Assessing the global warming potential of human settlement expansion in a mesic temperate landscape from 2005 to 2050

Reinmann, Andrew B.; Hutyra, Lucy R.; Trlica, Andrew; Olofsson, Pontus

doi:10.1016/j.scitotenv.2015.12.033

Cited by 30 publications

(23 citation statements)

References 55 publications

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“…The net change in albedo in the most intensively urbanized areas may be considerably smaller and more varied over space than has been assumed in some numerical modeling approaches for urban climate (e.g., Falasca et al, ). Analysis of trends between albedo and other land cover metrics in the study area show that on a regional basis (several kilometer scale) albedo gradients across an urban–rural gradient may be broadly predicted, opening the potential for further study on the implications for surface energy balance of urban land expansion (Reinmann et al, ). Further work will be necessary to investigate the generality of these albedo/land‐cover relationships in other urbanized regions with varying development histories, land use patterns, and local ecological and climatic context.…”

Section: Discussionmentioning

confidence: 99%

“…Decreases in albedo increase radiative energy absorption by the urban land surface, contributing to increases in air and surface temperature that characterize the UHI (Peng et al, 2012;Taha, 1997;Zhou et al, 2014). Beside the immediate local effects of the UHI, changes in land surface albedo with urban development may alter the regional surface energy balance and climate more broadly (Barnes & Roy, 2010), and could be an important component of the total climate forcing effect of current and future urban land cover (Bounoua et al, 2015;Reinmann et al, 2016). Conversely, the potential to counteract global and UHI temperature effects by albedo manipulation, for instance through using more reflective materials on roofs, has been the subject of widespread discussion and numeric modeling studies (e.g., Akbari et al, 2012;Jacobsen & Ten Hoeve, 2012;Li et al, 2014;Vahmani & Ban-Weiss, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape

et al. 2017

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Land surface albedo is a key parameter controlling the local energy budget, and altering the albedo of built surfaces has been proposed as a tool to mitigate high near‐surface temperatures in the urban heat island. However, most research on albedo in urban landscapes has used coarse‐resolution data, and few studies have attempted to relate albedo to other urban land cover characteristics. This study provides an empirical description of urban summertime albedo using 30 m remote sensing measurements in the metropolitan area around Boston, Massachusetts, relating albedo to metrics of impervious cover fraction, tree canopy coverage, population density, and land surface temperature (LST). At 30 m spatial resolution, median albedo over the study area (excluding open water) was 0.152 (0.112–0.187). Trends of lower albedo with increasing urbanization metrics and temperature emerged only after aggregating data to 500 m or the boundaries of individual towns, at which scale a −0.01 change in albedo was associated with a 29 (25–35)% decrease in canopy cover, a 27 (24–30)% increase in impervious cover, and an increase in population from 11 to 386 km−2. The most intensively urbanized towns in the region showed albedo up to 0.035 lower than the least urbanized towns, and mean mid‐morning LST 12.6°C higher. Trends in albedo derived from 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were comparable, but indicated a strong contribution of open water at this coarser resolution. These results reveal linkages between albedo and urban land cover character, and offer empirical context for climate resilient planning and future landscape functional changes with urbanization.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape

et al. 2017

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“…The impact of map input errors can also be seen in the practice of using well-known standard datasets, such as the National Land Cover Map (NLCD, [184]), to map quantities of interest, such as urban tree canopy biomass. Urban trees play a crucial role but in regional carbon cycles [185][186][187] but are often omitted from EO studies of carbon dynamics, e.g., MODIS Net Primary Productivity [188]. As urban lands are expected to triple between 2000 and 2030 [189,190], the need to factor them into The impact of map input errors can also be seen in the practice of using well-known standard datasets, such as the National Land Cover Map (NLCD, [184]), to map quantities of interest, such as urban tree canopy biomass.…”

Section: Impacts Of Training Data Errormentioning

confidence: 99%

“…As urban lands are expected to triple between 2000 and 2030 [189,190], the need to factor them into The impact of map input errors can also be seen in the practice of using well-known standard datasets, such as the National Land Cover Map (NLCD, [184]), to map quantities of interest, such as urban tree canopy biomass. Urban trees play a crucial role but in regional carbon cycles [185][186][187] but are often omitted from EO studies of carbon dynamics, e.g., MODIS Net Primary Productivity [188]. As urban lands are expected to triple between 2000 and 2030 [189,190], the need to factor them into carbon accounting is pressing, but remotely mapping urban tree cover is limited by (a) spatial resolutions that are too coarse for highly variable urban landscapes and (b) TD that are often biased to forested, agricultural, and other rural landscapes.…”

Section: Impacts Of Training Data Errormentioning

confidence: 99%

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

et al. 2020

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Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

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“…Due to geographic differences in human population and demographics, biophysical settings, and other factors, the impact of forest land cover conversion to new developed built-up land can be highly variable. Replacement of forest by urban development is also one of the most permanent changes to the environment [5] and may become even more important in regards to climate change effects on a growing number of people [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Regional Differences in Upland Forest to Developed (Urban) Land Cover Conversions in the Conterminous U.S., 1973–2011

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Xian

et al. 2016

Forests

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In this U.S. Geological Survey study of forest land cover across the conterminous U.S. (CONUS), specific proportions and rates of forest conversion to developed (urban) land were assessed on an ecoregional basis. The study period was divided into six time intervals between 1973 and 2011. Forest land cover was the source of 40% or more of the new urban land in 35 of the 84 ecoregions located within the CONUS. In 11 of these ecoregions this threshold exceeded in every time interval. When the percent of change, forest to urban, was compared to the percent of forest in each ecoregion, 58 ecoregions had a greater percent of change and, in six of those, change occurred in every time interval. Annual rates of forest to urban land cover change of 0.2% or higher occurred in 12 ecoregions at least once and in one ecoregion in all intervals. There were three ecoregions where the above conditions were met for nearly every time interval. Even though only a small number of the ecoregions were heavily impacted by forest loss to urban development within the CONUS, the ecosystem services provided by undeveloped forest land cover need to be quantified more completely to better inform future regional land management.

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Assessing the global warming potential of human settlement expansion in a mesic temperate landscape from 2005 to 2050

Cited by 30 publications

References 55 publications

Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape

Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Regional Differences in Upland Forest to Developed (Urban) Land Cover Conversions in the Conterminous U.S., 1973–2011

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