Presentation Type

Poster

DOI

https://doi.org/10.7275/fdkm-s966

Event Website

https://storymaps.arcgis.com/stories/a20062936eb84014ba457b87a949302c

Abstract

In the 1950s, Professor William P. MacConnell from the University of Massachusetts Forestry Department began working with his students to map the land cover in Massachusetts via the state’s earliest aerial photography program. These individual photographs are now part of the Special Collections and University Archives at the University of Massachusetts Amherst Libraries, and although they have been digitized and made available online, they have not yet been georeferenced.

In Spring 2021, our team (Alex and Matthew) began manually georeferencing the photos in ArcMap 10.8 software onto USGS 2019 color orthoimagery of Massachusetts available from MassGIS. Ideal ground control points include building corners, intersections, and any other distinctive features that have not resulted in much change between the historic and contemporary aerial photos. Some challenges we have faced include varying angles in which the photos were taken, cliffs that offset height in parts of the photo, or photos with a heavily forested area that makes georeferencing difficult to pinpoint ground control points.

The goal of the project is to georeference the approximately 4,800 individual photos from the 1951-52 series, which spans the entire state of Massachusetts, in order to make them available to the general public as downloadable GeoTIFFs. This result will allow anyone to access the photos and use them to analyze land-use changes in Massachusetts spanning the last 70 years. Manual georeferencing has come with its own challenges when encountering heavily forested aerial photos or aerial photos dominated by mostly urban areas. We find that future research into the potential automation of the georeferencing process would help alleviate the challenges associated with manual georeferencing.

Keyword

aerial photos, historical imagery, land use, georeferencing, storymaps, Massachusetts

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Heilmann, Alex, Matthew Martin, Camille Barchers, Forrest J. Bowlick, and Rebecca M. Seifried. 2021. "Georeferencing the MacConnell Aerial Photo Collection." StoryMap presented at Massachusetts GIS Day, November 17. https://doi.org/10.7275/fdkm-s966

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Georeferencing the MacConnell Aerial Photo Collection

In the 1950s, Professor William P. MacConnell from the University of Massachusetts Forestry Department began working with his students to map the land cover in Massachusetts via the state’s earliest aerial photography program. These individual photographs are now part of the Special Collections and University Archives at the University of Massachusetts Amherst Libraries, and although they have been digitized and made available online, they have not yet been georeferenced.

In Spring 2021, our team (Alex and Matthew) began manually georeferencing the photos in ArcMap 10.8 software onto USGS 2019 color orthoimagery of Massachusetts available from MassGIS. Ideal ground control points include building corners, intersections, and any other distinctive features that have not resulted in much change between the historic and contemporary aerial photos. Some challenges we have faced include varying angles in which the photos were taken, cliffs that offset height in parts of the photo, or photos with a heavily forested area that makes georeferencing difficult to pinpoint ground control points.

The goal of the project is to georeference the approximately 4,800 individual photos from the 1951-52 series, which spans the entire state of Massachusetts, in order to make them available to the general public as downloadable GeoTIFFs. This result will allow anyone to access the photos and use them to analyze land-use changes in Massachusetts spanning the last 70 years. Manual georeferencing has come with its own challenges when encountering heavily forested aerial photos or aerial photos dominated by mostly urban areas. We find that future research into the potential automation of the georeferencing process would help alleviate the challenges associated with manual georeferencing.

https://scholarworks.umass.edu/gisday/2021/posters/1

 

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