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1.
全球海洋次表层温度异常遥感反演的季节时空变化特征 总被引：1，自引：1，他引：0
卫星遥感反演海洋内部多时相、大尺度热力结构信息对于了解海洋内部复杂、多维的动力过程有重要意义。本文采用随机森林回归模型，利用卫星遥感观测的海表参量（海表高度异常（SSHA）、海表温度异常（SSTA）、海表盐度异常（SSSA）和海表风场异常（SSWA）），反演不同季节、不同深度层位（1000 m深度以上）的海洋次表层温度异常（STA），并用Argo实测数据作精度验证，采用均方根误差（RMSE）、归一化均方根误差（NRMSE）以及决定系数（R2）评价模型在全球及洋盆尺度上的反演精度。结果显示，全球海洋16个深度层位的平均R2在春、夏、秋、冬四季分别为0.53、0.60、0.54、0.66，NRMSE分别为0.051、0.031、0.043、0.044。随着季节的变化，模型反演性能有所波动。模型在印度洋的反演效果最佳，不同季节、不同深度层位上的平均R2和RMSE分别为0.71和0.18 ℃，而大西洋的反演精度最低，平均R2和RMSE分别为0.46和0.25 ℃。研究表明随机森林模型适用于全球不同季节的STA遥感反演，且在不同洋盆上均有较好的反演效果；不同季节上，上层STA有明显变化信号，空间异质性显著，但300 m以深，STA信号较弱且基本不随季节变化。本研究可为长时序、大尺度海洋内部参量信息遥感反演与重建提供依据，有助于进一步发展深海遥感方法。 相似文献
2.
红树林是世界上生产力最高、价值最高的湿地生态系统之一。冠层叶绿素含量CCC（Canopy Chlorophyll Content）作为红树林重要的生物物理参量,是估算其生产力和评价其健康状况的重要指标。本文利用珠海一号高光谱卫星（OHS）影像与Sentinel-2A多光谱数据计算传统植被指数与组合植被指数并构建了高维数据集,综合利用正态分布检验、最大相关系数法与变量重要性评价进行数据降维和变量优选;分别基于单一线性回归算法、机器学习回归算法和堆栈集成学习回归算法构建了红树林CCC遥感反演模型,探明北部湾红树林CCC的最佳遥感反演模型,验证OHS高光谱影像与Sentinel-2A数据反演红树林CCC的精度差异,评估SNAP-SL2P算法反演红树林CCC的适用性。研究结果表明：（1）通过数据降维和变量选择处理,从高维度OHS数据集选取了8个特征变量,其中RSI（12,17）、DSI（12,18）和NDSI（6,12）组合植被指数对红树林CCC反演精度的贡献率较高;（2）联合OHS数据和最优堆栈GBRT集成学习回归模型（Score=0.999,RMSE=0.963 μg/cm2）的训练精度优于最优RF机器学习回归模型（RMSE降低了7.531 μg/cm2）,明显优于最优Lasso线性回归模型（RMSE降低了19.383 μg/cm2）;（3）在最优堆栈集成学习回归模型下,OHS数据反演红树林CCC的精度（R2=0.761,RMSE=16.738 μg/cm2）高于Sentinel-2A影像（R2=0.615,RMSE=20.701 μg/cm2）;（4）联合OHS和Sentinel-2A数据的最优堆栈集成学习回归模型反演红树林CCC的精度都明显优于SNAP-SL2P算法（R2=0.356,RMSE=49.419 μg/cm2）。研究结果论证了正态分布检验、最大相关系数法和基于XGBoost的特征选择方法有效降低了高维数据集的维度,并得到了最优特征变量;OHS数据的最优堆栈GBRT集成学习回归模型训练精度最高,是估算红树林CCC的最优反演模型;OHS和Sentinel-2A数据都能有效反演红树林CCC（R2均大于0.61）,而OHS数据的估算精度更高（R2大于0.75）;SNAP-SL2P算法不能有效反演红树林CCC（R2小于0.4）,且对红树林CCC数值存在系统性低估。 相似文献
3.
支持向量机回归SVR（Support Vector Regression）方法作为叶面积指数反演的一种新思路,在LAI反演中具有一定的应用价值和前景,但SVR算法中惩罚系数C、核函数宽度参数g、不敏感损失函数参数ε的取值对回归精度有显著的影响。本文提出了一种基于人工蜂群算法ABC（Artificial Bee Colony）优化SVR参数的遥感影像叶面积指数反演方法。研究数据为美国土壤水分实验（SMEX02）2002年LAI实测数据和同期的Landsat 7 ETM+地表反射率数据,为了验证ABC算法优化SVR各个参数对反演精度的影响,建立了未优化参数（SVR）、优化单个参数（ABC-SVR-C,ABC-SVR-g,ABC-SVR-ε）、优化3个参数（ABC-SVR）的3类LAI反演模型,并比较了其回归拟合精度。在此基础上,分析了3个关键参数对LAI反演模型精度的敏感性,并对ABC算法优化SVR模型的精度进行显著性检验。研究表明：（1）相比未优化参数模型,ABC算法优化模型具有更高的反演精度,优化3个参数优于优化单个参数,回归直线斜率k达到0.797、决定系数r2达到0.775。（2）SVR的3个关键参数对模型精度都有影响,相较参数C和g,参数ε引起模型精度的不确定性更高。（3）95%的置信区间下,ABC-SVR模型与SVR模型的回归直线斜率k、r2、RMSE的差异显著性检验P值均小于0.005,ABC算法显著改善了SVR模型的精度。 相似文献
4.
光学与微波植被指数协同反演农作物叶面积指数的可行性分析 总被引：1，自引：0，他引：1
光学遥感是目前反演植被叶面积指数LAI（Leaf Area Index）的主要手段,但是当叶面积指数较大时存在光学遥感信息饱和、反演精度显著降低的问题。叶面积指数和平均叶倾角对光学、微波波段范围内反射和散射特性都有重要影响,主要表现在植被结构参数的变化可以引起冠层孔隙率和消光截面大小的改变。本文以典型农作物玉米为例,通过构建统一的PROSAIL和MIMICS模型输入参数,生成一套玉米全生长期光学二向反射率和全极化微波后向散射系数模拟库和冠层参数库。通过对模拟数据与LAI敏感性和相关性分析得出：（1）光学植被指数MNDVI(800 nm,2000 nm),在LAI为0—3时敏感,基于MNDVI与LAI的回归模型可以估算LAI变化 0.4的情况,RMSE是0.33,R2是0.958。（2）微波植被指数SARSRVI(1.4 GHz HH,9.6 GHz HV),在LAI为3—6时敏感,基于SARSRVI与LAI的回归模型可以估算LAI变化1的情况,RMSE为0.22,R2是0.9839。研究表明,采用分段敏感的植被指数,协同光学和微波遥感反演玉米全生长期叶面积指数是可行的。 相似文献
5.
中国南方森林冠顶高度Lidar反演—以江西省为例 总被引：1，自引：0，他引：1
激光雷达(Lidar)与光学遥感的有效结合对中国南方区域森林冠顶高度反演意义重大,而国产卫星将为中国森林生态研究提供新的数据源。本文联合利用大脚印激光雷达GLA和国产MERSI数据,在实现GLAS波形数据处理和不同地形条件下森林冠顶高度反演算法基础上,建立了区域尺度不同森林类型林分冠顶高度GLAS+MERSI联合反演关系模型,进行了江西地区森林冠顶高度反演。总体上,GLAS激光雷达森林冠顶高度估算精度较高;且在与MERSI 250 m数据的联合反演模型中,针叶林模型精度较好(R2=0.7325);阔叶林次之(R2=0.6095);混交林较差(R2=0.4068)。分析发现,考虑了光学遥感生物物理参数的GLAS+MERSI联合关系模型在区域森林冠顶高度估算中有较高精度,且在空间分布上与土地覆盖数据分布特征非常一致。 相似文献
6.
利用机载激光雷达点云数据,计算了9种度量指标,并将其分为冠层的高度指标、结构复杂度指标和覆盖度指标。利用高度指标和结构复杂度指标,结合大量实测单木结构与年龄估测数据,从样点和区域尺度分别分析了青海云杉林冠层垂直结构分布,分析得知实验区内主要以中龄林和成熟林为主,冠层垂直分布复杂程度偏低,高度分化程度一般。通过回归分析发现首次回波覆盖度指标FCI与实测的有效植被面积指数PAIe有良好的相关性（R2=0.66）,在此基础上基于辐射传输模型反演了实验区内PAIe的水平分布,且用实测数据验证发现反演的PAIe略高于实测值（R2=0.67）,绝对平均误差为0.65。分析结果很好地反映了激光雷达在森林空间结构信息提取方面的应用潜力。 相似文献
7.
氮素是植被整个生命周期的必要元素,红树林冠层氮素含量（CNC）遥感估算对红树林健康监测具有重要意义。以广东湛江高桥红树林保护区为研究区,本文旨在基于Sentinel-2影像超分辨率重建技术进行红树林CNC估算和空间制图。研究首先基于三次卷积重采样、Sen2Res和SupReMe算法实现Sentinel-2影像从20 m分辨率到10 m的重建;然后以重建后的影像和原始20 m影像为数据源构建40个相关植被指数,采用递归特征消除法（SVM-RFE）确定CNC估算的最优变量组合,进而构建CNC反演的核岭回归（KRR）模型;最后选取最优模型实现CNC制图。研究结果表明：基于Sen2Res和SupReMe超分辨率算法的重建影像不仅与原始影像具有很高的光谱一致性,且明显提高了影像的清晰度和空间细节。红树林CNC反演波段主要集中在红（B4）、红边（B5）、近红外波段（B8a）以及短波红外波段（B11和B12）,与“红边波段”相关的植被指数（RSSI和TCARIre1/OSAVI）也是红树林CNC反演的有效变量。基于3种方法重建后10 m的影像构建的模型反演精度（R2val>0.579）均优于原始20 m的影像（R2val=0.504）;基于Sen2Res算法重建影像构建的反演模型拟合精度（R2val=0.630,RMSE_val=5.133,RE_val=0.179）与基于三次卷积重采样重建影像的模型拟合精度（R2val=0.640,RMSE_val=5.064,RE_val=0.179）基本相当,前者模型验证精度（R2cv=0.497,RMSE_cv=5.985,RE_cv=0.214）较高且模型变量选择数量最为合理。综合重建影像光谱细节及模型精度,基于Sen2Res算法重建的Sentinel-2影像在红树林CNC估算中具有良好的应用潜力,能为区域尺度红树林冠层健康状况的精细监测提供有效的方法借鉴和数据支撑。 相似文献
8.
光能利用率表征植被通过光合作用将所截获/吸收的能量转化为有机碳的效率,是遥感估算植被生产力的关键参数。由于植被分布和气候环境的综合影响,光能利用率表现出显著的空间异质性和时间动态性,光能利用率的不确定性成为后续生产力模型估算精度不高的重要原因。本文以Fluxnet全球通量站点数据和MODIS LAI/fPAR产品为数据源,比较了5种遥感植被生产力模型中的光能利用率估算方法,并在此基础上考虑光照散射条件对光能利用率的影响,结合晴空指数,利用逐步线性回归方法和参数优化方法建立不同植被类型的光能利用率估算模型。验证结果表明,考虑晴空指数可提高光能利用率估算精度,两种方法估算得到的光能利用率值RMSE均低于0.5 gC·MJ-1,逐步线性回归法尽管机理欠缺,但由于选择因子较多,光能利用率估算精度较高（R2=0.461,RMSE=0.403 gC·MJ-1）;广泛应用的参数化方法由于考虑的因子较少、模型形式较固定,光能利用率估算精度稍低（R2=0.306,RMSE=0.489 gC·MJ-1）。本文所建立的光能利用率估算模型可应用于区域或全球植被光能利用率及生产力的估算。 相似文献
9.
应用随机辐射传输模型反演云南松林分郁闭度 总被引：1，自引：0，他引：1
随机辐射传输模型可用于模拟水平分布不均一森林的辐射传输过程。本文以云南松林分为研究对象，提出一种应用随机辐射传输模型的郁闭度反演方法。该方法以随机辐射传输模型中参数与林分郁闭度的定量关系为基础，提出了针对云南松的冠型等效模型，构建了郁闭度和卫星反射率（GF-1和Landsat 8卫星影像）的查找表，并实施了反演。基于野外实测的30个样地进行了郁闭度数据验证，并和基于NDVI回归模型的反演方法进行对比。结果表明，反演结果能够较准确反映云南松林分郁闭状况（R2=0.8345，RMSE=0.0688），通过冠型修正能够降低反演误差，冠型等效模型是合理的。反演方法机理清晰且适用范围广，研究成果可为大面积森林郁闭度反演提供模型和方法支持。 相似文献
10.
冰雪在短波区域具有很强的各向异性反射特征,对全球能量平衡及水循环等有重要作用。目前,国内外学者发展了一系列应用于冰雪的二向性反射分布函数BRDF（Bidirectional Reflectance Distribution Function）模型,全面比较和评估这些模型对星载多角度遥感产品的业务化模型选择有重要参考价值和指导意义。本文基于全球POLDER冰雪多角度反射率数据,选取3个模型,包括核驱动、半经验的MODIS业务化RTLSR模型、渐进辐射传输物理模型ART以及新发展的RTLSRS 模型进行了全面比较分析,研究结果表明：（1）在拟合所有POLDER数据时,RTLSRS模型都具有最高精度,对于单组纯雪数据,RTLSRS模型的最小二乘拟合的均方根误差（RMSE）比ART模型降低了45.45%,仅为RTLSR模型的18.46%。对于非纯雪数据,RTLSRS模型与RTLSR模型的拟合能力总体差别不大,但其RMSE比RTLSR模型降低了67.5%,ART模型的精度最差。（2）虽然RTLSRS可以高精度拟合所有数据,但该模型拟合纯雪（R2=0.969,RMSE=0.012）精度较优于非纯雪（R2=0.926,RMSE=0.013）。（3）对RTLSRS模型进行简化,仅保留其各向同性核和雪核ISM（Isotropic-Snow Model）,验证结果表明：简化后的模型能够很好地表征雪的二向散射能力,使用POLDER全部纯雪数据进行拟合时,R2达到了0.949,RMSE为0.034。本文有助于用户在应用冰雪多角度数据时选择更合适的BRDF模型,同时对理解这些模型的误差提供了有价值的参考 相似文献
11.
J.G.P.W. Clevers L. Kooistra M.E. Schaepman 《International Journal of Applied Earth Observation and Geoinformation》2010
Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper, in particular the spectral information provided by the canopy water absorption feature at 970 nm for estimating and predicting CWC was studied using a modelling approach and in situ spectroradiometric measurements. The relationship of the first derivative at the right slope of the 970 nm water absorption feature with CWC was investigated with the PROSAIL radiative transfer model and tested for field spectroradiometer measurements on two test sites. The first site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The second site was an extensively grazed fen meadow. 相似文献
12.
A simulation study has been carried out to investigate the Principal Component Inversion (PCI) technique for the retrieval of leaf area index (LAI). The PROSAIL model has been used for the forward analysis, i.e., estimation of reflectance for various combinations of LAI, soil reflectance, leaf angle distribution (ø1), chlorophyll a+b concentration (Cab), etc. Independent test on sample with LAI range 0.1-7.0 indicated that the retrieved LAI from PCI has higher accuracy (RMSE=0.137) than the classical NDVI-LAI empirical approach (RMSE=1.139). The study needs to be extended to cover retrieval from different types of soil and simultaneous retrieval of different biophysical parameters viz., LAI, Cab, ø1 to test the wider applicability of the PCI technique. 相似文献
13.
Janne Heiskanen Miina Rautiainen Lauri Korhonen Matti Mõttus Pauline Stenberg 《International Journal of Applied Earth Observation and Geoinformation》2011
Spectral invariants provide a novel approach for characterizing canopy structure in forest reflectance models and for mapping biophysical variables using satellite images. We applied a photon recollision probability (p) based forest reflectance model (PARAS) to retrieve leaf area index (LAI) from fine resolution SPOT HRVIR and Landsat ETM+ satellite data. First, PARAS was parameterized using an extensive database of LAI-2000 measurements from five conifer-dominated boreal forest sites in Finland, and mixtures of field-measured forest understory spectra. The selected vegetation indices (e.g. reduced simple ratio, RSR), neural networks and kNN method were used to retrieve effective LAI (Le) based on reflectance model simulations. For comparison, we established empirical vegetation index-LAI regression models for our study sites. The empirical RSR–Le regression performed best when applied to an independent test site in southern Finland [RMSE 0.57 (24.2%)]. However, the difference to the best reflectance model based retrievals produced by neural networks was only marginal [RMSE 0.59 (25.1%)]. According to this study, the PARAS model provides a simple and flexible modelling tool for calibrating algorithms for LAI retrieval in conifer-dominated boreal forests. The advantage of PARAS is that it directly uses field measurements to parameterize canopy structure (LAI-2000, hemispherical photographs) and optical properties of foliage and understory. 相似文献
14.
In this paper, we carried out a laboratory experiment to study changes in canopy reflectance of Tamarugo plants under controlled water stress. Tamarugo (Prosopis tamarugo Phil.) is an endemic and endangered tree species adapted to the hyper-arid conditions of the Atacama Desert, Northern Chile. Observed variation in reflectance during the day (due to leaf movements) as well as changes over the experimental period (due to water stress) were successfully modelled by using the Soil-Leaf-Canopy (SLC) radiative transfer model. Empirical canopy reflectance changes were mostly explained by the parameters leaf area index (LAI), leaf inclination distribution function (LIDF) and equivalent water thickness (EWT) as shown by the SLC simulations. Diurnal leaf movements observed in Tamarugo plants (as adaptation to decrease direct solar irradiation at the hottest time of the day) had an important effect on canopy reflectance and were explained by the LIDF parameter. The results suggest that remote sensing based assessment of this desert tree should consider LAI and canopy water content (CWC) as water stress indicators. Consequently, we tested fifteen different vegetation indices and spectral absorption features proposed in literature for detecting changes of LAI and CWC, considering the effect of LIDF variations. A sensitivity analysis was carried out using SLC simulations with a broad range of LAI, LIDF and EWT values. The Water Index was the most sensitive remote sensing feature for estimating CWC for values less than 0.036 g/cm2, while the area under the curve for the spectral range 910–1070 nm was most sensitive for values higher than 0.036 g/cm2. The red-edge chlorophyll index (CIred-edge) performed the best for estimating LAI. Diurnal leaf movements had an effect on all remote sensing features tested, particularly on those for detecting changes in CWC. 相似文献
15.
Gonipterus scutellatus outbreaks may severely defoliate Eucalyptus plantations growing in South Africa. Therefore, detecting and mapping the severity and extent of G. scutellatus defoliation is essential for the deployment of suppressive measures. In this study, we tested the utility of spatially optimized vegetation indices and an artificial neural network in detecting and mapping G. scutellatus-induced vegetation defoliation, using both visual estimates of percentage defoliation and optical leaf area index (LAI) measures. We tested both field methods to determine which of the two were more superior in detecting vegetation defoliation using optimized vegetation indices. These indices were computed from a WorldView-2 pan-sharpened image, which is characterized with a 0.5-m spatial resolution and eight spectral bands. The indices were resampled to spatial resolutions that best represented levels of G. scutellatus-induced defoliation. The results showed that levels of defoliation, using visual percentage estimates, were detected with an R2 of 0.83 and an RMSE of 1.55 (2.97% of the mean measured defoliation), based on an independent test data-set. Similarly, LAI subjected to defoliation was detected with an R2 of 0.80 and an RMSE of 0.03 (0.06% of the mean measured LAI), based on an independent test data-set. Therefore, the results indicate that the cheaper less-complicated visual percentage estimates of defoliation was the more superior model of the two. A sensitivity analysis revealed that NDRE, MCARI2 and ARI ranked as the top three most influential indices in developing both percentage defoliation and LAI models. Furthermore, we compared the optimized model with a model developed using the original image spatial resolution. The results indicated that the optimized model performed better than the original 0.5-m spatial resolution model. Overall, the study showed that vegetation indices optimized to specific spatial resolutions can effectively detect and map levels of G. scutellatus-induced defoliation and LAI subjected to defoliation. 相似文献
16.
大光斑激光雷达数据已广泛应用于森林冠层高度提取,但通常仅限于地形坡度小于20°的平缓地区。在地形坡度大于20°的陡峭山区,地形引起的波形展宽使得地面回波和植被回波信息混合在一起,给森林冠层高度提取带来巨大挑战。本文利用激光雷达回波模型和地形信息,提出了一种模型辅助的坡地森林冠层高度反演算法。该方法以激光雷达回波信号截止点为参考,定义了波形高度指数H50和H75,使用激光雷达回波模型与已知地形信息模拟裸地的激光雷达回波,将裸地回波信号截止点与森林激光雷达回波信号截止点对齐,利用裸地回波计算常用的波形相对高度指数RH50和RH75,对森林冠层高度进行反演。并与高斯波形分解法和波形参数法的反演结果进行了比较。研究结果表明:(1)利用所提取的波形指数RH50和RH75对胸高断面积加权平均高(Lorey’s height)进行了估算,在坡度小于20°时,高斯波形分解法、波形参数法和模型辅助法的估算结果与实测值线性拟合的相关系数(R2)分别为0.70,0.78和0.98,对应的均方根误差(RMSE)分别为2.90 m,2.48 m和0.60 m,模型辅助法略优于其他两种方法;(2)在坡度大于20°时,高斯波形分解法、波形参数法和模型辅助法的R2分别为0.14,0.28和0.97,相应的RMSE分别为4.93 m,4.53 m和0.81 m,模型辅助法明显优于其他两种方法;(3)在0°—40°时,模型辅助法对Lorey’s height估算结果与实测值的R2为0.97,RMSE为0.80 m。本研究提出的模型辅助法具有更好的地形适应性,在0°—40°的坡度范围内具备对坡地森林冠层高度反演的潜力。 相似文献
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18.
Roshanak Darvishzadeh Clement Atzberger Andrew Skidmore Martin Schlerf 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(6):894-906
Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R2 between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches (R2 = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data (R2 = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements. 相似文献
19.
The authors discuss a method by which the image characteristics of forest vegetation can be used to determine various valuational characteristics of forest stands through the combination of air photo interpretation and ground surveys at selected training sites. Construction of curves showing changes in image texture and tone occurring at different stages in the growth cycle of a pine forest community are used to estimate the age of a forest stand, and through known relationships between age and other stand characteristics incorporated into yield tables, to approximate such valuational characteristics as mean diameter, mean height, and stand volume. Translated from: Distantsionnyye issledovaniya rel'yefa Sibiri, A. L. Yanshin and V. N. Sharanov, eds. Novosibirsk, Nauka, 1985, pp. 73-78. 相似文献
20.
结合树龄信息的遥感森林生态系统生物量制图 总被引：10，自引：0，他引：10
森林生态系统是陆地生态系统中的重要组成部分，其中的地上生物量（AGB，Aboveground Biomass）在全球气候变化和碳循环研究中起着重要的作用。本文利用ETM^＋遥感影像，首先建立了实测叶面积指数（LAI，Leaf Area Index）与实测生物量数据的回归关系，基于遥感叶面积指数图像得到初步地上生物量空间分布图；同时在短波植被指数（SWVI，Short Wave Vegetation Index）与实测树龄之间建立了回归关系，在此基础上得到了树龄空间分布图。然后通过将植被指数（VI，Vegetation Index），LAI，树龄等变量针对不同的树种类型进行逐步回归，得到了较好的回归模型，并结合土地利用／土地覆盖估算了贵州省黎平县的地上生物量，绘制了其空间分布图。统计结果显示：总体森林生态系统的AGB与LAI和RSR（Reduced Simple Ratio）之间有一定的相关关系（R^2=0．895）；杉木林的AGB与LAI和归一化植被指数（NDVI，Normalized Difference Vegetation Index）之间有较强的相关性（R^2=0．93）；针叶树种的LAI与年龄是AGB较好的估算因子（R^2=0．937）；阔叶林的AGB与年龄有一定的相关性（R^2=0．792）；混交林的AGB与LAI和SR（Simple Ratio）有较强的相关性（R^2=0．931）。结果表明，将树龄和土地覆盖／土地利用类型的信息加入到地上生物量估算模型的建立中，是一种改善利用多光谱遥感估算精度的较好的方法。结合土地覆盖／土地利用类型的高分辨率的树龄空间分布图，可为森林生态系统的可持续发展和管理提供科学的论据。 相似文献