deforestation decreased from 1.27% annually between 1980 and 2000 to 1.26% annually between 2000 and 2020.

Spatiotemporal Dynamics of Land Use and Land Cover through Physical–Hydraulic Indices: Insights in the São Francisco River Transboundary Region, Brazilian Semiarid Area

This article presents a study on the spatiotemporal dynamics of land cover and use, vegetation indices, and water content in the semiarid region of Pernambuco, Brazil. This study is based on an analysis of satellite images from the years 2016, 2018, and 2019 using the MapBiomas platform. The results show changes in the predominant land cover classes over time, with an increase in the caatinga area and a decrease in the pasture area. An analysis of the vegetation indices (NDVI and LAI) indicated low vegetation cover and biomass in the study area, with a slight increase in the NDVI in 2018. An analysis of the Modified Normalized Difference Water Index (MNDWI) showed that the water content in the study area was generally low, with no significant variations over time. An increase in the water bodies, mainly due to the construction of a reservoir, was noted. The results of this study have provided important information for natural resource management in the region, including the development of strategies for the sustainable use and management of natural resources, particularly water resources, vegetation cover, and soil conservation.

Land Use Change and the Structural Diversity of Affem Boussou Community Forest in the Tchamba 1 Commune (Tchamba Prefecture, Togo)

Affem Boussou community forest (AFC) abounds in important biological resources. This study, which contributes to its better management, examines the spatiotemporal dynamics of the vegetation and its ecological and structural characteristics to propose a zoning plan for said forest. The analysis of the spatiotemporal dynamics of land use in the AFC from Google Earth images of 2015, 2018, and 2021 revealed a regressive trend of formations: crops and fallows (−33.98%), dense dry forests (−7.92%), gallery forests (−3.46%), plantations (−100%), grassy savannahs, and meadows (−18.84%), except for tree/shrub savannahs (484.23%). The floristic inventory identified 163 species divided into 129 genera and 55 families. Fabaceae (14.02%), and Combretaceae (10.55%) are the most represented families. Anogeissus leiocarpa (5.19%) and Vitellaria paradoxa (4.72%) are the most frequent species. We note the dominance of individuals of small diameters. The regeneration potential of the AFC is 64 feet/ha due to 21 feet/ha of suckers, 29 feet/ha of seedlings, and 14 feet/ha of shoots. As a zoning plan, the AFC was subdivided into four zones: the agroforestry zone (18.80%), the sustainable production forest zone (42.22%), the buffer zone (11%), and the biological conservation zone (28%). These results constitute a scientific basis for testing ecological indicators of the sustainable management of community forests in Togo.

Land use-land cover dynamics, and local perceptions of change drivers among Nyangatom agro-pastoralists, Southwest Ethiopia

Understanding the spatio-temporal dynamics of land use and land cover (LULC) and their drivers is instrumental in synthesizing knowledge for informed natural resource management, planning, and associated decisions. The objective of this study was to analyze LULC dynamics and their drivers in the Nyangatom district of southwest Ethiopia. This study adopted a mixed-method approach, comprising remote sensing and geographic information system-based analysis, focus-group discussions, key informant interviews, and structured interviews covering 384 agro-pastoral households. Land use and land cover maps were compiled with Landsat image data from 1992, 1999, 2010, and 2020 using supervised pixel-based classification with a maximum likelihood algorithm. The study revealed that grassland (33%) and shrub land (27%) are the dominant LULC classes in the study periods. The results show a significant increase in settlement, cropland, and bare land, between 1992 and 2020 following demand for fertile lands, overgrazing, and climate changes. Reductions in the water body, open woodland, riverine forest, and dense woodland of − 23%, − 18%, − 18%, and − 7%, respectively, were observed for the same period with an overall accuracy of 80%. Agro-pastoralists identified agricultural expansion, overgrazing, and drought as the main drivers of LULC changes, followed by conflicts, invasion of alien plant species, and change in policy institutions. The impacts of unplanned LULC change can be reduced through appropriate land-use planning with genuine stakeholder engagement. Updated information on present and future LULC dynamics can support these social dynamics and align a shared understanding among stakeholders that replaces polarization and erosion of mutual trust.

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use.

Spatiotemporal Dynamic of Land Use/Land Cover Changes and Their Drivers in the Fincha' a-Neshe Sub-Basin, Southeastern Blue Nile Basin, Ethiopia

It is paramount to evaluate the spatiotemporal dynamics of land use and land cover (LULC) changes and their drivers. This is because it helps generate information on biodiversity, land productivity, ecology, and livelihoods for decision-making. Due to land degradation, deforestation, shifting cultivation, high population pressure, and the three national megaprojects (NMPs), the Fincha'a Neshe sub-basin (FNSB) LULC, changes may be unprecedented. This study aimed to investigate the spatiotemporal dynamics of LULC changes and their drivers using remote sensing (RS) data and geographic information systems (GIS). Landsat images 5, 7, and 8 were used for the discrete periods of 1986, 2000, and 2016, respectively. Field observations (Ground control points) and interviews were conducted with key participants to validate the data. Supervised classification with a maximum likelihood algorithm was used to classify the Landsat imagery. The results showed that the FNSB experienced substantial changes in LULC between 1986 and 2016, of which 13.8% (457.3 km2 ) were due to NMPs. The cropland cover has expanded by 694.4 km2 (57.81%) at a rate of 24.60 km2 year-1 at the expense of shrubland, forest, wetland, and grassland. In contrast, shrubland, forest, wetland, and grassland have declined at the rates of 16.8, 3.9, 3.4, and 1.7 km2 year-1 over the entire study period. Population growth and NMPs were the principal drivers of the changes in the LULC of the sub-basin. Thus, the LULC transformation rate observed in the sub-basin requires due attention and mitigation strategies, as it might seriously threaten the sustainability of natural resources and NMPs.

Urbanization Trends Analysis Using Hybrid Modeling of Fuzzy Analytical Hierarchical Process-Cellular Automata-Markov Chain and Investigating Its Impact on Land Surface Temperature over Gharbia City, Egypt

Quick population increase and the desire for urbanization are the main drivers for accelerating urban expansion on agricultural lands in Egypt. This issue is obvious in governorates with no desert backyards. This study aims to (1) explore the trend of Land Use Land Cover Change (LULCC) through the period of 1991–2018; (2) upgrade the reliability of predicting LULCC by integrating the Cellular Automata (CA)-Markov chain and fuzzy analytical hierarchy process (FAHP); and (3) perform analysis of urbanization risk on LST trends over the Gharbia governorate for the decision makers to implement effective strategies for sustainable land use. Multi-temporal Landsat images were used to monitor LULCC dynamics from 1991 to 2018 and then simulate LULCC in 2033 and 2048. Two comparable models were adopted for the simulation of spatiotemporal dynamics of land use in the study area: CA-Markov chain and FAHP-CA-Markov chain hybrid models. The second model upgrades the potential of the CA-Markov chain for prediction by its integration with FAHP, which can determine the locations of high potential to be urbanized. The outcomes stated a significant LULCC in Gharbia during the study period—specifically, urban sprawl on agricultural land, and this trend is predicted to carry on. The agricultural sector represented 91.2% in 1991 and reduced to 83.7% in 2018. The built-up area is almost doubled by 2048 with respect to 2018. The regression analysis revealed the LST increase due to urbanization, causing an urban heat island phenomenon. Criteria-based analysis reveals the district’s vulnerability to rapid urbanization, which is efficient for data-gap zones. The simulation results make sense since the FAHP-CA-Markov simulated the LULCC in a thoughtful way, considering the driving forces of LULCC, while the CA-Markov chain results were relatively random. Therefore, the FAHP-CA-Markov chain is the pioneer to be relied upon for future projection. The findings of this work provide a better understanding of LULCC trends over the years supporting decision makers toward sustainable land use. Thus, further urbanization should be planned to avert the loss of agricultural land and uninterrupted increasing temperatures.

The driving forces of changes in land use of a Peri-urban area: The case of Tselinograd district, Kazakhstan

This study focuses on the spatiotemporal dynamics of land use and land cover (LULC) in the Tselinograd district, which is a peri-urban area (PUA) of the metropolis Nur-Sultan. The analysis of LULC dynamics in PUA was carried out based on a supervised classification in the twenty-year interval (1998, 2008, and 2018). During the study period, noticeable changes occurred in the structure of LULC. At the border with the city, the built-up area increased dramatically. In the PUA the area of arable land and forests has grown steadily and pasture land has been declining. That is, there is an intensification of land use due to an increase in the share of arable land in the study area. The main drivers of LULC change in PUA are urban expansion and population growth of the Nur Sultan metropolis. In general, as a result of reasonable economic and legislative measures, the influence of the Urban sprawl of the capital of Kazakhstan on the PUA is still accompanied by a slight effect on its sustainable development. Our approach by using Geo-Information Techniques is useful for the rapid detection of phenomena and processes that can lead to the unstable development of PUA.

Spatiotemporal differentiation and the coupling analysis of ecosystem service value with land use change in Hubei Province, China

Rapid urbanization has exacerbated land use transformation, which in turn has affected ecosystem service value (ESV). Clarifying the spatial association between land use change (LUC) and ESV is of relevance to the development of policies for sustainable land development and ecosystem service (ES) management. In this paper, taking Hubei Province as an example, the biomass coefficient, resource scarcity coefficient and socioeconomic adjustment coefficient are introduced to improve the ESV assessment model, and then the spatiotemporal dynamics of land use and ESV from 2000 to 2020 are analyzed. Furthermore, the coupling coordination degree (CCD) between land use intensity and ESV was explored through a CCD model. The results show that (1) land use in Hubei Province changed significantly from 2000 to 2020, mainly in cropland and built-up land. (2) The change in ESV showed a fluctuating upward trend, with an increase of 71.30%, and the spatial differences generally decreased, showing a spatial pattern of “low in the center and high in the surroundings”. (3) All individual ESVs first decreased and then increased, with regulating services and supporting services contributing more to ESV and hydrological regulation, maintenance of biodiversity and soil conservation being the top three ecosystem functions with high service value. (4) The CCD between ESV and land use intensity in Hubei Province and its cities (prefecture and forest area) decreased and then increased, being at the lowest and highest values in 2005 and 2020, respectively. This paper revealed the spatiotemporal evolution process of LUC and ESV in Hubei Province and their coupling coordinated relationship, which can provide effective guidance for the formulation of interzonal ES maintenance policies to reconcile the conflicts between land use and ecosystem management.