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Low-cost unmanned aerial vehicle-based digital hemispherical photography for estimating leaf area index: a feasibility assessment

Low-cost unmanned aerial vehicle-based digital hemispherical photography for estimating leaf area index: a feasibility assessment
Low-cost unmanned aerial vehicle-based digital hemispherical photography for estimating leaf area index: a feasibility assessment

Unmanned aerial vehicles (UAVs) have the potential to provide highly detailed information on vegetation status useful in precision agriculture. However, challenges are associated with existing techniques for UAV-based retrieval of vegetation biophysical variables such as leaf area index (LAI), including variable illumination, bidirectional reflectance effects, and the need for image calibration, mosaicking, and normalization. We investigated an alternative approach that avoids these challenges whilst still providing spatially explicit estimates of LAI, using UAV-based digital hemispherical photography (DHP). LAI estimates were obtained using a low-cost UAV-based DHP system over a winter wheat field in Southern England. Point-based estimates were interpolated to provide spatially continuous datasets, which successfully described patterns of vegetation condition. The UAV-based DHP data were compared to ground-based LAI estimates, demonstrating good agreement (root mean square error (RMSE) = 0.10, normalized RMSE (NRMSE) = 3%).

0143-1161
9064-9074
Brown, Luke
6a693f08-df9e-4494-bb89-8d2897470d4a
Sutherland, David H.
c303dbd2-059f-430c-a3b6-fe95f0ea9e1e
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Brown, Luke
6a693f08-df9e-4494-bb89-8d2897470d4a
Sutherland, David H.
c303dbd2-059f-430c-a3b6-fe95f0ea9e1e
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8

Brown, Luke, Sutherland, David H. and Dash, Jadunandan (2020) Low-cost unmanned aerial vehicle-based digital hemispherical photography for estimating leaf area index: a feasibility assessment. International Journal of Remote Sensing, 41 (23), 9064-9074. (doi:10.1080/2150704X.2020.1802527).

Record type: Article

Abstract

Unmanned aerial vehicles (UAVs) have the potential to provide highly detailed information on vegetation status useful in precision agriculture. However, challenges are associated with existing techniques for UAV-based retrieval of vegetation biophysical variables such as leaf area index (LAI), including variable illumination, bidirectional reflectance effects, and the need for image calibration, mosaicking, and normalization. We investigated an alternative approach that avoids these challenges whilst still providing spatially explicit estimates of LAI, using UAV-based digital hemispherical photography (DHP). LAI estimates were obtained using a low-cost UAV-based DHP system over a winter wheat field in Southern England. Point-based estimates were interpolated to provide spatially continuous datasets, which successfully described patterns of vegetation condition. The UAV-based DHP data were compared to ground-based LAI estimates, demonstrating good agreement (root mean square error (RMSE) = 0.10, normalized RMSE (NRMSE) = 3%).

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Accepted/In Press date: 21 July 2020
e-pub ahead of print date: 29 September 2020
Published date: 1 December 2020

Identifiers

Local EPrints ID: 444554
URI: http://eprints.soton.ac.uk/id/eprint/444554
ISSN: 0143-1161
PURE UUID: 9b576c8d-3ec6-4973-a786-6ae33650725d
ORCID for Luke Brown: ORCID iD orcid.org/0000-0003-4807-9056
ORCID for Jadunandan Dash: ORCID iD orcid.org/0000-0002-5444-2109

Catalogue record

Date deposited: 23 Oct 2020 16:33
Last modified: 26 Nov 2021 05:39

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Contributors

Author: Luke Brown ORCID iD
Author: David H. Sutherland
Author: Jadunandan Dash ORCID iD

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