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.

Estimating Plant Nitrogen Concentration of Rice through Fusing Vegetation Indices and Color Moments Derived from UAV-RGB Images

“…In recent decades, various sensors mounted on UAVs have been widely used to monitor crop growth traits such as LiDAR [54], hyperspectral [55] and multispectral [26] devices, NIR [56][57][58], and RGB cameras [59,60]. Compared to other sensors, the UAV-RGB camera was cheaper and lighter which made it possible for a longer working time to apply to larger areas in precision agriculture.…”

Leaf Age Matters in Remote Sensing: Taking Ground Truth for Spectroscopic Studies in Hemiboreal Deciduous Trees with Continuous Leaf Formation

“…We focused on the effect of leaf developmental category and leaf phenology on VI linear model performance. The effect of leaf age on modelling of biophysical traits from spectra is currently a topical theme for evergreen conifers bearing several needle age classes at once [20,36,80]; and also tropical tree species with leaf developmental asynchrony [81,82]. Moreover, in some tropical trees with asynchronous leaf development and long leaf lifespan (e.g., 360 days), even the leaf age could be modelled from leaf spectra [83].…”

“…With the current wide availability of hyperspectral data at leaf and canopy levels, the multivariate approaches to leaf trait modelling, such as PLSR, exploiting the whole spectral range, are preferentially used [20,46,81]. Although our source data were hyperspectral, we built the present study on vegetation indices, which are still widely used due to rise of various affordable multispectral sensors [80,[94][95][96] or satellite multispectral data [97]. On a large scale, starting in 2015 thanks to a NASA initiative, the Harmonized Landsat and Sentinel-2 (HLS) surface reflectance data set represents a novel type of multispectral satellite data [98,99] and vegetation indices derived from HLS have potential to monitor land surface phenology [100].…”

What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images?

“…The results showed that NDVI and red edge NDVI extracted by a multipectral UAV system could be used to estimate the PPC in a Norway spruce's individual tree canopy at a semiquantitative level. Furthermore, the needle age had a major influence on the estimation results, while the illumination conditions had little effect [23].…”

“…Moreover, no matter whether it is multispectral UAV or canopy PPC, the research on forestry is still immature (please refer to the Supplementary Materials), and there are still various problems that need to be further solved. Therefore, how to retrieve the PPC of forest canopy from multispectral UAV images becomes a quantitative remote sensing problem worthy of discussion at present [23].…”