Skip to main content

Assessment of different methods for shadow detection in high-resolution optical imagery and evaluation of shadow impact on calculation of NDVI, and evapotranspiration

Irrigation Science volume 37, pages 407–429 (2019)Cite this article

Abstract

Significant efforts have been made recently in the application of high-resolution remote sensing imagery (i.e., sub-meter) captured by unmanned aerial vehicles (UAVs) for precision agricultural applications for high-value crops such as wine grapes. However, at such high resolution, shadows will appear in the optical imagery effectively reducing the reflectance and emission signal received by imaging sensors. To date, research that evaluates procedures to identify the occurrence of shadows in imagery produced by UAVs is limited. In this study, the performance of four different shadow detection methods used in satellite imagery was evaluated for high-resolution UAV imagery collected over a California vineyard during the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) field campaigns. The performance of the shadow detection methods was compared and impacts of shadowed areas on the normalized difference vegetation index (NDVI) and estimated evapotranspiration (ET) using the Two-Source Energy Balance (TSEB) model are presented. The results indicated that two of the shadow detection methods, the supervised classification and index-based methods, had better performance than two other methods. Furthermore, assessment of shadowed pixels in the vine canopy led to significant differences in the calculated NDVI and ET in areas affected by shadows in the high-resolution imagery. Shadows are shown to have the greatest impact on modeled soil heat flux, while net radiation and sensible heat flux are less affected. Shadows also have an impact on the modeled Bowen ratio (ratio of sensible to latent heat) which can be used as an indicator of vine stress level.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

£ 29.95

Price includes VAT (United Kingdom)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

References

  • AgiSoft LLC (2016) and Russia St Petersburg. Agisoft photoscan. Professional Edition

  • Anderson MC, Neale CMU, Li F, Norman JM, Kustas WP, Jayanthi H, Chavez J (2004) Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Rem Sens Environ 92:447–464

    Article  Google Scholar 

  • Bethsda MD (1997) Manual of photographic interpretation. 2nd edition, American Society Photogrammetry and remote sensing (ASPRS)

  • Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Rem Sens Environ 37(1):35–46

    Article  Google Scholar 

  • Campbell GS, Norman JM (1998) An introduction to environmental biophysics. Springer, New York

    Book  Google Scholar 

  • Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Rem Sens Environ 62(3):241–252

    Article  Google Scholar 

  • Choi H, Bindschadler R (2004) Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision. Rem Sens Environ 91(2):237–242

    Article  Google Scholar 

  • Crowther BG (1992) Radiometric calibration of multispectral video imagery. Doctoral dissertation. State University. Department of biological and Irrigation Engineering, Utah

  • Elarab M, Ticlavilca AM, Torres-Rua AF, Maslova I, McKee M (2015) Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. Int J Appl Earth Obs Geoinform 43:32–42

    Article  Google Scholar 

  • Fuentes S, Poblete-Echeverra C, Ortega-Farias S, Tyerman S, De Bei R (2014) Automated estimation of leaf area index from grapevine canopies using cover photography video and computational analysis methods. Aust J Grape Wine Res 20(3):465–473

    Article  Google Scholar 

  • Gonzalez RC, Woods RE, Eddins SL (2004) Digital image processing using. MATLAB, Prentice Hall

  • Hsieh YT, Wu ST, Chen CT, Chen JC (2016) Analyzing spectral characteristics of shadow area from ADS-40 high radiometric resolution aerial images. Int Arch Photogram, Rem Sens Spatial Inf Sci XLI–B7:223–227

    Article  Google Scholar 

  • http://www.aggieair.usu.edu/

  • https://www.labsphere.com/

  • http://www.technologyreview.com/lists/technologies/2014/

  • Huang J, Chen C (2009a) A physical approach to moving cast shadow detection. IEEE international conference on acoustics, speech and signal processing, 769–772

  • Huang J, Chen C (2009b) Moving cast shadow detection using physics-based features. IEEE conference on computer vision and pattern recognition, 2310–2317

  • Kiran TS (2016) A framework in shadow detection and compensation of images. DJ J Adv Electron Commun Eng 2(3):1–9

    Article  Google Scholar 

  • Kumar P, Sengupta K, Lee A (2002) A comparative study of different color spaces for foreground and shadow detection for traffic monitoring system. In: The IEEE 5th International Conference on Intelligent Transportation Systems, 100–105

  • Kustas WP, Norman JM (1999) Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agric For Meteorol 94(1):13–29. https://doi.org/10.1016/S0168-1923(99)00005-2

    Article  Google Scholar 

  • Kustas WP, Anderson MC, Alfieri JG, Knipper K, Torres-Rua A, Parry CK, Hieto H, Agam N, White A, Gao F, McKee L, Prueger JH, Hipps LE, Los S, Alsina M, Sanchez L, Sams B, Dokoozlian N, McKee M, Jones S, Yang Y, Wilson TG, Lei F, McElrone A, Heitman JL, Howard AM, Post K, Melton F, Hain C (2018) The Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Bull Am Meteorol Soc. https://doi.org/10.1175/BAMS-D-16-0244.1

    Article  Google Scholar 

  • Leblon B, Gallant L, Charland SD (1996a) Shadowing effects on SPOT-HRV and high spectral resolution reflectance in Christmas tree plantation. Int J Rem Sens 17(2):277–289

    Article  Google Scholar 

  • Leblon B, Gallant L, Grandberg H (1996b) Effects of shadowing types on ground-measured visible and near-infrared shadow reflectance. Rem Sens Environ 58(3):322–328

    Article  Google Scholar 

  • Lillesand TM, Kiefer RW (2000) Remote sensing and image interpretation, 4th edn. New York, Wiley

    Google Scholar 

  • Miura T, Huete AR (2009) Performance of three reflectance calibration methods for airborne hyperspectral spectrometer data. Sensors 9(2):794–813

    Article  CAS  PubMed  Google Scholar 

  • MosaicMill Oy (2009) EnsoMOSAIC image processing users guide. Version 7.3. Mosaic Mill Ltd. Finland

  • Cook BD, Corp LW, Nelson RF, Middleton EM, Morton DC, McCorkel JT, Masek JG, Ranson KJ, Ly V, Montesano PM (2013) NASA Goddard’s lidar, hyperspectral and thermal (G-LiHT) airborne imager. Rem Sens 5:4045–4066. https://doi.org/10.3390/rs5084045

    Article  Google Scholar 

  • Neale CM, Crowther BG (1994) An airborne multispectral video/radiometer remote sensing system: development and calibration. Rem Sens Environ 49(3):187–194

    Article  Google Scholar 

  • Nemani RR, Running SW (1989) Estimation of regional surface resistance to evapotranspiration from NDVI and thermal IR AVHRR data. J Appl Meteorol 28(4):276–284

    Article  Google Scholar 

  • Nieto H, Kustas W, Torres-Rua A, Alfieri J, Gao F, Anderson M, White WA, Song L, Mar Alsina M, Prueger J, McKee M, Elarab M, McKee L (2018) Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery. Irrigation Sci. https://doi.org/10.1007/s00271-018-0585-9

    Article  Google Scholar 

  • Norman JM, Kustas WP, Humes KS (1995) Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agric For Meteorol 77:263–293

    Article  Google Scholar 

  • Ortega-Farías S, Ortega-Salazar S, Poblete T, Kilic A, Allen R, Poblete-Echeverra C, Ahumada-Orellana L, Zuiga M, Seplveda D (2016) Estimation of energy balance components over a drip-irrigated olive orchard using thermal and multispectral cameras placed on a helicopter-based unmanned aerial vehicle (uav). Rem Sens 8(8)

  • Parry C, Nieto H, Guillevic P, Agam N, Kustas B, Alfieri J, McKee L, McElrone AJ. An intercomparison of radiation partitioning models in vineyard row structured canopies. Irrigation Sci (In press)

  • Poblete T, Ortega-Farías S, Ryu D (2018) Automatic coregistration algorithm to remove canopy shaded pixels in UAV-borne thermal images to improve the estimation of crop water stress index of a drip-irrigated cabernet sauvignon vineyard. Sensors 18(2):397

    Article  Google Scholar 

  • Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–92

    Article  Google Scholar 

  • Qiao X, Yuan D, Li H (2017) Urban shadow detection and classification using hyperspectral image. J Indian Soc Rem Sens. https://doi.org/10.1007/s12524-016-0649-3

  • Ranson KJ, Daughtry CST (1987) Scene shadow effects on multispectral response. IEEE Trans Geosci Rem Sens 25(4):502–509

    Article  Google Scholar 

  • Rosin PL, Ellis T (1995) Image difference threshold strategies and shadow detection. Br Conf Mach Vision 1:347–356

    Google Scholar 

  • Ross J (1981) The radiation regime and architecture of plants. In: Lieth H (ed) Tasks for Vegetation Sciences 3. Dr. W. Junk, The Hague, Netherlands

  • Sandnes FE (2011) Determining the geographical location of image scenes based object shadow lengths. J Signal Process Syst 65(1):35–47

    Article  Google Scholar 

  • Sanin A, Sanderson C, Lovell B (2012) Shadow detection: a survey and comparative evaluation of recent methods. Pattern Recognit 45(4):1684–1689

    Article  Google Scholar 

  • Scanlan JM, Chabries DM, Christiansen R (1990) A shadow detection and removal algorithm for 2-d images.In: Proceeding IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp 2057–2060

  • Shiting W, Hong Z (2013) Clustering-based shadow edge detection in a single color image. International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), pp 1038–1041

  • Siala K, Chakchouk M, Besbes O, Chaieb F (2004) Moving shadow detection with support vector domain description in the color ratios space. In: Proceedings of the 17th IEEE International Conference on Pattern Recognition, pp 384–387

  • Sirmacek B, Unsalan C (2008) Building detection from aerial images using invariant color features and shadow information.” Proceedings of the 23rd International Symposium on Computer and Information Sciences (ISCIS 2008), Istanbul, Turkey, October 27–29, pp 1–5

  • Tolt G, Shimoni M, Ahlberg J, (2011) A shadow detection method for remote sensing images using VHR hyperspectral and LIDAR data. In: Proceedings of Geoscience and Remote Sensing Symposium, IGARSS, Vancouver Canada, pp 4423–4426

  • Torres-Rua A (2017) Vicarious calibration of sUAS microbolometer temperature imagery for estimation of radiometric land surface temperature. Sensors 17:1499

    Article  Google Scholar 

  • Trout TJ, Johnson LF (2007) Estimating crop water use from remotely sensed NDVI, crop models, and reference ET. USCID Fourth International Conference on Irrigation and Drainage, Sacramento, California, pp 275–285

  • Xia H, Chen X, Guo PA (2009) Shadow detection method for remote sensing images using affinity propagation algorithm. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, USA, pp 5–8

  • Zhu Z, Woodcock CE (2012) Object-based cloud and cloud shadow detection in Landsat imagery. Rem Sens Environ 118(15):83–94

    Article  Google Scholar 

Download references

Acknowledgements

This project was financially supported under Cooperative Agreement no. 58-8042-7-006 from the U.S. Department of Agriculture, from NASA Applied Sciences-Water Resources Program under Award no. 200906 NNX17AF51G, and by the Utah Water Research Laboratory at Utah State University. The authors wish to thank E&J Gallo Winery for their continued collaborative support for this research, and the AggieAir UAV Remote Sensing Group at the Utah Water Research Laboratory for their UAV technology and skill and hard work in acquiring the scientific-quality, high-resolution aerial imagery used in this project. USDA is an equal opportunity provider and employer.

Author information

Affiliations

  1. Department of Civil and Environmental Engineering, Utah State University, Logan, UT, USA

    Mahyar Aboutalebi & Alfonso F. Torres-Rua

  2. USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA

    William P. Kustas

  3. IRTA, Institute of Agriculture and Food Research and Technology, Lleida, Spain

    Héctor Nieto

  4. Department of Electrical and Computer Engineering, Utah State University, Logan, UT, USA

    Calvin Coopmans

  5. Utah Water Research Laboratory, Utah State University, Logan, UT, USA

    Mac McKee

Authors
  1. Mahyar Aboutalebi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alfonso F. Torres-Rua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. William P. Kustas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Héctor Nieto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Calvin Coopmans
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mac McKee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahyar Aboutalebi.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Communicated by N. Agam.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aboutalebi, M., Torres-Rua, A.F., Kustas, W.P. et al. Assessment of different methods for shadow detection in high-resolution optical imagery and evaluation of shadow impact on calculation of NDVI, and evapotranspiration. Irrig Sci 37, 407–429 (2019). https://doi.org/10.1007/s00271-018-0613-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00271-018-0613-9