An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

The availability of multispectral images, with both high spatial and spectral resolution, makes it possible to obtain valuable information about complex urban environment, reducing the need for more expensive surveying techniques. Here, a methodology is tested for the semi-automatic extraction of buildings and the mapping of the main roofing materials over a urban area of approximately 100 km², including the entire city of Bologna (Italy). The methodology follows an object-oriented approach and exploits a limited number of training samples. After a validation based on field inspections and close-range photos acquired by a drone, the final map achieved an overall accuracy of 94% (producer accuracy 79%) regarding the building extraction and of 91% for the classification of the roofing materials. The proposed approach proved to be flexible enough to catch the strong variability of the urban texture in different districts and can be easily reproducible in other contexts, as only satellite imagery is required for the mapping

Remote sensing to characterise vegetation fuel moisture content in the UK uplands

Wildfires are an important hazard globally as they lead to significant land degradation, carbon losses and impact on human activities. Recent research has demonstrated how dynamic fire risk estimates can be informed by the use of remote sensing technology. The focus here is on improving methods for fire risk evaluation, so that prediction about where and when fires are likely to start can become more accurate. Fuel moisture content (FMC) is one of the most important factors influencing wildfire risk, as it controls the probability of ignition and the rate of spread of a fire. This work aims to assess the potential of calibrated time-series Sentinel-2A MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) data to estimate and map FMC in upland areas of the UK. The work employs laboratory and field-scale measurements, and radiative transfer modelling, to test the relationships between reflectance and FMC. Calluna vulgaris samples were collected from a test site in the UK Peak District, and their FMC determined. Near-coincident multi-temporal satellite imagery was acquired for the test site and maps of FMC generated using relationships tested through the laboratory work and modelling. The results showed a strong relationship between the normalized difference water index (NDWI) and moisture stress index (MSI) with FMC, which was independent of scale. The relationship was not strongly affected by variations in soil background properties or differences in solar zenith angle. Spatial mapping of FMC across the Peak District National Park revealed temporal and spatial variations in FMC in Calluna-dominated areas. The results have implications for wildfire risk management and for upland vegetation management and conservation