MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING

MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING

Unmanned Aircraft Systems (UAS) are demonstrated cost- and time-effective remote sensing platforms for precision agriculture applications and crop damage monitoring. In this study, lodging damage on barley crops has been mapped from UAS imagery that was acquired over multiple barley fields with exte...

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Main Authors: O. Vlachopoulos, B. Leblon, J. Wang, A. Haddadi, A. LaRocque, G. Patterson
Format: Article
Language:English
Published: Copernicus Publications 2021-06-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B1-2021/203/2021/isprs-archives-XLIII-B1-2021-203-2021.pdf
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spelling doaj-b06f23dffb9141bba647f062546b79ea2021-06-28T22:00:30ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342021-06-01XLIII-B1-202120320810.5194/isprs-archives-XLIII-B1-2021-203-2021MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNINGO. Vlachopoulos0B. Leblon1J. Wang2A. Haddadi3A. LaRocque4G. Patterson5Faculty of Forestry and Environmental Management, University of New Brunswick, 2 Bailey Dr, Fredericton, NB E3B5A3 New Brunswick, CanadaFaculty of Forestry and Environmental Management, University of New Brunswick, 2 Bailey Dr, Fredericton, NB E3B5A3 New Brunswick, CanadaDepartment of Geography and Environment, University of Western Ontario, 1151 Richmond Street, ON N6A 5C2, London, CanadaA&L Canada Laboratories, 2136 Jetstream Rd., London, ON N5V 3P5, London, CanadaFaculty of Forestry and Environmental Management, University of New Brunswick, 2 Bailey Dr, Fredericton, NB E3B5A3 New Brunswick, CanadaA&L Canada Laboratories, 2136 Jetstream Rd., London, ON N5V 3P5, London, CanadaUnmanned Aircraft Systems (UAS) are demonstrated cost- and time-effective remote sensing platforms for precision agriculture applications and crop damage monitoring. In this study, lodging damage on barley crops has been mapped from UAS imagery that was acquired over multiple barley fields with extensive lodging damages in two aerial surveys. A Random Forests classification model was trained and tested for the discrimination of lodged barley with an overall accuracy of 99.7% on the validation dataset. The crop areas with lodging were automatically delineated by vector analysis and compared to manually delineated areas using two spatial accuracy metrics, the Area Goodness of Fit (AGoF) and the Boundary Mean Positional Error (BMPE). The average AGoF was 97.95% and the average BMPE was 0.235 m.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B1-2021/203/2021/isprs-archives-XLIII-B1-2021-203-2021.pdf
collection DOAJ
language English
format Article
sources DOAJ
author O. Vlachopoulos
B. Leblon
J. Wang
A. Haddadi
A. LaRocque
G. Patterson
spellingShingle O. Vlachopoulos
B. Leblon
J. Wang
A. Haddadi
A. LaRocque
G. Patterson
MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet O. Vlachopoulos
B. Leblon
J. Wang
A. Haddadi
A. LaRocque
G. Patterson
author_sort O. Vlachopoulos
title MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING
title_short MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING
title_full MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING
title_fullStr MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING
title_full_unstemmed MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING
title_sort mapping barley lodging with uas multispectral imagery and machine learning
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2021-06-01
description Unmanned Aircraft Systems (UAS) are demonstrated cost- and time-effective remote sensing platforms for precision agriculture applications and crop damage monitoring. In this study, lodging damage on barley crops has been mapped from UAS imagery that was acquired over multiple barley fields with extensive lodging damages in two aerial surveys. A Random Forests classification model was trained and tested for the discrimination of lodged barley with an overall accuracy of 99.7% on the validation dataset. The crop areas with lodging were automatically delineated by vector analysis and compared to manually delineated areas using two spatial accuracy metrics, the Area Goodness of Fit (AGoF) and the Boundary Mean Positional Error (BMPE). The average AGoF was 97.95% and the average BMPE was 0.235 m.
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B1-2021/203/2021/isprs-archives-XLIII-B1-2021-203-2021.pdf
work_keys_str_mv AT ovlachopoulos mappingbarleylodgingwithuasmultispectralimageryandmachinelearning
AT bleblon mappingbarleylodgingwithuasmultispectralimageryandmachinelearning
AT jwang mappingbarleylodgingwithuasmultispectralimageryandmachinelearning
AT ahaddadi mappingbarleylodgingwithuasmultispectralimageryandmachinelearning
AT alarocque mappingbarleylodgingwithuasmultispectralimageryandmachinelearning
AT gpatterson mappingbarleylodgingwithuasmultispectralimageryandmachinelearning
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