Water Bodies In Areas Research Articles

Seguimiento de los cambios en el uso del suelo y la cobertura del suelo utilizando tecnicas de teledeteccion y SIG en la Isla de Phu Quoc, Provincia de Kien Giang, Vietnam

The changes in land use and land cover (LULC) are among the primary drivers of global environmental changes in many developing countries. In this study, LULC changes were assessed on Phu Quoc Island, located in Kien Giang province, Vietnam, from 2001 to 2022. The study utilized remote sensing and Geographic Information System (GIS) technology using Landsat images for the years 2001, 2009, and 2022. Image classification for each year was conducted through supervised classification using a maximum likelihood classifier, with the main LULC classes being forests, bare land, agricultural areas, water bodies, and built-up areas. The accuracy of the classification was evaluated using the kappa coefficient, achieving values consistently above 0.8 for all three images. Over the 20-year period (2001-2022), the area of forest, agriculture, and water bodies decreased by 14.90 km2, 30.96 km2, and 0.64 km2, respectively. Meanwhile, the areas of bare land and built-up areas increased by 22.22 km2 and 24.28 km2, respectively. Additionally, this study employed the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) to quickly assess LULC changes, obtaining results consistent with the supervised classification. The findings underscore the importance of closely monitoring LULC changes to facilitate effective natural resource management and maintain a sustainable environment.

Comparative analysis of land use changes modeling based-on new hybrid models and CA-Markov in the Urmia lake basin

Land use change is one of the most significant environmental issues in many countries and is the main reason for ecosystem changes. This issue is one of the most important environmental crises in Iran due to the lack of planning and inappropriate human use of the natural environment. Many parts of Iran, especially the Urmia Lake basin, are involved in challenging land use changes. The trend of land use changes extracted from Landsat images in the Urmia lake basin with overall accuracy and Kappa coefficient of more than 85 % and 80 % respectively shows that during the 32-year period (1989–2021) agricultural lands, built-up lands and barren lands respectively 3330.83 km2, 466.86 km2 and 5325.8 km2 have increased in area. On the other hand, the areas of vegetation and water bodies decreased by 6625.79 km2 and 2497.7 km2, respectively. The current trend of land use changes in Urmia basin based on all models of Markov chain-Multi Criteria Evolution-Cellular Automata (MC-MCE-CA), Markov Chain-Multilayer Perceptron-Cellular Automata (MC-MLP-CA), Markov Chain-Sim Weight-Cellular Automata (MC-SW-CA), Markov Chain–Logistic Regression-Cellular Automata (MC-LR-CA) indicate the continuation of changes until 2037. Meanwhile, based on the most accurate models such as MC-MLP-CA and MC-LR-CA models, agricultural lands will increase by 1436.60 km2 and 1258.33 km2, built-up lands by 75.23 km2 and 65.52 km2, and barren lands by 808.94 km2 and 856.90 km2 respectively. Also, the vegetation lands will be reduced by 1890.64 km2 and 1755.31 km2 and water bodies by 430.13 km2 and 425.44 km2. In general, the results showed that MC-MLP-CA, MC-LR-CA, MC-SW-CA, MC-MCE-CA models with Overall Kappa 89.89, 87.09, 84.52 % and 84.84 and Percent of Correctness 93.65, 93.09, 91.52 and 91.77 respectively, with higher accuracy than the CA-Markov model (Overall Kappa = 81.49 % and Percent of Correctness = 87.49 %) are more effective for land use changes modeling.

A novel approach for quantifying the influence intensity of urban water and greenery resources on microclimate for efficient utilization

Climate changes have led to increasing global energy consumption, detrimental to the sustainable development of society. Urban blue-green infrastructure (UBGI) can improve urban microclimate. However, influence intensity of UBGI on microclimate has not been quantified deeply to improve the use efficiency of water and greenery resources. To solve research deficiencies, this study numerically simulated the UBGI for 44 scenarios with different resource configurations (various water body areas and greenery coverages) in summer. Based on simulations, this study developed novel mathematical models of UBGI thermo-environment (BGTE) to quantify influence intensity of UBGI. The results indicated that daytime synergies of UBGI first increased and then decreased with time. The significance intensity of the influence of time (t), water body area (Sw), tree coverage rate (TCR), shrub coverage rate (SCR), and grassland coverage rate (GLCR) on the synergy of water and greenery was quantified by artificial neural network: t (39.4 %), Sw (22.6 %), TCR (22.0 %), SCR (13.2 %), and GLCR (2.8 %). To make the overall effect of UBGI on microclimate relatively efficient, Sw should be less than 10,000 m2, SCR should be greater than 65 %, and TCR should be close to 15 %. This research provides practical ideas for efficient use of water and greenery resources.

Analysis of Changes in Water Body Area in Southwest, China Based on GEE

It is of great significance for policymakers to develop management strategies for water resource regulation, habitat protection and wetland restoration. Based on the Google Earth Engine platform, this study extracted remote sensing data of water quality change in Southwest China in the past 20 years, combined with ArcGIS for spatial analysis, and used M-K trend test and mutation test to analyze the long-term temporal variation of water bodies in Southwest China. The analysis shows that the water area in Southwest China increased every five years from 2000 to 2020, but the growth rate changed, while the growth rate slowed down from 2010 to 2020, and the increase mainly occurred in the central and western regions. The results show that in the past 20 years, the water surface area in Southwest China has shown a statistically significant increasing trend, especially after 2020, this trend is more statistically significant. This study can provide a theoretical basis for the protection of water environment in Southwest China.

Spatiotemporal Simulation of Soil Pb Accumulation Process in Urban-Rural Areas: A Case Study of a Large City in Central China

The high intensity of diverse human activities in urban-rural areas leads to complex soil Pb accumulation processes and high spatiotemporal heterogeneity, making it difficult to reveal the spatiotemporal characteristics of soil Pb accumulation in these areas. This study used a typical urban-rural area in a large city in Central China as the study area, constructed a soil Pb accumulation model, and established a spatiotemporal simulation method for soil Pb accumulation processes combining this model and land use classification and simulation results. Using this method, we simulated the soil Pb content in the study area from 2013 to 2040 and elucidated the future spatiotemporal variation characteristics of soil Pb content. The results showed that the average soil Pb content in the study area in 2013 was approximately 1.77 times the background value of the Pb content in the surface soil of the province where the city is located, indicating significant soil Pb pollution. The soil Pb content was predicted to continue increasing from 2013 to 2040, with relatively low increases (0.53-2.25 mg·kg-1) in the western, northern, and southern parts of the study area, accounting for 25.46 % of the total area, and relatively high increases (3.98-5.70 mg·kg-1) in the eastern part, accounting for 17.14 % of the total area. The increase in the area of forest land and the decrease in the area of water bodies and grassland in the eastern part of the study area led to a substantial rise in soil Pb content in this region; in addition, the spatial distribution of soil Pb content was highly correlated with the distribution of important factories and transportation facilities. This study overcomes the limitations of previous research that treated land use as unchanging and to a certain extent reflects the impact of regional land use changes on the heavy metal accumulation process. It provides a method for simulating the soil Pb accumulation process in urban-rural areas and a basis for controlling soil Pb pollution in the city's urban-rural areas.

Deformation Characteristics and Activation Dynamics of the Xiaomojiu Landslide in the Upper Jinsha River Basin Revealed by Multi-Track InSAR Analysis

The upper Jinsha River, located in a high-mountain gorge with complex geological features, is highly prone to large-scale landslides, which could result in the formation of dammed lakes. Analyzing the movement characteristics of the typical Xiaomojiu landslide in this area contributes to a better understanding of the dynamics of landslides in the region, which is of great significance for landslide risk prediction and analysis. True displacement data on the surface of landslides are crucial for understanding the morphological changes in landslides, providing fundamental parameters for dynamic analysis and risk assessment. This study proposes a method for calculating the actual deformation of landslide bodies based on multi-track Interferometric Synthetic Aperture Radar (InSAR) deformation data. It iteratively solves for the optimal true deformation vector of the landslide on a per-pixel basis under a least-squares constraint based on the assumption of consistent displacement direction among adjacent points on the landslide surface. Using multi-track Sentinel data from 2017 to 2023, the line of sight (LOS) accumulative de-formation of the Xiaomojiu landslide was obtained, with a maximum LOS deformation of −126 mm/year. The true surface displacement of the Xiaomojiu landslide after activation was calculated using LOS deformation. The development of two rotational sub-slipping zones on the landslide body is inferred based on the distribution of actual displacements along the central profile line. Analysis of temporal changes in water body area data revealed that the Xiaomojiu landslide was activated after a barrier lake event and continuously moved due to the influence of higher water levels’ in the river channel. In conclusion, the proposed method can be applied to calculate the true surface displacement of landslides with complex mechanisms for analyzing the movement status of landslide bodies. Furthermore, the spatiotemporal analysis of the Xiaomojiu landslide characteristics can support analyzing the mechanisms of similar landslides in the Jinsha River Basin.

Analysis of Lake Area Dynamics and Driving Forces in the Jianghan Plain Based on GEE and SEM for the Period 1990 to 2020

The lakes of Jianghan Plain, as an important component of the water bodies in the middle and lower reaches of the Yangtze River plain, have made significant contributions to maintaining the ecological health and promoting the sustainable development of the Jianghan Plain. However, there is a relatively limited understanding regarding the trends of lake area change for different types of lakes and their dominant factors over the past three decades in the Jianghan Plain. Based on the Google Earth Engine (GEE) platform, combined with the water body index method, the changes in area of three different types of lakes (area > 1 km2) in the Jianghan Lake Group from 1990 to 2020 were extracted and analyzed. Additionally, the Partial least squares structural equation model (PLS-SEM) was utilized to analyze the driving factors affecting the changes in water body area of these lakes. The results show that from 1990 to 2020, the area of the lakes of the wet season and level season exhibited a decreasing trend, decreasing by 893.1 km2 and 77.9 km2, respectively. However, the area of dry season lakes increased by 59.27 km2. The areas of all three types of lakes reached their minimum values in 2006. According to the PLS-SEM results, the continuous changes in the lakes’ area are mainly controlled by environmental factors overall. Furthermore, human factors mainly influence the mutation of the lakes’ area. This study achieved precise extraction of water body areas and accurate analysis of driving factors, providing a basis for a comprehensive understanding of the dynamic changes in the lakes of Jianghan Plain, which is beneficial for the rational utilization and protection of water resources.

Streamflow Variation under Climate Conditions Based on a Soil and Water Assessment Tool Model: A Case Study of the Bailong River Basin

We coupled the global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Future Land Use Simulation model (FLUS) to evaluate land use change in the Bailong River Basin (BRB) under three shared socioeconomic pathway and representative concentration pathway scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5). Additionally, we used calibrated soil and water assessment tools (SWATs) to evaluate the streamflow in the BRB from 2008 to 2100 under the combined influence of climate and land use changes. The results indicate that (1) under the SSP126-EP scenario, forests have been well preserved, and there has been an increase in the combined area of forests and water bodies. The SSP245-ND scenario has a similar reduction pattern in agricultural land as SSP126-EP, with relatively good grassland preservation and a moderate expansion rate in built-up land. In contrast, the SSP585-EG scenario features a rapid expansion of built-up land, converting a significant amount of farmland and grassland into built-up land. (2) From 2021 to 2100, the annual average flow increases under all three scenarios, and the streamflow change is most significant under SSP5–8.5. (3) Compared to the baseline period, the monthly runoff increases, with the most significant increase occurring during the summer months (June to August). This study offers a thorough assessment of potential future changes in streamflow. Its findings are expected to be applied in the future to improve the management of water resources at a local level.

Urban Growth Modeling in Kurunegala, Sri Lanka using Cellular Automata/Markov Chain Multi-criteria Analysis and Markov-Markovian Transition Estimator

With accelerated global urbanization, the expansion of cities has become a pressing concern for urban planners and researchers. The growth and dynamics of urban encroachment are closely tied to changes in land use, especially in urbanized areas. This study seeks to evaluate the accuracy of the CA-Markov (Cellular Automata) model in predicting land use/land cover changes (LULCC) in the Kurunegala district, Sri Lanka as a result of urban expansion. The investigation employs secondary data, including 2007, 2012, 2017, and 2022 Landsat 05, 07, 08, and 09 images respectively. Using software applications such as ArcGIS 10.8, IDRISI 17.0, and MS-Excel 2019, diverse techniques including supervised classification, Markovian transition estimation, and CAMarkov chain analysis, this study attempts to analyze the Urban Growth Modeling for 2027 and 2037 using CA-Markov Chain Multi-criteria Analysis (MCA) under Markov-Markovian Transition Estimator. The study used 32 spatial variables for determining the LULCC. As per the derived results, from 2022 to 2027, urban areas have increased quite markedly. The vegetation cover area has reduced and the areas of water bodies have increased spatially. From 2027 to 2037, the urban area increment is 72.552%. Also, vegetation cover and water body distribution have reduced by 8.051% and 39.91% respectively.

Autonomous Surface Water Cleaning Robots

t—Water resources are indispensable for sustaining life on Earth, playing a crucial role in various ecosystems and supporting human civilization. However, the escalating levels of plastic and waste pollution in water bodies have become a pressing concern in recent years. This pollution not only disrupts aquatic ecosystems but also compromises water quality and obstructs sunlight penetration. Additionally, the advent of the coronavirus pandemic has exacerbated the problem, with the widespread usage of disposable masks, gloves, and other biomedical waste further contributing to water pollution.The improper disposal of waste, especially biomedical waste, poses significant challenges to both public health and environmental integrity. Inadequate management of such waste not only jeopardizes human well-being but also threatens the delicate balance of ecosystems. Consequently, addressing waste management has become an urgent imperative, with potentially dire consequences if left unaddressed.In response to this critical issue, our paper proposes the design of an innovative product: an autonomous water cleaning robot. This robot is engineered to efficiently tackle the problem of improper waste disposal in water bodies. Its design features a sturdy mechanical frame, complemented by advanced sensor fusion and computer vision technologies, enabling it to operate autonomously in cleaning water surfaces.The autonomous water cleaning robot serves as a proactive solution to combat water pollution effectively. Equipped with sensors and cameras, it navigates water bodies with precision, identifying and collecting floating garbage and waste materials. By harnessing the power of automation, the robot can operate continuously, covering large areas of water bodies and efficiently removing pollutants.One of the key strengths of our design is its adaptability to diverse environments and types of pollutants. The robot's sensor fusion capabilities allow it to distinguish between various types of waste, enabling targeted and efficient cleaning. Moreover, its autonomous operation reduces the need for manual intervention, minimizing human labor and associated costs.Furthermore, the design prioritizes sustainability by incorporating environmentally friendly materials and energy-efficient components. By utilizing renewable energy sources such as solar power, the robot minimizes its carbon footprint while maximizing operational efficiency. This approach aligns with global efforts to promote sustainable practices and mitigate the impacts of climate change.In conclusion, the autonomous water cleaning robot represents a promising solution to the escalating problem of water pollution caused by improper waste disposal. Through its innovative design and advanced technologies, it offers a proactive and sustainable approach to preserving water quality and safeguarding aquatic ecosystems. By addressing the root causes of pollution and promoting responsible waste management, our design contributes to the collective effort to ensure a healthier and more sustainable future for generations to come. Keywords—Autonomous Robot, surface water cleaning,raspberry pi, machine learning, obstacle avoidance

Dynamic evolution and simulation of habitat quality in arid regions: A case study of the Hexi region, China

Habitat quality is an important characterization of the health level of ecosystems. Unreasonable development activities have led to a degradation in habitat quality and loss of biodiversity, especially in the arid areas of northwest China where the ecological environment is fragile. In this study, we focused on the arid area in northwestern China (Hexi region). By integrating the InVEST model and PLUS model, we evaluated the dynamics of habitat quality in the Hexi region from 1990 to 2020 under the background of land use change, and simulated the habitat quality in this area in 2030 under different scenarios. The results showed that: (1) From 1990 to 2020, the areas of farmland, waterbodies, and built-up land increased by 2343.26 km2, 416.42 km2 and 739.28 km2 respectively, and the area of unutilized land decreased by 3020.36 km2. In terms of changes in land use type, unutilized land changed the most. (2) From 1990 to 2020, the habitat quality increased by 1.07 %, spatially showed a high distribution pattern along the Qilian Mountains and a low distribution pattern in the remaining area. The spatial change was dominated by stabilization, and the rank change was mainly from poor habitat quality areas to good habitat quality areas. (3) Under the scenario of natural development (ND), the future built-up land area will rapidly increase by 989 km2. In the ecological protection (EP) scenario, the areas of forest and grassland will increase by 10476 km2, the expansion of cultivated land and built-up land will be effectively curbed, and the habitat quality will be greatly improved. (4) In 2020–2030 (ND scenario), land use change will cause a certain degree of degradation of habitat quality. In 2020–2030 (EP scenario), land use change will promote the improvement of habitat quality. In addition, through this study, we provide a new method to quantify the relationship between land use and habitat quality.

Analysis of Land Use and Land Cover Change Detection for Indore District of Malwa Plateau Region Using Supervised Machine Learning

The main aim of this work is to find out LULC classes for the Indore region. For the last seven years, Indore has been crowned the cleanest city in India according to the latest ranking of 2023. In the Indian state of Madhya Pradesh, the city and district of Indore are situated on the southern edge of the Malwa plateau. The municipalities of the Indore district majorly cover Manpur, Depalpur, Goutampura, Mhow, Hatod, Betma, Rau and Sanwer, with an area of approximately 3900 square kilometers. In contrast, Indore covers a land area of approximately 530 square kilometres. It is in charge of the nation’s slow but steady industrialization and population growth found in the last three decades. The city was much smaller and had a far smaller population thirty years ago. However, to evaluate the changes and decide on Indore city's future planning, Indore has taken over responsibility for the city's economic activities in recent years. New appropriate patterns should be recommended based on local factors like topography characteristics. The satellite image with multiple spectrums was used for the investigation. ArcGIS is based on pixel-by-pixel supervised classification using the maximum likelihood approach of Landsat satellite images from 2003 and 2023. The ground area, agriculture, and urbanization are linked to the LULC features. The various categories of classified land use and land cover features include the area of built-up regions, water bodies, cropland, forest, and barren land taken to predict the broad changes. Remotely sensed Landsat 5 images of 2003 and 8 images of 2023 were utilized to identify changes to accomplish this goal. This work clarifies the comparison of LULC classes for the Indore region.

An Overview of the Remote Sensing and GIS Techniques Application to Detect Changes in the Surface Area of Water Bodies in Iraq

The use of Remote Sensing data in monitoring water bodies and reservoirs is a new and advanced approach that helps to study large areas covered by water in a short time and at a reasonable amount compared to traditional methods. Accordingly, the Remote Sensing technique integrated with Geographic Information Systems (GIS) has been used in several studies to monitor changes in water surface body area locally and regionally. In this study, several studies in Iraq were reviewed, using Remote Sensing techniques with the help of GIS to detect changes in the areas covered by the water bodies using satellite images captured in different periods. Natural and artificial lakes represent a large proportion of the water bodies in Iraq, including eight (8) lakes distributed in different regions of Iraq. An evaluation of the hydrological system of the studied water bodies showed that the changes in the area and size of natural and artificial lakes are affected by political, economic, and climatic conditions as the areas increase and decrease over years.

Influence of river structure and hydrodynamics on water quality in the upper Taihu Basin, China

The Taihu Basin constitutes a complex river network and variable hydrodynamic forces, which contributing to alterations in regional water quality within the Yangtze River Delta. In this study, to explore the underlying mechanism of these dual factors on water quality in the upper Taihu Basin, a combination of graphical analysis, the water quality index (WQI) method, and the partial least square-structural equation model (PLS-SEM) were employed to quantitatively investigate the river structure indices (water surface ratio, WP; river system fractal, Δα; river network connectivity, γ) and hydrodynamic force indices (the flow index, FI), and their influence on water quality. Negative correlations were found between both WP and Δα with FI and a positive correlation between γ and FI. The southern water network had a higher water body area and greater difference in river network density, while the northern river network had enhanced connectivity and stronger water flow, thus exhibiting superior water quality. Regarding the influence of these factors on water quality, the impact of river structure on WQI was statistically insignificant (P > 0.05), while hydrodynamic force was significantly positively correlated with the WQI (P < 0.05). Moreover, in comparison to the path coefficient of 0.657 for the river structure's influence on the WQI, the hydrodynamic force exerted an even more significant impact on water quality with a path coefficient of 0.715. This study suggested that water quality improvements depend on optimizing the structural integrity of river systems and promoting favorable hydrodynamic conditions, with the latter having greater influence. This research provided theoretical underpinning for optimizing river structure and advancing river and lake water environments within the Taihu Basin.

ПУТИ ВОВЛЕЧЕНИЯ НЕВОСТРЕБОВАННЫХ СЕЛЬСКОХОЗЯЙСТВЕННЫХ ЗЕМЕЛЬ ВЛАДИМИРСКОЙ ОБЛАСТИ В ЭКОНОМИЧЕСКИЙ ОБОРОТ

The article, using the example of the territories of the Troitsky and Novomoskovsky administrative districts (hereinafter referred to as TiNAO) of Moscow, shows the possibility of a geoecological assessment of changes in the morphometric parameters of small reservoirs. Before joining the administrative structure of the Moscow territories in 2012, the studies on the ecology of these territories was minimal. Of particular importance for monitoring the geo-ecological situation in “New Moscow», – what the conjoined territories are also called, is the fact that its area is 1,480 km2, which is 57.8% of the entire territory of Moscow.&#x0D; The subject of the study were 1402 small reservoirs registered in the territory of the TiNAO. To carry out their environmental assessment, a cluster analysis method was used, which is a universal mathematical tool for scientific research. A negative change (decrease) in the average depth of the reservoir was taken as the main parameter of the study. Based on the results of the objects’ morphometry analysis carried out at an interval of two years, data on dynamic changes in the physical parameters of reservoirs were obtained and processed. It was established that shallowing problems exist in 4.51% of the total area of reservoirs in the TiNAO. Mapping stagnant small reservoirs made it possible to visually assess the scale of the problem and identify potentially problematic areas of the TiNAO from a geoecological point of view. Using the cluster analysis, zoning of problematic water bodies was carried out. Such identification (clustering) of water bodies’ grouping made it possible to see deviations at the level of groups of small river basins, which provided a large-scale understanding of the ecological situation at the level of catchment areas of larger water bodies.

Mangrove density and delta formation in Segara Anakan Lagoon as an impact of the riverine sedimentation rate

Abstract. Cahyo TN, Hartoko A, Muskananfola MR, Haeruddin, Hilmi E. 2024. Mangrove density and delta formation in Segara Anakan Lagoon as an impact of the riverine sedimentation rate. Biodiversitas 25: 1276-1285. Segara Anakan Lagoon (SAL) is in Southwestern of Central Java Province, Indonesia. The Western Part of SAL (WP-SAL) gets its sediment load from the Citanduy River. This research aimed to determine the temporal pattern of the shoreline, water body area, depth, delta formation, and mangrove density at WP-SAL. Shoreline data were extracted from a base map and the satellite imagery. Overlapping analysis of several shorelines and depth maps showed different results in terms of values or patterns. The western part of SAL had silted up and reached more than 85% in 161 years. The sediment accretion impacted the shoreline (between 177 ha yr-1 in 1999-2003), and the average decrease of the water bodies speed was 61 ha yr-1. The Pelawangan Barat waters (PBW) and the main lagoon had a decreasing depth because of the sediment deposits. The sedimentation also greatly impacts mangrove species distribution, clustering and association, and density and mangrove affinity. Mangrove density growth-based and the delta formation were formed and developed following the tidal pattern during floods and ebbs. It can be used to predict the future morphology of WP-SAL. The WP-SAL will be sediment filled and left the waterways channels, it was reached within 13.6 years from 2018, which would be in 2032.

Advanced CMD predictor screening approach coupled with cellular automata-artificial neural network algorithm for efficient land use-land cover change prediction

The prediction of future land use and land cover (LULC) change is essential for natural resource management and the development of environmental monitoring strategies, especially for a non-resilient system. Thus, a novel LULC prediction model was proposed by coupling a hybrid predictor screening approach with the Cellular Automata coupled Artificial Neural Network (CA-ANN) approach. The hybrid predictor screener was formed by integrating the outcomes of Correlation Coefficient (CC), Mutual Information (MI), and Decision Tree (DT) methods and termed as the CMD technique. The CA-ANN model was trained by LULC maps of 1995 and 2005 and tested by the map of 2020 for these four sets of variables (i.e., CC, MI, DT, and CMD). The results revealed that the CMD predictors were the most influential variables for LULC prediction, followed by the DT-based variables. In contrast, the CC-based predictors were found to be the least effective, followed by the MI variables. Thus, the CMD-CA-ANN model was established as the benchmark model in this study. The study showed that elevation and distance from roads were more critical variables for LULC prediction compared to slope and aspect parameters. The proposed model demonstrated that by 2080, there would be an increase in the area of built-up land (5.79%), cropland (4.35%), shrubland (1.62%), and water bodies (0.3%) and a decrease in the area of forested land (1.34%) and barren land (10%) in the study area. The research has the potential to significantly contribute to the identification and management of significant environmental changes.

Landscape Analysis and Ecological Risk Assessment during 1995–2020 Based on Land Utilization/Land Coverage (LULC) and Random Forest: A Case Study of the Fushun Open-Pit Coal Area in Liaoning, China

China’s Fushun open-pit mine is the largest century-old coal mine in Asia. Large-scale mining and the use of coal has caused dramatic changes in the regional urban landscape pattern, seriously affecting the ecological function and ecosystem stability of its surrounding landscape. Evaluating the ecological risks of the landscape in the urban areas of open-pit mines contributes to the risk management of regional ecosystems and the sustainable development of society. This study selected six-phase Landsat ETM/OLI remote sensing images from 1995 to 2020 and combined them with the random forest model to carry out an LULC classification of the open-pit mine and its surrounding areas and, on this basis, discusses the evolution of its landscape pattern and evaluates the ecological risks. It fills the gap in the research on the evolution of regional landscape patterns and ecological risks in the study area and improves the automatic classification efficiency of LULC for use in open-pit mines. The results show that the classification accuracy of LULC regarding open-pit mines based on image pixels and the random forest model can reach 30 m, and the rate of accuracy can reach 92–97%. From 1995 to 2020, the coverage area of forest land and building land in the study area has increased and is mainly composed of grassland and undeveloped land. The use of land was transferred to the mining area, the water body area maintained a relative dynamic balance, and the overall vegetation coverage of the mining area was greatly improved; the forest land began to expand from the surrounding area to the mining area in 2010, and the construction land began to move from the areas surrounding the mining area to the surrounding valleys in 2015. The landforms have extended radially, and the landscape sprawl index has increased, indicating the optimization of the ecological environment; the high- and medium-risk areas decreased by 75.51 km2, the low-risk areas expanded by 461.48 km2, and the overall ecological risk index decreased. From this, it is possible to conclude that the landscape restoration project adopted in the study area has achieved great results, and the improvement of the ecological environment also directly affects the increase of construction land. These research results can provide scientific guidance for the rational utilization and sustainable development of land resources in urban areas of open-pit mines.

Extreme drought alters waterfowl distribution patterns and spatial niches in floodplain wetlands

The Yangtze River Basin was among the areas most severely affected by the widespread extreme drought in the Northern Hemisphere in 2022. Drought conditions in this basin extended into the wintering season, significantly influencing waterfowl habitat availability. Exploring how waterfowl distributions and ecological niches respond to these changes in wetland environmental conditions and the underlying ecological mechanisms are important for biodiversity conservation and understanding wetland ecological processes. By integrating field observations, remote sensing, and ecological niche modelling, we analysed habitat changes over the past decade and investigated how extreme drought affected the distribution patterns and spatial niches of two waterfowl guilds, ducks and geese, in the Dongting Lake Region, a key wetland ecosystem in the Yangtze River Basin. The extreme drought led to a drastic reduction in the waterbody area of natural habitats and caused vegetation to enter the wilting stage prematurely. In response, a substantial proportion of waterfowl shifted to adjacent managed wetlands with stable water levels, especially the ducks, whose distribution pattern was fundamentally altered. The proportion of both waterfowl guilds in the managed wetland showed a significant correlation with the proportion of the waterbody area. Further, we compared the spatial niche of waterfowl in 2022 with that in the former year, which represented a typical year with normal hydrology. The spatial niche of ducks underwent a substantial contraction, while that of geese remained large in both years but shifted inward in 2022 towards areas with lower elevation, and foraging resource use was altered. Therefore, for both waterfowl guilds, the extent of habitat overlap between the two years was small. This study has important implications for understanding the impact of extreme climatic and hydrological events on wetland ecosystems. Furthermore, our results emphasise the importance of water level-managed wetlands for biodiversity conservation.

Predicting land cover driven ecosystem service value using artificial neural network model

Understanding the synergies and trade-offs of major cities' ecosystem services is vital to mitigating regional ecological and environmental risks and enhancing human well-being in this era of rapid urbanization and global climate change. This study aimed to assess and predict the land use- and land cover (LULC)-driven ecosystem service value (ESV) dynamics in Arkansas’s capital city, Little Rock. Historical LULC data were derived by applying support vector machine learning algorithms to Landsat satellite imagery. The benefit transfer method was utilized to identify nine types of ecosystem services and their corresponding economic values. A cellular automata artificial neural network model was used to simulate future potential LULC and ESV patterns. Vegetation accounted for more than 94% of total ESV over the past two decades. However, a 38.40% expansion of built-up areas resulted in a 45.28% decrease in vegetated areas, which reduced total ESV from $3619.73 × 106 to $2563.81 × 106 during 2003–2023. By 2033, the city’s urban area will expand to 72.75% of the total area and will witness further declines of 30.35 km2 in vegetation, 19.30 km2 in barren soil, and 1.69 km2 in waterbody areas. Consequently, the ESVs of these natural landscapes will decline by $708.58 × 106, $44.87 × 106, and $15.69 × 106, respectively. Provisioning services will be most affected, followed by supporting, regulating, and cultural services. The study findings provide reference information to policymakers and the local government for use in adopting sustainable land management policies, thereby promoting the ecological value of Little Rock.