Data · dataset · 2021
Autocorrelation between Fuel Type Fragmentation and Fire Severity at the Elephant Hill wildfire in British Columbia
Listed in Borealis and Agri-environmental Research Data Dataverse — shown once because both records carry DOI 10.5683/sp2/qri93b
Geospatial analyses focused on quantifying fuel types fragmentation and its autocorrelation with megafire severity inform decision making in contexts such as forest management and human activities regulation.
Description
Fuel type fragmentation plays a crucial role in fire severity contribution. I evaluated the landscape and class level fragmentation of fuel types in two maps: FuelSat (derived from remote sensing) and a completely randomized map.
Specifically, the C-2 (Boreal Spruce), C-3 (Mature Pine), slash, and standing grass were targeted for class level metrics calculation. Fire behavior could be described in two terms – burn probability and fire intensity. Flammability (burn probability) represent the likelihood of a given location on landscape burning, while the fire intensity is the rate of heat energy released by the fire.
Read the rest (2 more)
Burn probability and fire intensity of those four target classes were extracted from landscapes (FuelSat and Random). Boxplots were created to visualize the difference between burn probability and fire intensity of four classes from FuelSat and Random, respectively. Results indicated higher fragmentated fuel types would lower the fire intensity generally, but resulted in more extreme events.
It was not evident that fuel type fragmentation has significant impacts on burn probability. Moran’s I was computed and did indicate there was autocorrelation between fuel fragmentation and fire intensity. It helps fill the gap in forest fire prediction by considering effects of fuel fragmentation.
Links
Where it is published
- Dataverse dataset page borealisdata.ca/dataset.xhtml?persistentId=doi%3A10.5683%2FSP2%2FQRI93B ↗
landing page · from borealisdata ca
- DOI doi.org/10.5683/sp2/qri93b ↗
DOI / persistent id · from borealisdata ca
Catalogue records · 1
- Dataverse API borealisdata.ca/api/datasets/:persistentId/?persistentId=doi%3A10.5683%2FSP2%2… ↗
metadata API · from borealisdata ca
Topics
- Stated by source
- Earth and Environmental Sciences · Earth and Environmental Sciences
- From keywords
- Chemistry · Computer Science & AI · Earth & Environmental Science · Earth & Environmental Science · Economics & Finance · Energy · Energy · Engineering · Humanities · Life Sciences · Mathematics & Statistics · Medicine & Health · Ocean & Atmospheric Science · Social Science
- Inferred from text
- Fire ecology 76% · Satellite remote sensing 65%
Provenance · 2 source records, 24 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Borealis | doi:10.5683/SP2/QRI93B | 13 d ago | JSON v1 |
| Agri-environmental Research Data Dataverse | doi:10.5683/SP2/QRI93B | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:410205 | enrichment · borealisdata ca | taxonomy-embedding@1.0.0 | title+keywords+description (76%) |
| concepts[field].dataverse_subject:earth-and-environmental-sciences | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].dataverse_subject:earth-and-environmental-sciences | source · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:chemistry | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:economics-finance | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:energy | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:energy | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:engineering | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:humanities | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:mathematics-statistics | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:medicine-health | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:ocean-atmospheric | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:social-science | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[modality].local:modality:remote-sensing | enrichment · borealisdata ca | keyword-concept-rules@1.0.0 | title+description (65%) |
| created_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| description | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /description |
| publication_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| title | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /name |
| updated_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| version_label | source · borealisdata ca | connector:borealisdata_ca@1.0.0 |