干旱区生态环境知识资源中心(Arid): Plant Population Classification Based on PointCNN in the Daliyabuyi Oasis, China

Arid

DOI	10.3390/f14101943
	Plant Population Classification Based on PointCNN in the Daliyabuyi Oasis, China
	Li, Dinghao; Shi, Qingdong; Peng, Lei; Wan, Yanbo
通讯作者	Shi, QD
来源期刊	FORESTS
EISSN	1999-4907
出版年	2023
卷号	14 期号:10
英文摘要	Populus euphratica and Tamarix chinensis hold significant importance in wind prevention, sand fixation, and biodiversity conservation. The precise extraction of these species can offer technical assistance for vegetation studies. This paper focuses on the Populus euphratica and Tamarix chinensis located within Daliyabuyi, utilizing PointCNN as the primary research method. After decorrelating and stretching the images, deep learning techniques were applied, successfully distinguishing between various vegetation types, thereby enhancing the precision of vegetation information extraction. On the validation dataset, the PointCNN model showcased a high degree of accuracy, with the respective regular accuracy rates for Populus euphratica and Tamarix chinensis being 92.106% and 91.936%. In comparison to two-dimensional deep learning models, the classification accuracy of the PointCNN model is superior. Additionally, this study extracted individual tree information for the Populus euphratica, such as tree height, crown width, crown area, and crown volume. A comparative analysis with the validation data attested to the accuracy of the extracted results. Furthermore, this research concluded that the batch size and block size in deep learning model training could influence classification outcomes. In summary, compared to 2D deep learning models, the point cloud deep learning approach of the PointCNN model exhibits higher accuracy and reliability in classifying and extracting information for poplars and tamarisks. These research findings offer valuable references and insights for remote sensing image processing and vegetation study domains.
英文关键词	PointCNN decorrelation stretch vegetation information extraction oasis vegetation
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001093536400001
WOS关键词	POPULUS-EUPHRATICA ; TREE ; DECORRELATION
WOS类目	Forestry
WOS研究方向	Forestry
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396446
推荐引用方式 GB/T 7714	Li, Dinghao,Shi, Qingdong,Peng, Lei,et al. Plant Population Classification Based on PointCNN in the Daliyabuyi Oasis, China[J],2023,14(10).
APA	Li, Dinghao,Shi, Qingdong,Peng, Lei,&Wan, Yanbo.(2023).Plant Population Classification Based on PointCNN in the Daliyabuyi Oasis, China.FORESTS,14(10).
MLA	Li, Dinghao,et al."Plant Population Classification Based on PointCNN in the Daliyabuyi Oasis, China".FORESTS 14.10(2023).