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

Abstract

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.

A Fully Automated and Fast Approach for Canopy Cover Estimation Using Super High-Resolution Remote Sensing Imagery

“…It is noteworthy that the relaxation with the inclusion of only R, G, and B bands are important to robustly generalize the success of rule-based methods to regular digital RGB images for segmenting canopy cover. However, these remote sensing RGB images may require a normalization/calibration scheme to alleviate various illumination conditions and other sensing dynamics (Xu et al, 2020). It is noteworthy that, consistent with the observation for hyperspectral datasets, the rule-based method ( 1) is sensitive to mixed pixels that do not present a distinct green vegetation spectral pattern.…”

“…Those mixed pixels would possibly be classified as non-vegetation area by the rule-based method, which explains the relatively lower canopy cover derived by the 1 method for the UAV-sorghum scenes (Figure 3(n) and (r)). It is worth noting that, the performance of SVM and RF is more dependent on the quality of the samples selected for model training, and the criteria that those samples are defined/selected is subjective to user experiences and knowledge to some extent, for instance, whether the mixed pixels are treated as green vegetation area, or background soil area (Coy et al, 2016;Xu et al, 2020). However, the rule-based methods provided more objective and consistent results.…”

“…Canopy cover is one of the most important agronomic indicators and phenotypical traits that reflect crop growth and development status, as well as plant photosynthesis and water use. Additionally, canopy cover has also been used to study crop leaf nitrogen concentration, leaf area index, biomass and grain yield (Maimaitijiang et al, 2020c;Xu et al, 2020). Thus, rapid and accurate estimation of canopy cover, particularly in a fully automated manner, is imperative for improved high throughput plant phenotyping.…”

“…The most common approaches for canopy cover estimation include thresholding and classification/segmentation methods. Single or multiple thresholds are set to RGB imagery * Corresponding author transformed color space, such as CIE (Commission Internationale d'Eclairage) L*a*b* and HSI (hue saturation intensity) color (Liu et al, 2012), as well as vegetation indices such as ExG (Excessive Green Index) and CIVE (Color Index of Vegetation Extraction) (Meyer and Neto, 2008) through manual (Grieder et al, 2015), semi-automated (Xu et al, 2020) or fully automated (Coy et al, 2016;Yu et al, 2017) methods to separate the plant pixels from background non-green vegetation pixels such as soil, plant residue, and non-photosynthetic plant leaves etc. The thresholding approach is simple and widely employed in many applications, but it is less effective with images captured at various illumination conditions, or from different plant growth stages, or plants with variable water, nutrient and health status (Banerjee et al, 2020;Sadeghi-Tehran et al, 2017).…”

“…However, classification methods require a certain level of human intervention preventing automation. Additionally, sample selection is often time consuming and model training and application may also be computationally intensive (Coy et al, 2016;Xu et al, 2020). In addition to using RGB digital imagery for canopy cover estimation, multispectral (Yu et al, 2017) and hyperspectral imagery (Banerjee et al, 2020) were utilized for canopy cover estimation through thresholding, machine learningbased classification, as well as spectral angle mapping method.…”