Remote Sensing of Arctic Vegetation: Relations between the NDVI, Spatial Resolution and Vegetation Cover on Boothia Peninsula, Nunavut

Arctic tundra environments are thought to be particularly sensitive to changes in climate, whereby alterations in ecosystem functioning are likely to be expressed through shifts in vegetation phenology, species composition, and net ecosystem productivity (NEP). Remote sensing has shown potential as a tool to quantify and monitor biophysical variables over space and through time. This study explores the relationship between the normalized difference vegetation index (NDVI) and percent-vegetation cover in a tundra environment, where variations in soil moisture, exposed soil, and gravel till have significant influence on spectral response, and hence, on the characterization of vegetation communities. IKONOS multispectral data (4 m spatial resolution) and Landsat 7 ETM+ data (30 m spatial resolution) were collected for a study area in the Lord Lindsay River watershed on Boothia Peninsula, Nunavut. In conjunction with image acquisition, percent cover data were collected for twelve 100 m × 100 m study plots to determine vegetation community composition. Strong correlations were found for NDVI values calculated with surface and satellite sensors, across the sample plots. In addition, results suggest that percent cover is highly correlated with the NDVI, thereby indicating strong potential for modeling percent cover variations over the region. These percent cover variations are closely related to moisture regime, particularly in areas of high moisture (e.g., water-tracks). These results are important given that improved mapping of Arctic vegetation and associated biophysical variables is needed to monitor environmental change.On croit que les environnements de la toundra arctique sont particulièrement sensibles aux changements climatiques, en ce sens que toute altération du fonctionnement de l’écosystème est susceptible d’être exprimée dans le réarrangement de la phénologie de la végétation, de la composition des espèces et de la productivité nette de l’écosystème (PNÉ). La télédétection s’avère un outil efficace de quantification et de surveillance des variables biophysiques dans le temps et dans l’espace. Cette étude explore la relation entre l’indice d’activité végétale et le pourcentage de couverture végétale en milieu de toundra, où les variations propres à l’humidité du sol, au sol exposé et au till de gravier ont une influence considérable sur la réponse spectrale et, par conséquent, sur la caractérisation des communautés végétales. Des données multispectrales IKONOS (résolution spatiale de 4 m) et des données ETM+ de Landsat 7 (résolution spatiale de 30 m) ont été recueillies pour une zone d’étude visée par la ligne de partage des eaux à la hauteur de la rivière Lord Lindsay, dans la péninsule de Boothia, au Nunavut. De concert avec l’acquisition d’images, les données relatives au pourcentage de couverture ont été recueillies pour douze terrains d’étude de 100 m sur 100 m dans le but de déterminer la composition de la communauté végétale. De fortes corrélations ont été dénotées dans le cas des valeurs de l’indice d’activité végétale calculées à l’aide de détecteurs de surface et de détecteurs satellisés et ce, à l’échelle des terrains ayant servi d’échantillon. Par ailleurs, les résultats laissent entendre que le pourcentage de couverture est hautement corrélé avec l’indice d’activité végétale, ce qui indique une forte possibilité de modélisation des variations de pourcentage de couverture dans la région. Ces variations du pourcentage de couverture sont étroitement liées au régime d’humidité, particulièrement dans les régions où l’humidité est élevée (comme les traces d’eau). Ces résultats revêtent de l’importance étant donné qu’il y a lieu d’améliorer le mappage de la végétation arctique et les variables biophysiques connexes afin de surveiller la modification de l’environnement

An Incidence of Multi-Year Sediment Storage on Channel Snowpack in the Canadian High Arctic

During June 2005, we identified the presence of sediment buried within multi-year channel snowpack of a small river located near Cape Bounty, Melville Island, Nunavut (74°55' N, 109°35' W). Photographic evidence indicates that the sediment was deposited during the 2003 season by the initial meltwater flowing on the snowpack, which was dammed by snow upstream of a channel constriction. The resulting pond covered a minimum area of 180 m2 and contained an estimated minimum 27 Mg of sediment. Suspended sediment measurements during the 2003 season indicate that deposition on the snowpack at this location represented 49%–65% of the sediment transport prior to the ponding and emplacement of the sediment on the snow, and approximately 20% of the measured sediment flux for the entire season. Multi-year snow accumulations immediately downstream exhibited similar sediment deposition on snow, but no evidence of multi-year sediment storage was present. By contrast, a similar stream in an adjacent watershed channelized rapidly, with minimal sediment deposition on the snow, and delivered a large pulse of sediment to the downstream lake. These results provide quantitative evidence for the magnitude of sediment storage on snowpack and point to the unique role that snow plays in the fluvial geomorphology of High Arctic watersheds.En juin 2005, nous avons dénoté la présence de sédiment enterré dans une plaque de neige datant de plusieurs années d’une petite rivière située près de cap Bounty, sur l’île Melville, au Nunavut (74°55' N, 109°35' O). D’après des preuves photographiques, le sédiment a été déposé pendant la saison 2003 par l’eau de fusion initiale s’écoulant sur la plaque de neige, qui avait été endiguée par la neige en amont d’un canal confiné. L’étang qui en a découlé recouvrait une aire minimale de 180 m2 et contenait, selon les estimations, au moins 27 Mg de sédiment. Les mesures de sédiment en suspension pendant la saison 2003 indiquent que ce dépôt sur la plaque de neige à cet endroit représentait entre 49 % et 65 % du transport de sédiment avant l’accumulation d’eau et l’emplacement de sédiment sur la neige, et environ 20 % du flux de sédiment mesuré pour toute la saison. Les accumulations de neige de plusieurs années immédiatement en aval comptaient des dépôts de sédiment semblables sur la neige, quoi qu’aucun emmagasinage de sédiment sur plusieurs années n’était présent. Par contraste, un cours d’eau similaire d’un bassin hydrographique adjacent s’est canalisé rapidement, avec peu de dépôts de sédiment sur la neige, puis a laissé une grande quantité de sédiment au lac en aval. Ces résultats fournissent des preuves quantitatives quant à l’ampleur de l’emmagasinage de sédiment sur la plaque de neige et laissent envisager le rôle unique que joue la neige sur la géomorphologie fluviale des bassins hydrographiques de l’Extrême-Arctique

Spatial variability in carbon dioxide exchange processes within wet sedge meadows in the Canadian High Arctic

Wet sedge meadows are the most productive plant communities in the High Arctic. However, the controls on carbon dioxide (CO2) exchange processes within wet sedge communities – and the scale at which they operate – are poorly understood. Here, the factors controlling CO2 exchange of wet sedge meadows experiencing different moisture regimes are examined. Environmental data are used to create predictive models of CO2 exchange on multiple temporal scales. Automated chamber systems recorded CO2 fluxes at 30-minute intervals at wet sedge sites in the Canadian High Arctic from June to August in 2014 and 2015. Static chambers were also deployed over a larger spatial extent in 2014. Our results show that wet sedge communities were strong CO2 sinks during the growing season (−7.67 to −44.36 g C·m−2). CO2 exchange rates in wetter and drier areas within wet sedge meadows differed significantly (Wilcoxon, p<0.001), suggesting that soil moisture regimes within vegetation types influence net CO2 balance. Random Forest models explained a significant amount of the variability in CO2 flux rates over time (R2=0.46 to 0.90). The models showed that the drivers of CO2 exchange in these communities vary temporally. Variable moisture regimes indirectly influenced CO2 fluxes given that they exhibit different vegetation and temperature-response characteristics. We suggest that the response of a single vegetation type to environmental changes may vary depending on microenvironment variability within that community

Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data

Abstract: As a result of the warming observed at high latitudes, there is significant potential for the balance of ecosystem processes to change, i.e., the balance between carbon sequestration and respiration may be altered, giving rise to the release of soil carbon through elevated ecosystem respiration. Gross ecosystem productivity and ecosystem respiration vary in relation to the pattern of vegetation community type and associated biophysical traits (e.g., percent cover, biomass, chlorophyll concentration, etc.). In an arctic environment where vegetation is highly variable across the landscape, the use of high spatial resolution imagery can assist in discerning complex patterns of vegetation and biophysical variables. The research presented here examines the relationship between ecological and spectral variables in order to generate an ecologically meaningful vegetation classification from high spatial resolution remote sensing data. Our methodology integrates ordination and image classifications techniques for two non-overlapping Arctic sites across a 5 ° latitudinal gradient (approximately 70 ° to 75°N). Ordination techniques were applied to determine the arrangement of sample sites, in relation to environmental variables, followed by cluster analysis to create ecological classes. The derived classes were the

Estimating Stem Diameter Distributions in a Management Context for a Tolerant Hardwood Forest Using ALS Height and Intensity Data

Two types of nonparametric modeling techniques and various metrics derived from airborne laser scanning (ALS) data were examined in terms of their utility for modeling stem diameter distributions in an uneven-aged tolerant hardwood forest in Ontario, Canada. Using an area-based approach (ABA), the frequency distribution of trees across 6 size classes was predicted using k-nearest neighbor (k-NN) imputation and Random Forest (RF) regression. Predictor variables derived from ALS height and intensity data were divided into 3 groups: height only, intensity only, and all metrics. Prediction results demonstrated that the first 2 groups of predictor variables exhibited similar predictive accuracy, whereas the synergy of both resulted in enhanced performance. The utility of intensity-based metrics was corroborated by an importance measure obtained from RF. The size class-specific stem density estimation approach based on RF was more accurate and flexible than the simultaneous estimation approach based on k-NN models. After the predicted diameter distributions were grouped into 9 structural groups, heterogeneous accuracy scores revealed the challenges for predicting select diameter distributions. Although successes were observed for certain size classes, there remains additional research (e.g., development of additional metrics or data types) to be done to accurately predict a complete range of size classes

Ecologically informed bird habitat characterization with airborne laser scanning

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Spatiotemporal Variability of Arctic Soil Moisture Detected from High-Resolution RADARSAT-2 SAR Data

Various methods are used to determine soil moisture information from synthetic aperture radar (SAR) data, but none specific to High Arctic regions and their unique physical characteristics. This research presents a method for determining, at high spatial and temporal resolutions, surface soil moisture and its changes through time in the Canadian High Arctic. An artificial neural network (ANN) is implemented using input variables derived from RADARSAT-2 SAR data and previously modelled surface roughness information. The model is applied to SAR data collected at various incidence angles and acquisition dates across two study sites on Melville Island, Nunavut. The model results in absolute soil moisture errors of approximately 15% (r2 = 0.46) for the primary study sites and 12% (r2 = 0.26) for the verification study area. The ANN model is accurate for modelling (i) the spatial distribution of soil moisture and (ii) the changes in moisture through time across the study areas, two characteristics that are very important for inputs to hydrologic or climate models. In addition, the models appear to be scalable when applied at coarser spatial resolutions, showing potential for large-area mapping or modelling