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Article

Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems

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Department of Physical Geography and Geoinformatics, Faculty of Science and Technology, Doctoral School of Earth Sciences, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary
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Department of Physical Geography and Geoinformatics, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary
*
Author to whom correspondence should be addressed.
Academic Editor: Elias Symeonakis
Remote Sens. 2021, 13(15), 2980; https://doi.org/10.3390/rs13152980
Received: 6 June 2021 / Revised: 21 July 2021 / Accepted: 26 July 2021 / Published: 28 July 2021
(This article belongs to the Special Issue Land Degradation Assessment with Earth Observation)
The availability of aerial and satellite imageries has greatly reduced the costs and time associated with gully mapping, especially in remote locations. Regardless, accurate identification of gullies from satellite images remains an open issue despite the amount of literature addressing this problem. The main objective of this work was to investigate the performance of support vector machines (SVM) and random forest (RF) algorithms in extracting gullies based on two resampling methods: bootstrapping and k-fold cross-validation (CV). In order to achieve this objective, we used PlanetScope data, acquired during the wet and dry seasons. Using the Normalized Difference Vegetation Index (NDVI) and multispectral bands, we also explored the potential of the PlanetScope image in discriminating gullies from the surrounding land cover. Results revealed that gullies had significantly different (p < 0.001) spectral profiles from any other land cover class regarding all bands of the PlanetScope image, both in the wet and dry seasons. However, NDVI was not efficient in gully discrimination. Based on the overall accuracies, RF’s performance was better with CV, particularly in the dry season, where its performance was up to 4% better than the SVM’s. Nevertheless, class level metrics (omission error: 11.8%; commission error: 19%) showed that SVM combined with CV was more successful in gully extraction in the wet season. On the contrary, RF combined with bootstrapping had relatively low omission (16.4%) and commission errors (10.4%), making it the most efficient algorithm in the dry season. The estimated gully area was 88 ± 14.4 ha in the dry season and 57.2 ± 18.8 ha in the wet season. Based on the standard error (8.2 ha), the wet season was more appropriate in gully identification than the dry season, which had a slightly higher standard error (8.6 ha). For the first time, this study sheds light on the influence of these resampling techniques on the accuracy of satellite-based gully mapping. More importantly, this study provides the basis for further investigations into the accuracy of such resampling techniques, especially when using different satellite images other than the PlanetScope data. View Full-Text
Keywords: satellite imagery; gully mapping; machine learning; random forest; support vector machines; South Africa; semi-arid environment satellite imagery; gully mapping; machine learning; random forest; support vector machines; South Africa; semi-arid environment
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MDPI and ACS Style

Phinzi, K.; Abriha, D.; Szabó, S. Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems. Remote Sens. 2021, 13, 2980. https://doi.org/10.3390/rs13152980

AMA Style

Phinzi K, Abriha D, Szabó S. Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems. Remote Sensing. 2021; 13(15):2980. https://doi.org/10.3390/rs13152980

Chicago/Turabian Style

Phinzi, Kwanele, Dávid Abriha, and Szilárd Szabó. 2021. "Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems" Remote Sensing 13, no. 15: 2980. https://doi.org/10.3390/rs13152980

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