Verónica Andreo

Potential of remote sensing to predict species invasions A modelling perspective

Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results.

Estimating Hantavirus Risk in Southern Argentina: A GIS-Based Approach Combining Human Cases and Host Distribution

We use a Species Distribution Modeling (SDM) approach along with Geographic Information Systems (GIS) techniques to examine the potential distribution of hantavirus pulmonary syndrome (HPS) caused by Andes virus (ANDV) in southern Argentina and, more precisely, define and estimate the area with the highest infection probability for humans, through the combination with the distribution map for the competent rodent host (Oligoryzomys longicaudatus). Sites with confirmed cases of HPS in the period 1995–2009 were mostly concentrated in a narrow strip (~90 km × 900 km) along the Andes range from northern Neuquén to central Chubut province. This area is characterized by high mean annual precipitation (~1,000 mm on average), but dry summers (less than 100 mm), very low percentages of bare soil (~10% on average) and low temperatures in the coldest month (minimum average temperature −1.5 °C), as compared to the HPS-free areas, features that coincide with sub-Antarctic forests and shrublands (especially those dominated by the invasive plant Rosa rubiginosa), where rodent host abundances and ANDV prevalences are known to be the highest. Through the combination of predictive distribution maps of the reservoir host and disease cases, we found that the area with the highest probability for HPS to occur overlaps only 28% with the most suitable habitat for O. longicaudatus. With this approach, we made a step forward in the understanding of the risk factors that need to be considered in the forecasting and mapping of risk at the regional/national scale. We propose the implementation and use of thematic maps, such as the one built here, as a basic tool allowing public health authorities to focus surveillance efforts and normally scarce resources for prevention and control actions in vast areas like southern Argentina.

A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data

Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. We present a novel method to fully reconstruct MODIS daily LST products for central Europe at 1 km resolution and globally, at 3 arc-min. We combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. The reconstructed MODIS LST for central Europe was calibrated to air temperature data through linear models that yielded R2 values around 0.8 and RMSE of 0.5 K. This new method proves to scale well for both local and global reconstruction. We show examples for the identification of extreme events to demonstrate the ability of these new LST products to capture and represent spatial and temporal details. A time series of global monthly average, minimum and maximum LST data and long-term averages is freely available for download.

Análisis de una serie temporal de clorofila-a a partir de imágenes MODIS de un embalse eutrófico

Eutrophication is a phenomenon that affects many water bodies around the world. San Roque Dam is not an exception since it has been going through a huge increase in algae concentration. In this framework, its monitoring is a key matter, as is the source of water supply most important of Cordoba city. Remote sensing is a fundamental tool to complement traditional in situ monitoring and understand processes that occur in the reservoir. This study presents a semi-empirical algorithm obtained from MODIS data and in situ chlorophyll-a measurements, provided by the Ministry of Water Resources of Cordoba Province. From this model, a daily time chlorophyll-a time series was obtained from 2001 to 2014. Statistical analysis of the series indicate that water quality of the reservoir has worsened when comparing sets of last (2014–2010) and first (2010–2014) five years.

Remote Sensing of Phytoplankton Blooms in the Continental Shelf and Shelf-Break of Argentina: Spatio-Temporal Changes and Phenology

We studied the spatio-temporal variations of satellite chlorophyll-a (chl-a) and phytoplankton blooms in the continental shelf and shelf-break of the Argentinean patagonic region by means of an 11-years time series of level 3 (L3) MODIS/Aqua chlorophyll products. We aggregated data according to different granularities and estimated climatologies and anomalies. We also studied the phenology of phytoplankton blooms determining bloom starting date and date of maximum concentration. Finally, we estimated and described statistical indexes such as bloom occurrence frequency. The results obtained provide an overview of the evolution and the spatio-temporal variability of chl-a that, in general, and despite its limitations, was complementary and consistent with previous studies based on both satellite and in situ data. This study intended to set the baseline to study algal blooms and their variability in the Argentinian sea, which is valuable information to be included in predictive models related to the occurrence of harmful algal blooms, dynamics of marine system and the effects of global changes over climatic and biogeochemical cycles. In addition, this study also contributes with more up-to-date science data for the future Argentinian and Brazilian SABIA-Mar ocean color mission, which will provide high resolution data (200 m) over the Argentinian coastal zones and continental shelf. Besides the results per se, the relevance of this study is also related to the use of an novel free and open source tool.

Effect of Climate and Land Use on the Spatio-Temporal Variability of Tick-Borne Bacteria in Europe

The incidence of tick-borne diseases caused by Borrelia burgdorferi sensu lato, Anaplasma phagocytophilum and Rickettsia spp. has been rising in Europe in recent decades. Early pre-assessment of acarological hazard still represents a complex challenge. The aim of this study was to model Ixodes ricinus questing nymph density and its infection rate with B. burgdorferi s.l., A. phagocytophilum and Rickettsia spp. in five European countries (Italy, Germany, Czech Republic, Slovakia, Hungary) in various land cover types differing in use and anthropisation (agricultural, urban and natural) with climatic and environmental factors (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Land Surface Temperature (LST) and precipitation). We show that the relative abundance of questing nymphs was significantly associated with climatic conditions, such as higher values of NDVI recorded in the sampling period, while no differences were observed among land use categories. However, the density of infected nymphs (DIN) also depended on the pathogen considered and land use. These results contribute to a better understanding of the variation in acarological hazard for Ixodes ricinus transmitted pathogens in Central Europe and provide the basis for more focused ecological studies aimed at assessing the effect of land use in different sites on tick–host pathogens interaction.

Spatio-temporal variations in chlorophyll-a concentration in the patagonic continental shelf: An example of satellite time series processing with GRASS GIS temporal modules

We studied the spatio-temporal variations of chlorophyll-a concentration and phytoplankton blooms in the continental shelf and shelf break of the Argentinean patagonic region by means of a time series of 11-years of MODIS/Aqua level 3 (L3) chlorophyll product. We aggregated data according to different granularities and estimated annual and monthly anomalies. We also studied the phenology of phytoplankton blooms determining bloom starting date and date of maximum concentration. Finally, we estimated and described statistical indexes such as minimum and maximum bloom areas, their occurrence date and bloom occurrence frequency. All the temporal processing of this raster dataset was done with the recently implemented temporal modules of GRASS GIS 7. This Free and Open Source Software provides the advantage of automating all (or most of) the processing, allowing the application of the same methodology to analyze satellite time series of different variables, like sea surface temperature.