Remote Sensing Applications-Society and Environment最新文献

{"title":"Tourism and environmental change in Saint Martin Island, Bangladesh: Insights from remote sensing data","authors":"Jayanta Biswas ,&nbsp;Tanmoy Malaker ,&nbsp;Taposh Mollick","doi":"10.1016/j.rsase.2025.101484","DOIUrl":"10.1016/j.rsase.2025.101484","url":null,"abstract":"<div><div>This study uses remote sensing data and geospatial analysis to evaluate the impact of unregulated tourism on the ecological vulnerability and land use dynamics of Saint Martin Island, Bangladesh. Saint Martin, the only coral-bearing island in Bangladesh, has experienced significant environmental degradation due to increased tourist activities, population growth, and tourism-induced development. In this study, multi-temporal Sentinel-2 imagery from 2018 to 2024 has been utilized for land use and land cover (LULC) classification to assess the impact of tourism on ecology, achieving an accuracy range of 94.56%–98.89%. Key environmental indices were calculated to assess vegetation cover, water quality, and climate patterns, along with land surface temperature (LST). The results showed a 2.52% increase in developed areas and a 12.77% decrease in sandy water between 2018 and 2024. Polluted water areas shrank from 2.16 acres in 2018 to 0.74 acres in 2020, reflecting ecological recovery due to reduced tourist activity during the COVID-19 lockdown period. However, pollution resurged to 0.97 acres by 2024 after restrictions were lifted. Coral reef degradation reached 25% between 2015 and 2022, severely impacting the island's marine biodiversity and future marine life. Additionally, a rise in land surface temperature (LST) from 32 °C in 2020 to 36 °C in 2024 was observed, along with a decrease in vegetation cover. The study demonstrates a clear link between unregulated tourism and environmental degradation, emphasizing the urgency of sustainable tourism practices such as limiting tourist visits, enhancing waste management, and protecting sensitive ecological areas to prevent further harm to the island's ecosystem and livelihoods.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101484"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated floating debris monitoring using optical satellite imagery and artificial intelligence: Recent trends, challenges and opportunities","authors":"Kamakhya Bansal,&nbsp;Ashish Kumar Tripathi","doi":"10.1016/j.rsase.2025.101475","DOIUrl":"10.1016/j.rsase.2025.101475","url":null,"abstract":"<div><div>Unwanted and harmful floating debris creates aesthetic, economic, social, and ecological harm. The optical satellites provide frequent global coverage across multiple spectral bands. Utilizing this abundant multi-banded optical satellite data for floating debris monitoring, many artificial intelligence-based approaches were proposed. These approaches face various challenges due to the multidimensional nature of the earth observation data visualized on a reduced scale. This work identifies various stages of AI deployment for floating debris identification, classification, segmentation, density estimation, and/or temporal study. The challenges during each stage along with some potential solutions applied in this field or elsewhere have been identified. Since AI approaches are data-driven, the limitation of labeled data with real-time diversity of shape, color, texture, size, and composition of floating debris placed against different backgrounds is most acute. The work proposes the utilization of some recent AI-based systems, like continuous learning, transfer learning, attention-based transformers, explainable AI, etc., to resolve these identified challenges. The work calls for further research into the application of pre-trained models, semi-supervised learning, and multi-modal data fusion for overcoming the labeled data deficiency. Additionally, harmful debris density estimation and factors leading to a change in the estimated density need further research.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101475"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143346716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the link between spectra, inherent optical properties in the water column, and sea surface temperature and salinity","authors":"Solomon White ,&nbsp;Encarni Medina Lopez ,&nbsp;Tiago Silva ,&nbsp;Evangelos Spyrakos ,&nbsp;Adrien Martin ,&nbsp;Laurent Amoudry","doi":"10.1016/j.rsase.2025.101454","DOIUrl":"10.1016/j.rsase.2025.101454","url":null,"abstract":"<div><div>Sea surface salinity and temperature are important measures of ocean health. They provide information about ocean warming, atmospheric interactions, and acidification, with further effects on the global thermohaline circulation and as a consequence the global water cycle. In coastal waters they provide information about sub mesoscale circulations and tidal currents, riverine discharge and upwelling effects. This paper explores the methodology to extract sea surface salinity (SSS) and temperature (SST) from ground based hyperspectral ocean radiance. Water leaving radiance is linked to the inherent optical properties of the water column, effected by the constituent parts. Hyperspectral data at ground level is then used as input to train a linear regression model against temporally and spatially matched water data of SSS and SST. Furthermore, a neural network model to be able to estimate the SST and SSS with the hyperspectral data averaged to multispectral bands to emulate the satellite use case. The neural network model is able to learn the relationship between the multispectral radiance to both SSS and SST values, and can predict these with a root mean square error (RMSE) of 0.2PSU and 0.1 degree respectively. This demonstrates the feasibility of similar algorithms applied to multispectral ocean colour satellites with enhanced coverage and spatial resolution.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101454"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing grassland cut detection using Sentinel-2 time series through integration of Sentinel-1 SAR and weather data","authors":"Aleksandar Dujakovic ,&nbsp;Cody Watzig ,&nbsp;Andreas Schaumberger ,&nbsp;Andreas Klingler ,&nbsp;Clement Atzberger ,&nbsp;Francesco Vuolo","doi":"10.1016/j.rsase.2025.101453","DOIUrl":"10.1016/j.rsase.2025.101453","url":null,"abstract":"<div><div>The detection of grassland cuts is relevant for modelling grassland yield and quality because information on cut dates and cut intensity aids in the modelling of the nutrient biomass ratio of fodder. This research improves an existing grassland cut detection methodology developed for Austria based on Sentinel-2 (S2) optical time series. To further improve the detection accuracy, the new method incorporates Sentinel-1 (S1) Synthetic Aperture Radar (SAR) and daily weather data utilizing a machine learning-based model (Catboost). Cuts are first identified through a threshold-based comparison between a fitted idealized grassland growth curve and the observed NDVI values. The Catboost model subsequently addresses limitations in S2 data caused by cloud cover and other sub-optimum observation conditions. The Catboost model (1) identifies missing cuts in periods with no S2 data, and (2) eliminates false positive cuts. Weather data is utilized to identify the start of the cutting season and to define the (minimum required) time span between two consecutive cuts. Results demonstrate an improvement in cut date f-score (from 0.77 to 0.81), a reduced false detection rate (from 0.21 to 0.16), and a slight decrease in mean absolute error between true and estimated cut dates (from 4.6 to 4.1). The improvement in the accuracy was more evident for plots with high mowing frequency, while some remaining false detections were evident for extensively managed grasslands. The incorporation of S1 SAR and weather data enables the cut detection for the entire calendar year and eliminates the need for fixed growing season start/end dates. However, S1 SAR data alone did not provide reliable detection accuracy, showing its limitations in depicting vegetation dynamics for grassland. Overall, the improvements in accuracy and flexibility demonstrate the efficacy of the enhanced methodology, emphasizing the potential of combining S1 and S2 with weather data in large scale and cost-efficient grassland monitoring.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101453"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of soybean plants affected by Aphelenchoides besseyi using remote sensing and machine learning techniques","authors":"João Lucas Della-Silva ,&nbsp;Valeria de Oliveira Faleiro ,&nbsp;Tatiane Deoti Pelissari ,&nbsp;Amanda Ferreira ,&nbsp;Neurienny Ferreira Dias ,&nbsp;Daniel Henrique dos Santos ,&nbsp;Thaís Lourençoni ,&nbsp;Joelma Nayara ,&nbsp;Wendel Bueno Morinigo ,&nbsp;Larissa Pereira Ribeiro Teodoro ,&nbsp;Paulo Eduardo Teodoro ,&nbsp;Dthenifer Cordeiro Santana ,&nbsp;Izabela Cristina de Oliveira ,&nbsp;Ester Cristina Schwingel ,&nbsp;Renan de Almeida Silva ,&nbsp;Carlos Antonio da Silva Junior","doi":"10.1016/j.rsase.2025.101461","DOIUrl":"10.1016/j.rsase.2025.101461","url":null,"abstract":"<div><div>Soybeans (<em>Glycine max</em> (L.) Merrill) are a major player in food security, and pest loss control is a major focus of research and technological development by the agricultural sector. Among these pests, <em>Aphelenchoides besseyi</em> contaminates the aerial part of the plant, which can be detected in the leaf's spectral response, based on in situ hyperspectral sensors with the adoption of remote sensing techniques, such as spectral models. Assessing such data using machine learning allows the identification of optimal computational conditions to evaluate different levels of infection by the green stem nematode in soybeans. Thus, this research aimed to (i) discriminate the spectral bands most sensitive to nematode infection, (ii) identify the spectral model with the greatest accuracy for distinguishing different levels of nematode infection according to reflectance, and (iii) verify the resilience to the impact of <em>A. besseyi</em> on soybeans. From this approach, the near and short-wave infrared spectral portions contributed most to discriminating different amounts of nematodes in the plant, in a scenario in which the logistic regression algorithm had greater performance. Finally, this evaluation suggests that the best discrimination conditions occur with data obtained in the final half of the soybean cultivation cycle.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101461"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of different deficit irrigation regimens on soil moisture, production parameters of mango (Mangifera indica L.), and spectral vegetation indices in the Mediterranean region of Southern Spain","authors":"J.A. Sillero-Medina ,&nbsp;J. González-Pérez ,&nbsp;P. Hueso-González ,&nbsp;J.J. González-Fernández ,&nbsp;J.I. Hormaza-Urroz ,&nbsp;J.D. Ruiz-Sinoga","doi":"10.1016/j.rsase.2024.101415","DOIUrl":"10.1016/j.rsase.2024.101415","url":null,"abstract":"<div><div>Mediterranean region is facing a severe water resource crisis, exacerbated by climate change. In recent decades, the region has experienced increased anthropogenic pressure due to population growth, tourism, and urban and agricultural expansion, intensifying competition for water among economic sectors. The agri-food sector is one of the most affected by water scarcity. This presents significant challenges for the sustainability of irrigated crops and underscores the need for efficient irrigation strategies and adaptive mechanisms. Among innovative strategies is deficit irrigation. In this context, to ensure effective water management, it is essential to constantly monitor soil moisture and adapt water conditions to the specific requirements of each crop. Precision agriculture, supported by technologies such as remote sensing and UAVs, plays a fundamental role in this context, enabling detailed crop monitoring and facilitating more efficient irrigation management. This study aims to evaluate the impact of using three different irrigation treatments on mango cultivation, a subtropical crop of growing importance in the Mediterranean region. Specifically, Treatment 1 is based on conventional surface drip irrigation without restrictions; Treatment 2 involves conventional surface drip irrigation with a 65% water reduction; and Treatment 3 uses deep subsurface drip irrigation (20 cm), with a similar water restriction as the previous treatment. The effect on mango cultivation has been evaluated based on: (i) soil moisture, (ii) production data collected during the 2022–2023 growing season on the experimental plot; and (iii) two vegetation indices (NDVI and NDRE) derived from multispectral data collected via two UAV flights at different phenological stages. The results indicate that surface drip irrigation has shown better outcomes in terms of production, yield, and crop quality compared to other treatments involving significant water reductions or subsurface irrigation. Deep deficit irrigation has obtained the worst results in the evaluation of plant production, and yield.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101415"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV visual imagery-based evaluation of blue carbon as seagrass beds on a tidal flat scale","authors":"Takuya Akinaga ,&nbsp;Mitsuyo Saito ,&nbsp;Shin-ichi Onodera ,&nbsp;Fujio Hyodo","doi":"10.1016/j.rsase.2024.101430","DOIUrl":"10.1016/j.rsase.2024.101430","url":null,"abstract":"<div><div>Seagrass and seaweed beds (SSBs) have a high carbon sequestration function (blue carbon) in shallow coastal waters. Unmanned aerial vehicles (UAVs) are a highly useful tool for monitoring SSBs because of their ease of use and ability to acquire high-resolution photographs. In many previous studies using UAV, surveys of SSBs have been based on area alone, but it is insufficient to properly assess the habitat and carbon fixation of SSBs.</div><div>In this study, we estimated above-ground biomass and carbon of eelgrass in shallow coastal waters by combining aerial photography of visible images, quadrat surveys, and sampling of eelgrass. The analysis area was a tidal flat on an island located in the Seto Inland Sea in western Japan. Aerial photography was conducted by UAV to acquire high-resolution RGB visual images of the area. The quadrat survey and sampling were used to develop regression formulas for estimating biomass and carbon of eelgrass. The former was conducted to investigate the relationship between the coverage and Leaf Area Index (LAI), and the latter was conducted to investigate the relationship between leaf area and biomass, carbon of eelgrass. Those showed clear relationship between coverage and LAI (R² = 0.97) and between leaf area and biomass, carbon (biomass: R² = 0.98, carbon: R² = 0.98).</div><div>To identify eelgrass beds, the maximum likelihood classification was adapted. After calculating the coverage from the distribution, biomass and carbon were estimated by adapting regression formulas developed by quadrat survey and sampling.</div><div>The proposed method can be easily adapted from visible images taken by UAVs and robust to the effects of water, which provides high adaptability regarding the estimation for biomass and carbon of eelgrass on the tidal flat.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101430"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing earth target classification in hyperspectral imagery using graph convolutional neural networks and graph-regularized sparse coding","authors":"Geetha T S ,&nbsp;Chellaswamy C ,&nbsp;Kaliraja T ,&nbsp;Ramachandra Reddy K","doi":"10.1016/j.rsase.2024.101419","DOIUrl":"10.1016/j.rsase.2024.101419","url":null,"abstract":"<div><div>As hyperspectral remote sensing technology continues to advance, classification approaches using hyperspectral images (HSIs) have become increasingly important in earth target identification, mineral mapping, and environmental management. The strength of HSIs lies in their capacity to provide a detailed understanding of a target's composition. However, challenges such as high dimensionality, redundancy in HSI datasets, and potential class imbalances complicate their effective utilization. In this study, a novel framework combining graph convolutional neural networks (GCNNs) and graph-regularized sparse coding (GSC), referred to as GCNN-GSC, is proposed to address these challenges in HSI-based earth target classification. HSIs often exhibit irregular spatial structures, making traditional grid-based methods less effective. GCNNs excel in handling irregular grids, making them well-suited for hyperspectral data where spatial pixel arrangements deviate from regular patterns. GSC complements GCNNs by mitigating high dimensionality through compact and informative feature representation. To evaluate the efficacy of the proposed approach, a comparative study was conducted using key performance metrics, including overall accuracy, per-class accuracy, and Cohen's Kappa coefficient. The results demonstrate that GCNN-GSC outperforms state-of-the-art methods, achieving notable improvements across multiple benchmark datasets. Specifically, for the Indian Pines dataset, GCNN-GSC achieved increases of 5.74%, 5.49%, and 7.89% in Cohen's Kappa coefficient, per-class accuracy, and overall accuracy, respectively. Similar enhancements were observed for the Kennedy Space Center, Pavia University, and Houston 2013 datasets, with respective improvements of 6.58%, 6.55%, and 6.15% in Cohen's Kappa coefficient; 6.24%, 6.30%, and 5.57% in per-class accuracy; and 6.24%, 6.54%, and 6.30% in overall accuracy. These results underscore the robustness and effectiveness of GCNN-GSC in hyperspectral image classification tasks.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101419"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of support vector machine classifiers for lithological mapping using PRISMA hyperspectral remote sensing data: Sahand–Bazman magmatic arc, central Iran","authors":"Naer Rahmani ,&nbsp;Milad Sekandari ,&nbsp;Amin Beiranvand Pour ,&nbsp;Hojjatollah Ranjbar ,&nbsp;Hossein Nezamabadi pour ,&nbsp;Emmanuel John M. Carranza","doi":"10.1016/j.rsase.2025.101449","DOIUrl":"10.1016/j.rsase.2025.101449","url":null,"abstract":"<div><div>Mineral exploration is highly dependent on an accurate lithological map of a study area, which provides comprehensive information on geologic features for exploration target zones. Nowadays, the processing of hyperspectral image data for lithological mapping and mineral exploration using machine learning (ML) algorithms has greatly developed. The recently launched Italian hyperspectral sensor ‘PRecursore IperSpettrale della Missione Applicativa (PRISMA)’ offers an excellent capability for mineral detection and object classification with superior accuracy and efficiency for lithological mapping and mineral exploration. In this study, the performance of the support vector machine (SVM) algorithm was evaluated for processing PRISMA datasets to generate lithological maps of the Sar Cheshmeh porphyritic copper ore deposit in the Sahand–Bazman magmatic arc in central Iran. Three different SVM kernels, namely linear (LSVM), quadratic (QSVM) and cubic (CSVM), were comparatively evaluated for data classification in lithological mapping. The SVM classifiers were trained on the basis of prior knowledge from previous studies and field surveys. Approximately 5000 pixels from 14 different classes were used for training. There was a large misclassification between granodiorites and altered granodiorites in the LSVM result (78.3% accuracy for altered granodiorites), but this was greatly reduced in the QSVM and CSVM methods (with 96.1% and 99.1% accuracy, respectively). A significant improvement in classification was also seen for the vegetation, mine pits and Razak volcanism classes (with varying accuracy values). It is noteworthy that nine of the 14 classes had less than 400 training pixels and only one class had more than 1000 pixels used for training, indicating the power of ML for such studies. LSVM was the best method for mapping dacites with maximum accuracy (100%), but this accuracy was slightly lower for QSVM and CSVM (both had 97.9% accuracy). The results show that the LSVM, QSVM and CSVM methods achieved an accuracy of 80.22%, 85.81% and 86.05%, respectively, in the final classification. This study advocates the optimal SVM classifier (CSVM classifier) using PRISMA hyperspectral images for accurate lithological mapping for mineral exploration in metallogenic provinces.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101449"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing measurements of fresh volcanic ash during the Mount Etna emission of February 21, 2019","authors":"Salvatore Spinosa ,&nbsp;Antonella Boselli ,&nbsp;Luigi Mereu ,&nbsp;Giuseppe Leto ,&nbsp;Ricardo Zanmar Sanchez ,&nbsp;Simona Scollo","doi":"10.1016/j.rsase.2024.101413","DOIUrl":"10.1016/j.rsase.2024.101413","url":null,"abstract":"<div><div>Explosive activity can have a relevant impact in the atmosphere even during weak and continuous volcanic ash emissions. In fact, this type of activity can affect highly populated areas and needs to be investigated in order to reduce potential risks. In this paper, we analyze the volcanic ash emissions that took place on February 21, 2019 from the North East Crater, one of the summit craters of Mount Etna, in Italy. During the activity, a continuous ash emission caused the closure of the International Airport in Catania due to a large quantity of volcanic particles in the atmosphere that were dispersed by winds several kilometers away from the eruptive crater, mainly toward the west, south and south-east directions. This activity was analyzed using a dual depolarization LiDAR and visual and thermal cameras that are part of the instrumental network of the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo. The LiDAR derived aerosol backscattering coefficient and particle linear depolarization ratio profiles, both measured at 355 nm and 532 nm, gave insights on plume dynamics and variations of some features of the particles within the volcanic plume. During this event, we estimated a maximum volcanic plume height of about 3 km above sea level and LiDAR data show two distinct layers in the atmosphere, LiDAR derived aerosol properties were used for a first application of the Volcanic Ash LiDAR Retrieval - Maximum Likelihood (VALR-ML) algorithm on two volcanic ash layers, allowing to obtain a maximum value of volcanic ash concentration of 7.5 ± 3.7 mg/m³ and 8.1 ± 4.0 mg/m³, in the first layer at 355 and 532 nm, respectively; while in the second layer we obtained concentration values of 6.6 ± 3.3 and 8.5 ± 4.2 mg/m³ at 355 and 532 nm, respectively. Moreover, the plume was composed of very fine ash of about 1 μm dimensions. We found that weak and continuous volcanic ash emissions can reach thresholds that cause troubles to aviation operations. Our work shows how LiDAR systems are able to estimate critical information for aviation safety in the proximity of the airport, such as the altitude, the concentration and the size of emitted ash particles, even during low-intensity explosive activity.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101413"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}