1. Introduction
Mangroves are a type of wetland vegetation consisting of evergreen trees and shrubs dominated by mangrove plants that grow in the intertidal zones of the tropical and subtropical coasts. This ecosystem is unique because it serves as a transitional zone between land and sea [
1]. Mangrove forests are commonly referred to as “coastal guardians” and “marine green lungs” due to their vital roles in protecting coasts and maintaining the balance of marine life. They provide crucial habitats for rare and endangered waterfowls, as well as fish, shrimp, crabs, and shellfish. Mangroves are essential for slowing tidal currents, promoting sediment deposition, protecting coasts from erosion, sequestering carbon, and maintaining biodiversity. Mangrove ecosystems have an extraordinary diversity of species and the highest ecosystem service functions on the planet, making them of immense ecological value [
1,
2,
3]. Unfortunately, global climate change has led to an increase in sea levels, seawater warming, and coastal disasters such as storm surges, ocean waves, shoreline erosion, and typhoons [
4]. Human activities such as wetland reclamation, aquaculture expansion, and urbanization have also had devastating impacts on mangrove ecosystems and their biodiversity [
5,
6]. Consequently, the global mangrove area has decreased by 30–50% over the past 50 years [
7,
8]. Mangrove forests in most regions of the world are facing various challenges, including biodiversity decline, ecosystem function degradation, and inadequate protection [
9]. Therefore, it is essential to conduct a quantitative assessment of the spatial distribution pattern of mangrove biodiversity to carry out mangrove biodiversity assessments and identify key protected areas. This will provide a scientific basis for governments to formulate conservation strategies, propose conservation measures, and carry out conservation actions. The protection of global mangrove biodiversity is of great significance for achieving sustainable goals.
An efficient evaluation of biodiversity is crucial for conservation efforts [
10]. The United Nations Convention on Biological Diversity (CBD) aims to prevent biodiversity loss, with 196 nations serving as signatories. In 2022, the “Kunming Montreal Global Biodiversity Framework” established a target to protect at least 30% of land and marine areas by 2030, building on the “Aichi Biodiversity Goal 11” from 2010–2020 [
11,
12,
13]. Research shows that core protected areas have increased, but most critical marine areas, such as mangroves, remain under-protected [
14]. To achieve the 2030 goal, mangrove biodiversity protection must be reinforced. Traditional mangrove monitoring methods, which focus on field surveys and species changes, are affected by natural conditions and require substantial resources [
15,
16,
17]. Technological advancements have improved monitoring efficiency through satellite remote sensing, offering high-resolution hyperspectral data [
18,
19]. For example, Wang et al. developed a global biodiversity method using multispectral and hyperspectral images [
20]. However, this study did not consider the vertical structure of mangroves, which is essential for constructing a biodiversity index for mangrove species [
16,
17].
Low-altitude unmanned-aerial-vehicle (UAV) remote sensing technology has emerged as a promising tool for collecting high-resolution mangrove remote sensing data at any time with characteristics such as fast image acquisition, short application cycle, high definition, low constraints on the natural environment, and low operation and maintenance costs [
21,
22,
23]. UAV remote sensing can obtain high-precision hyperspectral images and LiDAR data, which can fully compensate for the limitations of traditional satellite remote sensing and ordinary aerial remote sensing and bridge the gap between satellite remote sensing and traditional ground survey methods. This study provides a new perspective and program for biodiversity investigation and monitoring and is considered to have an important revolutionary role in the field of ecological research [
24]. Low-altitude remote sensing technology for monitoring biodiversity can directly identify mangrove species, community types, and spatial distribution due to the high spatial and hyperspectral resolution provided by drones [
25,
26,
27], which reflects the biodiversity of mangroves. For instance, Cao et al. [
28] combined drone hyperspectral images with digital surface models and studied the classification of mangrove populations in Qiao, Guangdong Province, using the nearest neighbor method and support vector machine classification methods. Moreover, several indicators derived from remote sensing data that can reflect biodiversity, such as leaf area index [
29], spectral diversity parameters [
30,
31], and biomass [
32,
33], can be employed to assess biodiversity characteristics. Among the various biodiversity estimation methods, the “spectral variation hypothesis” suggests that the number of plant species increases with observed spectral diversity [
34,
35]. The spectral diversity index has gradually become a crucial approach for remote monitoring of biodiversity. For example, Bongalov et al. [
36] conducted spectral image clustering of airborne hyperspectral data in the Malaysian tropical rainforest, treating each cluster as a “species”, and successfully estimated the area’s β diversity; Gholizadeh et al. [
37] used airborne imaging hyperspectral data to evaluate the α and β diversity of grasslands and found that the performance of the coefficient of variation of mapping biodiversity was better than that of mapping convex hull volume and spectral angle over the entire study period, which was comparable to the performance of spectral species. Xu et al. [
38] established quantitative models for the coefficient of variation, minimum convex hull volume, minimum convex hull area, and Shannon–Wiener diversity index based on remote sensing data from unmanned aerial vehicles in alpine grasslands. Most of these methods rely on low-altitude hyperspectral remote sensing data to explore spectral diversity among species, which is an emerging dimension for measuring plant biodiversity that integrates the characteristics of traits within and between species. In recent years, the “height variation hypothesis” has been increasingly employed to study forest biodiversity [
39]. In complex mangrove ecosystems, drone laser point clouds can be used to obtain height and three-dimensional structural information within the mangrove canopy. By combining three-dimensional information, it can also be used to distinguish the canopies of different mangrove species [
16,
28]. For example, Tian et al. [
16] classified mangrove species in the Maowei Sea of Beibu Gulf based on UAV LiDAR point-cloud data and successfully classified the mangrove community in the area into exotic
Sonneratia apetala and
Aegiceras corniculatum species. Combining the “spectral variation hypothesis” with the “height variation hypothesis” to assess the forest diversity often yields satisfactory results. For example, Asner et al. [
40] combined LiDAR and hyperspectral techniques to evaluate the biochemical characteristics of canopy leaves in 79 sample plots in the Andes, and extended the study to the entire tropical rainforest of Peru. They retrieved the leaf nitrogen and phosphorus content of different forest canopy tree species and obtained the forest functional diversity index of the region. Zheng et al. [
41] obtained the morphological and physiological characteristics of each individual tree crown based on LiDAR and hyperspectral data, and plotted a distribution map of the functional diversity of subtropical forests in China at the individual level. However, from the current research and application perspective, a technical scheme for monitoring mangrove biodiversity by combining drones and ground-based methods has not yet been established, with insufficient attention being given to mangrove natural reserves as the primary research object. Most studies have focused on forest biodiversity assessment, and effective monitoring methods and research frameworks for mangrove biodiversity have not been established. There is limited interaction among multiple drone surveys in terms of monitoring technology integration, analysis, and processing. Key methods for managing and processing multi-source drone remote sensing monitoring data require further breakthroughs, particularly in areas lacking field surveys of mangrove biodiversity, based on low-altitude drone remote sensing technology. Few studies have evaluated mangrove biodiversity by coupling the “spectral variation hypothesis” and “height variation hypothesis” to construct a comprehensive indicator system.
The Pinglu Canal project spans approximately 140 km, originating at the mouth of the Pingtang River in Hengzhou City, Nanning, and traverses the watershed between the Shaping River and Jiuzhou River, a tributary of the Qinjiang River. After the reply to the environmental impact assessment, the construction of the Pinglu Canal was officially started on 28 August 2022, which was the first man-made canal built since the founding of the People’s Republic of China. The mangrove community at the estuary of the Pinglu Canal is located in the Maoweihai Mangrove Nature Reserve in the Beibu Gulf of Guangxi, which is a typical composite mangrove ecosystem reserve in China characterized by south subtropical estuaries, harbors, and coastal mudflat wetlands. The construction of the Pinglu Canal will alter the geomorphic characteristics, sedimentary facies distribution characteristics, and tidal flow characteristics of the mangrove area, resulting in changes in sediment erosion and deposition. Sedimentation changes will affect the tidal flat elevation information of the mangrove forest, while habitat changes will directly impact the mangrove ecosystem, indirectly affecting the quality of biological habitats and causing indirect damage to the habitat. Moreover, changes in the area and location of the intertidal zone will affect the degree of exposure and exposure time of mangroves, resulting in the disruption of the original biological community structure and a reduction in species. The environmental impact assessment results revealed that the Pinglu Canal project will occupy 13.8698 ha of mangrove forests, with the number of mangrove plants within the proposed land area being 255,325. There will be a significant decrease in the population of Aegiceras corniculatumn by 248,330, 5789 Kandelia candel, 362 Sonneratia apetala, and 844 Acanthus ilicifolius. However, limited research has been conducted on the assessment of mangrove biodiversity in this area, and few scholars have utilized LiDAR and hyperspectral technology to investigate the spatial distribution patterns of mangrove biodiversity.
To address the gaps in research, this study aims to combine hyperspectral and LiDAR technology to acquire both the spectral characteristics of mangrove plants and the vertical three-dimensional structural features of mangroves. Through data fusion and multi-source data collaboration, the study aims to address the challenges in mangrove structure parameter extraction, biodiversity assessment, and the identification of key protected species areas within complex coastal mudflat habitats. Building on the core biodiversity monitoring indicator system proposed by the Group on Earth Observations Biodiversity Observation Network (GEO BON) [
10], the study seeks to construct an evaluation index system for mangrove biodiversity at the species diversity, ecosystem diversity, and landscape diversity levels. After obtaining the biodiversity index of the study area through the proposed framework calculation, spatial zoning of mangrove biodiversity will be carried out based on two indicators: the elevation of mangrove growth and offshore distance. Measures were taken to protect the existing mangrove biodiversity, with the aim of minimizing the adverse effects on the local mangroves during the construction of the Pinglu Canal. This research primarily pursues two objectives: (1) Supported by the hypotheses of “spectral variation” and “height variation”, the applicability of UAV low-altitude LiDAR remote sensing and hyperspectral remote sensing in the construction and evaluation of mangrove biodiversity indicator system. (2) How to delineate the spatial zoning results of mangrove biodiversity to provide effective protection for mangrove biodiversity and reduce the impact of canal construction on mangrove biodiversity. The findings of this study can provide a scientific basis and technical support for developing mangrove biodiversity conservation strategies and offer a feasible solution for large-scale mangrove biodiversity mapping in the Maowei Sea at the estuary of the Pinglu Canal.
4. Discussion
4.1. Selection of Biodiversity Indicators
Mangrove forests are among the most diverse ecosystems on the planet and have the highest value for ecosystem services. Monitoring biodiversity is essential for the implementation of biodiversity conservation and mangrove biodiversity restoration. Rapid and accurate assessment of biodiversity and identification of core protected areas are prerequisites for supporting mangrove biodiversity restoration. However, because of differences in research areas and research objects, as well as limitations in data acquisition, biodiversity indicators and methods are not the same, and a universal biodiversity evaluation method has not been established [
65]. Previous researchers often used one of the “spectral variation hypothesis” [
66,
67] or “height variation hypothesis” [
39] as an indicator system to assess forest biodiversity in a certain area. This study is based on the “spectral variation” and “height variation” hypotheses, and constructs an evaluation index system for mangrove biodiversity at three levels: species diversity, ecosystem diversity, and landscape diversity. This method combines the two hypotheses in terms of three-dimensional structure and canopy spectral characteristics, overcoming the difficulty of assessing mangrove biodiversity with a single property. In this study, the canopy spectral variable characteristics generated from hyperspectral data of unmanned aerial vehicles [
68] and the height variable characteristics generated from laser point clouds of unmanned aerial vehicles [
69,
70,
71,
72,
73] are fused at the level of species diversity, which effectively solves the problem of selecting indicator systems at the level of species diversity. In the selection of species diversity and landscape diversity indicator systems, this study is based on the classification results of unmanned-aerial-vehicle remote sensing images, which derive mangrove aboveground biomass and three landscape pattern indices (Simpson diversity index (SIDI), landscape separation index (SPLIT), and landscape spread Index (CONTAG)). This indicator system selection emphasizes both the representativeness and practicality of indicator selection, fully analyzes the connotation of biodiversity, and reflects various levels of biodiversity. UAV data are easy to obtain with strong timeliness and operability, which solves the problem of difficult extraction of biodiversity indicators and parameters in coastal mudflat areas for biodiversity assessment.
4.2. Applicability of UAV Hyperspectral and Lidar in Biodiversity Assessment
Scholars have combined optical remote sensing data and laser radar to conduct forest biodiversity modeling based on the relationships between spectral heterogeneity, structural diversity, and species diversity. This has resulted in improved accuracy in estimating tree species diversity [
74,
75,
76]. Integrating optical and LiDAR or microwave remote sensing data can fully explain forest biodiversity variability from both canopy spectral and vertical structure features, providing a significant opportunity for biodiversity remote sensing monitoring. The hyperspectral data and LiDAR data obtained via drone remote sensing can be used to monitor forest biodiversity from both canopy spectral features and vertical structure features, which provides the possibility for rapid monitoring of mangrove biodiversity.
In this study, we extracted PRI (spectral reflectance of mangrove leaves), BGI2 (mangrove blue–green pigment index), and CI (chlorophyll content) from the hyperspectral data of unmanned aerial vehicles to characterize the mangrove canopy’s spectral variability. These physiological characteristics depict the biodiversity of mangroves on the scale of the photosynthetic effective rate, carotenoids in leaves, and chlorophyll properties, which can describe the biodiversity characteristics of mangroves in a more detailed spectral dimension than previous research results have. Previous researchers using the “spectral variation hypothesis” mainly used the variation characteristics (standard deviation, variance, and coefficient of variation) of NDVI indices extracted from multispectral data as predictive factors for tree species diversity modeling [
77,
78,
79,
80]. Their results showed that the standard deviations of the green light band reflectance and infrared band derivative have strong explanatory power for the Shannon diversity of tropical tree species. Therefore, this study used PRI, BGI2, and CI indices extracted from unmanned-aerial-vehicle LiDAR data to establish mangrove biodiversity indicators, which can take advantage of hyperspectral data in spectral dimensions. The contents of lutein pigments, carotenoids, and chlorophyll can better reflect the characteristics of mangroves at the mangrove level, providing the possibility for the application of UAV hyperspectral data to mangrove biodiversity. UAV LiDAR can quickly and accurately obtain the three-dimensional coordinates of research objects all day and under all weather conditions, which provides convenience for the classification of mangrove populations and extraction of structural diversity information [
16]. In this study, we extracted three indicators of mangroves (H95), LAI, and canopy coverage (CC) from UAV laser point-cloud data to characterize the biodiversity characteristics of the vertical structure of mangroves. These morphological characteristics represent the height, openness, and complexity of the mangrove canopy structure and have been widely used to characterize the diversity of the canopy structure [
81,
82,
83]. The correlation coefficient between the vegetation index generated by hyperspectral variables of unmanned aerial vehicles and the mangrove biodiversity index is generally lower than that generated by laser point-cloud data, indicating that the characteristic parameters of the vertical structure are better able to characterize the biodiversity status of mangroves. This is consistent with the results of Zheng et al. [
41], who investigated the effects of laser point-cloud variables (H95, LAI, and FHD) and hyperspectral variables (CRI, SLA, and NDNI) on the biodiversity of subtropical forests in China. The results showed that the H95 variable in the laser point cloud contributed the most to biodiversity, whereas hyperspectral variables contributed less to the functional diversity of forests.
4.3. Uncertainty of Model Evaluation Results and Future Research Directions
In this study, a specific indicator system was adopted at three levels to evaluate biodiversity indicators: species diversity, ecosystem diversity, and landscape diversity indicators. The generation of ecosystem and landscape diversity indicators was based on the UAV landscape classification results. The classification of mangrove landscape patterns in the study area was performed using various vegetation, UAV hyperspectral, and laser radar data with the support of the CART method [
45]. Although the CART decision tree is a supervised classification method that can improve classification accuracy, it is necessary to select a high-quality supervised classification sample set to construct and evaluate the decision tree. Thus, other classification methods, such as machine learning algorithms (random forest [
43], SVM [
25], and XGBoost [
16]), can be considered in the future by integrating UAV laser remote sensing data and hyperspectral remote sensing data for mangrove landscape classification. At the species diversity level, this study mainly selected the PRI, BGI2, and CI of mangroves as hyperspectral variables to characterize the mangrove’s canopy spectral variability. However, it is possible to obtain sensitive indices for other traits that contribute to tree growth, reproduction, survival, vegetation dynamics, and ecosystem function. Incorporating such traits, including functional traits such as chlorophyll, nitrogen, carbon content, and other functional traits of leaves of different mangrove species [
40,
84], into the biodiversity assessment index system may result in more accurate mangrove biodiversity assessment results.
It is important to consider the environmental conditions under which different mangrove species grow, such as salinity, soil, and climate factors, as they can affect mangrove biodiversity. Future research should incorporate these factors into a biodiversity assessment index system to improve the accuracy of the model. However, owing to the small study area, the salinity, soil, and climate factors of mangroves in this area are relatively uniform. If the methodology proposed in this study is applied to assess mangrove biodiversity on a larger scale, such as in the Maowei Sea and Beibu Gulf, the impact of salinity, soil, and climate factors in different regions on mangrove biodiversity must be considered. Finally, this study used unmanned-aerial-vehicle hyperspectral and laser point-cloud data to assess mangrove biodiversity in the estuary of China’s first Pinglu Canal. Although the study area is relatively small, the framework proposed in this study can be used for large-scale biodiversity mapping, providing a feasible local mangrove biodiversity conservation strategy after the construction of the Pinglu Canal. This study provides a feasible solution for large-scale biodiversity mapping.
4.4. Recommendations for the Protection of Mangrove Biodiversity
Guangxi Pinglu Canal Group Co., Ltd. was established on 30 June 2022. The company mainly focuses on the development of resources along the canal and the investment and construction of industries related to the Canal Economic Belt. Because the biodiversity value of the studied mangrove areas is much higher than that of the surrounding areas, it is important to carry out biodiversity conservation of mangroves during engineering construction. Therefore, this study proposes the following recommendations for Pinglu Canal Company:
Firstly, strictly abide by the relevant regulations on regional mangrove management. During the construction period of Pinglu Canal Group Co., Ltd., no damage or impact shall be caused to the surrounding mangrove forests, and no materials shall be piled up in the mangrove forests. During the construction of the Pinglu Canal, temporary sites such as borrow areas, spoil areas, stockpile areas, and worksheds should be set up outside the core protected area of the mangrove forest to ensure that the damage to the mangrove forest in the area during construction is minimized.
Secondly, the construction scope should be strictly controlled. The sediment from channel dredging should be transported out of the construction area in a timely manner and should not be directly dumped into the surrounding area to avoid covering the roots of mangrove plants and causing damage or even death to the mangroves. If it is found that the Pinglu Canal Company is damaged and endangers the survival of mangroves or even experiences abnormal situations of mangrove decline or death, it should be immediately reported to the Forestry Department or the Oceanic Administration and other competent departments, and emergency measures should be taken to control the situation in a timely manner. If necessary, the work should be stopped.
Thirdly, the core area of mangrove biodiversity should be absolutely protected. Except for the necessary field positioning observations for scientific research, facilities or activities that affect or interfere with the ecological environment of mangroves should not be established or engaged in. Owing to the blockage of seawater caused by construction equipment and facilities, seawater can be prevented from reaching the surrounding areas of mangroves, resulting in changes in the growth environment and death of mangroves. Therefore, construction equipment and facilities cannot be deployed in core areas of mangrove biodiversity protection.
Fourthly, try to shorten the construction period as much as possible. Pinglu Canal Company should try its best to use advanced and reasonable construction equipment and processes, strictly follow the operating procedures, scientifically arrange the operating procedures, shorten the construction period as much as possible, and minimize the impact of project construction on the regional mangrove ecosystem.