Search Results (96)

Accurate regional-scale mapping of soil organic matter (SOM) is crucial for land productivity management and global carbon pool monitoring. Current remote sensing inversion of SOM faces challenges, including the underutilization of temporal information and low feature selection efficiency. To address these limitations, this study developed an integrated framework combining multi-temporal Landsat imagery, field-measured SOM data, intelligent feature optimization, and machine learning. The framework employs two novel image-processing strategies: the Maximum Annual Bare-Soil Composite (MABSC) method to extract background spectral information and the Multi-temporal Feature Optimization Composite (MFOC) method to capture seasonal and environmental dynamics. These features, along with topographic covariates, were processed using an improved Feature-Optimized and Interpretable XGBoost (FOI-XGB) model for key variable selection and spatial mapping. Validation across two subtropical coastal mountainous regions at different scales in southeastern China demonstrated the framework’s effectiveness and robustness. Key findings include the following: (1) Both the MABSC-derived spectral bands and the MFOC-optimized indices significantly outperformed traditional single-season approaches. Their combined use achieved a moderate SOM inversion accuracy (R² = 0.42–0.44). (2) The FOI-XGB model substantially outperformed traditional feature selection methods (Pearson, SHAP, and CorrSHAP), achieving significant regional R² improvements ranging from 9.72% to 88.89%. (3) The optimal model integrating the MABSC-derived features, MFOC-optimized indices, and topographic covariates attained the highest accuracy (R² up to 0.51). This represents major improvements compared with using topographic covariates alone (R² increase of up to 160.11%) or the combined spectral features (MABSC + MFOC) alone (R² increase of up to 15.91%). This study provides a robust, scalable, and practical technical solution for accurate SOM mapping in complex environments, with significant implications for sustainable land management and carbon monitoring. Full article

The Advanced Topographic Laser Altimeter System (ATLAS) on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a photon-counting detection mode with a 532 nm laser to obtain high-precision Earth surface elevation data and offers a new remote sensing method for nearshore bathymetry. The key issues in using ATLAS ATL03 data for bathymetry are achieving automatic and accurate extraction of signal photons in different water environments. Especially for areas with sharply fluctuating topography, the interaction of various impacts, such as topographic fluctuations, sea waves, and laser pulse direction, can result in a sharp change in photon density and distribution at the seafloor, which can cause the signal photon detection at the seafloor to be misinterpreted or omitted during analysis. Therefore, an improved size and direction adaptive filtering (ISDAF) method was proposed for nearshore bathymetry using ATLAS ATL03 data. This method can accurately distinguish between the original photons located above the sea surface, on the sea surface, and the seafloor. The size and direction of the elliptical density filter kernel automatically adapt to the sharp fluctuations in topography and changes in water depth, ensuring precise extraction of signal photons from both the sea surface and the seafloor. To evaluate the precision and reliability of the ISDAF, ATLAS ATL03 data from different water environments and seafloor terrains were used to perform bathymetric experiments. Airborne LiDAR bathymetry (ALB) data were also used to validate the bathymetric accuracy and reliability. The experimental findings show that the ISDAF consistently exhibits effectiveness in detecting and retrieving signal photons, regardless of whether the seafloor terrain is stable or dynamic. After applying refraction correction, the high accuracy of bathymetry was evidenced by a strong coefficient of determination (R²) and a low root mean square error (RMSE) between the ICESat-2 bathymetry data and ALB data. This research offers a promising approach to advancing remote sensing technologies for precise nearshore bathymetric mapping, with implications for coastal monitoring, marine ecology, and resource management. Full article

An urban heat island (UHI) refers to urban areas that experience higher temperatures due to heat absorption and retention by impervious surfaces compared to the surrounding rural areas. Urban wetlands are crucial in mitigating the UHI effect and improving climate resilience via their cooling effect. This study examines Colombo, Sri Lanka, the RAMSAR-accredited wetland city in South Asia, to assess the cooling effect of urban wetlands based on 2023 dry season data for effective sustainable management. We used Landsat 8 and 9 data to create Land Use/Cover (LUC), Land Surface Temperature (LST), and surface-reflectance-based maps using the Google Earth Engine (GEE). The Enhanced Vegetation Index (EVI), Modified Normalized Difference Water Index (mNDWI), topographic wetness, elevation, slope, and impervious surface percentage were identified as the influencing variables. The results show that urban wetlands in Colombo face tremendous pressure due to rapid urban expansion. The cooling intensity positively correlates with wetland size. The threshold value of efficiency (TVoE) of urban wetlands in Colombo was 1.42 ha. Larger and more connected wetlands showed higher cooling effects. Vegetation- and water-based wetlands play an important role in <10 km urban areas, while more complex shape configuration wetlands provide better cooling effects in urban and peri-urban areas due to edge effects. Urban planners should prioritize protecting wetland areas and ensuring hydrological connectivity and interconnected wetland clusters to maximize the cooling effect and sustain ecosystem services in rapidly urbanizing coastal cities. Full article

In the context of sea levels rising, monitoring spatial and temporal topographic changes along coastal dunes is crucial to understand their dynamics since they represent natural barriers against coastal flooding and large sources of sediment that can mitigate coastal erosion. Different technologies are currently used to monitor coastal dune topographic changes (GNSS, UAV, airborne LiDAR, etc.). Satellites recently emerged as a new source of topographic data by providing high-resolution images with a rather short revisit time at the global scale. Stereoscopic or tri-stereoscopic acquisition of some of these images enables the creation of 3D models using stereophotogrammetry methods. Here, the Ames Stereo Pipeline was used to produce digital elevation models (DEMs) from tri-stereo panchromatic and high-resolution Pleiades images along three 19 km long stretches of coastal dunes in SW France. The vertical errors of the Pleiades-derived DEMs were assessed by comparing them with DEMs produced from airborne LiDAR data collected a few months apart from the Pleiades images in 2017 and 2021 at the same three study sites. Results showed that the Pleiades-derived DEMs could reproduce the overall dune topography well, with averaged root mean square errors that ranged from 0.5 to 1.1 m for the six sets of tri-stereo images. The differences between DEMs also showed that Pleiades images can be used to monitor multi-annual coastal dune morphological changes. Strong erosion and accretion patterns over spatial scales ranging from hundreds of meters (e.g., blowouts) to tens of kilometers (e.g., dune retreat) were captured well, and allowed to quantify changes with reasonable errors (30%). Furthermore, relatively small averaged root mean square errors (0.63 m) can be obtained with a limited number of field-collected elevation points (five ground control points) to perform a simple vertical correction on the generated Pleiades DEMs. Among different potential sources of errors, shadow areas due to the steepness of the dune stoss slope and crest, along with planimetric errors that can also occur due to the steepness of the terrain, remain the major causes of errors still limiting accurate enough volumetric change assessment. However, ongoing improvements on the stereo matching algorithms and spatial resolution of the satellite sensors (e.g., Pleiades Neo) highlight the growing potential of Pleiades images as a cost-effective alternative to other mapping techniques of coastal dune topography. Full article

High-resolution (2 km), high-frequency (hourly) SST data of the Advanced Himawari Imager (AHI) flown onboard the Japanese Himawari-8 geostationary satellite were used to derive the monthly climatology of temperature fronts in the South China Sea. The SST data from 2015 to 2022 were processed with the Belkin–O’Reilly algorithm to generate maps of SST gradient magnitude GM. The GM maps were log-transformed to enhance contrasts in digital maps and reveal additional features (fronts). The combination of high-resolution, cloud-free, four-day-composite SST imagery from AHI, the advanced front-preserving gradient algorithm BOA, and digital contrast enhancement with the log-transformation of SST gradients allowed us to identify numerous mesoscale/submesoscale fronts (including a few fronts that have never been reported) and document their month-to-month variability and spatial patterns. The spatiotemporal variability of SST fronts was analyzed in detail in five regions: (1) In the Taiwan Strait, six fronts were identified: the China Coastal Front, Taiwan Bank Front, Changyun Ridge Front, East Penghu Channel Front, and Eastern/Western Penghu Islands fronts; (2) the Guangdong Shelf is dominated by the China Coastal Front in winter, with the eastern and western Guangdong fronts separated by the Pearl River outflow in summer; (3) Hainan Island is surrounded by upwelling fronts of various nature (wind-driven coastal and topographic) and tidal mixing fronts; in the western Beibu Gulf, the Red River Outflow Front extends southward as the Vietnam Coastal Front, while the northern Beibu Gulf features a tidal mixing front off the Guangxi coast; (4) Off SE Vietnam, the 11°N coastal upwelling gives rise to a summertime front, while the Mekong Outflow and associated front extend seasonally toward Cape Camau, close to the Gulf of Thailand Entrance Front; (5) In the Luzon Strait, the Kuroshio Front manifests as a chain of three fronts across the Babuyan Islands, while west of Luzon Island a broad offshore frontal zone persists in winter. The summertime eastward jet (SEJ) off SE Vietnam is documented from five-day mean SST data. The SEJ emerges in June–September off the 11°N coastal upwelling center and extends up to 114°E. The zonally oriented SEJ is observed to be located between two large gyres, each about 300 km in diameter. Full article

Accurately identifying pollution risks and sources is crucial for regional land resource management. This study takes a certain coastal county in eastern China as the object to explore the spatial distribution, pollution risk, and source apportionment of heavy metals in topsoil. A total of 633 samples were collected from the topsoil with a depth ranging from 0 to 20 cm, which came from different topographical and land use types (e.g., farmland, industrial areas, and mining areas), and the concentrations of HMs and As were measured by using atomic fluorescence spectrometry and inductively coupled plasma mass spectrometry. Firstly, the spatial distribution of soil HMs (Cd, Cr, Hg, Ni, and Pb) and arsenic (As) was predicted by incorporating environmental variables strongly affecting soil formation into geostatistical methods and machine learning approaches. Then, various pollution indicators were employed to conduct pollution evaluations, and potential ecological risk assessments were implemented based on the generated soil map. Finally, source apportionment was conducted using random forest (RF), absolute principal component score–multiple linear regression (APCS-MLR), correlation analysis, and spatial distribution of soil HMs and As. Findings in this research reveal that the RF approach yielded the best spatial prediction performance (0.59 ≤ R² ≤ 0.73). The Nemerow and geoaccumulation indices suggest that various pollution levels exist in this area. The average concentrations of As, Hg, and Ni are 7.233 mg/kg, 0.051 mg/kg, and 27.43 mg/kg respectively, being 1.14 times, 1.27 times, and 1.15 times higher than the background levels, respectively. The central–northern region presented a slight potential ecological risk, with Hg and Cd being identified as the primary risk factors. Natural, agricultural, transportation, and industrial and mining activities were identified as the main HMs and As sources. These findings will assist in the design of targeted policies to reduce the risks of HMs and As in urban soil and offer useful guidelines for soil pollution research in similar regions. Full article

Oil spills pose significant threats to marine and coastal ecosystems, necessitating advanced methodologies for environmental sensitivity and vulnerability assessments. This study enhances existing frameworks to better manage oil spill risks in the Caspian Sea, a region characterized by its ecological sensitivity and economic dependence on oil extraction. Utilizing the Environmental Sensitivity Index (ESI), we adapted global standards to the unique conditions of the Caspian Sea and built a sensitivity map of the coastline, which later became one of the components of the integral sensitivity map for the entire Caspian Sea, which includes several biotic and abiotic components. We also developed a comprehensive geodatabase incorporating topographic, infrastructural, and hydrodynamic data. Through the sophisticated modeling of oil spill scenarios using the Oil Spill model of the MIKE 21 software (Release 2016) suite, we simulated spills of varying magnitudes to analyze their potential impacts on the marine and coastal environment. The results enabled the creation of vulnerability maps, pinpointing areas at highest risk and facilitating strategic response planning. Our study demonstrates the critical importance of integrating advanced geospatial analyses and dynamic modeling techniques to improve oil spill preparedness and response strategies. The findings of this study suggest that enhanced monitoring and adaptive management strategies are essential for protecting the Caspian Sea from environmental risks posed by its oil industry. Full article

Flash flood hazard assessment is a critical component of disaster risk management, particularly in regions vulnerable to extreme rainfall and climatic events. This study focuses on evaluating the flash flood susceptibility of the Rabigh area, located along the Red Sea coast in Makkah province, Saudi Arabia. Using advanced GIS tools and a spatial multi-criteria analysis approach, the research integrates a variety of datasets, including remotely sensed satellite data, the SRTM Digital Elevation Model (DEM), and topographic indices. The main goal was to produce detailed flood susceptibility maps based on the morphometric characteristics of the region’s drainage basins. These basins were delineated and assessed for their flood vulnerability using three distinct modeling techniques, each highlighting different aspects of flood behavior. The results show that the northern basin (Dulaidila) and the central basins (Rabigh, Algud, and Al Nuaibeaa) exhibit the highest flood risk, with significant susceptibility also observed in the southern basins (Ofoq and Saabar). Other basins in the region display moderate susceptibility levels. A key aspect of this analysis was the overlay of the integrated flood susceptibility map with the Topographic Position Index (TPI), a crucial topographic indicator, which helped refine the understanding of flood-prone areas by linking basin morphometry with in-situ topographic features. This study’s comprehensive approach offers valuable insights that can be applied to other coastal regions where hydrological and climatic data are scarce, contributing to more effective flood risk mitigation and strategic planning. Full article

Flash floods are catastrophic phenomena that pose a serious risk to coastal infrastructures, towns, villages, and cities. This study assesses the risk of flash floods in the ungauged Mekkah province region based on specific and effective morphometric and topographic features characterizing the study region. Shuttle Radar Topography Mission (SRTM) data were employed to construct a digital elevation model (DEM) for a detailed analysis, and the geographical information systems software 10.4 (GIS) was utilized to assess the linear, area, and relief aspects of the morphometric parameters. The ArcHydro tool was used to prepare the primary parameters, including the watershed border, flow accumulation, flow direction, flow length, and stream ordering. The study region’s flash flood hazard degrees were assessed using several morphometric characteristics that were measured, computed, and connected. Two different and effective methods were used to independently develop two models of flood vulnerability behaviors. The integrated method analysis revealed that most of the eastern and western parts of the studied province provide high levels of flood vulnerability. Due to it being one of the most helpful topographic indices, the integrated flood vulnerability final map was overlayed with the topographic position index (TPI). The integrated results aided in understanding the link between the general basins’ morphometric characteristics and their topographical features for mapping the different flood susceptibility locations over the entire studied province. Thus, this can be applied to investigate a surface-specific reduction plan against the impacts of flood hazards in the studied landscape. Full article

We developed a spatiotemporal mapping approach utilizing multiple techniques for distinguishing and mapping known reclaimed mine sites from “unreclaimed” mine sites in a historic uranium mining district along the South Texas Coastal Plains. Lidar laser scanning penetrates the vegetation canopy to expose anthropogenic modifications to the landscape. The Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features. Visual interpretation of Landsat imagery and time-series analysis augmented the Lidar analysis revealing the temporal life cycle of mining. The combination of bare earth texture with time-lapse and time-series analyses revealed areas of disturbance for reclaimed mines. The spatiotemporal mapping approach proved to be most useful in identifying and characterizing the known mine pit and pile features, reclamation status, and areas of disturbance due to mining. Two mine waste volume estimation methods resulted in a 21% difference indicating that although the approach helps to map mine features and areas of mining disturbance for the purposes of mine land inventory, additional information is needed to improve the estimation of buried mine waste at reclaimed mine sites. Full article

Given global climate change and rapid land cover changes due to human activities, accurately identifying, extracting, and monitoring the long-term evolution of wetland resources is profoundly significant, particularly in areas with fragile ecological conditions. Gansu Province, located in northwest China, contains all wetland types except coastal wetlands. The complexity of its wetland types has resulted in a lack of accurate and comprehensive information on wetland changes. Using Gansu Province as a case study, we employed the GEE platform and Landsat time-series satellite data, combining high-quality sample datasets with feature-optimized multi-source feature sets. The random forest algorithm was utilized to create wetland classification maps for Gansu Province across eight periods from 1987 to 2020 at a 30 m resolution and to quantify changes in wetland area and type. The results showed that the wetland mapping method achieved robust classification results, with an average overall accuracy (OA) of 96.0% and a kappa coefficient of 0.954 across all years. The marsh type exhibited the highest average user accuracy (UA) and producer accuracy (PA), at 96.4% and 95.2%, respectively. Multi-source feature aggregation and feature optimization effectively improve classification accuracy. Topographic and seasonal features were identified as the most important for wetland extraction, while textural features were the least important. By 2020, the total wetland area in Gansu Province was 10,575.49 km², a decrease of 4536.86 km² compared to 1987. The area of marshes decreased the most, primarily converting into grasslands and forests. River, lake, and constructed wetland types generally exhibited an increasing trend with fluctuations. This study provides technical support for wetland ecological protection in Gansu Province and offers a reference for wetland mapping, monitoring, and sustainable development in arid and semi-arid regions. Full article

Coastal wetland ecosystems are critical due to their diverse ecological and economic benefits, yet they have been significantly affected by human activities over the past century. Understanding the spatiotemporal changes and underlying factors influencing these ecosystems is crucial for developing effective ecological protection and restoration strategies. This study examines the Tianjin–Hebei coastal wetlands using topographic maps from the 1940s and Landsat satellite imagery from 1975, 2000, and 2020, supplemented by historical literature and field surveys. The aim is to analyze the distribution and classification of coastal wetlands across various temporal intervals. The findings indicate an expansion of the Tianjin–Hebei coastal wetlands from 7301.34 km² in the 1940s to 8041.73 km² in 2020. However, natural wetlands have declined by approximately 44.36 km²/year, while constructed wetlands have increased by around 53.61 km²/year. The wetlands have also become increasingly fragmented, with higher numbers of patches and densities. The analysis of driving factors points to human activities—such as urban construction, cultivated land reclamation, sea aquaculture, and land reclamation—as the primary contributors to these changes. Furthermore, the study addresses the ecological and environmental issues stemming from wetland changes and proposes strategies for wetland conservation. This research aims to enhance the understanding among researchers and policymakers of the dynamics and drivers of coastal wetland changes, as well as the major challenges in their protection, and to serve as a foundation for developing evidence-based conservation and restoration strategies. Full article

Coastal upwelling that cools sea temperatures and nutrifies the euphotic layer is the focus of this research, motivated by how these processes benefit the marine ecosystem. Here, atmosphere–ocean reanalysis fields and satellite radiance data are employed to link South African coastal upwelling with nearshore winds and currents in the 2000–2021 period. Temporal behavior is quantified in three regimes—Benguela, transition, and Agulhas—to distinguish the influence of offshore transport, vertical pumping, and dynamic uplift. These three mechanisms of coastal upwelling are compared to reveal a leading role for cyclonic wind vorticity. Daily time series at west, south, and east coast sites exhibit pulsing of upwelling-favorable winds during summer. Over the western shelf, horizontal transport and vertical motion are in phase. The south and east shelf experience greater cyclonic wind vorticity in late winter, due to land breezes under the Mascarene high. Ekman transport and pumping are out of phase there, but dynamic uplift is sustained by cyclonic shear from the shelf-edge Agulhas current. Temporal analysis of longshore wind stress and cyclonic vorticity determined that vertical motion of ~5 m/day is pulsed at 4- to 11-day intervals due to passing marine high/coastal low-pressure cells. Height sections reveal that 15 m/s low-level wind jets diminish rapidly inshore due to topographic shearing by South Africa’s convex mountainous coastline. Mean maps of potential wind vorticity show a concentration around capes and at nighttime, due to land breezes. Air–land–sea coupling and frequent coastal lows leave a cyclonic footprint on the coast of South Africa that benefits marine productivity, especially during dry spells with a strengthened subtropical atmospheric ridge. This work has, for the first time, revealed that South Africa is uniquely endowed with three overlapping mechanisms that sustain upwelling along the entire coastline. Amongst those, cyclonic potential vorticity prevails due to the frequent passage of coastal lows that initiate downslope airflows. No other coastal upwelling zone exhibits such a persistent feature. Full article

We investigate Venezuela’s potential “futures” under Shared Socioeconomic Pathways (SSPs) through a systematic literature review, including systematic mapping and thematic analysis of 50 scientific articles. We categorised the SSP scenarios into two generational categories and classified the outcomes into positive, negative, and neutral futures. Under first-generation SSP scenarios, increasing poverty could be reversed, and the country’s economic growth could be stimulated by adopting unambitious climate measures. However, second-generation SSP scenarios paint a more challenging picture. They suggest that Venezuela could face heat waves, droughts, an increase in diseases, loss of biodiversity, and an increase in invasive species and pests during the remainder of the 21st century as a direct consequence of climate change. Venezuela’s geographic and topographic diversity could exacerbate these impacts of climate change. For instance, coastal areas could be at risk of sea-level rise and increased storm surges, while mountainous regions could experience more frequent and intense rainfall, leading to landslides and flash floods. The urgency of conducting additional research on the factors that could influence the severity of climate change’s impact, considering Venezuela’s geographic and topographic diversity, cannot be overstated. We also identified the critical need to explore alternative paths to move away from the current extractive development model. The potential actions in this regard could be instrumental in aligning the country with global adaptation and mitigation commitments. Full article

Thermokarst lakes in permafrost regions are highly dynamic due to drainage events triggered by climate warming. This study focused on mapping lake drainage events across the Northeast Siberian coastal tundra from 2000 to 2020 and identifying influential factors. An object-based lake analysis method was developed to detect 238 drained lakes using a well-established surface water dynamics product. The LandTrendr change detection algorithm, combined with continuous Landsat satellite imagery, precisely dated lake drainage years with 83.2% accuracy validated against manual interpretation. Spatial analysis revealed the clustering of drained lakes along rivers and in subsidence-prone Yedoma regions. The statistical analysis showed significant warming aligned with broader trends but no evident temporal pattern in lake drainage events. Our machine learning model identified lake area, soil temperature, summer evaporation, and summer precipitation as the top predictors of lake drainage. As these climatic parameters increase or surpass specific thresholds, the likelihood of lake drainage notably increases. Overall, this study enhanced the understanding of thermokarst lake drainage patterns and environmental controls in vulnerable permafrost regions. Spatial and temporal dynamics of lake drainage events were governed by complex climatic, topographic, and permafrost interactions. Integrating remote sensing with field studies and modeling will help project lake stability and greenhouse gas emissions under climate change. Full article