Ecotone conditions along pinon-juniper and ponderosa pine elevational ranges, Jemez Mountains, NM

While climate variability is endemic to Southwest North America (SWNA), mounting evidence indicates the region is undergoing significant warming and becoming increasingly arid. Species are at or near their physiological limits at ecotone boundaries and are therefore particularly sensitive to climate change. Drought and warming associated tree mortality has been particularly acute in the semiarid forests and woodlands of the Jemez Mountains, New Mexico, USA, where ponderosa pine forests (Pinus ponderosa var. scopulorum) and piñon--juniper woodlands (Pinus edulis and Juniperus monosperma) have been subject to cambium-feeding pine beetle (Dendroctonus spp., Ips spp) attacks and increased wildfire activity and severity. Grazing and fire suppression have also impacted fire regimes leading to altered species composition and distribution. Projected warming and drought conditions in the 21st century will likely foster rapid (i.e. \u3c5 year) ecotone shifts in semiarid ponderosa pine forests and piñon-juniper woodlands. This study analyzed ponderosa pine ecotone characteristics within a 2100 to 2200 m. range of the Vallecita watershed of the Jemez Mountains. Identification of sample sites was accomplished using remote sensing Landsat imagery, a moderate resolution earth observation data, coupled with a Geographic Information System (GIS) embedded semi--automated land cover classification method for raster-based analysis. Field procedures devised for this study determined past vegetation elements, current vegetation structure and composition, and present successional trajectory. The results of this research established baseline conditions and suggest the study area is undergoing a climate generated compositional shift from ponderosa pine dominated sites to piñon-juniper woodlands. Analysis of log and snag decay classes showed evidence of recent mortality and indicated ponderosa pine was formerly the climax species in sampled areas. As piñon-juniper woodlands exhibit different fire characteristics than ponderosa pine forests, fire behavior will likely change if trends continue

Subset Feature Learning for Fine-Grained Category Classification

Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy