Abstract
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.
Highlights
IntroductionTransportation, and deposition of soil particles by the erosive forces of raindrop and runoff [1,2], soil erosion by water represents one of the most typical forms of land degradation affecting many countries around the world [3]
An error matrix compared reference data to the classified map using various accuracy indices [54], but in this study, we only focused on class level accuracies/errors: producer’s accuracy (PA) and user’s accuracy (UA); PA was known as sensitivity or recall while UA was sometimes referred to as precision
Our study demonstrated that gullies could be better identified in the dry season with random forest (RF) combined with bootstrapping, whereas support vector machines (SVM) combined with k-fold CV is best for identifying gullies in the wet season
Summary
Transportation, and deposition of soil particles by the erosive forces of raindrop and runoff [1,2], soil erosion by water represents one of the most typical forms of land degradation affecting many countries around the world [3]. While soil erosion has many negative effects, the most concerning one include the decline in soil fertility, resulting in limited food production [4,5]. This, in turn, contributes to food insecurity in several developing countries, in those ones where a considerable segment of their population strongly relies on agriculture for their survival [6].
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