Analyzing plant cover class data quantitatively: Customized zero-inflated cumulative beta distributions show promising results

Herpigny, Basile; Gosselin, Frédéric

doi:10.1016/j.ecoinf.2014.12.002

Cited by 24 publications

(25 citation statements)

References 55 publications

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“…The implemented method allows obtaining the leaf coverage value easily and affectively, without relying on the mathematic model variables (Damgaard, 2013;Herpigny & Gosselin, 2015 and University of Idaho, 2015), the economic investment, availability, weather conditions and updating of satellite images (Weber et al, 2013 andKarl et al, 2014). The investigation made it possible to improve the work done by Laliberte et al (2007), obtaining the photographic captures by using a pattern of known length, and introducing it in the scenario avoiding the pixel scale reduction calculation in each obtained image.…”

Section: Resultsmentioning

confidence: 99%

“…Several studies submitted by Damgaard (2013), Herpigny & Gosselin (2015) and the University of Idaho (2015) showed that leaf coverage estimation by mathematical models is not an easy task to perform, due to the measurement variation caused by the weather, and the possible presence of pest and diseases, that prevent the determination of an accurate estimation. The research carried out by Weber et al (2013); Karl et al (2014) and Lehnert et al (2015).…”

Section: Introductionmentioning

confidence: 99%

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Method for estimating leaf coverage in strawberry plants using digital image processing

Sandino

Ramos-Sandoval

Amaya

2016

Rev. bras. eng. agríc. ambient.

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A B S T R A C TIn farming the measurement of leaf coverage is considered as an exhaustive task for the researchers due to most of the time they do not have access to the adequate tool for this purpose. A new algorithm, implemented in this investigation, allows to estimate by means of a non-destructive method, the leaf coverage value of strawberry plants (fragaria x ananassa) of the cultivar Albion in the Cajicá region, Colombia, by using digital image processing techniques ( DPI). The DPI based technique includes the smoothing, dilatation, contour detection, threshold and edges detection operations. The image acquisition system was conducted by means of photographic images in plants in study, directly from the beds of the crop and the captures were subsequently processed through the proposed algorithm. The obtained results show the measured values of the plants leaf coverage in cm 2 , with up to 90% of accuracy. This system gives an important contribution to the crop evolution analysis by computational tools, making easier the monitoring work.Método para estimar a cobertura foliar das plantas de morango usando processamento de imagem digital R E S U M O Na agricultura a medição da cobertura de folha é considerada como uma tarefa exaustiva para os pesquisadores, devido na maioria das vezes eles não terem acesso à ferramenta para este proposito. Um novo algoritmo, implementado nesta investigação permite estimar, por meio de um método não destrutivo, o valor de cobertura foliar das plantas de morango (fragaria x ananassa) da cultivar Albion na região de Cajicá, Colômbia, usando técnicas de processamento digital de imagens (PDI). A técnica baseada em PDI inclui as operações de suavização, dilatação, detecção de contorno, limiarização e detecção de bordas. O sistema de aquisição de imagem foi realizado por meio de imagens fotográficas nas plantas em estudo, diretamente dos canteiros da cultura e as capturas foram posteriormente processadas mediante o algoritmo proposto. Os resultados obtidos mostram os valores medidos da cobertura foliar de plantas em cm 2 , com até 90% de precisão. Este sistema proporciona uma importante contribuição para a análise da evolução das culturas por ferramentas computacionais, tornando mais fácil o trabalho de monitoramento. Key words:imaging fragaria x ananassa non-destructive method Palavras-chave: imagem fragaria x ananassa método não-destrutivo

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Method for estimating leaf coverage in strawberry plants using digital image processing

Sandino

Ramos-Sandoval

Amaya

2016

Rev. bras. eng. agríc. ambient.

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“…Numerous authors have instead demonstrated that percent plant cover are more appropriately analysed by assuming that cover approximates a two-parameter beta distribution (Ferrari & Cribari-Neto 2004; Chen et al 2008a; Cribari-Neto & Zeileis 2010; Herpigny & Gosselin 2015). Beta distributions are attractive because fitted values are constrained between the interval 0<y<1 and they can accommodate asymmetrical distributions with left- or right-skew.…”

Section: Methodsmentioning

confidence: 99%

Species abundance distributions should underpin ordinal cover-abundance transformations

McNellie

Dorrough²,

Oliver³

2019

Preprint

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4 Questions: The cover and abundance of individual plant species have been 5 recorded on ordinal scales for millions of plots world-wide. Many ecological 6 questions can be addressed using these data. However ordinal cover data may need 7 to be transformed to a quantitative form (0 to 100%), especially when scrutinising 8 summed cover of multiple species. Traditional approaches to transforming ordinal 9 data often assume that data are symmetrically distributed. However, skewed 1 0 abundance patterns are ubiquitous in plant community ecology. A failure to account 1 1 for this skew will bias plant cover estimates, especially when cover of multiple 1 2 species are summed. The questions this paper addresses are (i) how can we 1 3 estimate transformation values for ordinal data that accounts for the underlying right-1 4 skewed distribution of plant cover; (ii) do different plant groups require different 1 5 transformations and (iii) how do our transformations compare to other commonly 1 6 used transformations within the context of exploring the aggregate properties of 1 7 vegetation? 1 8 Location: Global 1 9Methods: Using a continuous cover dataset, each occurrence record was mapped to 2 0 its commensurate ordinal value, in this case, the ubiquitous Braun-Blanquet cover-2 1 abundance (BBCA) scale. We fitted a Bayesian hierarchical beta regression to 2 2 estimate the predicted mean (PM) cover of each of six plant growth forms within 2 3 different ordinal classes. We illustrate our method using a case study of 2 809 plots 2 4 containing 95 812 occurrence records with visual estimates of cover for 3 967 2 5 2 species. We compare the model derived estimates to other commonly used 2 6 transformations. 7Results: Our model found that PM estimates differed by growth form and that 2 8 previous methods overestimated cover, especially of smaller growth forms such as 2 9forbs and grasses. Our approach reduced the cumulative compounding of errors 3 0 when transformed cover data were used to explore the aggregate properties of 3 1 vegetation and was robust when validated against an independent dataset.3 2 Conclusions: By accounting for the right-skewed distribution of cover data, our 3 3 alternate approach for estimating transformation values can be extended to other 3 4 ordinal scales. A more robust approach to transforming floristic data and aggregating 3 5 cover estimates can strengthen ecological analyses to support biodiversity 3 6 conservation and management. 3 7 3 8 3 9 ordinal transformation; species abundance distribution; sPlot; summed foliage cover; 4 0 VegBank; vegetation cover. 4 1 4 2 Abbreviations: Braun-Blanquet cover-abundance -BBCA 4 3 4 4 4 5Field-based assessment of the cover and abundance of individual plant species is 4 6 complex. Observers making on-ground visual estimates of plant cover need to 4 7 account for, and assess, foliage cover of different densities, dimensions, shapes and 4 8 structures across multiple species, growth forms and strata. So too, counting cryptic, 4 9 6 1 effective, rapid and non-des...

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“…A common approach to address these problems is to apply arcsine or logit transformations to the response variable, prior to regression (Warton & Hui, ), although the results can be difficult to interpret (Ferrari & Cribari‐Neto, ). Numerous authors have instead demonstrated that percent plant cover is more appropriately analysed by assuming that cover approximates a two‐parameter beta distribution (Chen, Shiyomi, Bonham, et al., ; Cribari‐Neto & Zeileis, ; Ferrari & Cribari‐Neto, ; Herpigny & Gosselin, ). Beta distributions are attractive because fitted values are constrained between the interval 0 < y < 1 and they can accommodate asymmetrical distributions with left‐ or right‐skew.…”

Section: Methodsmentioning

confidence: 99%

Species abundance distributions should underpin ordinal cover‐abundance transformations

McNellie

Dorrough²,

Oliver

2019

Applied Vegetation Science

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Questions: The cover and abundance of individual plant species have been recorded on ordinal scales for millions of plots world-wide. Ordinal cover data often need to be transformed to a quantitative form (0%-100%), especially when scrutinising summed cover of multiple species. Traditional approaches to transforming ordinal data often assume that data are symmetrically distributed. However, skewed abundance patterns are ubiquitous in plant community ecology. The questions this paper addresses are (a) how can we estimate transformation values for ordinal data that account for the underlying right-skewed distribution of plant cover; (b) do different plant groups require different transformations; and (c) how do our transformations compare to other commonly used transformations within the context of exploring the aggregate properties of vegetation? Location: Global. Methods:We assigned Braun-Blanquet cover-abundance ordinal values to continuous cover observations. We fitted a Bayesian hierarchical beta regression to estimate the predicted mean (PM) cover of each of six plant growth forms within six ordinal classes. We illustrate our method using a case study (2,809 plots containing 95,812 observations), compare the model-derived estimates to other commonly used transformations and validate our model using an independent dataset (2,227 plots containing 51,497 observations) accessed through the VegBank database. Results: Our model found that PM estimates differed by growth form and that previous methods overestimated cover, especially of smaller growth forms such as forbs and grasses. Our approach reduced the cumulative compounding of errors and was robust when validated against an independent dataset. Conclusions:By accounting for the right-skewed distribution of cover data, our alternate approach for estimating transformation values can be extended to other ordinal scales. A more robust approach to transforming floristic data and aggregating cover estimates can strengthen ecological analyses to support biodiversity conservation and management. |

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Analyzing plant cover class data quantitatively: Customized zero-inflated cumulative beta distributions show promising results

Cited by 24 publications

References 55 publications

Method for estimating leaf coverage in strawberry plants using digital image processing

Method for estimating leaf coverage in strawberry plants using digital image processing

Species abundance distributions should underpin ordinal cover-abundance transformations

Species abundance distributions should underpin ordinal cover‐abundance transformations

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