We evaluated the ability of a low-cost, low point density, rapid-scan terrestrial laser system (TLS), namely the Compact Biomass Lidar (CBL), to assess complex forest structures in terms of stem attributes, canopy structure, canopy complexity (e.g., leaf area index), and digital elevation models in this study. Our CBL system produces relatively low resolution 3D point cloud data when compared to higher-cost commercial scanners. This relatively low- resolution data introduces challenges for automatic detection and assessment of forest structure, mainly due to a higher laser beam divergence (15 mrad) and angular sampling resolution (0.25°), resulting in ~850,000 discrete lidar returns for an enclosed environment. However, the system scans rapidly (~33s/scan), collects a 270x360° scanned point cloud, is highly portable, and has low power requirements. We therefore developed algorithms to assess the structural environment in complex forest ecosystems, and evaluated them via field data, tailored to this kind of rapid-scan system. Specifically, we attempted to detect stems and roots and assess tree volume in Micronesia mangrove forests, and estimate LAI using leaf area density (LAD) assessment in a Hawai’ian study site. In order to evaluate our assessment of sediment elevation changes, we compared the TLS-derived results to those recorded using surface elevation table (SETs) in the field.
Examples of our results indicate that we can assess the elevation changes in mangrove forest using TLS data with an average consistency of 72% and an RMSE of 1.36 mm. More importantly, the standard error values for TLS results were 10-70x lower than field measurements. One important challenge in this work was differentiating between the root segments close to the ground and actual ground points needed for elevation change analysis. We addressed this issue by filtering the points resulting from ground segmentation, based on the angular orientation of the points, thus segmenting out the remaining root points. However, more complex root structures in different species may have a higher impact on ground detection. Future research should incorporate more species- and structural- related parameters toward optimizing this algorithm and addressing the species-related root structure complexity. An issue in assessing SEC using a CBL-type system in wetlands, including mangrove forests, is the inability of the laser beam at 905 nm to penetrate the water. To address this issue and assess the validity/error of our approach, we suggest a follow-up experiment in a controlled environment to develop an algorithm for detection of standing water regions, using parameters like unusually flat regions (“puddles”) or a decrease in the normal gradient. The ground detection can be further optimized using such approach, which in turn could result in a more accurate SEC assessment.
When evaluating the aboveground stem and root structures, we found an accuracy and precision value of 82% and 77%, respectively, for stem classification. The values for root classification were lower due to higher structural complexity, namely 76% and 68%, respectively. Additionally, we acquired consistency values ranging between 66%-98% across plots when comparing the measured stem volume using TLS and field data. We implemented a 3D classification approach using a random forest classifier, followed by a kernelized SVM. A potential improvement involves augmenting data and incorporating more sophisticated machine learning techniques, e.g., deep learning and neural networks, to enhance the classification, as well as studying the species-related impacts on the classification and modeling.
Finally, we evaluated the leaf area index as a canopy-level metric that can be used in studying the structural changes in complex forest environments. Using a 3D point density- based approach, we obtained an average accuracy of 90% and a RMSE = 0.31 when evaluating leaf area index, and an R2 = 0.88 when modeling the field-measured leaf area index
using only TLS-derived leaf density values. The data we used in this study are of low-density and as a result, deriving leaf-level geometric features were not practical. Future work should incorporate leaf geometric features, which can be acquired by sensors like full waveform lidar, to improve the leaf detection in lidar point clouds and expanding the application of such approach to other types of forest environments.
All the findings in this research can be used collectively with other structural attributes of complex forest environments, such as canopy complexity, to build a biophysically-accurate 3D model of such forests. This 3D model can then be used for modeling of radiative transfer in these environments and for other simulation studies. This can reduce the cost of time-consuming field work, by simulating these forests in ways to enable modifications to meet the needs of each specific study. Also, the application of the algorithms developed in this work can be expanded to other data sets, in order to evaluate their performance in environments with different structures, thereby identifying the modifications needed to adapt our algorithms more broadly to generic, but “typical” complex forest environments. Furthermore, with inevitable future improvements in TLS systems, e.g., increases in point density for low-cost scanners, the algorithms developed in this research can be adapted to these types of denser data. Typically, structural evaluation using high- density data is less challenging due to the ability of extracting detailed structural features from these data, especially for canopy-level assessment. We argue that all the algorithms in this work would provide better results with higher-density data. However, to increase efficiency in terms of computational time, one can tailor select algorithms to better fit a denser data scenario. An example may be the use of a simpler geometric reconstruction technique, instead of alpha shapes, for stem reconstruction, or incorporating gap fraction
and leaf geometric features in LAI assessment to supplement the density-based method provided in this work.
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