Abstract
Landscapes are meaningful ecological units that strongly depend on the environmental conditions. Such dependencies between landscapes and the environment have been noted since the beginning of Earth sciences and cast into conceptual models describing the interdependencies of climate, geology, vegetation and geomorphology. Here, we ask whether landscapes, as seen from space, can be statistically predicted from pertinent environmental conditions. To this end we adapted a deep learning generative model in order to establish the relationship between the environmental conditions and the view of landscapes from the Sentinel-2 satellite. We trained a conditional generative adversarial network to generate multispectral imagery given a set of climatic, terrain and anthropogenic predictors. The generated imagery of the landscapes share many characteristics with the real one. Results based on landscape patch metrics, indicative of landscape composition and structure, show that the proposed generative model creates landscapes that are more similar to the targets than the baseline models while overall reflectance and vegetation cover are predicted better. We demonstrate that for many purposes the generated landscapes behave as real with immediate application for global change studies. We envision the application of machine learning as a tool to forecast the effects of climate change on the spatial features of landscapes, while we assess its limitations and breaking points.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allen, M.R., et al.: IPCC fifth assessment synthesis report-climate change 2014 synthesis report (2014)
Brando, V.E., Dekker, A.G.: Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality. IEEE Trans. Geosci. Remote Sens. 41(6), 1378–1387 (2003)
Bunce, R.G.H., Barr, C., Clarke, R., Howard, D., Lane, A.: Land classification for strategic ecological survey. J. Environ. Manage. 47(1), 37–60 (1996)
Cardille, J.A., Turner, M.G.: Understanding landscape metrics. In: Gergel, S.E., Turner, M.G. (eds.) Learning Landscape Ecology, pp. 45–63. Springer, New York (2017). https://doi.org/10.1007/978-1-4939-6374-4_4
Cushman, S.A., McGarigal, K., Neel, M.C.: Parsimony in landscape metrics: strength, universality, and consistency. Ecol. Ind. 8(5), 691–703 (2008)
Forman, R.T.: Some general principles of landscape and regional ecology. Landscape Ecol. 10(3), 133–142 (1995)
Fox, J., Vogler, J.B.: Land-use and land-cover change in montane mainland southeast asia. Environ. Manag. 36(3), 394–403 (2005)
Franklin, S., Dickson, E., Hansen, M., Farr, D., Moskal, L.: Quantification of landscape change from satellite remote sensing. Forestry Chronicle 76(6), 877–886 (2000)
Getzin, S., Wiegand, K., Schöning, I.: Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods Ecol. Evol. 3(2), 397–404 (2012)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680 (2014)
Groom, G., Mücher, C., Ihse, M., Wrbka, T.: Remote sensing in landscape ecology: experiences and perspectives in a european context. Landscape Ecol. 21(3), 391–408 (2006)
Haase, G., Richter, H.: Current trends in landscape research. GeoJournal 7(2), 107–119 (1983)
Hartmann, J., Moosdorf, N.: The new global lithological map database GLiM: a representation of rock properties at the Earth surface. Geochemistry, Geophysics, Geosystems, 13(12) (2012)
Hijmans, R.J., Cameron, S., Parra, J., Jones, P., Jarvis, A., Richardson, K.: WorldClim-Global Climate Data. Free Climate Data for Ecological Modeling and GIS (2015)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint (2017)
Jarvis, A., Reuter, H.I., Nelson, A., Guevara, E.: Hole-filled srtm for the globe version, 4 (2008)
Jun, C., Ban, Y., Li, S.: China: open access to Earth land-cover map. Nature 514(7523), 434 (2014)
Kerr, J.T., Ostrovsky, M.: From space to species: ecological applications for remote sensing. Trends Ecol. Evol. 18(6), 299–305 (2003)
Klijn, J.: Hierarchical concepts in landscape ecology and its underlying disciplines. DLO winand staring centre report 100, (1995)
Langfelder, P., Horvath, S.: Fast R functions for robust correlations and hierarchical clustering. J. Stat. Softw. 46(11), i11 (2012)
McGarigal, K., Cushman, S.A., Neel, M.C., Ene, E.: FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. University of Massachusettes, Amherst, MA. http://www.umass.edu/landeco/research/fragstats/fragstats.html (2007) (2012). https://doi.org/citeulike-article-id:287784
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Mücher, C.A., Klijn, J.A., Wascher, D.M., Schaminée, J.H.: A new european landscape classification (lanmap): a transparent, flexible and user-oriented methodology to distinguish landscapes. Ecol. Ind. 10(1), 87–103 (2010)
Newton, A.C., et al.: Remote sensing and the future of landscape ecology. Prog. Phys. Geogr. 33(4), 528–546 (2009)
Otterman, J.: Anthropogenic impact on the albedo of the earth. Climatic Change 1(2), 137–155 (1977)
Roberts, D.R., et al.: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40(8), 913–929 (2017)
Simmons, M., Cullinan, V., Thomas, J.: Satellite imagery as a tool to evaluate ecological scale. Landscape Ecol. 7(2), 77–85 (1992)
Uuemaa, E., Antrop, M., Roosaare, J., Marja, R., Mander, Ü.: Landscape metrics and indices: an overview of their use in landscape research. Living Rev. Landscape Res. 3(1), 1–28 (2009)
Wilcox, R.R.: Robust generalizations of classical test reliability and cronbach’s alpha. Br. J. Math. Stat. Psychol. 45(2), 239–254 (1992)
Zachos, J., Pagani, M., Sloan, L., Thomas, E., Billups, K.: Trends, rhythms, and aberrations in global climate 65 ma to present. Science 292(5517), 686–693 (2001)
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Requena-Mesa, C., Reichstein, M., Mahecha, M., Kraft, B., Denzler, J. (2019). Predicting Landscapes from Environmental Conditions Using Generative Networks. In: Fink, G., Frintrop, S., Jiang, X. (eds) Pattern Recognition. DAGM GCPR 2019. Lecture Notes in Computer Science(), vol 11824. Springer, Cham. https://doi.org/10.1007/978-3-030-33676-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-33676-9_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33675-2
Online ISBN: 978-3-030-33676-9
eBook Packages: Computer ScienceComputer Science (R0)