9 Dec 2021

Semi-Automated Land Cover Mapping Using an Ensemble of Support Vector Machines with Moderate Resolution Imagery Integrated into a Custom Decision Support Tool

Abstract: Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.

17 Jun 2020

PUBLICATION NOTICE: Water Quality Visualization Tools: A Python Application (1/A)

Abstract: On May 4, 2016, US District Court Judge Simon ordered the US Army Corps of Engineers and two other Action Agencies to produce a comprehensive Environmental Impacts Statement (EIS) by March 26, 2021. To do this, the Columbia River Systems Operation (CRSO) EIS will evaluate and compare a range of alternatives to offset or minimize any remaining unavoidable impacts. Due to the unique large system model approach, there is a need to quickly develop and analyze water quality model results. Therefore, there was a need for several visualization tools to assist the CRSO EIS team in promptly analyzing the results and creating publication-ready graphics. To create the most accessible desktop application for the CRSO EIS team, the Python programming language was used to quickly create three visualization tools. These three tools are only useful for relatively small data sets. If the team wishes to expand the functionality for larger data sets, it is recommended that model execution and analysis be moved to the supercomputers.