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.     

截形叶螨危害下枣叶片叶绿素含量高光谱估算模型

以不同截形叶螨(Tetranychus truncatus Ehara)危害等级下枣叶片高光谱和叶绿素含量数据为基础,分析不同截形叶螨危害等级(0级、1级、2级、3级、4级)下枣叶片高光谱特征,构建基于一阶微分光谱的不同截形叶螨危害等级枣叶片叶绿素含量高光谱线性回归估测模型。结果表明:截形叶螨危害造成叶片中叶绿素含量减少,导致光谱反射率降低,表现为随危害等级的增加叶绿素含量呈逐级减少趋势。在不同截形叶螨危害等级枣叶片叶绿素估测模型中,危害等级为0级时,模型拟合度最好,达到0.810,表明利用高光谱数据构建不同危害等级枣叶片叶绿素含量估算模型具有一定的潜力,对危害植被叶片的虫害诊断意义重大。     

土壤有机质含量的高光谱特性及其反演

【目的】应用高光谱技术阐释土壤有机质光谱规律及对有机质在土壤中的含量进行定量分析，为土壤肥力测定和评价提供指导。【方法】利用ASD FieldSpec Pro地物光谱仪在自然环境条件下对不同有机质含量的土壤样本进行光谱测量。通过对获取的土壤样品高光谱反射率进行倒数、导数、对数和标准化比值变换，运用统计单相关方法进行分析。【结果】确定了511 nm波长为诊断土壤有机质含量的敏感波段，采用450～750 nm可见光波段反射率均值对507～516 nm敏感范围反射率均值进行标准化比值处理后获得的有机质诊断指数（OII）对土壤有机质含量的估算精度较高，它们存在着简单的线性相关关系。【结论】土壤有机质诊断指数（OII）反演模型为土壤肥力的快速测定提供了新的途径。     

Effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery – Case study in Norway Spruce-dominated stands in Southern Finland

The effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery was investigated in Norway Spruce-dominated stands in Southern Finland. Airborne thermal imagery, airborne scanning light detection and ranging (LiDAR) data and 92 field-measured sample plots were acquired at the area of interest. The surface temperature correlated most negatively with the logarithm of stem volume, Lorey’s height and the logarithm of basal area at a resolution of 254?m² (9?m radius). LiDAR-derived metrics: the standard deviations of the canopy heights, canopy height (upper percentiles and maximum height) and canopy cover percentage were most strongly negatively correlated with the surface temperature. Although forest structure has an effect on the detected surface temperature, higher temperatures were detected in severely defoliated canopies and the difference was statistically significant. We also found that the surface temperature differences between the segmented canopy and the entire plot were greater in the defoliated plots, indicating that thermal images may also provide some additional information for classifying forests health status. Based on our results, the effects of forest structure on the surface temperature captured by airborne thermal imagery should be taken into account when developing forest health mapping applications using thermal imagery.     

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。     

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.     

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。     

Reflectance-based assessment of spider mite “bio-response” to maize leaves and plant potassium content in different irrigation regimes

It is widely accepted that pest infestations elicit a change in plant physiology, which cause detectable changes in crop leaf reflectance. In this study, we test the hypothesis that crop leaf reflectance may also be used to forecast the risk of pest infestation before they actually occur. We collected reflectance data in 160 spectral bands from 405 to 907 nm from excised leaf pieces from field grown maize plants under 3 irrigation regimes. Leaf material was collected at weekly intervals in two growing seasons. The same leaf pieces were used in choice bioassays with carmine spider mites to assess attractiveness to mites (spider mite “bio-response”) across irrigation regimes. In one growing season, we also obtained nutritional element data (lipid, protein, soluble sugar, starch, lignin, Ca, P, Mg, K, S, and Cl) from whole maize plants. Principal component analysis showed that potassium content (K) was highly negatively correlated with spider mite bio-response. Relative reflectance at 740 nm showed a highly significant and positive trend across spider mite bio-response classes, and that potassium content showed a highly significant and negative trend across the same classes. Thus, we argue that relative reflectance at 740 nm may be used to predict both potassium content and risk of spider mite infestation. Based on extensive reviews, potassium leaf content is known to reduce susceptibility of crops to pests. The results presented provide encouraging support for remotely sensed risk assessment of pest infestations through reflectance-based monitoring of maize leaf attractiveness and highlight that reflectance based monitoring of crop susceptibility may be possible through careful management of macro element crop properties, such as potassium content.     

Use of airborne multispectral imagery to discriminate and map weed infestations in a citrus orchard

Reliable information on weed abundance and distribution within fields is essential for weed management in agricultural systems. Such information is necessary to adopt localized and variable rates of herbicide spraying, thus reducing chemical waste, crop damage, and environmental pollution. This paper examined the potential of airborne multispectral imagery to discriminate and map weed infestations in an experimental citrus orchard in Japan. Using an airborne digital sensor, multispectral imagery was acquired over the study site on 10 April 2003. The obtained reflectance imagery was analyzed using an image object-based approach in eCognition. After creating image objects on the image, the spectral information for weeds and citrus, represented by corresponding selected sample image objects, was extracted. Significant differences in the spectral characteristics between weeds and citrus were observed in each of the red, green, and blue wavebands. The simple average values of these wavebands were used to classify image objects with the nearest neighbor algorithm. Maps were generated with different classes or levels of class groups. A subsequent accuracy assessment demonstrated that the weeds were successfully discriminated from other image objects with a classification accuracy of 99.07%. Therefore, maps generated based on the classification result could provide valuable information for developing a site-specific weed management program for the study orchard.     

A method for mapping the distribution of willow at a catchment scale using bi-seasonal SPOT5 imagery

This study showed that seasonal imagery acquired at specific stages of phenology can be used to improve the mapping accuracy of invasive willow at a catchment scale. SPOT5 XI (10 m) satellite imagery was acquired for early autumn and winter to represent the phenological stages of leaf cover and leaf fall respectively. Four classification regimes were evaluated using single‐ and bi‐seasonal composite imagery to determine the most accurate method. Significant spectral noise was found in willow populations, especially in the winter image, due to the effects of undergrowth exposure, shadowing, topography and boundary‐mixed pixels. Two noise reduction techniques were applied to the bi‐seasonal composite image to improve the classification results. The noise‐reduced bi‐seasonal composite image was classified using the spectral angle mapper (SAM) algorithm before importation into a geographical information system. Aerial photography was used to reduce the errors of commission associated with misclassification of pastures. The class accuracy achieved for willow using the method described in this study was 77.5% (Kappa =0.87). The high cost of eradicating willow means that managers must establish priorities for control; this technique can provide a powerful tool for prioritizing control programmes and for monitoring results at a catchment scale.