Abstract
Pigments are the biochemical material basis for energy and material exchange between vegetation and the external environment, therefore quantitative determination of pigment content is crucial. Unmanned Aerial Vehicle (UAV)-borne remote sensing data coupled with radiative transfer models (RTM) provide marked strengths for three-dimensional (3D) visualization, as well as accurate determination of the distributions of pigment content in forest canopies. In this study, Light Detection and Ranging (LiDAR) and hyperspectral images acquired by a multi-rotor UAV were assessed with the PROSAIL model (i.e., PROSPECT model coupled with 4SAIL model) and were synthetically implemented to estimate the horizontal and vertical distribution of pigments in canopies of Ginkgo plantations in a study site within coastal southeast China. Firstly, the fusion of LiDAR point cloud and hyperspectral images was carried out in the frame of voxels to obtain fused hyperspectral point clouds. Secondly, the PROSAIL model was calibrated using specific model parameters of Ginkgo trees and the corresponding look-up tables (LUTs) of leaf pigment content were constructed and optimally selected. Finally, based on the optimal LUTs and combined with the hyperspectral point clouds, the horizontal and vertical distributions of pigments in different ages of ginkgo trees were mapped to explore their distribution characteristics. The results showed that 22-year-old ginkgo trees had higher biochemical pigment content (increase 3.37–55.67%) than 13-year-old ginkgo trees. Pigment content decreased with the increase of height, whereas pigment content from the outer part of tree canopies showed a rising tendency as compared to the inner part of canopies. Compared with the traditional vegetation index models (R2 = 0.25–0.46, rRMSE = 16.25–19.37%), the new approach developed in this study exhibited significant higher accuracies (R2 = 0.36–0.60, rRMSE = 13.53–16.86%). The results of this study confirmed the effectiveness of coupling the UAV-borne LiDAR and hyperspectral image with the PROSAIL model for accurately assessing pigment content in ginkgo canopies, and the developed estimation methods can also be adopted to other regions under different conditions, providing technical support for sustainable forest management and precision silvicuture for plantations.
Highlights
In order to fill the gap, this study explored the fusion of Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) datasets and hyperspectral images coupled with the PROSAIL model to estimate the 3D distribution of pigments in Ginkgo plantations
We attempted to perform multi-source remote sensing data inversion estimation in the PROSAIL model for biochemical pigments of Ginkgo, and fused hyperspectral imagery with LiDAR data to obtain the fused hyperspectral point clouds, which were put into the PROSAIL model to construct the exclusive look-up tables (LUTs) for Ginkgo
Following the LUT, we explored the 3D distribution of pigments within canopies and the regularities in the horizontal and vertical profiles of pigments with age and height
Summary
As an essential part of the forest ecosystem, planted forests play a significant role in maintaining the ecological environment, conserving biodiversity, and providing a variety of biochemical products for the ecosystem [1]. Report (2009–2013), the area of planted forests is approximately 7.95 × 107 km , accounting for approximately 32.94% of the national forest area [2]. Ginkgo (Ginkgo biloba L.) is a unique multi-purpose tree species in China, with important economic and ecological value [3]. As the principal component of the forest’s biochemical traits, total chlorophylls (Cab) and carotenoids (Car) can reflect the growth status of trees and environmental stress, and can Remote Sens.
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