干旱区生态环境知识资源中心(Arid): Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods

Arid

DOI	10.3390/s21217310
	Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods
	Yu, Xiaolei; Guo, Xulin
通讯作者	Guo, XL (corresponding author), Univ Saskatchewan, Dept Geog & Planning, Kirk Hall,117 Sci Pl, Saskatoon, SK S7N 5C8, Canada.
来源期刊	SENSORS
EISSN	1424-8220
出版年	2021
卷号	21 期号:21
英文摘要	Fractional vegetation cover is a key indicator of rangeland health. However, survey techniques such as line-point intercept transect, pin frame quadrats, and visual cover estimates can be time-consuming and are prone to subjective variations. For this reason, most studies only focus on overall vegetation cover, ignoring variation in live and dead fractions. In the arid regions of the Canadian prairies, grass cover is typically a mixture of green and senescent plant material, and it is essential to monitor both green and senescent vegetation fractional cover. In this study, we designed and built a camera stand to acquire the close-range photographs of rangeland fractional vegetation cover. Photographs were processed by four approaches: SamplePoint software, object-based image analysis (OBIA), unsupervised and supervised classifications to estimate the fractional cover of green vegetation, senescent vegetation, and background substrate. These estimates were compared to in situ surveys. Our results showed that the SamplePoint software is an effective alternative to field measurements, while the unsupervised classification lacked accuracy and consistency. The Object-based image classification performed better than other image classification methods. Overall, SamplePoint and OBIA produced mean values equivalent to those produced by in situ assessment. These findings suggest an unbiased, consistent, and expedient alternative to in situ grassland vegetation fractional cover estimation, which provides a permanent image record.
英文关键词	fractional vegetation cover SamplePoint image classification OBIA image analysis Northern Mixed Grasslands
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:000718535900001
WOS关键词	PHOTOSYNTHETICALLY ACTIVE RADIATION ; MIXED PRAIRIE ; GROUND-COVER ; GRASSLAND ; RESOLUTION ; ALGORITHM ; SAVANNA ; INDEXES ; PLOT ; SOIL
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368212
作者单位	[Yu, Xiaolei; Guo, Xulin] Univ Saskatchewan, Dept Geog & Planning, Kirk Hall,117 Sci Pl, Saskatoon, SK S7N 5C8, Canada
推荐引用方式 GB/T 7714	Yu, Xiaolei,Guo, Xulin. Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods[J],2021,21(21).
APA	Yu, Xiaolei,&Guo, Xulin.(2021).Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods.SENSORS,21(21).
MLA	Yu, Xiaolei,et al."Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods".SENSORS 21.21(2021).