Theme leader: Marie-Josée Fortin, University of Toronto
Project 7: Remote sensing as direct observation of lakes water quality and organic carbon content
Project leader: Simon Bélanger, Université du Québec à Rimouski
Summary: While there are rich datasets of land use and land-use change at the spatial scales relevant to most watersheds (30 meters resolution, e.g. Landsat imagery) that can be used for spatial modelling, until now, direct remote sensing of lakes has offered limited systematic applications. This is mostly because past satellite sensors were not well adapted to lakes in terms of spatial, spectral and radiometric resolutions. This is changing rapidly with the launch of a new generation of earth observation (EO) satellites offering high spatial resolution and good radiometric performance (Landsat-8, Sentinel-2), and future generations of satellites will continue to offer greater spectral and/or spatial resolution. The goal of this project is to build a pan-Canadian dataset of in situ optical properties and biogeochemical parameters to develop new bio-geo-optical algorithms required to fully exploit the Landsat-8 and Sentinel-2 multispectral imagery. We will establish relationships between satellite-derived bio-geo-optical parameters and lake health indicators and bulk biogeochemical quantities (e.g., organic carbon concentration) in order to map an optical water quality (OWQ) index. The algorithms and methods developed will be available to produce systematic water quality indicators in lakes or regions of concern, including those that were not sampled as part of the network. Therefore, the main outcome of this project will be tools to diagnose Canada’s millions of lakes OWQ from space.
Project 8: Spatial modelling as a tool to integrate results
Project leader: Marie-Josée Fortin, University of Toronto
Summary: This integrative project aims to predict Canadian lake health status in the face of land-use and climate change. We will combine spatio-temporal data from remote sensing, morphometric, and watershed property data as well as lake biogeochemistry data. Specifically, we will (1) Develop analytical regionalization methodologies and software tools to integrate the results from the large-scale sampling and to produce maps of the status of lake health. (2) Develop baseline and indicator data of lake health using paleolimnological, morphological, and climate data. (3) Integrate existing databases to determine trends and potential regions where lakes have comparable trajectories through time in terms of health status. (4) Quantify how temporal and spatial patterns in the water quality index change to gain an understanding of the key scales acting on lakes. (5) Model the relationships among the water quality index, biogeochemistry, land use and climatic data. (6) Provide a platform to merge several existing databases having different spatial extents, spatial and temporal resolutions, and data types, one of the core developments of the Network will be to integrate such diverse databases using metadata standards using existing software and platforms. Such integrated databases will allow the Network to map lake health accounting for land-use change and climate change.