Landscape diversity generally relates to species diversity at a range of ecological levels like species community diversity and genetic diversity. Species–based measures of diversity like species richness or species turnover are the most commonly used metrics for quantifying the diversity of an area. Nonetheless, the assessment of species diversity in relatively large areas has always been a challenging task for ecologists, mainly because of the intrinsic difficulty in judging the completeness of species lists and in quantifying the sampling effort (Palmer et al., 2002). Since the variability in the remotely sensed signal is expected to be related to landscape diversity, it could be used as a good proxy of diversity at species level.
It has been demonstrated that the relation between species diversity and landscape heterogeneity measured from remotely sensed data or land use maps varies with scale. However, Free and Open Source tools (allowing an access to the source code, see Rocchini and Neteler, 2012) for assessing landscape heterogeneity at different spatial scales are still lacking today. In this paper, we aim at: i) providing a theoretical background of the mostly used diversity indices stemmed from information theory that are commonly applied to quantify landscape heterogeneity from remotely sensed data and ii) proposing a free and robust Open Source tool (r.diversity) with its source code for calculating diversity indices (and allowing an easy potential implementation of new metrics by multiple contributors globally) at different spatial scales from remotely-sensed imagery or land use maps, running under the widely used Open Source program GRASS GIS.
r.diversity can be a valuable tool for calculating landscape heterogeneity in an Open Source space, on the strength of its major advantages like: i) the availability of multiple indices at a time and ii) the possibility to create new indices directly reusing the code, iii) the possibility to calculate landscape heterogeneity at multiple spatial scales in an explicit way based on varying moving windows thus iv) reducing the problems of hidden patterns of the relation between field- and landscape-based diversity due to scale mismatch.
We expect that the theme proposed in this paper will stimulate discussions on the opportunities offered by Free and Open Source Software to calculate landscape diversity
