Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data
Light detection and ranging (LiDAR) has greatly improved the spatial resolution and accuracy of operational forest inventories. However, a cost-effective method to impute species-specific tree-level inventory is needed, to be used as input to tree or stand growth models to project single-point-in-ti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2017-05-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2017.1324288 |
| _version_ | 1797661134175600640 |
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| author | Sean M. Lamb David A. MacLean Chris R. Hennigar Douglas G. Pitt |
| author_facet | Sean M. Lamb David A. MacLean Chris R. Hennigar Douglas G. Pitt |
| author_sort | Sean M. Lamb |
| collection | DOAJ |
| description | Light detection and ranging (LiDAR) has greatly improved the spatial resolution and accuracy of operational forest inventories. However, a cost-effective method to impute species-specific tree-level inventory is needed, to be used as input to tree or stand growth models to project single-point-in-time LiDAR estimates. We evaluated a method to match stand structural variables estimated from LiDAR to those in a library of over 5,500 sample plot measurements to impute tree lists for LiDAR grid cells across 83,000 ha of spruce (Picea sp.) plantations. Matches were determined based on planted species and minimum sum of squared difference between 6 inventory variables. Forest inventory variables obtained by the plot matches were highly correlated (r = 0.91–0.99) with those measured on 98 validation plots. Basal area distributions derived from plot matching were statistically equivalent to those observed on the validation plots 86% of the time (α = 0.05). When we aggregated the predictions for all validation plots, there was minimal difference between predicted and actual basal area distributions by planted species and species compositions were similar. Plot matching is a valid method to impute tree lists for LiDAR cells that combine the wealth of existing plot data with high resolution LiDAR-derived variables. |
| first_indexed | 2024-03-11T18:41:08Z |
| format | Article |
| id | doaj.art-aaf9f595b0fe4d879cbbdbf1c3b6dbba |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:41:08Z |
| publishDate | 2017-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-aaf9f595b0fe4d879cbbdbf1c3b6dbba2023-10-12T13:36:22ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712017-05-0143326928510.1080/07038992.2017.13242881324288Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot DataSean M. Lamb0David A. MacLean1Chris R. Hennigar2Douglas G. Pitt3University of New BrunswickUniversity of New BrunswickUniversity of New BrunswickNatural Resources CanadaLight detection and ranging (LiDAR) has greatly improved the spatial resolution and accuracy of operational forest inventories. However, a cost-effective method to impute species-specific tree-level inventory is needed, to be used as input to tree or stand growth models to project single-point-in-time LiDAR estimates. We evaluated a method to match stand structural variables estimated from LiDAR to those in a library of over 5,500 sample plot measurements to impute tree lists for LiDAR grid cells across 83,000 ha of spruce (Picea sp.) plantations. Matches were determined based on planted species and minimum sum of squared difference between 6 inventory variables. Forest inventory variables obtained by the plot matches were highly correlated (r = 0.91–0.99) with those measured on 98 validation plots. Basal area distributions derived from plot matching were statistically equivalent to those observed on the validation plots 86% of the time (α = 0.05). When we aggregated the predictions for all validation plots, there was minimal difference between predicted and actual basal area distributions by planted species and species compositions were similar. Plot matching is a valid method to impute tree lists for LiDAR cells that combine the wealth of existing plot data with high resolution LiDAR-derived variables.http://dx.doi.org/10.1080/07038992.2017.1324288 |
| spellingShingle | Sean M. Lamb David A. MacLean Chris R. Hennigar Douglas G. Pitt Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data Canadian Journal of Remote Sensing |
| title | Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data |
| title_full | Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data |
| title_fullStr | Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data |
| title_full_unstemmed | Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data |
| title_short | Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data |
| title_sort | imputing tree lists for new brunswick spruce plantations through nearest neighbor matching of airborne laser scan and inventory plot data |
| url | http://dx.doi.org/10.1080/07038992.2017.1324288 |
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