Asian Journal of Turfgrass Science (아시안잔디학회지)

Turfgrass Society of Korea (한국잔디학회)
권호 표지

Multi-Spectral Reflectance of Warm-Season Turfgrasses as Influenced by Deficit Irrigation

난지형 잔디의 가뭄 스트레스 상태로 인한 멀티스팩트럴 반사광 연구

  • 이준희 (함평 다이너스티 C.C.) ;
  • ;
  • Published : 2008.06.30
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Abstract

Remote sensing using multispectral radiometry may be a useful tool to detect drought stress in turf. The objective of this research was to investigate the correlation between drought stress and multispectral reflectance (MSR) from the turf canopy. St. Augustinegrass (Stenotaphrum secundatum[Walt.] Kuntze.) cultivars 'Floratam' and 'Palmetto', 'SeaIsle 1' seashore paspalum Paspalum vaginatum Swartz.), 'Empire' zoysiagrass (Zoysia japonica Steud.), and 'Pensacola' bahiagrass (Paspalum notatumFlugge) were established in lysimeters in the University of Florida Envirotron greenhouse facility in Gainesville. Irrigation was applied at 100%, 80%, 60%, or 40% of evapotranspiration (ET). Weekly evaluations included: a) shoot quality, leaf rolling, leaf firing b) soil moisture, chlorophyll content index; c) photosynthesis and d) multispectral reflectance. All the measurements were correlated with MSR data. Drought stress affected the infrared spectral region more than the visible spectral region. Reflectance sensitivity to water content of leaves was higher in the infrared spectral region than in the visible spectral region. Grasses irrigated at 100% and 80% of ET had no differences in normalized difference vegetation indices (NDVI), leaf area index (LAI), and stress indices. Grasses irrigated at 60% and 40% of ET had differences in NDVI, LAI, and stress indices. All measured wavelengths except 710nm were highly correlated (P < 0.0001) with turf visual quality, leaf firing, leaf rolling, soil moisture, chlorophyll content index, and photosynthesis. MSR could detect drought stress from the turf canopy.

Multi-spectral radiometer (MSR)를 사용한 리모트 센싱 기술이 향 후 잔디의 건조스트레스를 감지할 수 있는 도구로 사용될 수 있다. 본 연구의 목적은 네가지 각기 다른 조건의 건조스트레스를 받은 난지형 잔디의 잎에서 반사되는 Reflectance와 토양수분, 비주얼 잔디상태, 엽록소 함량, 광합성 등을 측정하여 각 factor간의 상관관계를 조사했으며 본 연구를 통해 모든 factor가 MSR 데이터와 질은 상관관계를 가지고 있었다. 또한 Reflectance 민감도는Visual spectral region보다 Infrared spectral region에서 더 높음을 알 수 있었다. 모든 결과를 종합해 볼 때 Multi-spectral radiometer (MSR)은 잔디의 건조상태를 미리 예측할 수 있는 도구로 사용될 수 있음을 확인할 수 있었다. 이 기술의 자료를 활용하게 된다면 향 후 MSR이 부착된 기구(Balloon)를 이용한 필드 스터디 연구로 확대될 수 있을 것이다.

Keywords

References

  1. Asrar, G., M. Fuchs, E. T. Kanemasu, and J. L. Hatfield. 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76:300-306 https://doi.org/10.2134/agronj1984.00021962007600020029x
  2. Carter, G. A. 1991. Primary and secondary effects of water content on the spectral reflectance of leaves. Am. J. Botany. 78:916-924 https://doi.org/10.2307/2445170
  3. Carter, G. A 1993. Responses of leaf spectral reflectance to plant stress. American Journal of Botany. 80(3):239-243 https://doi.org/10.2307/2445346
  4. Carter, G. A, and R. L. Miller. 1994. Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sensing of Environment. 50:295-302 https://doi.org/10.1016/0034-4257(94)90079-5
  5. Carter, G. A, W. G. Cibula, and R. L. Miller. 1996. Narrow-band reflectance imagery compared with thermal imagery for early detection of plant stress. J. Plant Physiol. 148:515-522 https://doi.org/10.1016/S0176-1617(96)80070-8
  6. Kenna, M P. 1995. Detecting turf stress with remote sensing. Grounds Maintenance. 10:17-20
  7. Knipling,E. B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment. 1:155-159 https://doi.org/10.1016/S0034-4257(70)80021-9
  8. Horler, D. N. H, M. Dockray, and J. Barber. 1983. The red edge of plant leaf reflectance. Int. J. Remote Sens. 4:273-288 https://doi.org/10.1080/01431168308948546
  9. Lee, J. H., L. E. Trenholm, and J. B. Unruh. 2003. Technology for irrigation scheduling for ST. Augustinegrass. Proc. Fla. State Hort. Soc. 116:319-321
  10. Lee, J. H., L. E. Trenholm, and J. B. Unruh. 2005. Evaluating methods of predicting irrigation needs of warm-season turfgrasses. Crop Science. Submitted
  11. Perry, C. A., and L. P. Lautenschlager. 1984. Functional equivalence of spectral vegetation indices. Remote Sensing of Environment. 14:169-182 https://doi.org/10.1016/0034-4257(84)90013-0
  12. SAS Institure. 1987. SAS user's guide: Statistics. 6th ed. SAS Inst., Cary, NC
  13. Tucker, C. J., J. H. Elgin, J. E. McMurtrey, and C. J. Fan. 1979. Monitoring corn and soybean crop development with hand-held radiometer spectral data. Remote Sensing of Environment. 13:461-474 https://doi.org/10.1016/0034-4257(83)90053-6
  14. Trenholm, L E., R. N. Carrow, and R. R. Duncan. 1999. Relationship of multispectral radiometry data to qualitative data in turfgrass research. Crop Science. 39:763-769 https://doi.org/10.2135/cropsci1999.0011183X003900030025x
  15. Wiegand, C. L., A. J. Richardson, E. E. Escobar, and A. H. Gerbermann. 1991. Vegetation indices in crop assessments. Remote Sensing of Environment. 35:105-119 https://doi.org/10.1016/0034-4257(91)90004-P