Advertisement

GeoJournal

pp 1–16 | Cite as

Mapping the extent of land cover colour harmony based on satellite Earth observation data

  • Oleksandr Karasov
  • Mart Külvik
  • Igor Chervanyov
  • Kostiantyn Priadka
Article
  • 141 Downloads

Abstract

The concept of colour harmony, being rarely used in geography, landscape and environmental studies, has been significantly developed in psychology, art and computer science within the different approaches: colour wheel geometry and, more recently, numerical models applied to colour combinations. Using the main numerical principles of colour harmony, borrowed from the psychological literature, this study aims to investigate the ways of mapping the extent of the colour harmony of land cover, based on satellite Earth observations and explain the spatial distribution of colour harmony scores. The naturalness of environment, as well as heat and moisture balance, are confirmed to be the main drivers of the colour harmony of land cover. Crowdsourced photographs, collected from Mapillary service, were used to link satellite and ground-based estimations of the colour harmony of land cover as “proof of concept”. They have a limited applicability for ground-based assessment of scenic colour harmony. Therefore, remote sensing data provide a significant support for nature conservation and sustainable management, being used for mapping of the colour harmony of land cover as an indicator of the visual quality of the perceived environment.

Keywords

Colour harmony Land cover Landscape aesthetics GLCM Landsat 

Notes

Acknowledgements

This research was supported by European Social Fund’s Dora Plus Programme.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Acar, C., & Sakıcı, Ç. (2008). Assessing landscape perception of urban rocky habitats. Building and Environment, 43(6), 1153–1170.CrossRefGoogle Scholar
  2. Amir, S., & Sobol, E. (1990). The use of geomorphological elements for evaluation of visual quality of Israeli coast. GeoJournal, 21(3), 233–240.CrossRefGoogle Scholar
  3. Antoniou, V., Fonte, C. C., See, L., Estima, J., Arsanjani, J. J., Lupia, F., et al. (2016). Investigating the feasibility of geo-tagged photographs as sources of land cover input data. ISPRS International Journal of Geo-Information, 5(5), 64.CrossRefGoogle Scholar
  4. Antrop, M. (2000). Geography and landscape science. Belgeo. Revue belge de géographie, (1-2-3-4) (pp. 9–36).Google Scholar
  5. Antrop, M., & Van Eetvelde, V. (2017). Landscape perspectives: The holistic nature of landscape. Berlin: Springer.CrossRefGoogle Scholar
  6. Arévalo, V., González, J., & Ambrosio, G. (2008). Shadow detection in colour high-resolution satellite images. International Journal of Remote Sensing, 29(7), 1945–1963.CrossRefGoogle Scholar
  7. Arriaza, M., Cañas-Ortega, J., Canas-Madueno, J., & Ruiz-Aviles, P. (2004). Assessing the visual quality of rural landscapes. Landscape and urban planning, 69(1), 115–125.CrossRefGoogle Scholar
  8. Baykan, N. A., & Yılmaz, N. (2010). Mineral identification using color spaces and artificial neural networks. Computers and Geosciences, 36(1), 91–97.CrossRefGoogle Scholar
  9. Bell, S. (2004). Elements of visual design in the landscape. London: Taylor & Francis.Google Scholar
  10. Bell, S. (2012). Landscape: pattern, perception and process. Abingdon: Routledge.Google Scholar
  11. Benčo, M., & Hudec, R. (2007). Novel method for color textures features extraction based on GLCM. Radioengineering, 16(4), 65.Google Scholar
  12. Bláha, J. D., & Štěrba, Z. (2014). Colour contrast in cartographic works using the principles of Johannes Itten. The Cartographic Journal, 51(3), 203–213.CrossRefGoogle Scholar
  13. BLM, U. (1986). Visual resource inventory. BLM manual handbook H-8410-1. Resource document. Bureau of Land Management, United States Department of the Interior. http://blmwyomingvisual.anl.gov/docs/BLM_VRI_H-8410.pdf. Accessed April 13, 2018.
  14. Blocker, L., Slider, T., Ruchman, J., Mosier, J., Kok, L., Silbemagle, J., et al. (1995). Landscape aesthetics (AH 701-f)—Scenery management system application (Chapter 5). Washington, D.C.: USDA Forest Service.Google Scholar
  15. Brewer, C. A. (1994). Color use guidelines for mapping and visualization. Modern Cartography Series, 2, 123–147.  https://doi.org/10.1016/B978-0-08-042415-6.50014-4.Google Scholar
  16. Brewer, C. A. (2004). Color research applications in mapping and visualization. In Color and imaging conference (pp. 1–3). Society for Imaging Science and Technology.Google Scholar
  17. Burchett, K. E. (2002). Color harmony. Color Research and Application, 27(1), 28–31.CrossRefGoogle Scholar
  18. Caivano, J. L. (1998). Color and semiotics: A two-way street. Color Research and Application, 23(6), 390–401.CrossRefGoogle Scholar
  19. Casalegno, S., Inger, R., DeSilvey, C., & Gaston, K. J. (2013). Spatial covariance between aesthetic value and other ecosystem services. PLoS ONE, 8(6), e68437.CrossRefGoogle Scholar
  20. Chamaret, C. (2016). Color harmony: Experimental and computational modeling. Resource document. Université Rennes 1. https://tel.archives-ouvertes.fr/tel-01382750/document. Accessed April 13, 2018.
  21. Chamaret, C., Urban, F., & Lepinel, J. (2014). Creating experimental color harmony map. In B. E. Rogowitz, T. N. Pappas, & H. de Ridder (Eds.), (Vol. 9014, pp. 901410). International Society for Optics and Photonics.  https://doi.org/10.1117/12.2039727.
  22. Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., et al. (2015). System for automated geoscientific analyses (SAGA) v. 2.1. 4. Geoscientific Model Development, 8(7), 1991–2007.CrossRefGoogle Scholar
  23. d’Andrimont, R., & Defourny, P. (2018). Monitoring African water bodies from twice-daily MODIS observation. GIScience and Remote Sensing, 55(1), 130–153.CrossRefGoogle Scholar
  24. de la Fuente de Val, G., Atauri, J. A., & de Lucio, J. V. (2006). Relationship between landscape visual attributes and spatial pattern indices: A test study in Mediterranean-climate landscapes. Landscape and Urban Planning, 77(4), 393–407.CrossRefGoogle Scholar
  25. Dhang, S., & Mudi, N. (2015). Study on importance of floricultural crops and aesthetic components in determining designs of landscape gardens. Journal Crop and Weed, 11(1), 194–196.Google Scholar
  26. Dong, W., Zhang, S., Liao, H., Liu, Z., Li, Z., & Yang, X. (2016). Assessing the effectiveness and efficiency of map colour for colour impairments using an eye-tracking approach. The Cartographic Journal, 53(2), 166–176.CrossRefGoogle Scholar
  27. Dronova, I. (2017). Environmental heterogeneity as a bridge between ecosystem service and visual quality objectives in management, planning and design. Landscape and Urban Planning, 163, 90–106.  https://doi.org/10.1016/j.landurbplan.2017.03.005.Google Scholar
  28. Granö, J. G. (1929; 1997). Pure geography. Baltimore: Johns Hopkins University Press.Google Scholar
  29. Guochao, Q., Shuyu, T., Min, Z., & Chun, J. (2014). Environmental landscape design of bridges and structures. In The environment and landscape in motorway design (pp. 191–235). Chichester, UK: Wiley.  https://doi.org/10.1002/9781118332962.ch6.
  30. Hall-Beyer, M. (2017a). GLCM texture: A tutorial. Resource document. University of Calgary. https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf?sequence=11&isAllowed=y. Accessed April 13, 2018.
  31. Hall-Beyer, M. (2017b). Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales. International Journal of Remote Sensing, 38(5), 1312–1338.CrossRefGoogle Scholar
  32. Hands, D. E., & Brown, R. D. (2002). Enhancing visual preference of ecological rehabilitation sites. Landscape and Urban Planning, 58(1), 57–70.CrossRefGoogle Scholar
  33. Haralick, R. M., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621.CrossRefGoogle Scholar
  34. Itten, J. (1973). The art of color: The subjective experience and objective rationale of color. New York: Reinhold Publishing Corporation.Google Scholar
  35. Jie, Z., Li, S., & Zhi, Y. (2016). Evaluating plant landscape in Shenyang City Park by applying SBE methods. In International conference on smart city and systems engineering (ICSCSE) (pp. 44–46). IEEE.Google Scholar
  36. Junge, X., Schüpbach, B., Walter, T., Schmid, B., & Lindemann-Matthies, P. (2015). Aesthetic quality of agricultural landscape elements in different seasonal stages in Switzerland. Landscape and Urban Planning, 133, 67–77.  https://doi.org/10.1016/j.landurbplan.2014.09.010.Google Scholar
  37. Kolen, J., Crumley, C., Burgers, G. J., Von Hackwitz, K., Howard, P., Karro, K., et al. (2015). HERCULES: Studying long-term changes in Europe’s landscapes. Analecta Praehistorica Leidensia, 45(15), 209–219.Google Scholar
  38. Laso Bayas, J. C., See, L., Fritz, S., Sturn, T., Perger, C., Dürauer, M., et al. (2016). Crowdsourcing in-situ data on land cover and land use using gamification and mobile technology. Remote Sensing, 8(11), 905.CrossRefGoogle Scholar
  39. Lenclos, J.-P. (2004). The geography of color. New York: W.W. Norton & Co.Google Scholar
  40. Lengen, C. (2015). The effects of colours, shapes and boundaries of landscapes on perception, emotion and mentalising processes promoting health and well-being. Health and Place, 35, 166–177.  https://doi.org/10.1016/j.healthplace.2015.05.016.Google Scholar
  41. Machajdik, J., & Hanbury, A. (2010). Affective image classification using features inspired by psychology and art theory. In Proceedings of the 18th ACM international conference on multimedia (pp. 83–92). ACM.Google Scholar
  42. Marcelino, E. V., Formaggio, A. R., & Maeda, E. E. (2009). Landslide inventory using image fusion techniques in Brazil. International Journal of Applied Earth Observation and Geoinformation, 11(3), 181–191.CrossRefGoogle Scholar
  43. Nemcsics, A. (2012). The complex theory of colour harmony. Obuda University e-Bulletin, 3(1), 249–257.Google Scholar
  44. Nishiyama, M., Okabe, T., Sato, I., & Sato, Y. (2011). Aesthetic quality classification of photographs based on color harmony. In 2011 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 33–40). IEEE.Google Scholar
  45. O’Connor, Z. (2006). Bridging tahe gap: Façade colour, aesthetic response and planning policy. Journal of Urban Design, 11(3), 335–345.CrossRefGoogle Scholar
  46. O’Connor, Z. (2010). Colour harmony revisited. Color Research and Application, 35(4), 267–273.CrossRefGoogle Scholar
  47. Ode, Å., Fry, G., Tveit, M. S., Messager, P., & Miller, D. (2009). Indicators of perceived naturalness as drivers of landscape preference. Journal of Environmental Management, 90(1), 375–383.CrossRefGoogle Scholar
  48. Orzechowska-Szajda, I. (2015). Complexity as an indicator of aesthetic quality of landscape. Czasopismo Techniczne.Google Scholar
  49. Ou, L. C., & Luo, M. R. (2006). A colour harmony model for two-colour combinations. Color Research and Application, 31(3), 191–204.CrossRefGoogle Scholar
  50. Palmer, S. E., & Schloss, K. B. (2010). An ecological valence theory of human color preference. Proceedings of the National Academy of Sciences, 107(19), 8877–8882.CrossRefGoogle Scholar
  51. Palmer, S. E., Schloss, K. B., & Sammartino, J. (2013). Visual aesthetics and human preference. Annual Review of Psychology, 64, 77–107.  https://doi.org/10.1146/annurev-psych-120710-100504.Google Scholar
  52. Pekel, J.-F., Ceccato, P., Vancutsem, C., Cressman, K., Vanbogaert, E., & Defourny, P. (2011). Development and application of multi-temporal colorimetric transformation to monitor vegetation in the desert locust habitat. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2), 318–326.CrossRefGoogle Scholar
  53. Pekel, J.-F., Vancutsem, C., Bastin, L., Clerici, M., Vanbogaert, E., Bartholomé, E., et al. (2014). A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data. Remote Sensing of Environment, 140, 704–716.  https://doi.org/10.1016/j.rse.2013.10.008.Google Scholar
  54. Peterson, G. N. (2009). GIS cartography: A guide to effective map design. Boca Raton: CRC Press.CrossRefGoogle Scholar
  55. Polat, A. T., & Akay, A. (2015). Relationships between the visual preferences of urban recreation area users and various landscape design elements. Urban Forestry and Urban Greening, 14(3), 573–582.CrossRefGoogle Scholar
  56. Rose, R. A., Byler, D., Eastman, J. R., Fleishman, E., Geller, G., Goetz, S., et al. (2015). Ten ways remote sensing can contribute to conservation. Conservation Biology, 29(2), 350–359.CrossRefGoogle Scholar
  57. Schloss, K. B., & Palmer, S. E. (2011). Aesthetic response to color combinations: preference, harmony, and similarity. Attention, Perception, and Psychophysics, 73(2), 551–571.CrossRefGoogle Scholar
  58. See, L., Foody, G., Fritz, S., Mooney, P., Olteanu-Raimond, A.-M., da Costa Fonte, C. M. P., et al. (2017). Mapping and the citizen sensor. London: Ubiquity Press.Google Scholar
  59. Shen, Y., Ge, M., Zhuang, C., & Ma, Q. (2016). Sightseeing value estimation by analyzing geosocial images. In 2016 IEEE second international conference on multimedia big data (BigMM) (pp. 117–124). IEEE.Google Scholar
  60. Smith, R. (2010). The heat budget of the earth’s surface deduced from space. Resource document. Yale University Center for Earth Observation: New Haven, CT, USA. https://yceo.yale.edu/sites/default/files/files/Surface_Heat_Budget_From_Space.pdf. Accessed April 13, 2018.
  61. Sowiſska-ſwierkosz, B. (2016). Index of Landscape Disharmony (ILDH) as a new tool combining the aesthetic and ecological approach to landscape assessment. Ecological Indicators, 70, 166–180.  https://doi.org/10.1016/j.ecolind.2016.05.038.Google Scholar
  62. Sullivan, R. G., & Meyer, M. E. (2016). Environmental reviews and case studies: The national park service visual resource inventory: Capturing the historic and cultural values of scenic views. Environmental Practice, 18(3), 166–179.CrossRefGoogle Scholar
  63. Swetnam, R. D., Harrison-Curran, S. K., & Smith, G. R. (2017). Quantifying visual landscape quality in rural Wales: A GIS-enabled method for extensive monitoring of a valued cultural ecosystem service. Ecosystem Services, 26, 451–464.  https://doi.org/10.1016/j.ecoser.2016.11.004.Google Scholar
  64. Szabo, F., Bodrogi, P., & Schanda, J. (2010). Experimental modeling of colour harmony. Color Research and Application, 35(1), 34–49.CrossRefGoogle Scholar
  65. Tarajko-Kowalska, J. (2016). Factors affecting the visual perception of colour in rural architecture and landscape. Czasopismo Techniczne.Google Scholar
  66. Team, R. C. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2016.Google Scholar
  67. Tveit, M., Ode, Å., & Fry, G. (2006). Key concepts in a framework for analysing visual landscape character. Landscape Research, 31(3), 229–255.CrossRefGoogle Scholar
  68. Uzun, O., & Muuml, H. (2011). Visual landscape quality in landscape planning: Examples of Kars and Ardahan cities in Turkey. African Journal of Agricultural Research, 6(6), 1627–1638.Google Scholar
  69. Westland, S., Laycock, K., Cheung, V., Henry, P., & Mahyar, F. (2007). Colour harmony. JAIC-Journal of the International Colour Association, 1(1), 1–15.Google Scholar
  70. Williams, D. (2009). Landsat-7 science data user’s handbook. Resource document. National Aeronautics and Space Administration. https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/08/Landsat7_Handbook.pdf. Accessed April 13, 2018.
  71. Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(1), 3–36.CrossRefGoogle Scholar
  72. Wood, S. N. (2017). Generalized additive models: An introduction with R. Boca Raton: CRC Press.Google Scholar
  73. Xin, D., Zhou, X., & Zheng, H. (2006). Contour line extraction from paper-based topographic maps. Journal of Information and Computing Science, 1(5), 275–283.Google Scholar
  74. Semenov-Tyan-Shansky, V. (1928). Raion i strana. M.-L.: Gosizdat (in Russian).Google Scholar
  75. Zennaro, P. (2017). Strategies in colour choice for architectural built environment. Journal of the International Colour Association, 19, 15–22. https://aic-color.org/resources/Documents/jaic_v19_02.pdf.
  76. Zhang, Z., Qie, G., Wang, C., Jiang, S., Li, X., & Li, M. (2017). Relationship between forest color characteristics and scenic beauty: Case study analyzing pictures of mountainous forests at sloped positions in Jiuzhai Valley, China. Forests, 8(3), 63.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Chair of Environmental Protection and Landscape Management, Institute of Agricultural and Environmental SciencesEstonian University of Life SciencesTartuEstonia
  2. 2.Physical Geography and Cartography Department, School of Geology, Geography, Recreation and TourismV. N. Karazin Kharkiv National UniversityKharkivUkraine

Personalised recommendations