Remote Sensing Applications-Society and Environment最新文献_第7页

Data-driven identification of high-nature value grasslands using Harmonized Landsat Sentinel-2 time series data

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-01-01 DOI: 10.1016/j.rsase.2024.101427

Kim-Cedric Gröschler , Tjark Martens , Joachim Schrautzer , Natascha Oppelt

Europe’s high-nature value (HNV) grasslands have significantly declined in recent decades. European conservation strategies are mainly confined to protected areas, and many national initiatives aiming for comprehensive coverage suffer from long and irregular monitoring intervals. Addressing this, we propose a data-driven approach to derive information about the location, extent and HNV status of grasslands to improve the efficiency of large-scale field mappings. Serving as a representative example of European and national grassland monitoring, we utilize the regional habitat map of Schleswig-Holstein, Germany, in conjunction with Harmonized Landsat Sentinel-2 time series data to train XGBoost models for the period of 2017-2022. Our models achieved high classification performance, distinguishing eight grassland classes with average F1-scores of 0.89 before and 0.86 after feature selection. We examined model decision-making patterns using an adapted version of SHapley Additive exPlanation values, finding that start-of-season, end-of-season, Red-Edge, and spectral change features significantly impacted predictions. We produced annual HNV grassland maps and, by aggregating yearly results, derived a robust estimate of the HNV status in our study area. Applying our HNV estimate to an independent dataset comprising 2363 km

^{2}

of grassland plots with unknown HNV status, we identified 84 km

^{2}

as HNV, highlighting the significance of our result. Overall, our study demonstrates how integrating remote sensing data enhances the efficiency and comprehensiveness of large-scale mapping initiatives.

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引用次数: 0

Satellite remote sensing for environmental sustainable development goals: A review of applications for terrestrial and marine protected areas

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-01-01 DOI: 10.1016/j.rsase.2025.101450

Matthew J. McCarthy , Hannah V. Herrero , Stephanie A. Insalaco , Melissa T. Hinten , Assaf Anyamba

With few years left to achieve the vital United Nations Sustainable Development Goals (SDGs), member nations must urgently leverage technological advancements in environmental monitoring to succeed. Remote sensing now provides decades of global observations at a variety of spatio-temporal scales and a litany of data products to guide comprehensive measures for climate action, and aquatic and terrestrial biota preservation. Protected areas, such as national parks and wildlife preserves, represent largely untapped resources for both applying robust conservation measures and testing ambitious new approaches to sustainable development that could jumpstart the much-needed adoption of strategies to efficiently pursue global sustainability. This review summarizes recent demonstrated utilities of remotely sensed data applied to protected areas for research related to SDG goals 13, 14, and 15: “Climate Action”, “Life below Water”, and “Life on Land”. We identify successful uses of such data for each SDG, identify areas for improvement, and provide recommendations from the literature on how to expand what others have done to achieve lofty goals with global impact. We demonstrate that remote sensing provides a valuable tool for achieving SDGs as it facilitates monitoring vegetation health, water quality and condition, and climate variables at large spatial and fine temporal scales, while also evaluating the effectiveness of management and conservation practices. Issues remain, however, in that there is currently no reference from which to relate goal progress to human livelihoods. The current relationship between remotely sensed indices and ecological services that determine sustainable development omit steps that would establish this connection.

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引用次数: 0

Integration of magnetic and remote sensing methods for mapping geothermal signatures in the middle part of Benue Trough, Northeastern Nigeria

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-01-01 DOI: 10.1016/j.rsase.2024.101434

Babatunde Joseph Fagbohun , Naheem Banji Salawu , Amin Beiranvand Pour , Suraju Adesina Adepoju

The Middle part of Benue Trough is one of the notable regions with geothermal manifestations in Nigeria, which is evident in the form of warm springs and mud pots. In this study, aeromagnetic and remote sensing data were utilized to evaluate the deep-seated and surface signatures of the geothermal system in the middle part of the Benue Trough. The analysis of aeromagnetic data involves the application of the reduction-to-pole (RTP) technique on total magnetic intensity (TMI) anomaly data to reposition anomalies above their magnetic sources. Filters were subsequently applied on the RTP map to suppress artefacts and anomalies produced by shallow magnetic sources. Additionally, the day-time land surface temperature (LST) of the study area was derived using Landsat-8 data. ASTER day-time and night-time images were subsequently used for detailed thermal anomaly mapping of a subset of the study area with elevated temperature in the Landsat derived LST. The STcorr algorithm was employed for the correction of topographic influences and generation of thermal anomaly maps from day-time and night-time ASTER data. Analysis of aeromagnetic data revealed that some of the geothermal manifestations within the study area are linked to deep-seated structures while others are related with the intrusive rocks. Generally, Akiri, Awe, Azara, and Ribi thermal springs display noticeable thermal anomalies in the night-time thermal anomaly map with the Akiri thermal spring having the most prominent thermal anomalies. The Ribi, Azara, and Akiri thermal springs, which are linked with deep-seated structures exhibit higher temperatures than Keana and Awe thermal springs, which are thought to be associated with intrusive rocks in the day-time LST and thermal anomaly images. The integration of geophysical and remote sensing data for the exploration of geothermal resources adopted in this study offers a rapid and cost-effective approach that can be adapted for geothermal resource exploration in other areas suspected to have blind geothermal systems with minimal surface manifestation.

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引用次数: 0

Temperate forest tree species classification with winter UAV images

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-01-01 DOI: 10.1016/j.rsase.2024.101422

Yunmei Huang , Baijian Yang , Joshua Carpenter , Jinha Jung , Songlin Fei

Tree species classification using unmanned aerial vehicle (UAV) images has gained increasing attention due to recent advancements in deep learning algorithms and UAV technology. Recent studies have primarily focused on the use of UAV images captured during the growing seasons. Despite the fact that winter is a critical and convenient period for forest inventory, limited studies have explored the application of winter images for species classification. By training a deep learning model (ResNet18), we achieved an average F1-score of 0.9 for classification among eight species using winter UAV images in a temperate forest. To enhance model interpretability, we applied the Grad-CAM method, which generated feature maps identifying critical regions for species classification. To examine the impact of color on species classification, we converted RGB images to grayscale. Model accuracy on grayscale images decreased slightly (F1-score 0.86) but it effectively learned features from canopy images. This study contributes to the field by pioneering the use of winter images for tree species classification in temperate forests, which provides new opportunities for year-round UAV-based forest inventory. Given winter provides the opportunity to inventory other under-canopy features such as trunk diameter, adding the capability of species classification with winter images could greatly improve the capacity and efficiency of UAV-based forest inventory.

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引用次数: 0

An assessment of different line-of-sight and ground velocity distributions for a comprehensive understanding of ground deformation patterns in East Jharia coalfield

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-01-01 DOI: 10.1016/j.rsase.2024.101446

Aditya Kumar Thakur, Rahul Dev Garg, Kamal Jain

Jharia Coalfield is one of the oldest and most crucial mining regions. It faces ongoing challenges with land surface deformation due to mining operations and coal seam fires. Previous studies often overlooked the complex interplay of various LOS and ground velocities and focused mainly on vertical subsidence. This paper used the PS-InSAR for detailed ground deformation analysis of East Jharia. Ascending and descending pass time series LOS deformation datasets were obtained using advanced multi-image sparse point processing. Further, the study employed IDW interpolation in LOS velocity, followed by velocity decomposition to derive horizontal and vertical velocity components. Multi-sparse point processing and IDW interpolation enhance spatial continuity and reduce noise, ensuring the robustness of decomposed velocities. Interdependency and distribution similarity between different velocities were explored using Correlation analysis and the Global Moran's Index. Analysis revealed significant ground movement patterns with weak spatial association and underscored the necessity of both vertical and horizontal velocity for a comprehensive understanding of deformation. Subsidence smaller than −30 mm/year was observed in Sahana Pahari, northeast of Rajapur opencast mines, Jharia Main Road, and southeast of Jharia Gurudwara to Kujama Colliery at Tisra. Upliftment greater than 30 mm/year occurred in Jorapokhar, Karmik Nagar, and Kustai Basti near Ena Colliery, while lateral displacement of the same value was notable in CIMFR Colony Dhaiya, Koyla Nagar Saraidhela, Ghanoodih, Dobari, Kujama, and Barari Colliery over dumps. Correlation coefficients of 0.9165 (horizontal) and 0.7933 (vertical) revealed the dominant influence of horizontal movement on overall ground deformation. Overall, the study provided valuable insights into the spatial distribution of subsidence for 2023, highlighting the importance of different velocities in assessing and managing ground movement in mining-affected regions.

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引用次数: 0

Accelerating Electric Vehicle (EV) adoption: A remote sensing data driven and deep learning-based approach for planning public car charging infrastructure

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-01-01 DOI: 10.1016/j.rsase.2024.101447

Prakash P.S., Jenny Hanafin, Divyajyoti Sarkar, Marta Olszewska

Car fleet electrification is critical for achieving ambitious climate action goals. Access to charging stations is a major barrier for widespread adoption of EV, especially impacting members of lower socio-economic groups who cannot easily install home chargers in their residences. This research aims to examine the demand for public EV charging stations in residential areas and their geographical distribution. By utilizing advanced deep learning models and high-resolution remote sensing imagery, the study aims to identify specific clusters of households that require access to the public infrastructure. The study uses high-resolution aerial images and property parcels as input to a deep learning model YOLOv8 to recognize properties that may require access to public charging stations. This study presents an innovative approach that addresses challenges pertaining to EV adoption using remote sensing data, machine learning, and geospatial analysis. The results of the study demonstrate spatial analysis using sociodemographic data and household parking data, generated through the innovative method developed in this work, to aid Irish towns in planning public EV charging facilities among residential neighbourhoods. The study's findings are expected to aid in the implementation of expansion strategies for the public EV charging network, which is vital for meeting ambitious EV fleet targets.

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引用次数: 0

GEE-based environmental monitoring and phenology correlation investigation using Support Vector Regression

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-01-01 DOI: 10.1016/j.rsase.2024.101445

Fatemeh Parto Dezfooli , Mohammad Javad Valadan Zoej , Ali Mansourian , Fahimeh Youssefi , Saied Pirasteh

Environmental changes over time and across different regions profoundly affect agriculture, forestry, water management, public health, and ecosystems. Therefore, monitoring these fluctuations is crucial for informing decision-making and developing strategies for long-term sustainability. While ground-based methods provide valuable insights into environmental dynamics, they are inherently limited in scope and coverage. Consequently, satellite-based techniques have become essential for comprehensive ecological monitoring over extensive spatial and temporal scales. This study investigates spatio-temporal patterns of environmental factors and their correlation with phenology in Ilam Province, Iran, from 2014 to 2021, utilizing remote sensing data and Google Earth Engine (GEE). Landsat 8 satellite data was used to generate time series maps and timelines for land cover, temperature, and soil moisture, using the Soil-Adjusted Vegetation Index (SAVI), Land Surface Temperature (LST) anomaly, and Soil Moisture Index (SMI). Subsequently, the Temporal Soil-Adjusted Vegetation Phenology Index (TSPI) was calculated to track annual vegetation variations and analyze its correlation with the specified parameters using Support Vector Regression (SVR). Our results revealed significant trends in environmental factors, highlighting robust correlations with the TSPI. Soil moisture peaked in late winter and early spring, declining during the summer, with the highest levels recorded in 2018. Vegetation reached its maximum density in mid-spring and its minimum in winter, with a notable greening surge observed in 2019. Temperatures were highest in summer and lowest in winter, showing minimal year-to-year variation. Spatial analysis indicated a consistent increase in land surface temperature from the northeast toward the southwest, corresponding to declines in vegetation and soil moisture levels. Regression analysis specified strong associations between the TSPI and environmental variables, with R-squared values of 0.83 for LST, 0.86 for SAVI, and 0.79 for SMI. These findings emphasize the effectiveness of remote sensing methods, such as time series satellite imagery and streamlined indices, for large-scale ecological analyses using the GEE platform and underscore the potential of TSPI as a proper indicator for future environmental management research.

{"title":"GEE-based environmental monitoring and phenology correlation investigation using Support Vector Regression","authors":"Fatemeh Parto Dezfooli , Mohammad Javad Valadan Zoej , Ali Mansourian , Fahimeh Youssefi , Saied Pirasteh","doi":"10.1016/j.rsase.2024.101445","DOIUrl":"10.1016/j.rsase.2024.101445","url":null,"abstract":"<div><div>Environmental changes over time and across different regions profoundly affect agriculture, forestry, water management, public health, and ecosystems. Therefore, monitoring these fluctuations is crucial for informing decision-making and developing strategies for long-term sustainability. While ground-based methods provide valuable insights into environmental dynamics, they are inherently limited in scope and coverage. Consequently, satellite-based techniques have become essential for comprehensive ecological monitoring over extensive spatial and temporal scales. This study investigates spatio-temporal patterns of environmental factors and their correlation with phenology in Ilam Province, Iran, from 2014 to 2021, utilizing remote sensing data and Google Earth Engine (GEE). Landsat 8 satellite data was used to generate time series maps and timelines for land cover, temperature, and soil moisture, using the Soil-Adjusted Vegetation Index (SAVI), Land Surface Temperature (LST) anomaly, and Soil Moisture Index (SMI). Subsequently, the Temporal Soil-Adjusted Vegetation Phenology Index (TSPI) was calculated to track annual vegetation variations and analyze its correlation with the specified parameters using Support Vector Regression (SVR). Our results revealed significant trends in environmental factors, highlighting robust correlations with the TSPI. Soil moisture peaked in late winter and early spring, declining during the summer, with the highest levels recorded in 2018. Vegetation reached its maximum density in mid-spring and its minimum in winter, with a notable greening surge observed in 2019. Temperatures were highest in summer and lowest in winter, showing minimal year-to-year variation. Spatial analysis indicated a consistent increase in land surface temperature from the northeast toward the southwest, corresponding to declines in vegetation and soil moisture levels. Regression analysis specified strong associations between the TSPI and environmental variables, with R-squared values of 0.83 for LST, 0.86 for SAVI, and 0.79 for SMI. These findings emphasize the effectiveness of remote sensing methods, such as time series satellite imagery and streamlined indices, for large-scale ecological analyses using the GEE platform and underscore the potential of TSPI as a proper indicator for future environmental management research.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101445"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092548","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}

引用次数: 0

Evaluation of InSAR applicability using a new multi-index and optical imagery: A case study in the Guangdong-Hong Kong-Macao greater bay area, China

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-01-01 DOI: 10.1016/j.rsase.2025.101474

Zhijie Zhang , Songbo Wu , Chaoying Zhao , Guoqiang Shi , Xiaoli Ding , Bochen Zhang , Ziyuan Li , Yan Wang , Zhong Lu

Satellite interferometric synthetic aperture radar (InSAR) is widely used for monitoring ground deformation. However, its effectiveness can be limited by factors such as dense vegetation and complex mountainous terrain, which may result in insufficient monitoring point distribution. Evaluating InSAR applicability in advance allows us to select and configure optimal SAR data, achieving better application outcomes. This study proposes a novel approach for assessing InSAR applicability using innovative multi-index and optical imagery. We developed two new spectral indices to define land cover types and performed statistical analysis to quantify the influence of land cover on interferometric phase quality. Regions with limited SAR visibility were excluded using layover and shadow maps and R-Index method. The resultant InSAR applicability map was graded into four categories: Good, Moderate, Low, and Poor. Given the diverse geological hazards in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China, prior evaluation of InSAR applicability can significantly improve geohazard investigations. We evaluated InSAR applicability in the GBA using Sentinel-2 and Copernicus DEM data and validated the results with Small Baseline Subset (SBAS) technique and Sentinel-1 SAR image dataset. The results indicate that 20.8% of the GBA is highly suitable for InSAR application, predominantly in built-up areas. In comparison, only 18.6% of the vegetated regions are moderately suitable due to sparse vegetation challenges. Over half of the GBA region faces challenges in InSAR application due to dense vegetation. The proposed method, executable via Google Earth Engine, can serve as an effective tool for InSAR suitability analysis in other geographical regions.

{"title":"Evaluation of InSAR applicability using a new multi-index and optical imagery: A case study in the Guangdong-Hong Kong-Macao greater bay area, China","authors":"Zhijie Zhang , Songbo Wu , Chaoying Zhao , Guoqiang Shi , Xiaoli Ding , Bochen Zhang , Ziyuan Li , Yan Wang , Zhong Lu","doi":"10.1016/j.rsase.2025.101474","DOIUrl":"10.1016/j.rsase.2025.101474","url":null,"abstract":"<div><div>Satellite interferometric synthetic aperture radar (InSAR) is widely used for monitoring ground deformation. However, its effectiveness can be limited by factors such as dense vegetation and complex mountainous terrain, which may result in insufficient monitoring point distribution. Evaluating InSAR applicability in advance allows us to select and configure optimal SAR data, achieving better application outcomes. This study proposes a novel approach for assessing InSAR applicability using innovative multi-index and optical imagery. We developed two new spectral indices to define land cover types and performed statistical analysis to quantify the influence of land cover on interferometric phase quality. Regions with limited SAR visibility were excluded using layover and shadow maps and R-Index method. The resultant InSAR applicability map was graded into four categories: Good, Moderate, Low, and Poor. Given the diverse geological hazards in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China, prior evaluation of InSAR applicability can significantly improve geohazard investigations. We evaluated InSAR applicability in the GBA using Sentinel-2 and Copernicus DEM data and validated the results with Small Baseline Subset (SBAS) technique and Sentinel-1 SAR image dataset. The results indicate that 20.8% of the GBA is highly suitable for InSAR application, predominantly in built-up areas. In comparison, only 18.6% of the vegetated regions are moderately suitable due to sparse vegetation challenges. Over half of the GBA region faces challenges in InSAR application due to dense vegetation. The proposed method, executable via Google Earth Engine, can serve as an effective tool for InSAR suitability analysis in other geographical regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101474"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091982","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}

引用次数: 0

Mapping crop water productivity of rice across diverse irrigation and fertilizer rates using field experiment and UAV-based multispectral data

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-01-01 DOI: 10.1016/j.rsase.2025.101456

Sumit Kumar Vishwakarma, Benu Bhattarai, Kritika Kothari, Ashish Pandey

Crop water productivity (CWP) is an important indicator for optimizing water use and yield in agriculture. In this study, an Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera was used for estimating the CWP of rice crop. To our knowledge, this is the first study that assessed CWP of rice at high spatial resolution in the tarai region of north India using UAV-based multispectral data. A field experiment was conducted in Roorkee, India, where rice was cultivated under two irrigation levels (continuous flooding (CF), and alternate wetting and drying (AWD)) and three nitrogen treatments (high nitrogen (HN): 150 kg/ha, medium nitrogen (MN): 120 kg/ha, and low nitrogen (LN): 60 kg/ha). There were a total of seven treatments (T0 = rainfed, T1 = CF-HN, T2 = CF-MN, T3 = CF-LN, T4 = AWD-HN, T5 = AWD-MN, and T6 = AWD-LN). UAV-derived Normalized Difference Vegetation Index (NDVI) was used for the estimation of crop evapotranspiration (ETa) and CWP. The highest and lowest ETa were found in treatments T4 (316.06 mm) and T0 (311.49 mm), respectively. The above-ground biomass (AGB) and grain yield were calculated using the radiation utilization efficiency (RUE). The model estimated AGB with R² 0.63 and RMSE 0.61 t ha⁻¹, and yield with R² 0.95 and RMSE 0.41 t ha⁻¹. The CWP was highest in treatment T5 (1.13 kg m⁻³) and lowest in treatment T0 (0.76 kg m⁻³). For treatments, T1, T2 T3, T4, and T6, the CWPs were 1.13, 1.13, 1.07, 1.05, and 1.12 kg m⁻³, respectively. Considering the global CWP categories for rice crop as low (≤0.70 kg m⁻³), medium (0.70–1.25 kg m⁻³), and high (>1.25 kg m⁻³), the CWP in the present study was within the medium category. Among the treatments, AWD with MN was found to be the most suitable strategy for achieving high CWP. Monitoring the CWP of rice fields using UAV and providing high-resolution CWP maps could be helpful for farmers and policymakers in better allocating the resources and enhancing resource use efficiency.

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引用次数: 0

Analysis of the gas emissions from volcanic activity in the East African Rift System using remote sensing over the past two decades

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-01-01 DOI: 10.1016/j.rsase.2025.101471

Sakine Moradi , Elham Ghasemifar

Monitoring volcanic gas emissions is crucial for assessing volcanic hazards, as they vary across spatial and temporal scales more than most other natural hazards and have significant environmental impacts. They can directly affect climate change, which in turn poses challenges to human society and global sustainable development. Satellite remote sensing plays a pivotal role in monitoring and studying atmospheric gases, particularly in regions like the East African Rift System (EARS) that hosts large and active intra-continental rift-related volcanoes. These predominantly mafic volcanoes sit above a major mantle upwelling, which are contributing to the breakup of the East African continent. Volcanic activity in this region has been persistent since Tertiary. Recent advancements in satellite remote sensing technology have greatly enhanced our ability to monitor gas emissions from volcanoes across the globe. However, data on gas composition and emissions in the EARS remains limited. Therefore, the present study focuses on eight volcanoes, including Erta Ale, Alu Dalafilla, Manda Hararo, Fentale, Mount Longonot, Ol Doinyo Lengai (from the eastern branch of the EARS), and Mt. Nyiragongo and Nyamulagira (from the western branch of the EARS). At each volcano, we used data from the atmospheric infrared sounder (AIRS) to measure H₂O, CO, and CH₄ gases, along with the spatial and temporal variability of NO₂ and SO₂ gases from the Ozone Monitoring Instrument (OMI). This combined dataset offers the most comprehensive dataset of gas variations during EARS activity from 2004 to 2024, establishing a robust baseline for future monitoring efforts. To assess the vertical profile of volcanic gases in atmosphere above the EARS, we analyzed Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission over a 12-month period from 2014 to 2024. June 2011 and January 2019 are selected as the representative months, reflecting periods of high and low gases anomalies, respectively. These analyses, which conducted for these months were evaluated using sea level pressure, geopotential height at 850 and 500 hPa, and meridional and zonal winds components, providing a detailed three-dimensional structure of volcanic gases over the EARS. These measurements can also support the development of effective policies to manage air pollution emissions from these volcanoes, which influence various aspects of human life and ecosystems in this region.

{"title":"Analysis of the gas emissions from volcanic activity in the East African Rift System using remote sensing over the past two decades","authors":"Sakine Moradi , Elham Ghasemifar","doi":"10.1016/j.rsase.2025.101471","DOIUrl":"10.1016/j.rsase.2025.101471","url":null,"abstract":"<div><div>Monitoring volcanic gas emissions is crucial for assessing volcanic hazards, as they vary across spatial and temporal scales more than most other natural hazards and have significant environmental impacts. They can directly affect climate change, which in turn poses challenges to human society and global sustainable development. Satellite remote sensing plays a pivotal role in monitoring and studying atmospheric gases, particularly in regions like the East African Rift System (EARS) that hosts large and active intra-continental rift-related volcanoes. These predominantly mafic volcanoes sit above a major mantle upwelling, which are contributing to the breakup of the East African continent. Volcanic activity in this region has been persistent since Tertiary. Recent advancements in satellite remote sensing technology have greatly enhanced our ability to monitor gas emissions from volcanoes across the globe. However, data on gas composition and emissions in the EARS remains limited. Therefore, the present study focuses on eight volcanoes, including Erta Ale, Alu Dalafilla, Manda Hararo, Fentale, Mount Longonot, Ol Doinyo Lengai (from the eastern branch of the EARS), and Mt. Nyiragongo and Nyamulagira (from the western branch of the EARS). At each volcano, we used data from the atmospheric infrared sounder (AIRS) to measure H<sub>2</sub>O, CO, and CH<sub>4</sub> gases, along with the spatial and temporal variability of NO<sub>2</sub> and SO<sub>2</sub> gases from the Ozone Monitoring Instrument (OMI). This combined dataset offers the most comprehensive dataset of gas variations during EARS activity from 2004 to 2024, establishing a robust baseline for future monitoring efforts. To assess the vertical profile of volcanic gases in atmosphere above the EARS, we analyzed Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission over a 12-month period from 2014 to 2024. June 2011 and January 2019 are selected as the representative months, reflecting periods of high and low gases anomalies, respectively. These analyses, which conducted for these months were evaluated using sea level pressure, geopotential height at 850 and 500 hPa, and meridional and zonal winds components, providing a detailed three-dimensional structure of volcanic gases over the EARS. These measurements can also support the development of effective policies to manage air pollution emissions from these volcanoes, which influence various aspects of human life and ecosystems in this region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101471"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421165","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}

引用次数: 0