370 Leveraging remote sensing products to estimate forage productivity in the Canadian Prairies
Notice bibliographique
Résumé
Abstract Remote sensing is widely used for land cover classification applications. This technology supports the development of land management practices and policies to achieve conservation and economic goals in agricultural landscapes. In animal production, remote sensing aids in reducing grassland degradation and enhancing livestock and crop production. The objective of this work is to discuss recent satellite- and drone-based remote sensing applications for grassland mapping in the Canadian Prairies. Using a satellite-based approach, the agreement between remotely sensed land cover maps and non-spatial government records was assessed for public agricultural Crown lands in southern Manitoba. Two different land cover classification products derived from satellite imagery [i.e., the Manitoba Grassland Inventory (MGI) prepared by the Province of Manitoba and the Annual Crop Inventory (ACI) prepared by Agriculture and Agri-Food Canada] were merged into a single mosaic covering the entire Agro-Manitoba region. Non-spatial official records were georeferenced and summarized through parcel delineation using geographic information system (GIS) and R programming tools. Comparison of the two datasets revealed low agreement between related grassland classes (i.e., forests and shrublands; and native and tame grasslands) due to spectral similarities. However, grouping these related vegetation types into broader categories (i.e., forests and shrublands as woody vegetation; native and tame grasslands as grassy vegetation) significantly improved overall agreement, with a difference of less than 3% observed between remotely sensed datasets and official records. The derived grassland classification was then used to assess the grazing status of the agricultural Crown lands by estimating stocking rates in animal unit months (AUM) and utilizing historical yield data available from three separate field surveys conducted between 2004 and 2020 across the region. Average carrying capacities ranged from 0.71 AUM/ha to 1.99 AUM/ha. Overall, the analysis using remotely sensed data indicated that the forage resources in the Crown lands were underutilized by 45%. To further explore the potential of using high-resolution remote sensing to map grasslands, ongoing research involves the use of a drone-mounted hyperspectral sensor (HySpex Mjolnir VS-620) covering a spectral range from 400 to 2,500 nm across 490 spectral channels. This sensor enables grassland mapping at significantly greater spatial and spectral resolutions. Monthly surveys during the snow-free period will be conducted in 12 study sites across the Agro-Manitoba region in 2024 and 2025, with biomass samples collected to model grassland distribution and productivity. The outcomes of this study will offer critical insights into leveraging specific wavelengths for grassland mapping and strategies for extrapolating these insights to larger geographical areas using satellite imagery. Overall, the efforts described above underscore the significance of remote sensing approaches in managing agricultural landscapes and provide valuable insights for land managers, policymakers, and stakeholders to promote sustainable agricultural production in grasslands.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».