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
Georectified Mosaic of Aerial Images of Baltimore City in 1927
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
- Award ID(s):
- 1855277
- NSF-PAR ID:
- 10474681
- Publisher / Repository:
- Environmental Data Initiative
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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
-
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
-
more » « less
Abstract Zoonotic diseases threaten human health worldwide and are often associated with anthropogenic disturbance. Predicting how disturbance influences spillover risk is critical for effective disease intervention but difficult to achieve at fine spatial scales. Here, we develop a method that learns the spatial distribution of a reservoir species from aerial imagery. Our approach uses neural networks to extract features of known or hypothesized importance from images. The spatial distribution of these features is then summarized and linked to spatially explicit reservoir presence/absence data using boosted regression trees. We demonstrate the utility of our method by applying it to the reservoir of Lassa virus,
Mastomys natalensis , within the West African nations of Sierra Leone and Guinea. We show that, when trained using reservoir trapping data and publicly available aerial imagery, our framework learns relationships between environmental features and reservoir occurrence and accurately ranks areas according to the likelihood of reservoir presence. -
Over the last century, direct human modification has been a major driver of coastal wetland degradation, resulting in widespread losses of wetland vegetation and a transition to open water. High-resolution satellite imagery is widely available for monitoring changes in present-day wetlands; however, understanding the rates of wetland vegetation loss over the last century depends on the use of historical panchromatic aerial photographs. In this study, we compared manual image thresholding and an automated machine learning (ML) method in detecting wetland vegetation and open water from historical panchromatic photographs in the Florida Everglades, a subtropical wetland landscape. We compared the same classes delineated in the historical photographs to 2012 multispectral satellite imagery and assessed the accuracy of detecting vegetation loss over a 72 year timescale (1940 to 2012) for a range of minimum mapping units (MMUs). Overall, classification accuracies were >95% across the historical photographs and satellite imagery, regardless of the classification method and MMUs. We detected a 2.3–2.7 ha increase in open water pixels across all change maps (overall accuracies > 95%). Our analysis demonstrated that ML classification methods can be used to delineate wetland vegetation from open water in low-quality, panchromatic aerial photographs and that a combination of images with different resolutions is compatible with change detection. The study also highlights how evaluating a range of MMUs can identify the effect of scale on detection accuracy and change class estimates as well as in determining the most relevant scale of analysis for the process of interest.more » « less
