Remote sensing of biodiversity: measuring ecological complexity from space
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1 Remote sensing of biodiversity: measuring ecological complexity from space Duccio Rocchini Fondazione Edmund Mach Research and Innovation Centre Department of Biodiversity and Molecular Ecology San Michele all Adige, Trento - Italy November 2014 Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 1 / 36
2 Outline Introduction 1 Introduction 2 Estimation of taxonomic diversity 3 Species Distribution Modelling 4 Uncertainty 5 Conclusion Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 2 / 36
3 Networks Introduction Courtesy: Dr. Martin Wegmann Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 3 / 36
4 Networks Introduction Courtesy: Dr. Martin Wegmann Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 4 / 36
5 Networks Introduction Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 5 / 36
6 Networks Introduction Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 6 / 36
7 Introduction The power of remote sensing Reflectances variability over an area should result in a positive outcome when dealing with biodiversity evaluation, strictly related to ecosystem entropy. In particular, when aiming to locate heterogeneous environments one might make use of external information suspected to be correlated with species richness. Rocchini (Remote Sens. Environ., 2007) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 7 / 36
8 Aim of this seminar Introduction Remote sensing power to improve: Estimation of taxonomic diversity Species Distribution Modelling Uncertainty Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 8 / 36
9 Outline Estimation of taxonomic diversity 1 Introduction 2 Estimation of taxonomic diversity 3 Species Distribution Modelling 4 Uncertainty 5 Conclusion Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 9 / 36
10 Estimation of taxonomic diversity Local species diversity estimate (α diversity) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 10 / 36
11 Estimation of taxonomic diversity Local species diversity estimate (α diversity) evd: - Functions for extreme value distributions - R package Extends simulation, distribution, quantile and density functions to univariate and multivariate parametric extreme value distributions, and provides fitting functions which calculate maximum likelihood estimates for univariate and bivariate maxima models, and for univariate and bivariate threshold models. Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 11 / 36
12 Estimation of taxonomic diversity Local species diversity estimate (α diversity) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 12 / 36
13 Estimation of taxonomic diversity Local species diversity estimate (α diversity) Oldeland et al. (Ecol. indic., 2010) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 13 / 36
14 Estimation of taxonomic diversity Local species diversity estimate (α diversity) Rocchini and Neteler (Int. J. Remote Sens., 2012) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 14 / 36
15 Estimation of taxonomic diversity Local species diversity estimate (α diversity) Rényi (1970) generalised entropy: H α = 1 1 α ln p α (1) where p=relative abundance of each spectral reflectance value (DN). Such measure is extremely flexible and powerful since many popular diversity indices are simply special cases of H α. Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 15 / 36
16 Estimation of taxonomic diversity Local species diversity estimate (α diversity) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 16 / 36
17 Estimation of taxonomic diversity Local species diversity estimate (α diversity) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 17 / 36
18 Estimation of taxonomic diversity Local species diversity estimate (α diversity) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 18 / 36
19 Estimation of taxonomic diversity Local species diversity estimate (α diversity) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 19 / 36
20 Estimation of taxonomic diversity Species turnover estimate (β diversity) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 20 / 36
21 Estimation of taxonomic diversity Species turnover estimate (β diversity) Baselga, A., Multiple site dissimilarity quantifies compositional heterogeneity among several sites, while average pairwise dissimilarity may be misleading. Ecography 36, Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 21 / 36
22 Estimation of taxonomic diversity Species turnover estimate (β diversity) i.colors.enhance - Performs auto-balancing of colors for RGB images. i.colors.enhance red=l B50 green=l B40 blue=l B30 Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 22 / 36
23 Estimation of taxonomic diversity Species turnover estimate (β diversity) d 1,1 d 1,2 d 1,3 d 1,n d 2,1 d 2,2 d 2,3 d 2,n M d = d 3,1 d 3,2 d 3,3 d 3,n d n,1 d n,2 d n,3 d n,n or more simply 123[2ex]123n123n123n, once n plots are considered, based on an a-priori defined statistical sampling design. (2) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 23 / 36
24 Estimation of taxonomic diversity Species turnover estimate (β diversity) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 24 / 36
25 Estimation of taxonomic diversity Species turnover estimate (β diversity) Rocchini, D., Andreini Butini, S., Chiarucci, A. (2005). Maximizing plant species inventory efficiency by means of remotely sensed spectral distances. Global Ecology and Biogeography, 14: Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 25 / 36
26 Outline Species Distribution Modelling 1 Introduction 2 Estimation of taxonomic diversity 3 Species Distribution Modelling 4 Uncertainty 5 Conclusion Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 26 / 36
27 Species Distribution Modelling Species Distribution Modelling Yanbin Jiang, C.A.J.M. de Bie, Tiejun Wang, A.K. Skidmore, Xuehua Liu, Shanshan Song and Xiaoming Shao Hyper-temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests (pages ) journal of vegetation Scinece. Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 27 / 36
28 Species Distribution Modelling Species Distribution Modelling Wegmann, M., Santini, L., Leutner, B., Safi, K., Rocchini, D., Bevanda, M., Latifi, H., Dech, S., Rondinini, C. (2014). Role of African protected areas in maintaining connectivity for large mammals. Philosophical Transactions of the Royal Society B-Biological Sciences, 369: Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 28 / 36
29 Species Distribution Modelling Species Distribution Modelling Gillespie, T.W., Foody, G.M., Rocchini, D., Giorgi, A.P., Saatchi, S. (2008). Measuring and modeling biodiversity from space. Progress in Physical Geography, 32: Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 29 / 36
30 Outline Uncertainty 1 Introduction 2 Estimation of taxonomic diversity 3 Species Distribution Modelling 4 Uncertainty 5 Conclusion Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 30 / 36
31 Uncertainty Uncertainty Decision = ( < E m > I ) < E m < I > E m > I > E m < I where E m = modelerror(oruncertainty), I = potentialinvasion (3) Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 31 / 36
32 Uncertainty Uncertainty Including such estimates alongside mean projections gives a map of ignorance as called for by Rocchini et al. (2011), highlighting areas where knowledge is lacking and could be improved with additional sampling effort or the inclu- sion of additional covariates. [Swanson et al., Global Ecol. Biogeogr., 2013] Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 32 / 36
33 Outline Conclusion 1 Introduction 2 Estimation of taxonomic diversity 3 Species Distribution Modelling 4 Uncertainty 5 Conclusion Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 33 / 36
34 Concluding remarks Conclusion Assessment of biodiversity at local and regional scales often relies on fieldwork-based data collection. Species assessment in relatively large areas has always been a challenging task for ecologists. Large spatial scales Remote sensing data sources and techniques are being rapidly developed, but quantitative tests need to be carried out to assess diversity using these data and techniques. Improving theoretical and empirical measurements of diversity by remote sensing should help to find the ecological gradients shaping diversity on a large spatial scale. Studying genetic structure at the landscape scale is a promising field of research which could provide us with a better understanding of species dispersal and gene flow across habitats. Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 34 / 36
35 Ecological informatics Conclusion The increasing availability of open ecological data [...] makes it increasingly possible to test cutting-edge ecological theories, such as dark diversity, evolutionary paths and climate change scenarios. In using a shared open-source code [...] researchers can be sure that their results are reliable and also that the code they have used is robust Rocchini, D., Neteler, M. (2012). Let the four freedoms paradigm apply to ecology. Trends in Ecology & Evolution, 27: Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 35 / 36
36 Thank you! Conclusion Duccio Rocchini (Fondazione E. Mach) Remote sensing of biodiversity SAGEO 2014, Grenoble 36 / 36
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