Science of Remote Sensing最新文献

{"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}

{"title":"Global soil moisture mapping at 5 km by combining GNSS reflectometry and machine learning in view of HydroGNSS","authors":"Emanuele Santi ,&nbsp;Davide Comite ,&nbsp;Laura Dente ,&nbsp;Leila Guerriero ,&nbsp;Nazzareno Pierdicca ,&nbsp;Maria Paola Clarizia ,&nbsp;Nicolas Floury","doi":"10.1016/j.srs.2024.100177","DOIUrl":"10.1016/j.srs.2024.100177","url":null,"abstract":"<div><div>The potential of GNSS reflectometry (GNSS-R) for the monitoring of soil and vegetation parameters as soil moisture (SM) and forest aboveground biomass (AGB) has been largely investigated in recent years.</div><div>In view of the ESA's HydroGNSS mission, planned to be launched in 2024, this study has explored the possibility to map SM at global scale and relatively high resolution of about 0.05° (corresponding approximately to 5 Km) using GNSS-R observations, by implementing and comparing two retrieval algorithms based on machine learning techniques, namely Artificial Neural Networks (ANN) and Random Forest Regressors (RF). Waiting for HydroGNSS commissioning and operation, the NASA's Cyclone GNSS (CyGNSS) land observations have been considered for this scope. Taking advantage of the versatility of both machine learning techniques, several combinations of input data, including CyGNSS observables and auxiliary information, have been exploited and the role of GNSS-R and auxiliary data has been assessed. Given the lack of global SM data at 0.05° resolution, the following novel strategy has been implemented to establish the training set: as first, training has been carried out at lower resolution by considering as target the SMAP SM on EASE-Grid 36 km. Then the trained algorithms have been applied to CyGNSS data at 0.05° to obtain global SM maps at this resolution. Finally, the SM at 0.05° has been validated against ISMN, to keep training and validation as much independent as possible. The two retrieval techniques exhibited similar accuracies and computational cost, with correlation coefficient R ≃ 0.9 between estimated and target SM computed globally, and RMSE ≃ 0.05 (m³/m³). Moreover, the SM maps at 0.05° revealed some finer details and small-scale patterns that are not shown by the original SMAP SM data at 36 km. Regardless of the ML technique applied, this study confirmed the promising potential of GNSS-R for the global monitoring of SM at improved resolution with respect to SM products available from microwave satellite radiometers.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100177"},"PeriodicalIF":5.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706863","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":"Coastal vertical land motion across Southeast Asia derived from combining tide gauge and satellite altimetry observations","authors":"Dongju Peng ,&nbsp;Grace Ng ,&nbsp;Lujia Feng ,&nbsp;Anny Cazenave ,&nbsp;Emma M. Hill","doi":"10.1016/j.srs.2024.100176","DOIUrl":"10.1016/j.srs.2024.100176","url":null,"abstract":"<div><div>Vertical land motion (VLM) is complex in Southeast Asia because this region is subject to a range of natural processes (e.g., earthquakes) and anthropogenic activities (e.g., groundwater withdrawal) that can change land heights. To aid in coastal management, long-term observations of VLM are as crucial as observations for climate-induced sea surface height changes; however, such long-term observations are sparse for Southeast Asian coasts. To fill this observational gap, here we derive monthly VLM time series from 1993 to 2020 at 50 coastal sites across Southeast Asia by combining tide-gauge records and newly generated satellite altimetry observations. These altimetry observations are reproduced sea-level products using new altimetry standards and more accurate geophysical corrections. Our 27-year-long VLM dataset shows high spatial variability and non-linear temporal changes in VLM across Southeast Asia. We identify several major sources that dominate the regional land-height changes, which include large subsidence due to groundwater extraction in Manila and Bangkok, land uplift in Indonesia and subsidence in Thailand from postseismic deformation resulting from the sequence of large Sumatran earthquakes since 2004, and land subsidence as a result of sediment compaction in Malaysia. Those signals are quantitatively or qualitatively consistent with observations from other sources. This VLM dataset can be used to advance our understanding of the physical mechanisms behind land-height changes and to improve sea level projections in the region.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100176"},"PeriodicalIF":5.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658607","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":"Identifying thermokarst lakes using deep learning and high-resolution satellite images","authors":"Kuo Zhang ,&nbsp;Min Feng ,&nbsp;Yijie Sui ,&nbsp;Jinhao Xu ,&nbsp;Dezhao Yan ,&nbsp;Zhimin Hu ,&nbsp;Fei Han ,&nbsp;Earina Sthapit","doi":"10.1016/j.srs.2024.100175","DOIUrl":"10.1016/j.srs.2024.100175","url":null,"abstract":"<div><div>Thermokarst lakes play a critical role in hydrologic connectivity, permafrost stability, and carbon exchange from local to regional scales. Due to the typically small sizes and highly dynamic nature of thermokarst lakes, their identification in large regions remains challenging. This study presented a deep-learning model and applied it to high-resolution (1.2 m) satellite imagery to automatically delineate and inventory thermokarst lakes. The method was applied in the Yellow River source region in eastern Tibetan Plateau and identified 52,486 thermokarst lakes, with the majority (90.9%) smaller than 0.01 km². It's the most comprehensive survey of thermokarst lakes within the region and more than 45% of these lakes were not covered by any existing lake datasets, thereby leading to a possible underestimation of the amount and effects of thermokarst lakes. Validation with visually interpreted data reported MIoU of 0.97, F1 score of 0.96, and PA of 0.97, confirming that thermokarst lakes we detected were matched very well with the reference. The experiment demonstrated great potential for investigating the distribution and impacts of thermokarst lakes in borad regions, such as the entire Tibetan Plateau or even the globe, to provide critical knowledge for their response to climate change and effects from their dynamics.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100175"},"PeriodicalIF":5.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587189","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}