Spatial scale and sampling resolution affect measures of gap disturbance in a lowland tropical forest: implications for understanding forest regeneration and carbon storage

Treefall gaps play an important role in tropical forest dynamics and in determining above-ground biomass (AGB). However, our understanding of gap disturbance regimes is largely based either on surveys of forest plots that are small relative to spatial variation in gap disturbance, or on satellite imagery, which cannot accurately detect small gaps. We used high-resolution light detection and ranging data from a 1500 ha forest in Panama to: (i) determine how gap disturbance parameters are influenced by study area size, and the criteria used to define gaps; and (ii) to evaluate how accurately previous ground-based canopy height sampling can determine the size and location of gaps. We found that plot-scale disturbance parameters frequently differed significantly from those measured at the landscape-level, and that canopy height thresholds used to define gaps strongly influenced the gap-size distribution, an important metric influencing AGB. Furthermore, simulated ground surveys of canopy height frequently misrepresented the true location of gaps, which may affect conclusions about how relatively small canopy gaps affect successional processes and contribute to the maintenance of diversity. Across site comparisons need to consider how gap definition, scale and spatial resolution affect characterizations of gap disturbance, and its inferred importance for carbon storage and community composition.     

黑龙江省黑河地区森林地上生物量和NPP估测及时空格局

根据黑龙江省黑河地区2005和2010年1390块固定样地数据和东北地区树种生物量异速模型,估算各样地单位面积森林地上生物量(AGB)和净初级生产力(NPP).结合该地区2005和2010年两期ETM⁺遥感图像,运用地统计克里格与协同克里格法对AGB和NPP进行插值,比较多种方差函数的拟合效果,并用最佳插值方法得出该地区AGB和NPP的分布图;通过两期分布图的对比,分析该区域AGB和NPP随时间和空间的动态变化趋势,及其与地形因子(坡向、坡度、海拔)和不同林分类型的时空变化规律.结果表明: 2005—2010年,黑河地区AGB呈现增加趋势,高于40 t·hm^-2的生物量面积明显增加;NPP有所下降,出现高NPP地区向低NPP地区转变的现象.AGB和NPP与各地形因子均有一定的相关性,其中,与海拔的相关性明显,说明AGB和NPP的分布受海拔影响显著.研究期间,AGB在各个坡向均有所提高,NPP则降低;AGB和NPP随着坡度增大、海拔增高有增加趋势;常绿针叶林AGB和NPP增长最多,针阔混交林AGB增长最少,落叶阔叶林NPP增长最少.     

Environmental and Biotic Controls over Aboveground Biomass Throughout a Tropical Rain Forest

The environmental and biotic factors affecting spatial variation in canopy three-dimensional (3-D) structure and aboveground tree biomass (AGB) are poorly understood in tropical rain forests. We combined field measurements and airborne light detection and ranging (lidar) to quantify 3-D structure and AGB across a 5,016 ha rain forest reserve on the northeastern flank of Mauna Kea volcano, Hawaii Island. We compared AGB among native stands dominated by Metrosideros polymorpha found along a 600–1800 m elevation/climate gradient, and on three substrate-age classes of 5, 20, and 65 kyr. We also analyzed how alien tree invasion, canopy species dominance and topographic relief influence AGB levels. Canopy vertical profiles derived from lidar measurements were strong predictors (r ² = 0.78) of AGB across sites and species. Mean AGB ranged from 48 to 363 Mg ha⁻¹ in native forest stands. Increasing elevation corresponded to a 53–84% decrease in AGB levels, depending upon substrate age. Holding climate constant, changes in substrate age from 5 to 65 kyr corresponded to a 23–53% decline in biomass. Invasion by Psidium cattleianum and Ficus rubiginosa trees resulted in a 19–38% decrease in AGB, with these carbon losses mediated by substrate age. In contrast, the spread of former plantation tree species Fraxinus uhdei corresponded to a 7- to 10-fold increase in biomass. The effects of topographic relief at both local and regional scales were evident in the AGB maps, with poorly drained terrain harboring 76% lower biomass than forests on well-drained relief. Our results quantify the absolute and relative importance of environmental factors controlling spatial variation in tree biomass across a rain forest landscape, and highlight the rapid changes in carbon storage incurred following biological invasion. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Author Contributions  GPA and RFH conceived of or designed the study. GPA, RFH, TAV, DEK, and TKB performed research and analyzed data. GPA, RFH, DEK, and TKB contributed new methods or models. GPA wrote the article.     

Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park,Vietnam

Forest biomass is one of the key measurement for carbon budget accounting, carbon flux monitoring, and climate change studies. Hence, it is essential to develop a credible approach to estimate forest biomass and carbon stocks. Our study applied Sentinel-2 satellite imagery combined with field-measured biomass using Random Forest (RF), a machine learning regression algorithm, to estimate forest aboveground biomass (AGB) in Yok Don National Park, Vietnam. A total of 132 spectral and texture variables were extracted from Sentinel-2 imagery (February 7, 2017) to predict AGB of the National Park using RF algorithm. It was found that a combination of 132 spectral and texture variables could predict AGB with an R² value of 0.94, RMSE of 34.5 Mgha⁻¹ and %RMSE of 18.3%. RF regression algorithm was further used to reduce the number of variables in such a way that a minimum number of selected variables can be able to estimate AGB at a satisfactory level. A combination of 11 spectral and texture variables was identified based on out-of-bag (OOB) estimation to develop an easy-to-use model for estimating AGB. On validation, the model developed with 11 variables was able to predict AGB with R² = 0.81, RMSE = 36.67 Mg ha⁻¹ and %RMSE of 19.55%. The results found in the present study demonstrated that Sentinel-2 imagery in conjunction with RF-based regression algorithm has the potential to effectively predict the spatial distribution of forest AGB with adequate accuracy.     

Assessing biotic and abiotic effects on forest productivity in three temperate forests

1. It is well understood that biotic and abiotic variables influence forest productivity. However, in regard to temperate forests, the relative contributions of the aforementioned drivers to biomass demographic processes (i.e., the growth rates of the survivors and recruits) have not received a great deal of attention. Thus, this study focused on the identification of the relative influencing effects of biotic and abiotic variables in the demographic biomass processes of temperate forests.
2. This study was conducted in the Changbai Mountain Nature Reserve, in northeastern China. Based on the observational data collected from three 5.2‐hectare forest plots, the annual above‐ground biomass (AGB) increment (productivity) of the surviving trees, recruits, and the total tree community (survivors + recruits) were estimated. Then, the changes in the forest productivity in response to biotic variables (including species diversity, structural diversity, and density variables) along with abiotic variables (including topographic and soil variables) were evaluated using linear mixed‐effect models.
3. This study determined that the biotic variables regulated the variabilities in productivity. Density variables were the most critical drivers of the annual AGB increments of the surviving trees and total tree community. Structural diversity enhanced the annual AGB increments of the recruits, but diminished the annual AGB increments of the surviving trees and the total tree community. Species diversity and abiotic variables did not have impacts on the productivity in the examined forest plots.
4. The results highlighted the important roles of forest density and structural diversity in the biomass demographic processes of temperate forests. The surviving and recruit trees were found to respond differently to the biotic variables, which suggested that the asymmetric competition had shaped the productivity dynamics in forests. Therefore, the findings emphasized the need to consider the demographic processes of forest productivity to better understand the functions of forests.

     

An integrated pan‐tropical biomass map using multiple reference datasets

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108 , 2011, 9899; Nature Climate Change, 2 , 2012, 182) into a pan‐tropical AGB map at 1‐km resolution using an independent reference dataset of field observations and locally calibrated high‐resolution biomass maps, harmonized and upscaled to 14 477 1‐km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South‐East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15–21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha^?1 vs. 21 and 28 Mg ha^?1 for the input maps). The fusion method can be applied at any scale including the policy‐relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country‐specific reference datasets.     

我国亚热带次生林乔木地上生物量估算的适宜样地面积初探

我国亚热带森林生物量估算研究常基于400~900 m²的小面积样地,但到底多大面积样地才较为适宜却鲜有探究。该文以浙江九龙山国家级自然保护区内三个1 hm²样地亚热带次生林为研究对象,利用生物量回归方程估算木本植物(胸径≥1 cm)的地上生物量,分析地上生物量的空间分布格局,并利用移动窗口法探讨三个次生林地上生物量估算的适宜样地面积。结果表明:(1)三个次生林木本植物的地上生物量分别为63.75 Mg·hm^-2(大岩前)、84.70 Mg·hm^-2(八通岭)和128.20 Mg·hm^-2(屁股窟),地上生物量集中分配在个体数量较少的大径级个体;屁股窟次生林的地上生物量空间变异程度高于大岩前和八通岭次生林。(2)利用移动窗口法确定的三个次生林木本植物地上生物量估算的适宜样地面积分别为2025 m²(大岩前)、2500 m²(八通岭)和3600 m²(屁股窟),森林地上生物量越高且空间变异程度越高,所需调查的样地面积越大。该研究结果可为我国亚热带森林地上生物量估算的样地面积设置提供证据,并为该区域森林生物量与碳储量的估算提供基础数据。     

Disentangling stand and environmental correlates of aboveground biomass in Amazonian forests

Tropical forests contain an important proportion of the carbon stored in terrestrial vegetation, but estimated aboveground biomass (AGB) in tropical forests varies two‐fold, with little consensus on the relative importance of climate, soil and forest structure in explaining spatial patterns. Here, we present analyses from a plot network designed to examine differences among contrasting forest habitats (terra firme, seasonally flooded, and white‐sand forests) that span the gradient of climate and soil conditions of the Amazon basin. We installed 0.5‐ha plots in 74 sites representing the three lowland forest habitats in both Loreto, Peru and French Guiana, and we integrated data describing climate, soil physical and chemical characteristics and stand variables, including local measures of wood specific gravity (WSG). We use a hierarchical model to separate the contributions of stand variables from climate and soil variables in explaining spatial variation in AGB. AGB differed among both habitats and regions, varying from 78 Mg ha^?1 in white‐sand forest in Peru to 605 Mg ha^?1 in terra firme clay forest of French Guiana. Stand variables including tree size and basal area, and to a lesser extent WSG, were strong predictors of spatial variation in AGB. In contrast, soil and climate variables explained little overall variation in AGB, though they did co‐vary to a limited extent with stand parameters that explained AGB. Our results suggest that positive feedbacks in forest structure and turnover control AGB in Amazonian forests, with richer soils (Peruvian terra firme and all seasonally flooded habitats) supporting smaller trees with lower wood density and moderate soils (French Guianan terra firme) supporting many larger trees with high wood density. The weak direct relationships we observed between soil and climate variables and AGB suggest that the most appropriate approaches to landscape scale modeling of AGB in the Amazon would be based on remote sensing methods to map stand structure.     

不同种源麻栎生长性状的地理变异

本研究在江西永丰、浙江开化和安徽滁州3个试验点,以32个种源的麻栎试验林为对象,分析了不同种源间林木生长变异、主要经济性状(地上单株生物量)随林龄的动态变化,并基于AMMI模型进行生长性状稳定性分析和优良种源选择。结果表明: 3个试验点麻栎不同种源间的树高、胸(地)径和地上单株生物量均具有显著差异。麻栎地上单株生物量受地点、种源、种源×地点交互作用的显著影响,其中地点对生长变异影响最大,其次为种源和种源×地点。不同地点麻栎苗期(1～3年生)和幼林期(4～11年生)优良种源的选择结果具有较大差异。根据第11年地上单株生物量分别筛选了在当地表现较好的种源,江西永丰试验点7个优良种源,高出试验点均值15.6%～57.8%;浙江开化试验点7个优良种源,高出试验点均值19.2%～45.2%;安徽滁州试验点8个优良种源,高出试验点均值24.9%～63.3%。综合生长量和稳定性表现,筛选出4个适于3个地理区域短轮伐期炭用林培育的优良种源,这些种源地上单株生物量均值为36.55 kg,稳定性参数均值为0.97。     

Forest biomass stocks and dynamics across the subtropical Andes

Forest biomass plays an important role in the global carbon cycle. Therefore, understanding the factors that control forest biomass stocks and dynamics is a key challenge in the context of global change. We analyzed data from 60 forest plots in the subtropical Andes (22–27.5° S and 300–2300 m asl) to describe patterns and identify drivers of aboveground biomass (AGB) stocks and dynamics. We found that AGB stocks remained roughly constant with elevation due to compensating changes in basal area (which increased with elevation) and plot‐mean wood specific gravity (which decreased with elevation). AGB gain and loss rates both decreased with elevation and were explained mainly by temperature and rainfall (positive effects on both AGB gains and losses). AGB gain was also correlated with forest‐use history and weakly correlated with forest structure. Mean annual temperature and rainfall showed minor effects on AGB stocks and AGB change (gains minus losses) over recent decades. Although AGB change was only weakly correlated with climate variables, increases in AGB gains and losses with increasing rainfall—together with observed increases in rainfall in the subtropical Andes—suggest that these forests may become increasingly dynamic in the future. Abstract in Spanish is available with online material     

Artificial neural networks for assessing forest fire susceptibility in Türkiye

Wildfires often threaten natural and economic resources and human lives. Wildfire susceptibility assessments have become essential for efficient disaster management and increasing resilience. In this study, we assessed the forest fire susceptibility in Istanbul Province and Thrace Region, Türkiye using a well-known machine learning technique, Artificial Neural Networks (ANN). Benefiting from freely available Earth Observation datasets such as Sentinel-2 images, Tree Cover Density from European Union (EU) European Environment Agency (EEA) Copernicus Land Monitoring Service, Shuttle Radar Topography Mission (SRTM) data, etc., and a forest inventory with ignition locations recorded over a period of eight years, we utilized a total of 16 independent and one dependent variables. The variables can be categorized as anthropogenic, topographic, vegetation, and hydrological factors. A ratio of 1:2 was preferred for the fire/non-fire location samples. The results show that the ANN exhibited high prediction performance with Area Under the Receiver Operating Characteristic Curve (AUC) value and F-1 score of 0.94 and 0.80, respectively. Based on feature importance analyses, we found that a human-related factor, proximity to forest roads, was the most predictive input variable. The ANN model trained with openly available data (i.e., without forest database) also yielded a high F-1 score, but produced maps with fewer details. Our results confirm that data-driven machine learning methods are promising for regional forest fire susceptibility assessments and can be extended further for other regions by deriving similar parameters from freely available Earth Observation datasets.     

Relationship Between Aboveground Biomass and Multiple Measures of Biodiversity in Subtropical Forest of Puerto Rico

Anthropogenic activities have accelerated the rate of global loss of biodiversity, making it more important than ever to understand the structure of biodiversity hotspots. One current focus is the relationship between species richness and aboveground biomass (AGB) in a variety of ecosystems. Nonetheless, species diversity, evenness, rarity, or dominance represent other critical attributes of biodiversity and may have associations with AGB that are markedly different than that of species richness. Using data from large trees in four environmentally similar sites in the Luquillo Experimental Forest of Puerto Rico, we determined the shape and strength of relationships between each of five measures of biodiversity (i.e., species richness, Simpson's diversity, Simpson's evenness, rarity, and dominance) and AGB. We quantified these measures of biodiversity using either proportional biomass or proportional abundance as weighting factors. Three of the four sites had a unimodal relationship between species richness and AGB, with only the most mature site evincing a positive, linear relationship. The differences between the mature site and the other sites, as well as the differences between our richness–AGB relationships and those found at other forest sites, highlight the crucial role that prior land use and severe storms have on this forest community. Although the shape and strength of relationships differed greatly among measures of biodiversity and among sites, the strongest relationships within each site were always those involving richness or evenness.     

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha^-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha^-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha^-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.     

Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region,Türkiye

Above-ground biomass (AGB) is an important component for identifying carbon stocks, monitoring the impacts of climate change, and evaluating merchantable timber. Accurate prediction of forest AGB is central to the correct interpretation of these components and to produce usable data for planners and researchers. In this study, remotely sensed time-series data derived from Landsat 8 (reflectance (R) and vegetation indices (VI)), topographic (T) and climate (C) data were used as independent variables to predict AGB of pure Calabrian pine (Pinus brutia Ten.) stands using multiple regression analysis (MLR) and support vector machines (SVM) methods. The AGB modeling was done by using independent variables individually and by combining variables, and the AGB maps of the most successful models obtained from MLR and SVM methods were produced. It was determined that the most successful variable group was the VI when the independent variables were used one by one (MLR Training R² = 0.50, SVM Training R² = 0.67). The most successful predictions in AGB modeling were obtained with combining all independent variables and using the SVM method (Training R² = 0.85, Validation R² = 0.69). In the combination of independent variables, VI and C data made the greatest contribution to the success of the AGB prediction. The ‘green leaf index’ vegetation indices had the most significant effect on the modeling AGB. In this study, T and C in addition to spectral data has increased the AGB estimation performance. It has been found that the SVM method yielded higher model accuracy than MLR method in predicting AGB. Overall, the spectral data and the SVM method can contribute to improving the accuracy of AGB estimates and provide an effective approach towards the capability for forest ecosystem monitoring.     

Damage to living trees contributes to almost half of the biomass losses in tropical forests

Accurate estimates of forest biomass stocks and fluxes are needed to quantify global carbon budgets and assess the response of forests to climate change. However, most forest inventories consider tree mortality as the only aboveground biomass (AGB) loss without accounting for losses via damage to living trees: branchfall, trunk breakage, and wood decay. Here, we use ~151,000 annual records of tree survival and structural completeness to compare AGB loss via damage to living trees to total AGB loss (mortality + damage) in seven tropical forests widely distributed across environmental conditions. We find that 42% (3.62 Mg ha⁻¹ year⁻¹; 95% confidence interval [CI] 2.36–5.25) of total AGB loss (8.72 Mg ha⁻¹ year⁻¹; CI 5.57–12.86) is due to damage to living trees. Total AGB loss was highly variable among forests, but these differences were mainly caused by site variability in damage-related AGB losses rather than by mortality-related AGB losses. We show that conventional forest inventories overestimate stand-level AGB stocks by 4% (1%–17% range across forests) because assume structurally complete trees, underestimate total AGB loss by 29% (6%–57% range across forests) due to overlooked damage-related AGB losses, and overestimate AGB loss via mortality by 22% (7%–80% range across forests) because of the assumption that trees are undamaged before dying. Our results indicate that forest carbon fluxes are higher than previously thought. Damage on living trees is an underappreciated component of the forest carbon cycle that is likely to become even more important as the frequency and severity of forest disturbances increase.     

Influences of vegetation,model, and data parameters on forest aboveground biomass assessment using an area-based approach

Although aboveground biomass (AGB) estimation using area-based approaches (ABAs) and its application to forestry have been actively researched through three decades, this technology has been little operationalized in the Central European forest sector. That means specific recommendations are needed in order to apply ABA for forest biomass modelling in this region. The present study was directed to filling such gaps while examining the effect of input ABA parameters on AGB model quality in conditions of mixed mountainous forests in Central Europe. Specific objectives were to assess whether the strength of the AGB model can be impacted by 1) canopy conditions (leaf-on and leaf-off), 2) airborne LiDAR point density (2.5, 5.0, 7.5, 10.0 points/m²), 3) field methods to estimate AGB (with regeneration components or without), and 4) machine learning methods (AdaBoost, Random decision forest, multilayer neural network, and Bayesian ridge regression). The results show that canopy conditions and airborne LiDAR point densities did not affect the strength of the AGB model, but that model's strength was affected by the vegetation regeneration component in the field biomass reference and by the machine learning method tested for modelling. AdaBoost and random decision forest were the most successful methods. To evaluate the quality of an AGB model it is recommended to combine several individual evaluation functions into the model score. The study highlights several recommendations to follow when estimating AGB from ALS using an ABA in Central European forests.     

岷江上游亚高山林区老龄林地上生物量动态变化

中国川西亚高山森林中的天然林大部分为成过熟的老龄林,对其生物量动态研究有助于了解其碳储量的动态变化规律.利用全国森林资源连续清查的27个固定样地数据,基于地上各器官生物量与树干胸径(D)和树高(h)的异速生长方程,估算了岷江上游亚高林山老龄林地上生物量密度的动态变化特征及其时空变化规律.结果表明,(1)从1988～2002年期间,老龄林地上生物量密度净增量为(27.311±15.580)Mg·hm-2,平均每年增长率为(1.930±1.091 )Mg·hm-2·a-1,平均每年枯损率为(2 271±1.424)Mg·hm-2·a-1;(2)地上生物量变化受各径级保留木生长量、枯损量及进界生长量影响,其中20～40cm径级保留木生长量与生物量净增量最大,>80cm径级生物量增量最小,40～60cm和60～80cm径级生物量在调查期间净增量出现负增长.(3)岷江上游老龄林地上生物量动态变化具有时空异质性,同一样地在不同调查间隔期或同一调查期间不同样地间生物量变化不同,不仅有增量数值大小差异,还表现为生物量增量的正负差异.     

Aboveground biomass accumulation and tree size distribution in seasonal Atlantic Forest restoration sites

Tropical forests undergoing restoration can present high biomass accumulation rates, especially in the first 20 years. However, native species reforestations often present a bias toward fast growth, low wood density, and small maximum adult size species, contrasting with most mature forest species. Since tree species adult size and wood density are key traits that influence biomass accumulation, these induce uncertainty regarding carbon uptake capabilities of restoration projects in the long term. We compared the density of individuals (DI), basal area (BA), aboveground biomass (AGB), and weighted average wood density (WDW) in 13–14-year-old restoration sites and in mature seasonal Atlantic Forest fragments. We also assessed the contribution of pioneer and non-pioneer and planted and non-planted species on these variables at restoration sites. Furthermore, we investigated the DI and WDW for saplings and seedlings, in order to foresee changes in forest structure that may result from natural recruitment of dense-wood mature species. The BA and WDW at restoration sites were similar to forest fragments, except for large trees (DBH ≥50 cm). Restoration sites recovered AGB to the level of forest fragments only for the smaller size class (DBH 5–19.9 cm). Planted pioneer and non-pioneer species accumulated the greatest AGB (93%), BA (94%), and DI (90%) at restoration sites. The DI of non-planted non-pioneer species with higher WDW increased among saplings and seedlings at restoration sites. The presence of species with a larger adult size and higher WD may indicate long-term increase in biomass accumulation at restoration sites.     

Predictive Models of Primary Tropical Forest Structure from Geomorphometric Variables Based on SRTM in the Tapajós Region,Brazilian Amazon

Surveying primary tropical forest over large regions is challenging. Indirect methods of relating terrain information or other external spatial datasets to forest biophysical parameters can provide forest structural maps at large scales but the inherent uncertainties need to be evaluated fully. The goal of the present study was to evaluate relief characteristics, measured through geomorphometric variables, as predictors of forest structural characteristics such as average tree basal area (BA) and height (H) and average percentage canopy openness (CO). Our hypothesis is that geomorphometric variables are good predictors of the structure of primary tropical forest, even in areas, with low altitude variation. The study was performed at the Tapajós National Forest, located in the Western State of Pará, Brazil. Forty-three plots were sampled. Predictive models for BA, H and CO were parameterized based on geomorphometric variables using multiple linear regression. Validation of the models with nine independent sample plots revealed a Root Mean Square Error (RMSE) of 3.73 m²/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% (21%) for CO. The coefficient of determination between observed and predicted values were r² = 0.32 for CO, r² = 0.26 for H and r² = 0.52 for BA. The models obtained were able to adequately estimate BA and CO. In summary, it can be concluded that relief variables are good predictors of vegetation structure and enable the creation of forest structure maps in primary tropical rainforest with an acceptable uncertainty.     

中国森林生态系统林冠层降雨截留特征

森林生态系统作为陆地生态系统的主体,其发达的林冠层通过调节降水量、改变降水强度等深刻影响着流域全过程水文通量及水分输出。以中国广泛开展的典型森林降雨再分配过程的年尺度监测数据为基础,揭示中国不同类型森林生态系统的降雨再分配及林冠层降雨截留特征,阐明森林生态系统林冠层截留特征与降雨、植被要素的关系。结果表明:我国不同森林生态系统年穿透雨量处于141.4-2450.0 mm之间,年穿透雨率为36.3%-92.3%。5种典型森林生态系统多年平均穿透雨量((445.3±252.9)-(1230.6±479.6) mm)占同期多年平均降雨量的(72.6±9.2)%-(77.4±8.9)%。不同森林生态系统年树干茎流量介于0-508.2 mm之间,占同期年降雨量的0-25.8%。5种典型森林生态系统树干茎流量多年平均值((9.8±17.3)-(87.8±81.6) mm)占同期多年平均降雨量的(1.4±1.9)%-(5.4±4.6)%。不同森林生态系统林冠层年降雨截留范围在25.7-812.9 mm之间,占年降雨量的4.2%-55.6%。5种典型森林生态系统多年平均林冠截留量((154.2±81.6)-(392.2±203.5) mm)占同期年平均降雨量的(18.7±7.4)%-(25.9±8.3)%。进一步分析表明,我国森林生态系统穿透雨量、树干茎流量和林冠层截留量随观测区年降雨量的增加而呈显著增大(P<0.05),年穿透雨率、年树干茎流率随年降雨量的增加呈显著线性上升趋势(P<0.05),而年林冠截留率与年降雨量呈显著的负相关关系(P<0.01),降雨量、叶面积指数是深刻影响森林生态系统林冠层降雨截留率等特征的重要因素。整体上,不同类型森林生态系统林冠截留降雨能力存在明显差异,林冠层截留率突出表现为:落叶林大于常绿林、针叶林大于阔叶林。