Advertisement

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

  • Mahyar Aboutalebi
  • Alfonso F. Torres-Rua
  • William P. Kustas
  • Héctor Nieto
  • Calvin Coopmans
  • Mac McKee
Original Paper

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.

Notes

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.

Compliance with ethical standards

Conflict of interest

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

References

  1. AgiSoft LLC (2016) and Russia St Petersburg. Agisoft photoscan. Professional EditionGoogle Scholar
  2. 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 92:447–464CrossRefGoogle Scholar
  3. Bethsda MD (1997) Manual of photographic interpretation. 2nd edition, American Society Photogrammetry and remote sensing (ASPRS)Google Scholar
  4. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Rem Sens Environ 37(1):35–46CrossRefGoogle Scholar
  5. Campbell GS, Norman JM (1998) An introduction to environmental biophysics. Springer, New YorkCrossRefGoogle Scholar
  6. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Rem Sens Environ 62(3):241–252CrossRefGoogle Scholar
  7. 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):237–242CrossRefGoogle Scholar
  8. Crowther BG (1992) Radiometric calibration of multispectral video imagery. Doctoral dissertation. State University. Department of biological and Irrigation Engineering, UtahGoogle Scholar
  9. 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 43:32–42CrossRefGoogle Scholar
  10. 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):465–473CrossRefGoogle Scholar
  11. Gonzalez RC, Woods RE, Eddins SL (2004) Digital image processing using. MATLAB, Prentice HallGoogle Scholar
  12. 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–B7:223–227CrossRefGoogle Scholar
  13. Huang J, Chen C (2009a) A physical approach to moving cast shadow detection. IEEE international conference on acoustics, speech and signal processing, 769–772Google Scholar
  14. Huang J, Chen C (2009b) Moving cast shadow detection using physics-based features. IEEE conference on computer vision and pattern recognition, 2310–2317Google Scholar
  15. Kiran TS (2016) A framework in shadow detection and compensation of images. DJ J Adv Electron Commun Eng 2(3):1–9CrossRefGoogle Scholar
  16. 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, 100–105Google Scholar
  17. 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 CrossRefGoogle Scholar
  18. 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 CrossRefGoogle Scholar
  19. 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):277–289CrossRefGoogle Scholar
  20. 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):322–328CrossRefGoogle Scholar
  21. Lillesand TM, Kiefer RW (2000) Remote sensing and image interpretation, 4th edn. New York, WileyGoogle Scholar
  22. Miura T, Huete AR (2009) Performance of three reflectance calibration methods for airborne hyperspectral spectrometer data. Sensors 9(2):794–813CrossRefGoogle Scholar
  23. MosaicMill Oy (2009) EnsoMOSAIC image processing users guide. Version 7.3. Mosaic Mill Ltd. FinlandGoogle Scholar
  24. 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 CrossRefGoogle Scholar
  25. Neale CM, Crowther BG (1994) An airborne multispectral video/radiometer remote sensing system: development and calibration. Rem Sens Environ 49(3):187–194CrossRefGoogle Scholar
  26. Nemani RR, Running SW (1989) Estimation of regional surface resistance to evapotranspiration from NDVI and thermal IR AVHRR data. J Appl Meteorol 28(4):276–284CrossRefGoogle Scholar
  27. 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 CrossRefGoogle Scholar
  28. 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 77:263–293CrossRefGoogle Scholar
  29. 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)CrossRefGoogle Scholar
  30. 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)Google Scholar
  31. 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):397CrossRefGoogle Scholar
  32. Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–92CrossRefGoogle Scholar
  33. 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 CrossRefGoogle Scholar
  34. Ranson KJ, Daughtry CST (1987) Scene shadow effects on multispectral response. IEEE Trans Geosci Rem Sens 25(4):502–509CrossRefGoogle Scholar
  35. Rosin PL, Ellis T (1995) Image difference threshold strategies and shadow detection. Br Conf Mach Vision 1:347–356Google Scholar
  36. Ross J (1981) The radiation regime and architecture of plants. In: Lieth H (ed) Tasks for Vegetation Sciences 3. Dr. W. Junk, The Hague, NetherlandsGoogle Scholar
  37. Sandnes FE (2011) Determining the geographical location of image scenes based object shadow lengths. J Signal Process Syst 65(1):35–47CrossRefGoogle Scholar
  38. Sanin A, Sanderson C, Lovell B (2012) Shadow detection: a survey and comparative evaluation of recent methods. Pattern Recognit 45(4):1684–1689CrossRefGoogle Scholar
  39. 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 2057–2060Google Scholar
  40. 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 1038–1041Google Scholar
  41. 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 384–387Google Scholar
  42. 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 1–5Google Scholar
  43. 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 4423–4426Google Scholar
  44. Torres-Rua A (2017) Vicarious calibration of sUAS microbolometer temperature imagery for estimation of radiometric land surface temperature. Sensors 17:1499CrossRefGoogle Scholar
  45. 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 275–285Google Scholar
  46. 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 5–8Google Scholar
  47. Zhu Z, Woodcock CE (2012) Object-based cloud and cloud shadow detection in Landsat imagery. Rem Sens Environ 118(15):83–94CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUtah State UniversityLoganUSA
  2. 2.USDA-ARSHydrology and Remote Sensing LaboratoryBeltsvilleUSA
  3. 3.IRTAInstitute of Agriculture and Food Research and TechnologyLleidaSpain
  4. 4.Department of Electrical and Computer EngineeringUtah State UniversityLoganUSA
  5. 5.Utah Water Research LaboratoryUtah State UniversityLoganUSA

Personalised recommendations