The use of spot simulation imagery for forestry mapping and management

SPOT simulation imagery was acquired for a test site in the Forest of Dean in Gloucestershire, U.K. This data was qualitatively and quantitatively evaluated for its potential application in forest resource mapping and management. A variety of techniques are described for enhancing the image with the aim of providing species level discrimination within the forest. Visual interpretation of the imagery was more successful than automated classification. The heterogeneity within the forest classes, and in particular between the forest and urban class, resulted in poor discrimination using traditional `per-pixel' automated methods of classification. Different means of assessing classification accuracy are proposed. Two techniques for measuring textural variation were investigated in an attempt to improve classification accuracy. The first of these, a sequential segmentation method, was found to be beneficial. The second, a parallel segmentation method, resulted in little improvement though this may be related to a combination of resolution in size of the texture extraction area. The effect on classification accuracy of combining the SPOT simulation imagery with other data types is investigated. A grid cell encoding technique was selected as most appropriate for storing digitised topographic (elevation, slope) and ground truth data. Topographic data were shown to improve species-level classification, though with sixteen classes overall accuracies were consistently below 50%. Neither sub-division into age groups or the incorporation of principal components and a band ratio significantly improved classification accuracy. It is concluded that SPOT imagery will not permit species level classification within forested areas as diverse as the Forest of Dean. The imagery will be most useful as part of a multi-stage sampling scheme. The use of texture analysis is highly recommended for extracting maximum information content from the data. Incorporation of the imagery into a GIS will both aid discrimination and provide a useful management tool.

Measuring the spatial extent of texture pooling using reverse correlation

The local image representation produced by early stages of visual analysis is uninformative regarding spatially extensive textures and surfaces. We know little about the cortical algorithm used to combine local information over space, and still less about the area over which it can operate. But such operations are vital to support perception of real-world objects and scenes. Here, we deploy a novel reverse-correlation technique to measure the extent of spatial pooling for target regions of different areas placed either in the central visual field, or more peripherally. Stimuli were large arrays of micropatterns, with their contrasts perturbed individually on an interval-by-interval basis. By comparing trial-by-trial observer responses with the predictions of computational models, we show that substantial regions (up to 13 carrier cycles) of a stimulus can be monitored in parallel by summing contrast over area. This summing strategy is very different from the more widely assumed signal selection strategy (a MAX operation), and suggests that neural mechanisms representing extensive visual textures can be recruited by attention. We also demonstrate that template resolution is much less precise in the parafovea than in the fovea, consistent with recent accounts of crowding. © 2014 The Authors.