Bond-Lamberty, Ben, Devaney, John L., Barrett, Brian, Barrett, Frank, Redmond, John and O`Halloran, John (2015) Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies. PLoS ONE, 10 (8). e0133583. ISSN 1932-6203
Preview
JD_forest cover.pdf
Download (6MB) | Preview
Abstract
Quantification of spatial and temporal changes in forest cover is an essential component of
forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar
(SAR) is an ideal source of information on forest dynamics in countries with near-constant
cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the
potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo)
in Ireland is investigated and compared to forest cover estimates derived from three national
(Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006)
and one global forest cover (Global Forest Change) product. Two machine-learning
approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–
98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase
in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall
accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest
overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of
SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar
could aid inventories in regions with low levels of forest cover in fragmented landscapes.
The reduced accuracies observed for the global and pan-continental forest cover maps in
comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting
Item Type: | Article |
---|---|
Keywords: | Forest Cover Estimation; Ireland; Radar Remote Sensing; Comparative Analysis; Forest Cover Assessment; Methodologies; |
Academic Unit: | Faculty of Science and Engineering > Biology |
Item ID: | 16190 |
Identification Number: | 10.1371/journal.pone.0133583 |
Depositing User: | John Devaney |
Date Deposited: | 28 Jun 2022 15:36 |
Journal or Publication Title: | PLoS ONE |
Publisher: | Public Library of Science |
Refereed: | Yes |
Related URLs: | |
URI: | https://mural.maynoothuniversity.ie/id/eprint/16190 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
Downloads
Downloads per month over past year