Characterization of <i>Vitis vinifera</i> L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques

Ballesteros, Rocío; Álvarez, José Fernando Ortega; Hernández, D.; Moreno, M.

doi:10.5344/ajev.2014.14070

Cited by 47 publications

(32 citation statements)

References 25 publications

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“…Using these UAS, higher spatial and temporal data resolution can be achieved, which makes possible precision agriculture applications to the submeter resolution per pixel. This allows research and practical applications applied to growth and vigor dynamic assessment, plant water status sensing for irrigation scheduling applications, and evapotranspiration modelling, among others [4][5][6][7][8][9].…”

Section: Remote Sensing Platform Considerationsmentioning

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: Remote Sensing Platform Considerationsmentioning

confidence: 99%

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

2017

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“…Zhang et al [28] estimated AGB of grassland by using PH at three different study sites selected from the Gansu, Inner Mongolia, and Jiangsu provinces of China, and indicated a high correlation between the PH and AGB with the coefficient of determination (R 2 ) values greater than 0.66. In addition to PH, vegetation indices (VIs) which could provide reliable information about crop growing status [26,29,30], such as green canopy cover and PH, have also been investigated as a reliable source to estimate AGB [31][32][33]. Sankaran et al[34] estimated biomass of dry bean at flowering and mid-pod fill stages by using the average green normalized difference vegetation index (GNDVI) in Othello, WA, USA, and indicated a high correlation between the average GNDVI and biomass with Pearson correlation coefficient values greater than 0.52.In general, VIs derived from the UAV imagery reflect the spectral characteristics of the top canopy and PH reflects the vertical structure properties of the entire canopy.…”

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confidence: 99%

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confidence: 99%

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

et al. 2019

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The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

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“…This issue has been recognised in previous work by the authors [21,22]. The overall ANN accuracy (81%) could therefore be improved through detailed flight planning that aims at minimising the presence of shadows.…”

Section: Discussionmentioning

confidence: 89%

“…Amongst all the existing supervised classification approaches, methods based on Artificial Neural Networks (ANNs) have been shown to enable image pattern recognition at particularly high precision with both coarse [16,17] and fine resolution imagery [18,19]. For example, [20,21,22] used ANNs to identify green canopy cover from background soil and shadows. ANNs have also been used successfully to map water bodies [23] and flood extent [24].…”

Section: Introductionmentioning

confidence: 99%

Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery

Casado

Ballesteros²,

Kriechbaumer

et al. 2015

Sensors

108

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European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management.

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Characterization of Vitis vinifera L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques

Cited by 47 publications

References 25 publications

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

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

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery

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