干旱区生态环境知识资源中心(Arid): Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems

Arid

DOI	10.3390/rs13152980
	Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems
	Phinzi, Kwanele; Abriha, David; Szabo, Szilard
通讯作者	Phinzi, K (corresponding author), Univ Debrecen, Fac Sci & Technol, Doctoral Sch Earth Sci, Dept Phys Geog & Geoinformat, Egyet Ter 1, H-4032 Debrecen, Hungary.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:15
英文摘要	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.
英文关键词	satellite imagery gully mapping machine learning random forest support vector machines South Africa semi-arid environment
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000682299100001
WOS关键词	EASTERN CAPE ; SOIL-EROSION ; SOUTH-AFRICA ; IMAGE CLASSIFICATION ; SPATIAL-DISTRIBUTION ; RANDOM FOREST ; DATA FUSION ; MACHINE ; ACCURACY ; REGION
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368581
作者单位	[Phinzi, Kwanele; Abriha, David] Univ Debrecen, Fac Sci & Technol, Doctoral Sch Earth Sci, Dept Phys Geog & Geoinformat, Egyet Ter 1, H-4032 Debrecen, Hungary; [Szabo, Szilard] Univ Debrecen, Fac Sci & Technol, Dept Phys Geog & Geoinformat, Egyet Ter 1, H-4032 Debrecen, Hungary
推荐引用方式 GB/T 7714	Phinzi, Kwanele,Abriha, David,Szabo, Szilard. Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems[J],2021,13(15).
APA	Phinzi, Kwanele,Abriha, David,&Szabo, Szilard.(2021).Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems.REMOTE SENSING,13(15).
MLA	Phinzi, Kwanele,et al."Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems".REMOTE SENSING 13.15(2021).