NTRS - NASA Technical Reports Server

Back to Results
Calibration and Validation of Landsat Tree Cover in the Taiga-Tundra EcotoneMonitoring current forest characteristics in the taiga-tundra ecotone (TTE) at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover greater than 80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010) by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE.
Document ID
Document Type
Reprint (Version printed in journal)
External Source(s)
Montesano, Paul Mannix
(Science Systems and Applications, Inc. Lanham, MD, United States)
Neigh, Christopher S. R.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Sexton, Joseph
(Maryland Univ. College Park, MD, United States)
Feng, Min
(Maryland Univ. College Park, MD, United States)
Channan, Saurabh
(Maryland Univ. College Park, MD, United States)
Ranson, Kenneth J.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Townshend, John R.
(Maryland Univ. College Park, MD, United States)
Date Acquired
April 6, 2017
Publication Date
June 29, 2016
Publication Information
Publication: Remote Sensing
Volume: 8
Issue: 7
ISSN: 2072-4292
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
Funding Number(s)
Distribution Limits

Available Downloads

There are no available downloads for this record.
No Preview Available