Use of Synthetic Aperture Radar (SAR) images for fuel moisture modelling and land cover mapping in the case of boreal forests and natural grasslands

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Boreal forests and natural grasslands are two important ecosystems in the world, but they are also prone to fires. The Fire Weather Index (FWI) system of the Canadian Fire Danger rating system is used in many places in the world to rate fire dangers based primarily on weather data, which are point-source measurements. A promising alternative is using satellite data. The long term goal of my research program is to map Fire Weather Index codes for boreal forests and natural grasslands using remote sensing. In the short term, using images and data available through on-going collaborations on Canadian/South African natural grasslands and Alaska boreal forests, we will (i) compare two classifiers that simultaneously use polarimetric synthetic aperture radar (polSAR), optical and other geospatial data for land cover mapping; (ii) develop an empirical model to estimate the FWI drought code (DC) for natural grasslands; and (iii) test a physics-based DC model using polSAR images. The study will use both RADARSAT-2 C-band and ALOS-PALSAR L-band polSAR images. Our research team has built unique expertise on the use of SAR images for mapping FWI codes and indices. The proposed research continues this innovative trend in many ways. From the classifier point of view, it is the first time that the proposed genetic algorithm-neural network classifier will be use to classify boreal forest and natural grassland ecosystems. From the DC modeling point of view, all the soil moisture models using polSAR data were mainly developed for agricultural fields. It is the first time that DC of natural grasslands will be modeled using polSAR data and that a physics-based model will be proposed to estimate DC from polSAR images acquired over both boreal forest and natural grasslands. In Canada, the proposed research addresses fire research priorities of the Canadian Council of Forest Ministers. Across the world, better prediction of FWI codes and indices using remote sensing will have significant benefits both from the economical and the human safety points of views. The proposed research has a potential for significant economic advancement for Canada, as it will produce new market opportunities for MDA Inc., the R-2 owner.