Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine

Hu, Tongxi; Toman, Elizabeth Myers; Chen, Gang; Shao, Gang; Zhou, Yuyu; Li, Yang; Zhao, Kunmin; Yong, Feng

doi:10.1016/j.isprsjprs.2021.04.008

Cited by 50 publications

(18 citation statements)

References 28 publications

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“… 23 Specifically, the superior model BEAST can manage the uncertainty by selecting the optimal candidate model and simultaneously relating the change, seasonality, and trend. 24 Moreover, the probability that a disturbance occurs at any specific time point can also be estimated to identify the potential changes in the time series. The BEAST modeling of the weekly and monthly HFMD time series was performed using the package “Rbeast” v0.2.2 ( https://CRAN.R-project.org/package=Rbeast ) in R v4.0.4.…”

Section: Methodsmentioning

confidence: 99%

The impact of anti‐COVID‐19 nonpharmaceutical interventions on hand, foot, and mouth disease—A spatiotemporal perspective in Xi'an, northwestern China

Shen

Sun

Song

et al. 2022

Journal of Medical Virology

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Growing evidence has shown that anti‐COVID‐19 nonpharmaceutical interventions (NPIs) can support prevention and control of various infectious diseases, including intestinal diseases. However, most studies focused on the short‐term mitigating impact and neglected the dynamic impact over time. This study is aimed to investigate the dynamic impact of anti‐COVID‐19 NPIs on hand, foot, and mouth disease (HFMD) over time in Xi'an City, northwestern China. Based on the surveillance data of HFMD, meteorological and web search data, Bayesian Structural Time Series model and interrupted time series analysis were performed to quantitatively measure the impact of NPIs in sequent phases with different intensities and to predict the counterfactual number of HFMD cases. From 2013 to 2021, a total number of 172,898 HFMD cases were reported in Xi'an. In 2020, there appeared a significant decrease in HFMD incidence (−94.52%, 95% CI: −97.54% to −81.95%) in the first half of the year and the peak period shifted from June to October by a small margin of 6.74% compared to the previous years of 2013 to 2019. In 2021, the seasonality of HFMD incidence gradually returned to the bimodal temporal variation pattern with a significant average decline of 61.09%. In particular, the impact of NPIs on HFMD was more evident among young children (0–3 years), and the HFMD incidence reported in industrial areas had an unexpected increase of 51.71% in 2020 autumn and winter. Results suggested that both direct and indirect NPIs should be implemented as effective public health measures to reduce infectious disease and improve surveillance strategies, and HFMD incidence in Xi'an experienced a significant rebound to the previous seasonality after a prominent decline influenced by the anti‐COVID‐19 NPIs.

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Section: Methodsmentioning

confidence: 99%

The impact of anti‐COVID‐19 nonpharmaceutical interventions on hand, foot, and mouth disease—A spatiotemporal perspective in Xi'an, northwestern China

Shen

Sun

Song

et al. 2022

Journal of Medical Virology

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“…All processes are realized through the Google Earth Engine (GEE) cloud computing platform (Also, the data is available independently from https://ladsweb.modaps.eosdis.nasa.gov/). GEE has a multi-petabyte database of an extensive and varied number of reanalysis and satellite imagery data collected over several decades, which allows for the quick and effective processing of numerous years of satellite data using an easy-to-use online cloud computing platform (Hu et al, 2021;Gorelick et al, 2017).…”

Section: Modis Mxd08_m3 Datamentioning

confidence: 99%

Comment on essd-2021-426

2022

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“…This algorithm was originally utilized to detect human and natural disturbances based on the time series Landsat imagery . BEAST combines several models to decompose time series data, improving change-point detection accuracy with robust performance …”

Section: Introductionmentioning

confidence: 99%

“…Inspired by deterministic dynamical systems inference − and correspondence between dissipation and structure, − a quantitative inference of self-assembly is desired for predicting its outcome and optimizing the process using external control. The Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) algorithm is a general framework for detecting trends and abrupt changes in any time series data and providing change occurrence probability at any time point . This algorithm was originally utilized to detect human and natural disturbances based on the time series Landsat imagery .…”

Section: Introductionmentioning

confidence: 99%

“…91 BEAST combines several models to decompose time series data, improving change-point detection accuracy with robust performance. 90 Here, we lay the foundation for inference and prediction in nonequilibrium self-assembly processes. Using Monte Carlo (MC) simulations 20,21,68,92 of a toy physical model, 24 we realize a system of distinguishable interacting particles, where self-assembly targets are encoded through specific interactions between the monomers, such that the assembled structures are global energy minima of the simulated system.…”

Section: ■ Introductionmentioning

confidence: 99%

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Nonequilibrium Self-Assembly Time Forecasting by the Stochastic Landscape Method

Faran

Bisker

2023

J. Phys. Chem. B

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Many biological systems rely on the ability to self-assemble target structures from different molecular building blocks using nonequilibrium drives, stemming, for example, from chemical potential gradients. The complex interactions between the different components give rise to a rugged energy landscape with a plethora of local minima on the dynamic pathway to the target assembly. Exploring a toy physical model of multicomponents nonequilibrium self-assembly, we demonstrate that a segmented description of the system dynamics can be used to provide predictions of the first assembly times. We show that for a wide range of values of the nonequilibrium drive, a log-normal distribution emerges for the first assembly time statistics. Based on data segmentation by a Bayesian estimator of abrupt changes (BEAST), we further present a general data-based algorithmic scheme, namely, the stochastic landscape method (SLM), for assembly time predictions. We demonstrate that this scheme can be implemented for the first assembly time forecast during a nonequilibrium self-assembly process, with improved prediction power compared to a naïve guess based on the mean remaining time to the first assembly. Our results can be used to establish a general quantitative framework for nonequilibrium systems and to improve control protocols of nonequilibrium self-assembly processes.

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Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine

Cited by 50 publications

References 28 publications

The impact of anti‐COVID‐19 nonpharmaceutical interventions on hand, foot, and mouth disease—A spatiotemporal perspective in Xi'an, northwestern China

The impact of anti‐COVID‐19 nonpharmaceutical interventions on hand, foot, and mouth disease—A spatiotemporal perspective in Xi'an, northwestern China

Comment on essd-2021-426

Nonequilibrium Self-Assembly Time Forecasting by the Stochastic Landscape Method

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