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.
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A Land-use/Land Cover Classification of Baltimore City in 1953
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.
more »
« less
- Award ID(s):
- 1855277
- NSF-PAR ID:
- 10474689
- Publisher / Repository:
- Environmental Data Initiative
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Landscape analyses are typically done using spatially explicit color aerial imagery. However, working with non-spatial black and white historical aerial photographs presents several challenges that require a combination of techniques and approaches. We analyzed 113 aerial images covering approx. 700 km2 (270 mi2) including all of Baltimore City, and a portion of Baltimore County surrounding the City. The images were taken between August 23rd 1952 and February 14th 1953. High-resolution scans were georeferenced and georectified against modern satellite imagery of the area and then combined to create a single raster mosaic. This process converted the images from a disparate set of photographs into a spatially explicit GIS data set that can be used to observe changes in land patches over time—and ultimately integrated with other long-term social, economic, and ecological data.more » « less
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Landscape analyses are typically done using spatially explicit color aerial imagery. However, working with non-spatial black and white historical aerial photographs presents several challenges that require a combination of techniques and approaches. We analyzed 93 aerial images covering 544 km2 (210 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. The images were taken from a biplane between October 1926 and February 1927. High-resolution scans were georeferenced and georectified against modern satellite imagery of the area and then combined to create a single raster mosaic. This process converted the images from a disparate set of photographs into a spatially explicit GIS data set that can be used to observe changes in land patches over time—and ultimately integrated with other long-term social, economic, and ecological data.more » « less
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Abstract Land-use change is highly dynamic globally and there is great uncertainty about the effects of land-use legacies on contemporary environmental performance. We used a chronosequence of urban grasslands (lawns) that were converted from agricultural and forested lands from 10 to over 130 years prior to determine if land-use legacy influences components of soil biodiversity and composition over time. We used historical aerial imagery to identify sites in Baltimore County, MD (USA) with agricultural versus forest land-use history. Soil samples were taken from these sites as well as from existing well-studied agricultural and forest sites used as historical references by the National Science Foundation Long-Term Ecological Research Baltimore Ecosystem Study program. We found that the microbiomes in lawns of agricultural origin were similar to those in agricultural reference sites, which suggests that the ecological parameters on lawns and reference agricultural systems are similar in how they influence soil microbial community dynamics. In contrast, lawns that were previously forest showed distinct shifts in soil bacterial composition upon recent conversion but reverted back in composition similar to forest soils as the lawns aged over decades. Soil fungal communities shifted after forested land was converted to lawns, but unlike bacterial communities, did not revert in composition over time. Our results show that components of bacterial biodiversity and composition are resistant to change in previously forested lawns despite urbanization processes. Therefore land-use legacy, depending on the prior use, is an important factor to consider when examining urban ecological homogenization.
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Abstract Context Land use history of urban forests impacts present-day soil structure, vegetation, and ecosystem function, yet is rarely documented in a way accessible to planners and land managers.
Objectives To (1) summarize historical land cover of present-day forest patches in Baltimore, MD, USA across land ownership categories and (2) determine whether social-ecological characteristics vary by historical land cover trajectory.
Methods Using land cover classification derived from 1927 and 1953 aerial imagery, we summarized present-day forest cover by three land cover sequence classes: (1) Persistent forest that has remained forested since 1927, (2) Successional forest previously cleared for non-forest vegetation (including agriculture) that has since reforested, or (3) Converted forest that has regrown on previously developed areas. We then assessed present-day ownership and average canopy height of forest patches by land cover sequence class.
Results More than half of Baltimore City’s forest has persisted since at least 1927, 72% since 1953. About 30% has succeeded from non-forest vegetation during the past century, while 15% has reverted from previous development. A large proportion of forest converted from previous development is currently privately owned, whereas persistent and successional forest are more likely municipally-owned. Successional forest occurred on larger average parcels with the fewest number of distinct property owners per patch. Average tree canopy height was significantly greater in patches of persistent forest (mean = 18.1 m) compared to canopy height in successional and converted forest patches (16.6 m and 16.9 m, respectively).
Conclusions Historical context is often absent from urban landscape ecology but provides information that can inform management approaches and conservation priorities with limited resources for sustaining urban natural resources. Using historical landscape analysis, urban forest patches could be further prioritized for protection by their age class and associated ecosystem characteristics.
