Swanton Pacific Ranch is an approximately 1,300 ha working ranch and forest in northern Santa Cruz County, California, managed by California Polytechnic State University, San Luis Obispo (Cal Poly). On August 12, 2009, the Lockheed Fire burned 300 ha of forestland, 51% of the forested area on the property, with variable fire intensity and mortality. This study used existing inventory data from 47 permanent 0.08 ha (1/5 ac) plots to compare the accuracy of classifying mortality resulting from the fire using digital multispectral imagery and LiDAR. The percent mortality of trees at least 25.4 cm (10”) DBH was aggregated to three classes (0-25, 25-50, and 50-100%). Three separate Classification Analysis and Regression Tree (CART) models were created to classify plot mortality. The first used the best imagery predictor variable of those considered, the Normalized Difference Vegetation Index (NDVI) calculated from 2010 National Agricultural Imagery Program (NAIP) aerial imagery, with shadowed pixel values adjusted, and non-canopy pixels removed. The second used the same NDVI in combination with selected variables from post-fire LiDAR data collected in 2010. The third used the same NDVI in combination with selected variables from differenced LiDAR data calculated using post-fire LiDAR and pre-fire LiDAR collected in 2008. The imagery alone was 74% accurate; the imagery and post-fire LiDAR model was 85% accurate, while the imagery and differenced LiDAR model was 83% accurate. These findings indicate that remote sensing data can accurately estimate post-fire mortality, and that the addition of LiDAR data to imagery may yield only modest improvement.
","abstract_html":"<p>Swanton Pacific Ranch is an approximately 1,300 ha working ranch and forest in northern Santa Cruz County, California, managed by California Polytechnic State University, San Luis Obispo (Cal Poly). On August 12, 2009, the Lockheed Fire burned 300 ha of forestland, 51% of the forested area on the property, with variable fire intensity and mortality. This study used existing inventory data from 47 permanent 0.08 ha (1/5 ac) plots to compare the accuracy of classifying mortality resulting from the fire using digital multispectral imagery and LiDAR. The percent mortality of trees at least 25.4 cm (10”) DBH was aggregated to three classes (0-25, 25-50, and 50-100%). Three separate Classification Analysis and Regression Tree (CART) models were created to classify plot mortality. The first used the best imagery predictor variable of those considered, the Normalized Difference Vegetation Index (NDVI) calculated from 2010 National Agricultural Imagery Program (NAIP) aerial imagery, with shadowed pixel values adjusted, and non-canopy pixels removed. The second used the same NDVI in combination with selected variables from post-fire LiDAR data collected in 2010. The third used the same NDVI in combination with selected variables from differenced LiDAR data calculated using post-fire LiDAR and pre-fire LiDAR collected in 2008. The imagery alone was 74% accurate; the imagery and post-fire LiDAR model was 85% accurate, while the imagery and differenced LiDAR model was 83% accurate. These findings indicate that remote sensing data can accurately estimate post-fire mortality, and that the addition of LiDAR data to imagery may yield only modest improvement.</p>","abstract_has_math":false,"creators":["Bishop, Brian David"],"institution":null,"degree_name":"MS in Forestry Sciences","degree_level":null,"degree_discipline":"Natural Resources Management","degree_department":null,"school":null,"contributors":["Brian Dietterick","Natural Resources Management","College of Agriculture, Food, and Environmental Sciences"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-03-01T08:00:00Z","date_published":"2014-03-01T08:00:00Z","updated_at":"2026-07-24T01:31:28Z","subjects":["Wildfire","NDVI","Forestry","Santa Cruz","Coast Redwood","spr_stu","Environmental Monitoring"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2014.19"],"render_values":[{"text":"10.15368/theses.2014.19","href":"https://doi.org/10.15368/theses.2014.19","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1171","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Brian Dietterick","Natural Resources Management","College of Agriculture, Food, and Environmental Sciences"]},{"key":"dc:creator","label":"Author","values":["Bishop, Brian David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-04T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Natural Resources Management"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Forestry Sciences"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Wildfire","NDVI","Forestry","Santa Cruz","Coast Redwood","spr_stu","Environmental Monitoring"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1171","10.15368/theses.2014.19"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Swanton Pacific Ranch is an approximately 1,300 ha working ranch and forest in northern Santa Cruz County, California, managed by California Polytechnic State University, San Luis Obispo (Cal Poly). On August 12, 2009, the Lockheed Fire burned 300 ha of forestland, 51% of the forested area on the property, with variable fire intensity and mortality. This study used existing inventory data from 47 permanent 0.08 ha (1/5 ac) plots to compare the accuracy of classifying mortality resulting from the fire using digital multispectral imagery and LiDAR. The percent mortality of trees at least 25.4 cm (10”) DBH was aggregated to three classes (0-25, 25-50, and 50-100%). Three separate Classification Analysis and Regression Tree (CART) models were created to classify plot mortality. The first used the best imagery predictor variable of those considered, the Normalized Difference Vegetation Index (NDVI) calculated from 2010 National Agricultural Imagery Program (NAIP) aerial imagery, with shadowed pixel values adjusted, and non-canopy pixels removed. The second used the same NDVI in combination with selected variables from post-fire LiDAR data collected in 2010. The third used the same NDVI in combination with selected variables from differenced LiDAR data calculated using post-fire LiDAR and pre-fire LiDAR collected in 2008. The imagery alone was 74% accurate; the imagery and post-fire LiDAR model was 85% accurate, while the imagery and differenced LiDAR model was 83% accurate. These findings indicate that remote sensing data can accurately estimate post-fire mortality, and that the addition of LiDAR data to imagery may yield only modest improvement.
"]},{"key":"dc:title","label":"Title","values":["Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and Lidar"]}]}],"canonical_facts":{"dc:contributor":["Brian Dietterick","Natural Resources Management","College of Agriculture, Food, and Environmental Sciences"],"dc:creator":["Bishop, Brian David"],"dc:date.available":["2014-03-04T08:00:00Z"],"dc:description.abstract":["Swanton Pacific Ranch is an approximately 1,300 ha working ranch and forest in northern Santa Cruz County, California, managed by California Polytechnic State University, San Luis Obispo (Cal Poly). On August 12, 2009, the Lockheed Fire burned 300 ha of forestland, 51% of the forested area on the property, with variable fire intensity and mortality. This study used existing inventory data from 47 permanent 0.08 ha (1/5 ac) plots to compare the accuracy of classifying mortality resulting from the fire using digital multispectral imagery and LiDAR. The percent mortality of trees at least 25.4 cm (10”) DBH was aggregated to three classes (0-25, 25-50, and 50-100%). Three separate Classification Analysis and Regression Tree (CART) models were created to classify plot mortality. The first used the best imagery predictor variable of those considered, the Normalized Difference Vegetation Index (NDVI) calculated from 2010 National Agricultural Imagery Program (NAIP) aerial imagery, with shadowed pixel values adjusted, and non-canopy pixels removed. The second used the same NDVI in combination with selected variables from post-fire LiDAR data collected in 2010. The third used the same NDVI in combination with selected variables from differenced LiDAR data calculated using post-fire LiDAR and pre-fire LiDAR collected in 2008. The imagery alone was 74% accurate; the imagery and post-fire LiDAR model was 85% accurate, while the imagery and differenced LiDAR model was 83% accurate. These findings indicate that remote sensing data can accurately estimate post-fire mortality, and that the addition of LiDAR data to imagery may yield only modest improvement.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1171","10.15368/theses.2014.19"],"dc:subject":["Wildfire","NDVI","Forestry","Santa Cruz","Coast Redwood","spr_stu","Environmental Monitoring"],"dc:title":["Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and Lidar"],"thesis:degree_discipline":["Natural Resources Management"],"thesis:degree_name":["MS in Forestry Sciences"]},"updated_at":"2026-07-24T01:31:28Z"}