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Open AccessArticle

Forest Canopy Height and Gaps from Multiangular BRDF, Assessed with Airborne LiDAR Data (Short Title: Vegetation Structure from LiDAR and Multiangular Data)

by Qiang Wang 1,2 and Wenge Ni-Meister 3,*
1
Department of Surveying Engineering, Heilongjiang Institute of Technology, Harbin 150040, China
2
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
3
Department of Geography, Hunter College of the City University of New York, New York, NY 10021, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2566; https://doi.org/10.3390/rs11212566
Received: 22 September 2019 / Revised: 27 October 2019 / Accepted: 29 October 2019 / Published: 1 November 2019
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
Both vegetation multi-angular and LiDAR (light detection and ranging) remote sensing data are indirectly and directly linked with 3D vegetation structure parameters, such as the tree height and vegetation gap fraction, which are critical elements in above-ground biomass and light profiles for photosynthesis estimation. LiDAR, particularly LiDAR using waveform data, provides accurate estimates of these structural parameters but suffers from not enough spatial samplings. Structural parameters retrieved from multiangular imaging data through the inversion of physical models have larger uncertainties. This study searches for an analytical approach to fuse LiDAR and multiangular data. We explore the relationships between vegetation structure parameters derived from airborne vegetation LiDAR data and multiangular data and present a new potential angle vegetation index to retrieve the tree height and gap fraction using multi-angular data in Howland Forest, Maine. The BRDF (bidirectional reflectance distribution factor) index named NDMM (normalized difference between the maximum and minimum reflectance) linearly increases with the tree height and decreases with the gap fraction. In addition, these relationships are also dependent on the wavelength, tree species, and stand density. The NDMM index performs better in conifer (R = 0.451 for tree height and R = 0.472 for the gap fraction using the near infrared band) than in deciduous and mixed forests. It is superior in sparse (R = 0.569 for tree height and R = 0.604 for the gap fraction using the near infrared band) compared to dense forest. Moreover, the NDMM index is more strongly related to tree height and the gap fraction at the near infrared band than at the three visible bands. This study sheds light on the possibility of using multiangular data to map vegetation’s structural parameters in larger regions for carbon cycle studies through the fusion of LiDAR and multiangular remote sensing data. View Full-Text
Keywords: multiangular remote sensing; forest-structure information; gap fraction; tree height; bidirectional reflectance distribution factor multiangular remote sensing; forest-structure information; gap fraction; tree height; bidirectional reflectance distribution factor
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MDPI and ACS Style

Wang, Q.; Ni-Meister, W. Forest Canopy Height and Gaps from Multiangular BRDF, Assessed with Airborne LiDAR Data (Short Title: Vegetation Structure from LiDAR and Multiangular Data). Remote Sens. 2019, 11, 2566.

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