Exploring spatial-based methods for assessing land-based stewardship
Keywords
stewardship, NDVI, spatial, conservation, RGB, drone, remote sensing, vegetation classification
Type of Proposal
Oral Presentation
Faculty
Faculty of Science
Faculty Sponsor
Dr. Catherine Febria and Ms. Alice Grgicak-Mannion
Proposal
In a time of heightened land degradation and biodiversity loss, spatial tools may offer an innovative method to monitor the health of ecosystems. In practice, this means assessing the recovery of ecosystems including restoration and conservation actions, however, this is labour-intensive and often hindered by limited resources including data, capacity, and an ever-changing environment. Under this context, our study examines the suitability of spatial technologies to assess vegetation-based management practices (i.e., replanting) to address such barriers. Here we assessed vegetation health and land use in the Sydenham River Watershed through remote sensing. We accessed normalized difference vegetation index (NDVI) imagery, which assigns higher values to areas of higher vegetation health. On a large scale, we used an unmanned aerial vehicle to assess vegetation species and assemblages through RGB (red, green, blue) imagery. We computed vegetation classification accuracy at both spatial and spectral scales. We found that their applicability remains contingent on the available ground-truth validation of planting events, and the information desired by on-the-ground practitioners. Through coordinated partnerships, spatial technology can offer assistance in assessing the effective restoration of terrestrial ecosystems and stewardship decisions.
Exploring spatial-based methods for assessing land-based stewardship
In a time of heightened land degradation and biodiversity loss, spatial tools may offer an innovative method to monitor the health of ecosystems. In practice, this means assessing the recovery of ecosystems including restoration and conservation actions, however, this is labour-intensive and often hindered by limited resources including data, capacity, and an ever-changing environment. Under this context, our study examines the suitability of spatial technologies to assess vegetation-based management practices (i.e., replanting) to address such barriers. Here we assessed vegetation health and land use in the Sydenham River Watershed through remote sensing. We accessed normalized difference vegetation index (NDVI) imagery, which assigns higher values to areas of higher vegetation health. On a large scale, we used an unmanned aerial vehicle to assess vegetation species and assemblages through RGB (red, green, blue) imagery. We computed vegetation classification accuracy at both spatial and spectral scales. We found that their applicability remains contingent on the available ground-truth validation of planting events, and the information desired by on-the-ground practitioners. Through coordinated partnerships, spatial technology can offer assistance in assessing the effective restoration of terrestrial ecosystems and stewardship decisions.