Loading…
Fall NEARC 2018 has ended
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

Filter the schedule to view only the presentations with slides available.

View and download a PDF of the final program.

Sign up or log in to bookmark your favorites and sync them to your phone or calendar.

Remote Sensing [clear filter]
Tuesday, October 30
 

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