基于机载激光雷达与Landsat 8 OLI数据的亚热带森林生物量估算

快速、定量、精确地估算区域森林生物量一直是森林生态功能评价以及碳储量研究的重要问题。该研究基于机载激光雷达(Li DAR)点云与Landsat 8 OLI多光谱数据,借助江苏省常熟市虞山地区55块调查样地数据,首先提取并分析了87个特征变量(53个OLI特征变量,34个LiDAR特征变量)与森林地上、地下生物量的Pearson’s相关系数以进行变量优选,然后利用多元逐步回归法建立森林生物量估算模型(OLI生物量估算模型和LiDAR生物量估算模型),并与基于两种数据建立的综合生物量估算模型的结果进行比较,讨论预测结果及其精确性。结果表明:3种模型(OLI模型、LiDAR模型和综合模型)在所有样地无区分分析时,地上和地下生物量的估算精度均达到0.4以上,基于不同森林类型(针叶林、阔叶林、混交林)分析时地上和地下生物量的估算精度均有明显提高,达到0.67及以上。利用分森林类型模型估算生物量,综合生物量估算模型精度(地上生物量:R2为0.88;地下生物量:R2为0.92)优于OLI生物量估算模型(地上生物量:R2为0.73;地下生物量:R2为0.81)和Li DAR生物量估算模型(地上生物量:R2为0.86;地下生物量:R2为0.83)。     

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。     

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.
     

联合GF-6和Sentinel-2红边波段的森林地上生物量反演

光谱反射率能反映地物差异,是森林地上生物量（Aboveground Biomass,AGB）遥感反演的理论基础。红边波段处于近红外与红光波段交界处快速变化的区域,能对植被冠层结构和叶绿素含量的微小变化做出快速反应,对植被生长状况较敏感。研究以GF-6和Sentinel-2多光谱影像作为数据源,结合野外调查AGB数据,构建落叶松和樟子松AGB线性和非线性估测模型,通过比较模型精度选择最优模型进行森林AGB反演和空间分布制图。结果表明：GF-6和Sentinel-2影像红边波段反射率与落叶松、樟子松AGB均呈显著相关（P<0.05）,红边波段对AGB估测较敏感。多变量估测模型整体估测效果优于单变量模型,所有模型中多元线性回归模型取得了最优的决定系数（落叶松R²=0.66,樟子松R²=0.65）和最低的均方根误差（落叶松RMSE=31.45 t/hm²,樟子松RMSE=54.77 t/hm²）。相比单个数据源,联合GF-6和Sentinel-2影像构建的多元线性回归模型估测效果得到了显著提升,模型RMSE对于落叶松和樟子松AGB估测分别最大降低了22.9%和11.2%。增加红边波段进行AGB估测能显著提高模型估测精度,三组数据源分别加入红边波段信息后进行建模,模型RMSE得到了显著降低。GF-6拥有800 km观测幅宽和高效的重访周期,可以快速地提供大尺度时间序列数据,在森林地上生物量反演和动态监测方面有着很大潜力。     

西藏自治区草地地上生物量遥感反演研究

草地地上生物量（Aboveground Biomass,AGB）是反映草地生态系统功能和质量的关键指标,大尺度地准确估算草地AGB对草地生态系统经营管理至关重要。研究以MODIS影像为数据源,提取反射率、植被指数和植被产品三种不同类型的特征变量,结合野外实测样地草地AGB数据,构建以多元线性逐步回归为代表的参数模型以及随机森林、支持向量机和kNN等非参数模型进行西藏自治区草地AGB估测及空间分布制图。结果表明：（1）多元线性逐步回归、随机森林、支持向量机和kNN模型在加入植被产品特征变量后,RMSE分别降低了15.8%、13.5%、4.1%和17.3%,表明植被产品作为建模变量用于草地AGB估测可有效提高模型精度;（2）三种组合变量构建的草地AGB估测模型中,反射率、植被指数、植被产品组合构建的模型效果最佳,其中kNN模型估测精度最高,R²达到0.60,RMSE和MAE分别为0.43、0.34 t/hm²;（3）草地AGB空间分布呈现出西北地区较低、中部地区较高且分布形态较破碎和东部地区较高的变化特征;（4）利用MODIS植被产品结合kNN模型的预测值与草地实测的AGB空间分布趋势基本一致。综上,MODIS植被产品结合kNN模型可作为大尺度区域草地AGB遥感估测的有效参考。     

滨海湿地植被地上生物量遥感反演

盐沼植被生物量是滨海湿地生态系统碳循环研究的重要参数,是湿地生态系统健康评价、资源可持续利用的关键指标,开展盐沼植被地上生物量监测方法研究具有重要意义。目前,遥感技术在湿地生物量监测领域已经得到广泛应用,但反演方法仍以统计模型为主,模型构建需要实测数据支撑,时空拓展性不强。选择江苏盐城丹顶鹤保护区为研究区,基于冠层辐射(PROSAIL)传输模型,通过局部和全局敏感性分析,对模型参数本地化,构建了互花米草地上生物量半经验反演模型,应用于Landsat 8 OLI遥感影像,获得了互花米草地上生物量的时空分布。研究结果表明,利用PROSAIL模型模拟互花米草冠层反射率,叶面积指数(LAI)、叶片干物质含量(Cm)、叶倾角分布参数(LIDF)、等效水厚度(Cw)、叶绿素含量(Cab)、叶片结构参数(N)为高敏感性参数,类胡萝卜素含量(Car)、土壤参数(P_soil)为低敏感性参数;利用不同时刻的遥感影像反演了地上生物量,遥感反演结果与实测数据对比,拟合度R²为0.83,均方根误差(RMSE)为0.43kg/m²,平均相对误差(MRE)为15.7%,精度较高,模型具有较好的时空普适性。研究发展了盐沼植被地上生物量遥感反演方法,解决了以往过于依赖现场实测数据构建反演模型的局限性,该方法可以为研究滨海湿地生态系统碳循环以及准确估算其碳汇潜力提供技术支持。     

基于HJ1B和ALOS/PALSAR数据的森林地上生物量遥感估算

森林地上生物量的精确估算能够减小碳储量估算的不确定性。为了探寻一种有效地提高森林生物量估算精度的方法,探讨了基于遥感物理模型和经验统计模型估算山地森林地上生物量的方法。首先,基于Li-Strahler几何光学模型和多元前向模式(MFM)进行模型模拟,结合查找表算法(LUT)从多光谱图像HJ1B估算贺兰山研究区的森林地上生物量。其次,采用统计方法建立了2种回归模型:(1)多光谱图像HJ1B进行混合像元分解(SMA),并与雷达图像ALOS/PALSAR进行图像融合建立生物量回归模型;(2)雷达图像ALOS/PALSAR后向散射系数和实测生物量建立了生物量回归模型。用实测数据对3种算法估算结果进行精度验证。研究结果表明:采用几何光学模型和MFM算法估算的森林地上生物量精度最好(决定系数R2=0.61,均方根误差RMSE=8.33 t/hm2,P0.001),其估算地上生物量与实测值一致性较好,估算生物量精度略优于SMA估算的精度(R2=0.60,RMSE=9.417 t/hm2);ALOS/PALSAR多元回归估算的精度最差(R2=0.39,RMSE=14.89 t/hm2)。由此可见,采用几何光学模型和混合像元分解SMA适合估算森林地上生物量,利用这2种方法进行森林地上生物量遥感监测研究具有一定的应用潜力。     

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。     

2004-2013年山东省森林碳储量及其碳汇经济价值

森林作为陆地生态系统的主体,其林分碳储量及其碳汇经济价值的估算是全球碳循环研究的热点和重要内容。基于2004-2008年和2009-2013年山东省森林资源清查数据以及实测样地数据改进的生物量蓄积量转换参数,利用生物量转换因子连续函数法,估算2004-2013年山东省森林碳储量及其碳汇经济价值动态。研究结果表明,2004-2013年山东省森林面积、碳储量和碳密度分别从2004-2008年的156.12×10⁴hm²、34.75Tg C和22.26Mg C/hm²增加到2009-2013年161.44×10⁴hm²、43.98Tg C和27.24Mg C/hm²。人工林是森林面积、碳储量和碳密度增加的主要贡献者,人工林和天然林对森林生物量碳汇的贡献分别为97.3%和2.7%。两次森林清查期间,杨树和硬阔软阔类森林的碳储量之和分别占全省总量的70.2%和69.6%,杨树的碳储量和碳密度增加最为显著。各龄组森林碳储量由大到小依次为：幼龄林 > 中龄林 > 成熟林 > 近熟林 > 过熟林。森林碳汇经济价值从2004-2008年的243.37亿元增长到2009-2013年的253.42亿元,年均增长2.01亿元,杨树的碳汇经济价值占全省所有森林类型的60%,赤松单位面积碳汇经济价值最强为2.08万元/ha。     

普洱季风常绿阔叶林乔木物种多样性高分辨率遥感估测

季风常绿阔叶林是我国南亚热带典型的地带性植被,也是云南省普洱地区重要森林类型。季风常绿阔叶林乔木物种多样性遥感估测对研究区域尺度生物多样性格局及其规律具有重要作用。根据光谱异质性假说和环境异质性假说,首先使用1m空间分辨率的机载高光谱数据和激光雷达数据提取了光谱多样性特征和垂直结构特征。然后利用基于随机森林算法的递归特征消除方法选择对研究区森林乔木物种多样性指数具有较好解释能力的遥感特征,并对Shannon-Winner物种多样性指数进行建模、制图。研究结果表明：（1）基于机载LiDAR数据提取的垂直结构特征和机载高光谱数据提取的光谱多样性特征均对研究区森林乔木物种多样性具有较好的解释能力,随机森林模型估测结果分别为R²=0.48,RMSE=0.46和R²=0.5,RMSE=0.45;两种数据源融合可以进一步提高遥感数据的森林乔木物种多样性估测精度,随机森林估测模型R²和RMSE分别为0.69和0.37。（2）机载激光雷达数据对研究区针阔混交林乔木物种多样性的估测能力优于机载高光谱数据。（3）机器学习方法有助于从高维遥感数据特征中选择适合于森林乔木物种多样性建模的少量特征。该研究在云南普洱开展对季风常绿阔叶林的遥感估测研究,可为森林生物多样性调查提供补充手段,有助于森林生物多样性大尺度、长期动态监测。     

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.     

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.     

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.     

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。