Wetlands are known to provide excellent waterfowl habitat but #DYK they are also home to many mammals? Let's celebrate #wetland mammals this #MammalWeek such as boreal caribou, moose, and of course, beavers! pic.twitter.com/RMHEJniH4I Retweeted by Fiera Biological

This is a first-time situation for me. #Coyote roaming about outside our @UofAALES building. We do a lot of work with conservation biology & wildlife issues but this is a bit surprising. #yeg pic.twitter.com/nOUroZe2j6 Retweeted by Fiera Biological

Remote Sensing, machine learning, remote sensing scientist, ecology, applied remote sensing, land cover, land use, watershed, LiDAR, riparian

Bing Lu
Senior Remote Sensing Scientist

PhD, Geography and Planning
University of Toronto, 2017

MSc, Cartography and GIS
East China Normal University, 2012

B.Sc. Resource, Environment, and Urban-Rural Planning
Qufu Normal University, China, 2009


Bing is a Sr. Remote Sensing Scientist with nine years of professional and academic experience in the areas of remote sensing, GIS, and ecology. He has extensive experience using remote sensing and GIS approaches to inventory, classify, and assess the condition of terrestrial and aquatic habitats, the results of which have been extensively published in peer reviewed scientific journals and books. Bing has also taught senior undergraduate GIS and Remote Sensing classes at the University of Toronto.


Bing has extensive experience using multi-source remote sensing data (e.g., satellite-, airplane-, and UAV-based) for biological and environmental inventories and assessments. He is familiar with a wide range of remote sensing and GIS software, including ENVI, ERDAS, eCognition, Agisoft Photoscan, and ArcGIS, which he routinely uses to classify and process imagery for various research or management purposes. Bing also has experience automating routine remote sensing and GIS tasks using Matlab and R, as well as experience analyzing historical images.

Bing’s experience using remote sensing for environmental applications is diverse, and includes investigating and monitoring biophysical and biochemical properties of vegetation in response to natural and anthropogenic disturbance across a range of habitat types. This includes assessing multi-decadal spatiotemporal variations of wetland habitats, assessing the response and recovery of vegetation after fire disturbance in prairie ecosystems, monitoring species composition and plant health in a tall grassland ecosystem, and evaluating disturbance as a result of disease in forested ecosystems . Within these projects, Bing has worked with multispectral and hyperspectral imagery and LiDAR data from UAV, airborne, and satellite platforms, and has used advanced image processing techniques, including machine learning and object-based image analysis. As a result of this experience, Bing has a strong understanding of novel and newly emerging remote sensing methods and techniques that can be used to identify, map, and assess environmental features, including riparian habitats. In addition to his extensive remote sensing experience, Bing is highly skilled in conducting complex spatial analysis using GIS. By combining up-to-date and accurate remote sensing inventories with advanced GIS modelling and analysis techniques, Bing is able to produce spatially explicit information about riparian habitat condition that can be used by land managers to make more informed land use decisions.

Notable Work:

Currently leading the remote sensing component of the North Saskatchewan Region Lakes Riparian Assessment for Alberta Environment and Parks; using LiDAR and SPOT satellite data to create a historic and current land use / land cover layer to model riparian and watershed condition at a regional level.

Quantified vegetation stress from the leaf to landscape scale using lab/field observations, radiative transfer modelling, and UAV hyperspectral imaging system. Remote Sensing and Spatial Ecosystem Modeling laboratory at Department of Geography, University of Toronto Mississauga.

Remote sensing of Canadian grasslands: using vegetation biochemistry to monitor grassland health; Remote Sensing and Spatial Ecosystem Modeling laboratory at Department of Geography, University of Toronto Mississauga.

Investigated wetland properties (e.g., vegetation growing condition and its spatio-temporal variations) using medium- and high-spatial resolution satellite imagery; State Key Laboratory of Estuarine and Coastal Research, East China Normal University.

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