WMS
National Forest Inventory kNN k-Nearest Neighbour
National Forest Inventory Project Office. This Webservice is for kNN Maps developed from NFI ground and photo plots. Published on Canada's National Forest Information System (NFIS)
ecology
botany
forest management
kNN
NFI
Forest
Information
System
Sustainable
Management
SFM
National Forest Inventory
Dr. Alex Song
Natural Resources Canada - CFS Pacific
NFI Data Manager
postal
506 West Burnside Rd.
Victoria
British Columbia
V8Z 1M5
CANADA
(250) 298-2415
(250) 363-0775
support@nfis.org
none
WMS image only
3000
3000
text/xml
image/png
image/jpeg
image/png; mode=8bit
image/tiff
text/plain
application/vnd.ogc.gml
text/xml
image/png
image/jpeg
image/png; mode=8bit
text/xml
XML
INIMAGE
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NFIS kNN Project Office WMS
National Forest Inventory kNN k-Nearest Neighbour
National Forest Inventory Project Office. This Webservice is for kNN Maps developed from NFI ground and photo plots. Published on Canada's National Forest Information System (NFIS)
ecology
botany
forest management
kNN
NFI
Forest
Information
System
Sustainable
Management
SFM
National Forest Inventory
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3978
EPSG:3979
EPSG:3857
-183.41
-7.31157
21.3303
83.3433
1000
5e+07
Canada_HillShadeLakes
ca_HillShadeLakes_r
Hill Shade and Lakes : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Hill Shade
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
simple_canada_boundary
ca_simple_canada_boundary_r
A simple raster representing Canada's boundaries (low resolution)
kNN
ecology
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-188.089
-0.27122
26.0029
87.3344
kNN_DominantGenus_250m
ca_kNN_DominantGenus_250m_r
kNN Dominant Genus 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Dominant Genus
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Genus_Abie_250m
ca_kNN_Genus_Abie_250m_r
kNN Genus Abies / True Firs 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Genus Abies
True Firs
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Genus_Acer_250m
ca_kNN_Genus_Acer_250m_r
kNN Genus Acer / Maples 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Genus Acer
Maples
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Genus_Betu
ca_kNN_Genus_Betu_250m_r
kNN Genus Betula / Birches 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Genus Betula
Birches
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Genus_Pice
ca_kNN_Genus_Pice_250m_r
kNN Genus Picea / Spruces 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Genus Picea
Spruces
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Genus_Pinu
ca_kNN_Genus_Pinu_250m_r
kNN Genus Pinus / Pine 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Genus Pinus
Pine
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Genus_Popu
ca_kNN_Genus_Popu_250m_r
kNN Genus Populus / Poplars, Aspens, Cottonwoods 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Genus Populus
Poplars
Aspens
Cottonwoods
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Genus_Pseu
ca_kNN_Genus_Pseu_250m_r
kNN Genus Pseudotsuga / Douglas-Firs 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Genus Pseudotsuga
Douglas-firs
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Genus_Thuj
ca_kNN_Genus_Thuj_250m_r
kNN Genus Thuja / Cedars 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Genus Thujia
Cedars
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Genus_Tsug
ca_kNN_Genus_Tsug_250m_r
kNN Genus Tsuga / Hemlocks 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Genus Tsuga
Hemlocks
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_LandCover_Veg_250m
ca_kNN_LandCover_Veg_250m_r
kNN Land Cover Vegetation 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Land Cover
Vegetation
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_LandCover_VegTreed_250m
ca_kNN_LandCover_VegTreed_250m_r
kNN Land Cover Vegetation Treed 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Land Cover
Vegetation
Treed
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_SpeciesDominant_broadleaf_250m
ca_kNN_SpeciesDominant_broadleaf_250m_r
kNN Species Dominant Broad Leaf 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Species
Dominant
Broad leaf
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_SpeciesDominant_needleleaf_250m
ca_kNN_SpeciesDominant_needleleaf_250m_r
kNN Species Dominant Needle Leaf 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Species
Dominant
Needle leaf
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_SpeciesGroups_Lari_250m
ca_kNN_SpeciesGroups_Lari_250m_r
kNN Species Groups Larix / Larch 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Species
Groups
Larix
Larch
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Structure_Biomass_TotalLiveAboveGround_400_250m
ca_kNN_Structure_Biomass_TotalLiveAboveGround_400_250m_r
kNN Structure Biomass Total Live Above Ground 400 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Structure Biomass Total Live Above Ground
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Structure_Stand_Age_250yr_250m
ca_kNN_Structure_Stand_Age_250yr_250m_r
kNN Structure Stand Age 250 yr 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Structure
Stand
Age
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Structure_Stand_CrownClosure_250m
ca_kNN_Structure_Stand_CrownClosure_250m_r
kNN Structure Stand Crown Closure 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Structure
Stand
Crown Closure
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Structure_Stand_Height_40m_250m
ca_kNN_Structure_Stand_Height_40m_250m_r
kNN Structure Stand Height 40m 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Structure
Stand
Height
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Structure_Volume_Merch_600_250m
ca_kNN_Structure_Volume_Merch_600_250m_r
kNN Structure Volume Merch 600 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Structure
Volume
EPSG:42304
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
kNN_Structure_Volume_Total_600_250m
ca_kNN_Structure_Volume_Total_600_250m_r
kNN Structure Volume Total 600 250 meter : Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org
ecology
botany
forest management
kNN
NFI
National Forest Inventory
Structure
Volume Total
EPSG:3979
EPSG:42101
EPSG:3978
EPSG:4269
EPSG:4326
-177.292
-9.97764
34.3016
84.4537
prov_bound
ca_prov_r
Provincial Boundaries
Boundaries
political
province
provincial
territory
territorial
EPSG:42101
EPSG:4269
EPSG:4326
-149.927
-52.6363
41.3806
86.4421