Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine

Mao, Yongjing; Harris, David; Xie, Zunyi; Phinn, Stuart

doi:10.1016/j.isprsjprs.2021.09.021

Cited by 34 publications

(23 citation statements)

References 47 publications

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“…Several studies have used composite imagery (Luijendijk et al, 2018;Bishop-Taylor et al, 2021;Mao et al, 2021), which average spatially overlapping images within a time window (e.g., yearly) into a single image. Composite images simplify shoreline mapping by smoothing over dynamic atmospheric (e.g., clouds) and hydrodynamic processes (e.g., tide, wave runup), which obscure and oscillate important features like the water line, respectively.…”

Section: Optical Satellitesmentioning

confidence: 99%

The future of coastal monitoring through satellite remote sensing

Vitousek

Buscombe

Vos

et al. 2022

Camb. prisms Coast. futures

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Satellite remote sensing is transforming coastal science from a 'data-poor' field into a 'data-rich' field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change has outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in-situ surveys. The longterm coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the best-monitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earth-observing satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and ever-expanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, i.e., (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer-vision to fully leverage this massive treasure-trove of data. Extensive

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Section: Optical Satellitesmentioning

confidence: 99%

The future of coastal monitoring through satellite remote sensing

Vitousek

Buscombe

Vos

et al. 2022

Camb. prisms Coast. futures

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“…Approaches include using ML as an alternative to traditional inverse modeling , for surrogate modeling in support of uncertainty quantification (see Surrogate Models and Emulators, chapter 9), or to construct data-driven representations of component subsystems. While multifidelity models (e.g., Meng and Karniadakis 2020;) that use ML to relate results from fast-running but lower-fidelity models with more expensive, higher-fidelity models could be promising to accelerate watershed modeling (e.g., Fu et al 2020), but only initial steps have been taken in that direction in the watershed modeling context (e.g., Mao et al 2021).…”

Section: Strategies Of Scaling (Multifidelity ML Surrogate Models)mentioning

confidence: 99%

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

Hickmon¹,

Varadharajan²,

Hoffman³

et al. 2022

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“…The Google Earth Engine (GEE) is a cloud geospatial computing platform that supports freely available petabyte remote sensing data, multiple machine learning algorithms, and shared computing resources (Gorelick et al, 2017). With GEE's support, researchers in the remote sensing community have completed numerous classification works on a planetary scale (Deines et al, 2019;Li et al, 2019;Gong et al, 2019Gong et al, , 2020Xie et al, 2019;Mao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine

Zhang

Wang

et al. 2022

Earth Syst. Sci. Data

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Abstract. Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established PV power plants. However, a comprehensive map regarding the PV power plants' locations and extent remains scarce on the country scale. This study developed a workflow, combining machine learning and visual interpretation methods with big satellite data, to map PV power plants across China. We applied a pixel-based random forest (RF) model to classify the PV power plants from composite images in 2020 with a 30 m spatial resolution on the Google Earth Engine (GEE). The resulting classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km2. We found that most PV power plants were situated on cropland, followed by barren land and grassland, based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants. The dataset of photovoltaic power plant distribution in China by 2020 is available to the public at https://doi.org/10.5281/zenodo.6849477 (Zhang et al., 2022).

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Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine

Cited by 34 publications

References 47 publications

The future of coastal monitoring through satellite remote sensing

The future of coastal monitoring through satellite remote sensing

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine

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