Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC)

Wanjuan Song,Guangjian Yan,Shuai Huang,Xihan Mu

doi:10.3390/rs70810425

Abstract

Taking photographs with a commercially available digital camera is an efficient and objective method for determining the green fractional vegetation cover (FVC) for field validation of satellite products. However, classifying leaves under shadows in processing digital images remains challenging and results in classification errors. To address this problem, an automatic shadow-resistant algorithm in the Commission Internationale d’Eclairage L*a*b* color space (SHAR-LABFVC) based on a documented FVC estimation algorithm (LABFVC) is proposed in this paper. The hue saturation intensity (HSI) is introduced in SHAR-LABFVC to enhance the brightness of shaded parts of the image. The lognormal distribution is used to fit the frequency of vegetation greenness and to classify vegetation and the background. Real and synthesized images are used for evaluation, and the results are in good agreement with the visual interpretation, particularly when the FVC is high and the shadows are deep, indicating that SHAR-LABFVC is shadow resistant. Without specific improvements to reduce the shadow effect, the underestimation of FVC can be up to 0.2 in the flourishing period of vegetation at a scale of 10 m. Therefore, the proposed algorithm is expected to improve the validation accuracy of remote sensing products.

Highlights

Fractional vegetation cover (FVC) is widely used to describe vegetation quality and ecosystem changes and is a controlling factor in transpiration, photosynthesis and other terrestrial processes [1,2,3].Estimating FVC in field measurements is critical because it provides a baseline for improving remote sensing algorithms and validating products.Visual estimation, sampling [4], photography [5] and other techniques are commonly used in field measurements
Image enhancement is an important module in SHAR-LABFVC
After the intensity histogram is equalized in the hue saturation intensity (HSI) color space, the entire image becomes brighter, the shaded leaves in the red boxes (Figure 5b), facilitating the identification of shaded components

Summary

Introduction

Visual estimation, sampling [4], photography [5] and other techniques are commonly used in field measurements. The parts of an image that contain vegetation can be determined based on their physical, shape and color characteristics and other features [6,7,8,12]. These methods can be grouped into two classes: (1). The result for SHAR-LABFVC is 0.237, identical to the reference FVC (0.237). The result for the LABFVC algorithm is lower, because it cannot distinguish leaves with deep shadows from the background (Figure 6c)

Methods

Results

Discussion

Conclusion