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Open AccessArticle

Vegetation Horizontal Occlusion Index (VHOI) from TLS and UAV Image to Better Measure Mangrove LAI

by Xianxian Guo 1,2,3, Le Wang 4,*, Jinyan Tian 1,2,3,*, Dameng Yin 4, Chen Shi 1,2,3 and Sheng Nie 5
1
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
2
State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Ministry of Science and Technology, Capital Normal University, Beijing 100048, China
3
College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
4
Department of Geography, The State University of New York at Buffalo, Buffalo, NY 14261, USA
5
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1739; https://doi.org/10.3390/rs10111739
Received: 13 August 2018 / Revised: 16 October 2018 / Accepted: 30 October 2018 / Published: 3 November 2018
(This article belongs to the Special Issue Remote Sensing of Mangroves)
Accurate measurement of the field leaf area index (LAI) is crucial for assessing forest growth and health status. Three-dimensional (3-D) structural information of trees from terrestrial laser scanning (TLS) have information loss to various extents because of the occlusion by canopy parts. The data with higher loss, regarded as poor-quality data, heavily hampers the estimation accuracy of LAI. Multi-location scanning, which proved effective in reducing the occlusion effects in other forests, is hard to carry out in the mangrove forest due to the difficulty of moving between mangrove trees. As a result, the quality of point cloud data (PCD) varies among plots in mangrove forests. To improve retrieval accuracy of mangrove LAI, it is essential to select only the high-quality data. Several previous studies have evaluated the regions of occlusion through the consideration of laser pulses trajectories. However, the model is highly susceptible to the indeterminate profile of complete vegetation object and computationally intensive. Therefore, this study developed a new index (vegetation horizontal occlusion index, VHOI) by combining unmanned aerial vehicle (UAV) imagery and TLS data to quantify TLS data quality. VHOI is asymptotic to 0.0 with increasing data quality. In order to test our new index, the VHOI values of 102 plots with a radius of 5 m were calculated with TLS data and UAV image. The results showed that VHOI had a strong linear relationship with estimation accuracy of LAI (R2 = 0.72, RMSE = 0.137). In addition, as TLS data were selected by VHOI less than different thresholds (1.0, 0.9, …, 0.1), the number of remaining plots decreased while the agreement between LAI derived from TLS and field-measured LAI was improved. When the VHOI threshold is 0.3, the optimal trade-off is reached between the number of plots and LAI measurement accuracy (R2 = 0.67). To sum up, VHOI can be used as an index to select high-quality data for accurately measuring mangrove LAI and the suggested threshold is 0.30. View Full-Text
Keywords: mangrove; leaf area index (LAI); terrestrial laser scanning (TLS); vegetation horizontal occlusion index (VHOI); unmanned aerial vehicle (UAV) imagery mangrove; leaf area index (LAI); terrestrial laser scanning (TLS); vegetation horizontal occlusion index (VHOI); unmanned aerial vehicle (UAV) imagery
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Guo, X.; Wang, L.; Tian, J.; Yin, D.; Shi, C.; Nie, S. Vegetation Horizontal Occlusion Index (VHOI) from TLS and UAV Image to Better Measure Mangrove LAI. Remote Sens. 2018, 10, 1739.

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