A high-resolution map of coastal vegetation for two Arctic Alaskan parklands: An object-oriented approach with point training data

• Published in: PLOS ONE
• Main Authors: Hampton-Miller, Celia J., Neitlich, Peter N., Swanson, David K.
• Format: Text
• Language: English
• Published: Public Library of Science 2022
• Subjects: Research Article; Arctic; Arctic Ocean; Bering Strait; Cape Krusenstern; Chukchi Sea; Shallow Bay; Bering Land Bridge; Chukchi; Alaska
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9432696/; https://doi.org/10.1371/journal.pone.0273893

Bering Land Bridge National Preserve and Cape Krusenstern National Monument in northwest Alaska have approximately 1600 km of predominantly soft-sediment coastlines along the Chukchi Sea, a shallow bay of the Arctic Ocean. Over the past decade, marine vessel traffic through the Bering Strait has grown exponentially to take advantage of new ice-free summer shipping routes, increasing the risk of oil spills in these fragile ecosystems. We present a high-resolution coastal vegetation map to serve as a baseline for potential spill response, restoration, and change detection. We segmented 663 km(2) of high-resolution multispectral satellite images by the mean-shift method and collected 40 spectral, topographic and spatial variables per segment. The segments were classified using photo-interpreted points as training data, and verified with field based plots. Digitizing points, rather than polygons, and intersecting them with the segmentation allows for rapid collection of training data. We classified the map segments using Random Forest because of its high accuracy, computational speed, and ability to incorporate non-normal, high-dimensional data. We found creating separate classification models by each satellite scene gave highly similar results to models combining the entire study area, and that reducing the number of variables had little impact on accuracy. A unified, study area-wide Random Forest model for both parklands produced the highest accuracy of various models attempted. We mapped 18 distinct classes, with an out-of-bag error of 11.6%, resulting in an improvement to the past per-pixel classification of this coast, and in higher spatial and vegetation classification resolution. The resulting map demonstrates the utility of our point-based method and provides baseline data for incident preparedness and change detection. Elevation is highly correlated with the ordination of the vegetation types, and was the most important variable in all tested classification models. The vegetation classification brings ...