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.

Assuntos

RESUMO

Wind disturbance can create large forest blowdowns, which greatly reduces live biomass and adds uncertainty to the strength of the Amazon carbon sink. Observational studies from within the central Amazon have quantified blowdown size and estimated total mortality but have not determined which trees are most likely to die from a catastrophic wind disturbance. Also, the impact of spatial dependence upon tree mortality from wind disturbance has seldom been quantified, which is important because wind disturbance often kills clusters of trees due to large treefalls killing surrounding neighbors. We examine (1) the causes of differential mortality between adult trees from a 300-ha blowdown event in the Peruvian region of the northwestern Amazon, (2) how accounting for spatial dependence affects mortality predictions, and (3) how incorporating both differential mortality and spatial dependence affect the landscape level estimation of necromass produced from the blowdown. Standard regression and spatial regression models were used to estimate how stem diameter, wood density, elevation, and a satellite-derived disturbance metric influenced the probability of tree death from the blowdown event. The model parameters regarding tree characteristics, topography, and spatial autocorrelation of the field data were then used to determine the consequences of non-random mortality for landscape production of necromass through a simulation model. Tree mortality was highly non-random within the blowdown, where tree mortality rates were highest for trees that were large, had low wood density, and were located at high elevation. Of the differential mortality models, the non-spatial models overpredicted necromass, whereas the spatial model slightly underpredicted necromass. When parameterized from the same field data, the spatial regression model with differential mortality estimated only 7.5% more dead trees across the entire blowdown than the random mortality model, yet it estimated 51% greater necromass. We suggest that predictions of forest carbon loss from wind disturbance are sensitive to not only the underlying spatial dependence of observations, but also the biological differences between individuals that promote differential levels of mortality.

Assuntos

RESUMO

Impervious surface area (ISA) is an important parameter related to environmental change and socioeconomic conditions, and has been given increasing attention in the past two decades. However, mapping ISA using remote sensing data is still a challenge due to the variety and complexity of materials comprising ISA and the limitations of remote sensing data spectral and spatial resolution. This paper examines ISA mapping with Landsat Thematic Mapper (TM) images in urban and urban-rural landscapes in the Brazilian Amazon. A fractional-based method and a per-pixel based method were used to map ISA distribution, and their results were evaluated with QuickBird images based on the 2010 Brazilian census at the sector scale of analysis for examining the mapping performance. This research showed that the fraction-based method improved the ISA estimation, especially in urban-rural frontiers and in a landscape with a small urban extent. Large errors were mainly located at the sites having ISA proportions of 0.2-0.4 in a census sector. Calibration with high spatial resolution data is valuable for improving Landsat-based ISA estimates.