Next Article in Journal
Glide-Symmetric Holey Structures Applied to Waveguide Technology: Design Considerations
Next Article in Special Issue
Monitoring and Landscape Dynamic Analysis of Alpine Wetland Area Based on Multiple Algorithms: A Case Study of Zoige Plateau
Previous Article in Journal
Utilization of an OLED-Based VLC System in Office, Corridor, and Semi-Open Corridor Environments
Article

A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands

by 1,2,*, 1 and 3
1
Department of Ecology, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
2
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
3
Department of Geography and Planning, University of Saskatchewan, 117 Science Place, Saskatoon, SK S7N5C8, Canada
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(23), 6870; https://doi.org/10.3390/s20236870
Received: 9 October 2020 / Revised: 24 November 2020 / Accepted: 30 November 2020 / Published: 1 December 2020
(This article belongs to the Special Issue Remote Sensing Application for Monitoring Grassland)
Green (GV) and non-photosynthetic vegetation (NPV) cover are both important biophysical parameters for grassland research. The current methodology for cover estimation, including subjective visual estimation and digital image analysis, requires human intervention, lacks automation, batch processing capabilities and extraction accuracy. Therefore, this study proposed to develop a method to quantify both GV and standing dead matter (SDM) fraction cover from field-taken digital RGB images with semi-automated batch processing capabilities (i.e., written as a python script) for mixed grasslands with more complex background information including litter, moss, lichen, rocks and soil. The results show that the GV cover extracted by the method developed in this study is superior to that by subjective visual estimation based on the linear relation with normalized vegetation index (NDVI) calculated from field measured hyper-spectra (R2 = 0.846, p < 0.001 for GV cover estimated from RGB images; R2 = 0.711, p < 0.001 for subjective visual estimated GV cover). The results also show that the developed method has great potential to estimate SDM cover with limited effects of light colored understory components including litter, soil crust and bare soil. In addition, the results of this study indicate that subjective visual estimation tends to estimate higher cover for both GV and SDM compared to that estimated from RGB images. View Full-Text
Keywords: CAI; green cover; NDVI; RGB imagery; standing dead matter CAI; green cover; NDVI; RGB imagery; standing dead matter
Show Figures

Figure 1

MDPI and ACS Style

Xu, D.; Pu, Y.; Guo, X. A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands. Sensors 2020, 20, 6870. https://doi.org/10.3390/s20236870

AMA Style

Xu D, Pu Y, Guo X. A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands. Sensors. 2020; 20(23):6870. https://doi.org/10.3390/s20236870

Chicago/Turabian Style

Xu, Dandan, Yihan Pu, and Xulin Guo. 2020. "A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands" Sensors 20, no. 23: 6870. https://doi.org/10.3390/s20236870

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop