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ORIGINAL RESEARCH
Adaptive Detection of Diverse Forest Disturbances
Using Sparse Landsat Time Series
1
The Second Surveying and Mapping Institute of Hunan Province, Changsha, 410004, China
2
School of Information Engineering, China University of Geosciences, Beijing, 100083, China
Submission date: 2024-12-18
Final revision date: 2025-02-14
Acceptance date: 2025-03-04
Online publication date: 2025-04-16
Publication date: 2026-04-21
Corresponding author
Pol. J. Environ. Stud. 2026;35(2):2563-2581
KEYWORDS
TOPICS
ABSTRACT
Most change detection algorithms are designed to detect specific forest disturbances, which may
not effectively capture diverse events. These algorithms typically model seasonal changes using dense
observations to reduce phenological noise, making them unsuitable for regions with frequent cloud
cover. In this study, we propose a change detection algorithm for diverse forest disturbances of varying
magnitudes using sparse Landsat time series. The Normalized Difference Moisture Index (NDMI) was
spatially normalized (SNDMI) to remove forest seasonality, reducing the need for dense observations.
Residuals obtained from SNDMI fitting using the spatial error model were input into the Exponentially
Weighted Moving Average t (EWMA-t) chart, designed for sparse data and sensitive to low-magnitude
disturbances. An adaptive strategy to the EWMA-t chart (AEWMA-t) that adjusts the weights of
historical chart values and current residual statistics is introduced. Low-magnitude disturbances exceed
control limits with small values, while high-magnitude disturbances prioritize current residuals for
rapid detection. Disturbances are identified when chart values consecutively exceed control limits.
Applied to a cloudy subtropical forest with diverse disturbances, the proposed algorithm achieved
84.6% and 89.3% accuracy in spatial and temporal domains, offering a reliable approach for detecting
diverse disturbances in low-data regions.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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