Development of GIS Tools to Optimize Identification of Road Segments Prone to Flood Damage

This report investigates the peak flow at culverts in Vermont. A tool was developed using a geographic information system to quickly identify the parameters for estimating flow volume in order to assess the likelihood of flood damage to existing or planned infrastructure. The tool was developed to analyze environments in New England, whereas previous studies were insufficient for the region\u2019s unique climate and terrain. The tool was found to be successful in deriving an equation to predict peak flow estimation at all of Vermont\u2019s bridges and culverts. The report recommends further fine-tuning in order to expand its usage

Climate and lawn management interact to control C4 plant distribution in residential lawns across seven U.S. cities.

Author Posting. © Ecological Society of America, 2019. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Trammell, T. L. E., Pataki, D. E., Still, C. J., Ehleringer, J. R., Avolio, M. L., Bettez, N., Cavender-Bares, J., Groffman, P. M., Grove, M., Hall, S. J., Heffernan, J., Hobbie, S. E., Larson, K. L., Morse, J. L., Neill, C., Nelson, K. C., O'Neil-Dunne, J., Pearse, W. D., Chowdhury, R. R., Steele, M., & Wheeler, M. M. Climate and lawn management interact to control C4 plant distribution in residential lawns across seven U.S. cities. Ecological Applications, 29(4), (2019): e01884, doi: 10.1002/eap.1884.In natural grasslands, C4 plant dominance increases with growing season temperatures and reflects distinct differences in plant growth rates and water use efficiencies of C3 vs. C4 photosynthetic pathways. However, in lawns, management decisions influence interactions between planted turfgrass and weed species, leading to some uncertainty about the degree of human vs. climatic controls on lawn species distributions. We measured herbaceous plant carbon isotope ratios (δ13C, index of C3/C4 relative abundance) and C4 cover in residential lawns across seven U.S. cities to determine how climate, lawn plant management, or interactions between climate and plant management influenced C4 lawn cover. We also calculated theoretical C4 carbon gain predicted by a plant physiological model as an index of expected C4 cover due to growing season climatic conditions in each city. Contrary to theoretical predictions, plant δ13C and C4 cover in urban lawns were more strongly related to mean annual temperature than to growing season temperature. Wintertime temperatures influenced the distribution of C4 lawn turf plants, contrary to natural ecosystems where growing season temperatures primarily drive C4 distributions. C4 cover in lawns was greatest in the three warmest cities, due to an interaction between climate and homeowner plant management (e.g., planting C4 turf species) in these cities. The proportion of C4 lawn species was similar to the proportion of C4 species in the regional grass flora. However, the majority of C4 species were nonnative turf grasses, and not of regional origin. While temperature was a strong control on lawn species composition across the United States, cities differed as to whether these patterns were driven by cultivated lawn grasses vs. weedy species. In some cities, biotic interactions with weedy plants appeared to dominate, while in other cities, C4 plants were predominantly imported and cultivated. Elevated CO2 and temperature in cities can influence C3/C4 competitive outcomes; however, this study provides evidence that climate and plant management dynamics influence biogeography and ecology of C3/C4 plants in lawns. Their differing water and nutrient use efficiency may have substantial impacts on carbon, water, energy, and nutrient budgets across cities.This research was funded by a series of collaborative grants from the U.S. National Science Foundation Macrosystems Biology Program (EF‐1065548, 1065737, 1065740, 1065741, 1065772, 1065785, 1065831, 121238320). The authors thank La'Shaye Ervin, William Borrowman, Moumita Kundu, and Barbara Uhl for field and laboratory assistance

Climate and Lawn Management Interact to Control C\u3csub\u3e4\u3c/sub\u3e Plant Distribution in Residential Lawns Across Seven U.S. Cities

In natural grasslands, C4 plant dominance increases with growing season temperatures and reflects distinct differences in plant growth rates and water use efficiencies of C3 vs. C4 photosynthetic pathways. However, in lawns, management decisions influence interactions between planted turfgrass and weed species, leading to some uncertainty about the degree of human vs. climatic controls on lawn species distributions. We measured herbaceous plant carbon isotope ratios (δ13C, index of C3/C4 relative abundance) and C4 cover in residential lawns across seven U.S. cities to determine how climate, lawn plant management, or interactions between climate and plant management influenced C4 lawn cover. We also calculated theoretical C4carbon gain predicted by a plant physiological model as an index of expected C4 cover due to growing season climatic conditions in each city. Contrary to theoretical predictions, plant δ13C and C4 cover in urban lawns were more strongly related to mean annual temperature than to growing season temperature. Wintertime temperatures influenced the distribution of C4 lawn turf plants, contrary to natural ecosystems where growing season temperatures primarily drive C4 distributions. C4 cover in lawns was greatest in the three warmest cities, due to an interaction between climate and homeowner plant management (e.g., planting C4 turf species) in these cities. The proportion of C4 lawn species was similar to the proportion of C4 species in the regional grass flora. However, the majority of C4 species were nonnative turf grasses, and not of regional origin. While temperature was a strong control on lawn species composition across the United States, cities differed as to whether these patterns were driven by cultivated lawn grasses vs. weedy species. In some cities, biotic interactions with weedy plants appeared to dominate, while in other cities, C4 plants were predominantly imported and cultivated. Elevated CO2 and temperature in cities can influence C3/C4competitive outcomes; however, this study provides evidence that climate and plant management dynamics influence biogeography and ecology of C3/C4plants in lawns. Their differing water and nutrient use efficiency may have substantial impacts on carbon, water, energy, and nutrient budgets across cities

Ecological homogenization of urban USA

Author Posting. © Ecological Society of America, 2014. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Frontiers in Ecology and the Environment 12 (2014): 74-81, doi:10.1890/120374.A visually apparent but scientifically untested outcome of land-use change is homogenization across urban areas, where neighborhoods in different parts of the country have similar patterns of roads, residential lots, commercial areas, and aquatic features. We hypothesize that this homogenization extends to ecological structure and also to ecosystem functions such as carbon dynamics and microclimate, with continental-scale implications. Further, we suggest that understanding urban homogenization will provide the basis for understanding the impacts of urban land-use change from local to continental scales. Here, we show how multi-scale, multi-disciplinary datasets from six metropolitan areas that cover the major climatic regions of the US (Phoenix, AZ; Miami, FL; Baltimore, MD; Boston, MA; Minneapolis–St Paul, MN; and Los Angeles, CA) can be used to determine how household and neighborhood characteristics correlate with land-management practices, land-cover composition, and landscape structure and ecosystem functions at local, regional, and continental scales.We thank the MacroSystems Biology Program in the Emerging Frontiers Division of the Biological Sciences Directorate at NSF for support. The “Ecological Homogenization of Urban America” project was supported by a series of collaborative grants from this program (EF-1065548, 1065737, 1065740, 1065741, 1065772, 1065785, 1065831, 121238320). The work arose from research funded by grants from the NSF Long Term Ecological Research Program supporting work in Baltimore (DEB-0423476), Phoenix (BCS-1026865, DEB-0423704 and DEB-9714833), Plum Island (Boston) (OCE-1058747 and 1238212), Cedar Creek (Minneapolis–St Paul) (DEB-0620652), and Florida Coastal Everglades (Miami) (DBI-0620409)

Tree Canopy Change, 2009-2014, Prince George's County, MD

<pre><p>This layer is a high-resolution tree canopy change-detection layer for Prince Georges County, Maryland. It contains three tree-canopy classes for the period 2009-2014: (1) No Change; (2) Gain; and (3) Loss. It was created by first mapping tree canopy in 2014 using LiDAR and multispectral data and then comparing the new map directly to an existing tree-canopy map for the year 2009. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2009 and 2014 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). No accuracy assessment was conducted, but the dataset was subjected to comprehensive manual review and correction.</p></pre

3D High-Resolution Land Cover Example for Syracuse, NY

<p>3D perspective of land cover for Syracuse, NY derived from high-resolution LiDAR and aerial imagery.  Object-based image analysis (OBIA) techniques were used to automtically extract seven land cover classes: 1) tree canopy, 2) grass/shrub, 3) bare soil, 4) water, 5) buildings, 6) roads/railroads, and 7) other paved surfaces.</p

Canopy Height Model

<p>An example of an object-based approach to generating Canopy Height Models (CHM). The object based approach to CHM generation has advantages over traditional raster CHMs based on LiDAR alone, particularly when leaf-of LiDAR are used. Using a data-fusion object-based approach to land cover mapping leaf-on imagery (A) and leaf-off LiDAR (B) were used to map tree canopy (C). This process overcomes the limitations inherent in the imagery (no clear spectral signature for trees) and the LiDAR (leaf-off returns resulting in tree canopy gaps) to create a highly accurate tree canopy map. The LiDAR (B) and tree canopy (C) are then fed into a second object-based system that creates polygons approximating tree crowns and returns the max (D) and average (E) canopy height using only those LiDAR returns that are actually trees. The result is a vector polygon database that can be easily queried and merged with other vector datasets for subsequent analysis.</p

The advantage of using LiDAR for tree mapping in Philadelphia

<p>Individual trees can contribute substantially to a city’s overall tree canopy. The Urban Tree Canopy (UTC) Assessment makes use of advanced technology, such Light Detection and Ranging (LiDAR), which can detect small trees, even in building shadows. In Philadelphia, previous estimates, which ignored small trees put the total tree canopy at 10%. The actual amount, from the LiDAR-based UTC assessment, is 20%.</p

Tree canopy patch configuration

<p>The tree canopy patch analysis was developed to support the USDA Forest Service's Urban Tree Canopy (UTC) Assessment protocols (http://www.nrs.fs.fed.us/urban/utc/).  The UTC Assessment has historically focused on measuring the amount of tree canopy, the new tree canopy patch analysis gives resource managers a better understanding of the type of tree canopy they have by dividing the tree canopy into large, medium, and small patches.  Patches are delineated using a customized object-based approach that takes into account morphology, area, perimeter, and edge metrics.</p