Estimating Canopy Cover Via VNIR/SWIR Hyperspectral Detection Methods

Canopy cover is a significant factor in assessing the performance of target detection algorithms in forested environments. This is true of electro-optical (EO), radar frequency (RF), light detection and ranging (LIDAR), multi/hyperspectral (MSI/HSI), and other remote sensing methods. This research compares traditional ground based methods of estimating canopy closure with estimates of canopy cover via spectral detection methods applied to VNIR/SWIR hyperspectral imagery. This paper uses canopy cover and canopy closure as defined by Jennings, et al. [1]. In the Summer of 2009, a pushbroom VNIR/SWIR hyperspectral sensor collected data over a forested region of the Naval Surface Warfare Center, Dahlgren Division, Virginia. This forested region can be best described as single canopy cover with multiple tree species. Hyperspectral imagery was collected over multiple days and at multiple altitudes in August and September, 2009. On the ground, densiometer measurements and hemispherical photography were used to estimate canopy closure at 10 meter intervals across a 2500 m(2) grid. Several spectral detection methods including vegetation indices, matched filtering, linear un-mixing, and distance measures, are used to calculate canopy coverage at varying ground sample distances and across multiple days. These multiple estimates are compared to the ground based measurements of canopy closure. Results indicate that estimates of canopy coverage via VNIR/SWIR hyperspectral imagery compare well to the ground based canopy closure estimates for this single canopy region. This would lead to the conclusion that it is possible to use airborne VNIR/SWIR hyperspectral alone to provide an accurate estimate of canopy cover.