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Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects

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  • Jesper Muren
  • Vilhelm Niklasson
  • Dmitry Otryakhin
  • Maxim Romashin

Abstract

This article is devoted to the problem of detection of forest and nonforest areas on Earth images. We propose two statistical methods to tackle this problem: one based on multiple hypothesis testing with parametric distribution families, another one—on nonparametric tests. The parametric approach is novel in the literature and relevant to a larger class of problems—detection of natural objects, as well as anomaly detection. We develop mathematical background for each of the two methods, build self‐sufficient detection algorithms using them and discuss practical aspects of their implementation. We also compare our algorithms with each other and with those from standard machine learning using satellite data.

Suggested Citation

  • Jesper Muren & Vilhelm Niklasson & Dmitry Otryakhin & Maxim Romashin, 2024. "Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects," Environmetrics, John Wiley & Sons, Ltd., vol. 35(5), August.
    • Handle: RePEc:wly:envmet:v:35:y:2024:i:5:n:e2848
    DOI: 10.1002/env.2848
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    References listed on IDEAS

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    1. R. Shooter & E. Ross & A. Ribal & I. R. Young & P. Jonathan, 2021. "Spatial dependence of extreme seas in the North East Atlantic from satellite altimeter measurements," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.
    2. Behnaz Pirzamanbein & Johan Lindström, 2022. "Reconstruction of past human land use from pollen data and anthropogenic land cover changes," Environmetrics, John Wiley & Sons, Ltd., vol. 33(6), September.
    3. Paulo Canas Rodrigues & Elisabetta Carfagna, 2023. "Data science applied to environmental sciences," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    4. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
    5. Victor Muthama Musau & Carlo Gaetan & Paolo Girardi, 2022. "Clustering of bivariate satellite time series: A quantile approach," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.
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