气候变化领域集成服务门户(CCPortal): Sub-alpine shrub classification using UAV images: Performance of human observers vs DL classifiers

CCPortal

DOI	10.1016/j.ecoinf.2024.102462
	Sub-alpine shrub classification using UAV images: Performance of human observers vs DL classifiers
	Moritake, Koma; Cabezas, Mariano; Nhung, Tran Thi Cam; Caceres, Maximo Larry Lopez; Diez, Yago
发表日期	2024
ISSN	1574-9541
EISSN	1878-0512
起始页码	80
卷号	80
英文摘要	In recent years, the automatic analysis of natural environment images acquired with unmanned aerial vehicles (UAV) has rapidly gained popularity. UAVs are specially important in mountainous forests where access is difficult and large areas need to be surveyed. In Zao mountains in northeastern Japan, regenerated fir saplings are competing with sub-alpine vegetation shrubs after a severe fir tree mortality caused by bark beetle infestation. A detailed survey of vegetation distribution is key to improve our understanding of species succession and the influence of climate change in that process. To that end, we evaluated the suitability of deep-learning-based automatic image classification of UAV images in order to map sub-alpine vegetation succession in large areas and the potential of fir regeneration. In order to assess the contribution of this technology in this research field, we first conducted an observer study to assess the difficulty for humans of the task of classifying vegetation from images. Afterwards, we compared the observers' accuracy to four state-of-the art deep learning networks for automatic image classification. The best observer accuracy of 55% demonstrates the limitations of species classification using only images. Furthermore, a detailed analysis of the sources of error showed that even though humans could differentiate between deciduous and evergreen species with an accuracy of 96%, identifying the correct species within each group proved much more challenging. In contrast, deep learning networks achieved accuracy values in the range of 70-80% for species classification, clearly demonstrating capabilities beyond human experts. Our experiments also indicated that the performance of these networks was significantly influenced by the similarity between the datasets used to fine-tune them and evaluate them. This fact highlights the importance of building publicly available images databases to further improve the results. Nevertheless, the results presented in this paper show that the analysis of UAV-acquired with deep learning networks can usher in a new type of large-scale study, spanning tenths or even hundreds of hectares with high spatial resolution (of a few cms per pixel), providing the ability to assess challenging vegetation dynamics problems that go beyond the ability of conventional fieldwork methodologies.
英文关键词	Sub-alpine vegetation; Vegetation change monitoring; Deep learning; Observer study; ConvNeXt; Swin
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Ecology
WOS记录号	WOS:001166333200001
来源期刊	ECOLOGICAL INFORMATICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305393
作者单位	Yamagata University; University of Sydney; Yamagata University
推荐引用方式 GB/T 7714	Moritake, Koma,Cabezas, Mariano,Nhung, Tran Thi Cam,et al. Sub-alpine shrub classification using UAV images: Performance of human observers vs DL classifiers[J],2024,80.
APA	Moritake, Koma,Cabezas, Mariano,Nhung, Tran Thi Cam,Caceres, Maximo Larry Lopez,&Diez, Yago.(2024).Sub-alpine shrub classification using UAV images: Performance of human observers vs DL classifiers.ECOLOGICAL INFORMATICS,80.
MLA	Moritake, Koma,et al."Sub-alpine shrub classification using UAV images: Performance of human observers vs DL classifiers".ECOLOGICAL INFORMATICS 80(2024).