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Background: Fast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China. Results: The results show that stacking achieved the best AGB estimation accuracy among the models, with an R2 of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively. Conclusion: Compared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring. [ABSTRACT FROM AUTHOR]

• ICESat-2 has the potential to improve the estimation accuracy and efficiency of forest aboveground biomass on a large scale. • The Google Earth Engine cloud platform was used for Sentinel-2 images acquisition and composition. • The optimized extreme learning machine achieved the highest estimation accuracy and reasonable forest aboveground biomass spatial distribution. Natural forests have the most complex structure and richest biodiversity among terrestrial ecosystems and are essential for maintaining the carbon balance and stability of the biosphere. Aboveground biomass (AGB) is a primary indicator used to evaluate forests and can directly measure forest growth and the quality of natural forests. Accurate and rapid AGB estimations can significantly improve the efficiency of forest management and deepen the understanding of the forest carbon cycle. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), one of the most recently launched spaceborne light detection and ranging (LiDAR) instruments, can penetrate forest canopies and obtain accurate, large-scale forest vertical parameter measurements. However, the discrete footprints offered by ICESat-2 cannot provide comprehensive spatial AGB distribution data. In this study, an optimized extreme learning machine (ELM) method was proposed to estimate the AGB of natural forests on the eastern Qinghai-Xizang Plateau, China. The synthetic Sentinel-2 images acquired from the Google Earth Engine (GEE) were synergized with ICESat-2 to realize continuous AGB mapping. To verify the effectiveness of the optimized method, support vector machine (SVM), k-nearest neighbor (kNN), and backpropagation (BP) neural network models were also established for comparison. The measured AGB data extracted from the forest management inventory (FMI) were used for model accuracy evaluation. The results show that the optimized ELM achieved the best estimation effect among all the analyzed models, with an R2 value of 0.68 and a root mean square error (RMSE) value of 25.14 mg/ha. The optimized ELM obtained the minimum RMSE and greatly improved the AGB prediction efficiency. These findings prove the ability of ICESat-2 in estimating the AGB of natural forests, which can provide a new way for large-scale forest resources investigation in high-altitude areas with a harsh environment. [ABSTRACT FROM AUTHOR]

Oil tea (Camellia oleifera) is one of the world's major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum–minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management. [ABSTRACT FROM AUTHOR]

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction. [ABSTRACT FROM AUTHOR]

Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage. [ABSTRACT FROM AUTHOR]