Assessing the distribution of disease vectors and fruit crop pests from satellite in GRASS GIS 7

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Titel Assessing the distribution of disease vectors and fruit crop pests from satellite in GRASS GIS 7
Serientitel FOSS4G 2014 Portland
Autor Neteler, Markus
Lizenz CC-Namensnennung - keine kommerzielle Nutzung - keine Bearbeitung 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt in unveränderter Form zu jedem legalen und nicht-kommerziellen Zweck nutzen, vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/31623
Herausgeber FOSS4G
Open Source Geospatial Foundation (OSGeo)
Erscheinungsjahr 2014
Sprache Englisch
Produzent FOSS4G
Open Source Geospatial Foundation (OSGeo)
Produktionsjahr 2014
Produktionsort Portland, Oregon, United States of America

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Fachgebiet Informatik
Abstract Over the past decades, disease vectors like the Asian tiger mosquito (Aedes albopictus) transmitting Dengue Fever and other infections and the Spotted Wing Drosophila (Drosophila suzukii), an economically important fruit crop pest, have continued to globally expand. In Europe, the already invaded areas comprise the Mediterranean basin while the spread to the north of the Alps is ongoing. Likewise many regions in the world face an increasing risk for new or re-emerging vector-borne diseases transmitted by mosquitoes. Given this spread, there is an urgent need to gain better understanding of spatio-temporal patterns in disease transmission and agro-pest diffusion. The life cycles of mosquitoes and fruit flies depend on climatic and environmental conditions which can be observed using satellite sensors. We identified the potential distribution areas linked to the current climatic suitability through the evaluation of remotely sensed land surface temperature (LST) data for Northern Italy and Switzerland. For this we processed with GRASS GIS 7 more than a decade of daily MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor data at continental scale (250 m resolution, four maps per day) as an alternative to meteorological data. Since LST data often contain gaps due to cloud cover, these gaps were filled by reconstructing any missing LST values before environmental indicators have been derived from these data. From the gap-filled LST data (in the multi terabyte range) we derived threshold maps like January mean temperatures as a threshold to estimate the survival chances of overwintering diapausing eggs, whereas the annual mean temperature can be used as a threshold to estimate population stability. We derived growing degree days (GDD) as well by temporal aggregation. The approach can be applied to continents other than Europe, too. The resulting potential distribution maps can be leveraged to assess the spread of disease vectors and agro-pests in order to assist decision makers and public health authorities to develop surveillance plans and vector control.
Schlagwörter MODIS
time series
infectious diseases
ecological indicators

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