Skip to main content

Advertisement

Log in

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

  • Published:
GeoJournal Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Acar, C., & Sakıcı, Ç. (2008). Assessing landscape perception of urban rocky habitats. Building and Environment, 43(6), 1153–1170.

    Article  Google Scholar 

  • Amir, S., & Sobol, E. (1990). The use of geomorphological elements for evaluation of visual quality of Israeli coast. GeoJournal, 21(3), 233–240.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Antrop, M. (2000). Geography and landscape science. Belgeo. Revue belge de géographie, (1-2-3-4) (pp. 9–36).

  • Antrop, M., & Van Eetvelde, V. (2017). Landscape perspectives: The holistic nature of landscape. Berlin: Springer.

    Book  Google Scholar 

  • Arévalo, V., González, J., & Ambrosio, G. (2008). Shadow detection in colour high-resolution satellite images. International Journal of Remote Sensing, 29(7), 1945–1963.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Baykan, N. A., & Yılmaz, N. (2010). Mineral identification using color spaces and artificial neural networks. Computers and Geosciences, 36(1), 91–97.

    Article  Google Scholar 

  • Bell, S. (2004). Elements of visual design in the landscape. London: Taylor & Francis.

    Google Scholar 

  • Bell, S. (2012). Landscape: pattern, perception and process. Abingdon: Routledge.

    Book  Google Scholar 

  • Benčo, M., & Hudec, R. (2007). Novel method for color textures features extraction based on GLCM. Radioengineering, 16(4), 65.

    Google Scholar 

  • Bláha, J. D., & Štěrba, Z. (2014). Colour contrast in cartographic works using the principles of Johannes Itten. The Cartographic Journal, 51(3), 203–213.

    Article  Google Scholar 

  • 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.

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • Burchett, K. E. (2002). Color harmony. Color Research and Application, 27(1), 28–31.

    Article  Google Scholar 

  • Caivano, J. L. (1998). Color and semiotics: A two-way street. Color Research and Application, 23(6), 390–401.

    Article  Google Scholar 

  • Casalegno, S., Inger, R., DeSilvey, C., & Gaston, K. J. (2013). Spatial covariance between aesthetic value and other ecosystem services. PLoS ONE, 8(6), e68437.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • d’Andrimont, R., & Defourny, P. (2018). Monitoring African water bodies from twice-daily MODIS observation. GIScience and Remote Sensing, 55(1), 130–153.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Granö, J. G. (1929; 1997). Pure geography. Baltimore: Johns Hopkins University Press.

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • Hands, D. E., & Brown, R. D. (2002). Enhancing visual preference of ecological rehabilitation sites. Landscape and Urban Planning, 58(1), 57–70.

    Article  Google Scholar 

  • Haralick, R. M., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621.

    Article  Google Scholar 

  • Itten, J. (1973). The art of color: The subjective experience and objective rationale of color. New York: Reinhold Publishing Corporation.

    Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Lenclos, J.-P. (2004). The geography of color. New York: W.W. Norton & Co.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Nemcsics, A. (2012). The complex theory of colour harmony. Obuda University e-Bulletin, 3(1), 249–257.

    Google Scholar 

  • 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.

  • O’Connor, Z. (2006). Bridging tahe gap: Façade colour, aesthetic response and planning policy. Journal of Urban Design, 11(3), 335–345.

    Article  Google Scholar 

  • O’Connor, Z. (2010). Colour harmony revisited. Color Research and Application, 35(4), 267–273.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Orzechowska-Szajda, I. (2015). Complexity as an indicator of aesthetic quality of landscape. Czasopismo Techniczne.

  • Ou, L. C., & Luo, M. R. (2006). A colour harmony model for two-colour combinations. Color Research and Application, 31(3), 191–204.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Peterson, G. N. (2009). GIS cartography: A guide to effective map design. Boca Raton: CRC Press.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Schloss, K. B., & Palmer, S. E. (2011). Aesthetic response to color combinations: preference, harmony, and similarity. Attention, Perception, and Psychophysics, 73(2), 551–571.

    Article  Google Scholar 

  • 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 

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Szabo, F., Bodrogi, P., & Schanda, J. (2010). Experimental modeling of colour harmony. Color Research and Application, 35(1), 34–49.

    Article  Google Scholar 

  • Tarajko-Kowalska, J. (2016). Factors affecting the visual perception of colour in rural architecture and landscape. Czasopismo Techniczne.

  • Team, R. C. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2016.

  • Tveit, M., Ode, Å., & Fry, G. (2006). Key concepts in a framework for analysing visual landscape character. Landscape Research, 31(3), 229–255.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

  • 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.

    Article  Google Scholar 

  • Wood, S. N. (2017). Generalized additive models: An introduction with R. Boca Raton: CRC Press.

    Book  Google Scholar 

  • 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 

  • Semenov-Tyan-Shansky, V. (1928). Raion i strana. M.-L.: Gosizdat (in Russian).

  • 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.

  • 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.

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oleksandr Karasov.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karasov, O., Külvik, M., Chervanyov, I. et al. Mapping the extent of land cover colour harmony based on satellite Earth observation data. GeoJournal 84, 1057–1072 (2019). https://doi.org/10.1007/s10708-018-9908-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10708-018-9908-x

Keywords

Navigation