Land-use and land cover classifications are typically created using
automated methods to analyze modern, spatially explicit color aerial
imagery. However, creating classifications from black and white
historical aerial imagery presents a number of challenges that require
a combination of more traditional, manual techniques and approaches. A
georectified mosaic of 113 aerial images was digitized in ArcGIS to
create a land-use/land cover classification. The analyzed area covered
700 km2 (270 mi2) including all of Baltimore City, and a portion of
Baltimore County immediately surrounding the city. A combination of 8
land-use and land cover classes were used: Agriculture, Barren, Built
(Other), Forest, Grass/Shrubland, Industrial, Residential, and Water.
This geospatial data set captures an ecologically and socially
important moment in the post-war history of the city. It can be used
to examine relationships between property ownership and forest patch
dynamics across time. These insights may help inform future
environmental planning, conservation, management, and stewardship
goals for Baltimore City forest patches, and other cities throughout
the region.
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A Land-use/Land Cover Classification of Baltimore City in 1927
Land-use and land cover classifications are typically created using
automated methods to analyze modern, spatially explicit color aerial
imagery. However, creating classifications from black and white
historical aerial imagery presents a number of challenges that require
a combination of more traditional, manual techniques and approaches. A
georectified mosaic of 93 aerial images was digitized in ArcGIS to
create a land-use/land cover classification. The analyzed area covered
585 km2 (226
mi2) including all of Baltimore City, and
an area immediately adjacent to the city known at the time as the
Metropolitan District of Baltimore County. A combination of 8 land-use
and land cover classes were used: Agriculture, Barren, Built (Other),
Forest, Grass/Shrubland, Industrial, Residential, and Water. This
geospatial data set captures a moment of dynamic expansion in the
city, just prior to the Great Depression and can be used to examine
relationships between property ownership and forest patch dynamics
across time. These insights may help inform future environmental
planning, conservation, management, and stewardship goals for
Baltimore City forest patches, and other cities throughout the region.
more »
« less
- Award ID(s):
- 1855277
- NSF-PAR ID:
- 10474682
- Publisher / Repository:
- Environmental Data Initiative
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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