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UBC Theses and Dissertations
Remote sensing to inform precision forestry management in P. radiata plantations Gavilan Acuna, Gonzalo A.
Abstract
The increasing global demand for timber and pulp has intensified the management of fast-growing plantation species like Pinus (pine), with Precision Forestry (PF) emerging as a critical framework for improving forest productivity and sustainability. For PF to effectively inform forest management decisions, accurate, high-resolution, and up-to-date data on forest characteristics at the stand, sub-stand, or individual tree level are essential. Traditional field inventories are insufficient for gathering such detailed information. The growing availability of remote sensing (RS) technologies, including Airborne Laser Scanning (ALS) and satellite time series data, presents a valuable opportunity to enhance decision-making in a PF context. However, several key gaps remain in utilizing these tools for plantation management, including the need for fine-resolution soil property data for improved fertilizer allocation, identifying trees for thinning based on individual productivity, monitoring volume growth over time, and developing a comprehensive PF framework throughout the forest rotation. This research addresses these gaps by integrating high-resolution remote sensing data to enhance forest management decisions, with a focus on P. radiata plantations in central-southern Chile. First, ALS-derived terrain attributes are used to predict soil properties through a digital soil mapping (DSM) approach, generating high-resolution maps of soil texture, depth, and organic matter, with associated uncertainties, to support fertilizer allocation. Second, individual tree growth is modeled through a single ALS survey, accounting for tree competition and predicting growth until the end of the rotation, aiding in thinning prescriptions. Third, volume growth over time is assessed using Leaf Area Index (LAI) and combined ALS and satellite data. Finally, the outcomes of these steps, along with a review of PF research, are synthesized into a comprehensive framework for applying RS to PF across the entire rotation. These findings demonstrate that integrating ALS and satellite time series technologies offers significant potential for optimizing management practices, such as site-specific fertilization, thinning, and adaptive silviculture. By improving our understanding of forest productivity through precise spatial data, this thesis contributes to more sustainable and efficient management of fast-growing plantations.
Item Metadata
Title |
Remote sensing to inform precision forestry management in P. radiata plantations
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Creator | |
Supervisor | |
Publisher |
University of British Columbia
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Date Issued |
2024
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Description |
The increasing global demand for timber and pulp has intensified the management of fast-growing plantation species like Pinus (pine), with Precision Forestry (PF) emerging as a critical framework for improving forest productivity and sustainability. For PF to effectively inform forest management decisions, accurate, high-resolution, and up-to-date data on forest characteristics at the stand, sub-stand, or individual tree level are essential. Traditional field inventories are insufficient for gathering such detailed information. The growing availability of remote sensing (RS) technologies, including Airborne Laser Scanning (ALS) and satellite time series data, presents a valuable opportunity to enhance decision-making in a PF context. However, several key gaps remain in utilizing these tools for plantation management, including the need for fine-resolution soil property data for improved fertilizer allocation, identifying trees for thinning based on individual productivity, monitoring volume growth over time, and developing a comprehensive PF framework throughout the forest rotation.
This research addresses these gaps by integrating high-resolution remote sensing data to enhance forest management decisions, with a focus on P. radiata plantations in central-southern Chile. First, ALS-derived terrain attributes are used to predict soil properties through a digital soil mapping (DSM) approach, generating high-resolution maps of soil texture, depth, and organic matter, with associated uncertainties, to support fertilizer allocation. Second, individual tree growth is modeled through a single ALS survey, accounting for tree competition and predicting growth until the end of the rotation, aiding in thinning prescriptions. Third, volume growth over time is assessed using Leaf Area Index (LAI) and combined ALS and satellite data. Finally, the outcomes of these steps, along with a review of PF research, are synthesized into a comprehensive framework for applying RS to PF across the entire rotation.
These findings demonstrate that integrating ALS and satellite time series technologies offers significant potential for optimizing management practices, such as site-specific fertilization, thinning, and adaptive silviculture. By improving our understanding of forest productivity through precise spatial data, this thesis contributes to more sustainable and efficient management of fast-growing plantations.
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Genre | |
Type | |
Language |
eng
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Date Available |
2024-12-19
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0447561
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2025-05
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
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DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International