Data · dataset · 2026
Detecting Cold-Air Pools from UAV-Derived Digital Terrain Models for Vineyard Frost Management
Listed in Borealis
Spring frost events pose a severe threat to grapevine production, with cold-air pooling (CAP) in topographic depressions creating localized frost pockets that can cause significant crop damage during the most vulnerable stages of vine development.
Description
The spatial distribution of cold air pooling is largely governed by terrain, yet precise information on frost-prone zones remains unavailable to growers, who often rely on intuition or trial-and-error to guide frost management decisions.
This study applied a high-resolution UAV-derived Digital Terrain Model to the Osoyoos Larose Vineyard, located in the Okanagan Valley of British Columbia, using a combination of Topographic Position Index and flow accumulation analysis to map terrain-driven cold air pooling susceptibility at the vineyard scale. Potential cold air pooling zones were identified across 13 of 18 vineyard blocks, with depressions of Strong and Moderate CAP susceptibility covering a combined area of 2,042 square meters, concentrated primarily in blocks 6, 2, and 1.
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These findings suggest that UAV-derived DTMs can provide growers with spatially explicit information to guide frost management decisions, offering an adaptable and reproducible framework for terrain-driven CAP susceptibility characterization across viticultural landscapes.
Links
Where it is published
- Dataverse dataset page borealisdata.ca/dataset.xhtml?persistentId=doi%3A10.5683%2FSP3%2FMFIGVO ↗
landing page · from borealisdata ca
- DOI doi.org/10.5683/sp3/mfigvo ↗
DOI / persistent id · from borealisdata ca
Catalogue records · 1
- Dataverse API borealisdata.ca/api/datasets/:persistentId/?persistentId=doi%3A10.5683%2FSP3%2… ↗
metadata API · from borealisdata ca
Topics
- Stated by source
- Agricultural Sciences · Computer and Information Science · Earth and Environmental Sciences
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Borealis | doi:10.5683/SP3/MFIGVO | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:401304 | mapping · borealisdata ca | vocabulary-mapper@1.0.0 | keywords['Remote Sensing'] |
| concepts[field].dataverse_subject:agricultural-sciences | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].dataverse_subject:computer-and-information-science | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].dataverse_subject:earth-and-environmental-sciences | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[modality].local:modality:remote-sensing | mapping · borealisdata ca | vocabulary-mapper@1.0.0 | keywords['Remote Sensing'] |
| 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 |