Fifty years of Landsat science and impacts

Wulder, Michael A.; Roy, David P.; Radeloff, Volker C.; Loveland, Thomas R.; Anderson, Martha C.; Johnson, David M.; Healey, Sean P.; Zhu, Zhe; Scambos, T. A.; Pahlevan, Nima; Hansen, Matthew C.; Gorelick, Noel; Crawford, Christopher J.; Masek, Jeffrey G.; Hermosilla, Txomin; White, Joanne C.; Belward, Alan; Schaaf, Crystal B.; Woodcock, Curtis E.; Huntington, Justin L.; Lymburner, Leo; Hostert, Patrick; Gao, Feng; Lyapustin, Alexei; Pekel, Jean-François; Strobl, Peter; Cook, Bruce D.

doi:10.1016/j.rse.2022.113195

Cited by 307 publications

(118 citation statements)

References 227 publications

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“…As a consequence of coarse spatial resolution and data scarcity, images acquired by Landsat-1, -2, and -3 with resolution of approximate 60 m would be extremely challenging for feature tracking. Therefore, a total of 373 radiometrically and geometrically corrected images from Landsat 5, 7 and 8 imageries were downloaded from USGS Earth Resources Observation and Science Center (EROS) (https://glovis.usgs.gov/) (Wulder et al, 2022) to investigate glacial lake area changes. Additionally, feature tracking was applied to all acquired images and the results from 60 pairs with good correlation performance were finally selected to present annual surface displacements from 1989 to 2021 as well as monthly displacements during surge events.…”

Section: Landsat Series Imagerymentioning

confidence: 99%

Characterizing the surge behaviour and associated ice-dammed lake evolution of the Kyagar Glacier in the Karakoram

Quincey

et al. 2023

Preprint

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Abstract. Glacier surges are prevalent in the Karakoram and occasionally threaten local residents by inundating land and initiating mass movement events. The Kyagar Glacier is well-known for its surge history, and in particular its frequent-blocking of the downstream valley, leading to a series of high-magnitude glacial lake outburst floods (GLOFs). Although the surge dynamics of the Kyagar Glacier have been broadly described in the literature, there remains an extensive archive of remote sensing observations that have great potential for revealing specific surge characteristics and their relationship with historic lake outburst floods. In this study, we propose a new perspective on quantifying the surging process using successive Digital Elevation Models (DEMs), which could be applied to other sites where glacier surges are known to occur. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs, High Mountain Asia 8-meter (HMA) DEMs and the Shuttle Radar Topography Mission (SRTM) DEM were used to characterize surface elevation changes throughout the period 2000 to 2021. We also used Landsat time-series imagery to quantify glacier surface velocities and associated lake changes over the course of two surge events between 1989 and 2021. Using these data, we reconstruct the surging process of Kyagar Glacier in unprecedented detail and find a clear signal of surface uplift over the lower glacier tongue, along with uniformly increasing velocities, associated with the period of surge initiation. Seasonal variations in flow are still evident throughout the surge phase indicating the presence of water at the glacier bed. Surge activity is strongly related to the development and drainage of the terminal ice-dammed lake, which itself is controlled by the drainage system beneath the glacier terminus.

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Section: Landsat Series Imagerymentioning

confidence: 99%

Characterizing the surge behaviour and associated ice-dammed lake evolution of the Kyagar Glacier in the Karakoram

Quincey

et al. 2023

Preprint

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“…As one of the most eminent satellite series, Landsat has been observing the Earth for 50 years since 1972, which provides mediumresolution images for scientific research (Wulder et al, 2022). The Landsat eight is one of the newest Landsat satellites, and its data has been widely used for surface water detection (Donchyts et al, 2016a;Pekel et al, 2016;Albarqouni et al, 2022).…”

Section: Lake Inventory Datasetmentioning

confidence: 99%

Spatiotemporal variations and overflow risk analysis of the Salt Lake in the Hoh Xil Region using machine learning methods

Wang

Guo²,

Chen³

et al. 2023

Front. Earth Sci.

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Global warming is inducing dramatic changes in fluvial geomorphology and reshaping the hydrological connections between rivers and lakes. The water level and area of the Salt Lake have increased rapidly since the outburst of the Zonag Lake in the Hoh Xil region of the Qinghai–Tibet Plateau in 2011, threatening the downstream infrastructure. However, fewer studies have focused on its spatiotemporal variation and overflow risk over long time series. Here, we used three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) to extract the area of the Salt Lake for a long time series, analyzed its spatiotemporal variation from 1973 to 2021, and finally assessed the overflow risk. The Kappa coefficient (KAPPA) and the overall accuracy (OA) were used to evaluate the performance of the models. The results showed that Random Forest performs superior in lake extraction (KAPPA = 0.98, overall accuracy = 0.99), followed by Classification and Regression Trees and Support Vector Machine. normalized difference water index is the relatively important feature variable in both RF and CART. Before the outburst event, the area change of the Salt Lake was consistent with the variation in precipitation; after that, it showed a remarkable area increase (circa 350%) in all orientations, and the main direction was the southeast. Without the construction of the emergency drainage channel, the simulation result indicated that the earliest and latest times of the Salt Lake overflow event are predicted to occur in 2020 and 2031, respectively. The results of this paper not only demonstrate that RF is more suitable for water extraction and help understand the water system reorganization event.

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“…Providing an uninterrupted supply of satellite imagery since 1972 makes it the only option for a assessing long-term dynamics. The entire archive was made available to the public in 2008, leading to widespread use (Wulder et al, 2022), including refinement into datasets closer to analysis-ready status. For this work, we used the Landsat ARD product developed by the UMD's GLAD lab, a globally consistent analysis-ready dataset for multi-decadal LULC monitoring (Potapov et al, 2020).…”

Section: Landsatmentioning

confidence: 99%

Ecodatacube.eu: Analysis-ready open environmental data cube for Europe

Witjes¹,

Parente²,

Krizan

et al. 2022

Preprint

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The paper describes production steps and accuracy assessment of an analysis-ready (complete, consistent, correct and current) open environmental data cube (2000–2021+) for continental Europe; at working resolutions from 10 m to 30 m and with quarterly to annual estimates. The data cube is based on processing and harmonizing earth observation (EO) images: Landsat GLAD ARD (2000–2020+), Sentinel-2 images (2017–2021+) and Digital Elevation data. These datasets were created with accessibility, user-friendliness, interoperability and synthesis in mind. This has required systematic spatiotemporal harmonization, efficient compression, and imputation of missing values. To ensure a missing value percentage below 1%, the EO data was first aggregated into four quarterly periods approximating the four seasons common in Europe (winter, spring, summer and autumn), and then split into three percentiles (25th, 50th and 75th). Remaining missing data in the Landsat time-series was imputed with a temporal moving window median (TMWM) approach. The accuracy assessment shows TMWM gap-filling achieves higher performance in Southern Europe, and lower performance in mountainous regions such as the Scandinavian Mountains, the Alps, and the Pyrenees. The intended uses of the EcoDataCube platform include vegetation, soil, land cover and land use mapping projects, environmental monitoring and automated generation of data for statistical offices including Eurostat. Results further show that combining all four datasets produced in this work (DTM, Landsat 30 m, Sentinel-2 30 m and Sentinel-2 10 m) yields the highest land cover classification accuracy, with different datasets improving the results for different land cover classes. The Environmental data cube for Europe is available under CC-BY license as Cloud-Optimized GeoTIFFs (ca. 12 TB in size) through STAC and the EcoDataCube data portal.

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Fifty years of Landsat science and impacts

Cited by 307 publications

References 227 publications

Characterizing the surge behaviour and associated ice-dammed lake evolution of the Kyagar Glacier in the Karakoram

Characterizing the surge behaviour and associated ice-dammed lake evolution of the Kyagar Glacier in the Karakoram

Spatiotemporal variations and overflow risk analysis of the Salt Lake in the Hoh Xil Region using machine learning methods

Ecodatacube.eu: Analysis-ready open environmental data cube for Europe

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