Change and Stability of Canada's Forested and Aquatic Landscapes: Big-data Remote Sensing on Multiple Platforms

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Recent developments in satellite remote sensing show that we have entered a new era. Where satellite information once quite scarce and expensive, vast archives have been opened for free public access. Where processing power and disk space once could throttle a plan for a large-area study, new tools with enormous capacity have emerged to put supercomputers at anyone's fingertips. Remote sensing's advances with this new paradigm have been rapid and impressive, producing global assessments of a wide range of phenomena: global tidal flat maps, high-resolution population estimates, times series maps of forest loss and gain-- to name only a few. Despite this enormous progress, considerable obstacles constrain our use of the vast potential of the world's earth-resources satellites. Combining data streams together from multiple platforms is quite complex: diverse sensors offer imagery with different spatial resolutions, have different numbers and specifications of spectral bands, arrive at irregular times, are often partially covered by clouds, and were designed for a wide range of purposes. It is exciting to conceive of these sensors as a single Earth-observing machine constantly grabbing views from different parts of the electromagnetic spectrum-but it is difficult to handle more than a few dimensions of this signal at a time.  There are two goals of this research. First, we will use our unique algorithms to blend information from a wide array of sensors, allowing us to view change and stability more rapidly and reliably. Second, we will integrate new machine-learning techniques to interpret older low-information imagery, allowing us to see how Canada looked near the beginning of the satellite era. Together these goals will more fully explore the immense trove of imagery available across Earth's surface, from the early 1970s until the very latest fires of this coming summer, on land and in water. Over the next five years my research team of pre-undergraduates, undergraduates, Master's students, PhD students, and postdocs will pursue four medium-term objectives: (1) Combine multiple data streams for weekly mapping of harvest, fire borders and other change across Canada; (2) track wildfire growth in near-real-time by fusing fire growth models with information streaming from multiple sensors; (3) create new estimations of the chemical properties of more than 100,000 lakes of Canada; and (4) implement new big-data approaches to old imagery to map Canada's land use / land cover in the 1970s. Built on solid prototypes created in my lab and fuelled by new data and powerful analysis platforms, this research is likely to provide concrete benefits to both researchers and non-scientists alike. We will better inform Canadians about the wildfires that increasingly capture the public's attention; we will peer into Canada's great aquatic resource; and we will develop approaches that we foresee will be of broad interest to the remote sensing community.