Sammanfattning

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.

Nyckelord

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

Publicerad i

Expert Systems with Applications
2022, Volym: 201, artikelnummer: 116961

SLU författare

• Ågren, Anneli

  • Institutionen för skogens ekologi och skötsel, Sveriges lantbruksuniversitet
    

Globala målen

SDG6 Säkerställa tillgången till och en hållbar förvaltning av vatten och sanitet för alla
SDG13 Vidta omedelbara åtgärder för att bekämpa klimatförändringarna och dess konsekvenser


UKÄ forskningsämne

Fjärranalysteknik
Skogsvetenskap


Publikationens identifierare

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

Permanent länk till denna sida (URI)

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