Greening dynamics and shrubland extent from remote sensing data using NDVI and NARI Indices: case study of the George River basin (Nunavik, Canada).
Notice bibliographique
Résumé
Arctic greening trends are well documented at various scales (Fraser et al., 2011; Tremblay et al., 2012; Bjorkman et al., 2018). In this context, Remote Sensing offers a unique tool for estimating the high latitude vegetation evolution in the relatively long-term, i.e. the Landsat archive since the 80’s. Spectral indices derived from visible and infra-red wavelengths provide relations that can be used to quantify vegetation dynamics, we will combine the well-used Normalized Difference Vegetation Index (NDVI) and the recent Normalized Anthocyanins Reflectance Index (Bayle et al., 2019), using red-edge spectral band (690 to 710 µm) from Sentinel-2, to better quantify vegetation change over 30 years. The application area is located in Nunavik, northern Québec (Canada), and concerns the George River catchment (565 km length, 41 700 km²). This large river basin covers vegetation from boreal forest (South) to arctic tundra (North). Local study sites stem from the Kangiqsualujjuaq village (Ungava Bay) to 300 km south, along the main river and its tributaries. NDVI: surface reflectance Landsat bands were gathered for three years 1985, 2000 and 2015 (respectively Landsat missions 5, 7 and 8). For each period of interest, the best August cloud-free scenes were chosen and merged to create a cloud free mosaic covering the study area. NDVI bands were calculated and compared after cloud and water masking. NDVI trends were compared between the main vegetation types following the newly released “Ecological mapping of the vegetation of northern Quebec” (MRNFP, 2018). Centroid of polygons within the main vegetation types of the map were used to classify the NDVI results and assess changes per type. Results of NDVI time evolution revealed a clear greening trend at the river basin scale. Although greening was observed across the whole latitudinal gradient, the relative NDVI increase was stronger on the northern half of the study area, mostly covered with tundra and subarctic vegetation. Both shrublands and sparsely vegetated zones dominated by rocks had the greatest relative NDVI increase. This is likely caused by improved growth of established prostrate vegetation over the past 30 years in response to increasing temperatures trend. NARI: greening trends in the Eastern Canadian Arctic have been partly attributed to increases in shrub cover (Myers-smith et al., 2011) and specifically to Betula glandulosa (e.g. Tremblay et al., 2012). Such land cover changes alter species competition (Shevtosa et al., 1997) and soil thermal regime (Domine et al., 2015; Paradis et al., 2016). Transformations in biotic and abiotic conditions reduce the fruit productivity of low stature shrubs of the Ericaceae family (Lussier 2017), which in turn is expected to impact animal (Prescott and Richard 2013) and human populations (Lévesque et al., 2013; Boulanger-Lapointe et al., 2019). An innovative method has been developed in the French Alps to detect the late-fall reddening of shrub leaves and map shrublands (Bayle et al., 2019). Quantifying NARI dynamics related to NDVI dynamics could allow to gain a better understanding of species composition change related to current landscape transformation.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».