Snitching on ditches: tracking salt marsh health using transfer learning
Loading...
Date
2023
Authors
Somerscales, Sophia
Journal Title
Journal ISSN
Volume Title
Publisher
University of Oregon
Abstract
Coastal salt marshes offer crucial ecological benefits, including carbon sequestration, habitat for many species, and protection against storm surges and erosion. However, human activity has led to significant dieback of these ecosystems on both a national and global scale. Much of the northeastern US salt marshes are experiencing exacerbated loss due to grid ditching, an outdated practice in which standing pools of water were drained by a series of narrow ditches to reduce mosquito populations. Identifying ditches is an important step in tracking salt marsh health, yet ecologists currently lack an efficient method to do so, mostly relying on walking the fields between tides or manually delineating ditches in aerial imagery. This project investigates an alternate workflow for identifying ditches in high-resolution drone imagery captured by the Salt Marsh UAV group at University of Massachusetts Amherst. I implement U-Net, a machine learning that originates from medical imaging, to sift through all the varied water features in a single salt marsh site and classify each pixel in an image as background, ditch, or non-ditch, a process called semantic segmentation. Ultimately, the goal is to produce georeferenced shapefiles that precisely locate ditches on the ground. I use pre-trained versions of U-net and experiment with various parameters to tune the models for optimal results. This is a form of transfer learning, taking models from one domain and repurposing them for another. MobileNet-UNet exhibits the highest performance and produces strong ditch segmentation results that ecologists can utilize with minimal post-processing. Future research should experiment with using multispectral bands like near-infrared (NIR) and short-wave infrared (SWIR) or a Digital Elevation Model (DEM) to provide the model with more information. This project provides ecologists with an automated method of identifying ditches and demonstrates that transfer learning is a viable alternative to traditional remote sensing water feature extraction methods.
Description
31 pages
Keywords
Transfer learning, Semantic segmenation, Remote sensing, Salt marsh, Water