Matte2020

Référence

Matte, O. (2020) Cartographie des forêts à haute valeur de stockage de carbone par apprentissage profond sur l’île de Bornéo. Thèse de doctorat, Université Laval. (URL )

Résumé

Forests in Southeast Asia are under heavy pressure from extensive land-use activities, including oil palm plantations. The desire to protect and manage habitats with high carbon storage potential has increased the need for preserving the unique ecosystems of local forests. To preserve tropical forest ecosystems from agricultural expansion, a methodology for classifying forests with high carbon storage potential, known as the High Carbon Stock Approach (HCSA) was developed. Our research goal is to assess the effectiveness of the combined use of airborne LiDAR and deep learning for HCSA classification across the island of Borneo. To do this, we will examine the above-ground biomass using the equation developed by Asner (2018) and Jucker (2017), established in the Sabah territory, as well as LiDAR metrics such as canopy height, canopy cover, and the forest basal area. LiDAR metrics of forest structure will also be used to try to differentiate HCS classes. LiDAR data and field surveys were collected from the Jet Propulsion Laboratory (JPL -NASA). The area of interest for this study covers part of the Kalimantan territory (Indonesian part of Borneo). The data collected has been part of the ongoing Carbon Monitoring System (CMS) project. Then, the training of a deep learning algorithm will allow, by the use of satellite images (Landsat 7 and Landsat 8), to make a spatial and temporal jump, in order to establish a cartography of the forests to be monitored in 2019 and on the entirety of Borneo Island.

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@PHDTHESIS { Matte2020,
    TITLE = { Cartographie des forêts à haute valeur de stockage de carbone par apprentissage profond sur l’île de Bornéo },
    AUTHOR = { Matte, O. },
    SCHOOL = { Université Laval },
    YEAR = { 2020 },
    NOTE = { CEFTMS, Beland, M. },
    ABSTRACT = { Forests in Southeast Asia are under heavy pressure from extensive land-use activities, including oil palm plantations. The desire to protect and manage habitats with high carbon storage potential has increased the need for preserving the unique ecosystems of local forests. To preserve tropical forest ecosystems from agricultural expansion, a methodology for classifying forests with high carbon storage potential, known as the High Carbon Stock Approach (HCSA) was developed. Our research goal is to assess the effectiveness of the combined use of airborne LiDAR and deep learning for HCSA classification across the island of Borneo. To do this, we will examine the above-ground biomass using the equation developed by Asner (2018) and Jucker (2017), established in the Sabah territory, as well as LiDAR metrics such as canopy height, canopy cover, and the forest basal area. LiDAR metrics of forest structure will also be used to try to differentiate HCS classes. LiDAR data and field surveys were collected from the Jet Propulsion Laboratory (JPL -NASA). The area of interest for this study covers part of the Kalimantan territory (Indonesian part of Borneo). The data collected has been part of the ongoing Carbon Monitoring System (CMS) project. Then, the training of a deep learning algorithm will allow, by the use of satellite images (Landsat 7 and Landsat 8), to make a spatial and temporal jump, in order to establish a cartography of the forests to be monitored in 2019 and on the entirety of Borneo Island. },
    URL = { https://corpus.ulaval.ca/jspui/handle/20.500.11794/66791 },
    TIMESTAMP = { 2022-06-20 },
}

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