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。     

基于森林资源清查资料的生物量估算模式及其发展趋势

基于森林资源清查资料的森林生物量估算是在景观、区域甚至全球尺度上评估森林碳收支的重要手段。且在陆地生态系统碳循环和全球变化研究中起着十分重要的作用．对3种常见的基于森林资源清查资料估算生物量的方法及其不足进行较为系统概述的基础上,指出了其未来的研究方向：1)综合考虑森林生物学因素与非生物学因素对森林生物量的影响,特别是蓄积量和林龄,以及气候因子在估算生物量中的作用;2)明确森林总生物量与活立木生物量的关系;3)建立基于森林资源清查资料的遥感驱动生物量估算模型,为森林生物量的准确估算提供方法和依据．     

上海城市森林叶生物量遥感监测

区域尺度城市森林叶生物量的估测对了解植物长势、碳同化过程和森林生态系统具有显著作用。本研究基于2011年6月—2012年6月样地实测叶生物量数据以及同期遥感信息,采用回归分析与空间分析相结合的方法,估测了上海城市森林叶生物量的空间分布,探讨了区域尺度森林叶生物量的遥感估测方法。结果表明:(1)上海城市森林叶生物量密度总体呈现出中心城区(静安区、黄浦区等)高,郊区县(松江区、金山区等)低的空间分布特征,其生物量密度分别介于4~10和1~6 t·hm-2。(2)研究区森林叶的平均生物量密度和生物量总量分别为2.55 t·hm-2和300.81×103t,郊区县与中心城区森林叶生物量分别占总量的94.16%和5.84%。在所有区县中,以林地面积最大的崇明县和浦东新区具有最高的森林叶生物量值,两者总量达到研究区总量的34.82%;以林地面积最小的静安区为最低,仅占总量的0.1%。(3)通过残差计算并引入空间分析的森林叶生物量遥感估算方法,其标准误差RMSE、平均绝对误差MAE、平均相对误差MRE较回归模型分别降低了58.46%、48.76%和48.71%,较空间插值的结果分别降低了47.74%、38%和49.24%。结合空间分析和回归分析的城市森林叶生物量研究方法为快速、便捷、客观、高效的区域生物量遥感监测提供了可能。     

基于遥感和空间分析的上海城市森林生物量分布特征

城市森林发挥着改善和维护城市生态环境质量的作用,研究城市森林生物量和分布特点对其生态系统服务评价和林分经营均具有重要意义。该文根据上海城市森林的种植分布和经营状况利用2011年6月~(–2)012年6月样地实测森林生物量数据和同期Landsat ETM+遥感图像,在基于逐步回归分析建立森林生物量反演模型的基础上,引入回归残差及空间分析,研究了城市森林及其主要优势树种樟(Cinnamomum camphora)林分的生物量分布特征,探讨了区域尺度森林生物量的遥感估测方法。结果表明:(1)上海城市森林生物量密度总体呈现中心城区(静安区、黄浦区等)较高,生物量密度集中在35–70 t·hm~(–2)之间,郊区(嘉定区、青浦区等)空间分布状况相对较低,生物量密度介于15–50 t·hm~(–2)之间的变化特征。上海优势树种樟林分生物量密度范围为20–110 t·hm~(–2);空间上呈现出东北部较高、西南部较低的变化特征。(2)上海城市森林及樟林分的生物量总量分别为3.57 Tg和1.33 Tg。林地面积小,具有较高森林生物量密度的上海中心城区,其森林生物量占总量的6.1%,其中林地面积最小的静安区生物量最低,仅占总量的0.11%。在所有区县中,林地面积最大的崇明县、浦东新区具有较高的森林生物量,分别占总量的20.08%和19.18%。(3)所建立的基于回归反距离插值的城市森林生物量估测模型,其标准误差、平均绝对误差、平均相对误差分别为8.39、6.86、24.22%,较回归模型分别降低了57.69%、55.43%、64.00%,较空间插值的方法分别降低了62.21%、58.50%、65.40%。残差的引入减少了由于空间变异引发的城市森林生物量遥感估测的不确定性。相比基于实测数据通过空间插值的估测,遥感为快速便捷、客观高效的森林生物量监测提供了可能,更加完善的结果和模型的优化有待引入其他信息源如高分高光谱信息或改善残差空间分析方法获得。     

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。     

利用TM数据提取粤西地区的森林生物量

通过样方调查获取森林材积 ,借助于 GPS技术为调查样方准确定位。通过研究针叶林和阔叶林材积与 Landsat TM数据各波段及 NDVI和 RVI等指数的相关性 ,筛选出估算针叶林和阔叶林材积的光谱因子。根据 TM数据 7个波段信息及其线形与非线形组合 ,应用逐步回归技术分别建立估算针叶林和阔叶林材积的最优光谱模型。进而研究了粤西及附近地区的森林生物量和森林覆盖。结果表明 :若不计少量云层及地形影响 ,粤西及附近地区的森林覆盖率约为 47.8%。西江干流以北地区的森林覆盖率明显高于西江以南 ,阔叶林主要分布在西江以北 ,西江以南主要为针叶林。粤西及附近地区的森林生物量多介于 2 3～ 45 1 t· hm- 2之间 ;在约 1 90 5 0 km2范围内 ,森林生物量共计 9.2 2× 1 0 7t左右。西江以北地区的森林生物量较高 ,西江以南的森林生物量较低。生物量 >40 0 t· hm- 2的森林主要分布在黑石顶自然保护区及附近、鼎湖山及附近、德庆东北部和广宁东部。     

Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data

 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(LiDAR)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LiDAR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)。     

黑龙江省大兴安岭森林生物量空间格局及其影响因素

基于样地实测数据和EVI指数,定量分析了黑龙江省大兴安岭森林生物量空间格局,并利用ArcGIS软件的空间分析与统计工具,分析了气候区、海拔、坡度、坡向和植被类型对森林生物量空间格局的影响.结果表明: 黑龙江省大兴安岭森林生物量为350 Tg,空间上呈聚集分布,生物量有巨大的增长空间.森林生物量密度大小顺序为:寒温带湿润区(64.02 t·hm^-2)＞中温带湿润区(60.26 t·hm^-2);各植被类型生物量密度大小顺序为:针阔混交林(65.13 t·hm^-2)＞云冷杉林(63.92 t·hm^-2)＞偃松 落叶松林(63.79 t·hm^-2)＞樟子松林(61.97 t·hm^-2)＞兴安落叶松林(61.40 t·hm^-2)＞落叶阔叶混交林(58.96 t·hm^-2).随海拔和坡度的增大,森林生物量密度先减小后增加,并且阴坡大于阳坡.大兴安岭森林生物量空间格局随气候区、植被类型和地形因子的梯度变化表现出差异性,在区域尺度上估算生物量密度时,需要充分考虑这种空间差异性.     

Carbon carry capacity and carbon sequestration potential in China based on an integrated analysis of mature forest biomass

Forests play an important role in acting as a carbon sink of terrestrial ecosystem.Although global forests have huge carbon carrying capacity(CCC)and carbon sequestration potential(CSP),there were few quantification reports on Chinese forests.We collected and compiled a forest biomass dataset of China,a total of 5841 sites,based on forest inventory and literature search results.From the dataset we extracted 338 sites with forests aged over 80 years,a threshold for defining mature forest,to establish the mature forest biomass dataset.After analyzing the spatial pattern of the carbon density of Chinese mature forests and its controlling factors,we used carbon density of mature forests as the reference level,and conservatively estimated the CCC of the forests in China by interpolation methods of Regression Kriging,Inverse Distance Weighted and Partial Thin Plate Smoothing Spline.Combining with the sixth National Forest Resources Inventory,we also estimated the forest CSP.The results revealed positive relationships between carbon density of mature forests and temperature,precipitation and stand age,and the horizontal and elevational patterns of carbon density of mature forests can be well predicted by temperature and precipitation.The total CCC and CSP of the existing forests are 19.87 and 13.86 Pg C,respectively.Subtropical forests would have more CCC and CSP than other biomes.Consequently,relying on forests to uptake carbon by decreasing disturbance on forests would be an alternative approach for mitigating greenhouse gas concentration effects besides afforestation and reforestation.     

Distribution of aboveground live biomass in the Amazon basin

The amount and spatial distribution of forest biomass in the Amazon basin is a major source of uncertainty in estimating the flux of carbon released from land‐cover and land‐use change. Direct measurements of aboveground live biomass (AGLB) are limited to small areas of forest inventory plots and site‐specific allometric equations that cannot be readily generalized for the entire basin. Furthermore, there is no spaceborne remote sensing instrument that can measure tropical forest biomass directly. To determine the spatial distribution of forest biomass of the Amazon basin, we report a method based on remote sensing metrics representing various forest structural parameters and environmental variables, and more than 500 plot measurements of forest biomass distributed over the basin. A decision tree approach was used to develop the spatial distribution of AGLB for seven distinct biomass classes of lowland old‐growth forests with more than 80% accuracy. AGLB for other vegetation types, such as the woody and herbaceous savanna and secondary forests, was directly estimated with a regression based on satellite data. Results show that AGLB is highest in Central Amazonia and in regions to the east and north, including the Guyanas. Biomass is generally above 300 Mg ha⁻¹ here except in areas of intense logging or open floodplains. In Western Amazonia, from the lowlands of Peru, Ecuador, and Colombia to the Andean mountains, biomass ranges from 150 to 300 Mg ha⁻¹. Most transitional and seasonal forests at the southern and northwestern edges of the basin have biomass ranging from 100 to 200 Mg ha⁻¹. The AGLB distribution has a significant correlation with the length of the dry season. We estimate that the total carbon in forest biomass of the Amazon basin, including the dead and belowground biomass, is 86 Pg C with ±20% uncertainty.     

Observation of Trends in Biomass Loss as a Result of Disturbance in the Conterminous U.S.: 1986–2004

The critical role of forests in the global carbon cycle is well known, but significant uncertainties remain about the specific role of disturbance, in part because of the challenge of incorporating spatial and temporal detail in the characterization of disturbance processes. In this study, we link forest inventory data to remote sensing data to derive estimates of pre- and post-disturbance biomass, and then use near-annual remote sensing observations of forest disturbance to characterize biomass loss associated with disturbance across the conterminous U.S. between 1986 and 2004. Nationally, year-to-year variability in the amount of live aboveground carbon lost as a result of disturbance ranged from a low of 61 T _g C (±16) in 1991 to a high of 84 T _g C (±33) in 2003. Eastern and western forest strata were relatively balanced in terms of their proportional contribution to national-level trends, despite eastern forests having more than twice the area of western forests. In the eastern forest stratum, annual biomass loss tracked closely with the area of disturbance, whereas in the western forest stratum, annual biomass loss showed more year-to-year variability that did not directly correspond to the area of disturbance, suggesting that the biomass density of forests affected by disturbance in the west was more spatially and temporally variable. Eastern and western forest strata exhibited somewhat opposing trends in biomass loss, potentially corresponding to the implementation of the Northwest Forest Plan in the mid 1990s that resulted in a shift of timber harvesting from public lands in the northwest to private lands in the south. Overall, these observations document modest increases in disturbance rates and associated carbon consequences over the 18-year period. These changes are likely not significant enough to weaken a growing forest carbon sink in the conterminous U.S. based largely on increased forest growth rates and biomass densities.     

Spatio-temporal changes in biomass carbon sinks in China’s forests from 1977 to 2008

Forests play a leading role in regional and global carbon (C) cycles. Detailed assessment of the temporal and spatial changes in C sinks/sources of China’s forests is critical to the estimation of the national C budget and can help to constitute sustainable forest management policies for climate change. In this study, we explored the spatio-temporal changes in forest biomass C stocks in China between 1977 and 2008, using six periods of the national forest inventory data. According to the definition of the forest inventory, China’s forest was categorized into three groups: forest stand, economic forest, and bamboo forest. We estimated forest biomass C stocks for each inventory period by using continuous biomass expansion factor (BEF) method for forest stands, and the mean biomass density method for economic and bamboo forests. As a result, China’s forests have accumulated biomass C (i.e., biomass C sink) of 1896 Tg (1 Tg=10¹² g) during the study period, with 1710, 108 and 78 Tg C in forest stands, and economic and bamboo forests, respectively. Annual forest biomass C sink was 70.2 Tg C a^?1, offsetting 7.8% of the contemporary fossil CO₂ emissions in the country. The results also showed that planted forests have functioned as a persistent C sink, sequestrating 818 Tg C and accounting for 47.8% of total C sink in forest stands, and that the old-, mid- and young-aged forests have sequestrated 930, 391 and 388 Tg C from 1977 to 2008. Our results suggest that China’s forests have a big potential as biomass C sink in the future because of its large area of planted forests with young-aged growth and low C density.     

Acclimation of Russian forests to recent changes in climate

Assessments made over the past few decades have suggested that boreal forests may act as a sink for atmospheric carbon dioxide. However, the fate of the newly accumulated carbon in the living forest biomass is not well understood, and the estimates of carbon sinks vary greatly from one assessment to another. Analysis of remote sensing data has indicated that the carbon sinks in the Russian forests are larger than what has been estimated from forest inventories. In this study, we show that over the past four decades, the allometric relationships among various plant parts have changed in the Russian forests. To this end, we employ two approaches: (1) analysis of the database, which contains 3196 sample plots; and (2) application of developed models to forest inventory data. Within the forests as a whole, when assessed at the continental scale, we detect a pronounced increase in the share of green parts (leaves and needles). However, there is a large geographical variation. The shift has been largest within the European Russia, where summer temperatures and precipitation have increased. In the Northern Taiga of Siberia, where the climate has become warmer but drier, the fraction of the green parts has decreased while the fractions of aboveground wood and roots have increased. These changes are consistent with experiments and mathematical models that predict a shift of carbon allocation to transpiring foliage with increasing temperature and lower allocation with increasing soil drought. In light of this, our results are a possible demonstration of the acclimation of trees to ongoing warming and changes in the surface water balance. Independent of the nature of the observed changes in allometric ratios, the increase in the share of green parts may have caused a misinterpretation of the satellite data and a systematic overestimation by remote sensing methods of the carbon sink for living biomass of the Russian forest.     

Increasing biomass in Amazonian forest plots

A previous study by Phillips et al. of changes in the biomass of permanent sample plots in Amazonian forests was used to infer the presence of a regional carbon sink. However, these results generated a vigorous debate about sampling and methodological issues. Therefore we present a new analysis of biomass change in old-growth Amazonian forest plots using updated inventory data. We find that across 59 sites, the above-ground dry biomass in trees that are more than 10 cm in diameter (AGB) has increased since plot establishment by 1.22 +/- 0.43 Mg per hectare per year (ha(-1) yr(-1), where 1 ha = 10(4) m2), or 0.98 +/- 0.38 Mg ha(-1) yr(-1) if individual plot values are weighted by the number of hectare years of monitoring. This significant increase is neither confounded by spatial or temporal variation in wood specific gravity, nor dependent on the allometric equation used to estimate AGB. The conclusion is also robust to uncertainty about diameter measurements for problematic trees: for 34 plots in western Amazon forests a significant increase in AGB is found even with a conservative assumption of zero growth for all trees where diameter measurements were made using optical methods and/or growth rates needed to be estimated following fieldwork. Overall, our results suggest a slightly greater rate of net stand-level change than was reported by Phillips et al. Considering the spatial and temporal scale of sampling and associated studies showing increases in forest growth and stem turnover, the results presented here suggest that the total biomass of these plots has on average increased and that there has been a regional-scale carbon sink in old-growth Amazonian forests during the previous two decades.     

The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates

The amount of carbon released to the atmosphere as a result of deforestation is determined, in part, by the amount of carbon held in the biomass of the forests converted to other uses. Uncertainty in forest biomass is responsible for much of the uncertainty in current estimates of the flux of carbon from land‐use change. In the present contribution several estimates of forest biomass are compared for the Brazilian Amazon, based on spatial interpolations of direct measurements, relationships to climatic variables, and remote sensing data. Three questions were posed: First, do the methods yield similar estimates? Second, do they yield similar spatial patterns of distribution of biomass? And, third, what factors need most attention if we are to predict more accurately the distribution of forest biomass over large areas? The answer to the first two questions is that estimates of biomass for Brazil's Amazonian forests (including dead and belowground biomass) vary by more than a factor of two, from a low of 39 PgC to a high of 93 PgC. Furthermore, the estimates disagree as to the regions of high and low biomass. The lack of agreement among estimates confirms the need for reliable determination of aboveground biomass over large areas. Potential methods include direct measurement of biomass through forest inventories with improved allometric regression equations, dynamic modelling of forest recovery following observed stand‐replacing disturbances, and estimation of aboveground biomass from airborne or satellite‐based instruments sensitive to the vertical structure plant canopies.     

Research on characteristics of biomass distribution in urban forests of Shanghai metropolis based on remote sensing and spatial analysis

Aims
Monitoring and quantifying the biomass and its distribution in urban trees and forests are crucial to understanding the role of vegetation in an urban environment. In this paper, an estimation method for biomass of urban forests was developed for the Shanghai metropolis, China, based on spatial analysis and a wide variety of data from field inventory and remote sensing.
Methods
An optimal regression model between forest biomass and auxiliary variables was established by stepwise regression analysis. The residual value of regression model was computed for each of the sites sampled and interpolated by Inverse-distance weighting (IDW) to predict residual errors of other sites not subjected to sampling. Forest biomass in the study area was estimated by combining the regression model based on remote sensing image data and residual errors of spatial distribution map. According to the distribution of plantations and management practices, a total of 93 sample plots were established between June 2011 and June 2012 in the Shanghai metropolis. To determine a suitable model, several spectral vegetation indices relating to forest biomass and structure such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and new images synthesized through band combinations such as the sum of TM2, TM3 and TM4 (denoted Band 234), and the sum of TM3, TM4 and TM5 (denoted Band 345) were used as alternative auxiliary parameters .
Important findings
The biomass density in urban forests of the Shanghai metropolis varied from 15 to 120 t·hm^-2. The higher densities of forest biomass concentrated mostly in the urban areas, e.g. in districts of Jing’an and Huangpu, mostly ranging from 35 to 70 t·hm^-2. Suburban localities such as the districts of Jiading and Qingpu had lower biomass densities at around 15 to 50 t·hm^-2. The biomass density of Cinnamomum camphora trees across the Shanghai metropolis varied between 20 and 110 t·hm^-2. The spatial biomass distribution of urban forests displayed a tendency of higher densities in northeastern areas and lower densities in southwestern areas. The total biomass was 3.57 million tons (Tg) for urban forests and 1.33 Tg for C. camphora trees. The overall forest biomass was also found to be distributed mostly in the suburban areas with a fraction of 93.9%, whereas the urban areas shared a fraction of only 6.1%. In terms of the areas, the suburban and urban forests accounted for 95.44% and 4.56%, respectively, of the total areas in the Shanghai metropolis. Among all the administrative districts, the Chongming county and the new district of Pudong had the highest and the second highest biomass, accounting for 20.1% and 19.18% of the total forest biomass, respectively. In contrast, the Jing’an district accounted for only 0.11% of the total forest biomass. The root-mean-square error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of the model for estimating urban forest biomass in this study were 8.39, 6.86 and 24.22%, respectively, decreasing by 57.69%, 55.43% and 64.00% compared to the original simple regression model and by 62.21%, 58.50%, 65.40% compared to the spatial analysis method. Our results indicated that a more efficient way to estimate urban forest biomass in the Shanghai metropolis might be achieved by combining spatial analysis with regression analysis. In fact, the estimated results based on the proposed model are also more comparable to the up-scaled forest inventory data at a city scale than the results obtained using regression analysis or spatial analysis alone.     

Lidar remote sensing of above-ground biomass in three biomes

Estimation of the amount of carbon stored in forests is a key challenge for understanding the global carbon cycle, one which remote sensing is expected to help address. However, estimation of carbon storage in moderate to high biomass forests is difficult for conventional optical and radar sensors. Lidar (light detection and ranging) instruments measure the vertical structure of forests and thus hold great promise for remotely sensing the quantity and spatial organization of forest biomass. In this study, we compare the relationships between lidar‐measured canopy structure and coincident field measurements of above‐ground biomass at sites in the temperate deciduous, temperate coniferous, and boreal coniferous biomes. A single regression for all three sites is compared with equations derived for each site individually. The single equation explains 84% of variance in above‐ground biomass (P < 0.0001) and shows no statistically significant bias in its predictions for any individual site.     

基于InVEST模型估算富阳市森林生态系统碳储量

基于森林资源遥感影像数据资料和ArcGIS10.0软件,以属于典型亚热带气候的富阳市为案例,运用InVEST-Carbon模型对其森林生态系统碳储量进行估算,可视化定量富阳市森林生态系统碳储量并明确其空间分布规律。结果表明：富阳市森林生态系统碳储量分布具有明显的区域差异性,由东向西呈现高－低－高－低的分布带规律。富阳市森林生态系统总的碳储量为26.7437×106 t,其价值量为39.9042亿元;得出富阳市各类森林类型平均碳密度的高低分布为常绿阔叶林碳密度>针阔混交林碳密度>竹林碳密度>马尾松林碳密度>杉木林碳密度,这与浙江省生态公益林各主要林型的碳密度分布规律基本一致,得到其森林生态系统总的碳密度约为180.75 t.hm-2,高于浙江省生态公益林平均碳密度和全国森林平均碳密度。与基于森林二类清查资料,由生物量与蓄积量的关系式估算出的碳储量(28.3780×106 t)相差不大,InVEST模型可适用于森林生态系统碳储量的总体估算。通过研究可以得出,InVEST模型评估结果简明直观,导入较少的数据,将量化的森林碳储量以地图的形式表现出来。 InVEST模型还可用于对未来或多种模拟场景情况下的预测估算等,可为政府、非盈利组织和公司企业等自然资源的管理提供决策信息,其多功能和模块化的设计为权衡评估得失提供了有效的工具。