Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/45842
Title: Snow-covered region improvements to a support vector machine-based semi-automated land cover mapping decision support tool
Authors: O'Neill, Francis D.
Lasko, Kristofer D.
Sava, Elena
Keywords: Land cover--Remote sensing
Remote-sensing images
Cold regions
Geospatial data
Machine learning
Algorithms
Geographic information systems
Publisher: Engineer Research and Development Center (U.S.)
Series/Report no.: Technical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC/GRL TR-22-3
Abstract: This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs.
Description: Technical Report
Gov't Doc #: ERDC/GRL TR-22-3
Rights: Approved for Public Release; Distribution is Unlimited
URI: https://hdl.handle.net/11681/45842
http://dx.doi.org/10.21079/11681/45842
Appears in Collections:Technical Report

Files in This Item:
File Description SizeFormat 
ERDC-GRL TR-22-3.pdf3.16 MBAdobe PDFThumbnail
View/Open