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。     

甘肃黑河流域上游森林地上生物量的多光谱遥感估测

[目的]以黑河流域上游祁连山森林保护区为研究区,利用133个森林样地调查数据、Landsat-5 TM影像和ASTER GDEM产品为数据源,探讨地形对该流域森林地上生物量(above-ground biomass,AGB)估测的影响,以及选择合适的遥感估测方法反演该流域的森林AGB.[方法]首先利用青海云杉特殊的生境范围和绿色植被对比值植被指数(ratio vegetation index,RVI)的灵敏程度,及不同地物对纹理特征的不同响应,制定相应的决策树分类器,将研究区的土地覆盖类型分为两大类:森林(青海云杉)-非森林,并利用133个森林样地调查数据和Google Earth 高分辨率影像的12 722个采样点对分类结果进行验证(总体分类精度达到90.39％,Kappa系数为0.81);然后运用多元线性逐步回归估测法,以及结合随机森林算法(random forest,RF)优化后的k最近邻分类法(k-nearest neighbors,k-NN)进行森林AGB的遥感估测,对比SCS+C地形校正前后青海云杉森林AGB的估测结果,同时比较2种不同估测方法的反演效果;最后利用得到的最优估测方法反演整个研究区的森林AGB,生成黑河流域上游祁连山森林保护区的森林AGB的等级分布图.[结果]SCS+C地形校正前多元线性逐步回归的估测精度为R2=0.31,RMSE =34.41 t·hm-2,地形校正后多元线性逐步回归的估测精度为R2 =0.46,RMSE =30.51 t·hm-2;而基于SCS+C地形校正后的k-NN的交叉验证精度不仅明显高于地形校正前的精度,且显著优于多元线性逐步回归的估测结果,达到R2=0.54,RMSE=26.62 t·hm-2;另外基于最优的k-NN估测模型(窗口为7×7,采用马氏距离,k=3)反演的该流域青海云杉在2009年总的森林地上生物量为8.4×107t,平均森林地上生物量为96.20 t·hm-2.[结论]在地形复杂地区,运用SCS+C模型对地形进行适当校正,能够有效地消除太阳入射角变化引起的地表反射亮度的差异,使影像能够更准确地反映地表信息,提高森林AGB的遥感估测精度;在样本有限的情况下,相对于以大数定律作为理论基础的多元线性逐步回归估测法,k-NN能够避免发生过学习现象和样本不平衡问题,更适于该研究区青海云杉的森林AGB的估测.     

川西亚高山针叶林生物量遥感估算模型研究

利用野外实测68个样地的森林生物量数据、TM影像的单波段数据、植被指数数据以及地形数据在内的18个自变量建立了川西亚高山针叶林生物量的回归估算模型。研究表明:在建立的一元线性回归、一元非线性回归和多元线性回归生物量模型中,以多元线性回归模型在森林生物量估算中有较好的精度。     

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。     

若尔盖高寒沼泽植被生物量遥感估算研究

采用若尔盖高寒沼泽2011—2012年TM5遥感数据和同期野外调查获得的32个样地湿地植被生物量数据,分析了遥感影像光谱特征、纹理特征以及地形特征与实测样点地上、地下和总生物量的相关关系并建立多元逐步回归模型,进而计算生物量,结果表明:草本沼泽植被生物量与地形因子和纹理特征的相关性不强,与遥感影像及其植被指数等自变量有显著的相关关系。若尔盖高寒沼泽的植被地上、地下、总生物量分别为367.05,3 680.36,4 048.01g/m2。随着地表水分的减少,生物量呈现先增加后减少的趋势。     

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DEM的地形辐射校正模型的研究

在地形复杂的地区,地形校正是影像预处理的一个重要步骤。它不仅能提高遥感识别与分类的精度,还是各种定量化遥感应用的前提。本文以2002年延庆县的ETM+为数据源,采用了余弦、SCS、C、SCS+C和CIVC0等五种不同的校正模型进行了对比分析研究。结果表明,5种方法均能在很大程度上消除地形阴影,更好地反映阴影区域的细节信息;从总体的光谱特性保真程度来说,余弦和SCS校正都因过度校正问题表现较差,C校正也表现一般。CIVC0校正和SCS+C校正光谱特性保持较好,但SCS+C校正也存在过度校正问题,CIVC0校正有点校正不足,但相比较CIVC0校正效果更好点。     

不同地形校正方法对黑松分布遥感提取的影响

以青岛黄岛区为研究区,利用资源三号卫星立体像对提取精细的DEM(Digital Elevation Model),使用5种地形校正模型(Teillet-回归,VECA,Cosine-C,C和SCS+C)对Quick Bird多光谱影像进行地形校正,并结合面向对象方法提取得到山区黑松的空间分布信息。结果表明:5种模型中,Quick Bird影像经VECA,SCS+C,C校正模型校正后山区阴影有较好的减弱效果,且山区黑松分布提取的精度均有所提高,其中以VECA模型的提取精度最佳,提取精度从70.25%提高到84.30%,提高了14.05%;Kappa系数从0.53提高到0.72,提高了0.19。本研究可为光学高分遥感影像在山区松树的分布提取上提供参考。     

基于Landsat5 TM遥感影像估算江山市公益林生物量

本研究基于Landsat5 TM遥感影像数据和样地调查数据,利用多元逐步回归、偏最小二乘回归和随机森林回归3种方法,建立江山市公益林生物量估算模型,分析和比较3种模型的精度结果,探究随机森林回归模型在估算生物量方面的应用,为提高估算森林生物量的精度提供参考。结果表明,多元逐步回归模型的预测精度为58.31%、均方根误差为31.02 t/hm2,偏最小二乘回归模型分别为60.84%、30.72 t/hm2,随机森林回归模型为70.02%,22.18 t/hm2。由此可得,随机森林回归模型的预测精度优于其他2种模型,随机森林算法能提高估算森林生物量的精度。     

华北落叶松人工林生物量及碳储量遥感模型研究

以华北落叶松人工林为研究对象,对赛罕乌拉生态系统定位站内华北落叶松人工林生物量及碳储量进行研究。应用Landsat TM影像,提取遥感影像各波段信息及相关的植被指数。将遥感影像的波段信息、相关的植被指数分别与野外实测的样地生物量数据进行一元回归分析,建立一元回归模型,分析比较后得出由波段信息建立的一元回归模型较合理;将提取的波段信息、植被指数分别与野外实测样地生物量数据进行相关分析,然后采用逐步回归的方法进行多元回归分析,建立森林生物量多元遥感回归模型;将选择出的一元回归模型与建立的多元回归模型进行对比分析,最后得到适用于研究区森林生物量研究的最优遥感回归模型,Y=-1617.863+573.312×SAVI+32.475×TM5-35.379×TM7,进而得到碳储量遥感模型。     

归一化地形校正模型研究进展及其对比实验

对遥感图像归一化地形校正模型的研究进行了回顾和总结。归一化模型包括二阶校正模型、地形均衡模型和坡度匹配模型3种方法。在云南省香格里拉县的TM遥感图像和DEM支持下,采用3种方法进行了校正对比实验。结果表明,3种方法中坡度匹配模型校正效果最好。对归一化地形校正模型今后可能的研究方向提出了建议。     

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.     

基于RS和GIS技术的植被景观结构分析

文章是以石林景区为例,利用TM影像、扫描地形图、野外样方地调查数据为基础,利用G IS、RS软件平台,对扫描地形图进行了基于公里格网的配准,对图像进行了投影变换和几何校正处理,并以建立掩膜层的方法提高了分类的精度。运用景观生态学原理和方法,选取分维数、斑块密度、景观多样性等指标,利用Fragstats3.3进行景观计算,对景区的景观格局进行了分析。     

Modeling forest aboveground biomass by combining spectrum, textures and topographic features

Many textural measures have been developed and used for improving land cover classification accuracy, but they rarely examined the role of textures in improving the performance of forest aboveground biomass estimations. The relationship between texture and biomass is poorly understood. In this paper, SPOT5 HRG datasets were ortho-rectified and atmospherically calibrated. Then the transform of spectral features is introduced, and the extraction of textural measures based on the Gray Level Co-occurrence Matrix is also implemented in accordance with four different directions (0°, 45°, 90° and 135°) and various moving window sizes, ranging from 3 × 3 to 51 × 51. Thus, a variety of textures were generated. Combined with derived topographic features, the forest aboveground biomass estimation models for five predominant forest types in the scenic spot of the Mausoleum of Sun Yat-Sen, Nanjing, are identified and constructed, and the estimation accuracies exhibited by these models are also validated and evaluated respectively. The results indicate that: 1) Most textures are weakly correlated with forest biomass, but minority textural measures such as ME, CR and VA play a significantly effective and critical role in estimating forest biomass; 2) The textures of coniferous forest appear preferable to those of broad-leaved forest and mixed forest in representing the spatial configurations of forests; and 3) Among the topographic features including slope, aspect and elevation, aspect has the lowest correlation with the biomass of a forest in this study. __________ Translated from Remote Sensing Information, 2006, 6: 6–9 [译自: 遥感信息]     

自然生草桃园表层土壤水分和草地地上生物量的空间分布及其关系

【目的】阐明桃树果园短期自然生草条件下土壤水分空间分布特征及其与草地地上生物量的关系。【方法】以信阳沙壤土五月鲜桃园为研究对象,在冬春季短期自然生草条件下,采用1 m×1 m网格采样法,以经典统计学和地统计学半方差函数为工具,研究表层(0~5 cm)土壤水分和草地地上生物量空间分布特征。【结果】表层(0~5 cm)土壤含水量和地上草地生物量符合正态分布,平均体积含水量为10.08%,95%置信区间为9.84%~10.32%,变异系数为16.36%,0.2 m×0.2 m草地地上生物量均值为9.17 g,变异系数为56.71%。区域田块尺度上,自然生草桃园草地地上生物量和表层(0~5 cm)土壤水分空间分布均符合指数函数模型,模型块金值/基台值分别为0.477 3和0.499 8,变程分别为7.20和2.51。即二者均具有中等程度的空间依赖性,草地地上生物量较表层(0~5 cm)土壤含水量具有更强的空间依赖性。克里格插值结果表明,表层(0~5 cm)土壤含水量和草地地上生物量空间上呈斑块状分布,且表现为距离树体越远值越大的分布特征。相关分析结果表明,地上生物量与表层(0~5 cm)土壤含水量之间极显著正相关(P<0.001)。【结论】果园短期自然生草有利于保蓄表层土壤水分,改善土壤水分条件,草地地上生物量是影响果园表层土壤水分空间分布的重要因素。     

Aboveground stand-level biomass estimation: a comparison of two methods for major forest species in northwest Spain

Context

The scaling-up approach (which requires the use of individual tree biomass equations and data) is one of the most commonly used methods for estimating stand biomass at a local scale. However, biomass prediction over large management areas requires more efficient methods.

Aims

Two methods of estimating aboveground stand biomass were developed and compared: stand biomass equations (SBE) including observed stand variables, and SBE including biomass expansion factors (BEF) and stand volume.

Methods

Two types of systems of additive equations were fitted simultaneously for components and total aboveground stand biomass, to ensure additivity. Inherent correlations among biomass components were also taken into account in the fitting process.

Results

The systems explained a high percentage of the observed variability. The SBE systems that included observed stand variables provided more accurate estimates than those that included BEF and stand volume. However, the latter were found to be more precise for stem wood and total aboveground biomass prediction.

Conclusions

Both approaches provide a direct link between forest inventory data, outputs from whole-stand growth models, and biomass estimates at stand level. Taking into account that the inventory effort is similar for both alternatives, the choice of which to use will depend on the data available and on the relative importance of the biomass components for the end-users.     

Topographic and biotic regulation of aboveground carbon storage in subtropical broad-leaved forests of Taiwan

There is a growing need to understand, and ultimately manage, carbon storage by forest ecosystems. Broad-leaved evergreen forests of Taiwan provide an outstanding opportunity to examine factors that regulate ecosystem carbon storage. We utilized data from three Taiwan Forest Dynamics Plots (FS, LHC, and PTY) in which every tree is identified, measured, tagged and mapped, to examine factors regulating carbon storage as estimated from aboveground biomass. Allometric equations were used to estimate the aboveground biomass of each tree, and a model building procedure was used to examine relationships between plot-level aboveground biomass (AGB; Mg/ha) and a suite of topographic and biotic factors. We found that our study sites have AGB values comparable to some of the most carbon dense forests in the world. Across all three sites, maximum biomass was contained in the taxonomic families Fagaceae, Lauraceae and Theaceae. In the FS site, we identified slope convexity (P = 0.03) and elevation (P < 0.001) as topographic predictors of AGB and found that maximum AGB was found in topographically flat areas. In FS, stem density (P < 0.001) was a significant biotic predictor of AGB and the maxima occurred at intermediate densities. In LHC, we found that convexity (P < 0.001) and slope (P < 0.001) were significantly related to AGB which was maximized along a topographic ridge in the plot. Species richness (P < 0.001) was a significant biotic predictor of AGB in LHC, and the relationship indicated slightly higher AGB at higher levels of species richness. The only significant factor related to AGB in PTY was species richness (P = 0.02). Further work is needed to seek a mechanistic understanding of topographic factors and species richness as drivers of carbon storage in forests.     

Developing individual tree-based models for estimating aboveground biomass of five key coniferous species in China

Estimating individual tree biomass is critical to forest carbon accounting and ecosystem service modeling. In this study, we developed one- (tree diameter only) and two-variable (tree diameter and height) biomass equations, biomass conversion factor (BCF) models, and an integrated simultaneous equation system (ISES) to estimate the aboveground biomass for five conifer species in China, i.e., Cunninghamia lanceolata (Lamb.) Hook., Pinus massoniana Lamb., P. yunnanensis Faranch, P. tabulaeformis Carr. and P. elliottii Engelm., based on the field measurement data of aboveground biomass and stem volumes from 1055 destructive sample trees across the country. We found that all three methods, including the one- and two-variable equations, could adequately estimate aboveground biomass with a mean prediction error less than 5%, except for Pinus yunnanensis which yielded an error of about 6%. The BCF method was slightly poorer than the biomass equation and the ISES methods. The average coefficients of determination (R ²) were 0.944, 0.938 and 0.943 and the mean prediction errors were 4.26, 4.49 and 4.29% for the biomass equation method, the BCF method and the ISES method, respectively. The ISES method was the best approach for estimating aboveground biomass, which not only had high accuracy but also could estimate stocking volumes simultaneously that was compatible with aboveground biomass. In addition, we found that it is possible to develop a species-invariant one-variable allometric model for estimating aboveground biomass of all the five coniferous species. The model had an exponent parameter of 7/3 and the intercept parameter a ₀ could be estimated indirectly from stem basic density (a ₀ = 0.294 ρ).