Uncertainty in species diversity mapping: unravelling a long lasting theme

Many geospatial tools have been advocated in spatial ecology and biogeography to estimate biodiversity and its changes over space and time. Such information is essential in designing effective strategies for biodiversity conservation and management. Remote sensing is one of the most powerful approaches to identify biodiversity hotspots and predict changes in species composition in reduced time and costs. This is because, with respect to field-based methods, it allows to derive complete spatial coverages of the Earth surface under study in a short period of time. Furthermore, remote sensing provides repeated coverages of field sites, thus making studies of temporal changes in biodiversity possible. Thus far, species diversity estimates from remote sensing have rarely taken into account uncertainty in an explicit manner. On the contrary, the spatial distribution of uncertainty should explicitly be shown on maps to avoid ignoring overall accuracy or model errors. In this talk I will discuss, from a conceptual point of view, the potential of remote sensing in estimating biodiversity using various diversity indices. I will also face challenges in the representation of uncertainty methods mainly based on Bayesian logistic regression coupled with simulation-based Monte Carlo techniques and Cartograms applied to European and worldwide datasets for explicitly mapping uncertainty in the distribution of species diversity in a Free and Open Source environment

Earth observation for ecosystems monitoring in space and time: a special issue in Remote Sensing

This Editorial introduces the papers published in the special issue “Earth Observation for Ecosystems Monitoring in Space and Time” which includes the most important researchers in the field and the most challenging aspects of the application of remote sensing to study ecosystems

A Lack of "Environmental Earth Data" at the Microhabitat Scale Impacts Efforts to Control Invasive Arthropods That Vector Pathogens

We currently live in an era of major global change that has led to the introduction and range expansion of numerous invasive species worldwide. In addition to the ecological and economic consequences associated with most invasive species, invasive arthropods that vector pathogens (IAVPs) to humans and animals pose substantial health risks. Species distribution models that are informed using environmental Earth data are frequently employed to predict the distribution of invasive species, and to advise targeted mitigation strategies. However, there are currently substantial mismatches in the temporal and spatial resolution of these data and the environmental contexts which affect IAVPs. Consequently, targeted actions to control invasive species or to prepare the population for possible disease outbreaks may lack efficacy. Here, we identify and discuss how the currently available environmental Earth data are lacking with respect to their applications in species distribution modeling, particularly when predicting the potential distribution of IAVPs at meaningful space-time scales. For example, we examine the issues related to interpolation of weather station data and the lack of microclimatic data relevant to the environment experienced by IAVPs. In addition, we suggest how these data gaps can be filled, including through the possible development of a dedicated open access database, where data from both remotely- and proximally-sensed sources can be stored, shared, and accessed

A virtual species set for robust and reproducible species distribution modelling tests

Predicting species potential and future distribution has become a relevant tool in biodiversity monitoring and conservation.In this data article we present the suitability map of a virtual species generated based on two bioclimatic variables, and a dataset containing more than 700,000 random observations at the extent of Europe. The dataset includes spatial attributes such as: distance to roads, protected areas, country codes, and the habitat suitability of two spatially clustered species (grassland and forest species) and a wide-spread species

Remote sensing object-oriented approaches coupled with ecological informatics to map invasive plant species

nvasive alien plants are considered one of the major threats to biodiversity conservation worldwide. Hence, understanding their distribution and abundance is important in order to assess the impact on native ecosystems. It is particularly important to be able to track the spread of invasive species across landscapes; a task best achieved using remotely sensed imagery. The availability of high resolution data, combined with efficient classification methods, can potentially improve early detection of invasive alien species thereby enhancing their management. This study aims to classify woody species with a focus on Melia azedarach (Meliaceae) trees in a moderately invaded coastal belt valley on the east coast of South Africa using WorldView-2 (WV-2) satellite imagery, and to compare the commonly used pixel-based classification with object-oriented approaches. The results show that object-oriented approaches are more suitable for classifying woody species, as well as other land cover classes when using high-resolution WV-2 imagery. The overall accuracy was 90% by object- oriented classification, while the pixel-based classification gave an overall accuracy of 78%. For Melia, a producer accuracy of 92% and user accuracy of 91% was obtained by object-oriented classification and a producer accuracy of 85% and user accuracy of 83% was obtained by pixel-based classification. Hence the combined use of new generation sensor imagery and the employment of object-oriented image classification techniques provided more accurate information on Melia invasion in the study area. This is an encouraging result given the high degree of intermingling of Melia with other plants at the study site. In particular, the vegetation maps produced from this study would aid in gathering accurate knowledge about the distribution and spreading status of Melia, a major invasivespecies over large areas of South Africa and elsewhere in the world

Terra and Aqua satellites track tiger mosquito invasion: modelling the potential distribution of Aedes albopictus in north-eastern Italy

<p>Abstract</p> <p>Background</p> <p>The continuing spread of the Asian tiger mosquito <it>Aedes albopictus </it>in Europe is of increasing public health concern due to the potential risk of new outbreaks of exotic vector-borne diseases that this species can transmit as competent vector. We predicted the most favorable areas for a short term invasion of <it>Ae. albopictus </it>in north-eastern Italy using reconstructed daily satellite data time series (MODIS Land Surface Temperature maps, LST). We reconstructed more than 11,000 daily MODIS LST maps for the period 2001-09 (i.e. performed spatial and temporal gap-filling) in an Open Source GIS framework. We aggregated these LST maps over time and identified the potential distribution areas of <it>Ae. albopictus </it>by adapting published temperature threshold values using three variables as predictors (0°C for mean January temperatures, 11°C for annual mean temperatures and 1350 growing degree days filtered for areas with autumnal mean temperatures > 11°C). The resulting maps were integrated into the final potential distribution map and this was compared with the known current distribution of <it>Ae. albopictus </it>in north-eastern Italy.</p> <p>Results</p> <p>LST maps show the microclimatic characteristics peculiar to complex terrains, which would not be visible in maps commonly derived from interpolated meteorological station data. The patterns of the three indicator variables partially differ from each other, while winter temperature is the determining limiting factor for the distribution of <it>Ae. albopictus</it>. All three variables show a similar spatial pattern with some local differences, in particular in the northern part of the study area (upper Adige valley).</p> <p>Conclusions</p> <p>Reconstructed daily land surface temperature data from satellites can be used to predict areas of short term invasion of the tiger mosquito with sufficient accuracy (200 m pixel resolution size). Furthermore, they may be applied to other species of arthropod of medical interest for which temperature is a relevant limiting factor. The results indicate that, during the next few years, the tiger mosquito will probably spread toward northern latitudes and higher altitudes in north-eastern Italy, which will considerably expand the range of the current distribution of this species.</p

Algorithmic Foundation of Spectral Rarefaction for Measuring Satellite Imagery Heterogeneity at Multiple Spatial Scales

Measuring heterogeneity in satellite imagery is an important task to deal with. Most measures of spectral diversity have been based on Shannon Information theory. However, this approach does not inherently address different scales, ranging from local (hereafter referred to alpha diversity) to global scales (gamma diversity). The aim of this paper is to propose a method for measuring spectral heterogeneity at multiple scales based on rarefaction curves. An algorithmic solution of rarefaction applied to image pixel values (Digital Numbers, DNs) is provided and discussed

High order singular value decomposition per la stima della biodiversità vegetale

We propose a new method to estimate plant biodiversity with Rényi and Rao indexes through the so called High Order Singular Value Decomposition (HOSVD) of tensors. Starting from NASA multispectral images we evaluate biodiversity and we compare original biodiversity estimates with those realised via the HOSVD compression methods for big data. Our strategy turns out to be extremely powerful in terms of storage memory and precision of the outcome. The obtained results are so promising that we can support the efficiency of our method in the ecological framework