Search Results (2)

Remotely sensed spectral diversity is a promising method for investigating biodiversity. However, studies designed to assess the effectiveness of tracking changes in diversity using historical satellite imagery are lacking. This study employs open-access multispectral Landsat imagery and the BiodivMapR package to estimate the multi-temporal alpha diversity in drylands affected by mining. Multi-temporal parameters of alpha diversity were identified, such as vegetation indices, buffer zone size, and the number of clusters. Variations in alpha diversity were compared for various plant communities over time. The results showed that this method could effectively assess the alpha diversity of vegetation (R², 0.68). The optimal parameters used to maximize the accuracy of alpha diversity were NDVI threshold, 0.01; size of buffer zones, 120 m × 120 m; number of clusters, 100. The root mean square error of the alpha diversity of herbs was lowest (0.26), while those of shrub and tree communities were higher (0.34–0.41). During the period 1990–2020, the study area showed an overall trend of increasing diversity, with surface mining causing a significant decrease in diversity when compared with underground mining. This illustrates that the quick development of remote sensing and image processing techniques offers new opportunities for monitoring diversity in both single and multiple time phases. Researchers should consider the plant community types involved and select locally suitable parameters. In the future, the generation of long-time series and finer resolution maps of diversity should be studied further in the aspects of spatial, functional, taxonomic, and phylogenetic diversity. Full article

As there is an urgent need to protect rapidly declining global diversity, it is important to identify methods to quickly estimate the diversity and heterogeneity of a region and effectively implement monitoring and conservation plans. The combination of remotely sensed and field-collected data, under the paradigm of the Spectral Variation Hypothesis (SVH), represents one of the most promising approaches to boost large-scale and reliable biodiversity monitoring practices. Here, the potential of SVH to capture information on plant diversity at a fine scale in an ecological network (EN) embedded in a complex landscape has been tested using two new and promising methodological approaches: the first estimates α and β spectral diversity and the latter ecosystem spectral heterogeneity expressed as Rao’s Quadratic heterogeneity measure (Rao’s Q). Both approaches are available thanks to two brand-new R packages: “biodivMapR” and “rasterdiv”. Our aims were to investigate if spectral diversity and heterogeneity provide reliable information to assess and monitor over time floristic diversity maintained in an EN selected as an example and located in northeast Italy. We analyzed and compared spectral and taxonomic α and β diversities and spectral and landscape heterogeneity, based on field-based plant data collection and remotely sensed data from Sentinel-2A, using different statistical approaches. We observed a positive relationship between taxonomic and spectral diversity and also between spectral heterogeneity, landscape heterogeneity, and the amount of alien species in relation to the native ones, reaching a value of R² = 0.36 and R² = 0.43, respectively. Our results confirmed the effectiveness of estimating and mapping α and β spectral diversity and ecosystem spectral heterogeneity using remotely sensed images. Moreover, we highlighted that spectral diversity values become more effective to identify biodiversity-rich areas, representing the most important diversity hotspots to be preserved. Finally, the spectral heterogeneity index in anthropogenic landscapes could be a powerful method to identify those areas most at risk of biological invasion. Full article