Coastal marshlands are among the world’s most highly productive ecosystems but they have diminished greatly in the past several decades owing to sea-level rise and direct anthropogenic influences. An effective means of quantifying loss or gain in marsh area is through the use of aerial image data, which offers synoptic views of the landscape at decadal-scale sampling frequencies. However, a potential problem with older panchromatic, or black-and-white, imagery is the absence of multispectral information that might be used otherwise in remote identification of vegetation types. Nevertheless, the analysis of horizontal variability in image brightness values, or image texture, can be used in deriving marsh areal coverage from even the oldest-available aerial photography. This project employed imagery acquired in 1955, 1992, and 2014 over Jackson County, Mississippi, to determine the extent of marshland loss or gain in the vicinity of the present-day Grand Bay National Estuarine Research Reserve (GBNERR). After pre-processing the images, image textural parameters were computed using the Grey-Level Co-Occurrence Matrix procedure (ENVI v X.X). A Maximum-Likelihood classification of the textural parameters to vegetation type was derived based on ground control point data. A change detection analysis then was applied among years. Preliminary results suggest that a net loss of around 5% in marsh area occurred in the GBNEER vicinity from 1955 to 2014. Results will assist resource managers in determining locations that may be most vulnerable to continued sea level rise and direct human impact.
","abstract_html":"<p>Coastal marshlands are among the world’s most highly productive ecosystems but they have diminished greatly in the past several decades owing to sea-level rise and direct anthropogenic influences. An effective means of quantifying loss or gain in marsh area is through the use of aerial image data, which offers synoptic views of the landscape at decadal-scale sampling frequencies. However, a potential problem with older panchromatic, or black-and-white, imagery is the absence of multispectral information that might be used otherwise in remote identification of vegetation types. Nevertheless, the analysis of horizontal variability in image brightness values, or image texture, can be used in deriving marsh areal coverage from even the oldest-available aerial photography. This project employed imagery acquired in 1955, 1992, and 2014 over Jackson County, Mississippi, to determine the extent of marshland loss or gain in the vicinity of the present-day Grand Bay National Estuarine Research Reserve (GBNERR). After pre-processing the images, image textural parameters were computed using the Grey-Level Co-Occurrence Matrix procedure (ENVI v X.X). A Maximum-Likelihood classification of the textural parameters to vegetation type was derived based on ground control point data. A change detection analysis then was applied among years. Preliminary results suggest that a net loss of around 5% in marsh area occurred in the GBNEER vicinity from 1955 to 2014. Results will assist resource managers in determining locations that may be most vulnerable to continued sea level rise and direct human impact.</p>","abstract_has_math":false,"creators":["Nicholson, Heather Michelle"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Masters Thesis","degree_discipline":"Geography and Geology","degree_department":null,"school":null,"contributors":["Gregory Carter","George Raber","Franklin Heitmuller"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-01T07:00:00Z","date_published":"2017-08-01T07:00:00Z","updated_at":"2026-07-24T05:44:41Z","subjects":["Salt Marsh","Textural Analysis","Historical Change","Environmental Monitoring","Geographic Information Sciences","Physical and Environmental Geography","Remote Sensing"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aquila.usm.edu/masters_theses/317","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gregory Carter","George Raber","Franklin Heitmuller"]},{"key":"dc:creator","label":"Author","values":["Nicholson, Heather Michelle"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-08-01T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Geography and Geology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Salt Marsh","Textural Analysis","Historical Change","Environmental Monitoring","Geographic Information Sciences","Physical and Environmental Geography","Remote Sensing"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://aquila.usm.edu/masters_theses/317"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Coastal marshlands are among the world’s most highly productive ecosystems but they have diminished greatly in the past several decades owing to sea-level rise and direct anthropogenic influences. An effective means of quantifying loss or gain in marsh area is through the use of aerial image data, which offers synoptic views of the landscape at decadal-scale sampling frequencies. However, a potential problem with older panchromatic, or black-and-white, imagery is the absence of multispectral information that might be used otherwise in remote identification of vegetation types. Nevertheless, the analysis of horizontal variability in image brightness values, or image texture, can be used in deriving marsh areal coverage from even the oldest-available aerial photography. This project employed imagery acquired in 1955, 1992, and 2014 over Jackson County, Mississippi, to determine the extent of marshland loss or gain in the vicinity of the present-day Grand Bay National Estuarine Research Reserve (GBNERR). After pre-processing the images, image textural parameters were computed using the Grey-Level Co-Occurrence Matrix procedure (ENVI v X.X). A Maximum-Likelihood classification of the textural parameters to vegetation type was derived based on ground control point data. A change detection analysis then was applied among years. Preliminary results suggest that a net loss of around 5% in marsh area occurred in the GBNEER vicinity from 1955 to 2014. Results will assist resource managers in determining locations that may be most vulnerable to continued sea level rise and direct human impact.
"]},{"key":"dc:title","label":"Title","values":["Textural Analysis of Historical Aerial Photography to Determine Change In Coastal Marsh Extent: Site of the Present-Day Grand Bay National Estuarine Research Reserve (GBNERR), Mississippi, 1955-2014"]}]}],"canonical_facts":{"dc:contributor":["Gregory Carter","George Raber","Franklin Heitmuller"],"dc:creator":["Nicholson, Heather Michelle"],"dc:date.available":["2019-08-01T07:00:00Z"],"dc:description.abstract":["Coastal marshlands are among the world’s most highly productive ecosystems but they have diminished greatly in the past several decades owing to sea-level rise and direct anthropogenic influences. An effective means of quantifying loss or gain in marsh area is through the use of aerial image data, which offers synoptic views of the landscape at decadal-scale sampling frequencies. However, a potential problem with older panchromatic, or black-and-white, imagery is the absence of multispectral information that might be used otherwise in remote identification of vegetation types. Nevertheless, the analysis of horizontal variability in image brightness values, or image texture, can be used in deriving marsh areal coverage from even the oldest-available aerial photography. This project employed imagery acquired in 1955, 1992, and 2014 over Jackson County, Mississippi, to determine the extent of marshland loss or gain in the vicinity of the present-day Grand Bay National Estuarine Research Reserve (GBNERR). After pre-processing the images, image textural parameters were computed using the Grey-Level Co-Occurrence Matrix procedure (ENVI v X.X). A Maximum-Likelihood classification of the textural parameters to vegetation type was derived based on ground control point data. A change detection analysis then was applied among years. Preliminary results suggest that a net loss of around 5% in marsh area occurred in the GBNEER vicinity from 1955 to 2014. Results will assist resource managers in determining locations that may be most vulnerable to continued sea level rise and direct human impact.
"],"dc:identifier":["https://aquila.usm.edu/masters_theses/317"],"dc:subject":["Salt Marsh","Textural Analysis","Historical Change","Environmental Monitoring","Geographic Information Sciences","Physical and Environmental Geography","Remote Sensing"],"dc:title":["Textural Analysis of Historical Aerial Photography to Determine Change In Coastal Marsh Extent: Site of the Present-Day Grand Bay National Estuarine Research Reserve (GBNERR), Mississippi, 1955-2014"],"thesis:degree_discipline":["Geography and Geology"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T05:44:41Z"}