1. Introduction
Vegetation is a crucial part of natural ecosystems and an indispensable natural resource [
1]. The root system of vegetation is closely related to the soil, and vegetation branches, stems, leaves, etc., are closely related to soil erosion [
2]. Photosynthesis, the conversion from inorganic into organic matter, is a crucial process in maintaining the Earth’s carbon–oxygen balance [
3]. Transpiration amplifies the circulation of water, which is a vital element of life in ecosystems [
4]. Therefore, changes in the vegetation type and quality can notably affect the cycle and profile of the whole ecosystem. The fractional vegetation cover (FVC), a critical parameter characterizing vegetation features, is important for the study of ecology, hydrology, and meteorology and in other aspects. It is often used as fundamental data for research on regional or globalization issues [
5,
6]. Consequently, FVC investigations are important.
The FVC is usually defined as the vertical projection of a vegetation canopy area to the ground and can be expressed as a fraction or percentage of the reference area [
7]. Within the context of remote sensing, the FVC can be defined as the percentage of the green vegetation area to the total observed area [
8]. The FVC is an important phenotypic factor in agriculture, forestry, and ecology and an important indicator for the study of changes in each circle of the entire ecosphere and their interactions. Moreover, the FVC is a key biophysical parameter for capturing horizontal exchange at the surface-atmosphere boundary using soil, vegetation, and atmospheric transfer models [
9,
10,
11].
Considering that the FVC is a vital indicator, it is important to measure it at different scales for different applications [
12]. For example, the remote sensing technique has provided an important data source for large-scale FVC studies for decades due to loaded positions and greater ease of efficient, large-scale monitoring [
13,
14]. The first commonly used method is the vegetation index-based method. It aims to extract the FVC with the help of various vegetation indices, such as the normalized difference vegetation index (NDVI), perpendicular vegetation index (PVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), and transformed soil adjusted vegetation index (TSAVI) [
15,
16]. Through the different vegetation and non-vegetation characteristics determined by these vegetation indices, thresholds obtained from various algorithms can be used to distinguish the vegetation cover from the background. Two aspects must be investigated in this method: one is the determination of a suitable vegetation index for different application scenarios, and the other is the establishment of a suitable algorithm to obtain a fixed or self-adaptive threshold for vegetation extraction. Using the vegetation index-based method, Bac et al. produced a map of the extent of mangroves in the Philippines using Google Earth MVI [
17]. Another commonly used technique is the regression method. This method involves first establishing a mathematical regression model based on several data sources and the FVC over a relatively small range of sample points and then generalizing the results to the entire study area [
18]. Currently, with the rapid development of computer technology, machine learning [
19,
20,
21,
22] and deep learning [
23,
24,
25] methods are emerging.
Large-scale remote sensing FVC measurements require more accurate ground data at smaller scales as validation data, which can be obtained from traditional small-scale FVC measurements [
26]. The validation dataset strives to meet the following objectives: (1) the instrument should be economical, practical, and easy to operate; (2) the instrument should capture valid, accurate, and objective ground observations; (3) the measurement duration should be short; and (4) the influence of human factors should be minimized [
27,
28]. The most traditional on-site FVC measurement method is the visual interpretation method, which can be divided into the direct visual estimation method [
29] and the photo visual estimation method [
30]. However, the visual estimation method is limited by subjectivity, an extreme dependence on observer experience, and a tendency to produce large errors. Therefore, various sampling methods, such as the point measurement method [
31], sample strip method [
1], shade method [
32], and square viewpoint frame method [
33], have been proposed for use in this area. The point measurement method entails the use of long, sharp needles stuck vertically in the sample plot, and the FVC is determined using the ratio of the number of needles passing through vegetation to the total number of needles. The shade method (ruler measurement) utilizes the relationship between a given scale and the dark shadows produced by vegetation to measure the FVC. However, the square viewpoint frame method considers the gaps in the small holes of the frame to obtain a sample within the study area. The quadrat method reduces the workload when increasing the observation accuracy due to the application of statistical principles. Relevant measurement instruments have also been developed to obtain FVC data via spatial quantitative meters and moving light meters. With the rapid development of large-scale integrated circuits, the photography technique is no longer an expensive, complex, or time-consuming option, as in film era, digital photography and digital image processing techniques have grown quickly [
34]. Digital cameras provide instant shooting results, greatly improve intuition, and reduce the learning costs of workflows; methods for extracting FVC products from digital images have been proposed and modified owing to their efficiency and convenience [
35]. Melville et al. used a portable photographic instrument to measure the FVC in the field [
36]. Liu et al. also proposed an FVC extraction algorithm based on the LAB color space [
20].
However, photography methods in actual shooting face certain challenges. Shadows often create extreme illumination contrasts, causing substantial luminance differences within a single image scene. A study revealed that the reflectance of shaded leaves is lower than that of sunlit leaves at all wavelengths [
37]. The presence of shadows could disrupt the accurate recording of pixel information. When the field scene is not covered by a hood or similar structure, shadows are inevitable [
38]. In many cases, shadows constitute a high-incidence area where commission and omission errors occur during FVC extraction. As a consequence, the FVC extraction capability in the shadow region must be addressed to improve the segmentation performance. To address the issue of low FVC accuracy based on photography methods under shadow conditions, several studies have proposed solutions from different perspectives. There are two primary approaches: first, specific techniques can be employed during data acquisition to compensate for the limitations due to shadows impeding the accurate recording of vegetation information [
39,
40]; second, algorithms have been designed for the vegetation extraction process to minimize the impact of shadows. In numerous studies, the characteristics of high-dynamic range (HDR) imaging have been utilized to effectively expand the dynamic range of photos. This technique allows for better capture of vegetation information and is often combined with thresholding methods or deep learning techniques to calculate FVC [
41,
42,
43,
44,
45]. Yang et al. proposed acquiring polarization images in the shooting process to complement and enhance pixel information that cannot be effectively captured under shadows [
46]. From a postprocessing perspective, Song et al. introduced the SHAR-LABFVC shadow-resistant algorithm, which alters the color space distribution of captured images to mitigate the adverse effect of shadows on the calculation of FVC [
47].
Although various methods have been proposed to address the issue of low FVC accuracy under shadow conditions, there are still areas that need improvement. First, the computational efficiency of these methods still needs to be enhanced. Methods using deep learning and machine learning techniques are extremely time-consuming. Moreover, the accuracy of these methods is closely tied to the quality of data samples, which means that they are significantly influenced by human factors. Second, there is a lack of solutions for FVC calculation under complex shadow conditions. Previous algorithms such as SHAR-LABFVC, methods based on HDR images, and algorithms incorporating polarized images essentially aim to supplement pixel information in shadowed areas to improve FVC accuracy. These studies all indicate that reconstructing shadow pixel information is an effective and feasible solution. However, they have not addressed complex shadow situations more specifically. For instance, the SHAR-LABFVC algorithm enhances shadow pixels based on the overall light distribution of the entire image.
To address the issue of shadows affecting the accurate recording of pixel information and subsequently reducing the FVC accuracy, a simple and effective solution is proposed in this study. The approach is based on a pixel-level classification strategy using a double-exposure algorithm with the ability to process images with different shadow levels. The double-exposure fusion data are derived from a normal-exposure image and a specific overexposed image. By increasing the exposure compensation during shooting, shadow regions are enhanced to show more vegetation details. Moreover, sunlit vegetation may be enhanced by overexposure. Therefore, a fusion operation is performed between the shadow pixels of the normal-exposure image and the corresponding pixels of the overexposed image. This process yields a fused double-exposure image containing a strengthened shadow region and a normal sunlit region. In the research process, multiple records of overexposed images at different exposure levels were captured. Various vegetation indices were assessed to identify the most suitable candidates for the double-exposure method and the optimal overexposed images under shadow conditions. This article is divided into four sections (materials and methods, experimental results and analysis, discussion, and conclusion) elaborating on the double-exposure method.