基于Landsat TM数据估算雷竹林地上生物量

结合Landsat TM遥感数据和雷竹林样地调查数据,采用偏最小二乘回归法(PLS)建立雷竹林地上生物量估算模型,利用该模型估算临安市雷竹林地上部分生物量。结果表明:雷竹单株地上部分生物量与胸径及雷竹林地上部分生物量与株数之间都呈极显著相关(P<0.01);通过PLS-Bootstrap法筛选自变量能够提高模型精度;模型预测的雷竹林地上生物量均方根误差为3.45t·hm-2,满足大范围估算的精度要求;临安市雷竹林地上生物量为13～25t·hm-2,均值为19.52t·hm-2。     

森林地上生物量遥感反演研究进展

森林地上生物量是反映森林生态系统状况的关键性指标之一,对全球气候变化、以及我国实现碳达峰和碳中和目标具有重要意义。遥感技术快速发展并日益成熟,已成为大区域尺度森林地上生物量反演的主要技术手段。通过系统梳理国内外相关文献资料,从数据源和反演模型两方面对森林地上生物量遥感反演研究进展进行讨论：从数据源角度,阐述分析光学遥感数据、合成孔径雷达数据、激光雷达数据等3种数据源提供的有效信息、优势及局限;从反演模型角度,结合实际应用案例讨论分析多元回归模型、机器学习算法、机理模型等3种模型的特点及适用范围。在总结现阶段利用遥感手段反演森林地上生物量存在问题的基础上,分析探讨未来森林地上生物量遥感反演的方向和热点。     

永安火炬松造林密度试验研究

对福建省永安国有林场12年生火炬松4种不同造林密度(1333、1667、2000、2500株·hm-2)试验林进行了生长调查和树高、胸径、单株材积方差分析,结果表明:造林密度对树高生长差异不显著,对胸径、单株材积生长差异极显著,呈现随着造林密度的增大而递减的趋势。造林密度1333株·hm-2的平均胸径14.92cm、14cm以上径阶频率0.782、单株材积0.00913m3,表现最佳。造林密度2000株·hm-2的林分蓄积123.36m3·hm-2,表现最佳。     

利用混合模型方法建立全国和区域相容性立木生物量方程

开展全国森林生物量监测和评估,建立适合较大区域范围的通用性立木生物量模型将成为必然趋势,而如何保证不同尺度范围森林生物量估计值的相容性,是必须面临的一个问题。以南方马尾松(Pinus massoniana)地上生物量数据为例,通过利用混合模型方法,同时建立全国和区域性立木生物量方程,为解决不同区域尺度范围内森林生物量估计的相容性问题提供有效途径。从模型反映的结果看,相同直径林木的地上生物量估计值随起源、地域的不同存在一定程度的差异,其差异大小可以通过混合模型中的随机效应来估计。该方法可推广应用于其它通用性模型(如材积方程)的建立。     

含地域和起源因子的马尾松立木生物量与材积方程系统

【目的】建立含地域和起源因子的相容性立木生物量与材积方程系统,为准确估计森林生物量提供依据。【方法】以我国南方主要针叶树种马尾松为研究对象,基于301和104株样木的实测地上生物量、树干材积和地下生物量数据,综合利用哑变量建模方法和误差变量联立方程组方法,建立集地上生物量、树干材积和地下生物量为一体、确保与生物量转换因子和根茎比等变量相容的一元和二元方程系统,并分析立木生物量和材积估计是否受地域和起源的影响。【结果】所建立的马尾松一元和二元相容性立木生物量方程与材积方程,确定系数(R~2)均在0.92以上,其中地上生物量方程的平均预估误差在4%以内,地下生物量方程的平均预估误差在8%以内。对马尾松地上生物量和树干材积的估计,二元模型均显著优于一元模型,其F统计量远大于临界值;但对地下生物量的估计,二元模型反而不如一元模型效果好。不论是一元模型还是二元模型,地域和起源对马尾松地上生物量估计均无显著影响,地上生物量模型具有很好的通用性,同时也进一步印证了曾伟生等(2012)提出的通用性地上生物量模型M=0.3ρD~(7/3)的广泛适用性。对马尾松地下生物量的估计,不同地域的模型存在显著差异;相同直径的林木,总体1地域范围内(长江流域东南部)的地下生物量要大于总体2(长江流域中西部)。对马尾松树干材积的估计,二元模型不受地域和起源影响,但一元模型受起源影响;相同直径的林木,人工林的材积估计值大于天然林。【结论】将哑变量引入误差变量联立方程组,可同时解决多个变量之间的相容性及地上生物量和地下生物量样本单元数不相等时如何联合建模的问题,是切实可行的生物量建模方法;研究所建立的马尾松立木生物量方程、材积方程及其相容的生物量转换因子和根茎比方程,达到相关技术规定预估精度要求,可推广应用。     

10年生灰木莲人工林生长规律和生物生产力

以广西南宁市10年生灰木莲人工林为研究对象,研究其生长过程和生物生产力特征。结果表明:110年生灰木莲人工林平均胸径(去皮)、树高、单株材积分别达到13.4 cm、12.9 m、0.0909 m3;灰木莲树高和胸径生长均以前6 a最快,随后随林龄的增长而下降;材积生长在10年生时仍未达到峰值。210年生灰木莲人工林林分生物量为104.73 t·hm-2,其中乔木层、林下植被层和凋落物层生物量依次为93.54、0.24、10.95 t·hm-2,分别占89.31%、0.23%、10.46%。林分乔木层年净生产力为11.80 t·hm-2·a-1,不同器官净生产力大小顺序为树干、树叶、树根、树枝、树皮。     

六盘山林区几种土地利用方式植被活体生物量C贮量的研究

通过野外调查测定,研究了六盘山林区天然次生林(杂灌林、山杨和辽东栎林)、农田、草地和人工林(13、18和25年生华北落叶松)植被活体生物量的C贮量.结果表明,天然次生林植被地上生物量C贮量为14.93～25.92 t·hm-2,根系为6.50～7.55 t·hm-2;人工林地上为11.97～45.39 t·hm-2,根系为6.48～7.64 t·hm-2;农田和草地地上分别为0.83和1.09 t·hm-2,根系分别为0.49和1.61 t·hm-2.植被活体生物量C年均积累量,天然次生林地上为2.97～5.15 t·hm-2·a-1,根系为1.67～2.86 t·hm-2·a-1;人工林地上为5.07～6.49 t·hm-2·a-1,根系为1.90～2.10 t·hm-2·a-1;农田与草地地上分别为0.83和1.09 t·hm-2·a-1,根系分别为1.38 和1.03 t·hm-2·a-1.在生长季,草本地上部分C积累呈逐步增长趋势,最高峰在9-10月,10月后下降.细根生物量C的积累量,天然次生林在5、6、9月较高,草地在8月份较低,农田在7和9月较高,人工林在5、9和10月较高.     

联合GF-3 PolSAR数据与Landsat-8 OLI数据的森林地上生物量估测

【目的】森林是陆地生态系统的重要组成部分,精确估测森林地上生物量对森林资源的经营管理具有指示作用,对研究全球碳循环具有重要意义。为了改善单一来源遥感数据估测森林地上生物量的不足,探讨了联合高分三号(Gaofen-3,GF-3)全极化(Polarimetric synthetic aperture radar,PolSAR)数据极化分解参数和Landsat-8 OLI数据估测森林地上生物量的可行性,并针对多源遥感数据的冗余问题优化特征组合。【方法】以广西南宁市高峰林场为研究区,结合森林样地调查数据,提取GF-3 PolSAR数据的后向散射系数、极化分解参数和Landsat-8 OLI数据的光谱信息、植被指数、纹理,使用基于序列前向特征选择的K最近邻法(K-nearest neighbor based on sequence forward feature selection,KNN-SFS)估测研究区的森林地上生物量,以留一法交叉验证得到的森林地上生物量预测值和实测值之间的均方根误差(Root mean square error,RMSE)最小为原则,对比验证使用多源遥感数据和单一来源遥感数据时的估测结果,寻求估测森林地上生物量的最优特征组合,基于最优特征组合绘制研究区的森林地上生物量空间分布图。【结果】结合GF-3 PolSAR数据和Landsat-8 OLI数据估测研究区森林地上生物量的精度为RMSE=21.05 t·hm~(-2),R~2=0.75,优于仅使用GF-3 PolSAR数据估测的精度(RMSE=28.38 t·hm~(-2),R~2=0.47)和仅使用Landsat-8 OLI数据估测的精度(RMSE=29.52 t·hm~(-2),R~2=0.42)。【结论】多源数据协同反演森林地上生物量可以提高估测的精度,基于KNN-SFS方法联合GF-3 PolSAR数据与Landsat-8 OLI数据可以较好地估测森林地上生物量。     

基于Landsat8影像的汝城县森林地上生物量遥感估算研究

精确估测森林生物量是分析森林碳动态和碳循环的基础。本研究采用汝城县森林资源连续清查数据,结合Landsat 8遥感影像,分析了森林地上生物量的空间自相关和空间异质性,并选取显著相关的植被指数因子,分别构建普通最小二乘模型、空间滞后模型以及地理加权回归模型,并绘制汝城县森林地上生物量的空间分布图。结果表明:通过对森林地上生物量的空间效应分析,发现样地生物量的空间自相关和空间异质性不容忽视。与普通最小二乘回归相比,空间滞后模型和地理加权回归模型可以减少空间效应对森林地上生物量估测的影响。地理加权回归模型可以最大程度地减少过高或过低估计,估测森林地上生物量的精度最高,决定系数达到0.756,均方根误差和平均相对误差最小,分别为17.288 t·hm~(-2)和-8.542%。因此使用Landsat 8遥感影像结合地理加权回归方法在改善森林地上生物量的估测中具有巨大潜力。     

长白山林区森林生物量遥感估测模型

采用黑龙江长白山地区TM图像和143块森林资源连续清查固定样地数据及野外调查补充样地数据,选择包括各波段灰度值、不同波段灰度值之间的线性和非线性组合(包括11种植被指数)、纹理信息以及环境因子在内的75个自变量,分别采用逐步回归分析法和偏最小二乘回归法建立黑龙江长白山林区森林生物量遥感估测模型:逐步回归法采用5个自变量所建模型平均拟合精度为76.5%,均方根误差为19.12t·hm-2,样地生物量真实值与预测值相关系数为0.5434;偏最小二乘回归法采用10个自变量所建模型平均拟合精度85.8%,均方根误差9.92t·hm-2,样地生物量真实值与预测值相关系数0.8603,偏最小二乘回归法要优于逐步回归法。利用建立的偏最小二乘回归模型计算得到黑龙江长白山2007生物量等级分布图,采用29个检验样本对反演结果进行检验,计算得到29个样本的平均预测精度为83.73%。     

Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data

Being able to accurately estimate and map forest biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving the effectiveness of forest management. In this study, forest plot sample data, forest resources inventory(FRI) data, and SPOT Vegetation(SPOT-VGT) normalized difference vegetation index(NDVI) data were used to estimate total forest biomass and spatial distribution of forest biomass in northeast China(with 1 km resolution). Total forest biomass at both county and provincial scales was estimated using FRI data of 11 different forest types obtained by sampling 1156 forest plots, and newly-created volume to biomass conversion models. The biomass density at the county scale and SPOT-VGT NDVI data were used to estimate the spatial distribution of forest biomass. The results suggest that the total forest biomass was 2.4 Pg(1 Pg = 10~(15) g), with an average of 77.2 Mg ha~(-1), during the study period. Forests having greater biomass density were located in the middle mountain ranges in the study area. Human activities affected forest biomass at different elevations, slopes and aspects. The results suggest that the volume to biomass conversion models that could be developed using more plot samples and more detailed forest type classifications would be better suited for the study area and would provide more accurate biomass estimates. Use of both FRI and remote sensing data allowed the down-scaling of regional forest biomass statistics to forest cover pixels to produce a relatively fineresolution biomass map.     

Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data

Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.     

云南省森林生物量与生产力研究

森林是陆地生态系统的主体,生物量是反映森林生态系统功能的重要指标.通过利用最新的(2002年)森林资源清查数据,以生物量与蓄积量之间的关系模型为基础,对云南省的森林生物量及生产力进行了估计,为碳汇研究及森林生态系统评价提供重要依据.     

Spatial variation and prediction of forest biomass in a heterogeneous landscape

Large areas assessments of forest biomass distribution are a challenge in heterogeneous landscapes, where variations in tree growth and species composition occur over short distances. In this study, we use statistical and geospatial modeling on densely sampled forest biomass data to analyze the relative importance of ecological and physiographic variables as determinants of spatial variation of forest biomass in the environmentally heterogeneous region of the Big Sur, California. We estimated biomass in 280 forest plots (one plot per 2.85 km2) and measured an array of ecological (vegetation community type, distance to edge, amount of surrounding non-forest vegetation, soil properties, fire history) and physiographic drivers (elevation, potential soil moisture and solar radiation, proximity to the coast) of tree growth at each plot location. Our geostatistical analyses revealed that biomass distribution is spatially structured and autocorrelated up to 3.1 km. Regression tree (RT) models showed that both physiographic and ecological factors influenced biomass distribution. Across randomly selected sample densities (sample size 112 to 280), ecological effects of vegetation community type and distance to forest edge, and physiographic effects of elevation, potential soil moisture and solar radiation were the most consistent predictors of biomass. Topographic moisture index and potential solar radiation had a positive effect on biomass, indicating the importance of topographically-mediated energy and moisture on plant growth and biomass accumulation. RT model explained 35% of the variation in biomass and spatially autocorrelated variation were retained in regession residuals. Regression kriging model, developed from RT combined with kriging of regression residuals, was used to map biomass across the Big Sur. This study demonstrates how statistical and geospatial modeling can be used to discriminate the relative importance of physiographic and ecologic effects on forest biomass and develop spatial models to predict and map biomass distribution across a heterogeneous landscape.     

基于无人机影像的森林生物量估测与制图

【目的】森林生物量的精确测定,对于全球气候变化和碳循环研究具有重要的意义。【方法】以东北林业大学城市林业示范基地为研究区域,首先利用无人机平台获取整个研究区域的高分辨率无人机影像;然后在研究区域四种人工林样地中分别选取20 m×20 m的4块建模样方和4块测试样方,通过每木检尺法实测建模样方内林木的树高和胸径数据,建立H-DBH(树高-胸径)估算模型,并结合已有的DBH-SB(胸径-树干生物量)模型得到测试样方的森林生物量数据;在处理后的数字冠层高度模型(DCHM)基础上利用局部最大值法提取树高与树冠中心点位置,建立一种结合无人机影像提取树高与H-SB(树高-树干生物量)经验模型的森林生物量制图方法。【结果】不同样方的H-DBH模型R2均大于0.70,测试样方的总地上生物量平均值为6915.85 kg,总的估测精度为87%。通过ArcGIS软件结合本研究提出的方法快速得到了整个研究区域的地上生物量分布图,估测总地上生物量为4396.18 t。【结论】研究结果可为快速准确的进行森林生物量的估测提供基础数据和技术参考。     

森林生物量与生产力研究综述

森林生态系统在维护全球碳平衡中有着重要作用，其生物量和生产力是研究生态系统碳平衡的基础数据，进一步推动了森林生物量和生产力的研究。阐述了森林生物量和生产力形成的生理生态学基础，不同森林类型的生物产量、区域分布格局，森林经营对生物产量的影响、研究方法和模型等。在多尺度上，采用先进的研究手段，围绕资源环境等热点问题来研究森林生物量和生产力，实现数据共享和网络化是森林生物量和生产力研究的发展趋势。     

The effect of thinning on microbial biomass C, N and basal respiration in black pine forest soils in Mudurnu, Turkey

Reducing the canopy cover (e.g., forest thinning) is one of the most commonly employed forest silvicultural treatments. Trees are partially removed from a forest in order to manage tree competition, thus favoring the remaining and often the most valuable trees. The properties of the soil are affected by forest thinning as a result of changes in key microclimatic conditions, microbial communities and biomass, root density, nutrient budgets and organic matter turnover. The aim of this study was to determine the soil microbial biomass C, N and respiration (basal respiration) in a black pine (Pinus nigra Arn. subsp. pallasiana) forest in the Mudurnu district of Bolu Province (Western Black Sea Region, Turkey). Whereas forest thinning was found to cause increases in the soil temperature, microbial biomass C and N and organic C, it was found to decrease the soil moisture, basal respiration and metabolic quotient (qCO₂). As expected, soil organic C exhibited a strong impact on soil microbial biomass C, N and basal respiration. It was concluded that the influence of forest thinning on the microbial biomass and soil respiration was the combined result of changing microclimatic conditions and soil properties, such as forest litter, soil temperature, soil moisture, soil pH and soil organic matter.     

基于森林生物量相容性模型长白山天然林生物量估测

利用中国第四次(1997年)二类森林调查数据,借助长白山天然林森林生物量相容性模型,以汪清天然林区为例,对阔叶林、针叶林及针阔混交林等不同森林群落进行森林生物量及其分量的估测,研究区森林生物量密度及碳密度估测值分别为110.06 t/hm2和51.73 t/hm2,碳库估测值为0.0119 Gt C.阔叶林生物量占总森林生物量的59%,在该研究区占主导地位。     

Urban land-use impacts on composition and spatiotemporal variations in abundance and biomass of earthworm community

Soil fauna can sensitively respond to alterations in soil environment induced by land-use changes.However,little is known about the impact of urban land-use changes on earthworm communities.In this study,three land-use types(i.e.,forest,nursery and abandoned lands)were chosen to identify differences in diversity,abundance and biomass of earthworm community in Kunming City.Urban land-use had a pronounced difference in species composition,evenness and diversity of earthworm communities.Forest land had the highest density,biomass and diversity of the earthworm communities.Total abundance was dominated by endogeic species in nursery land(70%)and abandoned land(80%),whereas in the forest land,the earthworm community comprised epigeic,endogeic and anecic species.Temporal changes in earthworm density and biomass were also significantly affected by land-use change.Total density and biomass of earthworms in the forest and nursery lands were highest in September,but highest in the abandoned land in October.The influence of soil physicochemical properties on the earthworm density and biomass also varied with land-use types.Soil temperature significantly affected earthworm density and biomass in the three land-use types.Soil pH was positively correlated with earthworm biomass in the forest land,but negatively associated with earthworm density in the abandoned land.Soil organic matter was positively correlated only with density and biomass of earthworms in the nursery and abandoned lands.Our results suggest that the species composition,abundance and biomass of earthworm communities can be determined by the modification of soil properties associated with urban land-use type.     

国外立木生物量模型研究现状与展望

对国外立木生物量模型的研究现状进行了综述, 从样本采集、建模方法、模型评价和模型应用等方面系统总结了立木生物量模型研建的技术要点, 并从开展国家级森林生物量监测的角度提出了建立通用性立木生物量模型的研究重点。