Use of remote sensing techniques to estimate plant diversity within ecological networks: a worked example

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 fine scale in an ecological network (EN) embedded in a complex landscape, has been tested using two new and promising methodological approaches, based on “biodivMapR” and “rasterdiv” R packages. The first estimates α and β spectral diversity and the latter ecosystem spectral heterogeneity expressed as Rao's Quadratic heterogeneity measure (Rao’s Q). Our aims were to investigate if spectral diversity and heterogeneity provide reliable information to assess and/or monitor over time floristic diversity maintained in an EN selected as an example and located in North-East 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. 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. While the spectral heterogeneity index in anthropogenic landscapes could be a powerful method to identify those areas most at risk of biological invasion.