Urban tree cover change in Detroit and Atlanta, USA, 1951–2010

Merry, Krista; Siry, Jacek P.; Bettinger, Pete; Bowker, J.M.

doi:10.1016/j.cities.2014.06.012

Cited by 28 publications

(8 citation statements)

References 36 publications

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“…The results of this sampling approach indicated a decline of forest cover for most US cities at a rate of approximately 7900 ha/year, or 4 million trees per year. Other work of Merry et al [ 131 ] indicated only slightly changes in tree canopy size for longer periods of time for selected US cities (1951–2010, Detroit and Atlanta). However, they suggested advanced remote sensing techniques to consider variations in spatial distribution more adequately.…”

Section: Main Textmentioning

confidence: 94%

High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments

Tigges

Lakes

2017

Carbon Balance Manage

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BackgroundUrban forests reduce greenhouse gas emissions by storing and sequestering considerable amounts of carbon. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging for long-term prognoses. Therefore, this review aims to identify uncertainties in urban forest carbon offset assessment and discuss the extent to which such uncertainties can be reduced by recent progress in high resolution remote sensing. We do this by performing an extensive literature review and a case study combining remote sensing and life cycle assessment of urban forest carbon offset in Berlin, Germany.Main textRecent progress in high resolution remote sensing and methods is adequate for delivering more precise details on the urban tree canopy, individual tree metrics, species, and age structures compared to conventional land use/cover class approaches. These area-wide consistent details can update life cycle inventories for more precise future prognoses. Additional improvements in classification accuracy can be achieved by a higher number of features derived from remote sensing data of increasing resolution, but first studies on this subject indicated that a smart selection of features already provides sufficient data that avoids redundancies and enables more efficient data processing. Our case study from Berlin could use remotely sensed individual tree species as consistent inventory of a life cycle assessment. However, a lack of growth, mortality and planting data forced us to make assumptions, therefore creating uncertainty in the long-term prognoses. Regarding temporal changes and reliable long-term estimates, more attention is required to detect changes of gradual growth, pruning and abrupt changes in tree planting and mortality. As such, precise long-term urban ecological monitoring using high resolution remote sensing should be intensified, especially due to increasing climate change effects. This is important for calibrating and validating recent prognoses of urban forest carbon offset, which have so far scarcely addressed longer timeframes. Additionally, higher resolution remote sensing of urban forest carbon estimates can improve upscaling approaches, which should be extended to reach a more precise global estimate for the first time.ConclusionsUrban forest carbon offset can be made more relevant by making more standardized assessments available for science and professional practitioners, and the increasing availability of high resolution remote sensing data and the progress in data processing allows for precisely that.

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Section: Main Textmentioning

confidence: 94%

High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments

Tigges

Lakes

2017

Carbon Balance Manage

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“…Many other cities across the US and Europe experienced similar post-industrialization processes, including depopulation and urban decay. In the US, these cities include Detroit, Michigan; Cleveland, Ohio; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania, and Holyoke, Massachusetts-generally considered part of America's rust belt in the midwest and northeast [27,[42][43][44]. In Europe, examples of post-industrial cities include Liverpool, England; Leipzig, Germany; and Turin, Italy [41,43].…”

Section: Urban Greening In Post-industrial Citiesmentioning

confidence: 99%

Linking Urban Tree Cover Change and Local History in a Post-Industrial City

Roman

Catton

Greenfield

et al. 2021

Land

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Municipal leaders are pursuing ambitious goals to increase urban tree canopy (UTC), but there is little understanding of the pace and socioecological drivers of UTC change. We analyzed land cover change in Philadelphia, Pennsylvania (United States) from 1970–2010 to examine the impacts of post-industrial processes on UTC. We interpreted land cover classes using aerial imagery and assessed historical context using archival newspapers, agency reports, and local historical scholarship. There was a citywide UTC increase of +4.3 percentage points. Substantial UTC gains occurred in protected open spaces related to both purposeful planting and unintentional forest emergence due to lack of maintenance, with the latter phenomenon well-documented in other cities located in forested biomes. Compared to developed lands, UTC was more persistent in protected open spaces. Some neighborhoods experienced substantial UTC gains, including quasi-suburban areas and depopulated low-income communities; the latter also experienced decreasing building cover. We identified key processes that drove UTC increases, and which imposed legacies on current UTC patterns: urban renewal, urban greening initiatives, quasi-suburban developments, and (dis)investments in parks. Our study demonstrates the socioecological dynamism of intra-city land cover changes at multi-decadal time scales and the crucial role of local historical context in the interpretation of UTC change.

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“… Presence of shadow in high spatial resolution imagery can reduce the accuracy of UGSs mapping (Zhang and Qiu, 2012;Merry, 2014). Considerable further research is therefore needed for recovering information from areas under shadow or at least to minimize the effects of shadow.…”

Section: Future Technical Requirementsmentioning

confidence: 99%