Discovering hidden saplings using cutting-edge LiDAR technology in forests.

March 14, 2024
1 min read


Researchers at Nanjing Agricultural University have developed a methodology to extract phenotypic parameters of understory saplings using high-density airborne LiDAR data. By segmenting upper mature trees and refining algorithms to detect understory saplings, they have successfully improved the accuracy of forest inventory and analysis. This study showcases the potential of advanced LiDAR technology in enhancing forest management and understanding sapling regeneration processes.

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In the world of forest management research, the regeneration of forest saplings plays a crucial role in maintaining biodiversity and ecosystem productivity. Traditional 2-dimensional remote sensing methods have struggled to accurately capture the complex dynamics of understory saplings. To address this challenge, researchers are turning to aerial laser scanning (ALS) to gain detailed 3-dimensional insights. Despite progress in estimating tree metrics using ALS data, accurately identifying and quantifying phenotypic parameters of understory saplings has remained a challenge.

The study published by Plant Phenomics, titled “Identifying Regenerated Saplings by Stratifying Forest Overstory Using Airborne LiDAR Data,” presents a comprehensive methodology to extract phenotypic parameters of understory regeneration saplings using advanced high-density airborne LiDAR data. By fusing data from multiple flights and implementing a Nyström-based spectral clustering algorithm to segment upper mature trees, the researchers successfully enhanced the detection and matching rates of tree segmentation.

The novel postprocessing method introduced in the study significantly improved the positional accuracy of mature trees, leading to a more precise detection of understory saplings under mature trees. By utilizing the local adaptive mean shift algorithm, the researchers successfully detected saplings with high matching rates and extraction rates, demonstrating the effectiveness of the proposed methodology.

Further validation using multisource reference data confirmed the efficacy of the method, with comparisons between ALS and terrestrial laser scanning (TLS) data showing strong correlations for tree height and sapling crown widths. The study not only highlights the successful application of high-density ALS data for understory sapling characterization but also underscores the potential of this technology to advance forest management practices and sapling growth studies.

Overall, the research showcases the importance of refining algorithms and utilizing advanced LiDAR technology to accurately segment and measure understory saplings, offering a significant step forward in the use of remote sensing technologies for detailed forest inventory and analysis.

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