Characterized by high phenotypic plasticity, salinity tolerance, and metal tolerance, Poaceae in mudflats and wetlands are considered to have great potentials for ecological restoration, coastal risk response, and climate change indication. Accordingly, in the context of severer climate change and fast-risen global mean sea level, there is a strong and urgent requirement of phenotypic traits extraction and growth monitoring for these plants. Terrestrial laser scanner (TLS) is a novel but effective way for retrieving phenotypic, biochemical, and physical parameters of Poaceae plants in intertidal wetlands. Before retrieving these various parameters, intelligent identification and precise separation for stalks and leaves are required. However, Poaceae plants in mudflats and wetlands are densely growing with tangled and complex leaves, making it more challenging to automatically separate the stalks and leaves. With the challenge above, we propose a new separation algorithm for stalks and leaves of individual Poaceae plants in intertidal wetlands using TLS three-dimensional point cloud data.

In the present algorithm, reflectance information (intensity data) and several spatial geometric characteristics (i.e., density, normal vectors, and spatial connectivity) are employed. Typically, there is an edge loss or edge effect in the laser scanning data of Poaceae in mudflats and wetlands. This results in low intensity for edge parts of stalk and leaves and intensity data errors on these parts. Additionally, differences in geometry and sizes of stalks and leaves can lead to discrepancies in the number of neighborhood points within a given search radius (i.e., density). Therefore, corrected intensity and density data can initially be used to separate stalks and leaves. Further, individual Poaceae plants are divided into two different types (i.e., upturned leaves and drooping leaves), and separation is continuously conducted from two different routes based on the geometric differences (i.e., density, normal vectors, and spatial connectivity). The specific procedures of the two routes are subtly different. The fundamental principles of the two routes are based on preliminary separation using normal vectors and density data, and stalk, in which leaf points are eventually classified according to the spatial connectivity logic.

Riegl VZ-4000, a long range full-waveform TLS, is used to obtain the point cloud data of a total of 16 Giant Reeds or Reeds from the western of Chongming Island in Shanghai to test and analyze the proposed method (Fig. 4). To assess the predictive performance of the proposed algorithm convincingly, we quantitatively assess all samples’ results using the confusion matrix (Table 1). Hence, the manual separation results are taken as truth reference data. By inputting a single parameter r_a into the entire algorithm, an averaged overall accuracy of 0.87, and an averaged Kappa coefficient of 0.68 are achieved (Table 2 and Fig. 5). r_a is empirically determined following the common stalk size of Giant Reed or Reed, is suitable to all samples in this research. However, when given a large number of samples, it is essential to adjust r_a to achieve more satisfactory separation results. In addition, future studies are recommended to address the adaptive estimation of r_a to improve the proposed method’s automatic and unsupervised performance. Results show that the proposed method has relatively high accuracy and fairly good robustness. However, because of the complexity of Poaceae morphology, surface heterogeneity in the reflectance (usually caused by withered stalks and leaves and speckles of diseases), and dearth of data points (especially for plants far from the instrument due to occlusion effects), the clustering process of intensity can be over-segmented. Therefore, under those circumstances, spatial connectivity of stalks and leaves may be destroyed, and misclassification will be inevitable. The proposed algorithm only uses some fundamental information or characteristics. Thus, it is more efficient and does not require time-consuming work like some existing methods, such as neural network training, regression statistical analysis, or grid construction. Moreover, the proposed method can be extended to stalk and leaf separation for other Poaceae species (e.g., wheat, maize, sorghum, and bamboo). More deep investigations should be conducted to separate stalks and leaves for natural growing Poaceae in mudflats and wetlands. Combining multiplatform and multi-type remote sensing observations may be a potential solution.

In this study, a novel separation algorithm is exploratively proposed for stalk and leaves of Poaceae (Giant Reed and Reed) in mudflats and wetlands using TLS three-dimensional point cloud data. Accordingly, an overall accuracy of 0.87 is acquired by setting a single parameter. The proposed method succeeds in providing a technical solution for retrieving phenotypic, biochemical, and physical parameters of Poaceae plants in mudflats and wetlands. It is worth mentioning that only very few existing methods can achieve effective stalk and leaf separation of Poaceae in mudflats and wetlands. The major innovation is that different kinds of spectral and geometric information are fully utilized in the proposed method, enabling the providing of an effective remote sensing solution for vegetation monitoring or biomass observation in estuarine and coastal zones.