XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

Bibliografische Daten

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Persistenter Identifier:: 1663813779

Titel:: XXII ISPRS Congress 2012

Untertitel:: Melbourne, Australia, 25 August-1 September 2012

Erscheinungsjahr:: 2013

Erscheinungsort des Originals:: Red Hook, NY

Verlag des Originals:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Sprache:: Englisch

Sonstige Anmerkungen:: Kongress-Thema: Imaging a sustainable future

Körperschaften:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Bearbeiter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Begründer eines Werks:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Sonstige Körperschaft:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Dokumenttyp:: Mehrbändiges Werk

Band

Persistenter Identifier:: 1663822514

Titel:: Technical Commission VIII

Umfang:: 590 Seiten

Erscheinungsjahr:: 2014

Erscheinungsort des Originals:: Red Hook, NY

Verlag des Originals:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustrationsangabe:: Illustrationen, Diagramme

Signatur der Quelle:: ZS 312(39,B8)

Sprache:: Englisch

Sonstige Anmerkungen:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Nutzungslizenz:: Attribution 4.0 International (CC BY 4.0)

Herausgeber:: Shortis, M.; Shimoda, H.; Cho, K.

Körperschaften:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Bearbeiter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Begründer eines Werks:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Sonstige Körperschaft:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Verlag des Digitalisats:: Technische Informationsbibliothek Hannover

Erscheinungsort des Digitalisats:: Hannover

Erscheinungsjahr des Digitalisats:: 2019

Dokumenttyp:: Band

Sammlung:: Geowissenschaften

Kapitel

Titel:: [VIII/2: Health]

Dokumenttyp:: Mehrbändiges Werk

Strukturtyp:: Kapitel

Kapitel

Titel:: MAPPING THE RISKS OF MALARIA, DENGUE AND INFLUENZA USING SATELLITE DATA R. K. Kiang, R. P. Soebiyanto

Dokumenttyp:: Mehrbändiges Werk

Strukturtyp:: Kapitel

et al., 2007). In addition to the significant burden of seasonal influenza, the persistent threat is that a pandemic causing strain may appear due to antigenic shift or reassortment. There are three types of influenza virus circulating in the world: A, B and C. Type A and B are the most commonly found in humans. Type A is further classified into subtypes (e.g., HIN1) based on the types of hemagglutinin and neuraminidase on the surface of the virus. In order to accommodate the ever changing circulating influenza strain, vaccine composition recommendations are updated twice a year. The recommendations are made by the World Health Organization (WHO) through its Global Influenza Surveillance Network (GISN). 2. ENVIRONMENTAL DETERMINANTS 2.1 Malaria and Dengue Many factors are known to contribute to malaria transmission, including meteorological and environmental conditions, socioeconomic status, military conflicts and natural disasters. Among these, meteorological and environmental factors are perhaps the most noticeable. For example, malaria transmission may increase with the arrival or the end of a rainy season, and living near forest or water bodies may pose greater risk of mosquito bites and getting malaria. The El Nifio-Southern Oscillation (ENSO) is a quasi-periodic climatic cycle that occurs every three to seven years across the tropical Pacific Ocean, and which causes excessive precipitation or droughts, has been shown to promote malaria transmission (Kovats 2003). Aside from precipitation, temperature and humidity are also important factors. Warmer temperatures hasten larval and vector development, and prolong mosquito life span and its consequent ability to transmit malaria. Warmer air retains more moisture and improves mosquito survivorship, so is higher humidity. Vegetation has also been linked to malaria as it indicates the vector’s breeding sites. For example, Anopheles dirus is a forest breeder, and An. maculates and An. sawadwongpori are rice field breeders. The Normalized Difference Vegetation Index (NDVI) (Tucker 1979) is one of the indices for vegetation condition. It is defined as the difference between the responses from the infrared and the red bands normalized by their mean. In modelling infectious diseases, NDVI is used most often to infer the precipitation which the area received before the satellite measurements were taken. The spatial distribution of NDVI can also be used to differentiate among urban, peri- urban, suburban and rural areas. Such information on the nature of the area is useful for malaria prevention and control. Like malaria, temperature, humidity and rainfall are the important environmental determinants for dengue transmission. However, their effects on dengue transmission may be less obvious than those for malaria transmission because dengue vectors can breed indoor. 2.2 Influenza The spatiotemporal variation of influenza across latitudes suggests that climate and environmental factors may have roles in influenza transmission and pathogenesis. It is well known that influenza transmission in temperate climates is seasonal and peaks in the winter months. In the US, for example, influenza outbreaks often start as early as October, peak in February, and diminish by April or May; thus forming a distinct inter-annual oscillation pattern. In the tropics, there are significant influenza cases throughout the year, with one or two less distinct peak(s) whose timing varies geographically. It has been shown (e.g., Viboud et al., 2006) that influenza seasonal patterns vary with latitude, forming a traveling wave across the globe. Several studies that have explored the global migration pattern of influenza show varying travel pattern of influenza virus A migration out of the tropics and China; and migration between northern and southern hemispheres. Another study in Brazil showed that influenza starts in a low-population state near the equator during March-April, and travels southward towards temperate and more populous states (Alonso et al., 2007). Temperature, humidity and rainfall are among the factors that have been frequently implicated in influenza transmission. 3. REMOTE SENSING MEASUREMENTS Satellite measurements of precipitation for estimating disease risks are most often derived from the Tropical Rainfall Monitoring Mission (TRMM) (Kummerow 1998). TRMM is a collaboration between the US and Japan. Japan built the precipitation radar (PR) and launched the spacecraft in 1999. There are five instruments on board: PR, TRMM microwave imager (TMI), Visible and Infrared Scanner (VIIRS), Cloud and Earth Radiation Energy Sensor (CERES), and Lighting Image Scanner (LIS). The main sensor that measures precipitation is TMI. Because the spatial resolution of the TRMM measurement is low, sometimes NDVI measured from a medium resolution satellite is used to infer the recent rainfall at a higher spatial resolution. Such inference is most effective for arid regions where little rainfall is received and vegetation growth is sensitive to rainfall, but less effective for regions with plenty rainfall. Land surface temperature and NDVI are both provided by the moderate resolution imaging spectroradiometer (MODIS). This sensor has thirty-six bands spanning the visible to the near-infrared wavelengths. Both the Terra and Aqua observatories are equipped with MODIS. NDVI, however, can be computed from any satellite instruments with red and infrared channels. However, because of the differences in band definitions, instrument characteristics, satellite orbits and measuring conditions, NDVI from different sensors must first be cross calibrated before they can be compared. In addition to the datasets described above, quite a few other satellites also provide data for ground cover classification, identification of potential larval habitats, and modelling malaria risks. For example, the well-known Landsat and SPOT series of satellites and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Advanced Land Imager (ALI) (USGS 2009) are some of the multispectral sensors used for health monitoring purposes. Microwave sensors like Radarsat and the Phased Array L-band Synthetic Aperture Radar (PALSAR) are used over areas that are obscured frequently by clouds (JAXA 2011). The geoscience laser altimeter system (GLAS) is a light detection and ranging (LIDAR) sensor that is useful for differentiating vegetation types (NASA 2011). For high spatial resolution imagery, commercial data from IKONOS, QuickBird, or WorldView can be used (GeoEye 2011; DigitalGlobe 2011). 4. Mt Many fac conditions seasonal i conditions these disea evident foi is directly conditions. can be use biological types of 1 meteoroloj remote sel also for : sensing-ba the impact In genera approache observatio In the sta with satell determine mean Squ Once the situations How the [ meteorolo modelled category network. network t Indonesia (ARIMA) discrete e among 23 4.1 Mal Figure 1 malaria c 2006). " future in expected environ precipita! agreemer

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