Detecting ditches using supervised learning on high-resolution digital elevation models

Abstract

Drained wetlands can constitute a large source of greenhouse gas emissions, but the drainage networks in these wetlands are largely unmapped, and better maps are needed to aid in forest production and to better understand the climate consequences. We develop a method for detecting ditches in high resolution digital elevation models derived from LiDAR scans. Thresholding methods using digital terrain indices can be used to detect ditches. However, a single threshold generally does not capture the variability in the landscape, and generates many false positives and negatives. We hypothesise that, by combining the digital terrain indices using supervised learning, we can improve ditch detection at a landscape-scale. In addition to digital terrain indices, additional features are generated by transforming the data to include neighbouring cells for better ditch predictions. A Random Forests classifier is used to locate the ditches, and its probability output is processed to remove noise, and binarised to produce the final ditch prediction. The confidence interval for the Cohen’s Kappa index ranges [0.655 , 0.781] between the evaluation plots with a confidence level of 95%. The study demonstrates that combining information from a suite of digital terrain indices using machine learning provides an effective technique for automatic ditch detection at a landscape-scale, aiding in both practical forest management and in combatting climate change.

Keywords

Machine learning; Geographic information systems; Classification and regression trees; Supervised learning by classification

Published in

Expert Systems with Applications
2022, Volume: 201, article number: 116961

SLU Authors

• Ågren, Anneli

  • Department of Forest Ecology and Management, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

SDG6 Clean water and sanitation
SDG13 Climate action


UKÄ Subject classification

Remote Sensing
Forest Science


Publication identifier

DOI: https://doi.org/10.1016/j.eswa.2022.116961

Permanent link to this page (URI)

https://res.slu.se/id/publ/116818