RESUMO
Giant kelp Macrocystis pyrifera is a brown alga with extensive global distribution, however, recent evidence suggests that its dynamics presents high degree of regional variability. In southern Chilean fjord region, largely unexplored kelp forests are currently being threatened by global change and human impacts. High-resolution satellite (Sentinel-2) imagery was used to describe temporal and spatial distribution patterns of kelp beds in Yendegaia Fjord (Beagle Channel) using Spectral Mixture Analysis (SMA), and to characterize water optical gradients of this habitat strongly influenced by river runoff from a melting glacier. The suitability of SMA for kelp classification was contrasted with other vegetation indices (NDVI, EVI, FAI). Validation was made using drone aerial photographs of kelp canopies. Different analysis tools resulted in up to 35% difference in kelp coverage estimation. The overall accuracy (66-82%) of kelp classification followed an order FAI < EVI < NDVI < SMA. Omission error of SMA and lower coincidence with vegetation indices occurred in pixels with low kelp pixel abundance (<0.50). Based on SMA, the lowest kelp abundance was observed in the river mouth with high turbidity, increasing towards the Beagle Channel. The highest kelp abundance was observed in late summer, but otherwise no clear seasonal patterns could be observed. Water turbidity presented both spatial and seasonal variation. Strong particle sedimentation (leading to light attenuation, interference with remote detection of kelps, and even to their detachment due to substrate quality) and tidal fluctuations in glacier-impacted fjord-type environments can be identified as key features affecting both the kelp population dynamics as well as their remote sensing. Also, low sun elevation at high latitudes in mid winter produces uncertainties in image analyses. In all, the remote sensing approach used in the present study can be regarded as a useful tool to map and monitor kelps forests from a remote region.