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冬小麦叶面积指数的高光谱估算模型研究 总被引：2，自引：0，他引：2
本文以山东禹城为研究区,利用地面实测光谱数据,探讨不同植被指数和红边参数建立高光谱模型反演冬小麦叶面积指数的精度。通过逐波段分析计算了4种植被指数(NDVI、RVI、SAVI、EVI),结合同步观测LAI数据,确定反演叶面积指数的最优波段;计算了5种常用的高光谱植被指数MCARI、MCARI2、OSAVI、MTVI2、MSAVI2,同时利用4种常用方法计算红边位置和红谷,与实测LAI进行回归分析,比较植被指数和红边参数模型对冬小麦LAI的估测精度。结果表明各因子与LAI均具有较高的相关性,整个研究区归一化植被指数具有最高的反演精度,确定了估算冬小麦LAI的最优模型,并使用独立的LAI观测数据对模型进行了验证。 相似文献
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不同传感器的模拟植被指数对水稻叶面积指数的估测精度和敏感性分析 总被引：5，自引：0，他引：5
本研究利用水稻冠层高光谱数据,模拟NOAA-AVHRR,Terra-MODIS和Landsat-TM的可见光波段反射率数据,计算各传感器的多种植被指数(NDVI,RVI,EVI,GNDVI,GRVI和Red-edge RVI),比较植被指数模型对水稻LAI的估测精度,分析不同植被指数对LAI变化的敏感性.相对于红波段植被指数,红边比值植被指数(Red-edge RVI)和绿波段指数GRVI与LAI有更好的线性相关关系,而GNDVI和LAI呈现更好的对数相关关系.MODIS的Red-edge RVI指数不仅模型拟合的精度最高,还有独立数据验证的估测精度也最高,而且它的验证精度较拟合精度下降幅度最小;其次是绿波段构建的GNDVI和GRVI植被指数的估测精度,再次是NDVI和EVI的估测精度,而RVI的估测精度最差.敏感性分析发现,13个植被指数对水稻LAI的估测能力都随着LAI的增加而下降,但归一化类植被指数和比值类植被指数对LAI变化反应的差异明显,归一化类植被指数在LAI较低时(LAI<1.5)对LAI变化的反应开始非常敏感,但迅速下降,而比值类植被指数在LAI较低时,明显小于归一化类植被指数,之后随着LAI的增大(LAI>1.5)比值类植被指数对LAI的变化敏感性,则明显高于归一化类植被指数.Red-edge RVI和绿波段指数GRVI和LAI不仅表现了很好的线性相关关系,而且在LAI大于2.9左右保持较高的敏感性. 相似文献
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无人机多光谱影像的天然草地生物量估算 总被引：1，自引：0，他引：1
地上草地生物量是衡量天然草地生态系统的重要指标,是草地资源合理利用和载畜平衡监测的重要依据。为了快速、准确、有效地估算天然草地地上生物量,掌握其变化规律,以天山北坡天然牧场为研究区,分析其地上生物量的时空分布特征。根据研究区阴坡与阳坡不同的草地类型和植被种类,利用多旋翼无人机获取的高分辨率多光谱影像(含近红外波段),结合地面实测数据,在进行天然草地地上生物量与植被指数相关性分析的基础上,运用回归分析方法,建立生物量和多种植被指数的估算模型。结果表明:考虑地形因子(阴阳坡)之后,植被地上生物量与各植被指数的相关性系数显著提高;不同坡向,同一植被指数拟合精度差异较大;同一坡向,各个植被指数的敏感性也有所不同。总体上,比值植被指数(RVI)与阴阳坡草地生物量拟合效果最好,模型精度均达到75%以上。利用植被指数建立的生物量估算方法结果与实际相符,可为天然草地生态系统检测和草地资源合理利用提供方法和依据。 相似文献
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基于环境星CCD数据的冬小麦叶面积指数遥感监测模型研究 总被引：11，自引：0，他引：11
以山东禹城为研究区,利用我国自主研发的环境星数据,计算了4种植被指数,即归一化植被指数(NDVI)、比值植被指数(RVI)、土壤调节植被指数(SAVI)及增强型植被指数(EVI);结合同步观测数据,将植被指数与实测叶面积指数(LAI)进行回归分析,比较各种植被指数模型对冬小麦LAI的估测精度。结果表明,4种植被指数与LAI均具有较高的相关性,其中,比值植被指数(RVI)对LAI反演精度最高,即LAI=2.967 lnRVI-1.201是估算冬小麦LAI的最优模型。使用2009年5月冬小麦LAI观测数据对模型进行验证,平均相对误差为19%。 相似文献
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遥感技术使大范围实时监测蓝藻水华成为可能,但大气效应与太阳、地物及传感器的几何关系会影响辐射传输方程,从而对基于遥感指数的蓝藻信息提取产生影响,因此,探讨这些影响因子对藻华信息的提取精度有着积极意义。基于MODIS大气校正前后2种产品(MOD02和MOD09),利用单波段、比值植被指数、归一化差值植被指数和归一化水体指数4种遥感指数对2006年全年太湖藻华信息进行提取,并定量分析了气溶胶光学厚度、太阳高度角和卫星观测角等因素对遥感指数的影响程度。结果表明:比值植被指数与归一化差值植被指数受大气影响程度较其他二者低,且比值植被指数对大气因素的敏感性较归一化差值植被指数低;气溶胶的光学厚度与太阳高度角在不同程度上对指数提取结果造成影响,因此利用遥感指数提取藻华信息时需谨慎,以避免误判。 相似文献
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大米草室内叶片光谱特征参数与叶绿素浓度关系研究 总被引：1，自引：0，他引：1
本文通过分析大米草叶片反射光谱特征,并提取红边位置、红边斜率和红边面积三个红边参数以及叶面叶绿素指数(LCI)、水分指数(WI)、三角植被指数(TVI)、结构相关色素指数(SIPI)四个高光谱植被指数,利用线性、对数、倒数、二次函数和三次函数曲线模拟算法得到大米草叶片叶绿素a浓度的高光谱估算模型。研究结果表明:叶绿素a浓度与红边斜率和红边面积在0.01水平上显著负相关,与LCI、WI和TVI在0.01水平上显著正相关。基于红边斜率、红边面积、TVI三个参数,选用倒数法构建叶绿素a浓度的估算模型精度明显高于其他算法。基于LCI和WI参数,应用三次函数法构建的叶绿素a浓度的高光谱检测模型精度明显高于其他算法。比较R2和模型估算误差,利用WI水分指数应用三次函数构建叶绿素a浓度的高光谱检测模型精度在所有模型中最高。因此,利用叶片光谱技术可以较高精度地估算叶绿素a浓度。 相似文献
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利用不同植被指数估算植被覆盖度的比较研究 总被引：5，自引：0，他引：5
选用蔬菜地和草地2种植被类型,利用ASD光谱仪实测二者在不同覆盖度下的光谱响应,分析了归一化植被指数(NDVI)、差值植被指数(DVI)、比值植被指数(RVI)、修正植被指数(MVI)、修改型土壤调节植被指数(MSAVI)以及全球环境监测植被指数(GEMI)等6种植被指数所用的最佳波段及其组合,进而研究了利用像元二分模型估算植被覆盖度时的不同植被指数的表现.结果表明,与蔬菜地植被指数相关系数较高的波段组合为620 ～ 740 nm谱段和780 ～ 900 nm谱段内波段的组合,与草地植被指数相关系数较高的波段组合为620 ～750 nm谱段和760 ～900 nm谱段内波段的组合,相关系数均达0.8以上;在高光谱数据构建的植被指数和模拟卫星数据构建的植被指数中,用DVI和MSAVI估算植被覆盖度,平均总体精度分别达到83.7％和79.5％,与其他4种植被指数相比,这2种指数更适合于利用像元二分模型进行植被覆盖度的估算. 相似文献
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高分六号红边特征的农作物识别与评估 总被引：3，自引：0，他引：3
红边作为植被敏感波段，其红边特征的运用是遥感识别农作物并实现精准农业的高新手段之一。以黑龙江松嫩平原北部为研究区，以国内首个提供红边波段的多光谱高分六号影像和玉米、大豆、水稻总计82859个作物样本同时作为研究对象，从以下几个方面研究了红边波段和红边指数波段等红边特征在农作物识别中的表现，并评估了农作物的识别精度。（1） 通过作物样本辐射亮度值的统计特征，初步显示了在两红边波段0.710 μm和0.750 μm处有比其他波段更好的区分；（2） 根据传统归一化植被指数形式构建了红边归一化植被指数NDVI710和NDVI750，综合两指数在J-M距离表征的作物样本类别区分度上比传统NDVI更显著；（3） 通过多种手段筛选了有效波段并且制定了支持向量机（SVM）框架下4种农作物识别的分类策略，分别在5∶5、6∶4、7∶3、8∶2、9∶1等5套随机样本分割方案下完成研究区域农作物的分类预测。在这20类分类精度中kappa系数均高于0.9609，总体精度高于0.9742；列向上5∶5分割方案的精度最高，8∶2的精度最低；横向上分类精度排序如下：SVM-RFE > SVM-RF > SVM-有红边波段 > SVM-无红边波段，该结果表明了红边指数和红边波段的参与显著地提高了作物的识别精度；（4） 由于水域等其他样本的缺少，SVM-RFE方法和SVM-RF方法的分类图像均存在少量错分现象。但从分类精度和图像细节展示上来看，SVM-RFE方法要优于SVM-RF方法，二者分类图像的交叉验证中kappa系数为0.8060，总体精度为0.8743。总之，高分六号红边特征在作物识别中表现优越，使得识别精度显著提高。后续研究者可开发更多与红边相关的植被指数，充分发挥红边特征在精准农业中的作用。 相似文献
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以广东惠州附近水域为研究区,利用ALOS AVNIR-2多光谱数据分析水体和其他主要地物在影像上的光谱特征。构建几种常用的波谱指数,分析阈值对各种指数模型提取水体的敏感性及所能达到的最大精度。发现利用这些指数均可以较好地提取图像中的水体信息,提取效果从好到差依次是:归一化差值水体指数法(NDWI)、近红外波段分割法(NIR)、归一化差值植被指数法(NDVI)和比值植被指数法(RVI)。其中NDWI指数模型,在选择合适阈值的情况下,水体提取的总体精度最高可达98%左右,并且提取过程对阈值影响不敏感。 相似文献
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The Sentinel-2 Multi-Spectral Imager (MSI) has three spectral bands centered at 705, 740, and 783 nm wavelengths that exploit the red-edge information useful for quantifying plant biochemical traits. This sensor configuration is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving canopy chlorophyll content (CCC) from Sentinel-2 in a heterogeneous mixed mountain forest. Amongst the algorithms presented in the literature, 13 different vegetation indices (VIs), a non-parametric statistical approach, and two radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field campaign was conducted in July 2017 to collect in situ measurements of CCC in Bavarian forest national park, and the cloud-free Sentinel-2 image was acquired on 13 July 2017. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches, such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. As such the modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m2 (R2 = 0.75). The PLSR resulted in the highest R2 of 0.78, and slightly higher RMSE =0.22 g/m2 than mSR3. The physical-based approach-INFORM inversion using look-up table resulted in an RMSE =0.31 g/m2, and R2 = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for long-term terrestrial ecosystems monitoring across the globe. 相似文献
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Timothy Dube Tawanda W. Gara Onisimo Mutanga Mbulisi Sibanda Cletah Shoko Amon Murwira 《国际地球制图》2018,33(2):178-188
Accurate and up-to-date information on forest dendrometric traits, such as above ground biomass is important in understanding the contribution of terrestrial ecosystems to the regulation of atmsopheric carbon, especially in the face of global environmental change. Besides, dendrometric traits information is critical in assessing the healthy and the spatial planning of fragile ecosystems, such as the savanna dry forests. The aim of this work was to test whether red-edge spectral data derived from WorldView-2 multispectral imagery improve biomass estimation in savanna dry forests. The results of this study have shown that biomass estimation using all Worldview-2 raw spectral bands without the red-edge band yielded low estimation accuracies (R2 of 0.67 and a RMSE-CV of 2.2 t ha?1) when compared to when the red-edge band was included as a co-variate (R2 of 0.73 and a RMSE-CV of 2.04 t ha?1). Also, similar results were obseved when all WorldView-2 vegetation indices (without the red-edge computed ones), producing slightly low accuracies (R2 of about 0.67 and a RMSE-CV of 2.20 t ha?1), when compared to those obtained using all indices and RE-computed indices(R2 of 0.76 and a RMSE-CV of 1.88 t ha?1). Overall, the findings of this work have demontrated the potential and importance of strategically positioned bands, such as the red-edge band in the optimal estimation of indigeonus forest biomass. These results underscores the need to shift towards embracing sensors with unique and strategeically positioned bands, such as the forthcoming Sentinel 2 MSI and HysPIRI which have a global footprint. 相似文献
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《地理信息系统科学与遥感》2013,50(2):195-209
Eight vegetation indices (VI) commonly used for above-ground biomass (AGB) estimation were derived from Satellite Pour l'Observation de la Terre 5 (SPOT 5) imagery and used to predict herbaceous AGB at a semiarid rangeland study site in southeastern Idaho. The relationship between herbaceous AGB and vegetation water content was also evaluated and as a result, a suite of water-sensitive vegetation indices (WSVI) were developed. Correlation coefficients between herbaceous AGB, VIs, and WSVIs were calculated, demonstrating that WSVIs were correlated (r 2 ≥ 0.51) with vegetation water content and performed better than standard VIs in herbaceous AGB estimates within the semiarid rangelands of Idaho. 相似文献
15.
Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression 总被引：7，自引：0，他引：7
Moses Azong Cho Andrew Skidmore Fabio Corsi Sipke E. van Wieren Istiak Sobhan 《International Journal of Applied Earth Observation and Geoinformation》2007,9(4):414-424
The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration (n = 30) and test (n = 12) data sets. The regression model derived from NDVI computed from bands at 740 and 771 nm produced a lower standard error of prediction (SEP = 264 g m−2) on the test data compared with the standard NDVI involving bands at 665 and 801 nm (SEP = 331 g m−2), but comparable results with REPs determined by various methods (SEP = 261 to 295 g m−2). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP = 149 to 256 g m−2) compared with NDVI and REP models. The lowest prediction error (SEP = 149 g m−2, 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park. 相似文献
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Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soybean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (∼daily), high spatial resolution (∼5 m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8 m2/m2. The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27%; and coefficients of determination ranging between 86 and 88%. The NDVI performed best for the estimation of dry leaf biomass (CV = 27% and r2 = 090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV ≤ 20%). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependant best-fit functions. This approach eliminates/reduces the need for reflectance resampling, VIs inter-calibration and spatial resampling. 相似文献
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WorldView-2纹理的森林地上生物量反演 总被引：1，自引：0，他引：1
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
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Mbulisi Sibanda Onisimo Mutanga Timothy Dube Thulile S Vundla Paramu L Mafongoya 《地理信息系统科学与遥感》2019,56(1):68-86
This study assessed the strength of Sentinel-2 multispectral instrument (MSI) derived Red Edge (RE) bands in estimating Leaf Area Index (LAI) and mapping canopy storage capacity (CSC) for hydrological applications in wattle infested ecosystems. To accomplish this objective, this study compared the estimation strength of models derived, using standard bands (all bands excluding the RE band) with those including RE bands, as well as different vegetation indices. Sparse Partial Least Squares (SPLSR) and Partial Least Squares Regression (PLSR) ensembles were used in this study. Results showed that the RE spectrum covered by the Sentinel-2 MSI satellite reduced the estimation error by a magnitude of 0.125 based on simple ratio (RE SR) vegetation indices from 0.157 m2· m?2 based on standard bands, and by 0.078 m2· m?2 based on red edge normalised difference vegetation (NDVI-RE). The optimal models for estimating LAI to map CSC were obtained based on the RE bands centered at 705 nm (Band 5), 740 nm (Band 6), 783 nm (Band 7) as well as 865 nm (Band 8a). A root mean square error of prediction (RMSEP) of 0.507 m2· m?2 a relative root mean square error of prediction (RRMSEP) of 11.3% and R2 of 0.91 for LAI and a RMSEP of 0.246 m2/m2 (RRMSEP = 7.9%) and R2 of 0.91 for CSC were obtained. Overall, the findings of this study underscore the relevance of the new copernicus satellite product in rapid monitoring of ecosystems that are invaded by alien invasive species. 相似文献
19.
Developing models for estimating aboveground biomass (AGB) in naturally growing forests is critical for climate change modelling. AGB models developed using satellite imagery varies with study area, depending on the complexity of vegetation and landscape structure, which affects the upwelling radiance. We assessed the potential of SPOT-6 imagery in predicting AGB of trees planted at different time periods, using image texture combinations. Image texture variables were computed from the SPOT6 pan-sharpened image data, which is characterised by a 1.5 m spatial resolution. In addition, we incorporated the minimal variance technique to select the optimum window sizes that best captures AGB variation in our study area. The results showed that image texture was able to detect AGB for both mature and young trees, however, models detecting mature trees were more superior, with accuracies of R2 = 0.70 and 0.25 for 2009–2011 and 2011–2013 plantation phases, respectively. In addition, our results showed that the three band texture ratios yielded the highest accuracy (R2 = 0.88 and RMSE = 54.54 kg m−2) compared to two texture (R2 = 0.85 and RMSE = 60.65 kg m−2) and single texture band combinations (R2 = 0.64 and RMSE = 94.13 kg m−2). A frequency analysis was also run to determine which bands appeared more frequently in the selected texture band models. The frequency analysis revealed that both the red and green bands appeared more frequently on the selected texture band variables, indicating that they were more sensitive to the variation of AGB in our study area. The results showed high variation in AGB within the Buffelsdraai reforestation site, especially due to varying tree plantation phases as well as topography. In essence, the study demonstrated the possibility of image texture combinations computed from the SPOT-6 image in estimating AGB. 相似文献
20.
The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha−1. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R2 = 0.62 > 0.52, RMSE = 50.36 Mg ha−1<56.63 Mg ha−1). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R2 = 0.62, RMSE = 51.03 Mg ha−1). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests. 相似文献