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Grant: Discovery Grants Program - Individual

Lakehead University

$30,000.00

Use of UAV images for precision agriculture and environmental applications

EF 2023-24

Résumé
For several environmental applications, airborne /satellite images are less suitable than unmanned aerial vehicles (UAV) images. UAVs can operate at much low altitudes that results in high spatial resolution images. They are more mobile, fast, adaptable, and easier to use. Furthermore, they allow surveys in a repetitive and much more cheaply way. Under a NSERC-CRD grant, we are developing a new multispectral UAV camera for crop management. The requested Discovery Grant will built upon this NSERC-CRD grant and several on-going projects. First, we will improve the automated method we developed to count apple orchard trees from UAV images to detect damaged trees. We will also test the method to other types of orchards. Second, we will test the use of UAV images for monitoring eelgrass bed restoration, in conjunction to a successful Environment Canada grant supporting field and UAV data acquisition. Third, UAV images will be used to monitor natural vegetation recolonization on recently restored salt marshes. For the three projects, novel methods that use spectral and textural image information will be developed. Given the high spatial resolution of the images, advanced classifiers will be tested in addition to standard pixel-based classifiers. UAV image processing can be challenging because UAV are more susceptible to windy conditions, which can affect the viewing and illumination geometries. Also, the low UAV flying altitude requires more images to cover the area, which can lead to increasing of the sensor temperature and of the probability of variable weather conditions. For the eelgrass project, an additional challenge is to consider the effect of the water column on the image acquisition and processing. Also for both coastal applications, image acquisition should take into account potential sun glint effects. The research is important to those who need an affordable and flexible monitoring tool that provides high spatial images. The damaged tree mapping method will be used by growers to assess the status of their orchards and by crop insurance agencies to determine compensations to growers. The eelgrass mapping method will be used by the Department of Fisheries and Oceans and other NGO groups working on eelgrass restoration projects. The salt marsh mapping method will be used by Ducks Unlimited Canada to monitor salt marsh restoration. Beyond these three applications, the research will also produce novel methods for UAV image processing that can be used in other applications. The proposed research will contribute to the development in Canada of a booming commercial UAV sector that is projected to more than quadruple in sales worldwide over the next five years.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
RGPIN-2018-04130
Name nom
Leblon, Brigitte
Department département
Faculty of Natural Resources Management
Organization id
25
Institution établissement
Lakehead University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2018
Award amount
30000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1506
Committee name en
Geosciences
Committee name fr
Géosciences
Area of application code
400
Area of application group en
Environment
Area of application group fr
Environnement
Area of application en
Environment
Area of application fr
Environnement
Research subject code
7003
Research subject group en
Other studies in natural sciences and engineering
Research subject group fr
Renewable and non-renewable resources management
Research subject en
Autres études en sciences naturelles et en génie
Research subject fr
Gestion des ressources renouvelables et non renouvelables
Application title
Use of UAV images for precision agriculture and environmental applications
Keyword
damaged orchard tree; eelgrass bed; leaf area index; machine learning; multispectral camera; natural vegetation recolonization; object-based image classification; Random Forests; salt marsh; unmanned aerial vehicle (UAV)
Application summary
For several environmental applications, airborne /satellite images are less suitable than unmanned aerial vehicles (UAV) images. UAVs can operate at much low altitudes that results in high spatial resolution images. They are more mobile, fast, adaptable, and easier to use. Furthermore, they allow surveys in a repetitive and much more cheaply way. Under a NSERC-CRD grant, we are developing a new multispectral UAV camera for crop management. The requested Discovery Grant will built upon this NSERC-CRD grant and several on-going projects. First, we will improve the automated method we developed to count apple orchard trees from UAV images to detect damaged trees. We will also test the method to other types of orchards. Second, we will test the use of UAV images for monitoring eelgrass bed restoration, in conjunction to a successful Environment Canada grant supporting field and UAV data acquisition. Third, UAV images will be used to monitor natural vegetation recolonization on recently restored salt marshes. For the three projects, novel methods that use spectral and textural image information will be developed. Given the high spatial resolution of the images, advanced classifiers will be tested in addition to standard pixel-based classifiers. UAV image processing can be challenging because UAV are more susceptible to windy conditions, which can affect the viewing and illumination geometries. Also, the low UAV flying altitude requires more images to cover the area, which can lead to increasing of the sensor temperature and of the probability of variable weather conditions. For the eelgrass project, an additional challenge is to consider the effect of the water column on the image acquisition and processing. Also for both coastal applications, image acquisition should take into account potential sun glint effects. The research is important to those who need an affordable and flexible monitoring tool that provides high spatial images. The damaged tree mapping method will be used by growers to assess the status of their orchards and by crop insurance agencies to determine compensations to growers. The eelgrass mapping method will be used by the Department of Fisheries and Oceans and other NGO groups working on eelgrass restoration projects. The salt marsh mapping method will be used by Ducks Unlimited Canada to monitor salt marsh restoration. Beyond these three applications, the research will also produce novel methods for UAV image processing that can be used in other applications. The proposed research will contribute to the development in Canada of a booming commercial UAV sector that is projected to more than quadruple in sales worldwide over the next five years.