Lara A. Arroyo

Learn More
This research presents a time-effective approach for mapping streambed and riparian zone extent from high spatial resolution LiDAR derived products, i.e. digital terrain model, terrain slope and plant projective cover. Geographic object based image analysis (GEOBIA) has proven useful for feature extraction from high spatial resolution image data because of(More)
The objectives of this research were to: (1) develop rule sets in Definiens Developer 7® for mapping and monitoring riparian zone land-cover classes within two QuickBird images; and (2) compare the results of four object-oriented and pixel-based change detection approaches. Two QuickBird images, atmospherically corrected to at-surface reflectance, were(More)
Although geographic object based image analysis (GEOBIA) has been successfully applied to derive local maps (1-10s km 2) from very high spatial resolution (VHR) image data (pixels < 1.0 x 1.0 m), its potential for automatically mapping large areas remains unknown. The aim of this study was to create and apply a GEOBIA method to automatically map land cover(More)
Wildland fires are one of the factors causing the deepest disturbances on the natural environment and severely threatening many ecosystems, as well as economic welfare and public health. Having accurate and up-to-date fuel type maps is essential to properly manage wildland fire risk areas. This research aims to assess the viability of combining Geographic(More)
Forest structure characterisation approaches using LiDAR data and object-based image analysis remain scarce to forestry agencies as these automated procedures usually require the use of expensive software and highly skilled analysts. The integration of forest expert opinion into semi-automated approaches would simplify the access of forest managers to new(More)
  • 1