基于Landsat TM数据的高山松林生物量研究

以香格里拉地区高山松林为对象,对香格里拉地区高山松林生物量进行了研究。生物量模型以香格里拉地区2009年Landsat-5TM遥感图像数据和2011年野外调查获得的45个样地调查数据为基础,利用遥感数据提取各波段灰度值、植被指数和地学数据共14个因子作为自变量,分别运用逐步回归分析方法和主成分分析方法提取的的主成分建立了以样地实测生物量为因变量的生物量估算的回归模型。两个模型经方差分析及相关性检验,均达到显著相关水平,相关系数R分别为0.519和0.581,可用于高山松林生物量的估测,但是估测精度都较低。其中利用主成分分析建立的模型精度高于利用逐步回归分析法建立的生物量模型。     

基于遥感信息的森林生物量、碳储量估测技术研究

利用遥感数据结合地面调查建立模型是定量评价森林生物量、碳储量的重要手段、以遥感数据及GIS易于获取的地学因子为主要信息源,应用逐步回归方法选取与森林生物量相关的7个遥感及地学因子为自变量,利用地面样地每木胸径调查数据结合生物量相对生长式获取样地生物量作为因变量,建立多元回归模型用于估测森林生物量．森林碳储量通过森林生物量结合碳转换系数获取、经方差分析检验,模型达到极显著相关水平．研究结果袁明,这一方法是获取较大尺度森林生物量、碳储量及其动态变化监测的有效方法．     

黑河地上生物量与遥感地学因子相关性模型研究

研究地上生物量(Above-ground Biomass,AGB)与遥感地学因子的相关性及模型,有利于分析影响生物量大小的因素,大致估算林分生物量。以黑龙江省黑河地区为研究区域,利用固定样地的每木检尺数据,根据东北地区主要树种生物量模型计算获得样地地上生物量;对Landsat ETM+遥感图像进行预处理,计算波段比、植被指数;分别对黑龙江省气象数据插值、验证及比较,获得最优插值的方法;利用DEM提取出坡度、坡向,之后利用Arc Toolbox值提取到点工具,分别获得样地的波段比、植被指数、平均气温、降水量、坡度、坡向等,从而进行样地地上生物量与遥感、地形、气象因子的相关性分析,并建立模型。研究结果表明:地上生物量与RVI、PC2、年平均气温、NDVI、年降水量以及(B2-B3)/(B2+B3),均在0.01水平上显著,其中与RVI的相关性最高,达到0.313,其次是第二主成分,达到0.294,最后是(B2-B3)/(B2+B3)为0.107;然后建立回归模型,模型的误差为:24.16%,对于估算黑河地区样地地上生物量、以及生物量与遥感地学因子的相关性提供了一定经验。     

基于TM影像纹理与光谱特征的红树林生物量估算

为研究红树林生物量的遥感估算方法,提取了广西和海南红树林TM遥感影像的光谱及纹理特征,结合地面调查所得126个样地生物量数据,进行多元逐步回归分析。结果表明,建立的生物量估算模型通过相关检验,可以高效、快速地进行红树林生物量的估测。     

基于BP神经网络模型思茅松天然林生物量遥感估测

以普洱市思茅松天然林为研究对象,以Landsat8 TM影像和DEM(30 m)数据为信息源,结合2006年森林资源二类调查小班数据和2012至2013年样地实测数据,在ENVI下提取14个自变量备选因子(11个遥感因子、3个地形因子),在MATLAB平台下利用BP神经网络模型建立研究区思茅松天然林生物量估测模型。结果表明,利用优选训练算法Ploak-Ribiere,隐含层节点数为9时效果最佳,得到决定系数R2=0.85,均方误差RMSE=14 t/hm2,预估精度P=74.75%。以像元为单位,分块提取思茅松对应的自变量,利用估测模型得到普洱市思茅松天然林总生物量为62 185 871.9 t,单位面积生物量为51.06 t/hm2。     

考虑地形效应的高山松地上生物量遥感估测模型构建

[目的]研究基于遥感因子与地形因子构建香格里拉市高山松地上生物量非线性混合效应估测模型,提高高山松地上生物量估测精度.[方法]以2015年和2018年Landsat 8 OLI与对应年份样地实测数据为基础,通过二元生物量模型计算出高山松地上生物量.提取植被指数、纹理等遥感因子.将地形因子按照一定等级进行划分后作为模型因...     

基于CEBERS-WFI遥感数据的森林生物量估测方法研究

以中巴卫星CEBERS-WFI遥感数据为基础,结合东北三省的地理、气象因子及森林资源连续清查固定样地信息,构建BP人工神经网络森林生物量估测模型,对我国东北三省的森林生物量进行估测,并反演了森林生物量的空间分布图像。结果表明,基于CEBERS-WFI遥感数据的BP人工神经网络应用于森林生物量估测简单实用,是一种快捷、有效的估测方法。     

基于 SVM 方法的高山松林蓄积量遥感估测研究

以香格里拉县高山松为研究对象,利用2006年香格里拉县TM遥感影像、2006年森林资源二类调查小班数据、2009年精度为30 m 的DEM数据以及2013年香格里拉县高山松实测样地数据,提取研究区内高山松林影像分布图及筛选出17个因子（13个遥感因子、3个地形因子、1个地面调查因子）作为备选自变量,在MAT-LAB下利用LIBSVM模块建立研究区高山松林蓄积量单位面积（30 m ×30 m）估测模型。结果表明,选用RBF核函数在参数范围内寻找出SVM模型的最佳参数C＝3.5809, g＝0.1、 p＝0.01,利用最佳寻优参数建立SVM非参数模型,对SVM模型进行测试得到,均方根误差MSE＝0.0087,复相关系数R＝0.51,相对误差RE＝23.4％,估测精度为76.6％。以像元为单位,分块提取高山松林对应的各像元自变量因子,利用估测模型预测得到香格里拉县高山松林总蓄积量为13318476.5 m3。     

基于不同立地质量的松树林分生物量遥感估测

基于2007年建德市森林资源二类调查数据和TM影像数据,采用蓄积量—生物量换算因子连续函数法计算松树生物量,对松树树种分立地质量等级和不分地位等级2种类型建立生物量的遥感估测模型,并进行精度检验。结果表明:(1)以TM遥感影像主成分分析中第一主成分为自变量的模型拟合效果最好,决定系数R2均在0.6以上,最高0.773。(2)利用预留独立样本对模型精度进行验证,不分地位级总体估测精度为92.51%,分立地质量等级好、中、差3种类型总体估测精度分别为97.66%、96.56%、97.32%,分不同立地质量等级建模精度明显优于统一建模的精度。研究结果为森林生物量遥感估测提供一种改进的思路,且为提高森林生物量和碳储量遥感估测精度提供一种参考方法。     

大兴安岭南段阔叶次生林生物量遥感模型研究

应用遥感技术对大兴安岭南段次生阔叶林生物量进行估测,融合2009年8月的SPOT及同期TM影像,结合DEM等资料,利用多源信息复合处理方法,在ENVI软件中计算归一化植被指数、比值植被指数,同时引入海拔、坡度、坡向与阔叶林生物量估测相关的因子,依据地面森林样地生物量实测数据,运用多元回归分析方法,建立阔叶林生物量遥感估测模型B=13220.418-(254.645S+7.218A+46.679RVI)。经过模型检验,各统计量均在合理范围之内,建立的多元回归生物量遥感估测值与实测值平均相对误差≤17.14%,模型预测结果合理精度较高,可用于赛罕乌拉国家级自然保护区阔叶林生物量预测,同时为大兴安岭南段阔叶次生林生物量的遥感估测奠定基础。     

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.     

间伐强度对云杉人工林下植物多样性的影响研究

调查了云杉人工林间伐后和未间伐的林分生长、林下植物种类、数量、盖度、高度,采用物种丰富度、Shannon-Wiener指数、Simpson指数和Pielou均匀度指数进行了多样性分析,以探讨间伐强度对云杉人工林下植物多样性的影响.结果表明：随着间伐强度的增大,保留木的生长显著优于对照区;林下植物种类、数量显著增加;不同间伐强度林下植物种类的多样性、丰富度、生物量、盖度、显著高于对照区,且随间伐强度的增强而增大,以重度间伐（＜450株/hm2）最为显著（灌木层多样性除外）.     

Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia

We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.     

广西百色细叶云南松天然林生物量研究

采用样地调查及标准木法,对广西国有雅长林场细叶云南松天然林中林（23年）乔木层、灌木层、草本层、凋落物层的生物量进行研究,结果表明,（1）23年生细叶云南松天然林生态系统总生物量为140.06 t/hm^2。（2）各层次的生物量分布为,乔木层127.75 t/hm^2,占总生物量91.21％,其中细叶云南松生物量124.01 t/hm^2,占总生物量88.54％;灌木层5.21 t/hm^2,占3.72％;草本层2.56 t/hm^2,占1.83％;枯枝落叶层4.54 t/hm^2,占3.24％。（3）乔木层生物量在各器官的分布为,干材生物量57.61 t/hm^2（46.46％）,皮9.97 t/hm^2（8.04％）,枝23.25 t/hm^2（18.75％）,叶9.26 t/hm^2（7.47％）,根23.92 t/hm^2（19.18％）。     

古交市植被类型与生物多样性分析

运用GPS辅助技术,以实地采样调查为主,对调查的样方资料进行统计分析,根据优势度原则对古交市的植被类型进行了分类。分类结果表明,古交市物种丰富,其植被类型可以分为5类,即针叶林、阔叶林、针阔混交林、灌丛、荒草丛。应用Margalef丰富度指数(R)、Shannon-Wiener指数(H')、Simpson指数(D)、群落均匀度指数(JH)和(JD)对古交市内5种植被类型的物种多样性进行研究。结果表明,乔木层中以针阔混交林的生物多样性最高,灌木层以荒草丛生物多样性最高,草本层灌丛的生物多样性最高,不同森林类型的生物多样性也不同。       

Variations in the biomass of Eucalyptus plantations at a regional scale in Southern China

We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method(BEF) versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation (70.94%) were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution (29.06%),with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.     

长江上游绵阳官司河流域低山丘陵区不同植被类型生物多样性分析

本文用多样性指数（Shannon—Wiener指数、Simpson指数）、均度指数（Pielou指数）、丰富度指数（Margalef指数、Menhinick指数）对绵阳官司河流域5种不同的人工林进行生物多样性分析,结果表明：经过植被恢复,各林分类型生物多样性都有所提高,同时水土保持效益也明显提高,土壤侵蚀量减少了42％;在5种植被类型中,乔木层中以针阔混交林的生物多样性最高,灌木层以松柏混交林和麻栎林的生物多样性最高,草本层以松柏混交林的生物多样性最高;5种植被类型中,灌木层地上部分生物量以针阔混交林最大为1849．37kg·hm^-2,马尾松纯林最小为747．37kg·hm^-2,其大小顺序为：针阔混交林〉柏木纯林〉栎类林〉松柏混交林〉马尾松纯林;草本层植物地上部分部总生物量大小顺序为：松柏混交林〉柏木纯林〉栎类林〉针阔混交林〉马尾松纯林。     

Variations in the biomass of <Emphasis Type="Italic">Eucalyptus</Emphasis> plantations at a regional scale in Southern China

We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method (BEF) versus estimates obtained from a local biomass model, based on large-scale empirical field inventory sampling data. The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method. Relative to the local model, BEF overestimated accumulative biomass by 22.12%. The predominant sources of the total deviation (70.94%) were stand-structure variables. Stand age and diameter at breast height are the major factors. Compared with biotic variables, abiotic variables had a smaller overall contribution (29.06%), with elevation and soil depth being the most important among the examined abiotic factors. Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data. To minimize deviations, stand age and elevation should be included in regional forest-biomass estimation.     

基于RS和GIS的毛乌素沙地荒漠化程度定量估测

选取内蒙古自治区毛乌素沙地东北部伊金霍洛旗为研究区域,以少量野外定位调查数据与其对应的RS和GIS信息为基础, 利用主成分分析、因子分析和多元线性回归分析方法,建立以像元为单位的植被盖度和生物量的定量估测模型.通过对数学模型的分析,得到影响植被盖度和生物量的主要指标(因子).在对荒漠化程度量化分级后,建立以植被盖度和生物量2个重要指标为影响因子的荒漠化程度定量估测模型,实现区域性荒漠化程度的定量估测.