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Unsupervised land cover change estimation using region covariance estimates

journal contribution
posted on 2023-05-19, 22:15 authored by Olding, WC, Jan OlivierJan Olivier, Brian SalmonBrian Salmon, Kleynhans, W
In this letter, we demonstrate the utility of estimating a probabilistic model of the underlying seasonal and interannual variations experienced by land cover time series in a given geographical region. Time series that deviate from these trajectories due to the human-induced change appear as outliers and can be detected using their Mahalanobis distance from the mean under the joint distribution of time samples. We apply this model to a collection of pixel time series acquired by the Moderate Resolution Imaging Spectroradiometer platform over Limpopo province, South Africa, for the task of identifying human settlement expansion. For estimation of the time of change, we present a hypothesis testing approach that tests for a decrease in correlation between samples before and after the change. This was found to be highly effective, yielding a mean absolute error of 52 days.

History

Publication title

IEEE Geoscience and Remote Sensing Letters

Volume

16

Pagination

347-351

ISSN

1545-598X

Department/School

School of Engineering

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

United States

Rights statement

Copyright 2018 IEEE

Repository Status

  • Restricted

Socio-economic Objectives

Environmental policy, legislation and standards not elsewhere classified

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