Detection of potential vernal pools on the Canadian Shield (Ontario) using object-based image analysis in combination with machine learning
Vernal pools are small, temporary, forested wetlands of ecological importance with a high sensitivity to changing climate and land-use patterns. These ecosystems are under consider- able development pressure in southeastern Georgian Bay, where mapping techniques are required to… Read More
Identification of most spectrally distinguishable phenological stage of invasive Phragmites australis in Lake Erie wetlands (Canada) for accurate mapping using multispectral satellite imagery
Phragmites australis (Cav.) Trin. ex Steudel subspecies australis is one of the worst plant invaders in wetlands of North America. Remote sensing is the most cost-effective method to track its spread given its widespread distribution and rapid colonization rate. We hypothesize… Read More
Mapping invasive Phragmites australis in highway corridors using provincial orthophoto databases in Ontario
We mapped the distribution of invasive Phragmites australis (European common reed) in MTO-operated roadways of southern Ontario using airphotos from a provincial database, the Southwestern Ontario Orthophotography Project (SWOOP), which covers all highways from Windsor east to… Read More
Assessing efficacy of treatment programs to control invasive Phragmites in highway corridors of southwestern Ontario
The invasive haplotype M of Phragmites australis originated in Europe and was introduced to the Atlantic coast in the 1800s. It eventually made its way to southwestern Ontario in the late 1940s. Since 2010, invasive Phragmites has greatly expanded into coastal and inland wetlands… Read More
Use of World View 3 (WV 3) satellite imagery for early detection of invasive Phragmites australis in roadway corridors in Ontario
We tested the suitability of high-resolution (80 cm) multi-spectral satellite data from World View 3 (WV 3) to detect small patches of invasive Phragmites within 20-m buffer of the centre-line of the road. We used ENVI 5.5 to classify the image into seven classes: roads, trees,… Read More