Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy

Zarco‐Tejada, Pablo J.; Berjón, A.; López-Lozano, Raúl; Miller, John R.; Martín, Pedro; Cachorro, Victoria E.; Rebollo, Miguel Ángel González; Baraja, Angel Máximo de Frutos

doi:10.1016/j.rse.2005.09.002

Cited by 603 publications

(289 citation statements)

References 62 publications

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“…These VIs are commonly associated with plant structural traits like leaf area index, biomass, and plant vigor (e.g., [30]) but show lower performance in the detection of physiological stress condition [65]. In addition, the results obtained by WI, specifically designed for estimation of plant water content [23], showed poor correlation with Ψpd, particularly in block 1 (Table 2).…”

Section: Discussionmentioning

confidence: 99%

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Pôças

Rodrigues²,

Gonçalves

et al. 2015

Remote Sensing

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Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region were tested for modelling grapevine water status estimated by the predawn leaf water potential (Ψpd). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation between VIs and Ψpd. A linear regression was defined using a parameterization dataset. The correlation analysis between Ψpd and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r 2 ) smaller than 0.67. However, the results of r 2 highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) showed the higher r 2 and stability index results. The equations obtained through the regression between measured Ψpd (Ψpd_obs) and optimal VARI and between Ψpd_obs and optimal NDGI when using the parameterization dataset were adopted for predicting Ψpd using a testing dataset. The comparison of Ψpd_obs with Ψpd predicted based on VARI led to R 2 = 0.79 and a regression coefficient b = 0.96. Similar R 2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating Ψpd, supporting vineyards irrigation management.

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Section: Discussionmentioning

confidence: 99%

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Pôças

Rodrigues²,

Gonçalves

et al. 2015

Remote Sensing

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“…28 These filters, whose transmittance curves are shown in Figure 1, were characterised using a calibration sphere Li-Cor 1800-12. 29 The filters were obtained from B+W (Berlin, Germany).…”

Section: Equipmentmentioning

confidence: 99%

Recognition of materials and damage on historical buildings using digital image classification

Larriva¹,

Perea²,

Ureña³

et al. 2015

S. Afr. J. Sci.

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Nowadays, techniques in digital image processing make it possible to detect damage, such as moisture or biological changes, on the surfaces of historical buildings. Digital classification techniques can be used to identify damages in construction materials in a non-destructive way. In this study, we evaluate the application of the object-oriented classification technique using photographs taken with a Fujifilm IS-Pro digital single lens reflex camera and the integration of the classified images in a three-dimensional model obtained through terrestrial laser scanning data in order to detect and locate damage affecting biocalcarenite stone employed in the construction of the Santa Marina Church (Córdoba, Spain). The Fujifilm IS-Pro camera captures spectral information in an extra-visible range, generating a wide spectral image with wavelengths ranging from ultraviolet to infrared. Techniques of object-oriented classification were applied, taking into account the shapes, textures, background information and spectral information in the image. This type of classification requires prior segmentation, defined as the search for homogeneous regions in an image. The second step is the classification process of these regions based on examples. The output data were classified according to the kind of damage that affects the biocalcarenite stone, reaching an overall classification accuracy of 92% and an excellent kappa statistic (85.7%). We have shown that multispectral classification with visible and near-infrared bands increased the degree of recognition among different damages. Post-analysis of these data integrated in a three-dimensional model allows us to obtain thematic maps with the size and position of the damage.

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“…They can be roughly subdivided into four different categories: i) Broadband vegetation indices, originally designed to estimate canopy structural variables such as LAI and fractional cover; ii) Chlorophyll indices which are narrow band indices designed to estimate leaf chlorophyll content; iii) Narrow band water indices, designed to estimate plant water content or detect water stress and having one or more bands in spectral regions sensitive to water absorption, and iv) dry matter indices which are sensitive to one or more of the chemicals composing Cdm, such as lignin and cellulose. For a detailed discussion of characteristics and performance the reader is referred to the original publications or to several publications describing and comparing various VIs [10,18,55]. In order to find the best predictive equation, both linear and exponential fits are tested for each combination of variable and VI.…”

Section: Using Predictive Equations For a First Guess Solutionmentioning

confidence: 99%

“…For this reason, the relationships between VIs and biochemical and physical variables are often established by the use of canopy radiative transfer models (RTMs), which explicitly describe the interactions between solar radiation and the elements constituting the canopy using physical laws. RTMs thus allow to generate a virtually endless number of canopy reflectance spectra for which the input variables are known and illumination and view conditions can be controlled [10,[17][18][19]. Regression equations developed using RTMs are therefore often more robust than those based on in situ measurements.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Dorigo

Richter

Baret³

et al. 2009

Remote Sensing

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Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data. CRASh, which is based on the inversion of the combined PROSPECT+SAILh radiative transfer model (RTM), explores the benefits of combining semi-empirical and physically based approaches. The approach exploits novel ways to address the underdetermined problem in the context of an automated retrieval from mono-temporal high resolution data. To regularize the inverse problem in the variable domain, RTM inversion is coupled with an OPEN ACCESSRemote Sens. 2009, 1 1140 automated land cover classification. Model inversion is based on a two step lookup table (LUT) approach: First, a range of possible solutions is selected from a previously calculated LUT based on the analogy between measured and simulated reflectance. The final solution is determined from this subset by minimizing the difference between the variables used to simulate the spectra contained in the reduced LUT and a first guess of the solution. This first guess of the variables is derived from predictive semi-empirical relationships between classical vegetation indices and the single variables. Additional spectral regularization is obtained by the use of hyperspectral data. Results show that estimates obtained with CRASh are significantly more accurate than those obtained with a tested conventional RTM inversion and semi-empirical approach. Accuracies obtained in this study are comparable to the results obtained by various authors for better constrained inversions that assume more a priori information. The completely automated and image-based nature of the approach facilitates its use in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers.

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Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy

Cited by 603 publications

References 62 publications

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Recognition of materials and damage on historical buildings using digital image classification

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

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