Visible-Region Photographic Remote Sensing of Nearshore Waters

Author(s):  
Tsuguo Sunamura ◽  
Kiyoshi Horikawa
Weed Science ◽  
1985 ◽  
Vol 33 (4) ◽  
pp. 569-581 ◽  
Author(s):  
R. M. Menges ◽  
P. R. Nixon ◽  
A. J. Richardson

Plant canopy reflectance over the 0.45- to 1.25-μm wavelength (WL) of weed species and crops was recorded with a field spectroradiometer to evaluate the possible use of remote sensing to distinguish weeds from crops. Weed and weed-crop species reflectance differences were generally greater at the 0.85 μm WL in the near-infrared spectral region than at the 0.55 μm WL in the visible region, indicating that color infrared (CIR) aerial photography may be useful to detect weed populations in crops. Canopy reflectance data were more directly related to photographic differences in weed-crop images than were single leaf or inflorescence reflectance data. Aerial photography at altitudes of 610 to 3050 m distinguished climbing milkweed (Sarcostemma cyancboides♯ SAZCY) in orange [Citrus sinensis(L.) Osbeck. ‘Valencia’) trees; ragweed parthenium (Parthenium hysterophorusL. ♯ PTNHY) in carrot (Daucus carotaL., var.sativa‘Long Imperator’); johnsongrass [Sorghum halepense(L.) Pers. ♯ SORHA) in cotton (Gossypium hirsutumL. ‘CP 3774’) and in sorghum (Sorghum bicolorL. Moench. ‘Oro’); London rocket (Sisymbrium irioL. ♯ SSYIR) in cabbage; and Palmer amaranth (Amaranthus palmeriS. Wats. ♯ AMAPA) in cotton. Johnsongrass was also detectable with CIR film in maturing grain sorghum from 18 290 m. Detection of weed species in crops was aided by differential stages of inflorescence and senescence, and by the chlorophyll content, color, area, intercellular space, and surface characteristics of the leaves. Discrete plant community areas were determined by computer-based image analyses from a 1:8000-scale positive transparency with the efficiency of 82, 81, 68, and 100% for Palmer amaranth, johnsongrass, sorghum, and cotton, respectively. The computer analyses should permit discrete aerial surveys of weed-crop communities that are necessary for integrated crop management systems.


1978 ◽  
Vol 1 (16) ◽  
pp. 85
Author(s):  
Tsuguo Sunamura ◽  
Kiyoshi Horikawa

By use of a synchronized camera system, multiband black and white photographs and conventional color photographs were taken respectively with the purposes of testing filters available for shallow-water photographic bathymetry, and of checking the availability of low-cost process imagery for the study of coastal processes. Kodak Wratten filters 29, 58, and 90 were employed for the multiband photography. A Wratten 90 filter provided the best correlation between water depth and the photographic density. The low-cost imagery, obtained in a laboratory from the color photographs by applying ordinary filters without using any expensive image processing devices, proved to be useful.


2016 ◽  
Vol 8 (4) ◽  
pp. 2175-2181 ◽  
Author(s):  
S. K. Singh ◽  
Sujay Dutta ◽  
Nishith Dharaiya

Detection of crop stress is one of the major applications of remote sensing in agriculture. Many researchers have confirmed the ability of remote sensing techniques for detection of pest/disease on cotton. The objective of the present study was to evaluate the relation between the mealybug severity and remote sensing indices and development of a model for mapping of mealybug damage using remote sensing indices. The mealybug-infested cotton crop had a significantly lower reflectance (33%) in the near infrared region and higher (14%) in the visible range of the spectrum when compared with the non-infested cotton crop having near infrared and visible region reflectance of 48 % and 9% respectively. Multiple Linear regression analysis showed that there were varying relationships between mealybug severity and spectral vegetation indices, with coefficients of determination (r2) ranging from 0.63 to0.31. Model developed in this study for the mealybug damage assessment in cotton crop yielded significant relationship (r2=0.863) and was applied on satellite data of 21st September 2009 which revealed high severity of mealybug and it was low on 24th September 2010 which confirmed the significance of the model and can be used in the identification of mealybug infested cotton zones. These results indicate that remote sensing data have the potential to distinguish damage by mealybug and quantify its abundance in cotton.


Author(s):  
Karl F. Warnick ◽  
Rob Maaskant ◽  
Marianna V. Ivashina ◽  
David B. Davidson ◽  
Brian D. Jeffs

Author(s):  
Dimitris Manolakis ◽  
Ronald Lockwood ◽  
Thomas Cooley

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