Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series

Pickens, Amy; Hansen, Matthew C.; Hancher, M.; Stehman, Stephen V.; Tyukavina, Alexandra; Potapov, Peter; Marroquin, Byron; Sherani, Zainab

doi:10.1016/j.rse.2020.111792

Cited by 282 publications

(201 citation statements)

References 40 publications

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“…GEE has been used for the mapping, and derived change detection, of surface waters from the Landsat data archive using multispectral indices. This approach includes regional‐ to planetary‐scale analyses of the changes in water and land occurrence (Donchyts et al, 2016) and the spatiotemporal variability of surface water dynamics over months, years, and decades (Pekel et al, 2016; Pickens et al, 2020; Zou et al, 2018). At the catchment scale, analyses have determined annual maximal and minimal surface water spatial extents (Wang, Jia, Chen, & Wang, 2018) and defined permanent and seasonal water bodies (Deng, Jiang, Tang, Ling, & Wu, 2019).…”

Section: Looking Beyond the Watermentioning

confidence: 99%

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

et al. 2020

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Cloud‐based computing, access to big geospatial data, and virtualization, whereby users are freed from computational hardware and data management logistics, could revolutionize remote sensing applications in fluvial geomorphology. Analysis of multitemporal, multispectral satellite imagery has provided fundamental geomorphic insight into the planimetric form and dynamics of large river systems, but information derived from these applications has largely been used to test existing concepts in fluvial geomorphology, rather than for generating new concepts or theories. Traditional approaches (i.e., desktop computing) have restricted the spatial scales and temporal resolutions of planimetric river channel change analyses. Google Earth Engine (GEE), a cloud‐based computing platform for planetary‐scale geospatial analyses, offers the opportunity to relieve these spatiotemporal restrictions. We summarize the big geospatial data flows available to fluvial geomorphologists within the GEE data catalog, focus on approaches to look beyond mapping wet channel extents and instead map the wider riverscape (i.e., water, sediment, vegetation) and its dynamics, and explore the unprecedented spatiotemporal scales over which GEE analyses can be applied. We share a demonstration workflow to extract active river channel masks from a section of the Cagayan River (Luzon, Philippines) then quantify centerline migration rates from multitemporal data. By enabling fluvial geomorphologists to take their algorithms to petabytes worth of data, GEE is transformative in enabling deterministic science at scales defined by the user and determined by the phenomena of interest. Equally as important, GEE offers a mechanism for promoting a cultural shift toward open science, through the democratization of access and sharing of reproducible code. This article is categorized under: Science of Water

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Section: Looking Beyond the Watermentioning

confidence: 99%

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

et al. 2020

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“…To assess the accuracy of annual disturbance detection and use in mixed-effect models, the sample grid reference data was collected using 6 × 6 km grids at a 10 m spatial resolution. The accuracy assessment using the sample data in grids and blocks was widely implemented in previous studies (e.g., [66][67][68]). In this study, the study area was first divided into 6 × 6 km grids, resulting in 1408 grids that overlapped with the land area.…”

Section: Sample Grid Reference Data For 2013-2017 At a 10 M Spatial Rmentioning

confidence: 99%

Accuracy Assessments of Local and Global Forest Change Data to Estimate Annual Disturbances in Temperate Forests

2020

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Forest disturbances are generally estimated using globally available forest change maps or locally calibrated disturbance maps. The choice of disturbance map depends on the trade-offs among the detection accuracy, processing time, and expert knowledge. However, the accuracy differences between global and local maps have still not been fully investigated; therefore, their optimal use for estimating forest disturbances has not been clarified. This study assesses the annual forest disturbance detection of an available Global Forest Change map and a local disturbance map based on a Landsat temporal segmentation algorithm in areas dominated by harvest disturbances. We assess the forest disturbance detection accuracies based on two reference datasets in each year. We also use a polygon-based assessment to investigate the thematic accuracy based on each disturbance patch. As a result, we found that the producer’s and user’s accuracies of disturbances in the Global Forest Change map were 30.1–76.8% and 50.5–90.2%, respectively, for 2001–2017, which corresponded to 78.3–92.5% and 88.8–97.1%, respectively in the local disturbance map. These values indicate that the local disturbance map achieved more stable and higher accuracies. The polygon-based assessment showed that larger disturbances were likely to be accurately detected in both maps; however, more small-scale disturbances were at least partially detected by the Global Forest Change map with a higher commission error. Overall, the local disturbance map had higher forest disturbance detection accuracies. However, for forest disturbances larger than 3 ha, the Global Forest Change map achieved comparable accuracies. In conclusion, the Global Forest Change map can be used to detect larger forest disturbances, but it should be used cautiously because of the substantial commission error for small-scale disturbances and yearly variations in estimated areas and accuracies.

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“…Tao et al, 2020). Owing to remote sensing techniques, numerous studies focus on largescale research, which substantially contributes to revealing the characteristics of lake changes in different global regions (Pekel et al, 2016;Pickens et al, 2020;J. Wang et al, 2018;J.…”

Section: Introductionmentioning

confidence: 99%

“…Lake plays a crucial role in the global water cycle and ecological balance (Verpoorter et al., 2014). Under ongoing climate change and intensified human activities in the recent several decades, the area, water level, and storage of numerous lakes have experienced significant changes (Pickens et al., 2020; C. Song et al., 2014, 2020; S. Tao et al., 2020). Owing to remote sensing techniques, numerous studies focus on large‐scale research, which substantially contributes to revealing the characteristics of lake changes in different global regions (Pekel et al., 2016; Pickens et al., 2020; J. Wang et al., 2018; J. Zhu et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Century‐Scale Reconstruction of Water Storage Changes of the Largest Lake in the Inner Mongolia Plateau Using a Machine Learning Approach

Fan

Song

et al. 2021

Water Resources Research

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Lake Hulun is the fifth‐largest lake in China, playing a substantial role in maintaining the balance of the grassland ecosystem of the Mongolia Plateau, which is a crucial ecological barrier in North China. To better understand the changing characteristics of Lake Hulun and the driving mechanisms, it is necessary to investigate the water storage changes of Lake Hulun on extended timescales. The main objective of this study is to reconstruct the water storage time series of Lake Hulun over the past century. We employed a machine learning approach termed the extreme gradient boosting tree (XGBoost) to reconstruct the water storage changes over a one‐century timescale based on the generated bathymetry and satellite altimetry data and investigated the relationships with hydrological and climatic variables in long term. Results show that the water storage changes from 1961 to 2019 were featured by four fluctuation phases, with the highest water storage observed in 1991 (14.02 Gt) and the lowest point in 2012 (5.18 Gt). The century‐scale reconstruction result reveals that the water storage of Lake Hulun reached the highest point in the 1960s within the period of 1910–2019. The lowest stage occurred in the sub‐period of the 1930s–1940s, which was even lower than the alerted shrinkage stage in 2012. The predictive model results indicate the effective performance of the XGBoost model in reconstructing century‐scale water storage variations, with the mean absolute error of 0.68, normalized root mean square error of 0.11, Nash–Sutcliffe efficiency of 0.97, and correlation coefficient of 0.94. The annual fluctuations of water storage were mostly affected by precipitation, followed by vapor pressure, temperature, potential evapotranspiration, and wet day frequency. The dominating characteristics of different variables vary evidently with different sub‐periods. The atmospheric circulations of the Arctic Oscillation, El Nino Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation have tight associations with the water storage variations of Lake Hulun, which change with different study periods.

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Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series

Cited by 282 publications

References 40 publications

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

Accuracy Assessments of Local and Global Forest Change Data to Estimate Annual Disturbances in Temperate Forests

Century‐Scale Reconstruction of Water Storage Changes of the Largest Lake in the Inner Mongolia Plateau Using a Machine Learning Approach

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