Real time estimation of chlorophyll content based on vegetation indices derived from multispectral UAV in the kinnow orchard

Muhammad Naveed Tahir, Syed Zaigham Abbas Naqvi, Yubin Lan, Yali Zhang, Yingkuan Wang, Muhammad Afzal, Muhammad Jehanzeb Masud Cheema, Shahid Amir

Abstract


Abstract: Nondestructive estimation of the biophysical properties of crops provide quick and real time information of crop health under wide range of environment.  The chlorophyll content is an important indicator of crop health and widely used for determination of nutritional status of the crops real time in precision agriculture.  Advancement in the low altitude remote sensing (LARS) technologies such as Unmanned Aerial vehicles (UAVs) provides high temporal and spatial resolution solution for nondestructive, rapid and accurate estimation of biophysical properties of various crops.  The main objective of this study was to evaluate the high resolution multispectral UAV images for nondestructive and real time estimation of the kinnow tree leaves chlorophyll content in district Sargodha, Pakistan.  Kinnow tree leaves chlorophyll contents were measured manually using chlorophyll meter (SPAD-502 Minolta) in the kinnow orchard along with GPS positions in district Sargodha.  The UAVs images were also acquired during the same time when ground-truthing campaign for kinnow leaves chlorophyll content was performed. Vegetation indices including Normalized Difference Vegetation Index (NDVI), Transformed Normalized Difference Vegetation Index (TNDVI), Modified Chlorophyll Absorbed Ratio Index (MCARI2), Soil adjusted vegetation Index (SAVI) and Modified soil adjusted vegetation index (MSAVI2) were derived by multispectral UAV images for chlorophyll estimation.  The regression analysis was performed between ground-truthing data of chlorophyll content and UAV derived vegetation indices for predicting kinnow leave chlorophyll content model. MSAVI2 and TNDVI were proved to be more robust indices to estimate the chlorophyll content in the kinnow orchard with the highest coefficients of determination (R2) 0.89 and 0.85 respectively.  The results showed that the multispectral UAV can be used for accurately estimation of chlorophyll content and assess crop health status in a wider range which will help in managing crop nutrition requirement in real time in the kinnow orchard.

Keywords: Chlorophyll content, kinnow orchard, Multispectral UAV, Vegetation indices

DOI: 10.33440/j.ijpaa.20180101.0001

Citation:Tahir M N, Naqvi S Z A, Lan Y B, Zhang Y L, Wang Y K, Afzal M, et al.  Real time monitoring chlorophyll content based on vegetation indices derived from multispectral UAVs in the kinnow orchard.  Int J Precis Agric Aviat, 2018; 1(1): 24–31.


Full Text:

PDF Word

References


Barton, C. V. A theoretical analysis of the influence of heterogeneity in chlorophyll distribution on leaf reflectance. Tree Physiology, 2001; 21(12-13): 789–795.

Blyenburgh, P. V. UAVs: An overview. Air & Space Eur. 1999; 1(5): 43–47.

Eisenbeiss, H. A mini unmanned aerial vehicle (UAV): system over and image acquisition. In: A. Gruen, Sh. Murai, T. Fuse, F. Remondino (Eds.). Proceedings of International Workshop on Processing and Visualization Using High-Resolution Imagery, XXXVI(5/W1), Pitsanulok, Thailand. CDROM. Retrieved March 12, 2012 from http://www.isprs.org/ proceedings/XXXVI/5-W1/papers/11.pdf.

Gitelson, A. A., A. Vina, V. Ciganda, D. C. Rundquist and T. J. Arkebauer. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 2005; 32(8).

Gitelson, A. A., Y. Gritz and M. N. Merzlyak. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of plant physiology, 2003; 160(3): 271–282.

Hu, B., S. E. Qian, D. Haboudane, J. R. Miller, A. B. Hollinger, N. Tremblay and E. Pattey. Retrieval of crop chlorophyll content and leaf area index from decompressed hyperspectral data: The effects of data compression. Remote Sensing of Environment, 2004; 92(2): 139–152.

Huang Y., Hoffmann, W. C., Lan, Y., Wu, W., Fritz, B. K.. Development of a spray system for an unmanned aerial vehicle platform. Appl. Eng. in Ag., 2009; 25, 803–809.

Hunt, E. R., Cavigelli, M., Daughtry, C. S. T., McMurtrey, J. E., & Walthall, C. L. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 2005; 6, 359–378.

Jensen, T., A. Apan, F. R. Young, L. Zeller, and K. Cleminson. Assessing grain crop attributes using digital imagery acquired from a low-altitude remote controlled aircraft. In Proc. Spatial Sci. 2003 Conf. Canberra, Australia: Spatial Sciences Institute, Deakin ACT, Australia.

Johnson, L. F., Herwitz, S. R., Lobitz, B. M., & Dunagan, S. E. Feasibility of monitoring coffee field ripeness with airborne multispectral imagery. Applied Engineering in Agriculture, 2004; 20, 845–849.

L. Hassan-Esfahani, A. Torres-Rua, A. M. Ticlavilca, A. Jensen, M. McKee, “Topsoil Moisture Estimation for Precision Agriculture Using Unmanned Aerial Vehicle Multispectral Imageryâ€, 2014 IEEE International Geoscience and Remote Sensing Symposium, 2014.

Laliberte, A. S., & Rango, A. Image processing and classification procedures for analysis of subdecimeter imagery acquired with an unmanned aircraft over arid rangelands. GIScience & Remote Sensing,

; 48, 4–23.

Le Maire, G., C. Francois and E. Dufrene. Towards universal broad leaf chlorophyll indices using prospect simulated database and hyperspectral reflectance measurements. Remote sensing of environment, 2004; 89(1): 1–28.

Moran, J. A., A. K. Mitchell, G. Goodmanson and K.A. Stockburger. Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: A comparison of methods. Tree physiology, 2000; 20(16): 1113–1120.

Richardson, A. D., S. P. Duigan and G. P. Berlyn. An evaluation of noninvasive methods to estimate foliar chlorophyll content. New phytologist, 2002; 153(1): 185–194.

Rouse, J., R. Haas, J. Schell, D. Deering and J. Harlan. Monitoring the vernal advancement of retrogradation of natural vegetation, nasa/gsfc, type iii, final report. Greenbelt, 1974; 371.

Sims, D. A. and J. A. Gamon. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote sensing of environment, 2002; 81(2-3): 337–354.

Swain, K. C., Thomson, S. J., & Jayasuriya, H. P. W. Adoption of an unmanned helicopter for low altitude remote sensing to estimate yield and total biomass of a rice crop. Transactions of the ASABE, 2010; 53, 21–27.

Tahir, M. N., J. Li, B. Liu, G. Zhao, Y. Fuqi and C. Chengfeng. Hyperspectral estimation model for nitrogen contents of summer corn leaves under rainfed conditions. Pak. J. Bot, 2013; 45(5): 1623–1630.

Wu, J. D., Wang, D., & Rosen, C. J. Comparison of petiole nitrate concentrations, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies. Field Crops Research, 2007b; 101, 96–103.

Yuan, W., S. Liu, G. Zhou, G. Zhou, L.L. Tieszen, D. Baldocchi, C. Bernhofer, H. Gholz, A. H. Goldstein and M. L. Goulden. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agricultural and Forest Meteorology, 2007; 143(3-4): 189–207.


Refbacks

  • There are currently no refbacks.