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Welcome to the interactive web schedule for the 2018 Fall NEARC Conference! To return to the NEARC website, go to: https://www.northeastarc.org/fall-nearc.html

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Remote Sensing [clear filter]
Monday, October 29
 

11:00am

UAV #1 Track. UAS Applications and ArcPro 2.1: The Waugh Arboretum’s Evolving Process of Tree Inventorying and Management
AUTHORS: Daniel J. Myers, GIS Technician, Physical Plant Grounds and Facilities; Christopher A. Copeland, GIS Technician, Physical Plant Landscape Management – University of Massachusetts, Amherst

ABSTRACT: Urban landscapes are dynamic systems inundated by physical, chemical, and biological inputs. As UMass Amherst continues its campaign for campus development, there are inherent conflicts with its urban forest. Trees are assets to any community, each with varying degrees of worth and functionality. A tree inventory is a method to monitor changes to each individual tree and detect patterns to the collective tree population. It serves as a guide to prioritize tree maintenance, preserve valuable specimens, and communicate forest functions to stakeholders. Campus arborists currently update the tree inventory using tablets equipped with ArcPad 10.2 and TreeWorks 2015. With the implementation of UAS technology and ArcPro, campus arborists can more accurately and efficiently measure trees and observe their environment. One centimeter pixel resolution RGB imagery can aid campus arborists in efficiently calculating dendrometric variables and through time series analysis. In our presentation, we will explore the benefits of ESRI Drone2Map and ArcGIS Pro 2.1 Image Analyst extension. These applications are used to create an accurately georeferenced orthomosaic and to conduct an object-oriented classification to identify tree canopy coverage and tree genus at a forty acre study area. As GIS technology evolves, our comprehensive GIS databases functionality can increase as a tool for organizing, analysis, and reporting with ancillary data collected by UAS.

Monday October 29, 2018 11:00am - 11:30am
Saratoga 1/2
 
Tuesday, October 30
 

9:00am

Data Analysis/Visualization Track. In Data We Trust? Using Data Science to Increase Locational Confidence
AUTHORS: Katie Budreski*, Jonnie Dunne, Lauren Padilla – Stone Environmental

ABSTRACT: As GIS analysts and data scientists, we rely on the high quality datasets prepared by authoritative sources, such as the National Land Cover Database (NLCD) published by the Multi-Resolution Land Characteristics Consortium (MRLC) and the Cropland Data Layer (CDL) published by the National Agriculture Statistics Service (NASS). These datasets allow us to do powerful and meaningful nationwide analyses. But to fully trust the data for planning and decision making, we need to understand its limitations and incorporate known uncertainty into analyses. Additionally, ancillary data and machine learning can be used to improve locational and information confidence, such as high resolution imagery and image segmentation.

Tuesday October 30, 2018 9:00am - 9:30am
Broadway 3

10:30am

UAV #2 Track. UAS Lidar and Imagery in the NERRS: Evaluating the Effectiveness of UAS Sensors and Platforms for Multi-Purpose Mapping of Marshes and Beaches in the NERRS Sentinel Site Network
AUTHORS: Kirk Waters, NOAA OCM; Sue Bickford, Wells NERR; Jamie Carter*, TBG at NOAA OCM; Nina Garfield, NOAA OCM; Andrea Habeck, Jacques Cousteau NERR; Nate Herold, NOAA OCM; Jared Lewis, San Francisco Bay NERR; Jonathan Pitchford, Grand Bay NERR; Melissa Rosa, TBG at NOAA OCM

ABSTRACT: There is a near universal need within the National Estuarine Research Reserve System (NERRS) and by other natural resource stakeholders for accurate Digital Elevation Models (DEMs) and habitat maps to support a diversity of applications. Applications include supporting sea level rise research and management and flood forecasts; evaluating the impact of specific vegetation management practices on elevation in marsh micro-environments; assessing beaches after storms for damage assessment and restoration purposes; and identifying high priority invasive and sensitive vegetation. Our intent with this project was to evaluate the effectiveness of unmanned aerial system (UAS) platforms to produce multiple mapping data and products for elevation and vegetation mapping in marshes and dune systems. We sought a UAS solution that could fly multi-spectral and lidar elevation instruments sequentially on the same platform. We contracted UAS data collection to the private sector (Quantum Spatial, Inc., and PrecisionHawk) and conducted the ground truth ourselves (NERRS and NOAA staff). We used multiple NERRS sentinel sites as test beds. Data from multiple high-resolution multi-spectral sensors and lidar elevation were acquired for three NERRS sites: Jacques Cousteau, NJ; Grand Bay, MS; and Rush Ranch in San Francisco Bay, CA. The data were evaluated on their ability to meet specifications, primarily positional accuracy and resolution, and their potential to improve habitat mapping.


Tuesday October 30, 2018 10:30am - 11:00am
Saratoga 1/2

11:00am

UAV #2 Track. Beyond the Drone
AUTHORS: Ted Covill, WSP USA

ABSTRACT: Small Unmanned Aerial Systems (sUAS) aka. Drones have been in use for several years and users have found many uses for data that can be produced using these systems. These applications range from basic imagery for marketing or personal use to more advanced applications such as orthophoto rectification or topographic modeling. This presentation will focus on the photogrammetric application know as Structure for Motion (SfM) and how sUAS imagery can be used to create ground surface model and 3D models of other objects such as tanks, towers and piers.


Tuesday October 30, 2018 11:00am - 11:30am
Saratoga 1/2

11:30am

Natural Resources #1 Track. How Interactive Online Technologies Advance the Connecticut’s Changing Landscape Study Website
AUTHORS: Emily H. Wilson, James Hurd, Chet Arnold – University of Connecticut

ABSTRACT: The University of Connecticut Center for Land Use Education and Research (CLEAR) has added a new date to the Connecticut’s Changing Landscape (CCL) land cover and land cover change study. The study now covers 30 years with 7 dates (1985, 1990, 1995, 2002, 2006, 2010 and 2015) of land cover. CLEAR has a long history of making the results of the study available on the web to all users in a variety of ways. Previous versions of the website used pdfs, static images and ArcIMS to name a few. Today, the information is being shared using the new geospatial technologies. ArcGIS server services are available to users, populate a story map and are consumed by a web app builder viewer with multiple, user-friendly tools. New, slick and interactive data visualizations tools, such as Tableau and Esri Insights, were assessed and are now part of the website. The presentation will show these tools, discuss how they are being used to share land cover and how their dynamic nature improves user experience. http://clear.uconn.edu/projects/landscape.

Tuesday October 30, 2018 11:30am - 12:00pm
Broadway 1&2

1:30pm

Remote Sensing Track. High Resolution Land Cover for the Northeast (and Beyond)
AUTHORS: Nate Herold, NOAA Office for Coastal Management; Jamie Carter, The Baldwin Group, Inc. on contract for NOAA's Office for Coastal Management

ABSTRACT: Understanding current land cover patterns and past change trends is essential to comprehensive management, assessment, and future planning. For more than two decades, NOAA’s Office for Coastal Management has been producing consistent, accurate land cover and change information for the coastal U.S through its Coastal Change Analysis Program (C-CAP), with the goal of continually updating these maps every 5 years. In recent years, NOAA has been working to establish an operational higher resolution land cover product line, bringing the national C-CAP framework to the local level and allowing for more site specific applications. This work has been possible because of the wealth of available imagery and lidar data, improved software and hardware capabilities, and artificial intelligence classification techniques. This talk will highlight the results of this work in the Northeast, with particular emphasis on products recently released for Massachusetts and Connecticut.


Tuesday October 30, 2018 1:30pm - 2:00pm
Saratoga 1/2

2:00pm

Remote Sensing Track. Statewide High-Resolution Land Cover Mapping
AUTHORS: Jaralth O'Neil-Dunne, University of Vermont

ABSTRACT: From statewide orthos to NAIP imagery to LiDAR, we are awash in high-resolution remotely sensed data. While these data can serve as great basemaps, the investment in these data truly pays off when we turn them into information. This presentation will discuss several statewide land cover mapping projects that are currently underway in New England. Participants will gain insight into the tools and techniques used to transform terabytes of high-resolution imagery and LiDAR data into land cover information, along with the challenges of doing so when datasets vary with respect to quality, acquisition date, and specifications. Prepared to be amazed at how far automated feature extraction techniques have come and how this technology can be leveraged to help resource managers make more informed decisions.


Tuesday October 30, 2018 2:00pm - 2:30pm
Saratoga 1/2

2:30pm

Remote Sensing Track. Estimating Percent Impervious Cover from Landsat-based Land Cover: An Evaluation of a Simple and Transferable Regression Model
AUTHORS: Jason R. Parent, Qian Lei – University of Connecticut

ABSTRACT: Percent impervious cover (PIC) is often estimated from moderate-resolution satellite data which is known to overestimate PIC in urban areas and underestimate PIC in rural areas. Regression-based models (e.g. ISAT, ETIS) have been developed to calibrate Landsat-based PIC estimates to improve accuracy. However, it is unknown how these models perform if they are used outside of the geographic area for which the models were developed or if the size of the analysis units (e.g. watershed) affects model performance. Furthermore, these models tend to be applicable only for specific land cover datasets and may require ancillary data such as population estimates. This study evaluated the robustness of a simple regression model, based solely on Landsat-based impervious land cover, to estimate PIC for different geographic areas, land cover datasets, and analysis units.

We tested the model for analysis units ranging in size from 2 to 100+ ha for four locations in Connecticut, Massachusetts, and Ohio. The model was developed in southwestern CT and validated in the three other locations. Model RMSE values ranged from 1.5% to 10.0% with the performance improving as the analysis unit size increased. The model had slightly lower performance (0.0 to 2.7% higher RMSE) when applied outside the area in which it was developed. Overall, this study showed that a simple PIC estimation model, based only on the impervious cover classes of Landsat-based land cover datasets, can be effective for a variety of analysis unit sizes and for locations outside of the model calibration areas.


Tuesday October 30, 2018 2:30pm - 3:00pm
Saratoga 1/2