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Comparison of visual assessment and digital image analysis for canopy cover estimation

Comparison of visual assessment and digital image analysis for canopy cover estimation

Büchi, Lucie ORCID: 0000-0002-1935-6176, Wendling, Marina, Mouly, Pauline and Charles, Raphaël (2018) Comparison of visual assessment and digital image analysis for canopy cover estimation. Agronomy Journal, 110 (4). pp. 1289-1295. ISSN 0002-1962 (Print), 1435-0645 (Online) (doi:https://doi.org/10.2134/agronj2017.11.0679)

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Abstract

Nowadays, in the context of agriculture, cover crops are crops cultivated with the sole aim of providing important ecosystem services such as erosion prevention. Many services offered by these crops are directly linked to the development of their vegetation, and especially of canopy cover. A proper estimation of this cover is thus necessary to evaluate cover crop performance. Many methods to estimate canopy cover exist, but differ in terms of effort and time needed to implement them. In this study, we compared visual assessment of canopy cover in the field with two methods of digital image analysis (Assess and Canopeo), for different cover crop species and vegetation types. Visual estimation was positively correlated with both type of image analysis estimations. However, it showed systematically lower values of canopy cover, especially at intermediate canopy cover values. The type of vegetation influenced the visual and digital image estimations, narrow leaf species being the most difficult to evaluate visually. This study showed that depending on its utilisation, visual canopy cover assessment could be useful, especially when only relative estimation of canopy cover is needed. When absolute canopy cover estimation is needed, the use of digital image analysis should be preferred.

Item Type: Article
Uncontrolled Keywords: soil cover, ground cover, fractional green cover, Canopeo, Assess
Subjects: S Agriculture > S Agriculture (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Agriculture, Health & Environment Department
Faculty of Engineering & Science > Natural Resources Institute > Ecosystem Services Research Group
Last Modified: 01 Mar 2019 14:42
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/20290

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