Geology topics

Austin Troy

Publications and source records attributed to Austin Troy.

6 recordsLinked to original sources

USGS2025

An enhanced national-scale urban tree canopy cover dataset for the United States

Moderate-resolution (30-m) national map products have limited capacity to represent fine-scale, heterogeneous urban forms and processes, yet improvements from incorporating higher resolution predictor data remain rare. In this study, we applied random forest models to high-resolution land cover data for 71 U.S. urban areas, moderate-resolution National Land Cover Database (NLCD) Tree Canopy Cover (TCC), and additional explanatory climatic and structural data to develop an enhanced urban TCC dataset for U.S. urban areas. With a coefficient of determination (R 2 ) of 0.747, our model estimated TCC within 3% for 62 urban areas and added 13.4% more city-level TCC on average, compared to the native NLCD TCC product. Cross validations indicated model stability suitable for building a national-scale TCC dataset (median R 2 of 0.752, 0.675, and 0.743 for 1,000-fold cross validation, urban area leave-one-out cross validation, and cross validation by Census block group median year built, respectively). Additionally, our model code can be used to improve moderate-resolution TCC in other parts of the world where high-resolution land cover data have limited spatiotemporal availability.

conterminous United States↗

USGS2022

Assessing the accuracy and potential for improvement of the national land cover database’s tree canopy cover dataset in urban areas of the conterminous United States

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input.

Remote Sensing↗

USGS2021

Piloting urban ecosystem accounting for the United States

In this study, we develop urban ecosystem accounts in the U.S., using the System of Environmental-Economic Accounting Experimental Ecosystem Accounting (SEEA EEA) framework. Most ecosystem accounts focus on regional and national scales, which are appropriate for many ecosystem services. However, ecosystems provide substantial services in cities, improving quality of life and contributing to resiliency for substantial parts of the population. Our models estimate energy savings for indoor cooling resulting from heat mitigated by trees and rainfall intercepted by trees. Both models cover major cities in the contiguous U.S. and report the results through physical supply and use tables for multiple accounting periods (2011 and 2016). Using conservative assumptions, urban trees provide substantial heat mitigation (4,098 and 4,229 GWh, valued at $523 and $539 million in 2011 and 2016, respectively) and rainfall interception (2,422 and 2,627 million m 3 , valued at $434 and $425 million for 2011 and 2016, respectively). Interannual differences largely reflect variations in weather patterns. Our work shows how Earth observation data can support urban ecosystem accounting. We provide model code within a public repository to facilitate model runs elsewhere, enabling the SEEA EEA and Earth observation user communities to reuse our models and provide feedback for improvement.

Ecosystem Services↗

USGS2020

A rasterized building footprint dataset for the United States

Microsoft released a U.S.-wide vector building dataset in 2018. Although the vector building layers provide relatively accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost. We used High-Performance Computing (HPC) to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state, excluding Alaska and Hawaii: (1) total footprint coverage, (2) number of unique buildings intersecting each cell, (3) number of building centroids falling inside each cell, and area of the (4) average, (5) smallest, and (6) largest area of buildings that intersect each cell. These values are represented as raster layers with 30 m cell size covering the 48 conterminous states. We also identify errors in the original building dataset. We evaluate precision and recall in the data for three large U.S. urban areas. Precision is high and comparable to results reported by Microsoft while recall is high for buildings with footprints larger than 200 m2 but lower for progressively smaller buildings.

Scientific Data↗

USGS2015

Linking biophysical models and public preferences for ecosystem service assessments: a case study for the Southern Rocky Mountains

Through extensive research, ecosystem services have been mapped using both survey-based and biophysical approaches, but comparative mapping of public values and those quantified using models has been lacking. In this paper, we mapped hot and cold spots for perceived and modeled ecosystem services by synthesizing results from a social-values mapping study of residents living near the Pike–San Isabel National Forest (PSI), located in the Southern Rocky Mountains, with corresponding biophysically modeled ecosystem services. Social-value maps for the PSI were developed using the Social Values for Ecosystem Services tool, providing statistically modeled continuous value surfaces for 12 value types, including aesthetic, biodiversity, and life-sustaining values. Biophysically modeled maps of carbon sequestration and storage, scenic viewsheds, sediment regulation, and water yield were generated using the Artificial Intelligence for Ecosystem Services tool. Hotspots for both perceived and modeled services were disproportionately located within the PSI’s wilderness areas. Additionally, we used regression analysis to evaluate spatial relationships between perceived biodiversity and cultural ecosystem services and corresponding biophysical model outputs. Our goal was to determine whether publicly valued locations for aesthetic, biodiversity, and life-sustaining values relate meaningfully to results from corresponding biophysical ecosystem service models. We found weak relationships between perceived and biophysically modeled services, indicating that public perception of ecosystem service provisioning regions is limited. We believe that biophysical and social approaches to ecosystem service mapping can serve as methodological complements that can advance ecosystem services-based resource management, benefitting resource managers by showing potential locations of synergy or conflict between areas supplying ecosystem services and those valued by the public.

Regional Environmental Change↗

USGS2005

Attitudes of Vermont dairy farmers regarding adoption of management practices for grassland songbirds

In the northeastern United States, most populations of grassland songbirds occur on private lands. However, little information exists about the attitudes of farmers toward habitat management for this guild. To address this information gap, we surveyed 131 dairy farmers in Vermont's Champlain Valley to assess current hayfield management practices and farmers' willingness to adopt more “bird-friendly” practices. Our results showed a clear trend toward earlier and more frequent hayfield cuts. Farmers indicated they have little flexibility to alter the timing of their cuts on most of their land. However, many farmers (49%) indicated a willingness to adopt alternative management practices on at least a small portion of their land. Combined with the fact that many farmers characterized parts of their land as “wasteland,” or economically unproductive land, this result suggests that some leeway exists for increasing songbird habitat quality on at least portions of dairy farms. Although significant differences existed in the amount of land for which farmers were willing to adopt alternative management based on herd size, acreage, and experience, the directionality of these relationships could not be established except tentatively for herd size, in which case it appeared that farmers with smaller herds were more likely to dedicate a greater percentage of their land to alternative management. The results of this study likely have relevance to dairy farms throughout the northern-tier dairy states. Given the increasing trend for agricultural land to be converted into housing, we recommend that extension and education efforts target farmers with large hayfield acreages, encouraging the maintenance of high-quality habitat for grassland songbirds.

Vermont↗