Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV)

Abstract

Remote sensing is one of the modern methods that have significantly developed over the last two decades and, nowadays, it provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for the accurate and timely monitoring of forests. In this study, multi-spectral Unmanned Aerial Vehicle (UAV) images were used to estimate canopy parameters (definition of crown extent, top, and height, as well as photosynthetic pigment contents). The UAV images in Green, Red, Red-Edge, and Near infrared (NIR) bands were acquired by Parrot Sequoia camera over selected sites in two small catchments (Czech Republic) covered dominantly by Norway spruce monocultures. Individual tree extents, together with tree tops and heights, were derived from the Canopy Height Model (CHM). In addition, the following were tested: (i) to what extent can the linear relationship be established between selected vegetation indexes (Normalized Difference Vegetation Index (NDVI) and NDVIred edge) derived for individual trees and the corresponding ground truth (e.g., biochemically assessed needle photosynthetic pigment contents) and (ii) whether needle age selection as a ground truth and crown light conditions affect the validity of linear models. The results of the conducted statistical analysis show that the two vegetation indexes (NDVI and NDVIred edge) tested here have the potential to assess photosynthetic pigments in Norway spruce forests at a semi-quantitative level; however, the needle-age selection as a ground truth was revealed to be a very important factor. The only usable results were obtained for linear models when using the second year needle pigment contents as a ground truth. On the other hand, the illumination conditions of the crown proved to have very little effect on the model’s validity. No study was found to directly compare these results conducted on coniferous forest stands. This shows that there is a further need for studies dealing with a quantitative estimation of the biochemical variables of nature coniferous forests when employing spectral data that were acquired by the UAV platform at a very high spatial resolution.