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%).
9064-9074
Brown, Luke
3f3ee47e-ee1f-4a44-a223-36059b69ce92
Sutherland, David H.
c303dbd2-059f-430c-a3b6-fe95f0ea9e1e
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
1 December 2020
Brown, Luke
3f3ee47e-ee1f-4a44-a223-36059b69ce92
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), .
(doi:10.1080/2150704X.2020.1802527).
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
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Local EPrints ID: 444554
URI: http://eprints.soton.ac.uk/id/eprint/444554
ISSN: 0143-1161
PURE UUID: 9b576c8d-3ec6-4973-a786-6ae33650725d
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Date deposited: 23 Oct 2020 16:33
Last modified: 14 Dec 2024 05:02
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Author:
Luke Brown
Author:
David H. Sutherland
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