Land Use Dynamics Research Articles

The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios

Peatlands only cover a small fraction of the global land surface (∼3%) but store large amounts of carbon (∼600 GtC). Drainage of peatlands for agriculture results in the decomposition of organic matter, leading to greenhouse gas (GHG) emissions. As a result, degraded peatlands are currently responsible for 2%–3% of global anthropogenic emissions. Preventing further degradation of peatlands and restoration (i.e. rewetting) are therefore important for climate change mitigation. In this study, we show that land-use change in three SSP scenarios with optimistic, recent trends, and pessimistic assumptions leads to peatland degradation between 2020 and 2100 ranging from −7 to +10 Mha (−23% to +32%), and a continuation or even an increase in annual GHG emissions (−0.1 to +0.4 GtCO2-eq yr−1). In default mitigation scenarios without a specific focus on peatlands, peatland degradation is reduced due to synergies with forest protection and afforestation policies. However, this still leaves large amounts of GHG emissions from degraded peatlands unabated, causing cumulative CO2 emissions from 2020 to 2100 in an SSP2-1.5 °C scenario of 73 GtCO2. In a mitigation scenario with dedicated peatland restoration policy, GHG emissions from degraded peatlands can be reduced to nearly zero without major effects on projected land-use dynamics. This underlines the opportunity of peatland protection and restoration for climate change mitigation and the need to synergistically combine different land-based mitigation measures. Peatland location and extent estimates vary widely in the literature; a sensitivity analysis implementing various spatial estimates shows that especially in tropical regions degraded peatland area and peatland emissions are highly uncertain. The required protection and mitigation efforts are geographically unequally distributed, with large concentrations of peatlands in Russia, Europe, North America and Indonesia (33% of emission reductions are located in Indonesia). This indicates an important role for only a few countries that have the opportunity to protect and restore peatlands with global benefits for climate change mitigation.

Modeling urban land use dynamics using Markov-chain and cellular automata in Gondar City, Northwest Ethiopia

Modeling urban land-use dynamics is critical for urban experts’ and infrastructure managers’ planning. This study attempts to explore the land-use/land-cover (LULC) dynamics of Gondar using satellite images from 1984 to 2020. Markov-Chain and Cellular Automata (MC-CA) models have been recognized as performing well in predicting urban land-use change. However, only a few models work in Ethiopia in general, and no study in Gondar has applied this approach to study urban land-use patterns. Therefore, Gondar land-use/land-cover changes of Gondar were predicted using the MC-CA model in IDRISI. The built-up area in Gondar city covered 1 413 ha (3% of the total area) in 1984 and increased to 2 380 ha (5%) in 1994; 21 153 ha (45.5%) in 2004; 22 622 ha (48.7%) in 2014; and 23 427 ha (50.5%) in 2020. The area has been predicted to reach 57.5% in the 2050s, showing a faster increase that will cause a very vast loss of farmland. This will increase urban sprawl challenges as well as overall environmental disequilibrium in the preceding decade. Thus, innovative and careful structures and systems in urban planning are required to secure a sustainable urban future and to make our cities livable and competitive in the paradigm of sustainable cities.

Human-Impact Gradients through Anthropogenic Pollen Indicators in a Mediterranean Mosaic Landscape (Balearic Islands)

This paper proposes new anthropogenic pollen indicators for the Balearic Islands and attempts to assess gradients of human impact on vegetation in Mediterranean islands. A combination of modern pollen analogue studies, complemented by phytosociological descriptions and ordination techniques using quantitative and presence/absence data was used. Redundancy analysis allowed us to evaluate the relationships between pollen types and significant environmental variables and propose regional (e.g., Centaurea, Rubus, Plantago lanceolata-t) and local/microregional anthropogenic pollen indicators (e.g., Cerealia, Poygonum aviculare, Matricaria-t). Additionally, an anthropogenic index score (AIS) for each sampled location was calculated to correlate each pollen type to a specific degree of human impact: (a) low (e.g., Cerastium-t, Erica arborea-t, Cistus albidus), (b) moderate (e.g, Sinapis-t, Sanguisorba minor-t, Plantago bellardii-t), (c) high (e.g., Papaveraceae undiff., Dipsacaceae, Secale-t). This paper contributes to a further understanding of land-use dynamics and to defining the degree of impact, which is especially necessary to assess colonization and anthropization rhythms in Mediterranean island environments.

Monitoring of Land Use Dynamics in the Autonomous District of Abidjan Between 2002 and 2022 using the Google Earth Engine Platform

In the countries of the South, capital cities such as the Autonomous District of Abidjan polarize both economic activity and demographic growth to such an extent that they are subject to rapid land artificialisation. This artificialization, in conjunction with poorly controlled urbanization, contributes to the thinning of vegetated areas. The objective of this work is to determine the changes in land use and land cover that have occurred in the Autonomous District of Abidjan over the last two decades, 2002 and 2022. The method uses the Random Forest classification algorithm in the Google Earth Engine platform from Landsat composite images. With an overall accuracy of 98.03% in 2002 and 98.7% in 2022 and kappa coefficients of 0.9 in 2002 and 2022, the cartographic and statistical results show important dynamics in land use in the Autonomous District of Abidjan during this period. These dynamics are characterized by a regression of vegetated and hydrographic areas that have lost 61% of their surface to the benefit of the habitat that has increased by 171% from 22.4 thousand hectares to 55.4 thousand hectares. Keywords: Autonomous District of Abidjan, random forest, google earth engine, urban dynamics, land use and land cover

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.

An application of land use, transport, and economy interaction model

Governments need to assess the benefits of transport projects to prioritize investments. It is imperative for governments to have tools that can closely estimate the actual benefits and impacts of investments in significant transport projects.Traditionally, the benefits of a transport project have been estimated by using a transport model, assuming fixed land use for the base and project case in the future. However, this approach cannot measure the impact of land-use changes as residents and businesses relocate to take advantage of lower travel and/or freight costs resulting from the project. Consequently, the benefits of the project may be under or overestimated, depending on its position within the transport network and how it reshapes land use patterns in the future. To overcome this drawback in existing models, this paper firstly presents a literature review of advances in the land use transport interaction model (LUTI), then discusses the development of a LUTI model for Victoria, Australia (VLUTI) by integrating the Victorian Integrated Transport Model with a Spatial Computable General Equilibrium model (SCGE). The land use component (SCGE) was developed to represent a highly resolved zone system, detailed classifications of occupations and industries, and spatial interactions via commuting, and trade in goods and services, and thus enables VLUTI to simulate a detailed and nuanced picture of land use and transport interactions in a complete economic environment.The literature review also indicates limited research in applying a LUTI to estimate land use change benefits. This paper presents an application of VLUTI by looking at differences in the conventional transport benefits under both static and dynamic land use scenarios. In the static method, the land use in the project case is unchanged from the base case. In the dynamic case, the land use in the project case, at a future point in time, is endogenously adjusted within VLUTI. It presents a method to correct, in the dynamic case, the benefits as estimated by the rule of a half, which usually assumes static land use. The paper concludes with suggestions for further developments and improvements of VLUTI such as incorporating an activity-based transport model and an environmental model to progress toward an integrated land-use, transport, economy, and environment model capable of assessing the full impact of a transport project in a holistic way.

Easy come, easy go: Short-term land-use dynamics vis à vis regional economic downturns

The present study postulates distinctive land-use dynamics along the economic cycle, and tests against diverging trends over time of urban and non-urban land-uses with characteristic economic potential. Short-term land-use changes over seven time windows encompassing the last three decades (1992–2020) were investigated in metropolitan Athens (Greece), a mono-centric region experiencing complex economic downturns. Based on diachronic land-use maps with homogeneous spatial resolution and nomenclature derived from ESA Climate Change Initiative (ESA-CCI), a change detection analysis was run considering mean patch size, distance from downtown, and specific entropy-based metrics of landscape diversification (Shannon-Wiener H’ diversity index and Pielou J evenness index). Results of a canonical correlation analysis document differential intensity and spatial direction of change during expansions and recessions associated with distinctive socio-demographic profiles. Metropolitan growth followed a radio-centric (land-saving) model during economic expansions with intense urbanization of fringe land. A more dispersed settlement model – reflecting urban sprawl – was associated with economic stagnations, involving land at progressively distant locations from downtown. Landscape diversification was higher under stagnations and lower during expansions.

Oil palm plantations in an aging agricultural landscape in the eastern Amazon: Pushing Amazon forests farther from biodiversity-friendly landscapes

Oil palm plantations across the tropics are intended to promote sustainable production by incorporating degraded lands while sparing old-growth forests. Here, we examine the trajectory of an Amazonian-forest focal landscape as it experiences the expansion of oil palm and its interactions with multiple land-uses. Land-use composition, landscape spatial configuration and structure in a 765.65-km2 watershed were examined over a 16-yr period (2002–2018) via Landsat imagery. Briefly, (1) old-growth forest coverage dropped from 37.3 % to 18.3 % across the whole landscape, (2) secondary forest and arable fields expanded until 2014, and (3) oil palm plantations emerged in 2010 with a positive trend thereafter, resulting in an 11.9 % expansion until 2018. In 2018, only 140.7 km2 of old-growth forest persisted, while oil palm plantations covered 91.8 km2. Oil palm expansion resulted from 14 land-use trajectories, with changes involving old-growth forests accounting for half the area (48.1 %) covered by plantations in 2018. However, only 0.4 % of the plantation area resulted from a direct conversion of old-growth forests. Landscape structure became increasingly degraded, which included the elimination of gallery forests, the proliferation of edge-affected habitats and the collapse of forest structural connectivity. Our results suggest that the eastern Amazon is still experiencing forest loss and exhibit highly dynamic land-use changes. Furthermore, landscapes continue to suffer degradation of key attributes related to biodiversity persistence and the provision of ecosystem services. Oil palm plantations have a catalytic effect on land-use dynamics, speeding up forest shrinking and landscape degradation. The oil palm sector requires a better governance based on guidelines able to guarantee either the persistence or the emergence of biodiversity-friendly landscapes.

Trends of changing land use dynamics in the Terkos Lake basin between 1980 and 2023 and their impact on natural ecosystems

Anthropogenic processes are one of the main causes of environmental change in this century. As an indicator of man’s struggle for dominance over nature, the environment has been directly affected. These changes, which we encounter in every region of the place where we live, on a local and global scale, and which are due to man, cause the natural cycle of ecosystems to be disrupted. As in Türkiye in general, land use changes in the study area under the influence of direct and indirect factors in some regions are very rapid. No matter how much effort are made to control them, factors that have a direct impact on the environment, such as population growth, agriculture, industrial facilities, and the design of transport infrastructure, are the most important causes of change. In this direction, this study aims to identify the trends of land use changes around Lake Terkos and to reveal the deficiencies and fragilities of the relationship networks. It is also an indication of possible changes in the ecological status of the lake and its surroundings in relation to this situation. Terkos Lake’s prolonged use as a crucial drinking water source for Istanbul makes it a valuable study site to investigate its ecological state and management practices. Nevertheless, it has recently been under pressure due to transportation (airport, Northern Marmara Motorway, Istanbul Canal and other transport networks), urban sprawl, and industry. In this context, first of all, the dynamics of land use and the rapidly changing areas (hot zones) between 1980 and 2023 have been identified and the extent of the changes over time has been shown. Possible future land use changes were analysed in the light of this data. As a result of the study, the speed of transformation of green areas was revealed. In this direction, especially in the field of the aquatic ecosystem, vulnerable areas were identified, the degree of being affected by future change was revealed and the trend in the dimensions of use and change was analysed.

Impact of land-use/cover change on water quality in the Mindu Dam drainage, Tanzania

Abstract Water pollution caused by land-use/cover change is one of the most pressing problems facing both industrialized and developing countries. Freshwater quality deterioration suggests the collective outcomes of natural courses and alterations in land-use/cover. Understanding the correlation among land-use/cover and water quality parameters is vital for future water quality management. In this work, land-use/cover pattern and its effect on water quality in the Mindu Dam drainage area were analysed using Remote Sensing (RS) and Geographical Information System (GIS) techniques together with cellular automata–Markov model. The land-use/cover images between 1990 and 2020 were used to assess historical and spatial change of land-use/cover change, and projected for 2030 and 2040. We discovered that the dynamics of land use and land cover during the study period were significant. A strong correlation was revealed between changes in the land-use/cover and water quality parameters. Furthermore, a strong correlation exists between cultivated land and measured nutrient (nitrate and phosphate) and chlorophyll-a concentration. The natural vegetation buffer around Mindu Dam should be sufficient to prevent long-term water quality degradation from agricultural runoff in order to manage water quality sustainably. Therefore, land-use/cover management practices must be considered for sustainable resource management and water quality monitoring.

Remote sensing based investigation of coastal LULC dynamics in the coastal region of Bangladesh

Rapid land-use transition on the landscape is accelerating the overall land cover change by altering naturally productive land into more complex land-uses. This study explored the spatial and temporal dynamics of land use and land cover (LULC) as landscape exploration though satellite imagery in the southwest of Bangladesh from 2000 to 2020. The goal of this study was to quantify the overall trend for different LULC changes using satellite imagery from Landsat TM and Landsat OLI/TIRS. Sequential K-means-based unsupervised and more accurate maximum likelihood classification (MLC) algorithm-based supervised methods were used to analyze land-use change for each year. The trend of land-use changes and transition results between 2000 and 2020 showed a massive increase in built-up (2010.654 ha/year) land-use; 525546.18 ha of forest increase (1.7%) in two decades, whereas 13351.59 ha (1.10%) of forest or plantation area was transformed into the built-up area. Remote sensing (RS) based land dynamics is always land cover analysis, especially for the vast area of interest.

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.

Spatial Tools for Inclusive Landscape Governance: Negotiating Land Use, Land-Cover Change, and Future Landscape Scenarios in Two Multistakeholder Platforms in Zambia

Landscape approaches are being promoted as a form of negotiated governance to help reconcile competing land uses and identify common concerns for planning envisioned future landscapes. Multistakeholder platforms play a key role in these efforts. This paper aims to contribute to an emerging scholarship that explores how spatial tools can be used in such platforms as boundary objects and if and how they can contribute to inclusive landscape negotiations and governance. We used spatial mapping to observe and document stakeholder perceptions about drivers of land-use and land-cover change and desired future scenarios that accommodate competing land uses. We found that land-cover maps derived from satellite images helped participants identify land-use change dynamics and drivers. The ensuing community mapping of desired landscape scenarios in both multistakeholder platforms (MSPs) triggered a process of identifying common concerns and defining actionable priorities. However, in one MSP, stakeholders ultimately reached a compromise on a draft land-use map that was widely regarded as an entry point for further negotiations in Local Area Plans, while the other lacked consensus due to deep-seated social-cultural issues, such as social-class-based disagreements. This paper illustrates, first, that instead of focusing on the end product (participatory maps), understanding negotiation processes helps uncover why spatial tools may fail to achieve the intended purpose of reconciling land uses. Second, spatial tools only work for landscape approaches if MSPs are inclusive and foster a collaborative process that considers the views of all participants. The authors recommend that those steering MSPs stimulate them to evolve from “mere consultation forums” to “innovative, participatory platforms”, encouraging stakeholders to engage in genuine negotiation processes that allow negotiated and alternative outcomes. We contend that such an approach, supported by spatial tools, is likely to contribute to the implementation of landscape approaches. Policymakers and land users can use these spatial tools as boundary objects in user-focused strategies that engender inclusive stakeholder participation and ensure legitimate, acceptable, and sustainable outcomes.

Dynamics and Drivers of Land Use and Land Cover Change in the Upper Awash Basin, Central Rift Valley of Ethiopia.

This study analyzed the patterns and drivers of LULC dynamics in relation to the expansion of large-scale irrigated agriculture in the Central Rift Valley of Ethiopia from 1972 to 2016. Aerial photographs (1972), Landsat images (1980, 2000) and SPOT5 satellite images (2016) were analyzed using GIS tools to reveal LULC changes, and documentation, key informant interviews and focus group discussions were used to ascertain the biophysical and socioeconomic implications and drivers of these dynamics. The study revealed that cultivated and rural settlement land, and urban built-up areas had expanded at the expense of forestland, woodland, shrubland and grassland. While an increase in the production of cash crops had brought some benefits to smallholder farmers, such as access to irrigation and modern agricultural inputs and technologies, the unregulated conversion of natural vegetation to cultivated land resulted in a loss of biodiversity, deforestation, and reduction of pasture and firewood. We identified that significant LULC changes in the study area were caused by intersecting biophysical, economic, institutional, technological and demographic factors, which reinforced each other with varying magnitudes at different moments in time. These changes were underpinned by one key driver, that is, government agricultural policies that promoted investment in commercial agriculture for national and export markets. The study shows that understanding the complex interaction between the contributing factors and drivers of LULC change is crucial to inform decision-making and policies, in particular, by directing responses towards the underlying drivers of change rather than only the proximate causes.

Dynamics of deforestation and land use in rural settlement of the brazilian agrarian reform in the legal amazon

Este trabalho objetiva analisar a dinâmica temporal do desmatamento ao longo de 20 anos, suas possíveis causas e o uso e ocupação do solo, em um assentamento rural na Amazônia, através da classificação supervisionada de imagens de satélites pelo método MAXVER. Foram processadas imagens Landsat 5 dos anos de 1998 e 2008, e Landsat 8 do ano 2018, no software Qgis 3.6, sendo mapeadas as classes: floresta; vegetação secundária; mosaico de ocupações; área urbana; hidrografia; e área não observada. A acurácia da classificação foi obtida através de matriz de confusão com índices de Kappa geral, do usuário e produtor. Para as classes, gerou-se gráficos de dispersão e coeficiente de correlação linear de Pearson (r). O desmatamento reduziu 72,52% da classe Floresta em 20 anos, e é fortemente correlacionado com a agropecuária (r= -0,940), indicando que foi realizado para o exercício dessas atividades.

Dynamics of land use and land cover in Northern India: a systematic review

Dynamics of land use and land cover in Northern India: a systematic review

Dynamics of land use and land cover change in peri urban area of Burdwan city, India: a remote sensing and GIS based approach

Dynamics of land use and land cover change in peri urban area of Burdwan city, India: a remote sensing and GIS based approach

Main Urban Land of Jinan City Dynamic Changes based on the Landsat8 Images from 2016 to 2021

In recent years, remote sensing data has been increasingly applied to land use change with its advantages. In this paper, the dynamic land use of Jinan city over ten years are analysed based on Landsat 8 data. After the pre-processing, there are five land use types are given using the supervision classification. It can be seen from the analysis that in the main urban area of Jinan, part of the cultivated land has changed into construction land, and most of the unused land has changed into construction land. From the comparative analysis of the classified images, it can be seen that the construction land in the main urban area of Jinan has increased significantly by 195.2577 km².

Simulating multiple scenarios of land use/cover change using a coupled model to capture ecological and economic effects

AbstractThe conflict between the demands of ecological protection and economic development is increasingly prominent. Land use change is sensitive to whether economic or ecological profits dominate, and prediction of future land use changes has become an important scientific issue. This article constructs a coupled model that comprises the grey model (GM), the fuzzy multiple objective linear programming model (FMOLP) and the future land use simulation model (FLUS). We used this model to calculate a macro‐scale quantitative forecast using the top‐down GM‐FMOLP model, and calculated micro‐scale spatial simulations of land use/cover change (LUCC) using the bottom‐up FLUS model under three dissimilar future scenarios, with different emphases on ecological and economic benefits. The scenarios were designed to meet different planning requirements in Chongqing: ecological benefits maximization, economic benefits maximization, and combined ecological and economic benefits maximization. The proposed model, FLUS, was applied to a LUCC simulation for Chongqing in 2015, beginning from verified historical land use data from 2010. The results show that the simulation agrees well with the actual land use in 2015. The overall accuracy and kappa coefficient are 85.92% and 0.76, respectively, which indicates the model performs well. The three land use types with the highest producer and user accuracies are forest, cropland and water areas. The GM‐FMOLP model was used to forecast future LUCC quantity demand, and the FLUS model was then used to calculate spatial predictions for the three scenarios for 2040. The prediction results for the six land use types were significantly different under the three different scenarios. The total areas of forest, urban land and water increased by different degrees under the three scenarios, and the unused land area dwindled. Cropland areas were largely converted into forest, urban land and water areas. The increases in forest and urban land areas generally represents internal gap‐filling between disparate areas and peripheral expansion of areas of the same land use type. The increase in water area results from increased surface runoff in mountainous valleys with severe terrain. Areas of unused land are fully transformed into other land use types. The outcomes from the designed scenarios demonstrate that the proposed models are reliable and effective for future LUCC simulation, and highlight key areas where land use changes differently according to the scenario. In summary, in the predictions for the scenario that combined ecological and economic benefits maximization, each land use type tried to maximize ecological and economic benefits under the constraints, taking into account ecological safety and economic growth, which is in line with the coordination, comprehensiveness and binding requirements in Chongqing's development plan. Therefore, this scenario becomes the optimal scenario for land use optimization in Chongqing in the future. This study provides an example of the application of simulation and forecast models in land use management, remediation and urban planning, and addresses the growing requirement for low‐cost and effective tools for prediction of dynamic land use succession patterns.