Next Article in Journal
Allometric Equations for Estimating Compartment Biomass and Stem Volume in Mature Hybrid Poplars: General or Site-Specific?
Next Article in Special Issue
Prediction of Forest Canopy and Surface Fuels from Lidar and Satellite Time Series Data in a Bark Beetle-Affected Forest
Previous Article in Journal
Development of Sessile Oak and European Hornbeam Sprouts after Thinning
Previous Article in Special Issue
Assessing and Monitoring Forest Degradation in a Deciduous Tropical Forest in Mexico via Remote Sensing Indicators
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessFeature PaperArticle
Forests 2017, 8(9), 304; doi:10.3390/f8090304

Edyn: Dynamic Signaling of Changes to Forests Using Exponentially Weighted Moving Average Charts

Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, 310 West Campus Drive, Blacksburg, VA 24061, USA
Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, USA
Author to whom correspondence should be addressed.
Received: 21 July 2017 / Revised: 15 August 2017 / Accepted: 18 August 2017 / Published: 24 August 2017
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
View Full-Text   |   Download PDF [8576 KB, uploaded 24 August 2017]   |  


Remote detection of forest disturbance remains a key area of interest for scientists and land managers. Subtle disturbances such as drought, disease, insect activity, and thinning harvests have a significant impact on carbon budgeting and forest productivity, but current change detection algorithms struggle to accurately identify them, especially over decadal timeframes. We introduce an algorithm called Edyn, which inputs a time series of residuals from harmonic regression into a control chart to signal low-magnitude, consistent deviations from the curve as disturbances. After signaling, Edyn retrains a new baseline curve. We compared Edyn with its parent algorithm (EWMACD—Exponentially Weighted Moving Average Change Detection) on over 3500 visually interpreted Landsat pixels from across the contiguous USA, with reference data for timing and type of disturbance. For disturbed forested pixels, Edyn had a mean per-pixel commission error of 31.1% and omission error of 70.0%, while commission and omission errors for EWMACD were 39.9% and 65.2%, respectively. Edyn had significantly less overall error than EWMACD (F1 = 0.19 versus F1 = 0.13). These patterns generally held for all of the reference data, including a direct comparison to other contemporary change detection algorithms, wherein Edyn and EWMACD were found to have lower omission error rates for a category of subtle changes over long periods. View Full-Text
Keywords: change detection; EWMACD; forest disturbance; Fourier; Landsat; quality control; remote sensing change detection; EWMACD; forest disturbance; Fourier; Landsat; quality control; remote sensing

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Brooks, E.B.; Yang, Z.; Thomas, V.A.; Wynne, R.H. Edyn: Dynamic Signaling of Changes to Forests Using Exponentially Weighted Moving Average Charts. Forests 2017, 8, 304.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top