伊犁绢蒿荒漠草地退化等级的高光谱识别方法

本研究以伊犁绢蒿(Seriphidium transiliense)荒漠草地为研究对象,基于环境卫星HSI高光谱影像和群落冠层光谱,采用光谱角填图法和光谱信息散度法对草地退化等级进行识别。结果表明:以实地采集的冠层反射光谱为指导的HSI高光谱影像识别精度较差;基于HSI高光谱影像的退化等级识别结果较好,总体分类精度在76%以上,适合对伊犁绢蒿荒漠草地退化等级识别。     

Identification of eggs from different production systems based on hyperspectra and CS-SVM

1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied.

2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables combined with a Savitzky–Golay were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) methods to establish an SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model, while egg category was the output.

3. The SWR–CS–SVM model performed better than the other models, including SWR–GS–SVM, SWR–GA–SVM, SWR–PSO–SVM and others based on full spectral data. The training and test classification accuracy of the SWR–CS–SVM model were respectively 99.3% and 96%.

4. SWR–CS–SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.     

Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging

Healthy wheat kernels and wheat kernels damaged by the feeding of the insects: rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum) were scanned using a near-infrared (NIR) hyperspecrtal imaging system (700-1100 nm wavelength range) and a colour imaging system. Dimensionality of hyperspectral data was reduced and statistical and histogram features were extracted from NIR images of significant wavelengths and given as input to three statistical discriminant classifiers (linear, quadratic, and Mahalanobis) and a back propagation neural network (BPNN) classifier. A total of 230 features (colour, textural, and morphological) were extracted from the colour images and the most contributing features were selected and used as input to the statistical and BPNN classifiers. The quadratic discriminant analysis (QDA) classifier gave the highest accuracy and correctly identified 96.4% healthy and 91.0-100.0% insect-damaged wheat kernels using the top 10 features from 230 colour image features combined with hyperspectral image features.     

农牧交错区青贮玉米粗蛋白含量分析及高光谱反演

为了探讨青贮玉米全株粗蛋白含量的基因型差异和生育进程中的变化特点,对农牧交错区引进和选育的24份青贮玉米杂交种的粗蛋白含量进行了测定分析.结果表明,不同基因型全株粗蛋白含量具有较大的差异,变幅为6.24%～10.01%,平均值为7.77%;全株粗蛋白含量和生物产量之间总体没有明显的相关关系,分组分析可以呈现出正相关、负相关和无相关三种关系.叶片和全株粗蛋白含量有随生育期的推移而下降的趋势,而且叶片对粗蛋白含量的贡献在前期最高,在中期最低;茎部对粗蛋白含量的贡献在中期最高,在后期最低.各生育期的叶片和全株粗蛋白含量之间具有显著或极显著正相关关系;生育前期叶片粗蛋白含量和收获期全株粗蛋白含量之间具有极显著正相关关系,生育前期和中期全株粗蛋白含量与生育后期全株粗蛋白含量之间具有极显著正相关关系.选用植株冠层12个波长的高光谱反射率的一阶导数,与叶片和全株粗蛋白含量作回归分析,可以获得很好的效果,拟合度(R2)可以达到0.8以上。     

基于光谱分析的土壤重金属含量估算研究——以三江源区玉树县和玛多县为例

以三江源区玉树县和玛多县为研究区,利用实验室测定的As、Cu、Pb、Zn、Cr、Cd、Hg元素含量和室内采集的土壤原始光谱及其4种转换形式,建立了光谱指标与重金属含量的多元回归模型,利用决定系数(R2)、相对分析误差(RPD)及均方根误差(RMSE)评价模型的精度。研究结果表明,土壤As、Cu、Pb、Zn、Cr、Cd含量与SOM、Fe、Mn、Al、Mg等元素具有显著相关关系,Hg元素则未达到显著性水平。As、Cu、Pb、Zn、Cr和Cd元素估算模型回归方程R2达到了0.5以上,均通过了显著性检验,其中Pb、Zn和Cr元素验证样本RPD均达到了1.4以上,模型具备粗略估算能力;As、Cu和Cd元素验证样本RPD均低于1.4,模型不具备粗略估算能力。Hg元素估算模型回归方程的R2为0.28,未能通过显著性检验,无法用于对Hg含量的估算。     

Soil heterogeneity at the field scale: a challenge for precision crop protection

Crop protection seldom takes into account soil heterogeneity at the field scale. Yet, variable site characteristics affect the incidence of pests as well as the efficacy and fate of pesticides in soil. This article reviews crucial starting points for incorporating soil information into precision crop protection (PCP). At present, the lack of adequate field maps is a major drawback. Conventional soil analyses are too expensive to capture soil heterogeneity at the field scale with the required spatial resolution. Therefore, we discuss alternative procedures exemplified by our own results concerning (i) minimally and non-invasive sensor techniques for the estimation of soil properties, (ii) the evidence of soil heterogeneity with respect to PCP, and (iii) current possibilities for incorporation of high resolution soil information into crop protection decisions. Soil organic carbon (SOC) and soil texture are extremely interesting for PCP. Their determination with minimally invasive techniques requires the sampling of soils, because the sensors must be used in the laboratory. However, this technique delivers precise information at low cost. We accurately determined SOC in the near-infrared. In the mid-infrared, texture and lime content were also exactly quantified. Non-invasive sensors require less effort. The airborne HyMap sensor was suitable for the detection of variability in SOC at high resolution, thus promising further progress regarding SOC data acquisition from bare soil. The apparent electrical conductivity as measured by an EM38 sensor was shown to be a suitable proxy for soil texture and layering. A survey of arable fields near Bonn (Germany) revealed widespread within-field heterogeneity of texture-related ECa, SOC and other characteristics. Maps of herbicide sorption and application rate were derived from sensor data, showing that optimal herbicide dosage is strongly governed by soil variability. A phytoassay with isoproturon confirmed the reliability of spatially varied herbicide application rates. Mapping areas with an enhanced leaching risk within fields allows them to be kept free of pesticides with related regulatory restrictions. We conclude that the use of information on soil heterogeneity within the concept of PCP is beneficial, both economically and ecologically.     

玉米叶片高光谱特征及与叶绿素、类胡萝卜素相关性的研究

测定了不同品种类型、不同株型、不同发育期的春玉米叶片及其它器官、不同叶位叶片及叶片不同部位的高光谱反射率和叶片的叶绿素、类胡萝卜素含量。结果表明,玉米不同鲜器官的反射率随波长的变化趋势相似,但数值存在较大差异;不同叶位的叶片、不同品种同一叶位的叶片、叶片的不同部位的光谱反射率有差异;叶片光谱反射率随含水率减少而升高,它们的变化趋势相似;叶片叶绿素和类胡萝卜素浓度与光谱植被指数R800/R550、R673/R640、PSSRa、PSNDa、RCh、CARI、λred、Dλred和Sred具有极显著相关。说明可以通过光谱方法来监测玉米长势和估算叶绿素、类胡萝卜素含量。     

Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data

Forest fire management practices are highly dependent on the proper monitoring of the spatial distribution of the natural and man-made fuel complexes at landscape level. Spatial patterns of fuel types as well as the three-dimensional structure and state of the vegetation are essential for the assessment and prediction of forest fire risk and fire behaviour. A combination of the two remote sensing systems, imaging spectrometry and light detection and ranging (LiDAR), is well suited to map fuel types and properties, especially within the complex wildland–urban interface. LiDAR observations sample the spatial information dimension providing explicit geometric information about the structure of the Earth's surface and super-imposed objects. Imaging spectrometry on the other hand samples the spectral dimension, which is sensitive for discrimination of surface types. As a non-parametric classifier support vector machines (SVM) are particularly well adapted to classify data of high dimensionality and from multiple sources as proposed in this work. The presented approach achieves an improved land cover mapping adapted to forest fire management needs. The map is based on a single SVM classifier combining the spectral and spatial information dimensions provided by imaging spectrometry and LiDAR.     

高光谱遥感在荒漠化监测中的应用

论述并建立了适合于高光谱遥感技术的荒漠化监测指标,提出了基于高光谱分辨率数据处理算法的荒漠化监测评价指标信息的提取方法。初步构建了ＴＭ、高光谱分辨率成像光谱仪和地面调查相结合的荒漠化监测的技术框架。     

Yield Estimation Model of Citrus Based on Spectral Data and Agronomic Parameters

With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren- nial citrus trees as the research object, using ASD spectrometer to collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.