Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data
Satellite forest inventories are the only feasible way to map Canada’s vast, remote forest regions, such as those in the Northwest Territories (NWT). A method used to create such inventories is the k-nearest neighbour (k-NN) algorithm, which spatially extends information from forest inventory (FI) p...
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ftmdpi:oai:mdpi.com:/2072-4292/14/5/1181/ 2023-08-20T04:08:50+02:00 Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data André Beaudoin Ronald J. Hall Guillermo Castilla Michelle Filiatrault Philippe Villemaire Rob Skakun Luc Guindon agris 2022-02-27 application/pdf https://doi.org/10.3390/rs14051181 EN eng Multidisciplinary Digital Publishing Institute Forest Remote Sensing https://dx.doi.org/10.3390/rs14051181 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 5; Pages: 1181 forest vegetation inventory PALSAR Landsat LiDAR GLAS k -NN boreal forest Northwest Territories Text 2022 ftmdpi https://doi.org/10.3390/rs14051181 2023-08-01T04:18:40Z Satellite forest inventories are the only feasible way to map Canada’s vast, remote forest regions, such as those in the Northwest Territories (NWT). A method used to create such inventories is the k-nearest neighbour (k-NN) algorithm, which spatially extends information from forest inventory (FI) plots to the entire forest land base using wall-to-wall features typically derived from Landsat data. However, the benefits of integrating L-band synthetic aperture radar (SAR) data, strongly correlated to forest biomass, have not been assessed for Canadian northern boreal forests. Here we describe an optimized multivariate k-NN implementation of a 151,700 km2 area in southern NWT that included ca. 2007 Landsat and dual-polarized Phased Array type L-band SAR (PALSAR) data on board the Advanced Land Observing Satellite (ALOS). Five forest attributes were mapped at 30 m cells: stand height, crown closure, stand/total volume and aboveground biomass (AGB). We assessed accuracy gains compared to Landsat-based maps. To circumvent the scarcity of FI plots, we used 3600 footprints from the Geoscience Laser Altimeter System (GLAS) as surrogate FI plots, where forest attributes were estimated using Light Detection and Ranging (LiDAR) metrics as predictors. After optimization, k-NN predicted forest attribute values for each pixel as the average of the 4 nearest (k = 4) surrogate FI plots within the Euclidian space of 9 best features (selected among 6 PALSAR, 10 Landsat, and 6 environmental features). Accuracy comparisons were based on 31 National Forest Inventory ground plots and over 1 million airborne LiDAR plots. Maps that included PALSAR HV backscatter resulted in forest attribute predictions with higher goodness of fit (adj. R2), lower percent mean error (ME%), and percent root mean square error (RMSE%), and lower underestimation for larger attribute values. Predictions were most accurate for conifer stand height (RMSE% = 32.1%, adj. R2 = 0.58) and AGB (RMSE% = 47.8%, adj. R2 = 0.74), which is much more abundant in the area ... Text Northwest Territories MDPI Open Access Publishing Northwest Territories Canada Remote Sensing 14 5 1181 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
forest vegetation inventory PALSAR Landsat LiDAR GLAS k -NN boreal forest Northwest Territories |
spellingShingle |
forest vegetation inventory PALSAR Landsat LiDAR GLAS k -NN boreal forest Northwest Territories André Beaudoin Ronald J. Hall Guillermo Castilla Michelle Filiatrault Philippe Villemaire Rob Skakun Luc Guindon Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data |
topic_facet |
forest vegetation inventory PALSAR Landsat LiDAR GLAS k -NN boreal forest Northwest Territories |
description |
Satellite forest inventories are the only feasible way to map Canada’s vast, remote forest regions, such as those in the Northwest Territories (NWT). A method used to create such inventories is the k-nearest neighbour (k-NN) algorithm, which spatially extends information from forest inventory (FI) plots to the entire forest land base using wall-to-wall features typically derived from Landsat data. However, the benefits of integrating L-band synthetic aperture radar (SAR) data, strongly correlated to forest biomass, have not been assessed for Canadian northern boreal forests. Here we describe an optimized multivariate k-NN implementation of a 151,700 km2 area in southern NWT that included ca. 2007 Landsat and dual-polarized Phased Array type L-band SAR (PALSAR) data on board the Advanced Land Observing Satellite (ALOS). Five forest attributes were mapped at 30 m cells: stand height, crown closure, stand/total volume and aboveground biomass (AGB). We assessed accuracy gains compared to Landsat-based maps. To circumvent the scarcity of FI plots, we used 3600 footprints from the Geoscience Laser Altimeter System (GLAS) as surrogate FI plots, where forest attributes were estimated using Light Detection and Ranging (LiDAR) metrics as predictors. After optimization, k-NN predicted forest attribute values for each pixel as the average of the 4 nearest (k = 4) surrogate FI plots within the Euclidian space of 9 best features (selected among 6 PALSAR, 10 Landsat, and 6 environmental features). Accuracy comparisons were based on 31 National Forest Inventory ground plots and over 1 million airborne LiDAR plots. Maps that included PALSAR HV backscatter resulted in forest attribute predictions with higher goodness of fit (adj. R2), lower percent mean error (ME%), and percent root mean square error (RMSE%), and lower underestimation for larger attribute values. Predictions were most accurate for conifer stand height (RMSE% = 32.1%, adj. R2 = 0.58) and AGB (RMSE% = 47.8%, adj. R2 = 0.74), which is much more abundant in the area ... |
format |
Text |
author |
André Beaudoin Ronald J. Hall Guillermo Castilla Michelle Filiatrault Philippe Villemaire Rob Skakun Luc Guindon |
author_facet |
André Beaudoin Ronald J. Hall Guillermo Castilla Michelle Filiatrault Philippe Villemaire Rob Skakun Luc Guindon |
author_sort |
André Beaudoin |
title |
Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data |
title_short |
Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data |
title_full |
Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data |
title_fullStr |
Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data |
title_full_unstemmed |
Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data |
title_sort |
improved k-nn mapping of forest attributes in northern canada using spaceborne l-band sar, multispectral and lidar data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14051181 |
op_coverage |
agris |
geographic |
Northwest Territories Canada |
geographic_facet |
Northwest Territories Canada |
genre |
Northwest Territories |
genre_facet |
Northwest Territories |
op_source |
Remote Sensing; Volume 14; Issue 5; Pages: 1181 |
op_relation |
Forest Remote Sensing https://dx.doi.org/10.3390/rs14051181 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs14051181 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
5 |
container_start_page |
1181 |
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1774721359085043712 |