LAI is a critical parameter for forest management and global ecosystem monitoring. GEDI provides global-scale vegetation structure data, yet its L2B LAI product often exhibits systematic biases. This study investigates the Maoer Mountain forest in China, utilizing a total of 60 validated GEDI footprints as the primary dataset. To address the limitations of the standard GEDI L2B algorithm, which assumes a horizontally uniform canopy, we integrated a four-scale geometric optical model to characterize canopy clumping effects. This model was employed to simulate the geometric proportions of sunlit/shaded canopy and ground components within each footprint to derive a footprint-specific clumping index, thereby refining the gap rate estimates. The accuracy of the revised leaf area index was rigorously verified by using the measured data from the sample plots in the Maoer Mountain area. The results indicate that the original GEDI L2B data underestimates LAI, with a mean absolute error (MAE) of 1.79 m2/m2, a root mean square error (RMSE) of 1.47 m2/m2, and a bias of −1.25 m2/m2. After correcting for canopy clumping, accuracy improved significantly, reducing the MAE to 0.65 m2/m2 and the RMSE to 0.82 m2/m2, while effectively mitigating underestimation. These findings demonstrate that accounting for non-uniform canopy distribution effectively reduces errors, providing a robust methodological basis for high-precision LAI retrieval using spaceborne lidar. Despite these improvements, this method still has certain limitations: the model’s performance is constrained in extremely steep terrain due to waveform aliasing and in fragmented vegetation areas where sub-footprint heterogeneity is high. Future research should incorporate topographic corrections and multi-source data fusion to enhance the model’s robustness in complex landscapes.

Forests comprise the largest ecosystem in the world and provide significant value to natural ecosystems and human society [...]

The clumping index (CI) is a key parameter for modeling radiative transfer in plant canopies, but its retrieval leads to significantly reduced accuracy in highly heterogeneous environments. To improve the accuracy of CI quantification and to assess the performance of various CI algorithms under ecologically heterogeneous conditions, five conventional methods based on the tracing radiation and architecture of canopies (TRAC) instrument, namely, the logarithm methods (LX), the gap size distribution methods (CC and P), the combination of gap size distribution and logarithm methods (CLX), and the modified gap size distribution algorithm (CMN), along with a novel LX+ method developed from the LX approach in this study, are evaluated in three $100\times 100$ -m natural secondary forests stands (hardwood, coniferous, and mixed broadleaf coniferous) located at Maoer Mountain Forest Farm in Heilongjiang Province, China. The key findings include: 1) the LX+ method demonstrates enhanced sensitivity and accuracy in highly heterogeneous stands; 2) the P and LX methods show no significant variation trends with increasing view zenith angle. In contrast, the CC, CLX, and LX+ methods exhibit a pattern in which the CI values first increase rapidly and then stabilize as the zenith angle increases. In particular, the LX+ method yields the highest precision for CI estimates among all methods; and 3) the LX+ method achieves optimal accuracy with varying segment lengths (coefficient of variation = 11.5%) and has strong explanatory ability for heterogeneous canopy structures with different segment lengths. The results demonstrate that the LX+ method significantly enhances characterization of multiscale canopy heterogeneity CI while maintaining the highest accuracy across all experimental scenarios.

The clumping index (CI) is a key parameter for quantifying foliage aggregation within vegetation canopies and is essential for accurately characterizing related physiological and ecological processes. Critically, even with identical leaf-level biochemical parameters, differences in canopy structure can yield distinct reflectance signatures, underscoring the role of CI in reducing uncertainties in retrieving vegetation biochemical properties. Traditional CI estimation often relies on coarse-resolution, multi-angular MODIS data, whose limited spectral and spatial resolution hinders accurate trait retrieval and reduces accuracy. In this study, a novel CI inversion framework that leverages the rich spectral information from satellite-based hyperspectral imagery ZY1F-02E data was developed to estimate CI timely and conveniently. CI was measured using TRAC with the LX+ method across 44 forest plots in Maoer Mountain, Heilongjiang Province. Three machine learning algorithms-Support Vector Machine (SVM), Random Forest (RF), and Tree-based Pipeline Optimization Tool (TPOT)-were compared for CI estimations. The optimal model was selected based on 5-fold cross-validation accuracy and uncertainty analysis. Results indicate that: (1) vegetation indices derived from second-derivative spectra (e.g., mRVI and EVI) exhibited stronger correlation with CI; (2) the model constructed by TPOT achieved higher accuracy and lower uncertainty than those by SVM and RF; (3) hyperspectral-based indices correlated more strongly with CI than multispectral ones.

Chinese forest ecosystems are key carbon sinks that significantly contribute to lowering carbon emissions. Accurate Net Ecosystem Productivity (NEP) estimations are essential for evaluating their carbon sequestration capabilities and overall health. This study employed the Physiological Principles Predicting Growth-Satellites (3-PGS) and soil heterotrophic respiration models to simulate China’s forest carbon sinks and sources distribution from 2013 to 2023. Then, climatic factors influencing NEP changes were examined through the application of a geographical detector model. The net carbon sequestered was 1.71 ± 0.09 PgC with an annual average of 0.156 ± 0.0071 PgC, signifying a substantial carbon sink in China’s forest. The annual NEP was highest in evergreen broadleaf forests (352.12 gC m−2) and lowest in deciduous needleleaf forests (148.31 gC m−2). NEP in China’s forests increased by a rate of 1.67 gC m−2 annually, with most regions exhibiting a 275.32 gC m−2 annual carbon sink. The geographical detector model analysis showed that solar radiation, precipitation, and vapor pressure deficit were the main drivers of NEP change, while temperature and frost days had a secondary influence. Furthermore, the interaction between solar radiation and temperature variables showed the greatest impact. This study can enhance the understanding of carbon sink and source distribution in China, serve as a reference for regional carbon cycle research, and provide key insights for policymakers in developing effective climate strategies.

We developed a method of comprehensive forest disturbance identification system based on the distur-bance characteristics of forest ecosystem and the integrated multi-source remote sensing data to evaluate the overall forest disturbance intensity of Heilongjiang Province from 2001 to 2023. We further conducted disturbance extraction and types identification. The results showed that forest disturbance intensity peaked in 2003, primarily due to large-scale forest fires. The spatial consistency between disturbance detection using the LandTrendr method and the Global Forest Change dataset exceeded 90%. Forest disturbances could be categorized into three types, including fire disturbance, pest disturbance, and logging disturbance. The overall classification accuracy for disturbance types was 87.8% (Kappa coefficient=0.81). Different spectral indices had different responses to disturbance types. Specifically, the normalized burn ratio was the most sensitive to fire disturbance. The normalized difference vegetation index was more responsive to overall vegetation change. The normalized difference moisture index made a more significant contribution to the identification of pest disease, while the modified greenness difference index could assist in detecting logging activities. In conclusion, the integrative analysis of multi-spectral indices and the fusion of temporal features could effectively improve the accuracy of identifying forest disturbance types, which would provide a scientific basis for forest ecosystem management in cold temperate zone of Northeast Asia.

The Ice, Cloud, and Land Elevation 2 (ICESat-2) mission uses a micropulse photon-counting lidar system for mapping, which provides technical support for capturing forest parameters and carbon stocks over large areas. However, the current algorithm is greatly affected by the slope, and the extraction of the forest canopy height in the area with steep terrain is poor. In this paper, an improved algorithm was provided to reduce the influence of topography on canopy height estimation and obtain higher accuracy of forest canopy height. First, the improved clustering algorithm based on ordering points to identify the clustering structure (OPTICS) algorithm was developed and used to remove the noisy photons, and then the photon points were divided into canopy photons and ground photons based on mean filtering and smooth filtering, and the pseudo-signal photons were removed according to the distance between the two photons. Finally, the photon points were classified and interpolated again to obtain the canopy height. The results show that the improved algorithm was more effective in estimating ground elevation and canopy height, and the result was better in areas with less noise. The root mean square error (RMSE) values of the ground elevation estimates are within the range of 1.15 m for daytime data and 0.67 m for nighttime data. The estimated RMSE values for vegetation height ranged from 3.83 m to 2.29 m. The improved algorithm can provide a good basis for forest height estimation, and its DEM and CHM accuracy improved by 36.48% and 55.93%, respectively.

Chlorophyll plays a significant role in evaluating vegetation health and forest carbon sink. In this study, a total of 36 characteristic variables from hyperspectral image and lidar point cloud data acquired through an unmanned aerial vehicle (UAV) platform were used to evaluate the accuracy of statistical models including multiple stepwise regression, BP neural network, BP neural network optimized by firefly algorithm, random forest, and the mixed data-driven mechanistic model PROSPECT model in estimating chlorophyll content for three different forest types in Maoershan Forest Farm of Northeast Forestry University in Heilongjiang Province, namely coniferous forest, broad-leaved forest, and coniferous–broad-leaved mixed forest. The accuracy of the models was evaluated by the coefficient of determination (R2) and root mean square error (RMSE). The results show that random forest (R2 = 0.59–0.64, RMSE = 3.79–5.83 µg·cm−2) among all statistical models is superior to other models. The accuracy of the mechanism model was the highest (R2 = 0.97, RMSE = 3.40 µg·cm−2). There were significant differences in chlorophyll content among different forest types. It ranged from 25.25 to 31.60 µg·cm−2 for broad-leaved forests, which was higher than that of coniferous and broad-leaved mixed forests (13.52–23.93 µg·cm−2) and coniferous forests (6.40–13.71 µg·cm−2). In the horizontal direction, the chlorophyll content near the center of the canopy was lower than that at the edge of the canopy. In the vertical direction, there was no significant difference in chlorophyll content between different canopies of Pinus sylvestris var. mongolica, while there was a significant difference in chlorophyll content between the upper, middle, and lower canopies of Juglans mandshurica. For different tree species, the variation in chlorophyll with crown height was different.

Forest ecosystems serve as a vital component of the global carbon cycle, particularly under accelerating climate change. As a key parameter in regulating forest ecosystems, the length of the growing season (LOS) is critical in determining forest photosynthesis and makes a positive contribution to carbon storage. Therefore, accurate LOS estimation is essential for improving the precision of carbon stock assessments and understanding forest responses to global warming. In this study, LOS was extracted from MODIS time-series remote sensing data and subsequently incorporated into the Integrated Terrestrial Ecosystem Carbon Cycle Model (InTEC) to simulate the dynamics of Net Ecosystem Productivity (NEP) from 2001 to 2022. The estimation errors of NEP were evaluated under four SSP-RCP climate scenarios for the period 2023 to 2060. The results demonstrated that the LOS derived using the optical time-series method (OptPhen) consistently outperformed the traditional temperature-threshold method (TemPhen), which systematically overestimated LOS. Under future climate scenarios, LOS derived from OptPhen exhibited moderate increase of 3.7-5.18 days, whereas TemPhen predicted larger expansion of 12.58-26.27 days. For NEP estimation, the TemPhen method showed an overestimation trend-by approximately 28 % for coniferous forests, 23 % for broadleaf forests, and 17 % for mixed forests-compared to OptPhen. Furthermore, NEP predictions increased by 8.3 %-15.1 % using OptPhen and by 11.4 %-25.8 % using TemPhen across different emission pathways. This research provides critical evidence for more accurate carbon sink assessments and improved carbon cycle modeling in Northeast China forests, offering theoretical support for the formulation of adaptation strategies under global climate change.

As carbon cycling and global environmental protection gain increasing attention, forest disturbance research has intensified worldwide. Constrained by limited data availability, existing frameworks often rely on extracting individual spectral bands for simple binary disturbance detection, lacking systematic approaches to visualize and classify causes of disturbance over large areas. Accurately identifying disturbance types is critical because different disturbances (e.g., fires, logging, pests) exhibit vastly different impacts on forest structure, successional pathways and, consequently, forest carbon sequestration and storage capacities. This study proposes an integrated remote sensing and deep learning (DL) method for forest disturbance type identification, enabling high-precision monitoring in Northeast China from 1992 to 2023. Leveraging the Google Earth Engine platform, we integrated Landsat time-series data (30 m resolution), Global Forest Change data, and other multi-source datasets. We extracted four key vegetation indices (NDVI, EVI, NBR, NDMI) to construct long-term forest disturbance feature series. A comparative analysis showed that the proposed convolutional neural network (CNN) model with six feature bands achieved 5.16% higher overall accuracy and a 6.92% higher Kappa coefficient than a random forest (RF) algorithm. Remarkably, even with only six features, the CNN model outperformed the RF model trained on fifteen features, achieving a 0.4% higher overall accuracy and a 0.58% higher Kappa coefficient, while utilizing 60% fewer parameters. The CNN model accurately classified forest disturbances—including fires, pests, logging, and geological disasters—achieving a 92.26% overall accuracy and an 89.04% Kappa coefficient. This surpasses the 81.4% accuracy of the Global Forest Change product. The method significantly improves the spatiotemporal accuracy of regional-scale forest monitoring, offering a robust framework for tracking ecosystem dynamics.

Against the backdrop of the deepening implementation of the “Double Carbon” goals, reducing carbon emissions poses great pressure on China. As major agricultural and industrial provinces, the industrial structure of the three northeastern provinces has a crucial impact on carbon emissions. In order to explore this phenomenon, this study employed provincial and municipal data from 2007 to 2019 to simulate the spatial and temporal patterns of carbon emissions and GDP in Northeast China. The Tapio decoupling model was applied to assess the elasticity coefficient between economic development and carbon emissions, while the Theil index was used to evaluate the rationalization of the industrial structure. Then, a multiple linear regression model (MLR) was innovatively applied to explore the relationship between the indexes of the two models. This study found that carbon emissions and GDP in the three provinces both exhibited the characteristic of Liaoning > Heilongjiang > Jilin. In the decoupling analysis, 64.7% of the cities were dominated by benign decoupling. The negative decoupling areas were primarily composed of industrial cities in the southwest and resource-based cities in the east. In the rationalization analysis, there were large-scale irrational areas in 2019, which were concentrated in northwestern and southwestern industrial cities, and occasionally in eastern resource-based cities. There was a certain degree of spatial overlap between these two problematic areas. The MLR result showed that there was a positive correlation between the elasticity coefficient and the Theil index, indicating that optimizing the industrial structure can promote the upgrading of the decoupling status toward strong decoupling. This study provided a theoretical basis for improving the decoupling of carbon emissions and economic development through industrial structure rationalization. For overlapping regions, emission reduction can be prioritized through the rationalization of the industrial structure to achieve a better decoupling status.

This study was aimed at examining the contribution of forest products to rural livelihoods and the socio-economic factors that influence household forest dependence in the Luki Biosphere Reserve. A structured questionnaire poll of 193 households randomly chosen from two enclaves in the Luki Biosphere Reserve, and focus group discussions were used to gather the data. For data analysis, a binary logistic regression model was used. The study revealed a substantial contribution of forest products to household livelihood based on household wealth strata and the gender of the household head. The contribution of forest income has been found to be higher for poor households than to other wealth categories, although their mean income from forest was low. However, the present research further revealed that household forest dependence was significantly determined by socioeconomic factors such as length of residency, age, sex, education, employment and household size. Compared to their elderly counterparts, the youth were probably more dependent on forest products. Therefore, there should be increased capacity-building efforts among the young people to enable them enlighten the local communities about the need for sustainable forest management. Meanwhile, highly educated people were observed to be less dependent on forests. The findings of this research provides empirical evidence from the Mayombe tropical forest, thus contributing to the growth of knowledge on the impact of socioeconomic factors on the household dependence on forest resources, especially in the tropical forest of the Democratic Republic of Congo where the complexity of the relationship between local communities and their environment is still being studied.

Vegetation plays a vital role in connecting ecosystems and climate features. The biodiversity of vegetation is one of the most important features for evaluating ecosystems and it is becoming increasingly important with the threat of global warming. To clarify the effects of climate change on forest biodiversity in Northeast China, time-series NDVI data, meteorological data and land cover data from 2010 to 2021 were acquired, and the forest biodiversity of Northeast China was evaluated. The effect of climate change on forest biodiversity was analyzed, and the results indicated that the forest biodiversity features increased from west to east in Northeast China. There was also an increasing trend from 2010 to 2021, but the rate at which forest biodiversity was changing varied with different forest types of Northeast China, as different climatic factors had a different impact on forest biodiversity in different forest types. Average annual temperature, annual accumulated precipitation, CO2 fertilization and solar radiation were the main factors affecting forest biodiversity changing trends. This research indicated the potential impact of climate change on forest ecosystems, as it emphasized with evidence that climate change has a catalytic effect on forest biodiversity in Northeast China.

Fast-growing wood generally has drawbacks such as soft texture, low strength, and poor photostability, which greatly limits its use value and application scope. In this article, nano-Al2O3/ZnO modified wood was prepared by magnetron sputtering with fast-growing poplar as the substrate. The results showed that when Al2O3 and composite films were deposited on fast-growing wood, their load-displacement curves were significantly shifted to the left, meaning that the deformation resistance of wood was enhanced. The maximum pressure depth of nano-Al2O3/ZnO/wood was 246.3 nm, a decrease of 80.1% from CTRL wood. The average elastic modulus was 4.683 GPa, an increase of 20.48 times; the average hardness was 0.731 GPa, an increase of 28.41 times. After 222 h ultraviolet irradiation, total color difference values (Delta E-& lowast;) of nano-ZnO/wood, nano-Al2O3/ZnO/wood, and nano-ZnO/Al2O3/wood were 4.31, 4.73, and 5.25, respectively, which were more than 70% lower than that of the CTRL wood (19.76). Continuous and uniform nanofilms were observed on the surface, and the highest weight percentage of Al and Zn elements reached 9.38% and 60.21% respectively. It was concluded that the nano-Al2O3/ZnO/wood prepared by magnetron sputtering resulted in good Young's modulus, hardness, and photostability, achieving multi-function improvement of fast-growing wood.

With the widespread use of nitrogen (N) and phosphorus (P) fertilizers, increased N and P deposition also regulate microbial growth by altering the effectiveness of forest soil nutrients. Soil microbes residing within or among soil aggregates serve as functional components of soil ecosystems and serve as sources and reservoirs of soil nutrients capable of reflecting minor alterations in soil ecosystems. Understanding the impact of N and P additions on soil microbial community structure is vital for predicting the consequences of N and P additions on ecosystems. Therefore, soils from short-term NP (nitrogen plus phosphorus) addition experiments were utilized, and high-throughput sequencing analysis was performed to ascertain how bacterial and fungal alpha - diversity and community composition across different aggregate-size fractions (macroaggregates: 2000-250 mu m; and microaggregates: < 250 mu m) were altered in response to short-term NP addition in a broad-leaved Korean pine forest. There were four treatments: CK (no addition), N1P1 (50 kg N ha(-1) a(-1) + 50 kg P ha(-1) a(-1)), N2P2 (150 kg N ha(-1) a(-1) + 100 kg P ha(-1) a(-1)), and N3P3 (300 kg N ha(-1) a(-1) + 200 kg P ha(-1) a(-1)). In this study, all the NP-treated soil samples had lower fungal diversity than bacterial diversity and demonstrated a tendency toward a negative response to NP addition. NMDS and RDA analyses revealed that the bacterial communities in macroaggregates and microaggregates differed in composition, while the fungal composition was similar. In addition, the bacterial and fungal communities in the CK, medium-concentration NP addition treatment, and low-concentration NP addition groups were closely related and were distinct from those in the high-concentration NP addition treatment group. PERMANOVA further revealed that the community composition and structure of bacteria and fungi were significantly affected by NP addition, with a greater responsiveness observed in fungal communities. Bacterial and fungal functional groups were predicted using the PICRUSt2 and FUNGuild databases. It was observed that high-NP addition led to a significant reduction in the number of bacterial metabolism-related pathways and an increase in the relative abundance of fungal saprotrophs within the soil aggregates. Overall, our findings suggest that short-term NP addition results in a reduction in fungal alpha -diversity and alters the community composition of soil aggregates, with a comparatively lesser impact on bacteria. Higher NP concentrations exerted a more pronounced influence on both bacterial and fungal communities compared to lower concentrations. These results contribute valuable insights into the influence of NP addition on diverse microbial communities within aggregates and their associated functions. This approach enhances our comprehensive understanding of the impact of environmental changes on microbial communities within soil aggregates in temperate forest ecosystems, facilitating predictions related to microbial-mediated soil carbon, nitrogen, and phosphorus cycles.

【Objective】 The new generation of the space-based altimetry global ecosystem dynamics investigation（GEDI） system is of great significance to forest observation and management. In order to explore the performance of GEDI version 2 data（V2 data） inversion of understory topography, this study uses airborne radar data to verify the accuracy of understory topography inversion, and explores the factors affecting the accuracy.【Method】Taking the Cibola forest in the United States and the Maoer Mountain forest in China as the research objects, the performances of GEDI V2 data in coniferous forests and mixed coniferous and broad-leaved forests were verified using G-liht and Maoer Mountain high-precision airborne radar data. The effects of different beam intensities, spot times, slopes and vegetation coverage on the accuracy of terrain inversion were analyzed.【Result】The root mean square error（RMSE） of topographic inversion accuracy in the Cibola taiga area of the United States was 2.33 m, and the average absolute error（MAE） was 1.48 m. The RMSE value of the topographic inversion accuracy in the coniferous and broad-leaved mixed forest area of Maoer Mountain was 4.49 m, and the MAE value was 3.33 m. With the increase in slope and vegetation coverage, the topographic inversion accuracy of the two forest types decreased.【Conclusion】The GEDI V2 data inversion accuracy of understory topography in coniferous forests was higher than that of mixed coniferous and broad-leaved forests. Strong beams were better than coverage beams, and the accuracy was higher during the daytime in humid areas, and better at night in arid areas. The accuracy of steep areas was reduced, the terrain inversion accuracy was higher in areas with medium and low vegetation coverage, and the performances of terrain determination in areas with high vegetation coverage were decreased.

The Lambertian property of objects is one of the basic hypotheses in remote sensing research. However, the spectral radiance of natural objects is always anisotropic. On the sea surface, a large amount of sea foam is generated at the water–air interface, induced by wind speed and breaking gravity waves. Additionally, the scattering characteristic at the water–air interface significantly influences the accuracy of ocean color remote sensing and its output. The bidirectionality of the water light field is one of the sources of errors in ocean color inversion. Therefore, the knowledge of the bidirectional reflectance distribution of water surfaces is of great significance in quantitative remote sensing or for the evaluation of measurement errors in surface optical parameters. To clarify the bidirectional reflectance distribution, we used the coupled ocean–atmosphere radiative transfer (COART) model to simulate the bidirectional radiance of water bodies and explored the anisotropy of radiance at the water–air interface. The results indicate that the downward and upward irradiance just below the water surface and the water-leaving radiance changed with the sun-viewing geometry. The downward and upward radiance just below the water surface decreased as the zenith angle of the incident light increased. This effect can be mitigated using a function of the viewing angle. Additionally, the viewing azimuth angle and rough sea surface had no significant effect on the downward and upward radiance. The water-leaving radiance had an obvious bidirectional reflectance characteristic. Additionally, a backward hotspot was found in the simulated results. Then, the transmission coefficient was calculated, and the bidirectional distribution characteristic was found for flat and rough sea surfaces. This study can be used as a reference to correct bidirectional errors and to guide the spectral measurements of water and its error control for rough sea surfaces.

为验证新一代冰、云和陆地高程卫星ICESat-2/ATLAS陆地与植被高程产品(Land and Vegetation Height)ATL08数据地面高程和植被冠层高度的反演精度,以ICESat-2/ATLAS ATL08产品为研究对象,以黑龙江省帽儿山国家森林公园为研究区域,以高精度机载激光雷达数据及样地实测数据为参考,分析不同波束强度、时间、坡度及植被覆盖度下地面高程和植被冠层高度反演精度差异.研究表明,1)对地面高程来说,ATL08强波束的反演精度均方根误差(RMSE)为1.9 m,平均绝对误差(MAE)为1.1 m,弱波束的精度RMSE为4.1 m,MAE为2.0 m;2)对植被冠层高度来说,以高精度机载激光雷达数据提取冠层高度为参考,强波束的反演精度RMSE为2.7 m,MAE为2.3 m,弱波束RMSE为5.4 m,MAE为3.7 m.以样地实测树高数据为参考,夜间所有波束精度RMSE为1.8 m,MAE为1.6 m;3)随着坡度从0°增加到20°以上,地面高程反演精度的RMSE从2.3 m增大到7.7 m,植被冠高反演的RMSE从3.8 m增大到10.4 m;4)中低植被覆盖度范围(0％～80％)内,ATL08产品能较好地测量出地面高程,RMSE均小于1 m.高植被覆盖度(80％～100％)区域反演精度RMSE为3.5 m.在中等植被覆盖范围(40％～80％)内,ATL08产品能较为准确地测量出植被冠层高度,RMSE为2.6 m.植被覆盖度过高(80％～100％)或者过低(0％～40％),其精度都会下降,RMSE为4.6 m和3.3 m.ATL08数据反演地面高程和植被冠层高度的精度强波束优于弱波束,夜晚波束优于白天波束,夜晚强波束精度最高.随着坡度的增加,不同波束的地面高程和植被冠高反演误差均逐渐增大,坡度越大误差越大.地面高程反演在中低植被覆盖度情况下较为准确,植被冠层高度反演精度在中等植被覆盖度情况下达到最高.

Stand age is a significant factor when investigating forest resource management. How to obtain age data at a sub-compartment level on a large regional scale conveniently and in real time has become an urgent scientific challenge in forestry research. In this study, we established two strategies for stand-age estimation at sub-compartment and pixel levels, specifically object-based and pixel-based approaches. First, the relationship between canopy height and stand age was established based on field measurement data, which was achieved at the Mao'er Mountain Experimental Forest Farm in 2020 and 2021. The stand age was estimated using the relationship between the canopy height, the stand age, and the canopy-height map, which was generated from multi-resource remote sensing data. The results showed that the validation accuracy of the object-based estimation results of the stand age and the canopy height was better than that of the pixel-based estimation results, with a root mean squared error (RMSE) increase of 40.17% and 33.47%, respectively. Then, the estimated stand age was divided into different age classes and compared with the forest inventory data (FID). As a comparison, the object-based estimation results had better consistency with the FID in the region of the broad-leaved forests and the coniferous forests. In addition, the pixel-based estimation results had better accuracy in the mixed forest regions. This study provided a reference for estimating stand age and met the requirements for stand-age data at the pixel and sub-compartment levels for studies involving different forestry applications.

The clumping index (CI) is a commonly used vegetation dispersion parameter used to characterize the spatial distribution of the clumping or random distribution of leaves in canopy environments, as well as to determine the radiation transfer of the canopy, the photosynthesis of the foliage, and hydrological processes. However, the method of CI estimation using the measurement instrument produces uncertain values in various forest types. Therefore, it is necessary to clarify the differences in CI estimation methods using field measurements with various segment lengths in different forest types. In this study, three 100 m × 100 m plots were set, and the CI and leaf area index (LAI) values were measured. The CI estimation results were compared. The results show that the accuracy of CI estimation was affected by different forest types, different stand densities, and various segment lengths. The segment length had a significant effect on CI estimation with various methods. The CI estimation accuracy of the LX and CLX methods increased alongside a decrease in the segment length. The CI evidently offered spatial heterogeneity among the different plots. Compared with the true CI, there were significant differences in the CI estimation values with the use of various methods. Moreover, the spatial distribution of the CI estimation values using the ΩCMN method could more effectively describe the spatial heterogeneity of the CI. These results can provide a reference for CI estimation in field measurements with various segment lengths in different forest types.