草业科学2024，Vol.41Issue(6)：1506-1518,13.DOI:10.11829/j.issn.1001-0629.2023-0105
基于定位观测站图像实现草原植被覆盖度自动化提取的方法比较
Comparison of automatic extraction methods of vegetation cover based on the grassland positioning observation station
辛玉春 1赵新来 2李宏达 1王九峦 1马文文 1王迎旭2
作者信息
- 1. 青海省草原总站,青海西宁 810008
- 2. 江苏及象生态环境研究院有限公司,江苏南京 210003
- 折叠
摘要
Abstract
Vegetation cover is an important index in grassland ecological monitoring.In this study,positioning observation stations were used to obtain the image of the sampling plot,and vegetation cover extraction algorithms suitable for different grassland positioning observation stations were selected and compared.This was conducted with the aim of solving the problem of how to automatically extract vegetation cover indices from images of different plots in continuous positioning observation.Different image segmentation methods such as the greenness index method,random forest(RF),support vector machine(SVM),and back propagation(BP)neural networks were used to obtain the cover extraction results for different grassland types.The pros and cons of threshold segmentation methods such as the greenness index and machine learning methods,the reasons for the different classification effects of three machine learning algorithms,and the main reasons for the error of coverage value were discussed in this paper.The results have shown that the machine learning algorithm could be flexibly applied to the rapid automatic extraction of vegetation cover in the quadrat image of the positioning observation station.The greenness index threshold segmentation method applied to the segmentation of vegetation cover was relatively poor.RF algorithm has a higher level of accuracy in the segmentation of alpine grassland.SVM has a higher level of accuracy in temperate grassland and temperate desert grassland.The BP neural network has more advantages in the cover extraction of alpine meadow.This study can provide an important reference for the development of information and intelligent monitoring equipment for grassland ecological monitoring in the new era.关键词
青藏高原/高寒草原/机器学习/图像分割/定位观测站/植被覆盖度/动态监测Key words
Qinghai-Tibet Plateau/alpine grassland/machine learning/image segmentation/positioning observation station/vegetation cover/dynamic monitoring引用本文复制引用
辛玉春,赵新来,李宏达,王九峦,马文文,王迎旭..基于定位观测站图像实现草原植被覆盖度自动化提取的方法比较[J].草业科学,2024,41(6):1506-1518,13.
