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How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms?
Open AccessArticle

Patch-Based Forest Change Detection from Landsat Time Series

College of Earth Ocean & Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
College of Forestry, Wildlife and Fisheries, University of Tennessee, Knoxville, TN 37996, USA
School of Forest Resources, University of Maine, Orono, ME 04469, USA
Author to whom correspondence should be addressed.
Academic Editors: Sean P. Healey and Warren B. Cohen
Forests 2017, 8(5), 166;
Received: 20 February 2017 / Revised: 28 April 2017 / Accepted: 5 May 2017 / Published: 11 May 2017
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
In the species-rich and structurally complex forests of the Eastern United States, disturbance events are often partial and therefore difficult to detect using remote sensing methods. Here we present a set of new algorithms, collectively called Vegetation Regeneration and Disturbance Estimates through Time (VeRDET), which employ a novel patch-based approach to detect periods of vegetation disturbance, stability, and growth from the historical Landsat image records. VeRDET generates a yearly clear-sky composite from satellite imagery, calculates a spectral vegetation index for each pixel in that composite, spatially segments the vegetation index image into patches, temporally divides the time series into differently sloped segments, and then labels those segments as disturbed, stable, or regenerating. Segmentation at both the spatial and temporal steps are performed using total variation regularization, an algorithm originally designed for signal denoising. This study explores VeRDET’s effectiveness in detecting forest change using four vegetation indices and two parameters controlling the spatial and temporal scales of segmentation within a calibration region. We then evaluate algorithm effectiveness within a 386,000 km2 area in the Eastern United States where VeRDET has overall error of 23% and omission error across disturbances ranging from 22% to 78% depending on agent. View Full-Text
Keywords: forest disturbance; remote sensing; Landsat; total variation regularization; change detection; segmentation forest disturbance; remote sensing; Landsat; total variation regularization; change detection; segmentation
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Hughes, M.J.; Kaylor, S.D.; Hayes, D.J. Patch-Based Forest Change Detection from Landsat Time Series. Forests 2017, 8, 166.

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