Science of Remote Sensing最新文献

{"title":"Detecting shallow subsurface anomalies with airborne and spaceborne remote sensing: A review","authors":"Adam M. Morley,&nbsp;Tamsin A. Mather,&nbsp;David M. Pyle,&nbsp;J-Michael Kendall","doi":"10.1016/j.srs.2024.100187","DOIUrl":"10.1016/j.srs.2024.100187","url":null,"abstract":"<div><div>Advances in air and space sensor technology reveal new opportunities and innovative ways to remotely sense the Earth's subsurface. Considerable spatial coverage, fast and frequent image acquisition and very high radiometric, spectral, spatial and temporal resolution imaging systems can now detect near subsurface anomalies with impressive accuracy. The merits are extensive, with archaeological prospecting, environmental risk mitigation, natural resource exploration, defence and security and speleological research all benefitting from subsurface imaging capabilities over unknown territory, difficult terrain, hazardous environments and inaccessible ground. In this paper, we categorise the ground indicators and potential field characteristics of a general subsurface anomaly before reviewing and documenting over seventy air and space subsurface detection techniques using: photogrammetry, multispectral sensors, thermal infrared, hyperspectral imaging, synthetic aperture radar (SAR), airborne light detection and ranging (LiDAR), airborne gravity and aeromagnetics. The capabilities of each technique are evaluated by reviewing their ability to detect specific characteristics from subsurface anomalies and then they are tabulated by investigable feature and sensor type in seven technique tables. Research trends in motive, sensor type and subsurface anomaly characteristic are discussed and a short review of the major ground-truthing techniques used to verify airborne and spaceborne observations is considered. To close, we take a brief look at future research opportunities with very high resolution (VHR) datasets, multi-branch convolutional neural networks (CNNs) and active remote sensing in variable potential fields.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100187"},"PeriodicalIF":5.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099566","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":"A meta-analysis for the nighttime light remote sensing data applied in urban research: Key topics, hotspot study areas and new trends","authors":"Baiyu Dong ,&nbsp;Ruyi Zhang ,&nbsp;Sinan Li ,&nbsp;Yang Ye ,&nbsp;Chenhao Huang","doi":"10.1016/j.srs.2024.100186","DOIUrl":"10.1016/j.srs.2024.100186","url":null,"abstract":"<div><div>Nighttime light (NTL) data have become an essential tool for urban remote-sensing research in the past 25 years because of its ability to intuitively detect human activities. With new data and technologies constantly emerging leading to accumulated research, there is an urgent need for a comprehensive review of this subject. Although there are currently some review articles focusing on NTL-based urban studies, they lack visual analysis of research keywords based on co-occurrence analysis, as well as the research topics and changes of global countries and regions. Furthermore, they not yet delved into research methods and their relationship with research topics. Addressing these gaps, this study thoroughly investigated 545 relevant publications from 1992 to 2023 via comprehensive meta-analysis and visual co-occurrence analysis. The results indicate an increasing trend in NTL-based urban studies. ‘China’ appears as the most frequently mentioned keyword. Based on the co-occurrence clustering results, this study categorized the research topics into 4 groups. The most attention was given to identifying urban spatial dynamics, especially urban expansion. We found that the research topics of the 6 most frequently studied countries/regions varied across different time stages and were correlated with the urbanization levels of those regions at that time. Regarding the research methods, we observed an increase in the use of machine learning and index-based evaluation methods, with the former most commonly applied to urban area extraction and environmental variable prediction. We also highlighted emerging trends including: (1) Growing significance of machine learning models; (2) Transition of NTL from a leading role to an auxiliary tool; (3) An increased focus on the physical modelling of NTL, and challenges including: (1) Difficulties faced when applying medium-high resolution NTL imagery; (2) Limited applications of deep learning models; (3) Unable to genuinely reflect the urban artificial light information; (4) Inadequate temporal flexibility and consistency of observations. This study expects to systematically demonstrate the current status, trends and challenges of NTL-based urban research through Meta-analysis, so as to provide scientific references for more future innovative research and the management of urban nighttime environment.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100186"},"PeriodicalIF":5.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099664","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":"The impacts of training data spatial resolution on deep learning in remote sensing","authors":"Christopher Ardohain,&nbsp;Songlin Fei","doi":"10.1016/j.srs.2024.100185","DOIUrl":"10.1016/j.srs.2024.100185","url":null,"abstract":"<div><div>Deep learning (DL) is ubiquitous in remote sensing analysis with continued evolution in model architectures and advancement of model types. However, DL is still constrained by the need for extensive training datasets, which are costly and time-consuming to produce. One potential solution is adapting training data annotations from different spatial resolutions, though the feasibility of such an application has yet to be tested. In this study, we explore the effects of using forest boundary training data derived from the 3D Elevation Program (3DEP) at 1.5m resolution and the National Land Cover Database (NLCD) at 30m to compare the effects on DL model performance. Our research covers diverse landscapes across 11 counties in Indiana (∼11,636 km²), developing 36 DL models to assess the impact of spatial resolution, model architectures, land cover, and training chip sizes. Our results show that higher-resolution training data yield more accurate models, regardless of imagery resolution, though the performance gap (F1 score) was limited to ∼2.7% even at its most extreme. We also found significant variation in performance based on land cover, with average F1 scores of 0.923 in homogeneous forested areas compared to 0.684 in complex urban settings. Despite similar training times between data sources, chipping 3DEP data took roughly five times longer. We expect that the findings from this study will assist future research in optimizing the development of DL training datasets, selection of source imagery at the proper resolution given training data availability, and application of appropriate model tuning depending on landscape complexity.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100185"},"PeriodicalIF":5.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094976","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":"Salinity Indian Ocean Dipole: Another facet of the Indian Ocean Dipole phenomenon from satellite remote sensing","authors":"Wei Shi ,&nbsp;Menghua Wang","doi":"10.1016/j.srs.2024.100184","DOIUrl":"10.1016/j.srs.2024.100184","url":null,"abstract":"<div><div>Using satellite-measured sea surface salinity (SSS) from the Aquarius and Soil Moisture Active Passive (SMAP) missions since 2011, we show that SSS in the Equatorial Indian Ocean (EIO) experienced dipolar changes in the well-defined east EIO and west EIO regions during the Indian Ocean Dipole (IOD) events. Similar to the concepts of dipole mode Index (DMI) and biological dipole mode index (BDMI), a salinity dipole mode index (SDMI) is proposed using the same definition for the east and west IOD zones. The results show that the salinity IOD in this study is in general co-located and co-incidental with the sea surface temperature (SST) IOD and biological IOD in previous studies. In the positive IOD event in 2019, the SSS anomaly was &gt;1 psu for most of the east IOD zone, while the average SSS in the west IOD zone was ∼0.2–0.3 psu lower than the climatology monthly SSS. The reversed SSS dipolar variability in the EIO was also found during the 2022 negative IOD event. The SSS anomaly difference between the east IOD zone and west IOD zone shows the same variation as the SST-based DMI and chlorophyll-a (Chl-a)-based BDMI. The in situ measurements show that, in the 2019 positive IOD event, the significant IOD-driven salinity change reached water depths at ∼70–80 m and ∼50 m in the east and the west IOD zones, respectively. Results also reveal that the salinity IOD is not only driven by the various ocean processes (e.g., upwelling, downwelling, propagation of the planetary waves, etc.), which are also the main driving forcing for the SST IOD and biological IOD, but also the precipitation and evaporation in the two IOD zones, especially in the west IOD zone. In addition to the traditional SST IOD and recently proposed biological IOD, the salinity IOD indeed features another facet of the entire IOD phenomenon.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100184"},"PeriodicalIF":5.7,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099661","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":"Developing a forest description from remote sensing: Insights from New Zealand","authors":"Grant D. Pearse ,&nbsp;Sadeepa Jayathunga ,&nbsp;Nicolò Camarretta ,&nbsp;Melanie E. Palmer ,&nbsp;Benjamin S.C. Steer ,&nbsp;Michael S. Watt ,&nbsp;Pete Watt ,&nbsp;Andrew Holdaway","doi":"10.1016/j.srs.2024.100183","DOIUrl":"10.1016/j.srs.2024.100183","url":null,"abstract":"<div><div>Remote sensing is increasingly being used to create large-scale forest descriptions. In New Zealand, where radiata pine (<em>Pinus radiata</em>) plantations dominate the forestry sector, the current national forest description lacks spatially explicit information and struggles to capture data on small-scale forests. This is important as these forests are expected to contribute significantly to future wood supply and carbon sequestration. This study demonstrates the development of a spatially explicit, remote sensing-based forest description for the Gisborne region, a major forest growing area. We combined deep learning-based forest mapping using high-resolution aerial imagery with regional airborne laser scanning (ALS) data to map all planted forest and estimate key attributes. The deep learning model accurately delineated planted forests, including large estates, small woodlots, and newly established stands as young as 3-years post planting. It achieved an intersection over union of 0.94, precision of 0.96, and recall of 0.98 on a withheld dataset. ALS-derived models for estimating mean top height, total stem volume, and stand age showed good performance (<em>R</em>² = 0.94, 0.82, and 0.94 respectively). The resulting spatially explicit forest description provides wall-to-wall information on forest extent, age, and volume for all sizes of forest. This enables stratification by key variables for wood supply forecasting, harvest planning, and infrastructure investment decisions. We propose satellite-based harvest detection and digital photogrammetry to continuously update the initial forest description. This methodology enables near real-time monitoring of planted forests at all scales and is adaptable to other regions with similar data availability.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100183"},"PeriodicalIF":5.7,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099663","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":"Nonparametric quantification of uncertainty in multistep upscaling approaches: A case study on estimating forest biomass in the Brazilian Amazon","authors":"Denis Valle ,&nbsp;Leo Haneda ,&nbsp;Rafael Izbicki ,&nbsp;Renan Akio Kamimura ,&nbsp;Bruna Pereira de Azevedo ,&nbsp;Silvio H.M. Gomes ,&nbsp;Arthur Sanchez ,&nbsp;Carlos A. Silva ,&nbsp;Danilo R.A. Almeida","doi":"10.1016/j.srs.2024.100180","DOIUrl":"10.1016/j.srs.2024.100180","url":null,"abstract":"<div><div>The use of multistep upscaling approaches in which field data are combined with data from multiple remote sensors that operate at different spatial scales (e.g., UAV LiDAR, GEDI, and Landsat) is becoming increasingly popular. In these approaches, a series of models are fitted linking the information from these different sensors, often resulting in improved predictions over large areas. Quantifying the uncertainty associated with individual models can be challenging as these models may not generate uncertainty estimates (e.g., machine learning models such as random forest), a problem that is further exacerbated if the results from multiple models are combined within a multistep upscaling methodology. In this article, we describe a nonparametric conformal approach to quantify uncertainty. This approach is straight-forward to apply, is computationally inexpensive (differently from bootstrapping), and generates improved predictive intervals. Importantly, this methodology can be used regardless of the number of models adopted in the upscaling approach and the nature of the intermediate models, as long as the final model can generate predictive intervals. We illustrate the improved empirical coverage of the conformalized predictive intervals using simulated data for a two-step upscaling scenario involving field, UAV LiDAR, and Landsat data. This simulation exercise shows how increasing uncertainty in the first stage model (which relates biomass field data to UAV LiDAR data) leads to an increase in the severity of uncertainty underestimation by naïve predictive intervals. On the other hand, conformalized predictive intervals do not exhibit this shortcoming. Finally, we illustrate uncertainty quantification for a multistep upscaling methodology using data from a large-scale carbon project in the Brazilian Amazon. Our validation exercise using these empirical data confirms the improved performance of the conformalized predictive intervals. We expect that the conformal approach described here will be key for uncertainty quantification as multistep upscaling approaches become increasingly more common.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100180"},"PeriodicalIF":5.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099662","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":"Snow observation from space: An approach to improving snow cover detection using four decades of Landsat and Sentinel-2 imageries across Switzerland","authors":"Charlotte Poussin ,&nbsp;Pascal Peduzzi ,&nbsp;Gregory Giuliani","doi":"10.1016/j.srs.2024.100182","DOIUrl":"10.1016/j.srs.2024.100182","url":null,"abstract":"<div><div>Landsat and Sentinel-2 satellites offer significant advantages for monitoring snow cover over mountainous countries like Switzerland. Starting in the 1970s, Landsat data provides over 50 years of medium resolution imagery. However, the main limitation of optical imagery is cloud cover. Cloud obstruction is particularly challenging for Landsat and Sentinel-2 data, which have limited temporal resolutions. In this study we present the Snow Observation from Space (SOfS) algorithm composed of seven successive temporal and spatial techniques to reduce cloud coverage in the final snow cover products. We used long-term Landsat and Sentinel-2 datasets available from the Swiss Data Cube. The results indicate that the filtering techniques are efficient in reducing cloud cover by half while still leaving an average of less than 30% of cloud cover. The accuracy of the entire algorithm is evaluated over Switzerland, using <em>in-situ</em> measurements of 263 climate stations in the period 1984–2021. The validation results show an agreement between SOfS dataset and ground snow observations with an average accuracy of 93%.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100182"},"PeriodicalIF":5.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099666","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":"Quantifying forest stocking changes in Sundarbans mangrove using remote sensing data","authors":"Yaqub Ali ,&nbsp;M. Mahmudur Rahman","doi":"10.1016/j.srs.2024.100181","DOIUrl":"10.1016/j.srs.2024.100181","url":null,"abstract":"<div><div>The Sundarbans, the world's largest mangrove ecosystem, faces significant challenges from forest stocking changes due to natural and anthropogenic factors. Scientific studies on these changes are not available. This study uses remote sensing techniques to quantify long-term changes in mangrove forest canopy height, aboveground biomass (AGB), and forest carbon stocks. Using Shuttle Radar Topography Mission (SRTM) and Global Ecosystem Dynamics Investigation (GEDI) LiDAR data sets, we assessed canopy height and forest stocking changes, and changes in AGB carbon fluxes over the last two decades in the Sundarbans mangrove. Calibrated SRTM data provided tree canopy height (TCH) estimates for 2000, while calibrated GEDI LiDAR data facilitated assessments of TCH for 2023. The findings show substantial changes in TCH, AGB, and carbon stock distribution in the Sundarbans mangrove between 2000 and 2023. TCH in the 5–10 m class notably increased from 58.3% in 2000 to 70.8% in 2023, while TCH above 15 m decreased, and those under 5 m regrew. Higher AGB carbon classes (&gt;50 tons ha⁻¹) decreased, with only the lowest class (&lt;50 tons ha⁻¹) increased, indicating notable forest carbon stock reduction due to deforestation and forest degradation. Approximately 1571 Kt of AGB carbon were lost over 23 years, which represents around 4% of the total stock. The driving forces of forest stocking changes could be the changes in the dynamic energy balance from the estuarine river system and the tidal waves, relative sea-level change, increases of salinity in various zones of Sundarbans mangrove, other anthropogenic factors, etc. This research provides valuable insights into Sundarbans mangrove dynamics, aiding global forest degradation and forest growth in understanding forest stocking change and their role in terrestrial carbon flux and global climate change. The results will be helpful for the forest manager in identifying the locations where there is forest degradation or enhancement of forest growing stock and planning any silvicultural operations that are needed in the forest. This is also useful for climate change scientists to understand probable man-made or natural driving forces of the changes in forest stocking in the Sundarbans mangrove forests. It underscores the urgency of integrating deforestation and forest degradation into climate strategies for effective carbon management and conservation efforts, that align with carbon sequestration goals, contributing to broader climate change mitigation strategies.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100181"},"PeriodicalIF":5.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099665","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":"Assessing the reliability of woody vegetation structural characterisation from UAV-LS in a tropical savanna","authors":"Rafael Bohn Reckziegel ,&nbsp;Thomas Lowe ,&nbsp;Timothy Devereux ,&nbsp;Stephanie M. Johnson ,&nbsp;Ellen Rochelmeyer ,&nbsp;Lindsay B. Hutley ,&nbsp;Tanya Doody ,&nbsp;Shaun R. Levick","doi":"10.1016/j.srs.2024.100178","DOIUrl":"10.1016/j.srs.2024.100178","url":null,"abstract":"<div><div>Terrestrial laser scanning (TLS) represents the gold standard in remote quantification of woody vegetation structure and volume, but is costly and time consuming to acquire. TLS data is typically collected at spatial scales of 1 ha or smaller, which limits its suitability for representing heterogeneous landscapes, and for training and validating satellite-based models which are needed for larger area monitoring. Advances in unoccupied aerial vehicle laser scanning (UAV-LS) sensors have recently narrowed the gap in quality between what TLS delivers and what can be acquired over larger areas from UAV platforms. We tested how well new nadir-forward–backward (NFB) UAV-LS technology can capture the structure of individual trees in a tropical savanna setting with a diversity of tree sizes and growth forms. UAV-LS data was acquired with a RIEGL VUX-120 LiDAR sensor mounted on a Acecore NOA hexacopter. Reference data was obtained with a RIEGL VZ-2000i TLS scanner using a multi-scan approach. Point clouds were segmented into individual trees and volumetrically reconstructed with RayCloudTools (RCT). We found no statistical difference between UAV-LS and TLS derived estimates of tree height, canopy cover, diameter, and wood volume. Mean tree height and DBH derived from UAV-LS were within 3% of the TLS estimate, and there was less than 1% deviation in stand wood volume. Our findings ease the advancements on the detailed monitoring of open forests, potentially achieving large-scale mapping and multi-temporal investigations. The open structure of savanna systems is well suited to UAV-LS sensing, but more research is needed across diverse ecosystems to understand the generality of these findings in landscapes with greater canopy closure or complex understorey conditions.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100178"},"PeriodicalIF":5.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099660","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":"Quantifying volumes of volcanic deposits using time-averaged ASTER digital elevation models","authors":"Ian T.W. Flynn,&nbsp;Daniel B. Williams,&nbsp;Michael S. Ramsey","doi":"10.1016/j.srs.2024.100179","DOIUrl":"10.1016/j.srs.2024.100179","url":null,"abstract":"<div><div>Quantifying the volume of erupted volcanic material, particularly lava flows and domes, provides critical insights into the dynamics of an eruption. This in turn aids in future hazard modeling, mitigation, and response. However, acquiring the necessary topographic datasets to calculate volumetric change is difficult, especially for active volcanoes in remote regions. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument has acquired global photogrammetric data since 2000, from which individual scene digital elevation models (DEMs) are created. We present a new straight forward method using ASTER DEMs to measure the volume of emplaced lava flows, domes, and tephra cones. We focus on five case studies that represent different eruption styles and products. For each of these we compare the results to those from previous studies that used alternative topographic datasets, such as synthetic aperture radar (SAR), airborne photogrammetry, or Light Detection and Ranging (LiDAR) measurements. These datasets, however, are expensive to acquire or lack the needed temporal resolution. We show that in nearly all cases, our volume results are either within the reported range for the eruption or ≤0.05 km³ of the previously reported value derived from SAR or LiDAR. The simplicity of the ASTER DEM approach combined with the global coverage of the data products enables more timely production of accurate volumetric data during and following an eruption, which can then be used to assess past and future eruption dynamics.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100179"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745839","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}