{"id":{"repo_id":"brock","oai_identifier":"oai:brocku.scholaris.ca:10464/17941"},"canonical_url":"https://search.dev.ndltd.org/etd/brock/oai:brocku.scholaris.ca:10464/17941","repository":{"repo_id":"brock","name":"Brock University","base_url":"https://brocku.scholaris.ca/server/oai/request"},"display":{"title":"Feasibility study using remote sensing technologies to improve zonal vineyard management","abstract":"The primary purpose of this research was to examine the feasibility of using remote sensing data to improve efficiency of zonal vineyard management. To achieve this goal, correlation analysis between the significant vineyard management variables and different remote sensing data analysis tools were undertaken. The variables included leaf water potential, soil moisture, canopy size, vine health, vineyard yield, and fruit composition, which further impacts wine quality. The remote sensing data analysis tools included normalized difference vegetation index (NDVI), and other indices extracted from electromagnetic reflectance data of grapevine leaves and canopies. In each site, sentinel vines (i.e., 72-81) were identified in a grid form. GPS-based geolocation was carried out for six Cabernet Franc vineyards in Ontario's Niagara wine country. Even though remote sensing data analysis tools were not associated with several other important variables for quality grape production, this research still confirmed that remote sensing data analysis has significant potential to differentiate specific zones of canopy size, water stress, yield, some superior fruit compositions, and the resulting wine sensory attributes within a single vineyard site. This study also confirmed that the mechanism of plant defense systems against biotic stress could have impacts on the spectral behaviour of grapevine leaves and hyperspectral remote sensing technologies could be applied as a tool to identify the spectral behaviour changes due to stress. Overall, this study verified the feasibility of remote sensing technologies to enhance the efficiency of vineyard management in the correlation of data from various remote sensing data-analysis techniques and viticulturally important variables for plant health and growth, and fruit and wine quality. As a first step to develop a site-specific crop management (SSCM) model for vineyard management, it also proposes future research opportunities to test and develop an efficient vineyard management decision making model.","abstract_html":"The primary purpose of this research was to examine the feasibility of using remote sensing data to improve efficiency of zonal vineyard management. To achieve this goal, correlation analysis between the significant vineyard management variables and different remote sensing data analysis tools were undertaken. The variables included leaf water potential, soil moisture, canopy size, vine health, vineyard yield, and fruit composition, which further impacts wine quality. The remote sensing data analysis tools included normalized difference vegetation index (NDVI), and other indices extracted from electromagnetic reflectance data of grapevine leaves and canopies. In each site, sentinel vines (i.e., 72-81) were identified in a grid form. GPS-based geolocation was carried out for six Cabernet Franc vineyards in Ontario's Niagara wine country. Even though remote sensing data analysis tools were not associated with several other important variables for quality grape production, this research still confirmed that remote sensing data analysis has significant potential to differentiate specific zones of canopy size, water stress, yield, some superior fruit compositions, and the resulting wine sensory attributes within a single vineyard site. This study also confirmed that the mechanism of plant defense systems against biotic stress could have impacts on the spectral behaviour of grapevine leaves and hyperspectral remote sensing technologies could be applied as a tool to identify the spectral behaviour changes due to stress. Overall, this study verified the feasibility of remote sensing technologies to enhance the efficiency of vineyard management in the correlation of data from various remote sensing data-analysis techniques and viticulturally important variables for plant health and growth, and fruit and wine quality. As a first step to develop a site-specific crop management (SSCM) model for vineyard management, it also proposes future research opportunities to test and develop an efficient vineyard management decision making model.","abstract_has_math":false,"creators":["Lee, Leeko Hyun Suk"],"institution":"Brock University","degree_name":"Ph.D. Biological Sciences","degree_level":"Doctoral","degree_discipline":"Faculty of Mathematics and Science","degree_department":"Department of Biological Sciences","school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08-10T15:58:03Z","date_published":"2023-08-10T15:58:03Z","updated_at":"2026-07-24T01:23:09Z","subjects":["precision viticulture","remote sensing,","NDVI","remotely piloted aircraft system","GreenSeeker"],"languages":["eng"],"rights":["Attribution-ShareAlike 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by-sa/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10464/17941","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Department of Biological Sciences"]},{"key":"dc:creator","label":"Author","values":["Lee, Leeko Hyun Suk"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-08-10T15:58:03Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-08-10T15:58:03Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-08-10T15:58:03Z"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Faculty of Mathematics and Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D. Biological Sciences"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Brock University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["precision viticulture","remote sensing,","NDVI","remotely piloted aircraft system","GreenSeeker"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Attribution-ShareAlike 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-sa/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10464/17941"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The primary purpose of this research was to examine the feasibility of using remote sensing data to improve efficiency of zonal vineyard management. To achieve this goal, correlation analysis between the significant vineyard management variables and different remote sensing data analysis tools were undertaken. The variables included leaf water potential, soil moisture, canopy size, vine health, vineyard yield, and fruit composition, which further impacts wine quality. The remote sensing data analysis tools included normalized difference vegetation index (NDVI), and other indices extracted from electromagnetic reflectance data of grapevine leaves and canopies. In each site, sentinel vines (i.e., 72-81) were identified in a grid form. GPS-based geolocation was carried out for six Cabernet Franc vineyards in Ontario's Niagara wine country. Even though remote sensing data analysis tools were not associated with several other important variables for quality grape production, this research still confirmed that remote sensing data analysis has significant potential to differentiate specific zones of canopy size, water stress, yield, some superior fruit compositions, and the resulting wine sensory attributes within a single vineyard site. This study also confirmed that the mechanism of plant defense systems against biotic stress could have impacts on the spectral behaviour of grapevine leaves and hyperspectral remote sensing technologies could be applied as a tool to identify the spectral behaviour changes due to stress. Overall, this study verified the feasibility of remote sensing technologies to enhance the efficiency of vineyard management in the correlation of data from various remote sensing data-analysis techniques and viticulturally important variables for plant health and growth, and fruit and wine quality. As a first step to develop a site-specific crop management (SSCM) model for vineyard management, it also proposes future research opportunities to test and develop an efficient vineyard management decision making model."]},{"key":"dc:title","label":"Title","values":["Feasibility study using remote sensing technologies to improve zonal vineyard management"]}]}],"canonical_facts":{"dc:contributor.department":["Department of Biological Sciences"],"dc:creator":["Lee, Leeko Hyun Suk"],"dc:date.accessioned":["2023-08-10T15:58:03Z"],"dc:date.available":["2023-08-10T15:58:03Z"],"dc:date.issued":["2023-08-10T15:58:03Z"],"dc:description.abstract":["The primary purpose of this research was to examine the feasibility of using remote sensing data to improve efficiency of zonal vineyard management. To achieve this goal, correlation analysis between the significant vineyard management variables and different remote sensing data analysis tools were undertaken. The variables included leaf water potential, soil moisture, canopy size, vine health, vineyard yield, and fruit composition, which further impacts wine quality. The remote sensing data analysis tools included normalized difference vegetation index (NDVI), and other indices extracted from electromagnetic reflectance data of grapevine leaves and canopies. In each site, sentinel vines (i.e., 72-81) were identified in a grid form. GPS-based geolocation was carried out for six Cabernet Franc vineyards in Ontario's Niagara wine country. Even though remote sensing data analysis tools were not associated with several other important variables for quality grape production, this research still confirmed that remote sensing data analysis has significant potential to differentiate specific zones of canopy size, water stress, yield, some superior fruit compositions, and the resulting wine sensory attributes within a single vineyard site. This study also confirmed that the mechanism of plant defense systems against biotic stress could have impacts on the spectral behaviour of grapevine leaves and hyperspectral remote sensing technologies could be applied as a tool to identify the spectral behaviour changes due to stress. Overall, this study verified the feasibility of remote sensing technologies to enhance the efficiency of vineyard management in the correlation of data from various remote sensing data-analysis techniques and viticulturally important variables for plant health and growth, and fruit and wine quality. As a first step to develop a site-specific crop management (SSCM) model for vineyard management, it also proposes future research opportunities to test and develop an efficient vineyard management decision making model."],"dc:identifier.uri":["http://hdl.handle.net/10464/17941"],"dc:language.iso":["eng"],"dc:rights":["Attribution-ShareAlike 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by-sa/4.0/"],"dc:subject":["precision viticulture","remote sensing,","NDVI","remotely piloted aircraft system","GreenSeeker"],"dc:title":["Feasibility study using remote sensing technologies to improve zonal vineyard management"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Faculty of Mathematics and Science"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D. Biological Sciences"],"thesis:institution_name":["Brock University"]},"updated_at":"2026-07-24T01:23:09Z"}