Detecting ditches using supervised learning on high-resolution digital elevation models
| Autor: | Jonatan Flyckt, Filip Andersson, Niklas Lavesson, Liselott Nilsson, Anneli M. Å gren |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Classification trees
Digital terrain Skogsvetenskap Naturgeografi Decision trees Regression trees Digital instruments Surveying Landscape scale E-learning Remote Sensing Artificial Intelligence Machine learning Forest production Greenhouse gas emissions Classification and regression trees Climate change Information use Digital elevation model High resolution Fjärranalysteknik Gas emissions Metadata Classification (of information) Forest Science General Engineering Forestry Geomorphology Geographic information systems Supervised learning by classification Computer Science Applications Greenhouse gases Physical Geography Wetlands Drainage networks Supervised learning |
| Popis: | 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. © 2022 The Authors open access |
| Databáze: | OpenAIRE |
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