Detecting ditches using supervised learning on high-resolution digital elevation models
نویسندگان
چکیده
Drained wetlands can constitute a large source of greenhouse gas emissions, but the drainage networks in these are largely unmapped, and better maps needed to aid forest production understand climate consequences. We develop method for detecting ditches high resolution digital elevation models derived from LiDAR scans. Thresholding methods using terrain indices be used detect ditches. However, single threshold generally does not capture variability landscape, generates many false positives negatives. hypothesise that, by combining supervised learning, we improve ditch detection at landscape-scale. In addition indices, additional features generated transforming data include neighbouring cells predictions. A Random Forests classifier is locate ditches, its probability output processed remove noise, binarised produce final prediction. The confidence interval Cohen’s Kappa index ranges [0.655 , 0.781] between evaluation plots with level 95%. study demonstrates that information suite machine learning provides an effective technique automatic landscape-scale, aiding both practical management combatting change.
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ژورنال
عنوان ژورنال: Expert Systems With Applications
سال: 2022
ISSN: ['1873-6793', '0957-4174']
DOI: https://doi.org/10.1016/j.eswa.2022.116961