Spectral signatures of hydrilla from a tank and field setting

The invasion of hydrilla in many waterways has caused significant problems resulting in high main- tenance costs for eradicating this invasive aquatic weed. Present identification methods employed for detecting hydrilla invasions such as aerial photography and videos are difficult, costly, and time consuming. Remote sensing has been used for assessing wetlands and other aquatic vegetation, but very little information is available for detecting hydrilla invasions in coastal estuaries and other water bodies. The objective of this study is to construct a library of spectral signatures for identifying and classifying hydrilla invasions. Spectral signatures of hydrilla were collected from an experimental tank and field locations in a coastal estuary in the upper Chesapeake Bay. These measurements collected from the experimental tank, resulted in spectral signatures with an average peak surface reflectance in the near-infrared （NIR） region of 16% at a wavelength of 818 nm. However, the spectral measure- ments, collected in the estuary, resulted in a very different spectral signature with two surface reflectance peaks of 6% at wavelengths of 725 nm and 818 nm. The difference in spectral signatures between sites are a result of the components in the water column in the estuary because of increased turbidity （e.g., nutrients, dissolved matter and suspended matter）, and canopy being lower （submerged） in the water column. Spectral signatures of hydrilla observed in the tank and the field had similar characteristics with low reflectance in visible region of the spectrum from 400 to 700 nm, but high in the NIR region from 700 to 900 nm.

Forest mapping:a comparison between hyperspectral and multispectral images and technologies

Mapping forests is an important process in managing natural resources.At present,due to spectral resolution limitations,multispectral images do not give a complete separation between different forest species.In contrast,advances in remote sensing technologies have provided hyperspectral tools and images as a solution for the determination of species.In this study,spectral signatures for stone pine（Pinus pinea L.） forests were collected using an advanced spectroradiometer ＂ASD FieldSpec 4 Hi-Res＂ with an accuracy of 1 nm.These spectral signatures are used to compare between different multispectral and hyperspectral satellite images.The comparison is based on processing satellite images： hyperspectral Hyperion,hyperspectral CHRIS-Proba,Advanced Land Imager（ALI）,and Landsat 8.Enhancement and classification methods for hyperspectral and multispectral images are investigated and analyzed.In addition,a well-known hyperspectral image classification algorithm,spectral angle mapper（SAM）,has been improved to perform the classification process efficiently based on collected spectral signatures.The results show that the modified SAM is 9% more accurate than the conventional SAM.In addition,experiments indicate that the CHRIS-Proba image is more accurate than Landsat 8（overall accuracy 82%,precision 93%,and Kappa coefficient 0.43 compared to 60,67%,and 0.035,respectively）.Similarly,Hyperion is better than ALI in mapping stone pine（overall accuracy 92%,precision 97%,and Kappa coefficient 0.74 compared to 52,56%,and -0.032,respectively）.

Satellite Multi-Temporal Data and Cropping Pattern Approach for Green Gram Crop Management in the Lower Midland Zone IV and V in Kenya

Creation of a spectral signature reflectance data, which aids in the identification of the crops is important in determining size and location crop fields. Therefore, we developed a spectral signature reflectance for the vegetative stage of the green gram (Vigna. radiata L.) over 5 years (2020, 2018, 2017, 2015, and 2013) for agroecological zone IV and V in Kenya. The years chosen were those whose satellite resolution data was available for the vegetative stage of crop growth in the short rain season (October, November, December (OND)). We used Landsat 8 OLI satellite imagery in this study. Cropping pattern data for the study area were evaluated by calculating the Top of Atmosphere reflectance. Farms geo-referencing, along with field data collection, was undertaken to extract Top of Atmosphere reflectance for bands 2, 3, 4 and 7. We also carried a spectral similarity assessment on the various cropping patterns. The spectral reflectance ranged from 0.07696 - 0.09632, 0.07466 - 0.09467, 0.0704047 - 0.12188,0.19822 - 0.24387, 0.19269 - 0.26900, and 0.11354 - 0.20815 for bands 2, 3, 4, 5, 6, and 7 for green gram, respectively. The results showed a dissimilarity among the various cropping patterns. The lowest dissimilarity index was 0.027 for the maize (Zea mays L.) bean (Phaseolus vulgaris) versus the maize-pigeon pea (Cajanus cajan) crop, while the highest dissimilarity index was 0.443 for the maize bean versus the maize bean and cowpea cropping patterns. High crop dissimilarities experienced across the cropping pattern through these spectral reflectance values confirm that the green gram was potentially identifiable. The results can be used in crop type identification in agroecological lower midland zone IV and V for mung bean management. This study therefore suggests that use of reflectance data in remote sensing of agricultural ecosystems would aid in planning, management, and crop allocation to different ecozones.

指纹光谱特征揭示叶斑病时空动态发展以实现显症前诊断

植物病原菌不断危害农业生产和粮食安全。因此,病害发展早期的动态表征对病变监测和显症前诊断至关重要。高光谱成像(HSI)在跟踪病害初始侵染部位的动态进程以进行显症前诊断方面具有巨大潜力。然而,目前尚无相关文献提取出早期感染阶段活体叶片病变组织的指纹光谱特征(FSS),也没有探究HSI的检测机制。其中FSS是指能够表征特定植物病害的独特、有代表性的光谱特征。在本研究中,基于时序HSI数据分析,提取了接种Bipolaris sorokiniana的大麦叶片的FSS,以表征叶斑病症状发展,实现显症前诊断。还研究了叶斑病早期发展阶段叶片的光谱和生化响应。本文所提取的全波段FSS能够捕捉病变发展过程中褪绿组织和坏死组织的独特特征,从而原位可视化植物-病原菌像素级的早期互作动态进程。进一步,实现了接种后24 h叶斑病的显症前诊断,比传统的聚合酶链反应(PCR)测定或生化测定提前了12 h。为了揭示HSI显症前诊断的机制,还建立了叶片的平均光谱响应与其生化指标(叶绿素、类胡萝卜素、丙二醛、抗坏血酸和还原型谷胱甘肽)之间的定量关系,回归模型在预测集上的Rp2均高于0.84。总体结果表明,HSI反映了活体植物特性的变化,所提取的FSS可成功跟踪叶斑病发生发展的时空动态进程,实现显症前诊断。在其他植物病害上的试验表明,该方法在植物病害早期控制方面具有较大的推广潜力。