Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods

Fuentes, Sigfredo; Poblete-Echeverría, Carlos; Ortega-Farías, Samuel; Tyerman, Stephen D.; Bei, Roberta De

doi:10.1111/ajgw.12098

Cited by 58 publications

(78 citation statements)

References 41 publications

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“…An automated image acquisition and calculation method was proposed by Fuentes et al 2008 applied to Eucalyptus trees [26] and it has been successfully applied for other crops such as grapevines compared to allometric measurements and to validate NDVI calculated from satellite information (WorldView-2) [27], apple trees with increased accuracy by using a variable light extinction coefficient ( ) [28], and cherry trees improving the method by extracting nonleaf material such as branches for tall trees [29,30]. In late 2015, a computer application (App) for smartphones and tablet PCs called VitiCanopy was released for free use to assess canopy architecture parameters using the cover photography automated algorithms, which can be applied to any other tree crop by changing to a specific value [31,32].…”

Section: Vegetation Indices and Validation Processmentioning

confidence: 99%

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

2017

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Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used. Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface. In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas.

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Section: Vegetation Indices and Validation Processmentioning

confidence: 99%

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

2017

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“…The method developed specifically for avocado canopies was evolved from similar analysis calculating the leaf area index (LAI) of grapevine canopies [18,32] and Eucalyptus [31]. The predominant processing steps included segmentation of canopy from sky and image gap analysis (sky/leaf ratio) following methodologies described by Poblete-Echeverría et al [16] and Fuentes et al [31].…”

Section: Rgb Image Analysismentioning

confidence: 99%

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

et al. 2018

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Phytophthora root rot (PRR) infects the roots of avocado trees, resulting in reduced uptake of water and nutrients, canopy decline, defoliation, and, eventually, tree mortality. Typically, the severity of PRR disease (proportion of canopy decline) is assessed by visually comparing the canopy health of infected trees to a standardised set of photographs and a corresponding disease rating. Although this visual method provides some indication of the spatial variability of PRR disease across orchards, the accuracy and repeatability of the ranking is influenced by the experience of the assessor, the visibility of tree canopies, and the timing of the assessment. This study evaluates two image analysis methods that may serve as surrogates to the visual assessment of canopy decline in large avocado orchards. A smartphone camera was used to collect red, green, and blue (RGB) colour images of individual trees with varying degrees of canopy decline, with the digital photographs then analysed to derive a canopy porosity percentage using a combination of 'Canny edge detection' and 'Otsu's' methods. Coinciding with the on-ground measure of canopy porosity, the canopy reflectance characteristics of the sampled trees measured by high resolution Worldview-3 (WV-3) satellite imagery was also correlated against the observed disease severity rankings. Canopy porosity values (ranging from 20-70%) derived from RGB images were found to be significantly different for most disease rankings (p < 0.05) and correlated well (R 2 = 0.89) with the differentiation of three disease severity levels identified to be optimal. From the WV-3 imagery, a multivariate stepwise regression of 18 structural and pigment-based vegetation indices found the simplified ratio vegetation index (SRVI) to be strongly correlated (R 2 = 0.96) with the disease rankings of PRR disease severity, with the differentiation of four levels of severity found to be optimal.

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“…On the other hand, [77] indicated that the use of NDVI computed from images of low-spatial-resolution such as Landsat (pixel resolution: 900 m 2 ) could generate poor estimations of LAI because pixels contain mixed pixels reflectance information from the surface occupied by plants (row) and the between row space. Recently, in two vineyards, [58] indicated that the LAI can be overestimated up to 17% when contrasting geo-referenced LAI data from cover photography method against NDVI obtained from the satellite World-View 2 (pixels resolution: 2 m 2 ). Vineyard satellite-based estimations of zom using the Perrier function (Equation (19)) generated zom values ranging between 0.05 and 0.11 m, which are within the values indicated by [12,60].…”

Section: Comparison Between Measured and Estimated Variablesmentioning

confidence: 99%

“…This model was developed using values of LAI obtained from an allometric correlation [5,58] and NDVI computed from the handheld multi-spectral radiometer. It is important to indicate that values of NDVI calculated from Landsat scenes were significantly similar to those obtained from the handheld multi-spectral radiometer (R 2 = 0.99; MAE = 0.03; RMSE = 0.04).…”

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Parameterization of the Satellite-Based Model (METRIC) for the Estimation of Instantaneous Surface Energy Balance Components over a Drip-Irrigated Vineyard

et al. 2014

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Abstract:A study was carried out to parameterize the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model for estimating instantaneous values of albedo (shortwave albedo) (αi), net radiation (Rni) and soil heat flux (Gi), sensible (Hi) and latent heat (LEi) over a drip-irrigated Merlot vineyard (location: 35°25′ LS; 71°32′ LW; 125 m.a.s. (l). The experiment was carried out in a plot of 4.25 ha, processing 15 Landsat images, which were acquired from 2006 to 2009. An automatic weather station was placed inside the experimental plot to measure αi, Rni and Gi. In the same tower an Eddy Covariance (EC) system was mounted to measure Hi and LEi. Specific 11343 sub-models to estimate Gi, leaf area index (LAI) and aerodynamic roughness length for momentum transfer (zom) were calibrated for the Merlot vineyard as an improvement to the original METRIC model. Results indicated that LAI, zom and Gi were estimated using the calibrated functions with errors of 4%, 2% and 17%, while those were computed using the original functions with errors of 58%, 81%, and 5%, respectively. At the time of satellite overpass, comparisons between measured and estimated values indicated that METRIC overestimated αi in 21% and Rni in 11%. Also, METRIC using the calibrated functions overestimated Hi and LEi with errors of 16% and 17%, respectively while it using the original functions overestimated Hi and LEi with errors of 13% and 15%, respectively. Finally, LEi was estimated with root mean square error (RMSE) between 43 and 60 W·m

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Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods

Cited by 58 publications

References 41 publications

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

Parameterization of the Satellite-Based Model (METRIC) for the Estimation of Instantaneous Surface Energy Balance Components over a Drip-Irrigated Vineyard

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