Terrain Research Articles

Topographic conditions dominate tree species recovery over 15 years post-fire in a temperate Pinus sylvestris forest

BackgroundIntensifying fire regimes and changing climatic conditions raise concerns about the capacity of forests to naturally recover after fire. Linking long-term observations of post-fire natural tree regeneration with the environmental and spatiotemporal context is thus becoming increasingly important to guide restoration efforts worldwide. Especially where fires have been rare, snapshot and short-term monitoring efforts prevail, thereby failing to recognize the post-fire species dynamics and wider community trends. Using multivariate Bayesian and spatial point process modeling, we evaluated the main environmental drivers of post-fire tree species recovery, its compositional and structural components, and species’ spatiotemporal co-occurrence patterns over 15 years post-fire in a Central European Pinus sylvestris forest.ResultsTopography-related conditions and ground cover were the prevalent drivers of tree species responses. In addition to diverse species-specific and size-dependent responses, higher site moisture and moss cover were beneficial for most species, while steep and warm habitats with exposed mineral soil and lack of litter mostly represented harsh conditions. We demonstrate the transition of the Pinus sylvestris forest to early successional broadleaves. Betula pendula seedlings and saplings began to dominate 10 and 5 years after the fire, respectively. Pinus sylvestris seedlings showed a similar abundance to Betula pendula and Populus tremula 5 years after the fire but declined sharply thereafter. We identified synchronized but species-specific patterns of seedling decline and sapling basal area build-up in early successional broadleaves. Fagus sylvatica, Picea abies, and Pinus sylvestris saplings showed synchronized recovery 15 years post-fire.ConclusionsWe highlight the need for a detailed recognition of species-environment relationships, particularly where species with diverse levels of natural recovery are challenged along wide topographic gradients. Our findings also demonstrate that the compositional and structural components of post-fire recovery were shaped by species and wider community dynamics depending on time since fire. Exploring these species’ unique and synchronized trajectories through spatiotemporal co-occurrence patterns is essential for setting realistic expectations of future forest recovery and can also help guide active restoration efforts in various post-disturbance contexts.

Decelerating Response of Western US Runoff to Shrinking Snowpacks

AbstractClimate warming threatens snowmelt‐derived water supplies in the western US (WUS) by reducing snowfall and snowmelt runoff, yet future rates of these declines remain highly uncertain in an evolving climate. Here, we analyze historical data, land surface model warming experiments, and climate projections across three major WUS river basins. We find that runoff loss become less sensitive to warming as snowpack shrinks, stemming from reduced snowmelt‐radiation feedback, a consequence of smaller snow‐cover changes and shifts in snowmelt timing to lower‐energy periods. Near‐linear projected warming with time (IPCC SSP245) exhibit a stable, possibly decelerating decline in runoff ratios. Although decelerating runoff declines do not eliminate broader water‐management challenges under continued warming, our findings complement the view that snowmelt‐radiation feedback drives runoff decline by highlighting the negative feedback from a shrinking snowpack on runoff warming sensitivity. Our findings should facilitate more comprehensive future water supply assessments in snow‐affected regions.

Pressure-Related Discrepancies in Landsat 8 Level 2 Collection 2 Surface Reflectance Products and Their Correction

Landsat 8 Level 2 Collection 2 (L2C2) surface reflectance (SR) products are widely used in various scientific applications by the remote sensing community, where their accuracy is vital for reliable analysis. However, discrepancies have been observed at shorter wavelength bands, which can affect certain applications. This study investigates the root cause of these differences by analyzing the assumptions made in the Land Surface Reflectance Code (LaSRC), the atmospheric correction algorithm of Landsat 8, as currently implemented at United States Geological Survey Earth Resources Observation and Science (USGS EROS), and proposes a correction method. To quantify these discrepancies, ground truth SR measurements from the Radiometric Calibration Network (RadCalNet) and Arable Mark 2 sensors were compared with the Landsat 8 SR. Additionally, the surface pressure measurements from RadCalNet and the National Centers for Environmental Information (NCEI) were evaluated against the LaSRC-calculated surface pressure values. The findings reveal that the discrepancies arose from using a single scene center surface pressure for the entire Landsat 8 scene pixels. The pressure-related discrepancies were most pronounced in the coastal aerosol and blue bands, with greater deviations observed in regions where the elevation of the study area differed substantially from the scene center, such as Railroad Valley Playa (RVUS) and Baotao Sand (BSCN). To address this issue, an exponential correction model was developed, reducing the mean error in the coastal aerosol band for RVUS from 0.0226 to 0.0029 (about two units of reflectance), which can be substantial for dark vegetative and water targets. In the blue band, there is a smaller improvement in the mean error, from 0.0095 to −0.0032 (about half a unit of reflectance). For the green band, the reduction in error was much less due to the significantly lesser impact of aerosol on this band. Overall, this study underscores the need for a more precise estimation of surface pressure in LaSRC to enhance the reliability of Landsat 8 SR products in remote sensing applications.

Exploration of coupled surface–subsurface hydrological model responses and challenges through catchment- and hillslope-scale examples

Selected runs with a physics-based model of surface water–groundwater interactions are used to examine in detail some numerical challenges and surprising behaviors that result from discretization, nested solution schemes, coupling, boundary condition, and other factors. Regardless of the spatial scale of the model domain (field, hillslope, catchment, …), the processes that are simulated by this class of integrated models can exhibit widely varying dynamics within and across the different subsystems comprising the land surface, the unsaturated zone, and deep groundwater formations. The presence of heterogeneities, nonlinearities, and complex boundary conditions can exacerbate numerical difficulties in resolving exchange fluxes across subsystems and lead to unexpected or undesired results, including localized numerical oscillations and an upper bound on adaptive time stepping. The need for accurate tracking of surface–subsurface exchanges and for better control of aspect ratio and mesh distortion can also influence and constrain spatial and temporal discretization choices. Finally, model performance assessments can be highly sensitive to the response variables of interest. We will illustrate some of these issues via test case simulations at large (13.66 km catchment transect) and small (450 m2 hillslope) spatial scales, run at time scales from 10 days to hundreds of years.

Information-Guided Diffusion Model for Downscaling Land Surface Temperature from SDGSAT-1 Remote Sensing Images

Land Surface Temperature (LST) is a parameter retrieved through the thermal infrared band of remote sensing satellites, and it is a crucial parameter in various climate and environmental models. Compared to other multispectral bands, the thermal infrared bands have lower spatial resolution, which limits their practical applications. Taking the Heihe River Basin in China as a case study, this research focuses on LST data retrieved from the SDGSAT-1 using the three-channel split-window algorithm. In this paper, we propose a novel approach, the Information-Guided Diffusion Model (IGDM), and apply it to downscale the SDGSAT-1 LST image. The results indicate that the downscaling accuracy of the SDGSAT-1 LST image using the proposed IGDM model outperforms that of Linear, Enhanced Deep Super-Resolution Network (EDSR), Super-Resolution Convolutional Neural Network (SRCNN), Discrete Cosine Transform and Local Spatial Attention (DCTLSA), and Denoising Diffusion Probabilistic Models (DDPM). Specifically, the RMSE of IGDM is reduced by 55.16%, 51.29%, 48.39%, 52.88%, and 17.18%. By incorporating auxiliary information, particularly when using NDVI and NDWI as auxiliary inputs, the performance of the IGDM model is significantly improved. Compared to DDPM, the RMSE of IGDM decreased from 0.666 to 0.574, MAE dropped from 0.517 to 0.376, and PSNR increased from 38.55 to 40.27. Overall, the results highlight the effectiveness of the auxiliary information-guided SDGSAT-1 LST downscaling diffusion model in generating high-resolution remote sensing LST data. Additionally, the study reveals the spatial feature impact of different auxiliary information in LST downscaling and the variations in features across different regions and temperature ranges.

An Integrated Multi-Media Modeling System for Regional- to National-Scale Nitrogen and Crop Productivity Assessments

Excessive nutrients transported from agricultural fields into the environment are causing environmental and ecological problems. This study uses an integrated multi-media modeling system version 1 (IMMMS 1.0) linking air, land surface, and watershed processes to assess corn grain yield and nitrogen (N) losses resulting from changing fertilization conditions across the contiguous United States. Two fertilizer management scenarios (FMSs) were compared and evaluated: 2006 FMS, developed based on the 2006 fertilizer sales data; and 2011 FMS, developed based on 2011 fertilizer sales and manure. Corn grain yields captured historical reported values with average percent errors of 4.8% and 0.7% for the 2006 FMS and 2011 FMS, respectively. Increased nitrogen (N) application of 21.2% resulted in a slightly increased corn grain yield of 5% in the 2011 FMS, but the simulated total N loss (through denitrification, volatilization, water, and sediment) increased to 49.3%. A better correlation was identified between crop N uptake and N application in the 2006 FMS (R2 = 0.60) than the 2011 FMS (R2 = 0.51), indicating that applied N was better utilized by crops in the 2006 FMS. Animal manure could create nutrient surpluses and lead to greater N loss, as identified in the regions of the Pacific and Southern Plains in the 2011 FMS. Manure nutrient management is important and urgently needed to protect our air and water quality. The IMMMS 1.0 is responsive to different FMSs and can be utilized to address alternative management scenarios to determine their impact when addressing the sustainability of food production and environmental issues.

Estimation of landslide volume by machine learning and remote sensing techniques in Himalayan regions

Abstract Topographical and geological conditions are typically regarded as the primary causes of landslides. However, accurately estimating landslide volumes on rock slopes using empirical equations remains challenging. In contrast, data science approaches, such as machine learning, leverage advanced data integration and processing capabilities, significantly enhancing the accuracy and reliability of landslide volume estimations. As such, an resemble method, XGBoost, was chosen in our study to estimate the potential landslide volume in Gyirong, China. A factor combination was proposed in this study. They are related to geomorphic (area of slope units (S) and mean elevation of slope units (El)) and geological (faults density (Fd) and geological index (GI)) conditions. The performance of the developed model was compared with three other machine learning models, including gradient boosting (GBDT), adaptive boosting (AdaBoost) and random forest (RF) based on the mean absolute percentage error (MAPE) and determination of coefficient (R-squared). The results demonstrate that XGBoost achieves the highest prediction accuracy, with an R-squared value of 0.986, and a mean absolute percentage error (MAPE) reduces to 8.19%. Additionally, the prediction outcomes of the machine learning model, using the proposed factor combination, were compared with several empirical models. Once again, XGBoost model exhibits the lowest error relative to measured values, highlighting the superiority of machine learning in landslide volume prediction and validating the effectiveness of the selected factors. Overall, the accurate estimation of landslide volume in remote areas can benefit the disaster management and decrease losses of human lives and properties.

Urban Heatwave Dynamics in Lagos State: Evidence from the Analysis of Land Surface Temperature Trends and Land Cover Changes (2000–2022)

Urban Heatwave Dynamics in Lagos State: Evidence from the Analysis of Land Surface Temperature Trends and Land Cover Changes (2000–2022)

A method for calculating background resistivity based on total magnetic intensity in ground-airborne frequency domain electromagnetic exploration in areas with topographic relief

Abstract Ground-airborne frequency domain electromagnetic (GAFDEM) is an important tool for mineral resource exploration and geological surveys in complex mountainous areas. The rapid and high-precision calculation of background resistivity is the foundation for high-resolution imaging. Due to the influence of attitude noise, the current accuracy of background resistivity calculations is not high. Furthermore, when the measurement area is large and the terrain is complex, the need to establish a terrain model for the measurement area leads to a decrease in efficiency. To solve the above problems, this paper proposes a method to calculate the background resistivity using the total magnetic intensity. Firstly, the magnetic field component is analyzed for its ability to identify anomalies, then the effects of transmitting frequency and source-receiver distance parameters are analyzed, and finally, simulation experiments and field experiments are conducted. Simulation experiments show that the method of calculating background resistivity using total magnetic intensity can accurately calculate the background resistivity of the double anomaly model and the model with topographic relief compared with the current method. Field experiments show that compared with the current method, the total magnetic intensity method of calculating background resistivity is not affected by attitude noise, and the results of background resistivity calculation are consistent with the real results with higher accuracy. The method in this paper supports GAFDEM to realize high-resolution and fast exploration of underground structures.

Spatiotemporal Analysis of Air Pollution and Climate Change Effects on Urban Green Spaces in Bucharest Metropolis

Being an essential issue in global climate warming, the response of urban green spaces to air pollution and climate variability because of rapid urbanization has become an increasing concern at both the local and global levels. This study explored the response of urban vegetation to air pollution and climate variability in the Bucharest metropolis in Romania from a spatiotemporal perspective during 2000–2024, with a focus on the 2020–2024 period. Through the synergy of time series in situ air pollution and climate data, and derived vegetation biophysical variables from MODIS Terra/Aqua satellite data, this study applied statistical regression, correlation, and linear trend analysis to assess linear relationships between variables and their pairwise associations. Green spaces were measured with the MODIS normalized difference vegetation index (NDVI), leaf area index (LAI), photosynthetically active radiation (FPAR), evapotranspiration (ET), and net primary production (NPP), which capture the complex characteristics of urban vegetation systems (gardens, street trees, parks, and forests), periurban forests, and agricultural areas. For both the Bucharest center (6.5 km × 6.5 km) and metropolitan (40.5 km × 40.5 km) test areas, during the five-year investigated period, this study found negative correlations of the NDVI with ground-level concentrations of particulate matter in two size fractions, PM2.5 (city center r = −0.29; p < 0.01, and metropolitan r = −0.39; p < 0.01) and PM10 (city center r = −0.58; p < 0.01, and metropolitan r = −0.56; p < 0.01), as well as between the NDVI and gaseous air pollutants (nitrogen dioxide—NO2, sulfur dioxide—SO2, and carbon monoxide—CO. Also, negative correlations between NDVI and climate parameters, air relative humidity (RH), and land surface albedo (LSA) were observed. These results show the potential of urban green to improve air quality through air pollutant deposition, retention, and alteration of vegetation health, particularly during dry seasons and hot summers. For the same period of analysis, positive correlations between the NDVI and solar surface irradiance (SI) and planetary boundary layer height (PBL) were recorded. Because of the summer season’s (June–August) increase in ground-level ozone, significant negative correlations with the NDVI (r = −0.51, p < 0.01) were found for Bucharest city center and (r = −76; p < 0.01) for the metropolitan area, which may explain the degraded or devitalized vegetation under high ozone levels. Also, during hot summer seasons in the 2020–2024 period, this research reported negative correlations between air temperature at 2 m height (TA) and the NDVI for both the Bucharest city center (r = −0.84; p < 0.01) and metropolitan scale (r = −0.90; p < 0.01), as well as negative correlations between the land surface temperature (LST) and the NDVI for Bucharest (city center r = −0.29; p< 0.01) and the metropolitan area (r = −0.68, p < 0.01). During summer seasons, positive correlations between ET and climate parameters TA (r = 0.91; p < 0.01), SI (r = 0.91; p < 0.01), relative humidity RH (r = 0.65; p < 0.01), and NDVI (r = 0.83; p < 0.01) are associated with the cooling effects of urban vegetation, showing that a higher vegetation density is associated with lower air and land surface temperatures. The negative correlation between ET and LST (r = −0.92; p < 0.01) explains the imprint of evapotranspiration in the diurnal variations of LST in contrast with TA. The decreasing trend of NPP over 24 years highlighted the feedback response of vegetation to air pollution and climate warming. For future green cities, the results of this study contribute to the development of advanced strategies for urban vegetation protection and better mitigation of air quality under an increased frequency of extreme climate events.

Simulation of latent heat flux over a high altitude pasture in the tropical Andes with a coupled land surface framework.

Simulation of latent heat flux over a high altitude pasture in the tropical Andes with a coupled land surface framework.

Impact of Wide-Graded Debris Flow on Sediment trap dams in Tibetan Plateau: An Experimental Study

In the Tibetan Plateau, pronounced topographic relief (steep mountains and deep valleys) coupled with intense weathering processes generates highly fragmented slope surfaces, creating debris flow source materials with exceptionally heterogeneous grain size distributions. These conditions frequently produce debris flows exhibiting extraordinary impact forces that cause severe damage to sediment trap dams. Through 27 sets of flume experiments systematically varying particle size distribution ( dmax ), bulk density ( γ ), and flume slope ( θ ), this study investigates the impact mechanisms of wide-graded debris flows on sediment trap dams. The results demonstrate that debris flow interactions with sediment trap dams occur through three distinct phases: (1) impact run-up, (2) rotational backflow, and (3) depositional back-silting. Lower-bulk-density flows exhibited greater run-up heights and more pronounced phase differentiation. Measured impact forces showed an inverse relationship with bulk density ( γ ↑→F↓), while displaying positive correlations with both slope gradient ( θ ↑→F↑) and maximum particle size ( dmax ↑→F↑). This occurs because higher- γ flows experience increased internal shear resistance, resulting in velocity reduction. Steeper slopes enhance kinematic energy, while larger particles generate more concentrated momentum transfer during impact. Sensitivity analysis revealed dmax exerts dominant control over impact dynamics compared to γ and θ . These findings provide critical insights for sediment trap dam design in high-altitude debris flow mitigation systems.

Assessing the Combined Impact of Land Surface Temperature and Droughts to Heatwaves over Europe Between 2003 and 2023

The increasing frequency, intensity, and duration of heatwaves and droughts pose significant societal and environmental challenges across Europe. This study analyzes land surface temperature (LST) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2003 and 2023 to identify thermal anomalies associated with heatwaves. Additionally, this study examines the role of different land cover types in modulating heatwave impacts, employing turbulent flux observations from micrometeorological towers. The interaction between heatwaves and droughts is further explored using the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture data, highlighting the amplifying role of water stress through land–atmosphere feedbacks. The results reveal a statistically significant upward trend in LST-derived thermal anomalies, with the 2022 heatwave identified as the most extreme event, when approximately 75% of Europe experienced strong positive anomalies. On average, 91% of heatwave episodes identified in reanalysis-based air temperature records coincided with LST-defined anomaly events, confirming LST as a robust proxy for heatwave detection. Flux tower observations show that, during heatwaves, evergreen coniferous and mixed forests predominantly enhance sensible heat fluxes (mean anomalies during midday of 74 W/m2 and 62 W/m2, respectively), while grasslands exhibit increased latent heat flux (89 W/m2). Notably, under extreme compound heat–drought conditions, this pattern reverses for grassed sites due to rapid soil moisture depletion. Overall, the findings underscore the combined influence of surface temperature and drought in driving extreme heat events and introduce a novel, multi-source approach that integrates satellite, reanalysis, and ground-based data to assess heatwave dynamics across scales.

Aerosol composition retrieval from a combination of three different spaceborne instruments: information content analysis

Abstract. This study focuses on the information content for retrieving aerosol optical depth (AOD) and its components from satellite measurements. We utilise an optimal estimation retrieval algorithm with data from three satellite-based instruments: the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3A and Sentinel-3B and the Infrared Atmospheric Sounding Interferometer (IASI) and the Global Ozone Monitoring Experiment-2 (GOME-2) on MetOp-A, MetOp-B and MetOp-C. Data are averaged to a common 40×80 km2 grid, temporally aligned within a 60 min window and cloud masked. A simulation study has been carried out to analyse the information content of the instrument combination, identify retrievable parameters, and initiate the development of a uniform retrieval algorithm for the AOD and aerosol components. The simulation study for the information content analysis is implemented using the radiative transfer model SCIATRAN and MERRA-2 reanalysis data for AOD and mass mixing ratios of different aerosol components. The study shows 6 to 15 degrees of freedom for the determination of aerosol components dependent on AOD and the underlying surface. The results will be used for the development of a synergistic multi-sensor retrieval algorithm for AOD and its components in cloud-free atmospheres across various surface types.

Factors influencing landslide occurrence in low-relief formerly glaciated landscapes: landslide inventory and susceptibility analysis in Minnesota, USA

Abstract In landscapes recently impacted by continental glaciation, landslides may occur where topographic relief has been generated by the drainage of glacial lakes and ensuing post-glacial fluvial network development into unconsolidated glacially derived sediments and exhumed bedrock. To investigate linkages among environmental variables, post-glacial landscape development, and landslides, we created a landslide inventory of nearly 10,000 landslides in five regions of the formerly glaciated low-relief state of Minnesota, United States. Multivariate logistic regression indicates the importance of slope angle, lithology, and the development of stream valleys to landslide distribution. Areas underlain by fine-grained glaciolacustrine and nearshore deposits that are incised by streams are particularly prone to shallow (< 1–2 m depth) landslides. Landslides also occur in a wide range of glacial and fluvial deposits, and as rockfall in layered Paleozoic sedimentary rocks in central and southern Minnesota and Precambrian igneous and sedimentary rocks in northeastern Minnesota. Although no more than 1–2% of the studied regions are susceptible to landslides, they can pose risk to life and safety, damage infrastructure, and impact water quality. The combination of recently generated low-relief steep slopes, extensive unconsolidated sediments, and layered sedimentary bedrock make this formerly glaciated landscape more susceptible to landslides than current national-scale models indicate.

Evaluation and Mapping of Snow Characteristics Using Remote Sensing Data in Astore River Basin, Pakistan

Being an agricultural country, Pakistan requires lots of water for irrigation. A major portion of its water resources is located in the upper indus basin (UIB). The snowmelt runoff generated from high-altitude areas of the UIB provides inflow into the Indus river system that boosts the water supply. Snow accumulation during the winter period in the highlands in the watershed(s) becomes a source of water inflow during the snow-melting period, which is described according to characteristics like snow depth, snow density, and snow water equivalent. Snowmelt water release (SWE) and snowmelt water depth (SD) maps are generated by tracing snow occurrence from MODIS-based images of the snow-cover area, evaluating the heating degree days (HDDs) from MODIS-derived images of the land surface temperature, computing the solar radiation, and then assimilating all the previous data in the form of the snowmelt model and ground measurements of the snowmelt water release (SWE). The results show that the average snow-cover area in the Astore river basin, in the upper indus basin, ranges from 94% in winter to 20% in summer. The maps reveal that the annual average values of the SWE range from 150 mm to 535 mm, and the SD values range from 600 mm to 2135 mm, for the snowmelt period (April–September) over the years 2010–2020. The areas linked with vegetation experience low SWE accumulation because of the low slopes in the elevated regions. The meteorological parameters and basin characteristics affect the SWE and can determine the SD values.

Using LiDAR‐Based DEM Elevation Difference Calculations to Estimate Net Streambank Erosion in an Iowa River, USA

ABSTRACTStreambank erosion is an important source of sediment to river systems but is difficult to quantify at watershed scales. In this study, high‐resolution Light Detection and Ranging (LiDAR) measurements collected from 2009 and 2020 were used to quantify the difference in land surface elevation that occurred along the fourth and fifth‐order streams in Old Mans Creek watershed in southeast Iowa. Study objectives were to quantify the volume of streambank sediment erosion and deposition occurring along the river systems and compare net channel erosion to watershed sediment export. Results indicated that streambank erosion and deposition along the fourth and fifth‐order channels totaled nearly 720,000 m3 and 148,000 m3, respectively, over the 11‐year study period. Five times more streambank erosion occurred than deposition, and the difference between the two totals (net sediment erosion) comprised 77% of the sediment export from the watershed. The contribution of streambank sediment to basin export, along with estimates of mean annual streambank recession derived from the analyses, were consistent with results reported in other studies of streambank erosion. The LiDAR differencing methodology was able to identify areas of both sediment erosion and deposition occurring in the stream channels and quantify the net difference, which is related to watershed‐scale sediment export.

Impact of urbanization on carbon emissions and ecological quality in theSemarang Metropolitan Region, Indonesia.

Rapid and dynamic urbanization encourages land use/land cover (LULC) change that threatens ecological quality and carbon emissions. However, spatial-temporal research on the impact of urbanization on carbon emissions and ecological quality is limited, particularly spatial distance-based analysis from urban to peri-urban areas. This study aims to explore the spatiotemporal effect of urbanization on carbon emission and ecological quality of the Semarang Metropolitan Region to support sustainable development. We used Landsat 7 ETM + (2003) and Landsat 8 OLI (2023) images. The analyses used were the stock-difference method and the Remote Sensing Ecological Index (RSEI). To calculate carbon emissions, we used land cover data, land use changes, and emission factors. As for the RSEI, we used the Tasseled Cap Wetness (TCW), Normalized Difference Vegetation Index (NDVI), Index-Based Built-Up Index (IBI), and Land Surface Temperature (LST). The results show a low positive correlation between urbanization and carbon emissions (R = 0.24). However, urbanization had a strong negative correlation with ecological quality (R = - 0.62), indicated by a significant decline in ecological quality in urban areas. The spatial correlation between ecological quality and carbon emissions was also highly negative (R = - 0.603). In other words, the higher the emissions, the lower the ecological quality. If this situation continues, the community's quality of life will decrease, and the government's incurred costs to repair the damage will be excessive. This study finally recommends the need for a spatial policy to control the growth of integrated cities as a mitigation effort against the impacts of urbanization to minimize environmental degradation and achieve carbon neutralization.

Accelerating Solar PV Site Selection: YOLO-Based Identification of Sound Barriers Along Highways

The exponential growth of the installation of solar photovoltaic systems has been a significant step in the energy transition toward reducing dependence on fossil fuels and mitigating climate change. This growth has raised concerns about land use, particularly in regions where large tracts are allocated to solar farms. Highway infrastructures such as sound barriers occupy large land surfaces which are under-utilized and could therefore contribute to renewable energy generation without increasing the land use. This study proposes the application of the YOLO object detection algorithm to automatically identify and analyse the locations of sound barriers along highways using video or image data, and to estimate the potential energy output from photovoltaic systems installed on these barriers. The model has been trained and tested on sound barriers along Portuguese highways, achieving a mean average precision exceeding 0.84 for YOLOv10 when using training datasets containing more than 600 images. Using the geolocation of the images and the identification of the number of sound barriers from YOLO, it is possible to estimate the potential generation of electricity and inform decision makers on the technical–economic feasibility of using this infrastructure for energy generation.

Evaluation of index-overlay methods for assessing shallow groundwater vulnerability in southeast Hungary

This study evaluated three index-overlay methods (i.e., DRASTIC, GOD, and susceptibility index (SI)) for their suitability to assessing the vulnerability of shallow aquifer in southeast Hungary to contamination from the land surface. Accordingly, the most recent information on the shallow aquifer depth, recharge rate, land use, and geology/hydrogeology of the groundwater basin was created and integrated in a geographic information system and through a linear combination to compose the methods indices. All three methods delineated approximately 95% of the groundwater basin as being moderately to highly susceptible to contamination, which was mainly due to the sandy soil, high recharge rate, gentle topography, and agricultural activities related to land use. A positive linear correlation was also found, during the validation of the final vulnerability maps, between the vulnerability indices and observed nitrate concentration. The vulnerability indices of SI, DRASTIC, and GOD showed correlations of 0.5635, 0.3615, and 0.3499, respectively, with the available nitrate concentration in the groundwater. Thus, SI was concluded as the most suitable method for assessing the vulnerability of shallow aquifers in southeast Hungary to contamination. The outcomes of this study provide useful information that will help policymakers identify the main contributors to groundwater contamination as well as adopt effective management strategies to avoid further pressure on this invaluable resource.