DNN-Based Lichen Mapping Using AVIRIS-NG Hyperspectral Imagery and UAV Images in a Rocky Canadian Shield Landscape

Abstract. Forage lichen fractional cover mapping using multi-spectral remote sensing (RS) data is challenging, especially over rocky landscapes where there is a high spectral correlation between lichens and non-lichen features. Given this, it is deemed that the use of airborne or satellite hyperspectral imagery may improve lichen mapping. In this study, we report the first results of using AVIRIS-NG hyperspectral imagery and UAV images to estimate forage lichen fractional cover (Cladonia spp.) in a rocky Canadian shield landscape where non-lichen bright features were prevalent. To estimate forage lichen fractional cover, we conducted a regression approach based on deep multi-layer perceptron (MLP) models whose number of hidden layers and neurons were determined using exhaustive grid search procedures. The three MLP models were trained and tested on four scenarios with different hyperspectral compression AVIRIS-NG band images and WorldView-3 (WV3) data of three sites. Our experiments showed that mapping lichen fractional cover using the 5 m AVIRIS-NG surface reflectance imagery was more accurate (i.e., higher R2 and lower RMSE values) than the one using a 4-band WorldView-3 (WV3) image with a spatial resolution of 2 m in most cases.