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Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications

Gevaert, C. M.; Tang, J. LU ; García-Haro, F. J.; Suomalainen, J. and Kooistra, L. (2017) 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 In 2014 6th Workshop on Hyperspectral Image and Signal Processing 2014-June.
Abstract

Remote sensing is a key tool for precision agriculture applications as it is capable of capturing spatial and temporal variations in crop status. However, satellites often have an inadequate spatial resolution for precision agriculture applications. High-resolution Unmanned Aerial Vehicles (UAV) imagery can be obtained at flexible dates, but operational costs may limit the collection frequency. The current study utilizes data fusion to create a dataset which benefits from the temporal resolution of Formosat-2 imagery and the spatial resolution of UAV imagery with the purpose of monitoring crop growth in a potato field. The correlation of the Weighted Difference Vegetation Index (WDVI) from fused imagery to measured crop indicators at... (More)

Remote sensing is a key tool for precision agriculture applications as it is capable of capturing spatial and temporal variations in crop status. However, satellites often have an inadequate spatial resolution for precision agriculture applications. High-resolution Unmanned Aerial Vehicles (UAV) imagery can be obtained at flexible dates, but operational costs may limit the collection frequency. The current study utilizes data fusion to create a dataset which benefits from the temporal resolution of Formosat-2 imagery and the spatial resolution of UAV imagery with the purpose of monitoring crop growth in a potato field. The correlation of the Weighted Difference Vegetation Index (WDVI) from fused imagery to measured crop indicators at field level and added value of the enhanced spatial and temporal resolution are discussed. The results of the STARFM method were restrained by the requirement of same-day base imagery. However, the unmixing-based method provided a high correlation to the field data and accurately captured the WDVI temporal variation at field level (r=0.969).

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
precision agriculture, STARFM, UAV, unmixing-based data fusion, WDVI
in
2014 6th Workshop on Hyperspectral Image and Signal Processing
volume
2014-June
publisher
IEEE Computer Society
conference name
6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014
external identifiers
  • scopus:85011045470
ISBN
9781467390125
DOI
10.1109/WHISPERS.2014.8077607
language
English
LU publication?
yes
id
0e398ec5-8f18-443d-83c4-e147e365a79a
date added to LUP
2018-01-05 09:02:33
date last changed
2018-01-05 09:02:33
@inproceedings{0e398ec5-8f18-443d-83c4-e147e365a79a,
  abstract     = {<p>Remote sensing is a key tool for precision agriculture applications as it is capable of capturing spatial and temporal variations in crop status. However, satellites often have an inadequate spatial resolution for precision agriculture applications. High-resolution Unmanned Aerial Vehicles (UAV) imagery can be obtained at flexible dates, but operational costs may limit the collection frequency. The current study utilizes data fusion to create a dataset which benefits from the temporal resolution of Formosat-2 imagery and the spatial resolution of UAV imagery with the purpose of monitoring crop growth in a potato field. The correlation of the Weighted Difference Vegetation Index (WDVI) from fused imagery to measured crop indicators at field level and added value of the enhanced spatial and temporal resolution are discussed. The results of the STARFM method were restrained by the requirement of same-day base imagery. However, the unmixing-based method provided a high correlation to the field data and accurately captured the WDVI temporal variation at field level (r=0.969).</p>},
  author       = {Gevaert, C. M. and Tang, J. and García-Haro, F. J. and Suomalainen, J. and Kooistra, L.},
  booktitle    = {2014 6th Workshop on Hyperspectral Image and Signal Processing},
  isbn         = {9781467390125},
  keyword      = {precision agriculture,STARFM,UAV,unmixing-based data fusion,WDVI},
  language     = {eng},
  month        = {10},
  publisher    = {IEEE Computer Society},
  title        = {Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications},
  url          = {http://dx.doi.org/10.1109/WHISPERS.2014.8077607},
  volume       = {2014-June},
  year         = {2017},
}