1,754 research outputs found

    Developing 2010 High-Resolution Impervious Cover Estimates for Selected Towns in the Piscataqua Region Estuaries Partnership: Final Report

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    Estimates of 2010 impervious cover (New Hampshire) and 2011 impervious cover (Maine) were generated to extend the coverage of previous work in Rockingham and Strafford Counties, New Hampshire, to include all of the Piscataqua Region Estuaries Partnership (PREP) footprint. The newly mapped area comprised the town of Alton in Belknap County, New Hampshire, the towns of Brookfield, Wakefield, and Wolfeboro in Carroll County, New Hampshire, and the towns of Acton, Berwick, Eliot, Kittery, Lebanon, North Berwick, Sanford, Shapleigh, South Berwick, Wells, and York in York County, Maine1. With these new data, standardized, high resolution impervious cover estimates are now available for the entire PREP watershed. Impervious features covered 3,026 acres (2.7%) in the New Hampshire towns and 13,612 acres (4.9%) in the Maine towns, with a total of 16,637 (4.3%) acres mapped in the entire study area. As expected, the more urbanized towns of Kittery (11.3%), Sanford (7.9%), Eliot (7.0%), and York (6.2%) contained the highest percentage of impervious cover

    Developing 2015 High-Resolution Impervious Cover Estimates for the 52 Towns in the Piscataqua Region Estuaries Partnership: Final Report

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    Estimates of 2015 impervious cover (IC) for the 52 towns of the Piscataqua Region Estuaries Partnership (PREP) were generated from 2015 1-foot imagery (for the 42 towns in NH) and 2015 1-meter NAIP imagery (for the 10 towns in Maine). The 2015 IC mapping updated previous high resolution mapping developed from 2010 (New Hampshire) and 2011 (Maine) orthophotography for the study area. Impervious features covered 32,462 acres (5.8% of the land area) in the New Hampshire towns and 13,295 acres (5.3% of the land area) in the Maine towns, with a total of 46,634 (5.6% of the land area) acres mapped in the entire study area. The towns with the highest percent impervious cover in 2015 were in New Hampshire, and included Portsmouth (26.7%), New Castle (20.0%), and Seabrook (20.0%). The largest increases in IC between 2010 and 2015 occurred in Rochester, NH (122 acres), Wells, ME (64 acres), and Seabrook, NH (64 acres). Minimal amounts of IC increases occurred in most towns, with the least amounts in Madbury, NH (4 acres), New Castle, NH (2 acres), and Brookfield, NH (2 acres)

    Methods and standards development for three-dimensional mapping of the Antioch Quadrangle, Lake County, Illinois a pilot study

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    The Pilot Study for the Central Great Lakes Geologic Mapping Coalition (CGLGMC) focused on the Antioch Quadrangle, Lake County, Illinois developing a series of maps and digital products, several protocols for database development and maintenance and field procedures to acquire and integrate drilling and geophysical data from a quadangle area featuring complex glacial geology over a 25,000 year period.U.S. Geological Survey, Central Great Lakes Geologic Mapping CoalitionOpe

    Stand types discrimination comparing machine-learning algorithms in Monteverde, Canary Islands.

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    Aim of study: The main objective is to determine the best machine-learning algorithm to classify the stand types of Monteverde forests combining LiDAR, orthophotography, and Sentinel-2 data, thus providing an easy and cheap method to classify Monteverde stand types.Area of study: 1500 ha forest in Monteverde, North Tenerife, Canary Islands.Material and methods: RF, SVML, SVMR and ANN algorithms are used to classify the three Monteverde stand types.¬† Before training the model, feature selection of LiDAR, orthophotography, and Sentinel-2 data through VSURF was carried out.¬† Comparison of its accuracy was performed.Main results: Five LiDAR variables were found to be the most efficient for classifying each object, while only one Sentinel-2 index and one Sentinel-2 band was valuable.¬† Additionally, standard deviation and mean of the Red orthophotography colour band, and ratio between Red and Green bands were also found to be suitable.¬† SVML is confirmed as the most accurate algorithm (0.904, 0.041 SD) while ANN showed the lowest value of 0.891 (0.073 SD).¬† SVMR and RF obtain 0.902 (0.060 SD) and 0.904 (0.056 SD) respectively.¬† SVML was found to be the best method given its low standard deviation.Research highlights: The similar high accuracy values among models confirm the importance of taking into account diverse machine-learning methods for stand types classification purposes and different explanatory variables.¬† Although differences between errors may not seem relevant at a first glance, due to the limited size of the study area with only three plus two categories, such differences could be highly important when working at large scales with more stand types.ADDITIONAL KEY WORDSRF algorithm, SVML algorithm, SVMR algorithm, ANN algorithm, LiDAR, orthophotography, Sentinel-2ABBREVIATIONS USEDANN, artificial neural networks algorithm; Band04, Sentinel-2 band 04 image data; BR, brezal; DTHM, digital tree height model; DTHM-2016, digital tree height model based on 2016 LiDAR data; DTM, digital terrain model; DTM-2016, digital terrain model based on 2016 LiDAR data; FBA, fayal-brezal-acebi√Īal; FCC, canopy cover; HEIGHT-2009, maximum height based on 2009 LiDAR data; HGR, height growth based on 2009 and 2016 LiDAR data; LA, laurisilva; NDVI705, Sentinel-2 index image data; NMF, non-Monteverde forest; NMG, non-Monteverde ground; P95-2016, height percentile 95 based on 2016 LiDAR data; RATIO R/G, ratio between Red and Green bands orthophotograph data; RED, Red band orthophotograph data; Red-SD, standard deviation of the Red band orthophotograph data; RF, random forest algorithm; SVM, support vector machine algorithm; SVML, linear support vector machine algorithm; SVMR, radial support vector machine algorithm; VSURF, variable selection using random forest.

    Shallow-water benthic habitats of St. John, U.S. Virgin Islands

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    Coral reef ecosystems of the Virgin Islands Coral Reef National Monument, Virgin Islands National Park and the surrounding waters of St. John, U.S. Virgin Islands are a precious natural resource worthy of special protection and conservation. The mosaic of habitats including coral reefs, seagrasses and mangroves, are home to a diversity of marine organisms. These benthic habitats and their associated inhabitants provide many important ecosystem services to the community of St. John, such as fishing, tourism and shoreline protection. However, coral reef ecosystems throughout the U.S. Caribbean are under increasing pressure from environmental and anthropogenic stressors that threaten to destroy the natural heritage of these marine habitats. Mapping of benthic habitats is an integral component of any effective ecosystem-based management approach. Through the implementation of a multi-year interagency agreement, NOAA’s Center for Coastal Monitoring and Assessment - Biogeography Branch and the U.S. National Park Service (NPS) have completed benthic habitat mapping, field validation and accuracy assessment of maps for the nearshore marine environment of St. John. This work is an expansion of ongoing mapping and monitoring efforts conducted by NOAA and NPS in the U.S. Caribbean and replaces previous NOAA maps generated by Kendall et al. (2001) for the waters around St. John. The use of standardized protocols enables the condition of the coral reef ecosystems around St. John to be evaluated in context to the rest of the Virgin Island Territories and other U.S. coral ecosystems. The products from this effort provide an accurate assessment of the abundance and distribution of marine habitats surrounding St. John to support more effective management and conservation of ocean resources within the National Park system. This report documents the entire process of benthic habitat mapping in St. John. Chapter 1 provides a description of the benthic habitat classification scheme used to categorize the different habitats existing in the nearshore environment. Chapter 2 describes the steps required to create a benthic habitat map from visual interpretation of remotely sensed imagery. Chapter 3 details the process of accuracy assessment and reports on the thematic accuracy of the final maps. Finally, Chapter 4 is a summary of the basic map content and compares the new maps to a previous NOAA effort. Benthic habitat maps of the nearshore marine environment of St. John, U.S. Virgin Islands were created by visual interpretation of remotely sensed imagery. Overhead imagery, including color orthophotography and IKONOS satellite imagery, proved to be an excellent source from which to visually interpret the location, extent and attributes of marine habitats. NOAA scientists were able to accurately and reliably delineate the boundaries of features on digital imagery using a Geographic Information System (GIS) and fi eld investigations. The St. John habitat classification scheme defined benthic communities on the basis of four primary coral reef ecosystem attributes: 1) broad geographic zone, 2) geomorphological structure type, 3) dominant biological cover, and 4) degree of live coral cover. Every feature in the benthic habitat map was assigned a designation at each level of the scheme. The ability to apply any component of this scheme was dependent on being able to identify and delineate a given feature in remotely sensed imagery

    IndianaMap GIS Resources

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    The IndianaMap.org web site makes over 260 separate layers of GIS data and services easily discoverable, viewable and available in various formats for use by everyone. This presentation will discuss how IndianaMap provides statewide GIS data layers and technology. Introduction to IGIC, the IndianaMap and how it works. IndianaMap County Data Sharing initiative Statewide Orthophotography-LiDAR data Local-Resolution National Hydrography Data (NHD) Development ISDP & IndianaMap resources IndianaOpenTopography Server What’s Next (Arcgis.com & Open Data Portal

    Climate Change In The Casco Bay Watershed: Past, Present, And Future

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    This report describes how the climate of Casco Bay watershed in Maine has changed over the past century and how the future climate of the region is likely to be affected by human emissions of heat-trapping greenhouse gases that are warming the planet. Overall, the region has been getting warmer and wetter over the last century, and these trends have increased over the last four decades. To generate future projections for Portland, Farmington, and Lewiston, simulated temperature and precipitation from four climate models were fitted to local, long-term weather observations. Unknowns regarding fossil fuel consumption were accounted for by using two future scenarios. The scenarios describe climate in terms of temperature and precipitation for three future periods: the near-term, 2010-2039, mid-century, 2040-2069, and end-of-century, 2070-2099. All changes are relative to a historical baseline, 1970-1999. Some future changes are inevitable, so smart choices must be made to ensure our society and our environment will be able to adapt to coming change. But with prompt action, many of the most extreme consequences of climate change could be avoided or their worst impacts reduced
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