Remote Sensing of Vegetation Water and Fire Risk: Selected Research Topics

The research presented in this book is aimed at reviewing, evaluating and improving remote sensing techniques for vegetation water and fire risk estimation. Coupled reflectance models, fire spread models and meteorological data is first used to estimate expected errors in remote sensing based vegetation moisture retrievals and understand the effect of such uncertainties on fire spread rate predictions. An analysis of the characteristics of soil and canopy reflectance is then used as a basis for designing a vegetation water index, which is more resistant to soil reflectance contamination. Vegetation water content estimation using the improved index is shown to reduce soil moisture related errors over low vegetation areas, where soil reflectance contamination is a major problem. This work also uses the physical concept of pre-ignition energy to propose a new remote sensing based fire risk index. The physical basis of this new Fire Susceptibility Index offers several advantages over other fire risk indices. The new approaches described in this book would be helpful for fire risk monitoring, agricultural applications and climate studies.

Dr. Dasgupta received his Ph.D in Computational Sciences & Informatics from George Mason University in 2007. In 2006 he was awarded the NASA Earth System Science Fellowship to conduct research on the remote sensing of fire risk and vegetation moisture. He is also involved in research and development of satellite direct broadcast technologies.