Search Results (2,642)

The service-learning (SL) approach has shown effectiveness in fulfilling both academic and community-oriented objectives. This paper focuses on a specific case study for a Cartography, Remote Sensing, and Geographical Information Systems (GIS) course for Environmental Sciences undergraduate students. The main goals for implementing SL practice were (1) to enhance students’ GIS knowledge and to develop cross-cutting skills by working with real-world problems; (2) to share with society the knowledge acquired by students and ensure that it is valued; and (3) to prompt reflection on urban waste issues among students. The activity consisted of analyzing the waste containers along the 1 km riverbanks in León (Spain) and elaborating a proposal for the location of new rubbish bins to deliver to a City Council’s environmental technician. The results showed an improvement in students’ GIS management skills to solve environmental problems compared to those from the previous 3 years and a satisfactory response from environmental professionals with delivering the results. Together, an increase in students discussing urban waste was observed during the sessions. Projects like this not only teach technical skills but also provide a deeper understanding of data collection and implementation processes in environmental issues, which are closely aligned with professional experiences, and awareness of the practical application of the knowledge acquired. Full article

Remote sensing (RS) has been widely used to monitor cyanobacterial blooms in inland water bodies. However, the accuracy of RS-based monitoring varies significantly depending on factors such as waterbody type, sensor characteristics, and analytical methods. This study comprehensively evaluates the current capabilities and challenges of RS for cyanobacterial bloom monitoring, with a focus on achievable accuracy. We find that chlorophyll-a (Chl-a) and phycocyanin (PC) are the primary indicators used, with PC demonstrating greater accuracy and stability than Chl-a. Sentinel and Landsat satellites are the most frequently used RS data sources, while hyperspectral images, particularly from unmanned aerial vehicles (UAVs), have shown high accuracy in recent years. In contrast, the Medium-Resolution Imaging Spectrometer (MERIS) and Moderate-Resolution Imaging Spectroradiometer (MODIS) have exhibited lower performance. The choice of analytical methods is also essential for monitoring accuracy, with regression and machine learning models generally outperforming other approaches. Temporal analysis indicates a notable improvement in monitoring accuracy from 2021 to 2023, reflecting advances in RS technology and analytical techniques. Additionally, the findings suggest that a combined approach using Chl-a for large-scale preliminary screening, followed by PC for more precise detection, can enhance monitoring effectiveness. This integrated strategy, along with the careful selection of RS data sources and analytical models, is crucial for improving the accuracy and reliability of cyanobacterial bloom monitoring, ultimately contributing to better water management and public health protection. Full article

This study focuses on the deposition of microplastics (MPs) on urban beaches along the central São Paulo coastline, utilizing advanced methodologies such as remote sensing, GNSS altimetric surveys, µ-Raman spectroscopy, and machine learning (ML) models. MP concentrations ranged from 6 to 35 MPs/m², with the highest densities observed near the Port of Santos, attributed to industrial and port activities. The predominant MP types identified were foams (48.7%), fragments (27.7%), and pellets (23.2%), while fibers were rare (0.4%). Beach slope and orientation were found to facilitate the concentration of MP deposition, particularly for foams and pellets. The study’s ML models showed high predictive accuracy, with Random Forest and Gradient Boosting performing exceptionally well for specific MP categories (pellet, fragment, fiber, foam, and film). Polymer characterization revealed the prevalence of polyethylene, polypropylene, and polystyrene, reflecting sources such as disposable packaging and industrial raw materials. The findings emphasize the need for improved waste management and targeted urban beach cleanups, which currently fail to address smaller MPs effectively. This research highlights the critical role of combining in situ data with predictive models to understand MP dynamics in coastal environments. It provides actionable insights for mitigation strategies and contributes to global efforts aligned with the Sustainable Development Goals, particularly SDG 14, aimed at conserving marine ecosystems and reducing pollution. Full article

Wildfires are frequently observed in watersheds with a Mediterranean climate and seriously affect vegetation, soil, hydrology, and ecosystems as they cause abrupt changes in land cover. Assessing wildfire effects, as well as the recovery process, is critical for mitigating their impacts. This paper presents a geospatial analysis approach that enables the investigation of wildfire effects on vegetation, soil, and hydrology. The prediction of regeneration potential and the period needed for the restoration of hydrological behavior to pre-fire conditions is also presented. To this end, the catastrophic wildfire that occurred in August 2021 in the wider area of Varybobi, north of Athens, Greece, is used as an example. First, an analysis of the extent and severity of the fire and its effect on the vegetation of the area is conducted using satellite imagery. The history of fires in the specific area is then analyzed using remote sensing data and a regrowth model is developed. The effect on the hydrological behavior of the affected area was then systematically analyzed. The analysis is conducted in a spatially distributed form in order to delineate the critical areas in which immediate interventions are required for the rapid restoration of the hydrological behavior of the basin. The period required for the restoration of the hydrological response is then estimated based on the developed vegetation regrowth models. Curve Numbers and post-fire runoff response estimations were found to be quite similar to those derived from measured data. This alignment shows that the SCS-CN method effectively reflects post-fire runoff conditions in this Mediterranean watershed, which supports its use in assessing hydrological changes in wildfire-affected areas. The results of the proposed approach can provide important data for the restoration and protection of wildfire-affected areas. Full article

Chlorophyll fluorescence is a useful indicator of a plant’s physiological status, particularly under stress conditions. Remote sensing is an increasingly adopted technology in modern agriculture, allowing the acquisition of crop information (e.g., chlorophyll fluorescence) without direct contact, reducing fieldwork. The objective of this study is to improve the monitoring of olive tree fluorescence (Fv′/Fm′) via remote sensing in a Mediterranean environment, where the frequency of stress factors, such as drought, is increasing. An advanced approach combining explainable artificial intelligence and multispectral Sentinel-2 satellite data was developed to predict olive tree fluorescence. Field measurements were conducted in southeastern Italy on two olive groves: one irrigated and the other one under rainfed conditions. Sentinel-2 reflectance bands and vegetation indices were used as predictors and different machine learning algorithms were tested and compared. Random Forest showed the highest predictive accuracy, particularly when Sentinel-2 reflectance bands were used as predictors. Using spectral bands preserves more information per observation, enabling models to detect variations that VIs might miss. Additionally, raw reflectance data minimizes potential bias that could arise from selecting specific indices. SHapley Additive exPlanations (SHAP) analysis was performed to explain the model. Random Forest showed the highest predictive accuracy, particularly when using Sentinel-2 reflectance bands as predictors. Key spectral regions associated with Fv′/Fm′, such as red-edge and NIR, were identified. The results highlight the potential of integrating remote sensing and machine learning to improve olive grove management, providing a useful tool for early stress detection and targeted interventions. Full article

Vegetation-covered water bodies (VCW) are a vital component of wetlands, and their distribution information is crucial for studying the dynamic interactions between vegetation and water. However, due to vegetation obstruction, optical remote sensing has limitations in extracting such water bodies, as it typically identifies only open water areas effectively. In contrast, microwave remote sensing, with its vegetation-penetrating capability and specular reflection characteristics, provides a more comprehensive identification of wetland water bodies. Previous studies have shown that the additional water body areas (SW) identified by SAR but not by optical sensors are often accompanied by significant vegetation cover. However, a systematic assessment of SW’s potential in mapping VCW is still lacking. This study uses the Caohai Wetland in Guizhou, China, as an example, leveraging Sentinel-2A and RadarSat-2 imagery from adjacent periods and multiple water body extraction methods to extract SW and explore its performance in mapping VCW during the dry season. Results show that during the initial stage of vegetation senescence (7 January 2019), the use of SW achieved high accuracy in mapping VCW, with overall accuracy, kappa coefficient, and F1 score reaching 84.2%, 68.4%, and 85.3%, respectively. However, as vegetation senescence deepened (12 January 2020), these metrics dropped to 76.2%, 60.7%, and 87%, respectively, indicating a significant decline in accuracy. During the vegetation regrowth stage (7 April 2020), the overall accuracy, kappa coefficient, and F1 score were 71.1%, 57.2%, and 70.9%, respectively. As vegetation continued to grow (21 April 2019), these metrics improved to 79.4%, 67.2%, and 86.6%. In summary, SW extracted from high-resolution optical and SAR imagery can preliminarily map VCW during the dry season. Furthermore, its identification accuracy improves significantly with increasing vegetation density. This study provides a novel perspective for wetland water body monitoring and the study of vegetation-water interactions. Full article

The aboveground biomass (AGB) of forests reflects the productivity and carbon-storage capacity of the forest ecosystem. Although AGB estimation techniques have become increasingly sophisticated, the relationships between AGB, spatial distribution, and growth stages still require further exploration. In this study, the Picea schrenkiana (Picea schrenkiana var. tianschanica) forest area in the Kashi River Basin of the Ili River Valley in the western Tianshan Mountains was selected as the research area. Based on forest resources inventory data, Gaofen-1 (GF-1), Gaofen-6 (GF-6), Gaofen-3 (GF-3) Polarimetric Synthetic Aperture Radar (PolSAR), and DEM data, we classified the Picea schrenkiana forests in the study area into three cases: the Whole Forest without vertical zonation and stand age, Vertical Zonality Classification without considering stand age, and Stand-Age Classification without considering vertical zonality. Then, for each case, we used eXtreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Residual Networks (ResNet), respectively, to estimate the AGB of forests in the study area. The results show that: (1) The integration of multi-source remote-sensing data and the ResNet can effectively improve the remote-sensing estimation accuracy of the AGB of Picea schrenkiana. (2) Furthermore, classification by vertical zonality and stand ages can reduce the problems of low-value overestimation and high-value underestimation to a certain extent. Full article

This study introduces Lake SkyWater (LSW), a novel radiometric buoy designed for the reliable measurement of remote sensing reflectance (R_rs) in lakes using the Skylight-Blocked Approach (SBA). LSW addresses key challenges in “on-water” field radiometry owing to its motorised rotating system, which maintains the radiance sensor in optimal geometrical conditions (i.e., facing the sun). Our device is easy to transport and deploy and can be controlled with a smartphone over Wi-Fi. Its modular design, which uses standard components and custom 3D-printed parts, facilitates customisation. A field experiment demonstrated excellent performance in the visible spectrum (400–700 nm) and no significant differences compared with handheld SBA measurements when measuring R_rs (coefficient of determination > 0.99 and general accuracy (median symmetric accuracy) of ~2.43%). Areas for potential improvement were identified, such as refinement of orientation control and addressing the occasional rotation of the float. Nonetheless, LSW shortens the acquisition time, reduces the risk of fore-optics contamination, and ensures that the measurements are conducted under optimal geometric conditions. In conclusion, LSW is a promising instrument for the operational collection of high-quality R_rs spectra in lakes, which is important for advancing both research and monitoring applications in aquatic remote sensing. Full article

Monitoring forest health has become essential due to increasing pressures caused by climate change and dust events, particularly in semi-arid regions. This study investigates the impact of dust events on forest vegetation in Paphos forest in Cyprus, which is a semi-arid area prone to frequent dust storms. Using multispectral and radar satellite data from Sentinel-1 and Landsat series, vegetation responses to eight documented dust events between 2015 and 2019 were analysed, employing BFAST (Breaks For Additive Season and Trend) algorithms to detect abrupt changes in vegetation indices and radar backscatter. The outcomes showed that radar data were particularly effective in identifying only the most significant dust events (PM10 > 100 μg/m³, PM2.5 > 30 μg/m³), indicating that SAR (Synthetic Aperture Radar) is more responsive to pronounced dust deposition, where backscatter changes reflect more substantial vegetation stress. Conversely, optical data were sensitive to a wider range of events, capturing responses even at lower dust concentrations (PM10 > 50 μg/m³, PM2.5 > 20 μg/m³) and detecting minor vegetation stress through indices like SAVI, EVI, and AVI. The analysis highlighted that successful detection relies on multiple factors beyond sensor type, such as rainfall timing and imagery availability close to the dust events. This study highlights the importance of an integrated remote sensing approach for effective forest health monitoring in regions prone to dust events. Full article

Chlorophyll fluorescence (ChlF) parameters serve as non-destructive indicators of vegetation photosynthetic function and are widely used as key input parameters in photosynthesis–fluorescence models. The rapid acquisition of the spatiotemporal dynamics of ChlF parameters is crucial for enhancing remote sensing applications and improving carbon cycle modeling. While hyperspectral reflectance offers a promising data source for estimating ChlF parameters, previous studies have relied primarily on spectral indices derived from specific datasets, which often lack robustness. In this study, we simultaneously monitored ChlF parameters and spectral reflectance in leaves from different species, growth stages, and canopy positions within a temperate deciduous forest. We developed a data-driven partial least squares regression (PLSR) model by integrating fractional-order derivative (FOD) spectral transformation with multiple feature selection methods to predict ChlF parameters. The results demonstrated that FOD spectra effectively improved prediction accuracy compared to conventional PLSR attempts. Among the feature selection algorithms, the least absolute shrinkage and selection operator (LASSO) and stepwise regression (Stepwise) methods outperformed others. Furthermore, the LASSO-based PLSR model that used low-order (<1) FOD spectra achieved high predictive performance for NPQ (R² = 0.60, RPD = 1.60, NRMSE = 0.16), ΦP (R² = 0.73, RPD = 1.94, NRMSE = 0.11), ΦN (R² = 0.62, RPD = 1.62, NRMSE = 0.12), and ΦF (R² = 0.54, RPD = 1.48, NRMSE = 0.15). These findings suggest that the integration of FOD spectral transformation and appropriate feature selection enables the simultaneous estimation of multiple ChlF parameters, providing valuable insights for the retrieval of ChlF parameters from hyperspectral data. Full article

Secondary metabolites (SM) in plants play crucial pharmacological, ecological, and nutritional roles for humans, wildlife, and livestock. Environmental Heterogeneity (EH) encompasses the variability of biotic and abiotic factors that influence biological responses of plant species. Advancements in remote sensing have enhanced the ability to assess plant functional traits more affordably and comprehensively by integrating spectral reflectance data with detailed plant metabolomics. However, studies investigating the relationship between EH—quantified using Rao’s Q heterogeneity index from remote sensing data—and SM diversity remain limited. Here, we present the first report demonstrating that the biotic component of EH, measured as Rao’s Q, is positively associated with SM diversity in mesquite pod extracts—higher Rao’s Q values correspond to greater SM diversity. Generalized additive models (GAMs) revealed that Rao’s Q contributed the most explanatory power, accounting for 21.2% of the deviance, compared to pod weight (13.7%) and pod length (2.03%). However, only the relationship between Rao’s Q and SM diversity was statistically significant (p = 0.029). The Rao’s Q index derived from remote sensing serves as a scalable proxy for identifying SM hotspots, facilitating the targeted discovery of regions with high pharmacological or nutritional value. Full article

The ongoing advancement of unmanned aerial vehicles (UAVs) and the evolution of small-scale cameras have bridged the gap between traditional ground-based surveys and orbital sensors. However, these systems present challenges, including limited coverage area, image stabilization constraints, and complex image processing. In water quality monitoring, these difficulties are further compounded by sun glint effects, which hinder the construction of accurate orthomosaics in homogeneous water surfaces and affect radiometric accuracy. This study focuses on evaluating these challenges by comparing two distinct airborne imaging platforms with different spectral resolutions, emphasizing Total Suspended Solids (TSS) monitoring. Hyperspectral airborne surveys were undertaken utilizing a pushbroom system comprising 276 bands, whereas multispectral airborne surveys were conducted employing a global shutter frame with 4 bands. Fifteen aerial survey campaigns were carried out over water bodies from two biomes in Brazil (Amazon and Savanna), at varying concentrations of TSS (0.6–130.7 mg L⁻¹, N: 53). Empirical models using near-infrared channels were applied to accurately monitor TSS in all areas (Hyperspectral camera—RMSE = 3.6 mg L⁻¹, Multispectral camera—RMSE = 9.8 mg L⁻¹). Furthermore, a key contribution of this research is the development and application of Sun Glint mitigation techniques, which significantly improve the reliability of airborne reflectance measurements. By addressing these radiometric challenges, this study provides critical insights into the optimal UAV platform for TSS monitoring in inland waters, enhancing the accuracy and applicability of airborne remote sensing in aquatic environments. Full article

Antarctic true-color imagery synthesized using multispectral remote sensing data is effective in reflecting sea ice conditions, which is crucial for monitoring. Deep learning has been explored for sea ice extraction, but traditional convolutional neural network models are constrained by a limited perceptual field, making it difficult to obtain global contextual information from remote sensing images. A novel model named GEFU-Net, a modification of U-Net, is presented. The self-established graph reconstruction module is employed to convert features into graph data and construct the adjacency matrix using a global adaptive average similarity threshold. Graph convolutional networks are utilized to aggregate the features at each pixel, enabling the rapid capture of global context, enhancing the semantic richness of the features, and improving the accuracy of sea ice extraction through graph reconstruction. Experimental results using the sea ice dataset of the Ross Sea in the Antarctic, produced by Sentinel-2, demonstrate that our GEFU-Net achieves the best performance compared to other commonly used segmentation models. Specifically, it achieves an accuracy of 97.52%, an Intersection over Union of 95.66%, and an F1-Score of 97.78%. Additionally, fewer model parameters and good inference speed are demonstrated, indicating strong potential for practical ice mapping applications. Full article

Surface soil moisture (SSM) plays a pivotal role various fields, including agriculture, hydrology, water environment, and meteorology. To investigate the impact of land use types and fractional vegetation cover (FVC) on the accuracy of SSM estimation, this study conducted a comprehensive analysis of SSM estimation performance across diverse land use scenarios (e.g., multiple land use combinations and cropland) and varying FVC conditions. Sentinel-2 NDVI and MOD09A1 NDVI were fused by the Enhanced Spatial and Temporal Adaptive Reflection Fusion Model (ESTARFM) to obtain NDVI with a temporal resolution better than 8 d and a spatial resolution of 20 m, which improved the matching degree between NDVI and the Sentinel-1 backscattering coefficient (σ⁰). Based on the σ⁰, NDVI, and in situ SSM, combined with the water cloud model (WCM), the SSM estimation model is established, and the model of each land use and FVC is validated. The model has been applied in Handan. The results are as follows: (1) Compared with vertical–horizontal (VH) polarization, vertical–vertical (VV) polarization is more sensitive to soil backscattering (${\mathsf{\sigma }}_{\mathrm{soil}}^0$). In the model for multiple land use combinations (Multiple-Model) and the model for the cropland (Cropland-Model), the R² increases by 0.084 and 0.041, respectively. (2) The estimation accuracy of SSM for the Multiple-Model and Cropland-Model is satisfactory (Multiple-Model, RMSE = 0.024 cm³/cm³, MAE = 0.019 cm³/cm³, R² = 0.891; Cropland-Model, RMSE = 0.023 cm³/cm³, MAE = 0.018 cm³/cm³, R² = 0.886). (3) When the FVC > 0.75, the accuracy of SSM by the WCM is low. It is suggested the model should be applied to the cropland where the FVC ≤ 0.75. This study clarified the applicability of SSM estimation by microwave remote sensing (RS) in different land uses and FVCs, which can provide scientific reference for regional agricultural irrigation and agricultural water resources management. The findings highlight that the VV polarization-based model significantly improves SSM estimation accuracy, particularly in croplands with FVC ≤ 0.75, offering a reliable tool for optimizing irrigation schedules and enhancing water use efficiency in agriculture. These results can aid in better water resource management, especially in regions with limited water availability, by providing precise soil moisture data for informed decision-making. Full article

Soil color serves as a critical indicator of its properties and conditions. It is shaped by a complex interplay of mineral and organic matter content, moisture levels, and other environmental variables. Additionally, human activities such as land-use changes and intensive agricultural practices can profoundly alter soil color. Soil color, driven by the presence of organic matter, plays a crucial role in understanding soil fertility. Its strong correlation with soil organic carbon content makes it a valuable parameter for assessing soil quality in agricultural practices. A variety of techniques have been developed to measure soil color, ranging from traditional Munsell color matching to modern color meters. Digital image colorimetry enables rapid on-site assessments of soil color, but environmental conditions such as soil water content and lighting conditions should be considered. Spectroscopic methods, particularly diffuse reflectance spectroscopy, pave the way for a more reliable and accurate composition analysis. Advances in remote sensing and computational methods are combined to explore the intricate relationships between soil color and environmental factors. Such an integrated approach not only enhances scalability but also leads to more insights and actionable strategies for environmental management and sustainable agriculture. Full article