Next Article in Journal
Automatic Ship Detection Using the Artificial Neural Network and Support Vector Machine from X-Band Sar Satellite Images
Next Article in Special Issue
Automated Open Cotton Boll Detection for Yield Estimation Using Unmanned Aircraft Vehicle (UAV) Data
Previous Article in Journal
Multi-Variable Classification Approach for the Detection of Lightning Activity Using a Low-Cost and Portable X Band Radar
Previous Article in Special Issue
High-Throughput Phenotyping of Crop Water Use Efficiency via Multispectral Drone Imagery and a Daily Soil Water Balance Model
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(11), 1798; https://doi.org/10.3390/rs10111798

How Far Can Consumer-Grade UAV RGB Imagery Describe Crop Production? A 3D and Multitemporal Modeling Approach Applied to Zea mays

1
TERRA Research and Teaching Center—Forest Is life, Gembloux Agro Bio-Tech, University of Liege, 5030 Gembloux, Belgium
2
TERRA Research and Teaching Center—Agriculture Is life, Gembloux Agro Bio-Tech (University of Liege), 5030 Gembloux, Belgium
3
TERRA Research and Teaching Center—Environment Is life, Gembloux Agro Bio-Tech (University of Liege), 5030 Gembloux, Belgium
*
Author to whom correspondence should be addressed.
Received: 24 September 2018 / Revised: 7 November 2018 / Accepted: 7 November 2018 / Published: 13 November 2018
Full-Text   |   PDF [1570 KB, uploaded 14 November 2018]   |  

Abstract

In recent decades, remote sensing has increasingly been used to estimate the spatio-temporal evolution of crop biophysical parameters such as the above-ground biomass (AGB). On a local scale, the advent of unmanned aerial vehicles (UAVs) seems to be a promising trade-off between satellite/airborne and terrestrial remote sensing. This study aims to evaluate the potential of a low-cost UAV RGB solution to predict the final AGB of Zea mays. Besides evaluating the interest of 3D data and multitemporality, our study aims to answer operational questions such as when one should plan a combination of two UAV flights for AGB modeling. In this case, study, final AGB prediction model performance reached 0.55 (R-square) using only UAV information and 0.8 (R-square) when combining UAV information from a single flight with a single-field AGB measurement. The adding of UAV height information to the model improves the quality of the AGB prediction. Performing two flights provides almost systematically an improvement in AGB prediction ability in comparison to most single flights. Our study provides clear insight about how we can counter the low spectral resolution of consumer-grade RGB cameras using height information and multitemporality. Our results highlight the importance of the height information which can be derived from UAV data on one hand, and on the other hand, the lower relative importance of RGB spectral information. View Full-Text
Keywords: unmanned aerial vehicles; unmanned aerial systems; drone; above-ground biomass; RGB imagery; photogrammetry; Zea mays unmanned aerial vehicles; unmanned aerial systems; drone; above-ground biomass; RGB imagery; photogrammetry; Zea mays
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Michez, A.; Bauwens, S.; Brostaux, Y.; Hiel, M.-P.; Garré, S.; Lejeune, P.; Dumont, B. How Far Can Consumer-Grade UAV RGB Imagery Describe Crop Production? A 3D and Multitemporal Modeling Approach Applied to Zea mays. Remote Sens. 2018, 10, 1798.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top