Abstract
Synthesizing and properly visualizing data in 2D systems is a key issue when aiming at explaining spatial patterns by spatial processes.
In this chapter we address the topics synthesis and visualization in relation to following ecological issues: (1) synthesizing species distribution models relying on virtual species, (2) visualizing spatial uncertainty in species distribution based on cartograms, (3) fuzzy methods to synthesize species distribution uncertainty, (4) remote sensing data synthesis by exploratory analysis and replotting data in new systems, (5) measuring and visualizing ecological diversity from space based on generalized entropy, and (6) neutral landscape for testing ecological theories. We will make use of examples from the free and open source software GRASS GIS and R.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barbosa AM (2015) fuzzySim: applying fuzzy logic to binary similarity indices in ecology. Methods Ecol Evol 6:853–858
Barbosa AM, Pautasso M, Figueiredo D (2013) Species- people correlations and the need to account for survey effort in biodiversity analyses. Divers Distrib 19:1188–1197
Bivand R, Krug R, Neteler M, Jeworutzki S (2016) rgrass7 – interface Between GRASS 7 Geographical Information System and R. R software package
Boggs S (1949) An atlas of ignorance: a needed stimulus to honest thinking and hard work. Proc Am Philos Soc 93:9–258
Comber A, Fisher P, Brunsdon C, Khmag A (2012) Spatial analysis of remote sensing image classification accuracy. Remote Sens Environ 127:237–246
Comber A, See L, Fritz S, Van der Velde M, Perger C, Foody GM (2013) Using ontrol data to determine the reliability of volunteered geographic information about land cover. Int J Appl Earth Obs Geoinf 23:37–48
Foody GM (2011) Impacts of imperfect reference data on the apparent accuracy of species presence/absence models and their predictions. Glob Ecol Biogeogr 20:498–508
Fourier J (1822) Thorie Analytique de la Chaleur. Didot, Paris
Gastner M, Newman M (2004) Diffusion-based method for producing density- equalizing maps. Proc Natl Acad Sci U S A 15:7499–7504
Gopal S, Woodcock C (1994) Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogramm Eng Remote Sens 60:181–188
Hanspach J, Kuehn I, Schweiger O, Pompe S, Klotz S (2011) Geographical patterns in prediction errors of species distribution models. Glob Ecol Biogeogr 20:779–788
Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans SMC 6:610–621
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978
Imre AR, Cseh D, Neteler M, Rocchini D (2011) Korcak dimension as a novel indicator of landscape fragmentation and re-forestation. Ecol Indic 11:1134–1138
Mandelbrot BB, Blumen A (1989) Fractal geometry: What is it, and what does it do? Proc R Soc Lond A 423:3–16
Neteler M, Bowman MH, Landa M, Metz M (2012) GRASS GIS: a multi-purpose open source GIS. Environ Model Softw 31:124–130
R Development Core Team (2016). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
Rényi A (1970) Probability theory. North Holland Publishing Company, Amsterdam
Ricotta C (2005) On possible measures for evaluating the degree of uncertainty of fuzzy thematic maps. Int J Remote Sens 26:5573–5583
Ricotta C, Avena G (2003) On the relationship between Pielou’s evenness and landscape dominance within the context of Hill’s diversity profiles. Ecol Indic 2:361–365
Rocchini D, Hortal J, Lengyel S, Lobo JM, Jiménez-Valverde A, Ricotta C, Bacaro G, Chiarucci A (2011) Accounting for uncertainty when mapping species distributions: the need for maps of ignorance. Prog Phys Geogr 35:211–226. doi:10.1177/0309133311399491
Rocchini D, Foody GM, Nagendra H, Ricotta C, Anand M, He KS, Amici V, Kleinschmit B, Foerster M, Schmidtlein S, Feilhauer H, Ghisla A, Metz M, Neteler M (2013) Uncertainty in ecosystem mapping by remote sensing. Comput Geosci 50:128–135. doi:10.1016/j.cageo.2012.05.022
Rocchini D, Boyd DS, Féret JB, Foody GM, He KS, Lausch A, Nagendra H, Wegmann M, Pettorelli N (2016) Satellite remote sensing to monitor species diversity: potential and pitfalls. Remote Sens Ecol Conserv 2:25–36. doi:10.1002/rse2.9
Walsh JL (1923) A closed set of orthogonal functions. Am J Math 45:5–24
Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Rocchini, D. et al. (2018). GIS-Based Data Synthesis and Visualization. In: Recknagel, F., Michener, W. (eds) Ecological Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-59928-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-59928-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59926-7
Online ISBN: 978-3-319-59928-1
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)