森林资源激光雷达遥感动态监测与蓄积量估测技术
Theme Keywords： terrain slopevertical structurevegetation indicesunmanned-aerial-vehicleterrestrialsubtropical forestsub-compartment levelrule-constrained all subsetreference datasetquality control
- The Paper
- Abstract：Satellite-based photon-counting LiDAR systems, represented by the Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), enable rapid acquisition of large-scale three-dimensional vegetation information and have been widely applied to forest parameter retrieval. However, their general applicability remains limited. To address this issue, this study focused on mixed coniferous and broadleaved forests in the canton of Aargau, Switzerland, and evaluated the role of canopy horizontal and vertical structural features in improving the accuracy of stand volume estimation from ICESat-2 data under complex forest conditions. Furthermore, we explored the optimal model form for this region and compared it against a baseline model using only conventional height-based statistical metrics. First, the denoised ICESat-2 ATLAS data were segmented into 100 m estimation units, and quality control was performed to identify and remove anomalous units, ensuring stable data quality. Next, feature grouping pre-screening combined with rule-constrained all-subset selection was applied to integrate point cloud height distribution metrics, canopy height and heterogeneity indices, and vertical structural features for stand volume estimation, yielding the optimal feature subset. Results indicated that the best-performing model for stand volume estimation in the Aargau mixed forests comprised the mean top-of-canopy height, the 65% height percentile, the leaf area–weighted canopy volume, and the mean value of the foliage profile. Ten-fold cross-validation demonstrated that this model achieved high accuracy, with an average R²=0.78, RMSE=92.48 m3/hm2, and rRMSE=0.24. By comparison, the baseline model using only traditional metrics yielded an R²=0.66 and an rRMSE reduced from 0.28 to 0.24, confirming that the incorporation of structural features substantially improved the accuracy of ICESat-2-based stand volume estimation, particularly in forests with high canopy heterogeneity. In conclusion, integrating three-dimensional structural attributes significantly enhances the applicability of ICESat-2 data for forest stand volume estimation under complex stand conditions, thereby providing methodological support for large-scale forest volume and carbon stock monitoring.Keywords：Forest Stock Volume;ICESat-2 ATLAS;forest structural features;canopy height heterogeneity;vertical structure;quality control;preliminary grouping feature selection;rule-constrained all subset522|738|0
<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-12-17 - Abstract：The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) is China’s first remote sensing satellite with space-borne LiDAR as the main payload, which aims at quantitatively monitoring terrestrial ecosystem carbon storage, forest resources, and forest productivity; serving the goals of “carbon peaking and carbon neutrality”; and monitoring and evaluating major projects for the protection and restoration of important ecosystems in China. In this study, the Relative Height metrics RHn (where n ranges from 0 to 100) , which is calculated by the full-waveform energy distribution, was used to evaluate the ability of characterizing forest canopy height for the full-waveform data of the full-waveform LiDAR onboard TECIS. The ability in canopy height estimation between fixed and variable gain waveform data was compared. Moreover, the influence of slope on canopy height extraction was analyzed. Six tracks of L2 products from TECIS full-waveform LiDAR passing the test area of a temperate coniferous-broadleaved mixed forest in Quebec, Canada were selected for analysis. Results show that the select of starting RH metrics for estimating forest canopy height significantly affects the accuracy of the results. Specifically, employing a lower RH metric tends to overestimate canopy height, whereas a higher RH metric results in an underestimation. In addition, the background noise threshold also has a certain influence on the accuracy of canopy height estimation. The Root Mean Square Error (RMSE) for forest canopy height can reach up to 3.58 meters, whereas the Median Error (ME) improves to less than 1.0 meter, and the Mean Absolute Error (MAE) is recorded at 2.48 meters after removing several anomalous laser points. Furthermore, in comparison to the final peak position derived from waveform decomposition, the RH5 metric demonstrates its superiority as a baseline for estimating canopy height, exhibiting reduced sensitivity in inversion accuracy to variations in terrain slope. The accuracy of canopy height retrieval using variable and fixed gain waveform data is comparable. The configuration of variable and fixed gains is beneficial for enhancing data effectiveness in forest areas. The conclusions drawn from this analysis will significantly aid in the application of the laser altimetry data from TECIS for canopy height mapping and biomass estimation in forests.Keywords：Terrestrial Ecosystem Carbon Inventory Satellite;full-waveform LiDAR;forest canopy height;Relative Height Metrics;Background noise threshold;terrain slope1648|2317|1<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-12-17
- Abstract：The successful launch of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), carrying the Advanced Topographic Laser Altimeter System (ATLAS), has made it possible to accurately quantify global vegetation structure. However, given the limitations of the sensitive photon detection system, its data contain a large amount of background noise photons. Aiming at the problem that ICESat-2/ATLAS has low signal extraction accuracy in mountainous forest areas, which leads to the difficulty of ground and canopy surface detection, the method of ground and canopy surface detection based on direction-adaptive ordering points to identify the clustering structure (DA-OPTICS) is proposed.First, the initial ground surface is obtained through segmented curve fitting based on random sample consensus (RANSAC), which is used to construct a direction-adaptive elliptical searching area to replace the traditional circle in OPTICS, forming the DA-OPTICS. On the basis of this algorithm, the reachability distance of all photon point clouds is obtained. Potential ground signals and surface are obtained successively via a “two-step method”: Otsu’s method is first introduced to obtain potential ground signals, and potential ground surface is then acquired by fitting potential ground signals through RANSAC. The “two-step method” is iterated several times until the similarity between the potential ground surface obtained before and after is greater than 90%, indicating that the potential ground surface is considered a fine ground surface. Second, the effect of terrain on photons is eliminated by referring to the fine ground surface, and the vegetation signal is extracted by vertical elliptical OPTICS. Finally, on the basis of the vegetation signal, the canopy surface is detected by combining the elevation percentile and piecewise cubic Hermite interpolation curve fitting.The ICESat-2 ATL03 data of Mengjiagang Forest Farm in Heilongjiang Province and Fushun Forest Farm in Liaoning Province are used as research objects to carry out experiments, and the accuracy is verified by manually labeled samples and unmanned aerial vehicle products. Results show that the extraction accuracy (F) of ground surface and vegetation signals in the mountainous forest areas is 0.97, which is about 0.07 higher than that of the OPTICS based on elliptical searching area. In addition, the RMSEs of ground and canopy surfaces detected by the proposed method are 1.08 m and 2.33 m, respectively, compared with 1.92 m and 3.29 m of ATL08, respectively, implying a significant improvement in accuracy.Therefore, compared with the OPTICS based on elliptical searching area and ATL08, DA-OPTICS has higher precision in extracting vegetation signal photons and detecting ground and canopy surfaces. It is more suitable for areas with large gradient changes such as mountainous forest areas and can provide a reliable data foundation for the subsequent inversion of forest spatial structure.Keywords：ICESat-2/ATLAS;direction adaptive;DA-OPTICS;iterative refinement;terrain slope;mountain forest area543|1539|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-12-17
- Abstract：The LiDAR Biomass Index (LBI) can calculate the aboveground biomass (AGB) of individual trees on the basis of airborne LiDAR data, and it has been verified to have high accuracy for biomass calculation at tree and plot levels. However, its ability to complete large-scale forest biomass mapping has not been fully explored. The aim of this research is to verify the accuracy of LBI for AGB estimation on a subcompartment scale, taking the widely planted Larix olgensis tree species in north China as an example and laying a theoretical foundation for the widespread application of this index.First, the existing tree species classification results based on hyperspectral data were used to select the point clouds of L. olgensis species in Mengjiagang Forest Farm. Second, the NSC algorithm was employed to complete the individual tree segmentation of the selected point clouds. Third, the LBI was used to calculate the forest biomass of each individual tree. Finally, with reference to the AGB_LBI biomass model of L. olgensis species constructed on the basis of 35 individual sample trees, the biomass of each individual tree was calculated, and the biomass of each subcompartment was obtained through accumulating the biomass of individual trees within the subcompartment. In this research, the calculation accuracy was verified through the silviculture survey data obtained from the local forestry department, including over 70000 individual trees. Meanwhile, the universality of LBI in estimating the biomass of the same tree species across different regions at the subcompartment level was evaluated on the basis of the existing AGB_LBI models of other forest farms, and the results were compared with those of the commonly used LiDAR Metric-based Regression (LMR) methods.The results indicated that LBI can achieve forest biomass estimation at the subcompartment level with high accuracy. When individual tree samples selected from different regions were used to calibrate the AGB_ LBI model, the obtained biomass values were comparable with the measured data, with R2 ranging from 0.86 to 0.87 and relative root-mean-square error (RMSE) ranging from 34.20% to 40.23%. The biomass results calculated from each model did not have significant differences. However, the increase in the number of sample trees used for model calibration still exerted a certain effect on the robustness and accuracy of biomass calculation. Overall, the accuracy of the LBI-based method was comparable to that of the LMR method, although the sample trees used to calibrate the AGB_LBI model only accounted for 1% of that used to calibrate the LMR model. Meanwhile, the LBI method exhibited stronger universality among the same tree species in different forest farms. The AGB_LBI model was used to calculate the biomass of each individual tree in the western region of Mengjiagang Forest Farm and complete the biomass mapping. The obtained biomass distribution presented a similar trend to the existing biomass map and was consistent with the forest subcompartment map, achieving high consistency at the scale of 20 m×20 m (R2=0.75, RMSE=1.55 t).The high-precision estimation of biomass by LBI at the subcompartment scale demonstrates its potential for conducting large-scale estimation of forest AGB. Because of the difficulty in obtaining validation data, this research only verified its accuracy on the species of L. olgensis and did not conduct experiments on other tree species. Nevertheless, previous studies have shown that this method can theoretically be applied to other tree species and forest situations, which is worth further exploration. This research provides a theoretical basis for precise, large-scale, and high-precision forest biomass estimation.Keywords：LiDAR Biomass Index;LBI;airborne LiDAR;individual tree;sub-compartment level;biomass2260|2174|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-12-17
- Abstract：This research aims to examine the key factors influencing the accuracy of tree species classification using airborne hyperspectral data combined with Light Detection And Ranging (LiDAR) data in forest environments. Accurate identification of individual tree species is essential for effective forest resource monitoring, management, ecosystem assessment, and biodiversity conservation. While many small-scale studies have explored tree species classification in forests with diverse species compositions and complex age structures, achieving this over larger areas remains a significant challenge. This study focuses on evaluating the effects of spectral consistency correction, canopy height information, and individual tree canopy segmentation on classification accuracy. Saihanba Mechanical Forest Farm, a large-scale artificial plantation, was selected as the study site to explore these factors.To assess the effect of different factors on tree species classification accuracy, this research utilized a random forest classification algorithm and developed four distinct classification strategies. The first strategy used vegetation indices derived from multiflightline images without applying Bidirectional Reflectance Distribution Function (BRDF) correction. The second strategy incorporated BRDF correction into the multiflightline images before deriving vegetation indices. The third strategy integrated canopy height information, specifically the Canopy Height Model (CHM), with the BRDF-corrected vegetation indices. The fourth and final strategy combined BRDF-corrected vegetation indices, CHM, and individual tree canopy segmentation data. The classification accuracy of each strategy was systematically compared to quantify the contribution of each factor toward improving tree species classification precision.Results indicated that individual tree canopy segmentation significantly reduced misclassification errors arising from the mixing of multiple species within a single canopy, leading to a notable 10.74% improvement in classification accuracy. Using the random forest model’s feature importance ranking, individual tree segmentation emerged as the most critical factor, followed by BRDF correction, then CHM. Although BRDF correction reduced spectral reflectance variability caused by differing Sun observation geometries across flight strips, it only led to a modest improvement in classification accuracy of 3.48%. The introduction of CHM yielded minimal gains in accuracy, contributing just 0.67%, particularly in areas with uniform vertical forest structures or species spanning multiple age cohorts.This study demonstrates that integrating airborne hyperspectral data with LiDAR data holds substantial promise for enhancing tree species classification in large-scale artificial plantations. Among the factors analyzed, individual tree segmentation proved to be the most impactful in improving accuracy. By contrast, the relatively minor influence of BRDF correction and canopy height features underscores the need for further refinement and optimization. Overall, the findings emphasize the importance of considering multiple factors in remote sensing workflows to enhance the efficiency and accuracy of forest resource monitoring, management, and other forestry-related applications, especially in expansive forest environments. These insights provide a valuable theoretical foundation and practical recommendations for future forest management and ecological monitoring efforts.Keywords：Tree Species Classification;airborne hyperspectral data;BRDF correction;LIDAR data;individual tree segmentation;Random Forest;vegetation indices;Saihanba mechanized forest farm2350|2290|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-12-17
- Abstract：Sharing multiplatform laser scanning point clouds of forests is of great significance for laser scanning research and applications in forestry. To this end, the Institute of Forest Resource Information Techniques, Chinese Academy of Forestry has constructed a multiplatform laser scanning point cloud dataset for forest plots in subtropical regions, featuring airborne laser scanning, unmanned-aerial-vehicle laser scanning, and Terrestrial Laser Scanning (TLS) point clouds, along with forest inventory data. Data was collected at Gaofeng Forest Farm in Guangxi, China, covering 25 plots with three tree species: Eucalyptus, Cunninghamia lanceolata, and Pinus massoniana. The field forest inventory data include plot locations, tree positions, diameter at breast height, tree height, height to the first live branch, and crown width. The dataset enables the analysis of forest three-dimensional structural information captured by laser scanners from various platforms, evaluating automated processing algorithms like point cloud registration and tree segmentation. It provides important references for forest research at the regional, plot, and tree levels. Additionally, this study developed a field inventory method guided by TLS data. This method utilizes tree stem point clouds to mark tree positions and measures individual trees according to tree maps, improving operational efficiency.Keywords：subtropical forest;laser scanning;airborne;unmanned-aerial-vehicle;terrestrial;multi-platform;forest plot;point cloud;reference dataset;field forest inventory2554|4385|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-12-17
