Farris
Amy S.
Farris
Amy S.
No Thumbnail Available
Search Results
Now showing
1 - 3 of 3
-
ArticleUAS-SfM for coastal research : geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery(MDPI AG, 2017-10-03) Sturdivant, Emily ; Lentz, Erika E. ; Thieler, E. Robert ; Farris, Amy S. ; Weber, Kathryn M. ; Remsen, David P. ; Miner, Simon ; Henderson, Rachel E.The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.
-
ArticleIdentifying salt marsh shorelines from remotely sensed elevation data and imagery(MDPI, 2019-07-31) Farris, Amy S. ; Defne, Zafer ; Ganju, Neil K.Salt marshes are valuable ecosystems that are vulnerable to lateral erosion, submergence, and internal disintegration due to sea level rise, storms, and sediment deficits. Because many salt marshes are losing area in response to these factors, it is important to monitor their lateral extent at high resolution over multiple timescales. In this study we describe two methods to calculate the location of the salt marsh shoreline. The marsh edge from elevation data (MEED) method uses remotely sensed elevation data to calculate an objective proxy for the shoreline of a salt marsh. This proxy is the abrupt change in elevation that usually characterizes the seaward edge of a salt marsh, designated the “marsh scarp.” It is detected as the maximum slope along a cross-shore transect between mean high water and mean tide level. The method was tested using lidar topobathymetric and photogrammetric elevation data from Massachusetts, USA. The other method to calculate the salt marsh shoreline is the marsh edge by image processing (MEIP) method which finds the unvegetated/vegetated line. This method applies image classification techniques to multispectral imagery and elevation datasets for edge detection. The method was tested using aerial imagery and coastal elevation data from the Plum Island Estuary in Massachusetts, USA. Both methods calculate a line that closely follows the edge of vegetation seen in imagery. The two methods were compared to each other using high resolution unmanned aircraft systems (UAS) data, and to a heads-up digitized shoreline. The root-mean-square deviation was 0.6 meters between the two methods, and less than 0.43 meters from the digitized shoreline. The MEIP method was also applied to a lower resolution dataset to investigate the effect of horizontal resolution on the results. Both methods provide an accurate, efficient, and objective way to track salt marsh shorelines with spatially intensive data over large spatial scales, which is necessary to evaluate geomorphic change and wetland vulnerability.
-
PublicationAccuracy of shoreline forecasting using sparse data(Elsevier, 2023-04-28) Farris, Amy S. ; Long, Joseph W. ; Himmelstoss, Emily A.Sandy beaches are important resources providing recreation, tourism, habitat, and coastal protection. They evolve over various time scales due to local winds, waves, storms, and changes in sea level. A common method used to monitor change in sandy beaches is to measure the movement of the shoreline over time. Typically, the rate of change is estimated by fitting a linear regression through a time series of shoreline positions. To best manage the valuable resources within a coastal environment, accurate forecasts of shoreline position are needed. A simple way to estimate future shoreline position is to extrapolate a linear regression into the future, this method is often used to establish management guidelines like construction setback lines. A more recently developed shoreline forecasting technique utilizes the Kalman filter to assimilate shoreline data and modify the linear regression. This paper calculates the uncertainty and accuracy of both the extrapolated linear regression and Kalman filter forecasting methods for 10- and 20-year hindcasts using data collected at five diverse study areas. These data are inherently sparse (8–10 measurements per location, collected over 150 years) and are representative of the observed historical data available for the continental United States for this timeframe. Both methods produced similar results and had regionally averaged forecast accuracies of 5–16 m. We determined that the inaccuracy of the forecasts is largely due to the effects of shorter time scale variability. This variability is roughly proportional to the standard error of the linear regression, which is a useful measure of forecast uncertainty.•Extrapolated linear regression (ELR) can provide suitable shoreline forecasts.•A new method using the Kalman filter has forecast accuracy similar to the ELR.•Standard error of the linear regression is useful to estimate forecast uncertainty.•The SE of the linear regression can be used to test usefulness of old shorelines.