Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review

High-resolution land use/cover forecasts for Switzerland in the 21st century

We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using > 200 predictors and accounting for climate and policy changes. We improved model performance by using a data augmentation algorithm that synthetically increased the number of cells of underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good prediction accuracy. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.

Application of geospatial techniques and binary logistic regression model for analyzing driving factors of urban growth in Bahir Dar city, Ethiopia

Understanding the drivers of urban growth and spatiotemporal land use change is important for rational land use and sustainable urban development. Based on the land use data, GIS data of explanatory variables, experts' knowledge and field observation, the study used a binary logistic regression model (BLRM) to analyze factors that drive rapid urban growth in Bahir Dar city, Ethiopia, using the LOGISTICREG module in IDRISI Selva software. Nine factors were used to reflect the influence of proximity and physical factors on urban growth from 1984 to 2019. This model helped in quantifying and identifying the factors of urban growth, which includes topography (slope, elevation and aspect) and accessibility (Dis. to the main road, Dis. to international airport, Dis. to CBD, Dis. to existing built-up area, Dis. to forest land and Dis. to water body). Furthermore, urban growth probability maps were created based on LRM results, revealing that the biggest urban growth would occur around existing built-up areas along the main roads and near Bahir Dar international airport. The Relative Operating Characteristic (ROC) values of 0.85, 0.90 and 0.93 and PCP values of 96.72 %, 98.46 % and 98.51 % indicate the urban growth probability maps are valid and BLRM had an ideal ability to predict urban growth. So, the study highlighted the relation between urban growth and its drivers in Bahir Dar, giving a decision making framework for better land use management and resource allocation.

Optimization of land use planning under multi-objective demand-the case of Changchun City, China

Modeling and scenario analysis are the core elements of land use change research, and in the face of the increasingly serious ecological and environmental problems in urbanization, it is important to carry out land use simulation studies under different protection constraints for scientific planning and policy formulation. Taking Changchun City, the capital of Jilin Province, a pilot national eco-province, as an example, a CLUE-S model with coupled landscape ecological security patterns was constructed to predict and simulate the land use structure and layout under multi-objective optimization scenarios in the planning target year (2030), and the results were analyzed based on landscape index evaluation. The study found the following: (i) the proportion of ecological land area under low, medium, and high security levels in the study area was 8.7%, 64.8%, and 26.5%, respectively; (ii) under the current development trend scenario, the trend of increasing fragmentation of cultivated land patches in Changchun in 2030 will remain unchanged, with construction land spreading along the periphery in a compact and continuous pattern, while ecological land will be seriously encroached upon; and (iii) in the 2030 multi-objective optimization scenario, land use patches of all types will begin to show a tendency to cluster, with less landscape fragmentation and more connectivity, while cultivated land and construction land will also begin to converge and do not deteriorate as a result of spatial conflicts over ecological land.

Spatio-temporal prediction of land use and land cover change in Bahi (Manyoni) Catchment, Tanzania, using multilayer perceptron neural network and cellular automata-Markov chain model

Evaluation of land use and land cover (LULC) change is among vital tools used for tracking environmental health and proper resource management. Remote sensing data was used to determine LULC change in Bahi (Manyoni) Catchment (BMC) in central Tanzania. Landsat satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used, and support vector machine (SVM) algorithm was applied to classify the features of BMC. The obtained kappa values were 0.74, 0.83 and 0.84 for LULC maps of 1985, 2005 and 2021, respectively, which indicates the degree of accuracy from produced being substantial to almost perfect. Classified maps along with geospatial, socio-economic and climatic drivers with sufficient explanatory power were incorporated into MLP-NN to produce transition potential maps. Transition maps were subsequently used in cellular automata (CA)-Markov chain model to predict future LULC for BMC in immediate-future (2035), mid-future (2055) and far-future (2085). The findings indicate BMC is expected to experience significant expansion of agricultural lands and built land from 31.89 to 50.16% and 1.48 to 9.1% from 2021 to 2085 at the expense of open woodland, shrubland and savanna grassland. Low-yield crop production, water scarcity and population growth were major driving forces for rapid expansion of agricultural lands and overall LULC in BMC. The findings are essential for understanding the impact of LULC on hydrological processes and offer insights for the internal drainage basin (IDB) board to make necessary measures to lessen the expected dramatic changes in LULC in the future while sustaining harmonious balance with livelihood activities.

LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia

Recurrent changes recorded in LULC in Guna Tana watershed are a long-standing problem due to the increase in urbanization and agricultural lands. This research aims at identifying and predicting frequent changes observed using support vector machines (SVM) for supervised classification and cellular automata-based artificial neural network (CA-ANN) models for prediction in the quantum geographic information systems (QGIS) plugin MOLUSCE. Multi-temporal spatial Landsat 5 Thematic Mapper (TM) imageries, Enhanced Thematic Mapper plus 7 (ETM+), and Landsat 8 Operational Land Imager (OLI) images were used to find the acute problem the watershed is facing. Accuracy was assessed using the confusion matrix in ArcGIS 10.4 produced from ground truth data and Google Earth Pro. The results acquired from kappa statistics for 1991, 2007, and 2021 were 0.78, 0.83, and 0.88 respectively. The change detection trend indicates that urban land cover has an increasing trend throughout the entire period. In the future trend, agriculture land may shoot up to 86.79% and 86.78% of land use class in 2035 and 2049. Grassland may attenuate by 0.03% but the forest land will substantially diminish by 0.01% from 2035 to 2049. The increase of land specifically was observed in agriculture from 3128.4 to 3130 km2. Judicious planning and proper execution may resolve the water management issues incurred in the basin to secure the watershed.

Assessment of the impact of wetland changes on carbon storage in coastal urban agglomerations from 1990 to 2035 in support of SDG15.1

The quantitative assessment and spatial representation of wetland carbon storage, which play a critical role in the global carbon cycle and human production, can provide useful data and knowledge for decision-making in achieving sustainable development goals (SDGs). Currently, human activities and climate change impacts pose a challenge for the assessment of wetland carbon storage in coastal urban clusters. We proposed a "past-present-future" long time series refined wetland carbon storage assessment model using Guangxi Beibu Gulf (GBG) and Guangdong, Hong Kong, Macao and the Greater Bay Area (GBA) as the study area. The CLUE-S and InVEST models were coupled to conduct a comparative analysis of the spatial and temporal changes in wetland carbon storage and the spatial identification of damages from 1990 to 2035 and finally explore the sensitivity of wetland changes to carbon storage and quantitatively assess the SDG15.1 target. The results showed that (1) both urban clusters are characterized by many reservoirs/farming ponds, large river areas and few lakes. 1990-2035 rivers, shallow waters and mudflats have a decreasing trend to be distributed in the middle of their respective regions, mangroves are on an increasing trend, GBG is mainly distributed in the Maowei Sea and GBA is mainly distributed in Shenzhen Bay. (2) Wetland carbon storage of the two urban clusters show an overall fluctuating downward trend, with rivers, lakes and beaches all showing a downward trend. The multiyear average carbon storage of the GBG are 3.2 times higher than those of the GBA. In ecological protection scenario (EPS) policy planning, it is reasonable to help wetland carbon sequestration in coastal urban clusters. (3) The trend of wetland change from 1990 to 2020 was positive for carbon storage. The rate of recovery of wetland carbon stocks is lower in GBA than in GBG under the natural increase scenario (NIS) and the ecological protection scenario (EPS). The economic development scenario (EDS) contributes least to the realisation of SDG15.1 for the coastal urban agglomeration. The ecological protection scenario (EPS) contributes the most to the realisation of SDG15.1 for the coastal urban agglomeration.

Prediction of the dynamics of land use land cover using a hybrid spatiotemporal model in Iran

Human activities are prone to be the main drivers of land use land cover (LULC) changes, which have cascading effects on the environment and ecosystem services. The main objective of this study is to assess the historical spatiotemporal distributions of LULC changes as well as estimated future scenarios for 2035 and 2045 by considering the explanatory variables of LULC changes in Zanjan province, Iran. The LULC time-series technique was applied using three Landsat images for the years 1987, 2002, and 2019. Multi-layer Perceptron Artificial Neural Network (MLP-ANN) is applied to model the relationships between LULC transitions and explanatory variables. Future land demand was calculated using a Markov chain matrix and multi-objective land optimization in a hybrid simulation model. Validation of the model's outcome was performed using the Figure of Merit index. The residential area in 1987 was 6406.02 ha which increased to 22,857.48 ha in 2019 with an average growth rate of 3.97%. Agriculture increased annually by 1.24% and expanded to 149% (890,433 ha) of the area occupied in 1987. Rangeland showed a decline concerning its area, with only about 77% (1,502,201 ha) of its area in 1987 (1,166,767 ha) remaining in 2019. Between 1987 and 2019, the significant net change was a conversion from rangeland to agricultural areas (298,511 ha). Water bodies were 8 ha in 1987, which increased to 1363 ha in 2019, with an annual growth rate of 15.9%. The projected LULC map shows the rangeland will further degrade from 52.43% in 2019 to 48.75% in 2045, while agricultural land and residential areas would be expanded to 940,754 ha and 34,727 ha in 2045 from 890,434 ha and 22,887 ha in 2019. The findings of this study provide useful information for the development of an effective plan for the study area.

A methodological proposal to analyze urban sprawl, negative environmental impacts, and land degradation in the case of João Pessoa City (Brazil) between 1991 and 2018

Urbanization and changes in urban spaces have caused severe environmental and social problems in large Brazilian cities. As such, this study presents a methodological proposal to analyze urban sprawl, negative environmental impacts, and land degradation. The methodology employed involves a combination of remote sensing data, environmental modeling techniques, and mixed-method analyses of environmental impacts from 1991 to 2018. Analyzed variables included vegetation, surface temperature, water quality, and soil degradation within the study area. These variables were assessed based on an interaction matrix used to evaluate environmental impacts (low, medium, or high impacts). The obtained results show conflicts of land use and land cover (LULC), a lack of urban sanitation infrastructure, and an absence of environmental monitoring and inspection. A reduction of 24 km² of arboreal vegetation was observed from 1991 to 2018. High values of fecal coliforms were found in March across nearly all analyzed points, indicating a seasonal discharge of effluents. The interaction matrix presented various negative environmental impacts, including increased land surface temperature, soil degradation, inappropriate solid waste disposal, devastation of remaining vegetation, water pollution by domestic effluents, and the incidence of erosive processes. Ultimately, the impact quantification determined that the study area has a medium degree of significance in terms of environmental impacts. Thus, refining this quantification method will contribute to future research by making the analysis processes more objective and efficient.

Assessing farmland suitability for agricultural machinery in land consolidation schemes in hilly terrain in China: A machine learning approach

Identifying available farmland suitable for agricultural machinery is the most promising way of optimizing agricultural production and increasing agricultural mechanization. Farmland consolidation suitable for agricultural machinery (FCAM) is implemented as an effective tool for increasing sustainable production and mechanized agriculture. By using the machine learning approach, this study assesses the suitability of farmland for agricultural machinery in land consolidation schemes based on four parameters, i.e., natural resource endowment, accessibility of agricultural machinery, socioeconomic level, and ecological limitations. And based on "suitability" and "potential improvement in farmland productivity", we classified land into four zones: the priority consolidation zone, the moderate consolidation zone, the comprehensive consolidation zone, and the reserve consolidation zone. The results showed that most of the farmland (76.41%) was either basically or moderately suitable for FCAM. Although slope was often an indicator that land was suitable for agricultural machinery, other factors, such as the inferior accessibility of tractor roads, continuous depopulation, and ecological fragility, contributed greatly to reducing the overall suitability of land for FCAM. Moreover, it was estimated that the potential productivity of farmland would be increased by 720.8 kg/ha if FCAM were implemented. Four zones constituted a useful basis for determining the implementation sequence and differentiating strategies for FCAM schemes. Consequently, this zoning has been an effective solution for implementing FCAM schemes. However, the successful implementation of FCAM schemes, and the achievement a modern and sustainable agriculture system, will require some additional strategies, such as strengthening farmland ecosystem protection and promoting R&D into agricultural machinery suitable for hilly terrain, as well as more financial support.

Analysis of the Future Evolution of Biocapacity and Landscape Characteristics in the Agro-Pastoral Zone of Northern China

The Agro-Pastoral Zone of Northern China (AZNC) is an ecologically fragile zone. It is a challenge to create scientifically sound plans for environmental conservation and agro-pastoral development due to the lack of future evolution prediction, and analysis of biocapacity (BC) and landscape characteristics. Using the Globeland30 dataset from 2000 to 2020, this study simulated 2030 land use/land cover (LULC) scenarios, and analyzed the future evolution of BC and landscape patterns. The results show that: (1) The Logistic and CA-Markov models can reasonably simulate the LULC changes in the research area, with ROC indices over 0.9 and Kappa approaching 0.805, after considering the driving factors such as physical geography, regional climate, and socio-economic development. (2) From 2000 to 2030, the spatial distribution pattern of LULC does not change significantly, and cultivated land, grassland, and forest are still the dominant land types in the research area. The regional BC exhibits an increasing trend (+4.55 × 10⁶ gha/a), and the spatial distribution pattern of BC is similar to that of LULC. (3) Changes in land miniaturization, landscape fragmentation, and decreased aggregation can be seen in the entire AZNC and specific land categories, including cultivated land, grassland, and forest. The study provides suggestions for formulating the AZNC's future ecological protection and agro-pastoral development strategies, and guidance for the LULC simulation in other agro-pastoral zones.

Analysis of land use/land cover changes and prediction of future changes with land change modeler: Case of Belek, Turkey

In the areas declared to be a tourism center by state planning, a rapid tourism-related development occurs depending on the investments in tourism, which causes a dramatic land use/land cover (LULC) change. Determining, monitoring, and modeling of LULC changes are required in order to ensure the conservation-use balance and sustainability within such vulnerable areas that are under development pressure. This study consists of four steps. In the first step, the Landsat images dated 1985, 2000, 2010, and 2021 were classified using the maximum likelihood method and the LULC of Belek Tourism Center located in Turkey were determined. The second step included the identification of areal and spatial changes between the LULC classes for the four periods. In the third step, the LULC changes in Belek Tourism Center for 2040 were modeled using the land change modeler. Last step evaluated the relationship between the modeled spatial development pattern and the current planning decisions. According to the results obtained during 36 years, the rates of built-up, forest, and water body areas have increased by 11.91%, 13.67%, and 0.82%, respectively, whereas the rates of barren land and agricultural areas have reduced by 22.25% and 4.15%, respectively. The LULC map modeled for 2040 predicts the built-up areas to expand by 8.25% and the agricultural areas to shrink by 5.42% by comparison with 2021. This study will contribute as a key measure for planners, policy-, and decision-makers to make decisions related to sustainable land use in the areas declared to be a tourism center.

Spatio-temporal assessment of land use dynamics and urbanization: linking with environmental aspects and DPSIR framework approach

Urbanization is the main force of the global environmental as well as land use land cover changes (LULC). Urbanization is caused by prompt increase in population growth, migration, and urge for employment. In this study, Geographic Information System (GIS) was applied for the analysis and representation of spatio-temporal changes in LULC in Peshawar district and these results were linked with environmental aspects and Driver-Pressure-State-Impact-Response (DPSIR) framework approaches. For LULC classification, the Landsat freely available satellite imageries were used. The analysis revealed that the vegetation cover has increased from 37.8% of the total area to 71.3% during 1990-2020 and this change in vegetation is attributed to the government initiatives of Billion Tree Tsunami afforestation project after 2014 which has substantially decreased the barren land (from 66% in 1990 to 19% in 2020) in southeastern part of Peshawar district. Although, there was reduction in the vegetation cover in the past but due to extensive plantation between 2014 and 2020 resulted rapid increase in vegetation cover in the study area. The results of the present study detected a remarkable increase in built-up area which has increased almost 224.6% from 1990 to 2020. The study area population has increased from 2.12 million during 1998 to 4.26 million in 2017. The DPSIR results revealed that drivers and pressure have adverse effects on the carrying capacity of natural resources which have resulted deterioration of ecosystem. The resulted reduced capacity leading towards land degradation, loss of agricultural land, decline the groundwater level and resulted in pluvial flooding in Peshawar district. Government and environmental protection agency should implement the land use bylaws to reduce the rapid and unplanned urban growth and its negative impacts on natural environment.

Spatial-Temporal Changes and Simulation of Land Use in Metropolitan Areas: A Case of the Zhengzhou Metropolitan Area, China

Metropolitan areas are the main spatial units sustaining development. Investigating internal factor changes in metropolitan areas are of great significance for improving the quality of development in these areas. As an emerging national central city of China, Zhengzhou has experienced rapid urban expansion and urbanization. In this study, principal component analysis and the model and Geodetector model were used to comprehensively analyze the influencing factors of land use change in Zhengzhou from 1980 to 2015. Based on the CA-Markov model, we improved the accuracy of multi-criteria evaluation of suitability factors and simulated land use change in 2015. The results show that land use conversions in the study area between 1980 and 2015 were frequent, with the areas of farmland, woodland, grassland, water, and unused land decreasing by 5.00%, 17.12%, 21.59%, 18.31%, and 94.48%, respectively, while construction land increased by 53.61%. The key influences on land use change are the urbanization and growth of residential or non-agricultural populations. In 2035, the area of farmland in the study area will decrease by 11.09% compared with that in 2015 and construction land will increase by 38.94%, while the area of other land use types will not significantly change. Zhengzhou, as the center city, forms a diamond-shaped core development area of Zhengzhou-Kaifeng-Xinxiang-Jiaozuo, while Xuchang is considered an independent sub-center uniting the surrounding cities for expansion. With its radiation power of unipolar core development for many years and the developmental momentum of Zhengzhou-Kaifeng integration, Zhengzhou city jointly drives the economic development of the surrounding cities. The protection of farmland and control of the expansion of construction land are the major challenges for the Zhengzhou metropolitan area to achieve sustainable development.

Prediction of Urban Sprawl by Integrating Socioeconomic Factors in the Batticaloa Municipal Council, Sri Lanka

Due to extensive population growth, urbanization increases urban development and sprawl in the world’s cities. Urban sprawl is a socioeconomic phenomenon that has not extensively incorporated socioeconomic factors in the prediction of most of the urban sprawl models. This study aimed to predict the urban sprawl pattern in 2030 by integrating socioeconomic and biophysical factors. NDBI, Cramer’s V, logistic regression, and CA-Markov analyses were used to classify and predict built-up patterns. The built-up area is the dominant land use, which had a gradual growth from 1990 to 2020. A total of 20 socioeconomic and biophysical factors were identified as potentials in the municipality, affecting the urban sprawl. Policy regulation was the most attractive driver with a positive association, and land value had a high inverse association. Three prediction scenarios for urban sprawl were achieved for 2030. Higher sprawling growth is expected in scenario 3, compared with scenarios 1 and 2. Scenario 3 was simulated with biophysical and socioeconomic factors. This study aids in addressing urban sprawl at different spatial and temporal scales and helps urban planners and decision makers enhance the development strategies in the municipality. Predicted maps with different scenarios can support evaluating future sprawling growth and be used to develop sustainable planning for the city.

Multi-Scenario Simulation of Ecosystem Service Values in the Guanzhong Plain Urban Agglomeration, China

Rapid urbanization and human activities enhanced threats to the degradation of various ecosystem services in modern urban agglomerations. This study explored the response of ecosystem service values (ESVs) to land use changes and the trade-offs among various ESVs in urban agglomerations under different future development scenarios. The patch-general land use simulation (PLUS) model and ESV calculation method were used to simulate the ESVs of Guanzhong Plain Urban Agglomeration under the Business As Usual scenario (BAU), Ecological Conservation scenario (EC), and Economic Development scenario (ED) in 2030. Global and local Moran’s I were used to detect the spatial distribution pattern, and correlation analysis was used to measure trade-offs among ecosystem services. The results showed that: (1) The simulated result of land use in Guanzhong Plain Urban Agglomeration showed high accuracy compared to the actual observed result of the same period, with a Kappa coefficient of 0.912. From 2000 to 2030, land use changes were significant, with the rapid decrease in farmland and an increase in construction land. The area of woodland increased significantly under the EC scenario, and the area of construction land increased rapidly under the ED scenario. (2) The decline of total ESV was CNY 218 million from 2000 to 2020, and ESVs remained the downward trend in the BAU and ED scenarios compared to 2020, decreasing by CNY 156 million and CNY 4731 million, respectively. An increasing trend of ESV showed under the EC scenario, with a growth of CNY 849 million. (3) Significant spatial autocorrelation showed in Guanzhong Plain Urban Agglomeration, as the Global Moran’s I were all positive and the p-values were zero. The ESV grids mainly showed “High-High” clusters in the mountainous areas and “Low-Low” clusters in plain areas. Except for food production, a majority of ecosystem services exhibited positive synergistic relationships. In future planning and development, policymakers should focus on the coordinated development of the urbanization process and ecological preservation to build an ecological safety pattern.

Land Use Change Simulation in Rapid Urbanizing Regions: A Case Study of Wuhan Urban Areas

Until now, few studies have used the mainstreaming models to simulate the land use changes in the cities of rapid urbanizing regions. Therefore, we aimed to develop a methodology to simulate the land use changes in rapid urbanizing regions that could reveal the land use change trend in the cities of the regions. Taking the urban areas of Wuhan, a typical rapid urbanizing region in China, as the study area, this study built a Markov chain–artificial neural network (ANN)–cellular automaton (CA) coupled model. The model used land use classification spatial data with a spatial resolution of 5 m in 2010 and 2020, obtained by remote sensing image interpretation, and data on natural and socio-economic driving forces for land use change simulation. Using the coupled model, the land use patterns of Wuhan urban areas in 2020 were simulated, which were validated in comparison with the actual land use data in 2020. Finally, the model was used to simulate the land uses in the study area in 2030. The model validation indicates that the land use change simulation has a high accuracy of 90.7% and a high kappa coefficient of 0.87. The simulated land uses of the urban areas of Wuhan show that artificial surfaces will continue to expand, with an area increase of approximately 7% from 2020 to 2030. Moreover, the area of urban green spaces will also increase by approximately 7%, while that of water bodies, grassland, cropland, and forests will decrease by 12.6%, 13.6%, 34.9%, and 1.3%, respectively, from 2020 to 2030. This study provides a method of simulating the land use changes in the cities of rapid urbanizing regions and helps to reveal the patterns and driving mechanisms of land use change in Wuhan urban areas.

Land Change Science and the STEPLand Framework: An Assessment of Its Progress

This contribution assesses a new term that is proposed to be established within Land Change Science: Spatio-TEmporal Patterns of Land (‘STEPLand’). It refers to a specific workflow for analyzing land-use/land cover (LUC) patterns, identifying and modeling driving forces of LUC changes, assessing socio-environmental consequences, and contributing to defining future scenarios of land transformations. In this article, we define this framework based on a comprehensive meta-analysis of 250 selected articles published in international scientific journals from 2000 to 2019. The empirical results demonstrate that STEPLand is a consolidated protocol applied globally, and the large diversity of journals, disciplines, and countries involved shows that it is becoming ubiquitous. In this paper, the main characteristics of STEPLand are provided and discussed, demonstrating that the operational procedure can facilitate the interaction among researchers from different fields, and communication between researchers and policy makers.

Exploring ecosystem carbon storage change and scenario simulation in the Qiantang River source region of China

To explore the impact of land-use change on carbon storage, this study coupled the InVEST model and the FLUS model to analyse the spatial and temporal characteristics of carbon storage in the Qiantang River source region from 2000 to 2030. The carbon storage in the study area is evaluated which declined rapidly from 166.22 × 106 t in 2000 to 164.41 × 106 t in 2020, and the spatial distribution of carbon storage could be characterized by "the northwest and the southwest of region with higher, the east and the centre of the region with lower". The carbon storage was simulated based on the historical trend development scenario, the food security scenario, and the ecological protection scenario. The carbon storage with the food security scenario could achieve 162.74 × 106 t in 2030. The carbon storage with the ecological protection scenario had an increase of 62.60 t/km2 compared to the historical natural tendency development. Interestingly, the food security scenario had the smallest carbon loss value which is about $1.39 × 109, and its net carbon storage value was the largest which is about $3.71 × 109. The results of this study could provide a scientific reference for the conservation of carbon storage and land use management for climate change and sustainable development. This paper also can lay the foundation for subsequent further studies such as artificial intelligence.

Scenario-Based Predictions of Urban Dynamics in Île-de-France Region: A New Combinatory Methodologic Approach of Variance Analysis and Frequency Ratio

Modelling land use dynamics is a critical scientific issue. Despite a diversity of models coming from the fields of remote sensing, geography, and economics, including multicriteria decision analysis and machine-learning models, taking into account the external driving factors of urbanization is still a main challenge. This study aims at simulating various land use development scenarios with global and local parameters. Thus, the developed approach is able to estimate and simulate the dynamic evolution of land use classes, the evolution of urban attractivity, both of which depend on several driving factors. The proposed scenarios incorporate anticipated global changes, such as an increase in oil prices and a decrease in wealth, and local spatial changes such as the provision of new rail lines and the development of new activity zones. The results of simulations, for the study area covering a great part of the Île-de-France region, show for the year 2050 an 18% increase in urban areas and a 25% decrease in bare soils, compared to the year 2018. Moreover, the increase of global prices and the reduction of income levels would increase the attractivity of public transport modes and drive urbanization around stations, reduce the accessible distances to public transport systems by 8.5%, reduce the dependency on private vehicles, and increase the concentrated saturation of urban development. These scenarios will serve as a basis for the deployment of nature-based solutions and renewable energy production.

Land-use changes and precipitation cycles to understand hydrodynamic responses in semiarid Mediterranean karstic watersheds

Non-planned agricultural land abandonment is affecting natural hydrological processes. This is especially relevant in vulnerable arid karstic watersheds, where water resources are scarce but vital for sustaining natural ecosystems and human settlements. However, studies assessing the spatiotemporal evolution of the hydrological responses considering land-use changes and precipitation cycles for long periods are rare in karstic environments. In this research, we selected a representative karstic watershed in a Mediterranean semiarid domain, since in this belt, karst environments are prone to land degradation processes due to human impacts. Geographic Information Systems-based tools and hydrological modeling considering daily time steps were combined with temporal analysis of climate variables (wavelet analysis) to demonstrate possible interactions and vulnerable responses. Observed daily flow data were used to calibrate/validate these hydrological models by applying statistic indicators such as the NSE efficiency and a self-developed index (the ANSE index). This new index could enhance goodness-of-fit measurements obtained with traditional statistics during the model optimization. We hypothesize that this is key to adding new inputs to this research line. Our results revealed that: i) changes in the type of sclerophyllous vegetation (Quercus calliprinos, ilex, rotundifolia, suber, etc.) from 81.5% during the initial stage (1990) to natural grasslands by 81.6% (2018); and, ii) decreases in agricultural areas (crops) by approximately 60% and their transformation into coniferous forests, rock outcrops, sparsely natural grasslands, etc. in the same period. Consequently, increases in the curve number (CN) rates were identified as a result of land abandonment. As a result, an increase in peak flow events jointly with a relevant decrease of the average flow rates (water scarcity) in the watershed was predicted by the HEC-HMS model and verified through the observed data. This research provides useful information about the effects of anthropogenic changes in the hydrodynamic behaviour of karstic watersheds and water resource impacts, especially key in water-scarce areas that depict important hazards for the water supply of related populations and natural ecosystems.

An integrated approach of logistic-MCE-CA-Markov to predict the land use structure and their micro-spatial characteristics analysis in Wuhan metropolitan area, Central China

As human interference with the natural environment accelerates, land use has undergone great changes. However, to realize rational land development in the rural-urban ecotone, the micro-spatial (MS) unit is the best scale for the management and planning of sustainable land use. Taking Wuhan metropolitan area as research area, the integrated logistic-multi-criteria evaluation (MCE)-cellular automata (CA)-Markov model was used to simulate land use pattern for 2025. In addition, the 1 km×1 km, 2 km×2 km, 3 km×3 km, and 4 km×4 km and typical sample belt were built to reveal the spatial microcosmic expression of land use structure. The results showed that the kappa coefficient and figure of merit (FoM) were 88.01% and 26.86%, respectively, indicating the integration model has high prediction accuracy. In 2005-2025, the diversification of land use in the Wuhan metropolitan area will be generally above the medium level, and the types of land combinations will be relatively abundant. As human activities increase, the land use degree will show increases continuously, it will expand outward from Wuhan, and there is a positive correlation between cultivated land-rural residential land and urban land-cultivated land. The spatial distribution of land use structure presents regional scale characteristics, and different regions have micro-spatial scale dependence. The selection of MS scales based on local conditions can be a good way to reflect land use internal structure and provide a better reference for the compilation of regional land use optimization.

Spatio-Temporal Evolution of Land Use Transition in the Background of Carbon Emission Trading Scheme Implementation: An Economic–Environmental Perspective

In the political context of “carbon peaking” and “carbon neutrality” proposed by the Chinese government, this paper investigates the spatio-temporal evolution of land use transition in China after the implementation of the carbon emission trading scheme (CETS). Based on the analysis of the spatio-temporal evolution, we discuss the spatial spillover of the policy effects. With the help of China’s CETS policy, this study explores the above issues with the main observation samples of the six provincial pilots included in CETS. Using the entropy weighting method, the indicator construction method, and local Moran’s I test, this paper takes 30 provincial areas in China from 2010 to 2017 as the full sample, and draws the following conclusions: (1) both the economic and environmental effects generated by CETS can optimize land use transition in the pilot areas, but the effective time points of the two are different; (2) the time for land use transition to be optimized by the two effects of CETS is different, among which the economic effect takes effect faster than the environmental effect; and (3) there is spatial spillover of the optimization effect of CETS on land use transition, but the specific effect depends on the industrial structure and development plan of the pilot areas.

Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning

Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural resources within the Okavango Basin is undoubtedly a complex issue. It is often difficult to accurately map land use and cover using remote sensing in heterogeneous landscapes. This study investigates the combined value of climate-based regionalization and integration of spectral bands with spectral indices to enhance the accuracy of multi-temporal land use/cover classification using deep learning and machine learning approaches. Two experiments were set up, the first entailing the integration of spectral bands with spectral indices and the second involving the combined integration of spectral indices and climate-based regionalization based on Koppen–Geiger climate zones. Landsat 5 TM and Landsat 8 OLI images, machine learning classifiers (random forest and extreme gradient boosting), and deep learning (neural network and deep neural network) classifiers were used in this study. Supervised classification using a total of 5140 samples was conducted for the years 1996, 2004, 2013, and 2020. Average overall accuracy and Kappa coefficients were used to validate the results. The study found that the integration of spectral bands with indices improves the accuracy of land use/cover classification using machine learning and deep learning. Post-feature selection combinations yield higher accuracies in comparison to combinations of bands and indices. A combined integration of spectral indices with bands and climate-based regionalization did not significantly improve the accuracy of land use/cover classification consistently for all the classifiers (p < 0.05). However, post-feature selection combinations and climate-based regionalization significantly improved the accuracy for all classifiers investigated in this study. Findings of this study will improve the reliability of land use/cover monitoring in complex heterogeneous TDBs.

A Declining Trend in China's Future Cropland-N2O Emissions Due to Reduced Cropland Area

Croplands are the largest anthropogenic source of nitrous oxide (N₂O), a powerful greenhouse gas that contributes to the growing atmospheric N₂O burden. However, few studies provide a comprehensive depiction of future cropland-N₂O emissions on a national scale due to a lack of accurate cropland prediction data. Herein, we present a newly developed distributed land-use change prediction model for the high-precision prediction of national-scale land-use change. The high-precision land-use data provide an opportunity to elucidate how the changes in cropland area will affect the magnitude and spatial distribution of N₂O emissions from China's croplands during 2020-2070. The results showed a declining trend in China's total cropland-N₂O emissions from 0.44 ± 0.03 Tg N/year in 2020 to 0.39 ± 0.07 Tg N/year in 2070, consistent with a cropland area reduction from (1.78 ± 0.02) × 10⁸ ha to (1.40 ± 0.15) × 10⁸ ha. However, approximately 31% of all calculated cities in China would emit more than the present level. Furthermore, different land use and climate change scenarios would have important impacts on cropland-N₂O emissions. The Grain for Green Plan implemented in China would effectively control emissions by approximately 12%.

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989–2020, which has led to habitat fragmentation, deforestation, and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regression model. The recent urban expansion of Delhi was mapped using LANDSAT images of 1989, 2000, 2010, and 2020. The urban expansion was analyzed using concentric rings to show the urban expansion intensity in each direction. Nine driving factors were analyzed to detect the influence of each factor on the urban expansion process. The results revealed that the proximity to urban areas, proximity to main roads, and proximity to medical facilities were the most significant factors in Delhi during 1989–2020, where they had the highest regression coefficients: −0.884, −0.475, and −0.377, respectively. In addition, the predicted pattern of urban expansion was chaotic, scattered, and dense on the peripheries. This pattern of urban expansion might lead to further losses of natural resources. The relative operating characteristic method was utilized to assess the accuracy of the simulation, and the resulting value of 0.96 proved the validity of the simulation. The results of this research will aid local authorities in recognizing the patterns of future expansion, thus facilitating the implementation of effective policies to achieve sustainable urban development in Delhi.

Landscape pattern change simulations in Tibet based on the combination of the SSP-RCP scenarios

Monitoring landscape pattern change can provide spatial explicit basis for future landscape management. The future socioeconomic and climate change drivers should be systematically combined in landscape pattern monitoring, while they are often regarded as independent parameters in landscape monitoring models. This study sought to project the detailed landscape pattern change based on landscape composition and configuration in Tibet by 2030, and combined the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). The results showed area of the unused land and forest will reduce by a minimum standard of 11.42 × 10⁴ and 9.04 × 10⁴ km² from 2010 to 2030, respectively. Other land use types will increase, and the highest increase in grassland will be 9.30 × 10⁵ km². Combined SSP1 and RCP2.6 scenario show high landscape aggregation and low edge density on cultivated land, urban land and grassland in Tibet as a whole. However, in typical cultivated and urban landscape, the abovementioned rule is appeared in the combined SSP4 and RCP6.0 scenario. These findings stress the importance of systematically modeling the socioeconomic demand and climate change in landscape pattern monitoring, and using both landscape composition and configuration indexes for scenario evaluation.

Proportional Variation of Potential Groundwater Recharge as a Result of Climate Change and Land-Use: A Study Case in Mexico

This work proposes a methodology whereby the selection of hydrologic and land-use cover change (LUCC) models allows an assessment of the proportional variation in potential groundwater recharge (PGR) due to both land-use cover change (LUCC) and some climate change scenarios for 2050. The simulation of PGR was made through a distributed model, based on empirical methods and the forecasting of LUCC stemming from a supervised classification with remote sensing techniques, both inside a Geographic Information System. Once the supervised classification was made, a Markov-based model was developed to predict LUCC to 2050. The method was applied in Acapulco, an important tourism center for Mexico. From 1986 to 2017, the urban area increased 5%, and by 2050 was predicted to cover 16%. In this period, a loss of 7 million m3 of PGR was assumed to be caused by the estimated LUCC. From 2017 to 2050, this loss is expected to increase between 73 and 273 million m3 depending on the considered climate change scenario, which is the equivalent amount necessary for satisfying the water needs of 6 million inhabitants. Therefore, modeling the variation in groundwater recharge can be an important tool for identifying water vulnerability, through both climate and land-use change.

Refining national greenhouse gas inventories

The importance of greenhouse gas inventories cannot be overstated: the process of producing inventories informs strategies that governments will use to meet emissions reduction targets. The Intergovernmental Panel on Climate Change (IPCC) leads an effort to develop and refine internationally agreed upon methodologies for calculating and reporting greenhouse gas emissions and removals. We argue that these guidelines are not equipped to handle the task of developing national greenhouse gas inventories for most countries. Inventory guidelines are vital to implementing climate action, and we highlight opportunities to improve their timeliness and accuracy. Such reforms should provide the means to better understand and advance the progress countries are making toward their Paris commitments. Now is the time to consider challenges posed by the current process to develop the guidelines, and to avail the policy community of recent major advances in quantitative and expert synthesis to overhaul the process and thereby better equip multi-national efforts to limit climate change.

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Urban growth and land-use change are a few of many puzzling factors affecting our future cities. Creating a precise simulation for future land change is a challenging process that requires temporal and spatial modeling. Many recent studies developed and trained models to predict urban expansion patterns using Artificial Intelligence (AI). This study aims to enhance the simulation capability of Cellular Automata Markov Chain (CA-MC) model in predicting changes in land-use. This study integrates the Artificial Neural Network (ANN) into CA-MC to incorporate several driving forces that highly impact land-use change. The research utilizes different socio-economic, spatial, and environmental variables (slope, distance to road, distance to urban centers, distance to commercial, density, elevation, and land fertility) to generate potential transition maps using ANN Data-driven model. The generated maps are fed to CA-MC as additional inputs. We calibrated the original CA-MC and our models for 2015 cross-comparing simulated maps and actual maps obtained for Irbid city, Jordan in 2015. Validation of our model was assessed and compared to the CA-MC model using Kappa indices including the agreement in terms of quantity and location. The results elucidated that our model with an accuracy of 90.04% substantially outperforms CA-MC (86.29%) model. The improvement we obtained from integrating ANN with CA-MC suggested that the influence imposed by the driving force was necessary to be taken into account for more accurate prediction. In addition to the improved model prediction, the predicted maps of Irbid for the years 2021 and 2027 will guide local authorities in the development of management strategies that balance urban expansion and protect agricultural regions. This will play a vital role in sustaining Jordan's food security.

Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China

Ground deformation (GD) has been widely reported as a global issue and is now an ongoing problem that will profoundly endanger the public safety. GD is a complex and dynamic problem with many contributing factors that occur over time. In the literature, there are only a few methods that can effectively monitor GD. Microwave remote sensing data such as interferometric synthetic aperture radar (InSAR) are mostly adopted to assess GD. These data can reveal the surface deforming areas with great precision, mapping GD results at a large scale. In this study, the effects of GD and the influencing factors, such as the building area, the water level, the cumulative precipitation, and the cumulative temperature, are modeled in the Erhai region with small baseline subset interferometric SAR (SBAS-InSAR) data that are applied using machine learning (ML) methods. The ML methods, namely, multiple linear regression (MLR), multilayer perceptron backpropagation (MLP-BP), least squares support vector machine (LSSVM), and particle swarm optimization (PSO)-LSSVM, are used to predict GD, and the results are compared. Particularly, the PSO-LSSVM method has obtained the least root mean square error (RMSE) and mean relative error (MRE) of 11.448 and 0.112, respectively. Therefore, the results have proven that the proposed PSO-LSSVM is very efficient in analyzing GD.

Coupling cellular automata with area partitioning and spatiotemporal convolution for dynamic land use change simulation

Urbanization processes have accelerated over recent decades, prompting efforts to model land use change (LUC) patterns for decision support and urban planning. Cellular automata (CA) are extensively employed given their simplicity, flexibility, and intuitiveness when simulating dynamic LUC. Previous research, however, has ignored the spatial heterogeneity among sub-regions, instead applying the same transition rules across entire regions; moreover, most existing methods extract neighborhood effects with only one data time slice, which is inconsistent with the nature of neighborhood interactions as a long-term process exhibiting obvious spatiotemporal dependency. Accordingly, we propose a hybrid cellular automata model coupling area partitioning and spatiotemporal neighborhood features learning, named PST-CA. We use a machine-learning-based partitioning strategy, self-organizing map (SOM), to divide entire regions into several homogeneous sub-regions, and further apply a spatiotemporal three-dimensional convolutional neural network (3D CNN) to extract the spatiotemporal neighborhood features. An artificial neural network (ANN) is then built to create a conversion probability map for each sub-region using both spatiotemporal neighborhood features and factors that drive the LUC. Finally, the dynamic simulation results of entire study area are generated by fusing these probability maps, constraints and stochastic factors. Land use data collected from 2000 to 2015 in Shanghai were selected to verify our proposed method. Four traditional models were implemented for comparison, including logistic regression (LR)-CA, support vector machine (SVM)-CA, random forest (RF)-CA and conventional ANN-CA. Results illustrate that the proposed PST-CA outperformed four traditional models, with overall accuracy increased by 4.66%~6.41%. Moreover, three distinctly different "coverage rate-growth rate" composite patterns of built-up areas are shown in the SOM partitioning results, which verifies SOM's ability to address spatial heterogeneity; while the optimal time steps in 3D CNN generally maintained a positive correlation with the growth rate of built-up areas, which implies longer temporal dependency should be captured for rapidly developing areas.