基于高光谱技术的茶鲜叶含水率检测与分析

茶鲜叶含水率是茶叶加工业中衡量茶叶品质的一个重要指标。为了实现茶叶加工过程中茶鲜叶含水率的快速检测,本文提出了一种应用高光谱技术分析茶鲜叶含水率的无损检测方法。通过对茶鲜叶高光谱图像感兴趣区域光谱数据的提取,利用4种不同的算法对原始数据进行预处理,采用逐步回归分析法对预处理后的数据提取特征波长,并采用多元线性回归法、偏最小二乘回归建立特征波长和茶鲜叶含水率定量分析模型。研究结果表明,经过卷积平滑处理后的正交信号校正的预处理结合逐步回归分析法所建立的偏最小二乘回归茶鲜叶含水率预测效果最佳,模型校正集、交叉验证集和预测集的相关系数分别为0.8977、0.8342和0.7749,最小均方根误差分别为0.0091、0.0311和0.0371。由此可见,高光谱技术能有效的实现茶鲜叶含水率的检测,这为茶叶加工业中衡量茶叶品质提供了新的检测方法。     

Spatial modelling of the fraction of photosynthetically active radiation absorbed by a boreal mixedwood forest using a lidar–hyperspectral approach

The spatial variability of the fraction of photosynthetically active radiation absorbed by the canopy (fPAR) was characterized for a heterogeneous boreal mixedwood forest site located in northern Ontario, Canada, based on relationships found between fPAR and light detection and ranging (lidar) data over different canopy architectures. Estimates of fPAR were derived from radiation measurements made above the canopy at a flux tower and below-canopy radiation was measured across a range of species compositions and canopy architectures. Airborne lidar data were used to characterize spatial variability of canopy structure around the flux tower and a map of mean canopy chlorophyll concentration was derived from airborne hyperspectral imagery. Different volumes of lidar points for the locations directly above each photosynthetically active radiation (PAR) sensor were examined to determine if there is an optimal method of relating lidar returns to estimated fPAR values.The strongest correlations between mean lidar height and fPAR occurred when using points that fell within a theoretical cone which originated at the PAR sensor having a solid angle α = 55°. For diffuse conditions, the correlation (r) between mean lidar height versus fPAR × chlorophyll was stronger than between mean lidar height versus fPAR by 8% for mean daily fPAR and from 10 to 20% for diurnal fPAR, depending on solar zenith angle. For direct light conditions, the relationship was improved by 12% for mean daily fPAR and 12–41% for diurnal relationships.Linear regression models of mean daily fPAR × chlorophyll versus mean lidar height were used in conjunction with gridded lidar data and the canopy chlorophyll map to generate maps of mean daily fPAR for direct and diffuse sunlight conditions. Site average fPAR calculated from these maps was 0.79 for direct light conditions and 0.78 for diffuse conditions. When compared to point estimates of mean daily fPAR calculated on the tower, the average fPAR was significantly lower than the point estimate. Subtracting the direct sunlight fPAR map from the diffuse sunlight fPAR map revealed a distinct spatial pattern showing that areas with open canopies and relatively low chlorophyll (e.g., black spruce patches) have a higher fPAR under direct sunlight conditions, while closed canopies with higher chlorophyll (e.g., deciduous species) absorb more PAR under diffuse conditions. These findings have implications for scaling from point measurements at flux towers to larger resolution satellite imagery and addressing local scale heterogeneity in flux tower footprints.     

Hyperspectral species mapping for automatic weed control in tomato under thermal environmental stress

This work studied the impacts of variations in environmental temperature on hyperspectral imaging features in the visible and near infrared regions for robust species identification for weed mapping in tomato production. Six major Californian processing tomato cultivars, black nightshade (Solanum nigrum L.) and redroot pigweed (Amaranthus retroflexus L.) were grown under a variety of diurnal temperature ranges simulating conditions common in the Californian springtime planting period and one additional treatment simulating greenhouse growing conditions. The principal change in canopy reflectance with varying temperature occurred in the 480-670 and 720-810 nm regions. The overall classification rate ranged from 62.5% to 91.6% when classifiers trained under single temperatures were applied to plants grown at different temperatures. Eliminating the 480-670 nm region from the classifier’s feature set mitigated the temperature effect by stabilizing the total crop vs. weed classification rate at 86.4% over the temperature ranges. A site-specific recalibration method was also successful in alleviating the bias created by calibrating the models on the extreme temperatures and increased the classification accuracy to 90.3%. A global calibration method, incorporating all four temperature conditions in the classifier feature space, provided the best average total classification accuracy of 92.2% out of the methods studied, and was fairly robust to the varying diurnal temperature conditions.     

高光谱技术在常规水稻种子活力检测中的应用

[目的]实现常规水稻种子活力的快速自动化检测。[方法]采用高光谱成像技术(波长范围874~1 734 nm),提取甬籼69和中早39 2种常规水稻种子未老化、老化48 h和老化72 h的光谱反射率,在提取样本光谱时采用小波变换(WT)剔除像素点光谱噪声部分,并基于全波段光谱建立了支持向量机(SVM)判别分析模型。[结果]未老化种子与老化种子可以准确识别,而老化48 h种子与老化72 h种子之间无法准确识别,与基于种子活力参数的测量结果相符,且不同水稻品种对老化的反应存在差异。[结论]高光谱成像技术结合化学计量学方法用于种子活力的快速自动化无损检测是可行的。     

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.     

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。     

TM影像VI提取植被信息技术研究

应用规一化植被指数（ＮＤＶＩ）、土壤调节植被指数（ＳＡＶＩ）和修正后的土壤调节植被指数（ＭＳＡＶＩ）,研究了ＴＭ影像植被信息的提取技术。结果表明,ＳＡＶＩ较之ＮＤＶＩ和ＭＳＡＶＩ,消除土壤背景影响和适应植被密度变化的能力更强,是植被信息提取效果较好的植被指数。使用ＳＡＶＩ,关键是要确定一个合适的土壤调节参数Ｌ,以最大限度地消除土壤背景的影响,ＳＡＶＩ提出时推荐的最佳Ｌ值（０．５）应用于卫片处理有很大局限性;基于研究结果,本文给出了适于不同植被条件卫片处理的最佳Ｌ取值。     

不同生育时期冬小麦叶片相对含水量高光谱监测

为实现冬小麦不同生育时期叶片水分含量的快速监测,以冬小麦冠层高光谱数据和红外热成像数据为基础,计算得到5种光谱参数,通过对不同生育时期叶片相对含水量与光谱参数拟合状况进行分析和筛选,分别构建了基于光谱参数的叶片相对含水量反演模型,并对模型进行检验。结果表明,不同生育时期叶片相对含水量与比值指数（RVI）、归一化差值植被指数（NDVI）、比值/归一化植被指数（R/ND）、优化土壤调整植被指数（OSAVI）、冠气温差（TDc-a）均呈极显著相关（P＜0.01）;拔节期、抽穗期、开花期、灌浆前期和灌浆后期叶片相对含水量分别与NDVI、OSAVI、R/ND、TDc-a和TDc-a拟合效果较好,决定系数分别为0.842、0.884、0.831、0.864和0.945;预测模型的均方根误差分别为0.019、0.016、0.027、0.032和0.024,相对误差分别为2.16%、1.80%、3.30%、3.81%和3.53%。因此,在拔节期、抽穗期、开花期、灌浆前期和灌浆后期,可以分别利用NDVI、OSAVI、R/ND、TDc-a和TDc-a估测冬小麦叶片相对含水量。     

Comparison of image and rapid field assessments of riparian zone condition in Australian tropical savannas

Suitable methods for measuring and monitoring the condition of riparian environments are being investigated by government agencies responsible for maintaining these environments in Australia. The objective of this work was to compare two riparian condition assessment approaches, the Tropical Rapid Appraisal of Riparian Condition (TRARC) method developed for rapid on-ground assessment of the environmental condition of savanna riparian zones and an image based riparian condition monitoring scheme. Measurements derived from these two approaches were compared and correlated. The sample representativeness of the TRARC method was evaluated and the cost-effectiveness and suitability for multi-temporal analysis of the two approaches were assessed. Two high spatial resolution multi-spectral QuickBird satellite images captured in 2004 and 2005 and coincident field data covering sections of the Daly River in the Northern Territory, Australia were used in this work. Both field and image data were processed to map indicators of riparian zone condition including percentage canopy cover, organic litter on the ground, canopy continuity, tree clearing, bank stability, and flood damage. Spectral vegetation indices, image segmentation, and supervised classification were used to produce riparian health indicator maps. QuickBird image data were used to examine if the spatial distribution of TRARC transects provided a representative sample of ground based estimates of riparian health indicators. Covering approximately 3% of the study area, the sample mean of the TRARC estimates of individual indicators of riparian zone condition were in most cases within 20% of the global mean derived from the whole imaged riparian area. The cost-effectiveness of the image based approach was compared to that of the ground based TRARC method. Results showed that the TRARC method was more cost-effective at spatial scales from 1 km to 200 km of river in relatively homogeneous riparian zones along rivers with only one channel, while image based assessment becomes more feasible at regional scales (200–2000 km of river). A change detection analysis demonstrated that image data can provide detailed information on gradual change, while the TRARC method is less suited for multi-temporal analysis due to the ranked data format, which inhibits precise detection of change. However, results from both methods were considered to complement each other for single date assessment of riparian zones if used at appropriate spatial scales.     

Seasonal variation in canopy reflectance and its application to determine the water status and water use by citrus trees in the Western Cape, South Africa

This study describes the diurnal and seasonal dynamics of the canopy reflectance, water use and water status of Midknight Valencia citrus trees under semi-arid conditions. Hyperspectral canopy reflectance data was collected on 30 trees at monthly intervals over a period of 16 months in a commercial orchard in South Africa. The mean canopy reflectance in the wavelength range 350-2500 nm followed a clear seasonal trend influenced by environmental conditions and tree phenology. Mean monthly reflectance peaked in summer (∼22%) while the lowest value (∼15%) was reached in winter with the seasonal changes in the sun's position accounting for a significant proportion of the variations. A sensitivity analysis of a Penman-Monteith transpiration model showed that water use by individual trees changed by up to 13% when the canopy reflectance was varied over the seasonal range of measured values. This suggested that the seasonal changes in tree water use influenced the seasonal trend of the canopy reflectance. Thus monitoring the canopy reflectance of citrus trees could offer information on the tree water status. To test this, sap flow data of water uptake and loss by the trees were compared with the canopy spectra. Sap flow data showed a heavy reliance by the citrus trees on the internally stored water with up to 25% of the daily total transpiration withdrawn from the trees’ internal water storage pools when soil water was limited. This depletion of internally stored water, and hence the change in tree water status, was detected using spectral indices based on the first order derivatives of the canopy reflectance centered at two and, at most, four spectral bands. We conclude that even if citrus trees are evergreen, their canopy reflectance changes significantly throughout the year with a considerable impact on tree energy balance and water use. In addition, the contribution of the internally stored water to daily transpiration is a possible indicator of drought stress for citrus trees detectable from changes in canopy reflectance and it has potential applications in irrigation scheduling using canopy level spectral information.