Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the U...

Full description

Saved in:
Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 12; no. 21; p. 3511
Main Authors Eskandari, Roghieh, Mahdianpari, Masoud, Mohammadimanesh, Fariba, Salehi, Bahram, Brisco, Brian, Homayouni, Saeid
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2020
Subjects
Accuracy
Aerial photography
Agriculture
agro-environmental monitoring
Aircraft
Algorithms
Classification
Data collection
Data processing
Environmental monitoring
Environmental studies
Forestry
Grasslands
Imagery
Learning algorithms
Machine learning
Mathematical models
Meta-analysis
Model accuracy
Photogrammetry
regression
Regression analysis
Regression models
Remote sensing
Reviews
Sensors
Software
Spatial analysis
Spatial discrimination
Spatial resolution
Statistical analysis
Statistical models
Systematic review
Unmanned Aerial Vehicle (UAV)
Unmanned aerial vehicles
Vegetation mapping
Wetlands
Online AccessGet full text

Cover

More Information
Summary:Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the UAV capabilities of various applications. This systematic review was performed on studies through a statistical meta-analysis of UAV applications along with machine learning algorithms in agro-environmental monitoring. For this purpose, a total number of 163 peer-reviewed articles published in 13 high-impact remote sensing journals over the past 20 years were reviewed focusing on several features, including study area, application, sensor type, platform type, and spatial resolution. The meta-analysis revealed that 62% and 38% of the studies applied regression and classification models, respectively. Visible sensor technology was the most frequently used sensor with the highest overall accuracy among classification articles. Regarding regression models, linear regression and random forest were the most frequently applied models in UAV remote sensing imagery processing. Finally, the results of this study confirm that applying machine learning approaches on UAV imagery produces fast and reliable results. Agriculture, forestry, and grassland mapping were found as the top three UAV applications in this review, in 42%, 22%, and 8% of the studies, respectively.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12213511