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A Practical and Automated Approach to Large Area Forest Disturbance Mapping with Remote Sensing
- Source :
- PLoS ONE, Vol 9, Iss 4, p e78438 (2014), PLoS ONE
- Publication Year :
- 2014
- Publisher :
- Public Library of Science (PLoS), 2014.
-
Abstract
- In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions.
- Subjects :
- Cartography
Computer and Information Sciences
Time Factors
Image Processing
lcsh:Medicine
Land cover
Forests
Cross-validation
Automation
Wisconsin
Geoinformatics
Forest ecology
lcsh:Science
Remote sensing
Data processing
Remote Sensing Imagery
Multidisciplinary
Geography
Pixel
Applied Mathematics
Ecology and Environmental Sciences
lcsh:R
Biology and Life Sciences
Agriculture
Forestry
Terrestrial Environments
Support vector machine
Statistical classification
Physical Geography
ROC Curve
Signal Processing
Physical Sciences
Remote Sensing Technology
Earth Sciences
Geographic Information Systems
Engineering and Technology
lcsh:Q
Mathematics
Algorithms
Change detection
Research Article
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....24977664cb1aa322568595fa854a4dac