David Riaño

Estimation of Fuel Conditions for Fire Danger Assessment

A review of physical and chemical properties of fuels relevant for fire ignition and propagation is presented, along with different methods to estimate those properties, with special emphasis on satellite imagery. The discussion is more extended on estimating fuel moisture trends and fuel geometrical properties.

Remote Sensing of Leaf, Canopy, and Vegetation Water Contents for Satellite Environmental Data Records

The absorption features of liquid water in plant leaves are readily detectable, and the amount of leaf water content may be determined by spectroscopy. Spectral reflectances at about 1240 and 1650 nm are the basis of numerous remote-sensing indices that could be used to estimate liquid water content of leaves and canopies. Two applications of remotely sensed water content are estimation of fuel moisture content for wildfire potential and estimation of vegetation water content for improving retrievals of soil moisture content from microwave sensors. The temporal record of MODIS, SPOT Vegetation, and AVHRR/3 sensors and the future record from VIIRS will create a global environmental data record of canopy water content for climate change studies.

Mapping Fire Risk in Mediterranean Ecosystems of California: Vegetation type, Density, Invasive Species, and Fire Frequency

California ecosystems and climate have characteristics that promote to wildfire, particularly in the dry late summer and fall seasons. Over recent decades, fire severity and number of fires has increased. In many cases, the changing fire regimes have been concurrent with the spread of invasive annual grasses into shrub and woodland habitats that provide dry fuel in the late summer/fall season. Remote sensing data, especially high spatial resolution airborne hyperspectral data has contributed to better mapping of vegetation types and characteristics and assessment of the biophysical condition of the vegetation, mostly related to drought status. These data can provide inputs to improved wildfire risk models. The temporal 1-day and 8-day resolution of the weather satellites contributes information on vegetation dynamics, particularly MODIS, which provides spatially distributed changes in canopy water content as the vegetation in these fire prone ecosystems dries and fire risk increases. Lastly, we describe a new multitemporal classifier to monitor early detection of near real-time fire ignitions using the half-hourly geostationary satellite.