Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Vegetation biomass is a key biophysical parameter for many ecological and environmental models. The accurate estimation of biomass is essential for improving the accuracy and applicability of these models. Light Detection and Ranging (LiDAR) data have been extensively used to estimate forest biomass. Recently, there has been an increasing interest in fusing LiDAR with other data sources for directly measuring or estimating vegetation characteristics. In this study, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China. A series of LiDAR and hyperspectral metrics were calculated to obtain the optimal biomass estimation model. To assess the prediction ability of the fused data, single and fused LiDAR and hyperspectral metrics were regressed against field-observed belowground biomass (BGB), aboveground biomass (AGB) and total forest biomass (TB). The partial least squares (PLS) regression method was used to reduce the multicollinearity problem associated with the input metrics. It was found that the estimation accuracy of forest biomass was affected by LiDAR plot size, and the optimal plot size in this study had a radius of 22 m. The results showed that LiDAR data alone could estimate biomass with a relative high accuracy, and hyperspectral data had lower prediction ability for forest biomass estimation than LiDAR data. The best estimation model was using a fusion of LiDAR and hyperspectral metrics (R² = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively, with p < 0.0001). Compared with LiDAR metrics alone, the fused LiDAR and hyperspectral data improved R² by 5.8%, 2.2% and 2.6%, decreased AIC value by 1.9%, 1.1% and 1.2%, and reduced RMSE by 8.6%, 7.9% and 8.3% for BGB, AGB and TB, respectively. These results demonstrated that biomass accuracies could be improved by the use of fused LiDAR and hyperspectral data, although the improvement was slight when compared with LiDAR data alone. This slight improvement could be attributed to the complementary information contained in LiDAR and hyperspectral data. In conclusion, fusion of LiDAR and other remotely sensed data has great potential for improving biomass estimation accuracy.     

南岭小坑藜蒴栲群落地上部分生物量分配规律

采用皆伐法对南岭小坑750m2天然藜蒴栲群落的生物量进行了实测,该群落有43个树种,其中藜蒴栲为优势种,获得了胸径2.0 cm以上的267株树的树干、枝、叶烘干重数据以及实测的胸径（D）、树高（H）数据。揭示了该森林群落地上部分总生物量(AGB)在森林各层次、各树种及乔木层各器官中的分配规律,并建立了该群落的生物量模型。结果表明,南岭小坑流域藜蒴栲群落地上部分总生物量是131.149 t.hm-2,其中乔木层是129.895 t.hm-2,下木层是1.563 t.hm-2,层间植物是0.267 t.hm-2,凋落物层是2.424 t.hm-2。树干、树枝、树叶生物量分别是乔木层地上部分总生物量的85.0%、10.6%和4.4%。优势树种藜蒴栲和小红栲生物量是乔木层地上部分总生物量的46.3%和9.8%,这说明在早期演替的森林群落中生物量主要集中分布在少数的几个优势种。乔木各径阶（DBH<5,5~10,10~15,15~20,20~25,≥25cm）的生物量占乔木层地上部分总生物量的百分比分别是1.0%, 13.1%,52.2%,26.4%,4.6%和2.7%。天然次生藜蒴栲群落以D为自变量的模型是Wtagb=0.116D2.384,R2=0.934,模型估算值比皆伐实测值低5.0%;以D2H为自变量的总生物量模型是Wtagb=184.274(D2H)0.881,R2=0.952,模型估算值比皆伐实测值低6.9%;这说明针对天然藜蒴栲群落,采用以D为自变量的总生物量模型更为实用。     

星载大光斑LiDAR与HJ-1A高光谱数据联合估测区域森林地上生物量

以吉林省汪清林业局经营区为研究区,利用HJ-1A/HSI高光谱数据和ICESat-GLAS波形数据,估测区域森林地上生物量。从平滑后的GLAS波形数据中提取波形长度W和地形坡度参数TS,建立GLAS森林最大树高估测模型;从GLAS波形数据中提取能量参数I(植被回波能量Ev和回波总能量E之比),建立GLAS森林郁闭度估测模型;利用GLAS估测的森林最大树高和森林郁闭度联合建立森林地上生物量模型。由于GLAS呈离散条带状分布,无法实现区域估测,因此研究将GLAS波形数据与HJ-1A/HSI高光谱数据联合,基于支持向量回归机算法实现森林地上生物量区域估测,得到研究区森林地上生物量分布图。研究结果显示,基于W和TS建立的GLAS森林最大树高估测模型的adj.R~2=0.78,RMSE=2.51m,模型验证的adj.R~2=0.85,RMSE=1.67m。地形坡度参数TS能够有效的降低地形坡度的影响;当林下植被高度为2m时,得到的基于参数I建立的GLAS森林郁闭度估测模型效果最好,模型的adj.R~2=0.64,RMSE=0.13,模型验证的adj.R~2=0.65,RMSE=0.12。利用森林最大树高和森林郁闭度建立的森林地上生物量模型的adj.R~2=0.62,RMSE=10.88 t/hm~2,模型验证的adj.R~2=0.60,RMSE=11.52 t/hm~2。基于支持向量回归机算法,利用HJ-1A/HSI和GLAS数据建立的森林地上生物量SVR模型,生成了森林地上生物量分布图,利用野外数据对得到的分布图进行验证,验证结果显示森林地上生物量估测值与实测值存在很强的线性关系(adj.R~2=0.62,RMSE=11.11 t/hm~2),能够满足林业应用的需要。因此联合ICESat-GLAS波形数据与HJ-1A高光谱数据,能够提高区域森林地上生物量的估测精度。     

毛竹林老竹水平和经营措施对新竹发育质量的影响

毛竹林是我国重要的森林资源类型,在森林固碳和林业应对气候变化中具有不可替代的重要作用。由于毛竹林的持续采伐与自我更新特性,在竹林经营过程中,新竹的发育数量和质量成为评价竹林固碳功能变化的决定性因子。利用两因素随机区组设计,排除地形因子等影响,选取施肥和采伐留养方式这两个因素,研究老竹水平和经营措施对2010年和2013年毛竹林新竹发育质量的影响。结果表明:无论2010年还是2013年,新竹平均胸径、株数和碳储量与3年生和5年生老竹的相关性均高于2年生和4年生老竹。新竹碳储量与3年生和5年生老竹碳储量呈线性相关,建立线性回归模型y=0.675x-2.2491,R~2=0.8561,而新竹碳储量与2年生和4年生老竹碳储量相关性较低,线性回归模型为y=-0.1109x+6.7287,R~2=0.0061。不同经营措施实施后,新老竹之间关系发生了很大的改变,新竹平均胸径与老竹的相关性大幅下降,新竹株数和碳储量与老竹几乎没有相关性,新竹碳储量与3年生和5年生老竹碳储量的线性回归模型为y=0.1036x+3.7539,R~2=0.0981,新竹碳储量与2年生和4年生老竹碳储量的线性回归模型为y=-0.0408x+5.9069,R~2=0.0151。不同经营措施的实施对新竹的平均胸径、株数和地上碳储量产生了很大的影响。处理A_1B_2(大量施肥中度采伐中密度留养)、A_2B_2(中等施肥中度采伐中密度留养)和A_3B_2(不施肥中度采伐中密度留养)新竹平均胸径、新竹株数和新竹碳储量都有所增加,新竹平均胸径增幅为:处理A_2B_2(8.78%)A_1B_2(2.43%)A_3B_2(2.06%),新竹株数增幅为:处理A_1B_2(81.0%)A3B2(35.4%)A2B2(15.2%),新竹地上碳储量增幅为:处理A_1B_2(90.8%)A_3B_2(35.7%)A_2B_2(49.7%),而其余处理基本都会减少,说明适度采伐留养最有利于提高毛竹林新竹的发育质量。仅仅从固碳最大化的角度出发,大量施肥中度采伐中密度留养最有利于新竹碳储量的增加,而从培养大径竹材的角度考虑,中等施肥中度采伐中密度留养能收到更好的效果。     

基于森林群落和光谱曲线特征分异的福建省森林生物量遥感反演

基于福建省Landsat8 OLI影像,利用混合像元分解模型筛选出"纯净"的植被像元,提取296个调查样地对应植被像元的红光和近红外波段的中心波长(分别CWR和CWNIR)及其对应的反射率(分别R和NIR),构建以(NIR-R)/(CWNIR-CWR)为特征指数的叶生物量回归模型。然后根据针叶林、阔叶林及针阔混交林叶生物量与干、枝、叶所组成的地上生物量的关系方程,结合福建省植被覆盖分类数据,估测了整个福建省针叶林、阔叶林、混交林的地上生物量,并绘制了福建省地上生物量分布图。结果表明:红光和近红外两个波段反射率和其中心波长所组成的斜率与叶生物量相关性显著,与针叶林、阔叶林、混交林叶生物量的精度分别达到70.55%、68.89%、51.75%,采用这种方法对福建省叶生物量和地上总生物量进行估算,并进行精度验证,其中,针叶林、阔叶林、混交林叶物量的模型误差(RMSE)分别达到29.2467 t/hm~2(R~2=66.64%)、14.0258 t/hm~2(R~2=61.13%)、10.1788 t/hm~2(R~2=55.43%),地上总生物量的模型精度分别达到49.8315 t/hm~2(R~2=54.65%)、45.1820 t/hm~2(R~2=49.01%)、41.5131 t/hm~2(R~2=38.79%),这说明,采用红光波段和近红外波段与其中心波长所组成的斜率估测森林叶生物量,进而估算其地上总生物量的方法是可行的。     

Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest

The estimation of forest aboveground biomass (AGB) is critical for quantifying carbon stocks and essential for evaluating global carbon cycle. Many previous studies have estimated forest AGB using airborne discrete-return Light Detection and Ranging (LiDAR) data, while fewer studies predicted forest AGB using airborne full-waveform LiDAR data. The objective of this work was to evaluate the utility of airborne discrete-return and full-waveform LiDAR data in estimating forest AGB. To fulfill the objective, airborne discrete-return LiDAR-derived metrics (DR-metrics), full-waveform LiDAR-derived metrics (FW-metrics) and structure parameters (combining height metrics and canopy cover) were used to estimate forest AGB. Additionally, the combined use of DR- and FW-metrics through a nonlinear way was also evaluated for AGB estimation in a coniferous forest in Dayekou, Gansu province of China. Results indicated that both height metrics derived from discrete-return and full-waveform LiDAR data were stronger predictors of forest AGB compared with other LiDAR-derived metrics. Canopy cover derived from discrete-return LiDAR data was not sensitive to forest AGB, while canopy cover estimated by full-waveform LiDAR data (CC_WF) showed moderate correlation with forest AGB. Structure parameters derived from full-waveform LiDAR data, such as H75_FW * CC_FW, were closely related to forest AGB. In contrast, structure parameters derived from discrete-return LiDAR data were not suitable for estimating forest AGB due to the less sensitivity of canopy cover CC_DR2 to forest AGB. This research also concluded that the synergistic use of DR- and FW-metrics can provide better AGB estimates in coniferous forest.     

中亚热带森林单木地上生物量的机载激光雷达估测

以雪峰山武冈林场为研究对象,利用遥感数据和地面实测样地数据,研究机载激光雷达（LiDAR）估测中亚热带森林乔木层单木地上生物量的能力.利用条件随机场和最优化方法实现LiDAR点云的单木分割,以单木尺度为对象提取的植被点云空间结构、回波特征以及地形特征等作为遥感变量,采用回归模型估测乔木层地上生物量.结果表明: 针叶林、阔叶林和针阔混交林的单木识别率分别为93%、86%和60%;多元逐步回归模型的调整决定系数分别为0.83、0.81和0.74,均方根误差分别为28.22、29.79和32.31 t·hm^-2;以冠层体积、树高百分位值、坡度和回波强度值构成的模型精度明显高于以树高为因子的传统回归模型精度.以单木为对象从LiDAR点云中提取的遥感变量有助于提高森林生物量估测精度.
     

Isometric scaling of above- and below-ground biomass at the individual and community levels in the understorey of a sub-tropical forest

Background and Aims Empirical studies and allometric partitioning (AP) theory indicate that plant above-ground biomass (M_A) scales, on average, one-to-one (isometrically) with below-ground biomass (M_R) at the level of individual trees and at the level of entire forest communities. However, the ability of the AP theory to predict the biomass allocation patterns of understorey plants has not been established because most previous empirical tests have focused on canopy tree species or very large shrubs.Methods In order to test the AP theory further, 1586 understorey sub-tropical forest plants from 30 sites in south-east China were harvested and examined. The numerical values of the scaling exponents and normalization constants (i.e. slopes and y-intercepts, respectively) of log–log linear M_A vs. M_R relationships were determined for all individual plants, for each site, across the entire data set, and for data sorted into a total of 19 sub-sets of forest types and successional stages. Similar comparisons of M_A/M_R were also made.Key Results The data revealed that the mean M_A/M_R of understorey plants was 2·44 and 1·57 across all 1586 plants and for all communities, respectively, and M_A scaled nearly isometrically with respect to M_R, with scaling exponents of 1·01 for all individual plants and 0·99 for all communities. The scaling exponents did not differ significantly among different forest types or successional stages, but the normalization constants did, and were positively correlated with M_A/M_R and negatively correlated with scaling exponents across all 1586 plants.Conclusions The results support the AP theory’s prediction that M_A scales nearly one-to-one with M_R (i.e. M_A ∝ M_R ^≈1·0) and that plant biomass partitioning for individual plants and at the community level share a strikingly similar pattern, at least for the understorey plants examined in this study. Furthermore, variation in environmental conditions appears to affect the numerical values of normalization constants, but not the scaling exponents of the M_A vs. M_R relationship. This feature of the results suggests that plant size is the primary driver of the M_A vs. M_R biomass allocation pattern for understorey plants in sub-tropical forests.     

Accelerating regrowth of temperate‐maritime forests due to environmental change

To understand how environmental changes have influenced forest productivity, stemwood biomass (B) dynamics were analyzed at 1267 permanent inventory plots, covering a combined 209 ha area of unmanaged temperate‐maritime forest in southwest British Columbia, Canada. Net stemwood production (ΔB) was derived from periodic remeasurements of B collected over a 40‐year measurement period (1959–1998) in stands ranging from 20 to 150 years old. Comparison between the integrated age response of net stemwood production, ΔB(A), and the age response of stemwood biomass, B(A), suggested a 58 ± 11% increase in ΔB between the first 40 years of the chronosequence period (1859–1898) and the measurement period. To estimate extrinsic forcing on ΔB, several different candidate models were developed to remove variation explained by intrinsic factors. All models exhibited temporal bias, with positive trends in (observed minus predicted) residual ΔB ranging between of 0.40 and 0.64% yr^?1. Applying the same methods to stemwood growth (G) indicated residual increases ranging from 0.43 and 0.67% yr^?1. Higher trend estimates corresponded with models that included site index (SI) as a predictor, which may reflect exaggeration of the age‐decline in SI tables. Choosing a model that excluded SI, suggested that ΔB increased by 0.40 ± 0.18% yr^?1, while G increased by 0.43 ± 0.12% yr^?1 over the measurement period. Residual G was significantly correlated with atmospheric carbon dioxide (CO₂), temperature (T), and climate moisture index (CMI). However, models driven with climate and CO₂, alone, could not simultaneously explain long‐term and measurement‐period trends without additional representation of indirect effects, perhaps reflecting compound interest on direct physiological responses to environmental change. Evidence of accelerating forest regrowth highlights the value of permanent inventories to detect and understand systematic changes in forest productivity caused by environmental change.     

Total Mercury Concentrations in Lake and Streams Sediments Related to Wet-Area Coverage and Geogenic Sources within Upslope Basins

Total mercury concentrations (THg) in lake and stream sediments generally decrease with wet-area coverage (A_W) per upslope basin area (A_B). This was determined by delineating the wet-area component of 12,653 basins above as many sediment-sampling locations of the Geological Survey of Canada. These locations represent four climate regions (maritime, boreal, arctic, alpine) comprising six stream and six lake study areas. The dependence of sediment THg on A_W/A_B was examined by dividing the 0 < A_W/A_B < 1 range into 40 equal segments, and obtaining the mean sediment THg value for each segment. The results were evaluated by way of regression analysis using the following equation: mean sediment THg = a (1 ? A_W/A_B)^b + c A_W/A_B, with a, b and c as area-specific coefficients. The “a” and “c” coefficients could – in part – be inferred from bedrock type, annual atmospheric Hg deposition, and mean monthly air temperatures, and mean annual precipitation. Both “a” and “c” increased with increasing atmospheric Hg deposition for lake sediments. For stream sediments, only “a” did so. The geogenic influence on the THg variations per study area was addressed through multiple regression analyses, using sediment concentrations of other heavy elements and organic matter as independent variables.     

基于机载激光雷达的小兴安岭温带森林组分生物量反演

使用小兴安岭温带森林机载遥感-地面观测同步试验获取的机载激光雷达(light detection and ranging, Lidar)点云数据和地面实测样地数据, 估测了典型森林类型的树叶、树枝、树干、地上、树根和总生物量等组分的生物量。从激光雷达数据中提取了两组变量(树冠高度变量组和植被密度变量组)作为自变量, 并采用逐步回归方法进行自变量选择。结果表明: 激光雷达数据得到的变量与森林各组分生物量有很强的相关性; 对于针叶林、阔叶林和针阔叶混交林三种不同森林类型生物量的估测结果是: 针叶林优于阔叶林, 阔叶林优于针阔叶混交林; 不区分森林类型的各组分生物量估测与地面实测值显著相关, 模型决定系数在0.6以上; 区分森林类型进行建模可以进一步提高生物量的估测精度。     

四川柏木人工林林下植被生物量与林分结构的关系

森林结构与林下植被生物量的关系是森林持续经营与森林碳计量监测的科学基础,但一直缺乏必要的研究。以四川柏木(Cupressus funebris)人工林为研究对象,揭示林下植被生物量(Wu)、灌木生物量(Ws)和草本生物量(Wh)与林分结构的关系,并试图构建区域性林下植被生物量估测的混合模型。结果表明:(1)乔、灌、草群体共12个结构因子中,灌木群体的平均基径(Ds)、盖度(Cs)、高度(Hs)、体积(Vs)与林下植被生物量关系更紧密,在林下植被生物量模型构建中更有效;(2)多模型拟合与比较表明,柏木林Ws最佳估算模型为Ws=0.0005V1.0411s(R2a=0.762,P0.001,n=40),而Wu的最佳估算模型为ln Wu=0.0158Hs+0.0111Cs-0.5358(R2a=0.695,P0.001,n=40),但对于Wh未能获得较为理想的估算模型(R2a0.410,P0.01,n=40);(3)林分密度(Du)整合进入多元线性模型提高了林下植被生物量的估测精度,ln Wu=a+b Du+c Hs+d Cs(R2a=0.721,P0.001,n=40)。研究为区域性林下生物量估测模型构建提供了新论据。     

Climate more important than soils for predicting forest biomass at the continental scale

Above-ground biomass in forests is critical to the global carbon cycle as it stores and sequesters carbon from the atmosphere. Climate change will disrupt the carbon cycle hence understanding how climate and other abiotic variables determine forest biomass at broad spatial scales is important for validating and constraining Earth System models and predicting the impacts of climate change on forest carbon stores. We examined the importance of climate and soil variables to explaining above-ground biomass distribution across the Australian continent using publicly available biomass data from 3130 mature forest sites, in 6 broad ecoregions, encompassing tropical, subtropical and temperate biomes. We used the Random Forest algorithm to test the explanatory power of 14 abiotic variables (8 climate, 6 soil) and to identify the best-performing models based on climate-only, soil-only and climate plus soil. The best performing models explained ~50% of the variation (climate-only: R² = 0.47 ± 0.04, and climate plus soils: R² = 0.49 ± 0.04). Mean temperature of the driest quarter was the most important climate variable, and bulk density was the most important soil variable. Climate variables were consistently more important than soil variables in combined models, and model predictive performance was not substantively improved by the inclusion of soil variables. This result was also achieved when the analysis was repeated at the ecoregion scale. Predicted forest above-ground biomass ranged from 18 to 1066 Mg ha⁻¹, often under-predicting measured above-ground biomass, which ranged from 7 to 1500 Mg ha⁻¹. This suggested that other non-climate, non-edaphic variables impose a substantial influence on forest above-ground biomass, particularly in the high biomass range. We conclude that climate is a strong predictor of above-ground biomass at broad spatial scales and across large environmental gradients, yet to predict forest above-ground biomass distribution under future climates, other non-climatic factors must also be identified.     

暖温带森林生态系统林下灌木生物量相对生长模型

灌木层作为暖温带森林生态系统的重要组成部分,其生物量估算的精确性及便捷性,成为森林生态系统能量流动、物质循环研究的重要环节。目前可用于暖温带森林生态系统灌木层生物量估算的相对生长模型较少。以河北雾灵山国家级自然保护区暖温带森林生态系统为研究对象,建立了该区域15种常见灌木的相对生长模型。研究发现:15种灌木全株和单一器官的最优相对生长方程均以D2H为自变量,分别以幂函数W=a(D2H)b或二项式函数W=a+b D2H+c(D2H)2为最优化回归方程。统计分析结果显示:判断系数R2值介于0.7331—0.9992之间,显著性检验各参数P0.01,满足回归模型的适用性要求。对研究区域常见灌木全株生物量(WTU)的普适性研究发现:以D2H为自变量的二项式函数回归模型WTU=0.0362+297.03D2H-127.1(D2H)2,R2=0.9434,P0.01,普遍适用于除去六道木(Zabelia biflora)和照山白(Rhododendron micranthum)之外的13种灌木植物的生物量估算。此模型对以上2种植物不适用的原因有待进一步研究。     

基于高分辨率遥感影像的北亚热带森林生物量反演

以北亚热带湖北省太子山林场为研究对象,基于高空间分辨率GF-2与SPOT-6卫星影像,提取不同窗口大小下的纹理信息与光谱信息,利用随机森林回归算法,并结合野外实测106块样地的生物量数据,建立不同影像下的太子山林场森林生物量反演模型。结果显示：（1） GF-2和SPOT-6虽然空间分辨率有差异,但是从其不同波段反射率的相关系数（0.75、0.78、0.73、0.61）发现,两种影像的波段反射率具有较高的相关性,说明两者的辐射性能相近;（2）通过分析不同纹理特征对生物量模型的影响,发现均值和对比度纹理参数对生物量反演具有很好的效果。（3）高分辨率的遥感数据在生物量反演中具有较好的表现,且GF-2生物量模型精度（R²=0.88,RMSE=27.11 Mg/hm²）与SPOT-6生物量模型的精度（R²=0.89,RMSE=23.93 Mg/hm²）相近。（4）两种影像对不同森林类型的生物量预测值不存在显著差异,都适合对不同林分类型的生物量进行预测。     

基于地统计学丰林自然保护区森林生物量估测及空间格局分析

摘要： 基于丰林保护区1997年样地调查数据,根据一元生物量估测模型,计算样地生物量,在此基础上,利用ArcGIS地统计插值方法得到整个研究区森林生物量分布,并从林分结构（林型、林龄组）和地形因子（海拔、坡度、坡向）两个方面对保护区森林生物量空间格局进行了分析。结果表明,利用地统计插值得到区域水平的森林生物量是可行的,保护区森林平均生物量水平为171.5t/hm2,总生物量为3.08Tg(1Tg=1012g);不同林分结构（林型、林龄组）有不同的生物量水平;地形因子与生物量有显著的相关性,并得到它们之间的回归方程,为保护区森林生态系统的可持续经营提供了科学依据。     

森林生物量遥感降尺度研究

森林生物量是评价全球碳氧平衡、气候变化的重要指标。目前已有基于星载激光雷达数据的全球森林生物量产品,但空间分辨率较低,不能很好地满足小区域森林调查和动态监测的需要。针对这一现状,以美国马里兰州两个森林分布状况不同的区域为研究区,基于CMS(Carbon Monitoring System)30 m分辨率和GEOCARBON 1 km分辨率森林地上生物量产品以及TM等数据源,通过升尺度模拟低分辨率生物量数据和直接使用低分辨率产品两种方式,分别尝试建立了多光谱地表参数和低分辨率森林地上生物量之间的统计关系,以此作为降尺度模型实现了森林地上生物量空间分辨率从1 km到30 m的转换,并对降尺度结果进行精度评价和误差分析。结果表明:模拟数据降尺度后的30 m分辨率森林地上生物量空间分布和CMS森林地上生物量分布状况大致相同,RMSE=59.2—65.5 Mg/hm~2,相关系数约为0.7;其降尺度结果优于GEOCARBON产品直接降尺度结果RMSE=75.3—79.9 Mg/hm~2;相较于线性模型,非线性模型能更好地呈现森林地上生物量和地表参数间的关系;总体上,降尺度生物量呈现高值区低估,低值区高估的现象。     

Allometric Models for Tree Volume and Total Aboveground Biomass in a Tropical Humid Forest in Costa Rica1

Allometric equations for the estimation of tree volume and aboveground biomass in a tropical humid forest were developed based on direct measurements of 19 individuals of seven tree species in Northern Costa Rica. The volume and the biomass of the stems represented about two‐thirds of the total volume and total aboveground biomass, respectively. The average stem volume varied between 4 and 11 Mg/tree and the average total aboveground biomass ranged from 4 to 10 mg/tree. The mean specific gravity of the sampled trees was 0.62 ± 0.06 (g/cm³). The average biomass expansion factor was 1.6 ± 0.2. The best‐fit equations for stem and total volume were of logarithmic form, with diameter at breast height (R²= 0.66 ? 0.81) as an independent variable. The best‐fit equations for total aboveground biomass that were based on combinations of diameter at breast height, and total and commercial height as independent variables had R² values between 0.77 and 0.87. Models recommended for estimating total aboveground biomass are based on diameter at breast height, because the simplicity of these models is advantageous. This variable is easy to measure accurately in the field and is the most common variable recorded in forest inventories. Two widely used models in literature tend to underestimate aboveground biomass in large trees. In contrast, the models developed in this study accurately estimate the total aboveground biomass in these trees.     

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world's most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR‐derived measures of three‐dimensional canopy structure and subcanopy topography using classification‐tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDAR‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the LiDAR‐based forest type classification (including native‐ and exotic‐dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR‐derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR‐derived variables of vegetation structure were better predictors of forest successional status (i.e., mid‐secondary, late‐secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.     

Mitochondrial Genetics. Xii. an Oligomycin-Resistant Mutant Localized at a New Mitochondrial Locus in SACCHAROMYCES CEREVISIAE

Three antibiotic-resistance mutations were isolated from strain FL496–2B: two are independent Mendelian genes, one conferring both oligomycin and venturicidin resistance (oli^R₄₉₆) and the other conferring cycloheximide resistance (cyh^R₄₉₆). The third is a mitochondrial mutation, O^R₉, and confers a low level of oligomycin resistance to cells (in vivo) but not to the extracted mitochondrial ATPase (in vitro). This mutation is located on the mitochondrial DNA at a new locus [OLI4] linked to [OLI2] and independent from [OLI1] and [OLI3] and from the other mitochondrial loci.

All three mutations (O ^R₉, oli^R₄₉₆, cyh^R₄₉₆ ) were found without any selection, in the same prototrophic haploid strain, which contained unknown resistances to antibiotics.

Some physiological, genetical and biochemical properties of the mitochondrial mutation are described.