Identification of Grassland Vegetation Coverage and Height Based on Vegetation Index and HSV Space
Year: 2025
Type: article
Abstract: In response to the demand for grassland vegetation monitoring, this study proposes a method based on the vegetation index and an automatic identification method of grassland vegetation coverage and height in the HSV color space. Hikvision smart ball machines and smartphones were used as fixed and mobile monitoring tools to conduct the experiments at the Sanqingyuan Nursery, Beijing Forestry University. Aiming at the time-series images from April to September 2021, five visible light vegetation indices, EXG, EXGR, NGRDI, GLI, and RGBVI, were comparatively analyzed in terms of vegetation coverage recognition. The experimental results show that the EXG index combined with the OTSU automatic threshold method has the best performance in extracting vegetation coverage, with an accuracy of 90% and an absolute error controlled within 3%; therefore, it was selected as the optimal algorithm. Second, this study converts the RGB color space into the HSV color space, realizes the accurate extraction of red color rings, and calculates the grass layer height accordingly. The experimental results show that the average error of this method is 2.3 cm, the maximum error is 6 cm, and the relative error is generally lower than 30%, indicating high reliability and accuracy. The results of this study provide an efficient and accurate automatic identification method for grassland vegetation monitoring and are expected to be widely used in grassland ecological protection and management.
Source: Journal of Resources and Ecology
Authors: Yang Wenbo, Guan Peng, Shi Honglei, Zhang Wei, Lei Fuqiang
Institutions: Hangzhou Dianzi University, Beijing Forestry University
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Zero hunger
Open Access status: bronze
OpenAlex ID: W4413381142