Search Results (249)

Multiple unmanned aerial vehicle (multi-UAV) confrontation is becoming an increasingly important combat mode in intelligent air combat. The confrontation highly relies on the intelligent collaboration and real-time decision-making of the UAVs. Thus, a decomposed and prioritized experience replay (PER)-based multi-agent deep deterministic policy gradient (DP-MADDPG) algorithm has been proposed in this paper for the moving and attacking decisions of UAVs. Specifically, the confrontation is formulated as a partially observable Markov game. To solve the problem, the DP-MADDPG algorithm is proposed by integrating the decomposed and PER mechanisms into the traditional MADDPG. To overcome the technical challenges of the convergence to a local optimum and a single dominant policy, the decomposed mechanism is applied to modify the MADDPG framework with local and global dual critic networks. Furthermore, to improve the convergence rate of the MADDPG training process, the PER mechanism is utilized to optimize the sampling efficiency from the experience replay buffer. Simulations have been conducted based on the Multi-agent Combat Arena (MaCA) platform, wherein the traditional MADDPG and independent learning DDPG (ILDDPG) algorithms are benchmarks. Simulation results indicate that the proposed DP-MADDPG improves the convergence rate and the convergent reward value. During confrontations against the vanilla distance-prioritized rule-empowered and intelligent ILDDPG-empowered blue parties, the DP-MADDPG-empowered red party can improve the win rate to 96% and 80.5%, respectively. Full article

As urbanization accelerates worldwide, understanding the impact of urban expansion on habitat quality has become increasingly critical in environmental science research. This study examines the impact of urban expansion on habitat quality in Beijing, forecasting land cover changes and ecological effects by 2030. Using CA–Markov and FLUS models, the research analyzes habitat quality from 2000 to 2030 through the InVEST model, revealing a significant urban land increase of 1316.47 km² and a consequent habitat quality decline. Predictions for 2030 indicate varying habitat quality outcomes across three scenarios: ecological priority (0.375), natural growth (0.373), and urban development (0.359). We observed that the natural growth scenario forecasts a further decline in habitat quality, primarily due to increased low–value habitat regions. Conversely, the ecological priority scenario projects a notable improvement in habitat quality. To mitigate habitat degradation in Beijing and enhance regional habitat quality and ecological conditions, it is recommended to control urban land cover expansion, adopt effective ecological conservation policies, and systematically carry out national spatial restructuring and ecological restoration. This research provides vital decision–making support for urban planning and ecological conservation, emphasizing the need for comprehensive land cover and ecological strategies in urban development. Additionally, our findings and methodologies are applicable to other rapidly urbanizing cities worldwide. This demonstrates the broader applicability and relevance of our research, providing a framework for sustainable urban planning in diverse global contexts. Full article

The conflict between grain production and wetland resource protection in plain wetland is prominent. Understanding the future impacts of current land use policies on wetlands is the key to rationally evaluating and adjusting these policies. Therefore, the objective of the research was to predict the potential distribution of Sanjiang plain wetland under the current land use and protection policy using remote sensing images and CA Markov models. Methodologically, Landsat TM remote sensing images of the Sanjiang Plain (2010–2020) were used to extract wetland distribution based on object-oriented methods, and the characteristics and patterns of wetland change caused by the land use and protection policies during this period were analyzed. A CA–Markov model was used to predict the potential distribution of Sanjiang Plain wetland in 2030, 2040, 2050, and 2060. Then, we summarized the advantages and disadvantages of current land use policies and put forward adjustment measures. The results indicate that during 2010 and 2020, the wetland area of Sanjiang Plain decreased by 22.34%. The conversion ratio of wetland to non-wetland type (mainly farmland) in the first half and the second half of the decade was 46.41% and 15.31%, respectively, and the decrease in wetland showed an obvious slowing trend. The spatial distribution prediction in future showed that the wetland area will continue to decline in 2030, and the decline will basically stop in 2040. Finally, the proportion of wetland area will remain at 8.68% of the total area of Sanjiang Plain, with that of some counties and cities less than 5%. It is concluded that, although the current land use policies in Sanjiang Plain can effectively slow down the wetland area shrinking and stabilize the spatial pattern, a very low proportion of wetland area in some areas will make it difficult for the wetland ecosystem to exert ecological functions and ensure regional ecological security. The wetland conservation managers should adjust the current land use policies according to relevant requirements of farmland protection policies and restore the areal proportion and spatial pattern of wetland in order to help with regional sustainable development. Full article

The spatio-temporal evolution of land use/land cover (LULC) and habitat quality (HQ) is vital to maintaining ecological balance and realizing regional sustainable development. Using the InVEST and CA-Markov model, with the Kendall coefficient as the sensitivity value, LULC and HQ in Lu’an City from 2000 to 2030 are simulated and evaluated. Then, Spearman is used to analyze the correlation between HQ and driving factors. Finally, the influence of policy factors on HQ is discussed. The results show the following: (1) from 2000 to 2030, the LULC of Lu’an is mainly cropland (about 40%) and forest land (about 30%) which are transferred to construction land; (2) the kappa coefficient is 0.9097 (>0.75), indicating that the prediction results are valid; (3) the Spearman coefficient shows that DEM (0.706), SLOPE (0.600), TRI (0.681), and HFI (−0.687) are strongly correlated with HQ, while FVC (0.356) and GDP (−0.368) are weakly correlated with HQ; (4) the main reasons for the decrease in HQ are the increase in construction land area, the decrease in forest area, the vulnerability of artificial forests to threat factors, and their low biodiversity. This study outlines exploratory research from two perspectives of HQ factors and policy effects to provide policy suggestions for the sustainable development of Lu’an City. Full article

With rapid economic development and urban expansion, China faces a serious imbalance between production, living, and ecological land use, in which the erosion of water ecological space by urban expansion is especially notable. In order to alleviate or solve this imbalance, this study constructs the water ecological space in the Mulan River Basin based on national land spatial planning using remote sensing statistics and the 2000–2020 statistical yearbooks for the Mulan River Basin. A landscape index is applied to explore this landscape in terms of its production–living–ecological space (PLES) patterns and evolutionary characteristics. Factors affecting the drivers of PLES changes are analyzed through Geo-Detector, and predictions are made using the cellular automata Markov (CA-Markov) model. It was found that (1) PLES distribution patterns in the Mulan River Basin from 2000 to 2020 are dominated by non-watershed ecological spaces, with a significant expansion of living space. Its ecological space is shrinking, and there is significant spatial variation between its near-river and fringe areas. (2) Of the PLES conversions, the most dramatic conversions are those of production space and living space, with 81.14 km² of production space being transferred into living space. Non-water ecological space and water ecological space are also mainly transferred into production space. (3) As shown by the results of the landscape index calculation, non-water ecological space in the Mulan River Basin is the dominant landscape, the values of the Shannon diversity index (SHDI) and Shannon homogeneity index (SHEI) are small, the overall level of landscape diversity is low, the aggregation index (AI) is high, and the degree of aggregation is obvious. (4) The progressive PLES changes in the Mulan River Basin are influenced by a combination of natural geographic and socioeconomic factors, with the mean population density and mean elevation being the most important factors affecting PLES changes among social and natural factors, respectively. (5) The Kappa coefficient of the CA-Markov model simulation is 0.8187, showing a good simulation accuracy, and it is predicted that the area of water ecological space in the Mulan River Basin will increase by 3.66 km² by 2030, the area of production space and non-water ecological space will further decrease, and the area of construction land will increase by 260.67 km². Overall, the aquatic ecological space in the Mulan River Basin has made progress in terms of landscape ecological protection, though it still faces serious erosion. Therefore, attaching importance to the restoration of the water ecological space in the Mulan River Basin, integrating multiple elements of mountains, water, forests, fields, and lakes, optimizing the spatial structure of its PLES dynamics, and formulating a reasonable spatial planning policy are effective means of guaranteeing its ecological and economic sustainable development. This study offers recommendations for and scientific defenses of the logical design of PLES spatial functions in the Mulan River Basin. Full article

This research focuses on the urban expansion occurring in the Kamrup Metropolitan District—an area experiencing significant urbanization—with the aim of understanding its patterns and projecting future growth. The research covers the period from 2000 to 2022 and projects growth up to 2052, providing insights for sustainable urban planning. The study utilizes the maximum likelihood method for land use/land cover (LULC) delineation and the Shannon entropy technique for assessing urban sprawl. Additionally, it integrates the cellular automata (CA)-Markov model and the analytical hierarchy process (AHP) for future projections. The results indicate a considerable shift from non-built-up to built-up areas, with the proportion of built-up areas expected to reach 36.2% by 2032 and 40.54% by 2052. These findings emphasize the importance of strategic urban management and sustainable planning. The study recommends adaptive urban planning strategies and highlights the value of integrating the CA Markov model and AHP for policymakers and urban planners. This can contribute to the discourse on sustainable urban development and informed decision-making. Full article

The land use change (LUC) and urbanization caused by human activities have markedly increased the occurrence of landslides, presenting significant challenges in accurately predicting landslide susceptibility despite decades of model advancements. This study, focusing on the Li River Valley (LRV) within the Yongding District, China, employs two common models, namely an analytic hierarchy process–comprehensive index (AHP-CI) model and a logistic regression (LR) model to assess landslide susceptibility (LS). The AHP-CI model is empirically based, with the advantage of being constructible and applicable at various scales without a dataset, though it remains highly subjective. The LR model is a statistical model that requires a training set. The two models represent heuristic and statistical approaches, respectively, to assessing LS. Meanwhile, the basic geological and environmental conditions are considered in the AHP-CI model, while the LR model accounts for the conditions of LUC and urbanization. The results of the multicollinearity diagnostics reflect the rationality of the predisposing factor selection (1.131 < VIF < 4.441). The findings reveal that the AHP-CI model underperforms in LUC and urbanization conditions (AUROC = 0.645, 0.628, and 0.667 for different validation datasets). However, when all the time-varying human activity predisposing factors are considered, the LR model (AUROC = 0.852) performs significantly better under the conditions of solely considering 2010 (AUROC = 0.744) and 2020 (AUROC = 0.810). The CA–Markov model was employed to project the future land use for the short-term (2025), mid-term (2030), and long-term (2040) planning periods. Based on these projections, maps of future LS were created. Importantly, this paper discussed the relationships between landslide management and regional sustainable development under the framework of the UN SDGs, which are relevant to Goal 1, Goal 11, Goal 13, and Goal 15. Finally, this study highlights the importance of integrating strategic land planning, reforestation efforts, and a thorough assessment of human impact predisposing factors with SDG-aligned LS predictions, advocating for a comprehensive, multi-stakeholder strategy to promote sustainable landslide mitigation. Full article

Land-use and land-cover changes constitute pivotal components in global environmental change research. Through an examination of spatiotemporal variations in land cover, we can deepen our understanding of land-cover change dynamics, shape appropriate policy frameworks, and implement targeted environmental conservation strategies. The judicious management of land is a critical determinant in fostering the sustainable growth of urban economies and enhancing quality of life for residents. This study harnessed remote sensing data to analyze land-cover patterns in Tianjin over five distinct time points: 2000, 2005, 2010, 2015, and 2020. It focused on evaluating the evolving dynamics, transition velocities, and transformation processes across various land categories within the region. Utilizing dynamic analysis and a transition matrix, the study traced shifts among different land-use classes. The center-of-gravity migration model was employed to elucidate land-cover pattern evolution. This research also integrated pertinent land-cover statistics to offer a holistic perspective on Tianjin’s land-cover transformations. Employing the CA–Markov model, we projected the prospective spatial layout of land cover for the area. Our findings revealed the following. (1) From 2000 to 2020, Tianjin experienced a significant reduction in cropland, forest, grassland, and water areas, alongside a substantial increase in impervious. (2) The impervious surface’s center of gravity, initially in Beichen District, shifted 4.20 km northwestward at an average rate of 0.84 km per year. (3) Principal component analysis indicated that the growth in the output value of the secondary and forestry industries is a key driver in expanding Tianjin’s impervious-surface area. (4) Predictions for 2025 suggest an increase in Tianjin’s impervious-surface area to 4659.78 km², with a concurrent reduction in cropland to 5656.18 km². The insights gleaned from this study provide a solid theoretical foundation and empirical evidence, aiding in the formulation of informed land-use strategies, the preservation of urban land resources, and guiding principles for sustainable urban development. Full article

Land use/land cover (LULC) changes significantly impact spatiotemporal groundwater levels, posing a challenge for sustainable water resource management. This study investigates the long-term (2000–2022) influence of LULC dynamics, particularly urbanization, on groundwater depletion in Kabul, Afghanistan, using geospatial techniques. A time series of Landsat imagery (Landsat 5, 7 ETM+, and 8 OLI/TIRS) was employed to generate LULC maps for five key years (2000, 2005, 2010, 2015, and 2022) using a supervised classification algorithm based on Support Vector Machines (SVMs). Our analysis revealed a significant expansion of urban areas (70%) across Kabul City between 2000 and 2022, particularly concentrated in Districts 5, 6, 7, 11, 12, 13, 15, 17, and 22. Urbanization likely contributes to groundwater depletion through increased population growth, reduced infiltration of precipitation, and potential overexploitation of groundwater resources. The CA-Markov model further predicts continued expansion in built-up areas over the next two decades (2030s and 2040s), potentially leading to water scarcity, land subsidence, and environmental degradation in Kabul City. The periodic assessment of urbanization dynamics and prediction of future trends are considered the novelty of this study. The accuracy of the generated LULC maps was assessed for each year (2000, 2005, 2010, 2015, and 2022), achieving overall accuracy values of 95%, 93.8%, 85%, 95.6%, and 93%, respectively. These findings provide a valuable foundation for the development of sustainable management strategies for Kabul’s surface water and groundwater resources, while also guiding future research efforts. Full article

As one of the four major bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a highly integrated mega urban agglomeration and its unparalleled urbanization has induced prominent land contradictions between humans and nature, which hinders its sustainability and has become the primary concern in this region. In this paper, we probed the historical characteristics of land use and land cover change (LUCC) in the GBA from 2005 to 2015, and forecasted its future land use pattern for 2030, 2050, and 2070, using a cellular automata–Markov (CA–Markov) model, under three typical tailored scenarios, i.e., urban development (UD), cropland protection (CP), and ecology security (ES), for land use optimization. The major findings are as follows: (1) The encroachments of build-up land on the other land uses under rapid urbanization accounted for the leading forces of LUCCs in the past decade. Accordingly, the urban sprawl was up to 1441.73 km² (23.47%), with cropland, forest land, and water areas reduced by 570.77 km² (4.38%), 526.05 km² (1.76%), and 429.89 km² (10.88%), respectively. (2) Based on the validated CA–Markov model, significant differences are found in future land use patterns under multiple scenarios, with the discrepancy magnified over time and driven by different orientations. (3) Through comprehensive comparisons and tradeoffs, the ES scenario mode seems optimal for the GBA in the next decades, which optimizes the balance between socio-economic development and ecological protection. These results serve as an early warning for future land problems and can be applied to land use management and policy formulation to promote the sustainable development of the GBA. Full article

In the face of geopolitical challenges and climate change, economic progress, safe production, and environmental protection have emerged as important directions for chemical industry development. However, the rational optimization of the chemical industry layout under the backdrop of ecological environmental protection necessitates further exploration. This study explores the evolution and future development direction of the chemical industry layout within the coastal region of Jiangsu Province, China, using the CA–Markov model. The findings reveal a trend of spatial agglomeration growth among coastal chemical enterprises, with Moran’s Index increasing from 0.109 in 2007 to 0.206 in 2017. The petrochemical industry, in particular, demonstrated the most significant agglomeration effect, with approximately 52.10% being concentrated in 14 coastal industrial parks in 2017. Under the constraints of the ecological environment and policy guidance, the land area allocated for the chemical industry experienced a reduction of over 10%, further strengthening the emphasis on spatial agglomeration. Chemical industries along Jiangsu’s coast have become agglomerated and concentrated near industrial parks and ports. Their spatial distribution and connectivity were mainly influenced by factors such as convenient transportation, the ecological environment, local policies, the distance from residential areas, and industrial agglomeration. Under different scenarios—including natural growth, ecological environment constraints, and policy guidance—chemical industries show diverse spatial patterns. Ecological environmental constraints and policy guidance can provide various intervention methods for the government to promote the optimization direction and focus of the chemical industry layout while minimizing its impact on the ecological environment. Full article

Land use and land cover (LULC) change detection and prediction studies are crucial for supporting sustainable watershed planning and management. Hence, this study aimed to detect historical LULC changes from 1985 to 2019 and predict future changes for 2035 (near future) and 2065 (far future) in the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. LULC classification for the years 1985, 2000, 2010, and 2019 was performed using Landsat images along with vegetation indices and topographic factors. The random forest (RF) machine learning algorithm built into the cloud-based platform Google Earth Engine (GEE) was used for classification. The results of the classification accuracy assessment indicated perfect agreement between the classified maps and the validation dataset, with kappa coefficients (K) of 0.92, 0.94, 0.90, and 0.88 for the LULC maps of 1985, 2000, 2010, and 2019, respectively. Based on the classified maps, cultivated land and settlement increased from 58.60 to 83.08% and 0.06 to 0.18%, respectively, from 1985 to 2019 at the expense of decreasing forest, shrubland and grassland. Future LULC prediction was performed using the cellular automata–Markov (CA–Markov) model under (1) the business-as-usual (BAU) scenario, which is based on the current trend of socioeconomic development, and (2) the governance (GOV) scenario, which is based on the Green Legacy Initiative (GLI) program of Ethiopia. Under the BAU scenario, significant expansions of cultivated land and settlement were predicted from 83.08 to 89.01% and 0.18 to 0.83%, respectively, from 2019 to 2065. Conversely, under the GOV scenario, the increase in forest area was predicted to increase from 2.59% (2019) to 4.71% (2065). For this reason, this study recommends following the GOV scenario to prevent flooding and soil degradation in the Gumara watershed. Finally, the results of this study provide information for government policymakers, land use planners, and watershed managers to develop sustainable land use management plans and policies. Full article

This paper is situated within the discussion of mega-urbanization, a particular urbanization process that entails a large-scale agglomeration. In this paper, our focus is on urbanization in Java, Indonesia’s most dynamic region. We add to the literature by investigating the change and prediction of the land use/land cover (LULC) of mega-urbanization in Java. This research uses a vector machine approach to support the classification of land cover change dynamics, cellular automata-Markov (CA Markov), and the Klassen typology technique. This paper indicates that major metropolitan areas are still expanding in terms of built-up areas, generating a larger urban agglomeration. However, attention should be also given to the urbanization process outside existing metropolis’ boundaries given that more than half of the built-up land coverage in Java is located in non-metropolitan areas. In terms of future direction, the projection results for 2032 show that the Conservative scenario can reduce and slow down the increase in built-up land on the island of Java. On the other hand, the Spatial Plan (RTRW) scenario facilitates a rapid increase in the LULC of built-up land from 2019. The urban spatial dynamics in Java raises challenges for urban and regional planning as the process is taking place across multiple administrative authorities. Full article

The quantitative analysis and prediction of spatiotemporal patterns of land use in Haihe River Basin are of great significance for land use and ecological planning management. To reveal the changes in land use and carbon stock, the spatial–temporal pattern of land use data in the Haihe River Basin from 2000 to 2020 was studied via Mann–Kendall (MK) trend analysis, the transfer matrix, and land use dynamic attitude. Through integrating the models of the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and the Long Short-Term Memory (LSTM), the results of the spatial distribution of land use and carbon stock were obtained and compared with Cellular Automation (CA-Markov), and then applied to predict the spatial distribution in 2025. The results show the following: (1) The land use and land cover (LULC) changes in the Haihe River Basin primarily involve an exchange between cultivated land, forest, and grassland, as well as the conversion of cultivated land to built-up land. This transformation contributes to the overall decrease in carbon storage in the basin, which declined by approximately 1.20% from 2000 to 2020. (2) The LULC prediction accuracy of LSTM is nearly 2.00% higher than that of CA-Markov, reaching 95.01%. (3) In 2025, the area of grassland in Haihe River Basin will increase the most, while the area of cultivated land will decrease the most. The spatial distribution of carbon stocks is higher in the northwest and lower in the southeast, and the changing areas are scattered throughout the study area. However, due to the substantial growth of grassland and forest, the carbon stocks in the Haihe River Basin in 2025 will increase by about 10 times compared with 2020. The research results can provide a theoretical basis and reference for watershed land use planning, ecological restoration, and management. Full article

Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems. Full article