MESc graduates get published
The first graduates of the interdisciplinary Master of Science in Environmental Science (MESc) program at Nipissing are already contributing new knowledge to their field, publishing their research in an international peer reviewed journal.
Under the supervision of Dr. John Kovacs, recent MESc graduates Jeff Cable and Jeff Wilson investigated the application of radar and hyperspectral remote sensing data, to study and monitor crops at the field and regional scale. The research is part of a collaborative project involving Nipissing’s Geography and Computer Science departments, as well as Agriculture and Agri-Food Canada, and Ferme Roberge (Verner). Cable’s and Wilson’s research was supported by funding from the Northern Ontario Heritage Fund Corporation. Wilson also received a postgraduate scholarship from the Natural Sciences and Engineering Research Council of Canada.
Here’s a brief summary of their work:
Multi-Temporal Polarimetric RADARSAT-2 for Land Cover Monitoring in Northeastern Ontario, Canada
by Jeffrey W. Cable, John M. Kovacs, Jiali Shang and Xianfeng Jiao
This paper examines the use of space-borne radar imagery of West Nipissing to delineate various land cover types typical of Northern Ontario (forests, urban infrastructure, open water/lakes, and wetlands). Unlike conventional optical imagery (i.e. what you see on Google Earth) which relies on reflected sunlight, radar satellites generate their own energy and thus can be collected regardless of time of day. Additionally, radar can be more advantageous than optical imagery since the radar waves can pass through cloud cover, a common occurrence in our part of the world. The ability to quickly acquire and analyze these data can be important for applications such as agricultural monitoring, land use planning and/or disaster management.
Agricultural Monitoring in Northeastern Ontario, Canada, Using Multi-Temporal Polarimetric RADARSAT-2 Data
by Jeffrey W. Cable, John M. Kovacs, Xianfeng Jiao and Jiali Shang
This article further investigates the potential application of agricultural monitoring using radar imagery in Northern Ontario. Once again focusing on West Nipissing, we intensively studied five cash crops common to the region (barley, canola, oat, soybean and wheat) and compared the in-field results to several radar images collected over the course of a growing season (May-September). It is necessary to understand how the growth of the crops affect the radar response, as it can provide information about crop health and condition. These data can one day be used by producers to determine whether changes (e.g. additional fertilizer, pesticides) need to be made in order to maximize crop productivity.
Separating Crop Species in Northeastern Ontario Using Hyperspectral Data
by Jeffrey H. Wilson, Chunhua Zhang and John M. Kovacs
The purpose of this investigation was to examine the capability of hyperspectral remote sensing to distinguish five cash crops (canola, wheat, soybean, oat and barley) commonly grown in Northeastern Ontario. The study compared optimal crop separability based on two scenarios; Scenario 1 involved testing separability of crops based on number of days after planting and Scenario 2 involved testing crop separability at specific dates across the growing season. The results indicate that the optimal time for crop discrimination is in late July or approximately 75-79 days after planting. Crop identification using remote sensing technology could potentially help reduce the costs of precision agriculture practices and improve crop production in Northern Ontario.