Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture

By A. Matese, P. Toscano, S. F. D. Gennaro, L. Genesio, F. Vaccari, J. Primicerio, C. Belli, A. Zaldei, R. Bianconi, B. Gioli. Remote Sensing, 2015

610 citations recorded by the source corpus.

Precision Viticulture is experiencing substantial growth thanks to the availability of improved and cost-effective instruments and methodologies for data acquisition and analysis, such as Unmanned Aerial Vehicles (UAV), that demonstrated to compete with traditional acquisition platforms, such as satellite and aircraft, due to low operational costs, high operational flexibility and high spatial resolution of imagery. In order to optimize the use of these technologies for precision viticulture, their technical, scientific and economic performances need to be assessed. The aim of this work is to compare NDVI surveys performed with UAV, aircraft and satellite, to assess the capability of each platform to represent the intra-vineyard vegetation spatial variability. NDVI images of two Italian vineyards were acquired simultaneously from different multi-spectral sensors onboard the three platforms, and a spatial statistical framework was used to assess their degree of similarity. Moreover, the pros and cons of each technique were also assessed performing a cost analysis as a function of the scale of application. Results indicate that the different platforms provide comparable results in vineyards characterized by coarse vegetation gradients and large vegetation clusters. On the contrary, in more heterogeneous vineyards, low-resolution images fail in representing part of the intra-vineyard variability. The cost analysis showed that the adoption of UAV platform is advantageous for small areas and that a break-even point exists above five hectares; above such threshold, airborne and then satellite have lower imagery cost.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/3c660ed6412425bf68dccedd6f098fcf6c4a5820

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Citations in the corpus

Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging (2017, background): “airborne platforms (for larger areas) and satellites show competitive results (Matese et al. 2015).”
Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds (2016, background): “, for areas the size of traditional forest plots up to areas of several km2) with an unrivalled combination of spatial and temporal resolution [10].”
A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles (2018, result): “One can also compare UAV channel models with satellite channel models [151], [152].”
Unmanned Aircraft System (UAS) Technology and Applications in Agriculture (2019, methodology): “In terms of aerial sensing platforms, a study by Matese and his colleagues found that farms smaller than 5 hectares and larger than 50 hectares would not benefit from using UAS and that satellite or aircraft sensing would be more economical [9].”
Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests (2017, background): “Lightweight unmanned aerial vehicles (UAVs) may be a suitable platform for acquiring digital aerial photographs for small areas at low cost [14,15].”
A compilation of UAV applications for precision agriculture (2020, neutral): “The UAV used for the experiments was a MikrokopterOktoXL multi-rotor [87] capable of ﬂying 15 min and carrying 2 kg weight.”
UAV & satellite synergies for optical remote sensing applications: A literature review (2021, positive): “In addition, UAVs provide low-cost VHSR data that reveal ﬁ ne patterns such as inter-row and intra-plot variability in vineyards (Aleem et al., 2019; Matese et al., 2015), the ﬁ ne morphology of glaciers (Fugazza et al., 2015), and even small water bodies (C ^ andido et al., 2016).”
Early season weed mapping in rice crops using multi-spectral UAV data (2018, background): “Among the advantages of RS are the synoptic view and the reduced cost per unit of area covered (Matese et al. 2015).”