Spectral characteristics of leafy spurge (Euphorbia esula) leaves and flower bracts

Weed Science ◽  
2004 ◽  
Vol 52 (4) ◽  
pp. 492-497 ◽  
Author(s):  
E. Raymond Hunt ◽  
James E. McMurtrey ◽  
Amy E. Parker Williams ◽  
Lawrence A. Corp

Leafy spurge can be detected during flowering with either aerial photography or hyperspectral remote sensing because of the distinctive yellow-green color of the flower bracts. The spectral characteristics of flower bracts and leaves were compared with pigment concentrations to determine the physiological basis of the remote sensing signature. Compared with leaves of leafy spurge, flower bracts had lower reflectance at blue wavelengths (400 to 500 nm), greater reflectance at green, yellow, and orange wavelengths (525 to 650 nm), and approximately equal reflectances at 680 nm (red) and at near-infrared wavelengths (725 to 850 nm). Pigments from leaves and flower bracts were extracted in dimethyl sulfoxide, and the pigment concentrations were determined spectrophotometrically. Carotenoid pigments were identified using high-performance liquid chromatography. Flower bracts had 84% less chlorophylla, 82% less chlorophyllb, and 44% less total carotenoids than leaves, thus absorptance by the flower bracts should be less and the reflectance should be greater at blue and red wavelengths. The carotenoid to chlorophyll ratio of the flower bracts was approximately 1:1, explaining the hue of the flower bracts but not the value of reflectance. The primary carotenoids were lutein, β-carotene, and β-cryptoxanthin in a 3.7:1.5:1 ratio for flower bracts and in a 4.8:1.3:1 ratio for leaves, respectively. There was 10.2 μg g−1fresh weight of colorless phytofluene present in the flower bracts and none in the leaves. The fluorescence spectrum indicated high blue, red, and far-red emission for leaves compared with flower bracts. Fluorescent emissions from leaves may contribute to the higher apparent leaf reflectance in the blue and red wavelength regions. The spectral characteristics of leafy spurge are important for constructing a well-documented spectral library that could be used with hyperspectral remote sensing.

1988 ◽  
Vol 18 (8) ◽  
pp. 1008-1016 ◽  
Author(s):  
D. G. Leckie ◽  
P. M. Teillet ◽  
G. Fedosejevs ◽  
D. P. Ostaff

Knowledge of the spectral characteristics of trees with varying degrees of needle loss is essential for developing remote sensing techniques for assessing defoliation. Spectra covering the range 400–2400 nm were acquired for single tree crowns suffering varying degrees of cumulative defoliation due to the spruce budworm (Choristoneurafumiferana (Clem.)), using a spectrometer mounted in the bucket of a boom truck. Spectra over the range 360–1100 nm were also obtained for the components of defoliated trees (i.e., needles, bare branches, and lichen), using a separate spectrometer and integrating sphere. Estimates of defoliation symptoms of each tree were made from the ground and above the tree. Changes in reflectance had a close and simple relationship with the defoliation symptoms measured. The spectral differences due to cumulative defoliation that were observed were broad-band features. The best spectral regions for differentiating levels of cumulative defoliation symptoms were the blue, red, shorter near-infrared wavelengths, and middle-infrared. Although currently available satellite and airborne sensors operate in these spectral regions, defoliation assessment may be improved by the use of optimized spectral bands.


2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.


2020 ◽  
Vol 71 (5) ◽  
pp. 593 ◽  
Author(s):  
A. Drozd ◽  
P. de Tezanos Pinto ◽  
V. Fernández ◽  
M. Bazzalo ◽  
F. Bordet ◽  
...  

We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012–16; 10 seasons) and spatial (30km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n=75) were used to select the most suitable spectral bands in seven high- and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and -6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ550 – λ650+λ800) ÷ (λ550+λ650+λ800). The best-performing chlorophyll algorithm was Sentinel 2 ((λ560 – λ660+λ703) ÷ (λ560+λ660+λ703); R2=0.80), followed by WorldView 2 ((λ550 – λ660+λ720) ÷ (λ550+λ660+λ720); R2=0.78), Landsat and the SPOT series ((λ550 – λ650+λ800) ÷ (λ550+λ650+λ800); R2=0.67–0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.


Author(s):  
H. R. Naveen ◽  
B. Balaji Naik ◽  
G. Sreenivas ◽  
Ajay Kumar ◽  
J. Adinarayana ◽  
...  

Aims/Objectives: Is to examine the use of spectral reflectance characteristics and explore the effectiveness of spectral indices under water and nitrogen stress environment. Study Design: Split-plot. Place and Duration of Study: Agro Climate Research Center, A.R.I., P.J.T.S. Agricultural University, Rajendranagar, Hyderabad, India in 2018-19. Methodology: Fixed amount of 5 cm depth of water was applied to each plot when the ratio of irrigation water and cumulative pan evaporation (IW/CPE) arrives at pre-determined levels of 0.6, 0.8 & 1.2 as main-plot and 3 nitrogen levels viz. 100, 200 & 300 kg N ha-1 as a subplot to create water and nitrogen stress environment. Spectral reflectance from each treatment was measured using Spectroradiometer and analyzed using statistical software package SPSS 17, SAS and trial version of UNSCRABLER. Results: At tasseling and dough stages, the reflectance pattern of maize was found to be higher in visible light spectrum of 400 to700 nm whereas lower in near-infrared region (700 to 900) in both underwater (IW/CPE ratio of 0.6) and nitrogen stress (100 kg N ha-1) environment as compared to moderate and no stress irrigation (IW/CPE ratio of 0.8 & 1.2) and nitrogen (200 and 300 kg N ha-1) treatments. The discriminant analysis of NDVI, GNDVI, WBI and SR indicated that 72.2% and 66.7% of the original grouped cases and 55.6% and 38.9% of the cross-validated grouped cases under irrigation and nitrogen levels, respectively were correctly classified. Conclusion: Hyperspectral remote sensing can be used as a tool to detect and quantify the water and nitrogen stress in maize non-destructively. Spectral vegetation indices viz. Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were found effective to distinguish water and nitrogen stress severity in maize.


2020 ◽  
Author(s):  
Yoichiro Hanaoka ◽  
Yukio Katsukawa ◽  
Satoshi Morita ◽  
Yukiko Kamata ◽  
Noriyoshi Ishizuka

Abstract Polarimetry is a crucial method to investigate solar magnetic elds. From the viewpoint of space weather, the magnetic eld in solar laments, which occasionally erupt and develop into interplanetary ux ropes, is of particular interest. To measure the magnetic eld in laments, high-performance polarimetry in the near-infrared wavelengths employing a high-speed, large-format detector is required; however, so far, this has been difficult to be realized. Thus, the development of a new infrared camera for advanced solar polarimetry has been started, employing a HAWAII-2RG (H2RG) array by Teledyne, which has 2048 2048 pixels, focusing on the wavelengths in the range of 1.0{1.6 m. We solved the problem of the difficult operation of the H2RGs under \fast readout mode" synchronizing with high-speed polarization modulation by introducing a \MACIE" (Markury ASIC Control and Interface Electronics) interface card and new assembly codes provided by Markury Scientic. This enables polarization measurements with high frame-rates, such as 29{117 frames per seconds, using a H2RG. We conducted experimental observations of the Sun and conrmed the high polarimetric performance of the camera.


2019 ◽  
Vol 34 (2) ◽  
pp. 250-259
Author(s):  
Kathryn M. Hooge Hom ◽  
Sreekala G. Bajwa ◽  
Rodney G. Lym ◽  
John F. Nowatzki

AbstractLeafy spurge (Euphorbia esula L.) and purple loosestrife (Lythrum salicaria L.) are invasive weeds that displace native vegetation. Herbicides are often applied to these weeds during flowering, so it would be ideal to identify them early in the season, possibly by the leaves. This paper evaluates the spectral separability of the inflorescences and leaves of these plants from surrounding vegetation. Leafy spurge, purple loosestrife, and surrounding vegetation were collected from sites in southeastern North Dakota and subjected to spectral analysis. Partial least-squares discriminant analysis (PLS-DA) was used to separate the spectral signatures of these weeds in the visible and near-infrared wavelengths. Using PLS-DA, the weeds were discriminated from their surroundings with R2 values of 0.86 to 0.92. Analysis of the data indicated that the bands contributing the most to each model were in the red and red-edge spectral regions. Identifying these weeds by the leaves allows them to be mapped earlier in the season, providing more time for herbicide application planning. The spectral signatures identified in this proof of concept study are the first step before using ultra–high resolution aerial imagery to classify and identify leafy spurge and purple loosestrife.


2014 ◽  
Vol 971-973 ◽  
pp. 1607-1610
Author(s):  
Yong Fei Che ◽  
Ying Jun Zhao ◽  
Wen Huan Wu

The traditional data processing and analysis method of remote sensing image processing system cannot meet the hyperspectral remote sensing mass data processing and the need of practical application in mineral resources exploration. By studying the systematical analysis and key technology on the hyperspectral mineral information identification module, and analyzing and thinking about the relevant theoretical methods and technical process, carried out the development of hyperspectral mineral information identification module based on IDL and integrated with ENVI software, providing the basic support platform for hyperspectral remote sensing mineral resources exploration. Meanwhile, the existing problems were discussed from the spectral characteristics mechanism analysis of rock and the hyperspectral mineral identification optimization algorithms.


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