森林生态学(四):森林生态系统的生产力(2)

三、森林生态系统的生产力各种森林生态系统的生产力由于所处环境条件、年龄阶段及经营集约程度等不同,存在很大差别。由大范围来看,森林生态系统中以热带雨林的净第一性生产力和生物量为最高,随着热量的减少而渐次减少,以北方针叶林为最低。根据日本、欧美对大量森林生态系统的生产力的调查,亦可得出类似的结论(表5),森林生态系统的净第一性生产力明显地受热量     

基于森林资源清查资料的落叶松林生物量和净生长量估算模式

 丰富的森林资源清查资料是了解各类森林材积准确信息的重要途径,如果能将这些资源用于估算森林生物量和生产力的动态变化,不仅对于科学地指导森林的经营管理,而且对于全球变化的研究,特别是区域尺度的生产力模型验证,都具有重要意义。根据我国落叶松(Larix)林生物量和材积的实际调查资料,探讨了基于森林资源清查资料(森林材积V和林龄A)估算森林生物量和生产力的方法,指出无论是人工林还是天然林,落叶松林的生物量与其蓄积量、生产力与其年均净生物生产量(B/A)和年均净蓄积生产量(V/A)均呈双曲线关系,但落叶松林的生产力与其生物量(B)关系不明显,并分别建立了人工和天然落叶松林的相关模型;所建模型克服了将森林生物量与其蓄积量之比作为常数的不足,并考虑了林龄对于森林生产力的影响。     

粤西南亚热带森林演替过程中的生物量与净第一性生产力动态

以粤西黑石顶自然保护区为对象,探讨了南亚热带森林群落演替系列上3个主要演替阶段的代表类型：针叶林(马尾松群落)、针阔混交林(马尾松+吊皮椎+木荷+枫香群落)、南亚热带常绿阔叶林(粘木+小叶胭脂+光叶红豆+黄果厚壳桂群落)的生物量和净第一性生产力及其分配规律。结果表明,针叶林生物量为246．697t·hm^-2,净第一性生产力为14．715t·hm^-2·yr^-1;针阔混交林生物量为287．367t·hm^-2,净第一性生产力为17．179t·hm^-2·yr^-1;常绿阔叶林生物量为357．976t·hm^-2,净第一性生产力为18．730t·hm^-2·yr^-1,可见黑石顶自然保护区南亚热带3种森林群落的发展阶段已比较接近,即针叶林、针阔混交林较为成熟,常绿阔叶林相对年轻,在不受或低度外界干扰的情况下,随着森林群落的正向演替,其生物量和净第一性生产力均呈增加趋势。     

利用CASA模型估算我国植被净第一性生产力

基于地理信息系统和卫星遥感应用技术,利用CASA模型估算了我国1997年植被净第一性生产力及其分布。结果表明：1997年我国植被净第一性生产力为1．95PgC,约是世界陆地植被年净第一性生产力的4．0％;我国植被净第一性生产力的主要分布趋势是从东南沿海向西北逐渐减小,其中海南岛南部、云南西南部、青藏高原东南部的热带雨林和季雨林地区植被年净第一性生产力最大,达900gC.m^2.a^-1以上,而西部塔克拉玛干沙漠地区植被年净第一性生产力最小,不足10gC.m^-2.a^-1。     

鹤山南亚热带草坡生态系统的生物量和生产力研究

以能量利用效率研究为中心,系统研究并分析了鹤山南亚热带草坡多年的光合作用与总第一性生产力、生物量与生物量增量、气候生产力模型和能量利用效率等能量学特征。草坡的总生物量为１１．３０ｔ·（ｈｍ）－２·ａ－１,其生物量增量为１．３９８ｔ·（ｈｍ）－２·ａ－１;草坡的总第一性生产力为４５．５４ｔ·（ｈｍ）－２·ａ－１,净第一性生产力为９．１０８ｔ·（ｈｍ）－２·ａ－１,用于净光合作用耗热１０５．５ＭＪ·ｍ－２·ａ－１,净光合耗热中又仅有２１．１ＭＪ·ｍ－２·ａ－１,用于净第一性生产力,净第一性生产力中又仅有３．２４ＭＪ·ｍ－２·ａ－１用于生物量增量;草坡生态系统的光能利用效率为０．０７％。草坡的能量利用效率是很低的     

遥感在森林地上生物量估算中的应用

生物量是地表C循环研究的重要组成部分,生物量研究有助于深入认识区域乃至全球的C平衡。森林作为地球最重要的陆地生态系统,区域乃至全球尺度的森林地上生物量估算一直是生态学研究的难点之一。在大的空间尺度上,遥感技术是估算森林地上生物量的有效手段。TM、AVHRR、SAR等数据以及多源数据的融合在森林生物量估算方面广泛应用,并取得了显著效果。运用遥感技术进行森林生物量估算时,所采用的数据源不同,分析方法也不相同,主要分析方法有:相关分析、多元回归分析、神经网络和数学模型模拟等。随着测定不同空间、时间和波谱分辨率的各种传感器的广泛使用,以及生物量遥感估算模型的进一步发展和完善,大尺度森林生物量的遥感估算研究必将向前迈进一大步。     

运用遥感估算中国陆地植被净第一性生产力

净第一性生产力 (NPP)研究方法很多 ,运用NOAA_AVHRR的可见光波段、近红外波段和热红外波段来提取和反演地面参数 ,进而准确估算陆地植被净第一性生产力 ,是一种全新的研究手段。利用遥感数据进行生物量和净第一性生产力的估算 ,主要是采用光能利用率模型 ,即通过NPP与植物吸收的光合有效辐射 (APAR)和植物将所吸收的光合有效辐射转化为有机物的转化率 (ε)的关系来实现的。用数学公式可表达为 :NPP =(FPAR×PAR)×[ε ×σT×σE×σS× (1-Ym)× (1-Yg) ]。在遥感和地理信息系统技术的支持下 ,以 1990年每旬的 8km分辨率的NOAA_AVHRR 1～ 5通道的影像为数据源 ,对中国每旬的陆地植被净第一性生产力进行估算 ,然后累加得出全年的NPP值。估算结果 :1990年中国陆地植被NPP总量为 6 .13× 10 9tC·a-1,NPP最高值为 1812 .9gC/m2 。根据计算的结果 ,对中国大陆植被NPP的分布规律进行了分析。遥感模型能够以面代点 ,比较真实地反映陆地植被NPP的时空分布状况 ,与中国植被分布的地理规律性相符 ,这是其他统计模型所无法比拟的。     

基于IBIS模型的东北森林净第一性生产力模拟

集成生物圈模型(the integrated biosphere simulator, IBIS)作为目前最复杂的基于动态植被模型的陆面生物模型之一,已经成为模拟大尺度(全球区域)的植被地理分布、净第一性生产力和碳平衡以及预测气候变化对陆地生态系统潜在影响的有效工具.应用IBIS模型对2004～2005年大小兴安岭的植被净第一性生产力(net primary productivity, NPP)进行了定量估算,模拟与研究了大小兴安岭森林生态系统植被NPP的空间分布格局以及不同植被类型的NPP季节变化特征,结果表明:大小兴安岭森林植被年均NPP值为494.7 gCm-2 · a-1,年吸收0.06Pg的大气碳.研究区年均NPP的空间分布主要受热量条件的影响,大兴安岭地区基本上呈现出由北向南增加的趋势,小兴安岭地区除单位面积年均NPP大于1.1kgCm-2 · a-1在小兴安岭北部孙吴和逊克地区分布外,基本上呈现出均匀分布的趋势.加强基础数据研究的同时如何根据中国的实际合理确定模型参数,使模型在我国典型生态系统中应用是值得进一步研究的.     

南亚热带森林群落光合作用的模拟研究

以广东黑石顶森林群落为对象,将群落分为４层,其高度分别为２１ｍ,１３ｍ,９ｍ和１．５ｍ,对每层植物种类的叶片光合作用水平及相关因子进行了测定．建立了３个以净光合速率对光合有效辐射和ＣＯ２浓度响应的数学方程式为基础的植物瞬间光合作用模型．不同模型对群落各层的模拟效果不同,模型中的参数反映了群落各层植物对光合有效辐射的不同利用效率,即有第一层＜第二层＜第三层＜第四层．并建立了黑石顶南亚热带群落光合作用模型,利用该群落光合作用模型计算了黑石顶南亚热带常绿阔叶林森林群落的净第一性生产力,每年群落光合作用固定的ＣＯ２为６１．７５ｔ·ｈｍ－２,换算为干物质则为每年３７．０４ｔ·ｈｍ－２．群落净第一性生产力中,群落第一层占８２．０９％,第二层占１０．７７％,第三层占６．２８％,第四层占０．０５％．一年中以６～９这４个月的净第一性生产力最高,而１２、１、２等３个月的净第一性生产力最低     

利用遥感技术实现作物模拟模型区域应用的研究进展

作物模拟模型从单点发展到区域应用时,模型中一些宏观资料的获取和参数的区域化方面出现困难,利用遥感技术将实现作物模拟模型的区域应用．文中综述了近年来遥感反演作物模型所需的地表生物物理参数的方法、利用遥感信息直接获取生物量的途径和遥感信息与作物模拟模型之间时空匹配问题等方面的研究概况,重点介绍了利用遥感技术实现作物模拟模型区域应用的3种解决方案(强迫型、调控型和验证型)及其研究进展,并讨论了目前存在的问题和今后研究的方向．     

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。     

Mapping the biomass of Bornean tropical rain forest from remotely sensed data

The biomass and biomass dynamics of forests are major uncertainties in our understanding of tropical environments. Remote sensing is often the only practical means of acquiring information on forest biomass but has not always been used successfully. Here the conventional approaches to the estimation of forest biomass from remotely sensed data were evaluated relative to techniques based on the application of artificial neural networks. Together these approaches were used to estimate and map the biomass of tropical forests in north‐eastern Borneo from Landsat TM data. The neural networks were found to be particularly suited to the application. A basic multi‐layer perceptron network, for example, provided estimates of biomass that were strongly correlated with those measured in the field (r = 0.80). Moreover, these estimates were more strongly correlated with biomass than those derived from 230 conventional vegetation indices, including the widely used normalized difference vegetation index (NDVI).     

Amazon Forest Structure from IKONOS Satellite Data and the Automated Characterization of Forest Canopy Properties

We developed an automated tree crown analysis algorithm using 1-m panchromatic IKONOS satellite images to examine forest canopy structure in the Brazilian Amazon. The algorithm was calibrated on the landscape level with tree geometry and forest stand data at the Fazenda Cauaxi (3.75° S, 48.37° W) in the eastern Amazon, and then compared with forest stand data at Tapajos National Forest (3.08° S, 54.94° W) in the central Amazon. The average remotely sensed crown width (mean ± SE) was 12.7 ± 0.1 m (range: 2.0–34.0 m) and frequency of trees was 76.6 trees/ha at Cauaxi. At Tapajos, remotely sensed crown width was 13.1 ± 0.1 m (range: 2.0–38.0 m) and frequency of trees was 76.4 trees/ha. At both Cauaxi and Tapajos, the remotely sensed average crown widths were within 3 percent of the crown widths derived from field measurements, although crown distributions showed significant differences between field-measured and automated methods. We used the remote sensing algorithm to estimate crown dimensions and forest structural properties in 51 forest stands (1 km²) throughout the Brazilian Amazon. The estimated crown widths, tree diameters (dbh), and stem frequencies differed widely among sites, while estimated biomass was similar among most sites. Sources of observed errors included an inability to detect understory crowns and to separate adjacent, intermingled crowns. Nonetheless, our technique can serve to provide information about structural characteristics of large areas of unsurveyed forest throughout Amazonia.     

Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large‐scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.     

Estimation of Mediterranean forest transpiration and photosynthesis through the use of an ecosystem simulation model driven by remotely sensed data

Aim This paper investigates the use of an ecosystem simulation model, FOREST‐BGC, to estimate the main ecophysiological processes (transpiration and photosynthesis) of Mediterranean coastal forest areas using remotely sensed data. Location Model testing was carried out at two protected forest sites in central Italy, one of which was covered by Turkey oak (Circeo National Park) and the other by holm‐oak (Castelporziano Estate). Methods At both sites, transpiration and photosynthesis measurements were collected in the field during the growing seasons over a four‐year period (1999 and 2001 for the Turkey oak; 1997, 1999 and 2000 for the holm‐oak). Calibration of the model was obtained through combining information derived from ground measurements and remotely sensed data. In particular, remote sensing estimates of the Leaf Area Index derived from 1 × 1‐km NOAA AVHRR Normalized Difference Vegetation Index data were used to improve the adaptation of the model to local forest conditions. Results The results indicated different strategies regarding water use efficiency, ‘water spending’ for Turkey oak and ‘water saving’ for holm‐oak. The water use efficiency for the holm‐oak was consistently higher than that for the Turkey oak and the relationship between VPD and WUE for the holm‐oak showed a higher coefficient of determination (R² = 0.9238). Comparisons made between the field measurements of transpiration and photosynthesis and the model estimates showed that the integration procedure used for the deciduous oak forest was effective, but that there is a need for further studies regarding the sclerophyllous evergreen forest. In particular, for Turkey oak the simulations of transpiration yielded very good results, with errors lower than 0.3 mm H₂O/day, while the simulation accuracy for photosynthesis was lower. In the case of holm‐oak, transpiration was markedly overestimated for all days considered, while the simulations of photosynthesis were very accurate. Main conclusions Overall, the approach offers interesting operational possibilities for the monitoring of Mediterranean forest ecosystems, particularly in view of the availability of new satellite sensors with a higher spatial and temporal resolution, which have been launched in recent years.     

遥感反演植被理化参数的光谱和空间尺度效应

植被理化参数是生态系统中碳和养分等物质循环与能量交换的重要指标,利用遥感技术反演是获取区域及全球植被理化参数的重要手段,但光谱和空间尺度效应的存在,限制了源自不同遥感传感器植被理化参数产品的统一应用。阐述了遥感反演植被理化参数光谱尺度效应的概念及其产生原因,主要从光谱波段位置和波段宽度两方面对国内外相关研究进行了介绍和评述。同时,从遥感反演植被理化参数的空间尺度效应产生原因、空间异质性描述方法和空间尺度转换方法等方面对其国内外研究现状进行了归纳和评述。最后,总结了遥感反演植被理化参数光谱和空间尺度效应研究的不足之处和发展趋势,并指出光谱和空间耦合效应的研究将是一大趋势,而在生态学等领域形成的尺度效应研究的理论和方法也值得借鉴参考。     

地形校正对森林生物量遥感估测的影响

基于常用的4种地形校正模型（Cosine模型、C模型、C+SCS模型、Minnaert模型）,以IDL语言为二次开发平台,对黑龙江省帽儿山地区2007年7月21日TM图像进行地形校正,从视觉差异、图像的定量统计特征两方面评价了4种地形校正模型的修正效果,并比较了地形校正后几种遥感因子与森林生物量的相关性,建立了森林生物量的遥感反演模型,分析了不同地形校正模型对森林生物量反演的影响.结果表明：由于K-T变换采用线性变换方式,地形校正后遥感数据与森林生物量的相关性出现了较大波动,应根据地表信息调整变换参数,因此该变换方式不适合与地形校正结合使用;植被指数的信息量在地形校正后明显提高,其与森林生物量的相关性显著增强;4种地形校正模型中,Cosine校正过度,不宜采用,C模型和C+SCS模型通过引入半经验参数,较好地消除了地形效应,Minnaert模型校正后降低了森林生物量估测的误差,有效地提高了遥感反演模型的精度.     

Predicting krill swarm characteristics important for marine predators foraging off East Antarctica

Open ocean predator‐prey interactions are often difficult to interpret because of a lack of information on prey fields at scales relevant to predator behaviour. Hence, there is strong interest in identifying the biological and physical factors influencing the distribution and abundance of prey species, which may be of broad predictive use for conservation planning and evaluating effects of environmental change. This study focuses on a key Southern Ocean prey species, Antarctic krill Euphausia superba, using acoustic observations of individual swarms (aggregations) from a large‐scale survey off East Antarctica. We developed two sets of statistical models describing swarm characteristics, one set using underway survey data for the explanatory variables, and the other using their satellite remotely sensed analogues. While survey data are in situ and contemporaneous with the swarm data, remotely sensed data are all that is available for prediction and inference about prey distribution in other areas or at other times. The fitted models showed that the primary biophysical influences on krill swarm characteristics included daylight (solar elevation/radiation) and proximity to the Antarctic continental slope, but there were also complex relationships with current velocities and gradients. Overall model performance was similar regardless of whether underway or remotely sensed predictors were used. We applied the latter models to generate regional‐scale spatial predictions using a 10‐yr remotely‐sensed time series. This retrospective modelling identified areas off east Antarctica where relatively dense krill swarms were consistently predicted during austral mid‐summers, which may underpin key foraging areas for marine predators. Spatiotemporal predictions along Antarctic predator satellite tracks, from independent studies, illustrate the potential for uptake into further quantitative modelling of predator movements and foraging. The approach is widely applicable to other krill‐dependent ecosystems, and our findings are relevant to similar efforts examining biophysical linkages elsewhere in the Southern Ocean and beyond.     

The impacts and implications of an intensifying fire regime on Alaskan boreal forest composition and albedo

Climate warming and drying are modifying the fire dynamics of many boreal forests, moving them towards a regime with a higher frequency of extreme fire years characterized by large burns of high severity. Plot‐scale studies indicate that increased burn severity favors the recruitment of deciduous trees in the initial years following fire. Consequently, a set of biophysical effects of burn severity on postfire boreal successional trajectories at decadal timescales have been hypothesized. Prominent among these are a greater cover of deciduous tree species in intermediately aged stands after more severe burning, with associated implications for carbon and energy balances. Here we investigate whether the current vegetation composition of interior Alaska supports this hypothesis. A chronosequence of six decades of vegetation regrowth following fire was created using a database of burn scars, an existing forest biomass map, and maps of albedo and the deciduous fraction of vegetation that we derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. The deciduous fraction map depicted the proportion of aboveground biomass in deciduous vegetation, derived using a RandomForest algorithm trained with field data sets (n=69, 71% variance explained). Analysis of the difference Normalized Burn Ratio, a remotely sensed index commonly used as an indicator of burn severity, indicated that burn size and ignition date can provide a proxy of burn severity for historical fires. LIDAR remote sensing and a bioclimatic model of evergreen forest distribution were used to further refine the stratification of the current landscape by burn severity. Our results show that since the 1950s, more severely burned areas in interior Alaska have produced a vegetation cohort that is characterized by greater deciduous biomass. We discuss the importance of this shift in vegetation composition due to climate‐induced changes in fire severity for carbon sequestration in forest biomass and surface reflectance (albedo), among other feedbacks to climate.     

Development of a satellite-based hazard rating system for Dendroctonus frontallis (Coleoptera: Scolytidae) in the Ouachita Mountains of Arkansas

The southern pine beetle, Dendroctonus frontalis Zimmermann (Coleoptera: Scolytidae), is the most damaging forest insect pest of pines (Pinus spp.) throughout the southeastern United States. Hazard rating schemes have been developed for D. frontalis, but for these schemes to be accurate and effective, they require extensive on-site measurements of stand attributes such as host density, age, and basal area. We developed a stand hazard-rating scheme for several watersheds in the Ouachita Highlands of Arkansas based upon remotely sensed data and a geographic information system. A hazard model was developed using stand attributes (tree species, stand age and density, pine basal area, and landform information) and was used to establish baseline hazard maps for the watersheds. Landsat 7 ETM+ data were used for developing new hazard maps. Two dates of Landsat imagery were used in the analyses (August 1999 and October 1999). The highest correlations between hazard rating scores and remotely sensed variables from either of the dates included individual Landsat 7 ETM+ bands in the near- and mid-infrared regions as well as variables derived from various bands (i.e., Tasseled cap parameters, principal component parameters, and vegetation indices such as the calculated simple ratio and normalized difference vegetation index). Best subset regression analyses produced models to predict stand hazard to southern pine beetle that consisted of similar variables that resembled but were more detailed than maps produced using inverse distance weighted techniques. Although the models are specific for the study area, with modifications, they should be transferable to geographically similar areas.