Assessment of active LiDAR data and passive optical imagery for double-layered mangrove leaf area index estimation: a case study in Mai Po, Hong Kong
Assessment of active LiDAR data and passive optical imagery for double-layered mangrove leaf area index estimation: a case study in Mai Po, Hong Kong
Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially for less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating the LAI of the main canopy, ignoring the understory. This study investigated the capability of multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), and airborne LiDAR data for overstory (OLe) and understory (ULe) LAI estimation of a multi-layered mangrove stand in Mai Po, Hong Kong, China. LiDAR data were employed to stratify the overstory and understory. Vegetation indices (VIs) and LiDAR metrics were generated as predictors to build regression models against the OLe and ULe with multiple parametric and non-parametric methods. The OLe model fitting results were typically better than ULe because of the dominant contribution of the overstory to the remotely sensed signal. A single red-edge VI derived from HSI data delivered the lowest RMSE of 0.12 and the highest R 2 adj of 0.79 for OLe model fitting. The synergetic use of LiDAR metrics and S2 VIs performed best for ULe model fitting with RMSE = 0.33, R 2 adj = 0.84. OLe estimation benefited from the high spatial and spectral resolution HSI that was found less confounded by the understory. In addition to their penetration attributes, LiDAR data could separately describe the upper and lower canopy, which reduced the noise from other components, thereby improving the ULe estimation.
LAI, LiDAR, hemispherical photography, overstory, understory, vegetation indices
Li, Qiaosi
83ade1f0-adcf-4d2c-a530-4e1ac750d3a3
Wong, Frankie Kwan Kit
d8ce3c9d-7018-4d25-aefe-ec7ce62f2a13
Fung, Tung
898972c9-8b73-4d38-82ed-a7d07896adb4
Brown, Luke A.
6a693f08-df9e-4494-bb89-8d2897470d4a
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
12 May 2023
Li, Qiaosi
83ade1f0-adcf-4d2c-a530-4e1ac750d3a3
Wong, Frankie Kwan Kit
d8ce3c9d-7018-4d25-aefe-ec7ce62f2a13
Fung, Tung
898972c9-8b73-4d38-82ed-a7d07896adb4
Brown, Luke A.
6a693f08-df9e-4494-bb89-8d2897470d4a
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Li, Qiaosi, Wong, Frankie Kwan Kit, Fung, Tung, Brown, Luke A. and Dash, Jadunandan
(2023)
Assessment of active LiDAR data and passive optical imagery for double-layered mangrove leaf area index estimation: a case study in Mai Po, Hong Kong.
Remote Sensing, 15 (10), [2551].
(doi:10.3390/rs15102551).
Abstract
Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially for less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating the LAI of the main canopy, ignoring the understory. This study investigated the capability of multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), and airborne LiDAR data for overstory (OLe) and understory (ULe) LAI estimation of a multi-layered mangrove stand in Mai Po, Hong Kong, China. LiDAR data were employed to stratify the overstory and understory. Vegetation indices (VIs) and LiDAR metrics were generated as predictors to build regression models against the OLe and ULe with multiple parametric and non-parametric methods. The OLe model fitting results were typically better than ULe because of the dominant contribution of the overstory to the remotely sensed signal. A single red-edge VI derived from HSI data delivered the lowest RMSE of 0.12 and the highest R 2 adj of 0.79 for OLe model fitting. The synergetic use of LiDAR metrics and S2 VIs performed best for ULe model fitting with RMSE = 0.33, R 2 adj = 0.84. OLe estimation benefited from the high spatial and spectral resolution HSI that was found less confounded by the understory. In addition to their penetration attributes, LiDAR data could separately describe the upper and lower canopy, which reduced the noise from other components, thereby improving the ULe estimation.
Text
remotesensing-15-02551-v2
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More information
Accepted/In Press date: 5 May 2023
e-pub ahead of print date: 12 May 2023
Published date: 12 May 2023
Additional Information:
Funding Information:
This study is supported by the Research Grant Council of Hong Kong General Research Fund (Project No. 14618715). This work was made possible by the ESRC’s on-going support for the Urban Big Data Centre (UBDC) [ES/L011921/1 and ES/S007105/1].
Publisher Copyright:
© 2023 by the authors.
Keywords:
LAI, LiDAR, hemispherical photography, overstory, understory, vegetation indices
Identifiers
Local EPrints ID: 477786
URI: http://eprints.soton.ac.uk/id/eprint/477786
ISSN: 2072-4292
PURE UUID: 0f0d3648-2de6-4564-b464-7c439e731a39
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Date deposited: 14 Jun 2023 16:46
Last modified: 27 Apr 2024 01:41
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Contributors
Author:
Qiaosi Li
Author:
Frankie Kwan Kit Wong
Author:
Tung Fung
Author:
Luke A. Brown
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