Next Article in Journal
Armillaria Pathogenesis under Climate Changes
Next Article in Special Issue
Patch-Based Forest Change Detection from Landsat Time Series
Previous Article in Journal
Estimating Large Area Forest Carbon Stocks—A Pragmatic Design Based Strategy
Previous Article in Special Issue
Effects of Burn Severity and Environmental Conditions on Post-Fire Regeneration in Siberian Larch Forest
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Forests 2017, 8(4), 98; doi:10.3390/f8040098

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

1
USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331, USA
2
USDA Forest Service, Rocky Mountain Research Station, 507 25th Street, Ogden, UT 84401, USA
3
Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, USA
4
College of Environmental Science & Forestry, State University of New York, 1 Forestry Drive, Syracuse, NY 13210, USA
5
USDA Forest Service, Rocky Mountain Research Station, P.O. Box 279, Florence, MT 59833, USA
6
Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, 310 West Campus Drive, Blacksburg, VA 24061, USA
7
Google Switzerland GmbH, Zurich 8002, Switzerland
8
College Park, University of Maryland, MD 20742, USA
9
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
10
U.S. Geological Survey, Earth Resources Observation and Science Center, 47914 252nd Street, Sioux Falls, SD 57198, USA
11
ASRC Federal InuTeq, U.S. Geological Survey, Earth Resources Observation and Science Center, 47914 252nd Street, Sioux Falls, SD 57198, USA
12
Department of Earth and Environment, Boston University, 675 Commonwealth Ave., Boston, MA 02215, USA
13
Stinger Ghaffarian Technologies, U.S. Geological Survey, Earth Resources Observation and Science Center, 47914 252nd Street, Sioux Falls, SD 57198, USA
Current affiliation: USDA Forest Service, Southern Research Station, 4700 Old Kingston Pike, Knoxville, TN 37919, USA.
Current affiliation: Texas Tech University, Department of Geosciences, 217 Holden Hall, Lubbock, TX 79409, USA.
*
Author to whom correspondence should be addressed.
Academic Editor: Timothy A. Martin
Received: 5 February 2017 / Revised: 16 March 2017 / Accepted: 23 March 2017 / Published: 26 March 2017
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
View Full-Text   |   Download PDF [6383 KB, uploaded 26 March 2017]   |  

Abstract

Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal data volume to mine subtle signals in Landsat time series, but as those signals become subtler, they are more likely to be mixed with noise in Landsat data. This study examines the similarity among seven different algorithms in their ability to map the full range of magnitudes of forest disturbance over six different Landsat scenes distributed across the conterminous US. The maps agreed very well in terms of the amount of undisturbed forest over time; however, for the ~30% of forest mapped as disturbed in a given year by at least one algorithm, there was little agreement about which pixels were affected. Algorithms that targeted higher-magnitude disturbances exhibited higher omission errors but lower commission errors than those targeting a broader range of disturbance magnitudes. These results suggest that a user of any given forest disturbance map should understand the map’s strengths and weaknesses (in terms of omission and commission error rates), with respect to the disturbance targets of interest. View Full-Text
Keywords: remote sensing; change detection; Landsat time series; forest disturbance remote sensing; change detection; Landsat time series; forest disturbance
Figures

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

Cohen, W.B.; Healey, S.P.; Yang, Z.; Stehman, S.V.; Brewer, C.K.; Brooks, E.B.; Gorelick, N.; Huang, C.; Hughes, M.J.; Kennedy, R.E.; Loveland, T.R.; Moisen, G.G.; Schroeder, T.A.; Vogelmann, J.E.; Woodcock, C.E.; Yang, L.; Zhu, Z. How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms? Forests 2017, 8, 98.

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

1

Comments

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