基于随机森林的高寒草地地上生物量高光谱估算

草地地上生物量（Aboveground biomass,AGB）是衡量草地生产力的关键因素,准确测定草地AGB具有重要意义。高光谱因具有时效性强、不破坏草地等特点被广泛用于草地生理生态指标的测定。本研究提取和计算了海北试验站高寒草地冠层的原始光谱（Original spectrum,OR）反射率、一阶微分光谱（First derivative spectrum,FD）反射率、光谱位置面积参数（Spectral parameters of spectral position and area,PA）和植被指数（Vegetation indices,VI）4种不同类型的特征变量,使用连续投影算法（Successive projections algorithm,SPA）和递归特征消除算法（Recursive feature elimination,RFE）进行特征选择,采用随机森林算法（Random forest,RF）构建草地AGB估测模型。结果表明：在由4种特征变量分别构建的草地AGB估测模型中,基于VI的RF模型精度最高（测试集R²=0.70,RMSE=557.87 kg·ha^-1）,实测AGB与估测AGB的线性R²达到0.72;不同类型特征变量组合构建的草地AGB估测模型中,PA+VI组合的RF模型精度最高（R²=0.71,RMSE=548.97 kg·ha^-1）,实测AGB和估测AGB的线性R²达到0.73。     

Identification of grapevine varieties using leaf spectroscopy and partial least squares

Grapevine variety identification is a matter of great interest in viticulture, which is currently addressed by visual ampelometry or wet chemistry genetic analysis. This paper reports the development of a simple and automatic method of classification of grapevine varieties from leaf spectroscopy. The method consists of a classifier based on partial least squares that discriminates among grapevine varieties using a hyperspectral image of a leaf measured in reflectance mode. Hyperspectral imaging was conducted with a camera with 1040 wavelength bands operating between 380 nm and 1028 nm. The classifier was created using 300 leaves, 100 of each of the varieties Vitis vinifera L., Tempranillo, Grenache and Cabernet Sauvignon. Monte-Carlo cross-validation confirmed the classifier’s performance for the three varieties, which exceeded 92% in all cases. The proposed method has proven to satisfactory classify among grape varieties, but certainly a wider range of grapevine cultivars should be tested before it gets implemented for local sensing with the aim of providing the wine industry with a fast, automatic, environmentally friendly and accurate tool for grapevine variety identification.     

Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data

Apple bruising, as a mechanical damage, occurs due to impact, compression, vibration or abrasion during handling. However, the symptoms of this damage, browning and softening of the tissue, appear not immediately but after a certain period of time after bruising. For sorting and grading systems, the information about how long the bruise exists in affected fruit can be valuable. VNIR (visible and near-infrared) and SWIR (short wavelength infrared) spectral characteristics of sound and bruised apple tissues were analyzed during a two week period after bruising. Supervised classification methods, including support vector machines, linear logistic regression, neural networks and decision trees, were used and compared to check their effectiveness for distinguishing time after bruising with respect to five varieties of apples. The detection system included hyperspectral cameras equipped with sensors working in the visible and near-infrared (400–1000 nm) and short wavelength infrared (1000–2500 nm) ranges. The results of supervised classification revealed good applicability of hyperspectral imaging in VNIR and SWIR spectral ranges for detecting the number of days after bruising. The linear logistic regression neural networks models were found to be the best classifiers in the majority of models developed. Prediction accuracies higher than 90% were obtained for classification models on spectral data pretreated with the second derivative.     

农畜产品品质无损检测中高光谱图像技术的应用进展

高光谱图像技术结合了计算机图像与光谱技术两者的优点,是农畜产品品质检测技术的发展趋势。阐述了农畜产品品质检测中高光谱图像技术的硬件组成和图像的获取方法。介绍了高光谱图像技术在农畜产品品质检测中的应用现状以及数据处理方法,重点分析了不均匀二次差分算法和独立分量分析算法。对高光谱图像技术应用于农畜产品品质检测的技术发展提出了建议。     

冬小麦叶片花青素相对含量高光谱监测

为探究冬小麦叶片花青素含量的高光谱监测方法,以陕西省关中地区冬小麦为研究对象,分析了叶片光谱反射率与花青素含量的相关性,建立以不同波段组合的RSI、DSI和NDSI光谱指数为自变量的一元回归模型以及利用偏最小二乘法构建的多元回归模型,并进行模型精度比较。结果表明,所有模型中,开花期的PLS模型精度最高,预测效果最好(建模r~2=0.872 3,RMSE=0.005 9;检验r~2=0.912 8,RMSE=0.004 8),是预测冬小麦花青素的最优模型;各生育时期中,开花期模型精度较高,表现稳定,是预测冬小麦花青素的最佳生育时期。     

武夷山地区土壤有机质高光谱模型建立与评价

[目的]利用高光谱遥感,对武夷山土壤有机质进行研究,建立了土壤有机质高光谱反演模型,为今后武夷山地区土壤有机质含量的快速测定提供理论依据。[方法]利用ASD Field Spec Pro地物光谱仪对不同有机质土壤进行光谱测量,采用有机质速测法对土壤有机质含量进行测定。[结果]762 nm为土壤有机质的敏感波段,其相关系数为-0.771,然后建立拟合方程为:Y=61.823-129.274x,拟合度为0.820。[结论]提出今后亚热带地区土壤高光谱的研究方向。     

干旱区典型绿洲土壤有机质的反演及影响因素研究

为了大面积、高精度地反演土壤有机质含量,为农业可持续发展提供数据支撑。以新疆渭干河-库车河三角洲绿洲为研究区,采用波段平均法将实测高光谱窄波段拟合为Landsat 8 OLI遥感影像的宽波段,建立土壤有机质含量的估算模型,并将最优估算模型应用到经过波段校正的Landsat 8OLI遥感影像中。结果表明:(1)反射率进行倒数、对数、平方、一阶微分等数学变换后与有机质含量的相关性显著提高;(2)土壤有机质的高光谱估算模型拟合度较高,最优估算模型的决定系数R2为0.852,采用比值法对多光谱波段反射率进行校正,校正后的遥感影像反演结果得到了较大提高,检验样本的决定系数R2从0.711提升至0.849。从反演结果来看,将高光谱估算模型应用到经过订正的多光谱影像,土壤有机质反演模型的精度得到了大幅度提高,运用此方法可以实现高精度的土壤有机质区域化反演。(3)有机质的分布受土地利用类型、土壤颗粒组成、土壤质地的影响,其中土壤质地对有机质的空间分布影响最为显著。     

水稻、玉米、棉花的高光谱及其红边特征比较

 测定了水稻、玉米、棉花 3种作物共 6个品种的冠层和主茎叶片不同生育期高光谱反射率及其对应叶片中叶绿素、类胡萝卜素含量 ,分析了它们的高光谱及其红边特征和红边参数与叶面积指数、地上生物量、鲜叶重及叶片色素含量的相关性。结果表明 :(1) 3种作物冠层高光谱反射率大小与其生育期有关。冠层和叶片光谱反射率最大值比较结果是 ,冠层光谱 :棉花 >玉米 >水稻 ;叶片光谱 :水稻 >棉花 >玉米。 (2 ) 3种作物冠层光谱的红边都具有“双峰”现象。红边幅值Dλr 和红边面积Sr 均呈增大、减少的变化规律 ,但红边     

棉花主要栽培生理参数的高光谱估测研究

 应用地物光谱仪获取近地高光谱遥感数据 ,用高光谱技术提取和估计棉花主要栽培生理参数。结果表明 ,棉花叶片具有绿色植物典型的反射光谱曲线特征 ;早衰的棉花反射光谱红谷区谷低变浅 ,最小波段光谱反射率Ro及数值积分面积SRo增大 ,与叶片光合速率呈显著负相关。经逐步回归分析 ,确定的一阶微分光谱值与棉叶叶绿素浓度的最大相关系数是 0 .734 4 (n =2 1) ,发生在 75 0nm波段处 ;群体叶面积指数 (LAI)与归一化差值植被指数 (ND VI)呈很好的对数相关关系 ;红边、蓝边、黄边每边的积分面积积累了多波段信息 ,在估测棉花冠层叶片全氮含量中有较大的应用潜力。研究建立了棉花主要栽培生理参数叶绿素含量、LAI和冠层叶片全氮含量等的遥感估测统计模型。     

联合主成分分析与最小二乘支持向量机估测冬小麦叶面积指数

利用单一植被指数估测叶面积指数存在高光谱遥感丰富的波段信息易丢失和外界因素干扰大的缺点,但若将波段信息全部引入模型又会增加建模难度。为解决利用多波段信息估测叶面积指数的问题,利用主成分分析法(PCA)对光谱数据进行降维,之后将提取的主成分与最小二乘支持向量机(LS-SVM)模型相结合,构建冬小麦叶面积指数的高光谱估测模型,并与以4类植被指数作为LS-SVM输入参数建立的模型进行比较。结果表明,以主成分作为LS-SVM模型的输入参数建立的模型精度最高,模型检验集R2为0.71,检验集RMSE为0.56,估测结果较使用植被指数作为输入参数建立的模型精度高,稳定性好。该方法可为利用多波段信息进行大范围冬小麦叶面积指数的无损测定提供参考。