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Enregistrement W4393760055 · doi:10.5281/zenodo.3620734

U.S.-EPA-BELD4-Equivalent Landuse Database for Canada – Version 2

2020· dataset· en· W4393760055 sur OpenAlexaffabout
Junhua Zhang, Michael D. Moran

Notice bibliographique

RevueFigshare · 2020
Typedataset
Langueen
DomaineEnvironmental Science
ThématiqueSmart Materials for Construction
Établissements canadiensEnvironment and Climate Change Canada
Organismes subventionnairesnon disponible
Mots-clésDatabaseLand useEnvironmental scienceGeographyComputer scienceEngineeringCivil engineering

Résumé

récupéré en direct d'OpenAlex

Air Quality Research Division, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada Email: Junhua.zhang@canada.ca The first version of a U.S.-EPA-BELD4-equivalent landuse database for Canada was compiled in 2018 by Environment and Climate Change Canada (ECCC) based on: (1) the first version of Canada-wide tree species composition maps based on the 2001 Canadian National Forest Inventory (NFI; Beaudoin et al., 2014); (2) the 2016 Canadian Annual Crop Inventory (ACI); and (3) the Land Cover Classification System surface hydrology (LCCS3) data set contained in the Collection 6 MODIS Land Cover product MCD12Q1 (Zhang and Moran, 2018). Recently, an improved mapping approach for estimating forest attributes from MODIS imagery was applied to reprocess the 2001 NFI-based species composition maps and to create a new set of species composition maps for 2011 using 2011 MODIS imagery (Beaudoin et al., 2017a,b). The new mapping approach resulted in an improved set of 2001 species composition maps as indicated by increased correlation coefficients and decreased mean deviations (MD) and root-mean-square deviations (RMSD) between 35,305 MODIS reference pixels for 2001 and the 2001 NFI photo-plot product (Beaudoin et al., 2017b). In addition, for the new 2011 species composition maps, expected reductions in forest coverage were seen for areas of Canada that had experienced rapid development between 2001 and 2011, such as the Athabasca Oil Sands (AOS) area in northeastern Alberta and areas near major urban centres such as Toronto and Vancouver. Given the improved mapping approach and the greater recentness of the 2011 tree species composition maps, the Canadian BELD4 landuse database was updated using these new 2011 maps and the same methodology described in Zhang and Moran (2018). Note that no change was made to the ACI and MODIS Land Cover product data sets that were used to develop this new database version. Because the number of tree species considered in the 2001 NFI-based forest composition maps was reduced from 109 in the first version to 75 in the second version due to the least abundant tree species being lumped with other related species (Beaudoin et al., 2017b), gridded fractional-coverage fields for the U.S.-EPA-BELD4-equivalent landuse categories compiled for Canada have also been reduced, from 92 in the first version of the Canadian BELD4 database to 80 in this second version. The mapping from the 75 NFI tree species to the U.S. Environmental Protection Agency (EPA) BELD4 landuse categories is shown in the attached spreadsheet “ACI_NFI_BELD4_species_match_V2.xlsx”, along with the unchanged mapping of 62 ACI species and other landuse categories. The mapping used to link the LCCS3 categories to the BELD4 categories also remains unchanged and is described in the attached Excel file “MODIS_LCCS3_BELD4_mapping.xlsx”. The updated version 2 of the Canadian BELD4 landuse database is provided here in GeoTIFF format at 1-km resolution for a Lambert conformal conic projection (+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs) in the compressed file “CAN-BELD4_tif_V2.7z”. Note that to use this dataset in conjunction with the U.S. EPA BELD4 database (https://www.epa.gov/air-emissions-modeling/biogenic-emission-sources), all MODIS landuse categories in the original EPA BELD4 database must be removed for Canada to avoid double-counting. Plots of the 80 matched Canadian and U.S. BELD4 vegetation species and other landuse categories in the new Canadian BELD4 database are shown in the attached file “CAN_US_Matched_BELD4_Species_Plots_V2.pdf”. Lastly, an overview and description of the updated Canadian BELD4 landuse database was presented at a recent conference (Zhang et al., 2019, https://www.epa.gov/sites/production/files/2019-08/documents/800am_zhang_2_0.pdf); this presentation is also provided in this package as file “2019EI_Updated_BELD4_Impact_on_VOC_emissions.pptx”. <strong>REFERENCES</strong> Beaudoin, A., Bernier, P.Y., Guindon, L., Villemaire, P., Guo, X.J., Stinson, G., Bergeron, T., Magnussen, S., and Hall, R.J.: Mapping attributes of Canada’s forests at moderate resolution through kNN and MODIS imagery. <em>Canadian Journal of Forest Research</em>, <strong>44</strong>, 521–532, https://doi.org/10.1139/cjfr-2013-0401, 2014. Beaudoin A., Bernier P.Y., Villemaire P., Guindon L., Guo X.-J., Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada, https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990, 2017a. Beaudoin, A., Bernier, P.Y., Villemaire, P., Guindon, L., Guo, X.-J., Tracking forest attributes across Canada between 2001 and 2011 using a kNN mapping approach applied to MODIS imagery, <em>Canadian Journal of Forest Research</em>, 48: 85–93, https://doi.org/10.1139/cjfr-2017-0184, 2017b. Zhang, J. and Moran, M. D., U.S.-EPA-BELD4-Equivalent Landuse Database for Canada [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2231047, 2018. Zhang, J., Moran, M.D., and He, Z.: Updates to Version 4 of the Biogenic Emissions Landuse Database (BELD4) for Canada and Impacts on Biogenic VOC Emissions, <em>2019 International Emissions Inventory Conference, </em>July 29th – Aug. 2nd, Dallas, Texas, USA, https://www.epa.gov/sites/production/files/2019-08/documents/800am_zhang_2_0.pdf, 2019. <strong>AcknowledgementS</strong> We are very grateful for the dedicated assistance of Dr. Zhuanshi He of SOLANA Networks Inc. in preparing this updated version of the database. <strong>RELATED DATA SETS AND MATERIALS</strong> “CAN-BELD4_tif_V2.7z” – Version 2 of the extended BELD4 GeoTIFF file for Canada “ACI_NFI_BELD4_species_match_V2.xlsx” – Version 2 of the NFI/ACI-BELD4 landuse-category crosswalk file “MODIS_LCCS3_BELD4_mapping.xlsx” – MODIS-BELD4 landuse-category crosswalk file (same as in Version 1) “CAN_US_Matched_BELD4_Species_Plots_V2.pdf” – Plots of 80 updated BELD4 landuse category fields over Canada. “2019EI_Updated_BELD4_Impact_on_VOC_emissions.pptx” – 2019 conference presentation on this new version of the Canadian BELD4 landuse database

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,543
Score d'incertitude au seuil0,979

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,5640,022

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,228
Écart entre enseignants0,203 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission2
Résumé présentoui

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