MétaCan
Menu
Back to cohort
Record W4393711093 · doi:10.5281/zenodo.2231046

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

2020· dataset· en· W4393711093 on OpenAlexaffabout
Junhua Zhang, Michael D. Moran

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsDatabaseLand useGeographyForestryEnvironmental scienceComputer scienceEngineeringCivil engineering

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0630.027

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.220
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicSmart Materials for ConstructionFrench-language works237,207