scholarly journals Hyper Spectral Image Characteristics of Aronia Melanocarpa Leaves under Saline Alkali Stress

2020 ◽  
Keyword(s):  
Spectral Reflectance ◽  
Near Infrared ◽  
Spectral Information ◽  
Alkali Stress ◽  
Plant Leaves ◽  
Infrared Band ◽  
Spectral Image ◽  
Aronia Melanocarpa ◽  
Image Characteristics ◽  
Hyper Spectral

<p>Objective: To obtain the characteristics analysis results of aronia melanocarpa leaves under saline alkali stress state and improve yield and quality of aronia melanocarpa. Methods: Using hyper spectral imaging system to obtain hyper spectral images of aronia melanocarpa leaves under saline alkali stress. The clear hyper spectral image is obtained by the conversion of reflectance, spectral envelope removal, and spectral denoising, and final hyper spectral image is obtained by the normalization of a clearer hyper spectral image. Results: Spectral information of aronia melanocarpa leaves under slight saline alkali stress: in the visible band, leaf reflectance is lower than leaf nutrient rich stress condition; in the near infrared band, the nutrient rich leaves was significantly higher than that of stress leaves. Spectral information of leaves under moderate saline alkali stress: spectral reflectance of different lesion spots for a same leaf in 550-680nm band is: severe &gt; moderate &gt; slight &gt; normal, in the near infrared band is on the contrary; spectral reflectance for different lesion grades in 550-680 nm, severe lesion leaves have highest reflectance, and normal leaves have the lowest reflectance. Under severe saline alkali stress, the leaf spectral information is: there is no significant difference in leaf spectral reflectance between the attachment aphid and the damaged leaf, but the difference is obvious for normal leaves in the band of 450-500 nm, 560-680 nm and 750-900 nm. Comparative results analysis for the three of saline alkali stress degree is: the near infrared band of 560-680 nm and the visible band of 780-900 nm is the sensitive band for the diagnosis of three kinds of stress; slight saline alkali stress has the most significant differences at 550 nm, and 780-900 nm; severe saline alkali stress has at 680 nm and 780-900 nm. Conclusion: The proposed method can analyze hyper spectral image characteristics of aronia melanocarpa leaves under different saline alkali stress the condition of is a kind of plant leaves, which is an efficient method for the analysis of characteristics of plant leaves under saline alkali stress.</p>

A Practical UAV Remote Sensing Methodology to Generate Multispectral Orthophotos for Vineyards

2015 ◽  
Vol 6 (4) ◽  
pp. 65-87 ◽  
Author(s):  
Adam J. Mathews
Keyword(s):  
Spectral Reflectance ◽  
Near Infrared ◽  
Aerial Imagery ◽  
Spectral Information ◽  
Surface Reflectance ◽  
Digital Cameras ◽  
Individual Bands ◽  
Aerial Vehicle ◽  
Best Fit

This paper explores the use of compact digital cameras to remotely estimate spectral reflectance based on unmanned aerial vehicle imagery. Two digital cameras, one unaltered and one altered, were used to collect four bands of spectral information (blue, green, red, and near-infrared [NIR]). The altered camera had its internal hot mirror removed to allow the sensor to be additionally sensitive to NIR. Through on-ground experimentation with spectral targets and a spectroradiometer, the sensitivity and abilities of the cameras were observed. This information along with on-site collected spectral data were used to aid in converting aerial imagery digital numbers to estimates of scaled surface reflectance using the empirical line method. The resulting images were used to create spectrally-consistent orthophotomosaics of a vineyard study site. Individual bands were subsequently validated with in situ spectroradiometer data. Results show that red and NIR bands exhibited the best fit (R2: 0.78 for red; 0.57 for NIR).

Quality Assurance of Torrefied Biomass Using RGB, Visual and near Infrared (Hyper) Spectral Image Data

2014 ◽  
Vol 22 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Susanne Wiklund Lindström ◽  
David Nilsson ◽  
Anders Nordin ◽  
Martin Nordwaeger ◽  
Ingemar Olofsson ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Near Infrared ◽  
Image Data ◽  
Spectral Image ◽  
Hyper Spectral ◽  
Torrefied Biomass

A Practical UAV Remote Sensing Methodology to Generate Multispectral Orthophotos for Vineyards

2019 ◽  
pp. 271-294 ◽  
Author(s):  
Adam J. Mathews
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Spectral Information ◽  
Digital Cameras ◽  
Individual Bands ◽  
Aerial Vehicle ◽  
Best Fit

This paper explores the use of compact digital cameras to remotely estimate spectral reflectance based on unmanned aerial vehicle imagery. Two digital cameras, one unaltered and one altered, were used to collect four bands of spectral information (blue, green, red, and near-infrared [NIR]). The altered camera had its internal hot mirror removed to allow the sensor to be additionally sensitive to NIR. Through on-ground experimentation with spectral targets and a spectroradiometer, the sensitivity and abilities of the cameras were observed. This information along with on-site collected spectral data were used to aid in converting aerial imagery digital numbers to estimates of scaled surface reflectance using the empirical line method. The resulting images were used to create spectrally-consistent orthophotomosaics of a vineyard study site. Individual bands were subsequently validated with in situ spectroradiometer data. Results show that red and NIR bands exhibited the best fit (R2: 0.78 for red; 0.57 for NIR).

Statistical Models for Prediction of Dry Weight and Nitrogen Accumulation Based on Visible and Near-Infrared Hyper-Spectral Reflectance of Rice Canopies

2000 ◽  
Vol 3 (4) ◽  
pp. 377-386 ◽  
Author(s):  
Wataru Takahashi ◽  
Vu Nguyen-Cong ◽  
Sachio Kawaguchi ◽  
Megumi Minamiyama ◽  
Seishi Ninomiya
Keyword(s):  
Statistical Models ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Dry Weight ◽  
Nitrogen Accumulation ◽  
Hyper Spectral

scholarly journals Hyperspectral Characteristics and Scale Effects of Leaf and Canopy of Summer Maize under Continuous Water Stresses

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1180
Author(s):  
Meng Li ◽  
Ronghao Chu ◽  
Xiuzhu Sha ◽  
Feng Ni ◽  
Pengfei Xie ◽  
...  
Keyword(s):  
Scale Effect ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Scale Effects ◽  
Spectral Characteristics ◽  
Summer Maize ◽  
Infrared Band ◽  
Area Index ◽  
Closed Canopy

The scale effect problem is one of the most challenging issues in remote sensing studies. However, the research on the methodology and theory of the scale effect is scarcely applied in practice. To this end, in this study, 3 years of field experimental data of continuous water stresses on summer maize were used for this purpose. Furthermore, the Prospect and Sail models were employed to investigate the scale effects of reflectance characteristics and vegetation indexes. The results indicated that the spectral characteristics of canopy and leaf of summer maize were similar under continuous water stresses at various stages. The reflectance at the canopy level was distinct from that at the leaf level, considering the soil background differences. From leaf to canopy scales, with the increase in the leaf area index (LAI), the spectral reflectance of all treatments in the visible band decreased, but increased in the near-infrared band, and the reflectance was saturated when LAI increased to 5. The reflectance difference caused by LAI variation was enlarged as the drought stress intensified in the short-wave infrared band. The spectral reflectance in the near-infrared band was susceptible to leaf inclination angle (LIA) variation and changed significantly, especially in the closed canopy. With the increase in LAI, the difference vegetation index (DVI) and normalized difference vegetation index (NDVI) values under each treatment showed a gradually increasing trend. With the increase in LIA, the DVI value decreased gradually, and the DVI value under the saturated canopy was significantly higher than that under the unclosed canopy. However, the NDVI values of all treatments did not change with LIA, mostly under the closed canopy. Overall, the results demonstrated that LAI had a more significant influence on canopy reflectance than LIA. In addition, NDVI was not able to capture the LAI and LIA information when the canopy was closed, but DVI performed better.

Air Dustfall Impact on Spectrum of Ficus Microcarpa’s Leaf

2013 ◽  
Vol 655-657 ◽  
pp. 813-815 ◽  
Author(s):  
Jin Hong Xu ◽  
Jin Ting Yu
Keyword(s):  
Spectral Reflectance ◽  
Near Infrared ◽  
Leaf Surface ◽  
Industrial Area ◽  
Infrared Band ◽  
Correlation Relationship ◽  
Guangzhou City ◽  
Visible Band ◽  
Economic Method ◽  
Ficus Microcarpa

This article has studied the correlation relationship between the spectral features of polluted leaf surface of Ficus microcarpa and air dustfall in Guangzhou City. The results show that the spectral reflectance of leaves in the industrial area and large traffic area is 3-5.5% higher than that of leaves in cleaning area in the visible band, but is 10-15% lower in the near infrared band. Compared to the spectral reflectance of the cleaned leaf, the spectral reflectance of leaf on nature dirty is 6.6％ higher in the visible band and 25.6% lower in the infrared band. The spectral reflectance difference between dirty leaf and cleaned leaf in the infrared band has a strong correlation with air dustfall in Guangzhou city. The correlation coefficient is 0.821. It is simple and convenient, fast, economic method to monitor the air dustfall using the spectral characteristic of Ficus microcarpa’s leaf.

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