Relationship between the normalized difference vegetation index and leaf area in vineyards

Junges, Amanda Heemann; Fontana, Denise Cybis; Lampugnani, Cristian Scalvi

doi:10.1590/1678-4499.2018168

Cited by 15 publications

(5 citation statements)

References 13 publications

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“…However, some similar trends were detected in both vineyards, an overall decline of the NDVI values in the last flight campaigns and an agreement between NDVI with the estimated grapevine volume and with the CWSI values was observed. The decline of NDVI in the last flight campaigns can be related to the leaf senescence and, consequently, its discolouration [62,63]. The relationships among the NDVI and volume relationships were verified in other studies [24] that showed that both of these parameters are related with LAI, which in fact, is also verified in this study.…”

Section: Multi-temporal Vineyard Monitoringsupporting

confidence: 86%

Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery

et al. 2020

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The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs’ capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants’ health status. In this way, the decision support process in precision viticulture can be greatly improved. However, despite the proliferation of these innovative technologies in viticulture, most of the published studies rely only on data from a single sensor in order to achieve a specific goal and/or in a single/small period of the vineyard development. In order to address these limitations and fully exploit the advantages offered by the use of UAVs, this study explores the multi-temporal analysis of vineyard plots at a grapevine scale using different imagery sensors. Individual grapevine detection enables the estimation of biophysical and geometrical parameters, as well as missing grapevine plants. A validation procedure was carried out in six vineyard plots focusing on the detected number of grapevines and missing grapevines. A high overall agreement was obtained concerning the number of grapevines present in each row (99.8%), as well as in the individual grapevine identification (mean overall accuracy of 97.5%). Aerial surveys were conducted in two vineyard plots at different growth stages, being acquired for RGB, multispectral and thermal imagery. Moreover, the extracted individual grapevine parameters enabled us to assess the vineyard variability in a given epoch and to monitor its multi-temporal evolution. This type of analysis is critical for precision viticulture, constituting as a tool to significantly support the decision-making process.

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Section: Multi-temporal Vineyard Monitoringsupporting

confidence: 86%

Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery

et al. 2020

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“…The lower model performances in the high vigor area can be related to the saturation of NDVI when LAI increases. In agreement with Junges et al (2019), the increase of NDVI with respect to the increase of leaf area suggests that NDVI shows a logarithmic behaviour rather than linear for LAI higher than 1, which prevents to highlight differences among plant vigor in vineyards. This issue might be overcome by the adoption of non-linear vegetation indices which are generally less affected by canopy variation and background effects (e.g.…”

Section: Discussionsupporting

confidence: 67%

Use of remote sensing-derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub-field level

Leolini

Bregaglio²,

Ginaldi³

et al. 2022

Precision Agric

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Grapevine simulation models are mostly used to estimate plant development, growth and yield at plot scale. However, the spatial variability of pedologic and micro-climatic conditions can influence vine growth, leading to a sub-field heterogeneity in plant vigor and final yield that may be better estimated through the assimilation of high spatial resolution data in crop models. In this study, the spatial variability of grapevine intercepted radiation at fruit-set was used as input for a grapevine simulation model to estimate the variability in biomass accumulation and yield in two Tuscan vineyards (Sites A and B). In Site A, the model, forced with intercepted radiation data as derived from the leaf area index (LAI), measured at canopy level in three main vigor areas of the vineyard, provided a satisfactory simulation of the final pruning weight (r2 = 0.61; RMSE = 19.86 dry matter g m−2). In Site B, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A images was firstly re-scaled to account for canopy fraction cover over the study areas and then used as a proxy for grapevine intercepted radiation for each single pixel. These data were used to drive the grapevine simulation model accounting for spatial variability of plant vigor to reproduce yield variability at pixel scale (r2 = 0.47; RMSE = 75.52 dry matter g m−2). This study represents the first step towards the realization of a decision tool supporting winegrowers in the selection of the most appropriate agronomic practices for reducing the vine vigor and yield variability at sub-field level.

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“…Thus reddening of canopy leaves (Mabhaudhi & Modi, 2013). Similarly, other studies show that temporal profiles of NDVI vary every fortnight, especially at the beginning and at the end of each cycle (Junges et al, 2019). These results show that NDVI has the potential to reflect the vegetation change and canopy development corresponding to canopy photosynthetic capacity (CPC) at different crop growth stages for Bambara groundnut.…”

Section: Evaluamentioning

confidence: 60%

Use of Unmanned Aerial Vehicles (UAVs) Imagery in Phenotyping of Bambara Groundnut

Suhairi¹,

Sinin²,

Wimalasiri³

et al. 2020

JAS

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In this experiment, proximal measurements and Unmanned Aerial Vehicle (UAV) imagery was used to determine growth stages for bambara groundnut (Vigna subterranea (L.) Verdc.). The crop is a high potential crop due to its ability to yield in marginal environments, but neglected and underutilised due to lack of information on its growth in different environments. This study evaluated the correlation between Normalised Difference Vegetation Index (NDVI) derived from the ground as well as airborne sensors to test the ability of remotely sensed data to identify growth stages. NDVI and chlorophyll content of bambara groundnut leaves were measured at ground level at 18, 32, 46 and 88 days after planting (DAP) comprising vegetative, flowering, pod formation and maturity growth stages. The UAV imagery for the experimental plots was acquired with 0.2m resolution at maturity. The result showed a significant (p < 0.05) linear relationship between proximal NDVI and chlorophylls content at all growth stages ofgrowth. The R2 varied from 0.57 in the vegetative stage to 0.78 in the flowering stage. Furthermore, NDVI derived from proximal measurements and UAV data showed a significant (p < 0.05) correlation. The observed high correlation between proximal sensors, UAV data and crop parameters suggest that remote sensing technologies can be used for rapid phenotyping to hasten the development of models to assess the performance of underutilised crops for food and nutrition security.

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Relationship between the normalized difference vegetation index and leaf area in vineyards

Cited by 15 publications

References 13 publications

Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery

Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery

Use of remote sensing-derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub-field level

Use of Unmanned Aerial Vehicles (UAVs) Imagery in Phenotyping of Bambara Groundnut

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