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1.
利用光谱指数和回归分析等方法进行叶绿素含量的反演,比较了归一化植被指数、比值植被指数、叶绿素吸收指数、三角植被指数和叶绿素指数反演叶绿素能力的差异,分析了光谱宽度对叶绿素指数反演叶绿素含量的影响,实现了森林叶片叶绿素含量的定量化反演。  相似文献   

2.
随着传感器技术的发展,高光谱数据光谱的波段宽度逐渐变窄,如何从海量的光谱数据中找到最优的光谱波段反演叶绿素含量,成为研究的难点问题。本文在测量华中农业大学狮子山6种主要树种的光谱数据和叶绿素含量的基础上,利用叶绿素指数(CI)和回归方法反演叶绿素含量,并分析了波段宽度对反演叶绿素含量结果的影响,结果发现波段宽度会影响到叶绿素反演的精度,当波段宽度为30nm时,叶绿素含量与"红边"区域(700nm-730nm)和近红外区域(770nm-800nm)叶绿素指标(CI)间的线性关系较好,决定系数可达到77.62%,均方根误差为10.6ug/cm2。  相似文献   

3.
吕杰 《测绘科学》2015,(9):88-91
针对现有研究在准确估算叶绿素含量方面的不足,该文运用粒子群优化算法和支持向量机构建叶片尺度作物叶绿素含量高光谱反演模型:利用PROSPECT模型模拟作物光谱,并运用所对应的叶绿素含量建立训练数据集,然后采用粒子群优化算法支持向量机学习训练数据集,最后建立实测叶片叶绿素含量估测模型。研究结果表明,粒子群优化算法和支持向量机构建的反演模型能准确预测作物的叶绿素含量,能够解决小样本作物采样点情况下叶绿素含量反演问题,可以作为作物叶绿素含量估测的参考方法。  相似文献   

4.
通过人工田间诱发不同等级条锈病,在不同生育期测定冬小麦感染条锈病严重度和冠层光谱,采用偏最小二乘(PLS)方法建立了冠层光谱和条锈病严重度的回归模型。结果显示: PLS反演冬小麦条锈病严重度的效果很好,与文献[4]中提出的利用高光谱指数进行反演的结果相比,精度更高; 通过对PLS回归系数的分析,发现叶绿素吸收谷两边(505~550 nm,640~670 nm,680~700 nm)的一阶微分光谱可用于诊断冬小麦条锈病病情,条锈病病害冬小麦在叶绿素吸收谷两边的一阶微分光谱的绝对值会比健康冬小麦的更大。  相似文献   

5.
对目前提出的光谱指数用以提取叶片叶绿素含量的适应性进行了分析和评价。通过分析,解释了为什么研究者得出这些指数与他们的观测样本叶绿素含量有显著的相关的结论以及为什么某个研究者提出的某个指数和叶绿素含量间的关系用于其他样本时会失效。此外,改进了一个农作物冠层叶绿素含量的提取模型,通过独立实测数据验证,效果较好,认为是可以用于其他地区农作物叶绿素含量提取的模型。  相似文献   

6.
针对三江平原洪河湿地保护区内主要特征植被冠层的叶绿素含量,采用PROSAIL模型从物理角度进行反演。首先将叶面积指数、叶片结构参数、等价水厚度、叶绿素实测含量等一些植被理化参数的实测值输入模型得到模拟光谱数据,然后与实测光谱数据对比验证其准确性。在模型中,通过固定其他参量不变,取叶绿素含量为唯一值时,考察在不同叶面积指数下叶绿素含量对冠层反射率的影响。结果显示,植被冠层叶绿素含量的敏感波段为555nm和720nm。基于PROSAIL模型的叶绿素反演方法较传统的统计模型相比是较好且稳健的方法。  相似文献   

7.
基于实测光谱估测密云水库水体叶绿素a浓度   总被引:2,自引:0,他引:2  
水中叶绿素a浓度是衡量水体初级生产力和富营养化程度的最基本的指标。利用野外便携式地物光谱仪对密云水库水体进行反射光谱测量并同步采集水样。通过分析叶绿素a浓度与光谱反射特征的相关关系,建立了叶绿素a反演模型。结果表明,利用单波段光谱反射率、光谱比值指数或微分光谱比值能够可靠反演叶绿素a浓度;但微分光谱与叶绿素a浓度相关性更高,更适用于密云水库水体叶绿素a浓度的高光谱反演。  相似文献   

8.
小波分析在植物叶绿素高光谱遥感反演中的应用   总被引:1,自引:0,他引:1  
监测叶绿素含量对研究作物与环境之间的相互影响具有重要的意义,高光谱遥感是提取叶绿素含量的可行技术.将小波分析的方法用于植物叶片的反射光谱,以小波系数作为回归变量来反演植物的叶绿素浓度.研究结果表明,通过对叶片光谱进行连续小波分解后得到的小波系数,可以准确地反演叶绿素浓度,反演的精度优于基于光谱指数的精度.  相似文献   

9.
植被生化组分的遥感反演方法研究   总被引:10,自引:2,他引:10  
颜春燕  刘强  牛铮  王长耀 《遥感学报》2004,8(4):300-308
从反演物理模型提取植被生化组分含量的角度 ,分别在叶片和冠层水平探讨了反演生化参量的方法。在叶片水平 ,利用实验室测量光谱数据 ,较为准确地提取了水分和叶绿素含量 ,通过比较真实光谱数据与利用模型和真实参数模拟的光谱数据 ,得出如下结论 :模型能否准确描述某个参数的作用是能否真正准确反演该参数的关键。在模拟的冠层水平 ,基于多阶段反演思想 ,采用了分步反演策略 ,最终较为准确地反演了生化参数。  相似文献   

10.
鄱阳湖叶绿素a浓度遥感定量模型研究   总被引:1,自引:0,他引:1  
江辉 《测绘科学》2012,37(6):49-52
叶绿素a浓度是反映湖泊水体营养状况的重要指标,本研究通过分析水体叶绿素a浓度与高光谱反射特征的相互关系,采用一阶微分值和峰值比值法分别建立了叶绿素a的高光谱定量反演模型,在此基础上与同步MODIS数据敏感波段建立卫星定量反演模型。结果表明:叶绿素a荧光峰出现在波段690nm-700nm,波段696nm一阶微分值相关系数最大;波段700nm与波段680nm的比值与其对数相关性较好,MODIS数据波段2和波段1比值的指数模型为最佳的回归模型。  相似文献   

11.
利用高光谱遥感图像估算小麦氮含量   总被引:29,自引:0,他引:29  
张霞  刘良云  赵春江  张兵 《遥感学报》2003,7(3):176-181
利用2001—04—26实用型模块化成像光谱仪(0MIS)在北京小汤山地区获取的航空高光谱遥感图像,对图像进行了精确的几何纠正和反射率转换,提取出43条小麦图像光谱与地面叶片全氮含量数据相对应,运用红边、光谱吸收特征分析方法和逐步回归算法,选择和设计了叶片全氮反演的特征波段和特征参数,并进行了全氮含量境图。实验结果表明:由吸收特征光谱(590-756nm,1096—1295nm,1295—1642nm)确定的特征深度与面积能够很好地对叶片全氮含量进行反演;NDVI(NRCA1175.8,NRCA733.9)和NDVI(dr745,dr699.2)与TN的关系最好(R^2分别为0.8145,0.769);全氮含量填图的值域和分布与地面调查和测量结果一致。  相似文献   

12.
通过田间开顶式小区熏气试验,研究在SO2急性伤害条件下水稻冠层导数光谱与叶片含硫量、叶液pH值以及叶绿素含量的相 关性。分别选择分蘖期和抽穗期显著相关的波段(分蘖期: 689 nm、584 nm、570 nm; 抽穗期: 689 nm、584 nm、585 nm)建立 预测叶片含硫量、叶液pH值及叶绿素含量的回归模型,并分别用拔节期和灌浆期相应导数光谱反射率检验模型预测精度。结果表明 ,由分蘖期建立的回归模型估测拔节期叶液pH值以及叶绿素含量与实测值之间相关系数分别为0.884和0.630; 由抽穗期建立的回 归模型估测灌浆期的叶片含硫量、叶绿素含量与实测值之间相关系数分别为0.659和0.768,均通过显著检验。  相似文献   

13.
WorldView-2纹理的森林地上生物量反演   总被引:1,自引:0,他引:1       下载免费PDF全文
使用高空间分辨率卫星WorldView-2的多光谱遥感影像,构建植被指数和纹理因子等遥感因子与森林地上生物量的关系方程,并计算模型估测精度和均方根误差,探索高分辨率数据的光谱与纹理信息在温带森林地上生物量估测应用中的潜力。以黑龙江省凉水自然保护区温带天然林及天然次生林为研究对象,通过灰度共生矩阵(GLCM)、灰度差分向量(GLDV)及和差直方图(SADH)对高分辨率遥感影像进行纹理信息提取,并利用外业调查的74个样地地上生物量与遥感因子建立参数估计模型。提取的遥感因子包括6种植被指数(比值植被指数RVI、差值植被指数DVI、规一化植被指数NDVI、增强植被指数EVI、土壤调节植被指数SAVI和修正的土壤调节植被指数MSAVI)以及3类纹理因子(GLCM、GLDV和SADH)。为避免特征变量个数较多对估测模型造成过拟合,利用随机森林算法对提取的遥感因子进行特征选择,将最优的特征变量输入模型参与建模估测。采用支持向量回归(SVR)进行生物量建模及验证,结果显示选入模型的和差直方图均值(sadh_mean)、灰度共生矩阵方差(glcm_var)和差值植被指数(DVI)等遥感因子对森林地上生物量有较好的解释效果;植被指数+纹理因子组合的模型获得较精确的AGB估算结果(R2=0.85,RMSE=42.30 t/ha),单独使用植被指数的模型精度则较低(R~2=0.69,RMSE=61.13 t/ha)。  相似文献   

14.
In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R2 ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error.  相似文献   

15.
Leaf pigment content provides valuable insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast, nondestructive method for pigment estimation. A number of methods were used previously for estimation of leaf pigment content, however, spectral bands employed varied widely among the models and data used. Our objective was to find informative spectral bands in three types of models, vegetation indices (VI), neural network (NN) and partial least squares (PLS) regression, for estimating leaf chlorophyll (Chl) and carotenoids (Car) contents of three unrelated tree species and to assess the accuracy of the models using a minimal number of bands. The bands selected by PLS, NN and VIs were in close agreement and did not depend on the data used. The results of the uninformative variable elimination PLS approach, where the reliability parameter was used as an indicator of the information contained in the spectral bands, confirmed the bands selected by the VIs, NN, and PLS models. All three types of models were able to accurately estimate Chl content with coefficient of variation below 12% for all three species with VI showing the best performance. NN and PLS using reflectance in four spectral bands were able to estimate accurately Car content with coefficient of variation below 14%. The quantitative framework presented here offers a new way of estimating foliar pigment content not requiring model re-parameterization for different species. The approach was tested using the spectral bands of the future Sentinel-2 satellite and the results of these simulations showed that accurate pigment estimation from satellite would be possible.  相似文献   

16.
Arsenic stress induces in subtle changes in the canopy chlorophyll content (CCC). Therefore, the establishment of a spectral index that is sensitive to subtle changes in the CCC is important for monitoring crop arsenic contamination in large areas by remote sensing. Experimental sites with three contamination levels were selected and were located in Chang Chun City, Jilin City, Jilin Province, China. Arsenic stress can induce small changes in the CCC, reflecting in the crop spectrum. This study created a new index to monitor the CCC. Then, the results from the index were compared with these from other indices and the random forest model, respectively. The final purpose of this study is to find an optimal index, which is sensitive to small changes in the CCC under arsenic stress for monitoring regional CCC in rice. The results indicate that the distribution of the CCC is aligned with the distribution of the arsenic stress level and that NVI (R640, R732, and R752) is the best index for monitoring CCC. The correlation coefficient R2 between the predicated values using NVI and the measured values of canopy chlorophyll content is 0.898, which performs better than the random forest model and other indices.  相似文献   

17.
ABSTRACT

Commercial forest plantations are increasing globally, absorbing a large amount of carbon valuable for climate change mitigation. Whereas most carbon assimilation studies have mainly focused on natural forests, understanding the spatial distribution of carbon in commercial forests is central to determining their role in the global carbon cycle. Forest soils are the largest carbon reservoir; hence soils under commercial forests could store a significant amount of carbon. However, the variability of soil organic carbon (SOC) within forest landscapes is still poorly understood. Due to limitations encountered in traditional systems of SOC determination, especially at large spatial extents, remote sensing approaches have recently emerged as a suitable option in mapping soil characteristics. Therefore, this study aimed at predicting soil organic carbon (SOC) stocks in commercial forests using Landsat 8 data. Eighty-one soil samples were processed for SOC concentration and fifteen Landsat 8 derived variables, including vegetation indices and bands were used as predictors to SOC variability. The random forest (RF) was adopted for variable selection and regression method for SOC prediction. Variable selection was done using RF backward elimination to derive three best subset predictors and improve prediction accuracy. These variables were then used to build the RF final model for SOC prediction. The RF model yielded good accuracies with root mean square error of prediction (RMSE) of 0.704 t/ha (16.50% of measured mean SOC) and 10-fold cross-validation of 0.729 t/ha (17.09% of measured mean SOC). The results demonstrate the effectiveness of Landsat 8 bands and derived vegetation indices and RF algorithm in predicting SOC stocks in commercial forests. This study provides an effective framework for local, national or global carbon accounting as well as helps forest managers constantly evaluate the status of SOC in commercial forest compartments.  相似文献   

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