|
|
|
|
|
| Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region |
| LIU Liang-yun, HUANG Wen-jiang, PU Rui-liang , WANG Ji-hua |
1、Key Laboratory of Digital Earth Science, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094,
P.R.China
2、Department of Geography, Environment, and Planning, University of South Florida, 4202 E. Fowler Ave., NES 107, Tampa, FL 33620, USA
3、National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China |
|
|
|
摘要 Spectral reflectance in the near-infrared (NIR) shoulder (750-900 nm) region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region (NSRI), defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices (water index and normalized difference water index) and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index (NDVI), water index (WI) and normalized difference water index (NDWI), with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.
Abstract Spectral reflectance in the near-infrared (NIR) shoulder (750-900 nm) region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region (NSRI), defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices (water index and normalized difference water index) and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index (NDVI), water index (WI) and normalized difference water index (NDWI), with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.
|
|
Received: 16 October 2012
Accepted:
|
| Fund: this research by the National High-Tech R&D Program of China (2012AA12A30701) and the National Natural Science Foundation of China (91125003, 41222008). |
|
Corresponding Authors:
LIU Liang-yun, Tel: +86-10-82178163, Fax: +86-10-82178177, E-mail: liuly@radi.ac.cn
E-mail: liuly@radi.ac.cn
|
Cite this article:
LIU Liang-yun, HUANG Wen-jiang, PU Rui-liang , WANG Ji-hua.
2014.
Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region. Journal of Integrative Agriculture, 13(4): 760-769.
|
| Aldakheel Y Y, Danson F M. 1997. Spectral reflectance of dehydrating leaves: measurements and modeling. International Journal of Remote Sensing, 18, 3683-3690 Berryman C A, Eamus D, Farrar J F. 1991. Water relations of leaves of barley infected with brown rust. Physiological and Molecular Plant Pathology, 38, 393- 405. Birchem R, Henk W G, Brown C L. 1979. Ultrastructure of paraquat-treated pine cells (Pinus elliottii Engelm) in suspension culture. Annals of Botany, 43, 683-691 Bowman W D. 1989. The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves. Remote Sensing of Environment, 30, 249-255 Carter G A. 1991. Primary and secondary effects of water content on the spectral reflectance of leaves. American Journal of Botany, 78, 916-924 Carter G A. 1993. Responses of leaf spectral reflectance to plant stress. American Journal of Botany, 80, 230-243 Ceccato P, Flasse S, Tarantola S, Jacquemoud S, Grégoire J M. 2001. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 77, 22-33 Devadas R, Lamb D W, Simpfendorfer S, Backhouse D. 2009. Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture, 10, 459-470 Filella I, Penuelas J. 1994. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15, 1459-1470 Foley S, Rivard B, Sanchez-Azofeifa G A, Calvo J. 2006. Foliar spectral properties following leaf clipping and implications for handling techniques. Remote Sensing of Environment, 103, 265-275 Gao B C. 1996. NDWI: A normalized difference water index for remote sensing vegetation liquid water from space. Remote Sensing of Environment, 58, 257-266 Gao B C, Goetz A F H. 1995. Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sensing of Environment, 52, 155-162 Hardisky M A, Klemas V, Smart R M. 1983. The influence of soft salinity, growth form, mad leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing, 49, 77-83 Harris N, Dodge A D. 1972. The effect of paraquat on flax cotyledon leaves: physiological and biochemical changes. Planta (Berl.), 104, 210-219 Huang W J, David W L, Niu Z, Liu L Y, Wang J H. 2007. Identification of yellow rust in wheat by in situ and airborne spectrum data. Precision Agriculture, 8, 187- 197. Hunt E R, Rock B N. 1989. Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sensing of Environment, 30, 43-54 Jacquemoud S, Baret F. 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75-91 Jacquemoud S, Ustin S L, Verdebout J, Schmuck G, Reoli G, Hosgood B. 1996. Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing of Environment, 56, 194-202 Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco- Tejada P J, Asner C, François G P, Ustin S. 2009. PROSPECT+SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, 113, 56-66 Jones C D, Jones J B, Lee W S. 2010. Diagnosis of bacterial spot of tomato using spectral signatures. Computers and Electronics in Agriculture, 74, 329-335 Larsolle A, Muhammed H H. 2007. Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Precision Agriculture, 8, 37-47 Li G B, Zeng S M, Li Z Q. 1989. Integrated Management of Wheat Pests. China Agricultural Science and Technology Press, Beijing. pp. 185-186 (in Chinese) Line R F. 2002. Stripe rust of wheat and barley in North America: A retrospective historical review. Annual Review of Phytopathology, 40, 75-118 Liu L Y, Song X Y, Li C J, Qi L, Huang W J, Wang J H. 2009. Monitoring and evaluation of the diseases of and yield winter wheat from multi-temporal remotely-sensed data. Transactions of the Chinese Society of Agricultural Engineering, 25, 137-143 (in Chinese) Liu L Y, Wang J H, Huang W J, Zhao C J. 2004. Estimating winter wheat plant water content using red edge parameters. International Journal of Remote Sensing, 25, 3331-3342 Liu L Y, Wang J H, Huang W J, Zhao C J. 2010. Detection of leaf and canopy EWT by calculating REWT from reflectance spectra. International Journal of Remote Sensing, 31, 2681-2695 Logan D C. 2006. The mitochondrial compartment. Journal of Experimental Botany, 57, 1225-1243 Mirik M, Michels J R, Kassymzhanova G J, Mirik S, Elliott N C, Bowling R. 2006. Spectral measurement of greenbug (Homoptera: Aphididae) density and damage to wheat growing under field and greenhouse conditions. Journal of Economic Entomology, 99, 1682-1690 Peñuelas J, Filella I, Biel C, Serrano L, Save R. 1993. The reflectance at the 950-970nm region as an indicator of plant water status International Journal of Remote Sensing, 14, 1887-1905 Peñuelas J, Pinol J, Ogaya R. 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18, 2869-2872 Purba E, Preston C, Powles S B. 1995. The mechanism of resistance to paraquat is strongly temperature dependent in resistant Hordeum leporinum Link and H. glaucum Steud. Planta, 196, 464-468 Sankaran S, Mishra A, Ehsani R, Davis C. 2010. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72, 1-13 Seelig H D, Hoehn A, Stodieck L S, Klaus D M, Adams III W W, Emery W J. 2008. The assessment of leaf water content using leaf reflectance ratios in the visible, near, and short-wave-infrared. International Journal of Remote Sensing, 29, 3701-3713 Smith R C G, Heritage A D, Stapper M, Barrs H D. 1986. Effect of stripe rust (Puccinia striiformis West.) and irrigation on the yield and foliage temperature of wheat. Field Crops Research, 14, 39-51 Suplick-Ploense M R, Alshammary S F, Qian Y L. 2011. Spectral reflectance response of three turfgrasses to leaf dehydration. Asian Journal of Plant Sciences, 10, 67-73 Vescovo L, Wohlfahrt G, Balzarolo M, Pilloni S, Sottocornola M, Rodeghiero M, Gianelle D. 2012. New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass. International Journal of Remote Sensing, 33, 2178-2195 Xu H R, Ying Y B, Fu X P, Zhu S P. 2007. Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf. Biosystems Engineering, 96, 447-454 Yang Z, Rao M N, Elliott N C, Kindler S D, Popham T W. 2005. Using ground-based multispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae) infestation. Computers and Electronics in Agriculture, 47, 121-135 Zhang J C, Yuan L, Wang J H, Huang W J, Chen L P, Zhang D Y. 2012. Spectroscopic leaf level detection of powdery mildew for winter wheat using continuous wavelet analysis. Journal of Integrative Agriculture, 11, 1474-1484 Zhang M H, Qin Z H, Liu X, Ustin S L. 2003. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 4, 295-310 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
