Canopy Cover Estimation from Landsat Images: Understory Impact onTop-of-canopy Reflectance in a Northern Hardwood Forest. Issue 5 (3rd September 2018)

• Record Type: Journal Article
• Authors: Landry, Stéphanie; St-Laurent, Martin-Hugues; Nelson, Peter R.; Pelletier, Gaetan; Villard, Marc-André
• Is Part Of: Canadian journal of remote sensing. Volume 44:Issue 5(2018)
• Page Start: 435
• Page End: 446
• Publication Date: 2018-09-03
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2018.1533399
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: In northern hardwood forests, light availability is considered to be the main factor limiting seedling and sapling growth. However, field measurement of this variable is time-consuming. To address this issue, we developed random forest regression models to estimate canopy cover from a Landsat 8 OLI image of a northern hardwood forest in northwestern New Brunswick, Canada. We then assessed the accuracy of model predictions with a canopy height model (CHM) derived from LiDAR data. We selected 2 threshold heights (1.3 and 5 m) to distinguish the understory from the overstory and to determine the impact of the understory on top-of-canopy reflectance. Our results show that the understory influenced top-of-canopy reflectance and that a 1.3 m height threshold provided the most accurate estimation of canopy cover. In contrast with studies conducted in softwood stands, we found no evidence that the shortwave infrared (SWIR1) band decreased the influence of the understory on top-of-canopy reflectance. In northern hardwood forests, the estimation of canopy characteristics, such as canopy cover and leaf area index, should be focused on the green band, as it was least influenced by understory vegetation.