Grant: Alliance Grants
York University
$212,464.00
An AI-driven approach that incorporates Two-Eyed Seeing to the accurate quantification of forest vegetation and integrity for nature-based climate solutions
EF 2024-25
Résumé
Accurate and relevant representation of forest vegetation and ecological integrity is crucial to assessing natural climate solutions that aim to reduce carbon emissions, support biodiversity conservation, and enhance forest resilience in a changing climate. It is also vital for and supports First Nations' forest management objectives, contributes to diversifying participation in forest management planning, developing conservation economies, and decision-making authority in their homelands. Despite considerable efforts to characterize forest vegetation, we lack accurate large-scale representations of canopy structure, understory vegetation and forest recovery. In addition, assessing integrity is difficult due to a lack of clarity on what observable and meaningful indicators to use and how to monitor those indicators. Furthermore, Indigenous knowledge of forest ecosystems is rarely integrated into forest management and monitoring. Capitalizing on the artificial intelligence (AI) revolution and the Ontario government's commitment to wall-to-wall coverage of high-density single photon LiDAR (SPL) data across Ontario's Forest management units, we will develop an individual tree-based AI approach to improve the representation of the species and structures of overstory canopies, cover type and abundance of understory vegetation, the quantification of forest recovery, and characterization of forest ecological integrity using measures that are meaningful to forest managers and Indigenous communities.
The map products and algorithms/tools we develop are crucial to informed decision-making in managing forests sustainably to reduce the dangers and damage associated with climate change while ensuring forest resource-based communities continue to thrive and contribute to Canada's social and economic well-being. This project will represent a significant advance in the applications of AI in digital representation, interpretation, and understanding of forest ecosystems and ensure that Canada remains at the forefront of the AI revolution.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
597627-2024
Name nom
Hu, Baoxin B
Department département
Earth and Space Science and Engineering
Organization id
38
Institution établissement
York University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2024
Award amount
212464
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
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
4102
Research subject group en
Geographical information
Research subject group fr
Information géographique
Research subject en
Remote sensing
Research subject fr
Télédétection
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
An AI-driven approach that incorporates Two-Eyed Seeing to the accurate quantification of forest vegetation and integrity for nature-based climate solutions
Keywords
artificial intelligence; forest integrity; forest recovery; individual tree based; nature-based climate solutions; remote sensing; understory mapping
Application summary
Accurate and relevant representation of forest vegetation and ecological integrity is crucial to assessing natural climate solutions that aim to reduce carbon emissions, support biodiversity conservation, and enhance forest resilience in a changing climate. It is also vital for and supports First Nations' forest management objectives, contributes to diversifying participation in forest management planning, developing conservation economies, and decision-making authority in their homelands. Despite considerable efforts to characterize forest vegetation, we lack accurate large-scale representations of canopy structure, understory vegetation and forest recovery. In addition, assessing integrity is difficult due to a lack of clarity on what observable and meaningful indicators to use and how to monitor those indicators. Furthermore, Indigenous knowledge of forest ecosystems is rarely integrated into forest management and monitoring. Capitalizing on the artificial intelligence (AI) revolution and the Ontario government's commitment to wall-to-wall coverage of high-density single photon LiDAR (SPL) data across Ontario's Forest management units, we will develop an individual tree-based AI approach to improve the representation of the species and structures of overstory canopies, cover type and abundance of understory vegetation, the quantification of forest recovery, and characterization of forest ecological integrity using measures that are meaningful to forest managers and Indigenous communities.
The map products and algorithms/tools we develop are crucial to informed decision-making in managing forests sustainably to reduce the dangers and damage associated with climate change while ensuring forest resource-based communities continue to thrive and contribute to Canada's social and economic well-being. This project will represent a significant advance in the applications of AI in digital representation, interpretation, and understanding of forest ecosystems and ensure that Canada remains at the forefront of the AI revolution.
