Search for: All records

Residential landscapes are essential to the sustainability of large areas of the United States. However, spatial and temporal variation across multiple domains complicates developing policies to balance these systems’ environmental, economic, and equity dimensions. We conducted multidisciplinary studies in the Baltimore, MD, USA, metropolitan area to identify locations (hotspots) or times (hot moments) with a disproportionate influence on nitrogen export, a widespread environmental concern. Results showed high variation in the inherent vulnerability/sensitivity of individual parcels to cause environmental damage and in the knowledge and practices of individual managers. To the extent that hotspots are the result of management choices by homeowners, there are straightforward approaches to improve outcomes, e.g. fertilizer restrictions and incentives to reduce fertilizer use. If, however, hotspots arise from the configuration and inherent characteristics of parcels and neighborhoods, efforts to improve outcomes may involve more intensive and complex interventions, such as conversion to alternative ecosystem types.

 

Addressing the challenges of sustainable and equitable city management in the 21st century requires innovative solutions and integration from a range of dedicated actors. In order to form and fortify partnerships of multi-sectoral collaboration, expand effective governance, and build collective resiliency it is important to understand the network of existing stewardship organizations. The term ‘stewardship’ encompasses a spectrum of local agents dedicated to the evolving process of community care and restoration. Groups involved in stewardship across Baltimore are catalysts of change through a variety of conservation, management, monitoring, transformation, education, and advocacy activities for the local environment – many with common goals of joint resource management, distributive justice, and community power sharing. The “environment” here is intentionally broadly defined as land, air, water, energy and more. The Stewardship Mapping and Assessment Project (STEW-MAP) is a method of data collection and visualization that tracks the characteristics of organizations and their financial and informational flows across sectors and geographic boundaries. The survey includes questions about three facets of environmental stewardship groups: 1) organizational characteristics, 2) collaboration networks, and 3) stewardship “turfs” where each organization works. The data have been analyzed alongside landcover and demographic data and used in multi-city studies incorporating similar datasets across major urban areas of the U.S. Additional information about the growing network of cities conducting stewmap can be found here: https://www.nrs.fs.usda.gov/STEW-MAP/ Romolini, Michele; Grove, J. Morgan; Locke, Dexter H. 2013. Assessing and comparing relationships between urban environmental stewardship networks and land cover in Baltimore and Seattle. Landscape and Urban Planning. 120: 190-207. https://www.fs.usda.gov/research/treesearch/44985 Johnson, M., D. H. Locke, E. Svendsen, L. Campbell, L. M. Westphal, M. Romolini, and J. Grove. 2019. Context matters: influence of organizational, environmental, and social factors on civic environmental stewardship group intensity. Ecology and Society 24(4): 1. https://doi.org/10.5751/ES-10924-240401 Ponte, S. 2023. Social-ecological processes and dynamics of urban forests as green stormwater infrastructure in Maryland, USA. Doctoral dissertation, University of Maryland, College Park, MD. 

The relationship between (a) the structure and composition of the landscape around an individual's home and (b) environmental perceptions and health outcomes has been well demonstrated (eg the value of vegetation cover to well‐being). Few studies, however, have examined how multiple landscape features (eg vegetation and water cover) relate to perceptions of multiple environmental problems (eg air or water quality) and whether those relationships hold over time. We utilized a long‐term dataset of geolocated telephone surveys in Baltimore, Maryland, to identify relationships between residents’ perceptions of environmental problems and nearby landcover. Residents of neighborhoods with more vegetation or located closer to water were less likely to perceive environmental problems. Water quality was one exception to this trend, in that people were more likely to perceive water‐quality problems when nearby water cover was greater. These trends endured over time, suggesting that these relationships are stable and therefore useful for informing policy aimed at minimizing perceived environmental problems.

 

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. 

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. 

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. 

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.