Remote Sensing and Human Health: New Sensors and New Opportunities.Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface Noun 1. Earth's surface - the outermost level of the land or sea; "earthquakes originate far below the surface"; "three quarters of the Earth's surface is covered by water" surface . An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Systeme Pour l'Observation de la Terre La Terre (The Earth) is a novel by Émile Zola, published in 1887. It is the fifteenth novel in Zola's Rougon-Macquart series. The action takes place in a rural community in La Beauce, an area of northern France. , and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer The Advanced Very High Resolution Radiometer (AVHRR) is a space-borne sensor embarked on the National Oceanic and Atmospheric Administration (NOAA) family of polar orbiting platforms. . New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases infectious diseases: see communicable diseases. . Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing Deriving digital models of an area on the earth. Using special cameras from airplanes or satellites, either the sun's reflections or the earth's temperature is turned into digital maps of the area. beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases. Remote sensing data enable scientists to study the earth's biotic biotic /bi·ot·ic/ (bi-ot´ik) 1. pertaining to life or living matter. 2. pertaining to the biota. bi·ot·ic adj. 1. Relating to life or living organisms. and abiotic a·bi·ot·ic adj. Nonliving: The abiotic factors of the environment include light, temperature, and atmospheric gases. a components. These components and their changes have been mapped from space at several temporal and spatial scales since 1972. A small number of investigators in the health community have explored remotely sensed environmental factors that might be associated with disease-vector habitats and human transmission risk. However, most human health studies using remote sensing data have focused on data from Landsat's Multispectral Scanner The Multispectral Scanner is one of the Earth observing sensors introduced in the Landsat program. A Multispectral Scanner (MSS) was placed aboard each of the first five Landsat satellites. NASA's web page on the Multispectral Scanner lists sensor specifics. (MSS) and Thematic Mapper One of the Earth observing sensors introduced in the Landsat program. A Thematic Mapper (TM) was first placed aboard Landsat 4 (decommissioned in 2001), and one is still operational aboard Landsat 5 as of May 2007. (TM), the National Oceanic and Atmospheric Administration Noun 1. National Oceanic and Atmospheric Administration - an agency in the Department of Commerce that maps the oceans and conserves their living resources; predicts changes to the earth's environment; provides weather reports and forecasts floods and hurricanes and (NOAA NOAA abbr. National Oceanic and Atmospheric Administration Noun 1. NOAA - an agency in the Department of Commerce that maps the oceans and conserves their living resources; predicts changes to the earth's environment; )'s Advanced Very High Resolution Radiometer (AVHRR AVHRR Advanced Very High Resolution Radiometer AVHRR Advanced Very High Resolution Radar ), and France's Systeme Pour l'Observation de la Terre (SPOT). In many of these studies (Table 1), remotely sensed data were used to derive three variables: vegetation cover, landscape structure, and water bodies. Table 1. Research using remote sensing data to map disease vectors(a)
Disease Vector
Dracunculiasis Cyclops spp.
Cyclops spp.
Eastern equine encephalomyelitis Culiseta melanura
Filariasis Culex pipiens
Cx. pipiens
Leishmaniasis Phlebotomus papatasi
Lyme disease Ixodes scapularis
I. scapularis
Malaria Anopheles albimanus
An. albimanus
An. albimanus
An. albimanus
An. spp.
An. albimanus
Rift Valley fever Aedes & Cx. spp.
Cx. spp.
Cx. spp.
Schistosomiasis Biomphalaria spp.
Trypanosomiasis Glossina spp.
Glossina spp.
Glossina spp.
Glossina spp.
Glossina spp.
Disease Location Sensor
Dracunculiasis Benin TM
Nigeria TM
Eastern equine encephalomyelitis Florida, USA TM
Filariasis Egypt AVHRR
Egypt TM
Leishmaniasis SW Asia AVHRR
Lyme disease New York, USA TM
Wisconsin, USA TM
Malaria Mexico TM
Belize SPOT
Belize SPOT
Mexico TM
Gambia AVHRR, Metosat
Mexico TM
Rift Valley fever Kenya AVHRR
Kenya TM, SAR
Senegal SPOT, AVHRR
Schistosomiasis Egypt AVHRR
Trypanosomiasis Kenya, Uganda AVHRR
Kenya TM
West Africa AVHRR
Africa AVHRR
Southern Africa AVHRR
Disease Ref.
Dracunculiasis 1
2
Eastern equine encephalomyelitis 3
Filariasis 4
56
Leishmaniasis 7
Lyme disease 89
10
Malaria 11
12
13
14
1,516
1,718
Rift Valley fever 1,920
21
22
Schistosomiasis 23
Trypanosomiasis 24
25
26
27
28
(a) See Appendix A for explanation of sensor acronyms International space agencies are planning an estimated 80 earth-observing missions in the next 15 years (29). During these missions [is greater than] 200 instruments will measure additional environmental features such as ocean color and other currently detectable variables, but at much higher spatial and spectral resolutions. The commercial sector is also planning to launch several systems in the next 5 years that could provide complementary data (30). These new capabilities will improve spectral, spatial, and temporal resolution Temporal resolution refers to the precision of a measurement with respect to time. Often there is a tradeoff between temporal resolution of a measurement and its spatial precision (spatial resolution). , allowing exploration of risk factors previously beyond the capabilities of remote sensing. In addition, advances in pathogen, vector, and reservoir and host ecology have allowed assessment of a greater range of environmental factors that promote disease transmission, vector production, and the emergence and maintenance of disease foci, as well as risk for human-vector contact. Advances in computer processing and in geographic information system and global positioning system Global Positioning System: see navigation satellite. Global Positioning System (GPS) Precise satellite-based navigation and location system originally developed for U.S. military use. technologies facilitate integration of remotely sensed environmental parameters with health data so that models for disease surveillance and control can be developed. In 1998, the National Aeronautics and Space Administration's (NASA NASA: see National Aeronautics and Space Administration. NASA in full National Aeronautics and Space Administration Independent U.S. ) Center for Health Applications of Aerospace Related Technologies (CHAART CHAART Center for Health Applications of Aerospace Related Technologies )(1) evaluated current and planned satellite sensor systems as a first step in enabling human health scientists to determine data relevant for the epidemiologic, entomologic en·to·mol·o·gy n. The scientific study of insects. en  to·mo·log , and ecologic aspects of
their research, as well as developing remote sensing-based models of
transmission risk. This article discusses the results of the evaluation
and presents two examples of how remotely sensed data have been used in
health-related studies. The first example, a terrestrial application,
illustrates how a single Landsat TM image was used to characterize the
spatial patterns of key components of the Lyme disease Lyme disease, a nonfatal bacterial infection that causes symptoms ranging from fever and headache to a painful swelling of the joints. The first American case of Lyme's characteristic rash was documented in 1970 and the disease was first identified in a cluster at transmission
cycle in New York New York, state, United StatesNew York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of . The second example, which focuses on the coastal environment, shows how remote sensing data from different satellite systems can be combined to characterize and map environmental variables in the Bay of Bengal Noun 1. Bay of Bengal - an arm of the Indian Ocean to the east of India Andaman Sea - part of the Bay of Bengal to the west of the Malay Peninsula Indian Ocean - the 3rd largest ocean; bounded by Africa on the west, Asia on the north, Australia on the east that are associated with the temporal patterns of cholera cases in Bangladesh. These examples demonstrate how remote sensing data acquired at various scales and spectral resolutions can be used to study infectious disease Infectious disease A pathological condition spread among biological species. Infectious diseases, although varied in their effects, are always associated with viruses, bacteria, fungi, protozoa, multicellular parasites and aberrant proteins known as prions. patterns. Lyme Disease in the Northeastern United States United States, officially United States of America, republic (2005 est. pop. 295,734,000), 3,539,227 sq mi (9,166,598 sq km), North America. The United States is the world's third largest country in population and the fourth largest country in area. During the past 10 years, NASA's Ames Research Center has been collaborating with the New York Medical College New York Medical College is a center for graduate medical education located in Westchester County, a suburb half an hour north of New York City. This private university comprises the School of Medicine, which grants the M.D. and the Yale School of Medicine The primary teaching hospital for the school is Yale-New Haven Hospital. The school is home to the Harvey Cushing/John Hay Whitney Medical Library, one of the largest modern medical libraries, also known for its historical collections. to develop remote sensing-based models for mapping Lyme disease transmission risk in the northeastern United States (31,32). The first study compared Landsat TM data with canine seroprevalence seroprevalence Immunology The proportion of a population that is seropositive–ie, has been exposed to a particular pathogen or immunogen; the seropositivity of a population is calculated as the number of individuals who produce a particular antibody divided rate (CSR (1) (Customer Service Representative) A person who handles a customer's request regarding a bill, account changes or service or merchandise ordered. Agents in call centers are known as CSRs. See call center. ) data summarized at the municipality level (31). The canine data were used as a measure of human exposure risk, the assumption being that dogs were more likely to acquire tick bites on or near their owner's property. The second study used TM data to map relative tick abundance on residential properties by using TM-derived indices of vegetation greenness and wetness (32). Figure 1 shows a subset of the TM data used in both studies, as well as some of the products (e.g., maps) derived from the data. Each product illustrated Lyme disease transmission variables, such as vector and reservoir habitats, as well as human risk for disease. Figure 1a shows raw Landsat-5 TM data, which are recorded in six spectral bands See optical bands and spectrum. (excluding a seventh thermal band) at a spatial resolution (Data West Research Agency definition: see GIS glossary.) A measure of the accuracy or detail of a graphic display, expressed as dots per inch, pixels per line, lines per millimeter, etc. It is a measure of how fine an image is, usually expressed in dots per inch (dpi). of 30 m. These data were processed to derive the products shown in Figures 1b-d. [Figure 1 ILLUSTRATION OMITTED] The image in Figure 1b was used to explore the relationship between forest patch size and deer distribution. Because white-tailed deer white-tailed deer or Virginia deer Common reddish brown deer (Odocoileus virginianus), an important game animal found alone or in small groups from southern Canada to South America. serve as a major host of the adult tick as well as its primary mode of transportation, deer distribution was a potentially important factor in a Lyme disease risk model. Figure 1c shows 12 classes used in two separate analyses of risk at two different scales (31). These classes include water, evergreen trees/vegetation, sparse deciduous trees, dense deciduous trees, clearings, golf courses (managed grass), urban/commercial, miscellaneous urban, residential-lawn, residential-sparse vegetation, residential-medium vegetation, and residential high-vegetation. In the first scale, the amount of remotely sensed deciduous deciduous /de·cid·u·ous/ (de-sid´u-us) falling off or shed at maturity, as the teeth of the first dentition. de·cid·u·ous adj. 1. forest was positively correlated (r-0.82) with canine exposure to Borrelia burgdorferi Borrelia burg·dor·fe·ri n. A spirochete causing Lyme disease in humans. Borrelia burgdorferi The spirochete agent of Lyme disease, which contains several outer membrane proteins and a highly immunogenic flagellar , as indicated by CSR data summarized by municipality. In the second analysis, a linear regression Linear regression A statistical technique for fitting a straight line to a set of data points. of the residential-high vegetation pixels (i.e., wood-edge) and CSR data resulted in a correlation coefficient Correlation Coefficient A measure that determines the degree to which two variable's movements are associated. The correlation coefficient is calculated as: of 0.84--indicating that human-host contact risk (e.g., deer leaving the forest to feed on residential ornamental vegetation) might be a good measure of human-vector contact risk. The image in Figure 1d was derived from the Landsat TM data by a Tasseled Cap Transformation (33). Tasseled Cap greenness and wetness were positively correlated with tick abundance on residential properties in this study area (32). Cholera in Bangladesh The second example of the use of remotely sensed data to provide information for health research and applications concerns cholera in Bangladesh. In this study, described by Lobitz et al. (34), remotely sensed datasets, downloaded from the Internet at no cost, were used to search for temporal patterns in the Bay of Bengal associated with cholera outbreaks in Bangladesh. Figure 2a shows a color-infrared image of the Ganges River Ganges River Hindi Ganga River, northern India and Bangladesh. Held sacred by followers of Hinduism, it is formed from five headstreams rising in Uttaranchal state. , where it empties into the Bay of Bengal. These data, which were acquired by NOAA's AVHRR sensor, have a spatial resolution of 1.1 km. The sediment load, transported to the Bay of Bengal by the Ganges and Brahmaputra rivers, includes nutrients that could support plankton plankton: see marine biology. plankton Marine and freshwater organisms that, because they are unable to move or are too small or too weak to swim against water currents, exist in a drifting, floating state. blooms. Plankton is an important marine reservoir of Vibrio cholerae Vibrio chol·er·ae n. A bacterium that causes Asiatic cholera in humans; Koch's bacillus. Vibrio cholerae Infectious disease The Vibrio , which attaches primarily to zooplankton zooplankton: see marine biology. zooplankton Small floating or weakly swimming animals that drift with water currents and, with phytoplankton, make up the planktonic food supply on which almost all oceanic organisms ultimately depend (see , which, in turn, is associated with phytoplankton phytoplankton Flora of freely floating, often minute organisms that drift with water currents. Like land vegetation, phytoplankton uses carbon dioxide, releases oxygen, and converts minerals to a form animals can use. (35). [Figure 2 ILLUSTRATION OMITTED] In Figure 2b, the AVHRR data shown in Fire, re 2A were processed to show sea surface temperature Sea surface temperature (SST) is the water temperature at the surface. In practical terms, the exact meaning of "surface" will vary according to the measurement method used. (SST SST: see airplane. ) (36). Because these data are for large-area studies, they have been processed at a spatial resolution of 18 kin. Figure 2c represents sea surface height Sea surface height (SSH) is the height (or topography or relief) of the ocean's surface. On a daily basis, SSH is most obviously affected by the tidal forces of the Moon and the Sun acting on the Earth. (SSH (Secure SHell) A security protocol for logging into a remote server. SSH provides an encrypted session for transferring files and executing server programs. Also serving as a secure client/server connection for applications such as database access and e-mail, SSH supports a ) anomaly data derived from the TOPEX/Poseidon satellite (37). These data have a spatial resolution of 1 degree. Increases in SST and SSH have preceded cholera outbreaks in Bangladesh (34). In the next 15 years, new sensors will provide valuable data for studies of infectious diseases similar to the ones described here. For Lyme disease, new sensors could provide similar information about ecotones, human settlement patterns, or forests. These sensors include ARIES-l, scheduled for launch by Australia; CCD CCD in full charge-coupled device Semiconductor device in which the individual semiconductor components are connected so that the electrical charge at the output of one device provides the input to the next device. and IR/MSS sensors onboard CBERS CBERS China-Brazil Earth Resources Satellite , launched by China and Brazil in late 1999; Ikonos, a commercial satellite with 4-m spatial resolution; LISS III, onboard the orbiting Indian IRS-1C and-1D satellites; and ASTER aster [Gr.,=star], common name for the Asteraceae (Compositae), the aster family, in North America, name for plants of the genus Aster, sometimes called wild asters, and for a related plant more correctly called China aster (Callistephus chinensis , onboard the recently launched Terra satellite. Information from these sensors could also be used to address other vector-borne diseases, such as malaria, schistosomiasis schistosomiasis (shĭs`təsōmī`əsĭs), bilharziasis, or snail fever, parasitic disease caused by blood flukes, trematode worms of the genus Schistosoma. , trypanosomiasis trypanosomiasis (trəpăn'əsōmī`əsis), infectious disease caused by a protozoan organism, the trypanosome, which exists as a parasite in the blood of a number of vertebrate hosts. , and hantavirus hantavirus, any of a genus (Hantavirus) of single-stranded RNA viruses that are carried by rodents and transmitted to humans when they inhale vapors from contaminated rodent urine, saliva, or feces. There are many strains of hantavirus. , whose patterns are likewise influenced by environmental variables. SeaWiFS, the Sea-Viewing Wide Field-of-View Sensor, with its increased spectral resolution of 1.1 km, is already providing imagery critical to understanding the temporal and spatial pattern of cholera risk (35). This sensor was specifically designed to gather information about ocean color (38) (Figure 2d). Sensor Evaluation Project(2) CHAART evaluated data from current and planned satellite instruments for mapping, surveillance, prediction, and control of human disease transmission activities, including vector ecology, reservoir and host ecology, and human settlement patterns. From hundreds of potential sensors, 54 were identified that were current (or would be launched within the next 5 years), operational (not reserved for the scientific community), and digital (not photographic). Beginning in 1985, NASA has held a series of workshops to elicit input from the health community on the use of remote sensing in the areas of entomology entomology, study of insects, an arthropod class that comprises about 900,000 known species, representing about three fourths of all the classified animal species. , ecology, epidemiology, vector control Vector control is any method to limit or eradicate the vectors of vector born diseases, for which the pathogen (e.g. virusor parasite) is transmitted by a vector which can be mammals, birds or arthropods, especially insects, and more specifically mosquitoes. , and infectious diseases. In addition, NASA has participated in sessions on remote sensing and health at professional meetings sponsored by national and international health organizations. On the basis of this experience as well as a review of the scientific literature (Table 1), there does not appear to be consensus in the health community regarding requirements for a remote sensing system. Some investigators use remotely sensed data to resolve questions regarding the relationship between an aspect of disease transmission and an environmental variable. Other researchers already have a model of disease transmission and have specific spatial, temporal, or spectral requirements for the remotely sensed variables. No single spatial, temporal, or spectral resolution is universally appropriate for understanding the transmission risk for any disease, given the variety of vectors, reservoirs, hosts, geographic locations, and environmental variables associated with that disease. Therefore, in evaluating the existing sensors, CHAART used an approach that allowed individual investigators to identify satellite data appropriate for their own needs. This approach defined 16 groups of physical factors that could be used for both research and applications. Each factor is essentially an environmental variable that might have a direct or indirect bearing on the survival of pathogens, vectors, reservoirs, and hosts. These factors may also affect many types of non-vector-borne diseases, such as waterborne diseases Waterborne diseases are caused by pathogenic microorganisms which are directly transmitted when contaminated drinking water is consumed. Contaminated drinking water used in the preparation of food can be the source of foodborne disease through consumption of the same microorganisms. . The factors are vegetation or crop type, vegetation green-up, ecotones, deforestation deforestation Process of clearing forests. Rates of deforestation are particularly high in the tropics, where the poor quality of the soil has led to the practice of routine clear-cutting to make new soil available for agricultural use. , forest patches, flooded forests, general flooding, permanent water, wetlands, soil moisture, canals, human settlements, urban features, ocean color, SST, and SSH. Precipitation, humidity, and surface temperature were not included because deriving these measurements from raw data requires highly specialized processing and calibration, routinely performed by qualified groups who often make the information available on Internet websites (Appendix B). The sensor evaluation project generated a series of tables that associated each of the 16 factors with the 54 sensors according to according to prep. 1. As stated or indicated by; on the authority of: according to historians. 2. In keeping with: according to instructions. 3. spatial, temporal, and spectral characteristics. For example, factors requiring frequent monitoring, such as vegetation green-up, are linked with sensors with shorter repeat overpasses. Similarly, factors requiring very high spatial resolution, such as mapping urban features, are linked with sensors having a spatial resolution of 10 m or less, regardless of their temporal or spectral resolutions. Perhaps the broadest use of Landsat and SPOT data has been to identify and map vegetation or crop types. This factor is important because the distribution of vegetation types integrates the combined impact of rainfall, temperature, humidity, topographic effects, soil, water availability, and human activities. Nearly all vector-borne diseases are linked to the vegetated environment during some aspect of their transmission cycle; in many cases, this vegetation can be sensed remotely from space. The spatial and temporal distribution of vector or reservoir and host species may relate to the occurrence and distribution of specific vegetation or crop types, not simply to whether an area has forests or grasslands. For example, food and cover preferences of the white-tailed deer, the host for adult ticks that transmit Lyme disease in the northeastern United States, might well encourage deer to live near certain types of forest. Crop-type information may also be important for studying the effects of pesticides (e.g., vector resistance; illnesses caused by exposure to toxins). The sensor evaluation procedure has identified many potentially useful sensors for mapping vegetation and crop type beyond the Landsat and SPOT systems (Table 2). A ground resolution threshold of 30 m was used as the upper limit for exploring the relationship between vegetation (or crop) type and disease vectors, reservoirs, and hosts; above 30 m, vegetation and crop type are more difficult to ascertain. Many of the sensors could also be used for mapping the boundary between vegetation types, or ecotones, which provide habitat for insects and animals critical to the maintenance and transmission of vectorborne diseases. These edges may be areas for increased risk for vector-human contact, as indicated by the relationship between Lyme disease transmission and suburban encroachment into forested areas in the northeastern United States. The movement of humans into forested edges where potential vectors are established could also be important for predicting malaria or yellow fever yellow fever, acute infectious disease endemic in tropical Africa and many areas of South America. Epidemics have extended into subtropical and temperate regions during warm seasons. transmission. Table 2. Current and proposed sensor systems for identifying and mapping vegetation and crop type(a)
Temporal Spatial resolution(b) (m)
resolution (d) 1-5 6-10
Daily (QuickBird)(c)
2-7 (Orbview-3,4) (Almaz-1b MSU-E2)
(QuickBird) (ALOS AVNIR-2)
(Orbview-4)
(SPOT-5a,b 3xHRG)
8-14 Ikonos Priroda/Mir MOMS-2P
15-30
>30 (ALOS AVNIR-2)
Temporal
resolution (d) 11-30
Daily
2-7 (ALOS AVNIR-2)
(ARIES-I)
SPOT-4 2xHRVIR
8-14 Priroda/Mir MOMS-2P
15-30 Terra ASTER
IRS-1C,D LISS III
Landsat TM
Landsat-7 ETM+
SPOT-2 2xHRV
>30 (ALOS AVNIR-2)
(a) This matrix is the output from an interactive search with the search engine located at http://geo.arc.nasa.gov/sge/health/sensor/senchar.html. (b) See Appendix A for explanations of sensor acronyms. (c) Sensors in parentheses See parenthesis. parentheses - See left parenthesis, right parenthesis. have not yet been launched. The list of 16 factors used in the CHAART evaluation includes some that have not yet been quantified because available sensors do not provide adequate spatial, spectral, or temporal resolutions. Two of these factors are briefly described below to illustrate how remotely sensed data might be used to explore their potential links to human health. More links between the factors and various diseases are listed in Table 3. Table 3. Potential links between remotely sensed factors and disease
Factor Disease
Vegetation/crop type Chagas disease
Hantavirus
Leishmaniasis
Lyme disease
Malaria
Plague
Schistosomiasis
Trypanosomiasis
Yellow fever
Vegetation green-up Hantavirus
Lyme disease
Malaria
Plague
Rift Valley fever
Trypanosomiasis
Ecotones Leishmaniasis
Lyme disease
Deforestation Chagas disease
Malaria
Yellow fever
Forest patches Lyme disease
Yellow fever
Flooded forests Malaria
Flooding Malaria
Rift Valley fever
Schistosomiasis
St. Louis encephalitis
Permanent water Filariasis
Malaria
Onchocerciasis
Schistosomiasis
Wetlands Cholera
Encephalitis
Malaria
Schistosomiasis
Soil moisture Helminthiases
Lyme disease
Malaria
Schistosomiasis
Canals Malaria
Onchocerciasis
Schistosomiasis
Human settlements Diseases
Urban features Chagas disease
Dengue fever
Filariasis
Leishmaniasis
Ocean color (Red tides) Cholera
Sea surface temp. Cholera
Sea surface height Cholera
Factor Mapping opportunity
Vegetation/crop type Palm forest, dry & degraded woodland
habitat for triatomines
Preferred food sources for host/
reservoirs
Thick forests as vector/reservoir
habitat in Americas
Preferred food sources and habitat
for host/reservoirs
Breeding/resting/feeding habitats;
crop pesticides vector resistance
Prairie dog and other reservoir
habitat
Agricultural association with snails,
use of human fertilizer
Glossina habitat (forests, around
villages, depending on species)
Reservoir (monkey) habitat
Vegetation green-up Timing of food sources for rodent
reservoirs
Habitat formation and movement of
reservoirs, hosts, vectors
Timing of habitat creation
Locating prairie dog towns
Rainfall
Glossina survival
Ecotones Habitats in and around cities that
support reservoir (e.g., foxes)
Ecotonal habitat for deer, other
hosts/reservoirs; human/vector
contact risk.
Deforestation New settlements in endemic-disease
areas
Habitat creation (for vectors requiring
sunlit pools)
Habitat destruction (for vectors
requiring shaded pools)
Migration of infected workers into
forests where vectors exist
Migration of disease reservoirs
(monkeys) in search of new habitat
Forest patches Habitat requirements of deer and other
hosts, reservoirs
Reservoir (monkey) habitat, migration
routes
Flooded forests Mosquito habitat
Flooding Mosquito habitat
Flooding of dambos, breeding habitat
for mosquito vector
Habitat creation for snails
Habitat creation for mosquitoes
Permanent water Breeding habitat for Mansonia
mosquitoes
Breeding habitat for mosquitoes
Simulium larval habitat
Snail habitat
Wetlands Vibrio cholerae associated with
inland water
Mosquito habitat
Mosquito habitat
Snail habitat
Soil moisture Worm habitat
Tick habitat
Vector breeding habitat
Snail habitat
Canals Dry season mosquito-breeding habitat;
ponding; leaking water
Simulium larval habitat
Snail habitat
Human settlements Source of infected humans; populations
at risk for transmission in general
Urban features Dwellings that provide habitat for
triatomines
Urban mosquito habitats
Urban mosquito habitats
Housing quality
Ocean color (Red tides) Phytoplankton blooms; nutrients,
sediments
Sea surface temp. Plankton blooms (cold water upwelling
in marine environment)
Sea surface height Inland movement of Vibrio-contaminated
tidal water
Urban Features The detection of urban features requires higher spatial resolution systems than needed for detecting the presence of human settlements. Some disease vectors are associated with specific urban features such as housing type, which can only be detected by sensors with very high spatial resolution. In the future, new sensors may be able to provide information on the urban environment (Table 4). Table 4. Current and proposed sensor systems for identifying and mapping urban features(a)
Temporal Spatial resolution(b) (m)
resolution
(d) 1-5 6-10
Daily (QuickBird)(c)
2-7 (ALOS AVNIR-2) (Almaz-1b MSU-E2)
(Orbview-3,4) (ALOS AVNIR-2)
(QuickBird) (ARIES-1)
(SPOT-5a,b 3xHRVIR) IRS-1C,D PAN
(Orbview-4)
SPOT-4 2xHRVIR
(SPOT-5a,b 3xHRVIR)
8-15 Ikonos IRS-1C,D PAN
Priroda/Mir MOMS-2P
15-30 IRS-1C,D PAN
SPOT-2 2xHRV
>30 (ALOS AVNIR-2) (ALOS AVNIR-2)
(a) This matrix is the output from an interactive search with the search engine located at http://geo.arc.nasa.gov/sge/health/ sensor/senchar.html (b) See Appendix A for explanations of sensor acronyms. (c) Sensors in parentheses have not yet been launched. Soil Moisture Wet soils indicate a suitable habitat tot species of snails, mosquito larvae Larvae, in Roman religion Larvae: see lemures. , ticks, and worms. Several types of sensors can detect soil moisture, including synthetic aperture radars (SARs), shortwave-infrared, and thermal-infrared sensors (Table 5). SARs are particularly important for sensing ground conditions in areas of cloud cover or vegetation canopy cover, two factors commonly found in the tropics tropics, also called tropical zone or torrid zone, all the land and water of the earth situated between the Tropic of Cancer at lat. 23 1-2°N and the Tropic of Capricorn at lat. 23 1-2°S. . Table 5. Current and proposed sensor systems for identifying and mapping soil moisture(a)
Temporal Spatial Resolution(b) (m)
resolution
(d) 11-30 101-500
Daily
2-7 (Almaz-1b SAR-70)(c)
(ARIES-1)
(ENVISAT-1 ASAR)
Radarsat
SPOT-4 2xHRVIR
8-14 (LightSAR)
Priroda/Mir MOMS-2P
15-30 Terra ASTER Landsat TM TIR
ERS-1,2 AMI-SAR
Landsat TM
Landsat-7 ETM+
>30 ERS-1,2 AMI-SAR
Temporal Spatial Resolution(b) (m)
resolution
(d) 501-1,000 1,001-4,000
Daily NOAA AVHRR
2-7 (ADEOS II GLI)
(Almaz-1b MSU-SK)
(Almaz-1b SROSM)
(ENVISAT-1 AATSR)
Terra MODIS
(EOS PM-1 MODIS)
Resurs-01 N2,3
MSU-SK
8-14
Priroda/Mir MSU-SK
15-30
>30
(a) This matrix is the output from an interactive search with the search engine located at http://geo.arc.nasa.gov/sge/health/ sensor/senchar.html. (b) See Appendix A for explanations of sensor acronyms. (c) Sensors in parentheses have not yet been launched. Conclusions The extent to which remotely sensed data are used for studying the spatial and temporal patterns of disease depends on a number of obstacles and opportunities. Many of the obstacles--including cost, inadequate spatial, spectral, or temporal resolutions, and long turnaround times for products--have restricted the use of remote sensing within the user community as a whole. Many of these barriers will be addressed by new sensor systems in the next 5 years. The recently launched Landsat-7 ETM (database) ETM - An active DBMS from the University of Karlsruhe. + sensor, for example, is now providing 30-m multispectral data, a 15-m panchromatic pan·chro·mat·ic adj. Sensitive to all colors: panchromatic film. pan·chro  ma·tism n. band, and an improved 60-m
thermal infrared band, all at a cost that is an order of magnitude A change in quantity or volume as measured by the decimal point. For example, from tens to hundreds is one order of magnitude. Tens to thousands is two orders of magnitude; tens to millions is three orders of magnitude, etc. less
than current Landsat-5 TM data.With the higher spatial and spectral resolutions, more frequent coverage, lower price, and increased availability offered by the range of new sensors, human health investigators should be able to extract many more environmental variables than previously realized. These improvements will provide new opportunities to extend the uses of remote sensing technology beyond a few vector-borne diseases to studies of water- and soil-borne diseases (for example, cholera and schistosomiasis [waterborne] and the helminthiases) and the mapping of human settlements at risk. The next generation of earth-observing remote sensing systems will also allow investigators in the human health community to characterize an increasing range of variables key to understanding the spatial and temporal patterns of disease transmission risk. These improved capabilities, when combined with the increased computing power and spatial modeling capabilities of geographic information systems, should extend remote sensing into operational disease surveillance and control. Acknowledgment The CHAART staff acknowledges the contributions made to the sensor project by their colleague Michael A. Spanner (1952-1998). Funding for the sensor evaluation project was provided by the Office of Earth Science at NASA Headquarters, RW#179. Funding for the Lyme disease and cholera examples described in this article was provided by NASA's Life Sciences Division and the NASA Ames Director's Discretionary Fund. [1] CHAART was established at Ames Research Center by NASA's Life Sciences Division, within the Office of Life & Microgravity mi·cro·grav·i·ty n. 1. An environment in which there is very little net gravitational force, as of a free-falling object, an orbit, or interstellar space. 2. Sciences & Applications, to make remote sensing, geographic information systems, global positioning systems, and computer modeling available to investigators in the human health community. (2) The information gathered during the CHAART sensor evaluation process is available at http://geo.arc.nasa.gov/sge/health/sensor/sensor.html. References (1.) Clarke KC, Osleeb JR, Sherry JM, Meert JP, Larsson RW. The use of remote sensing and geographic information systems in UNICEF's dracunculiasis (Guinea worm guinea worm or medina worm or dragon worm Nematode (Dracunculus medinensis) that is a common parasite of humans and other mammals in tropical Asia and Africa and has been introduced into the West Indies and tropical South America. ) eradication effort. Prev Vet Meal 1990;11:229-35. (2.) Ahearn SC, De Rooy C. Monitoring the effects of dracunculiasis remediation on agricultural productivity Agricultural productivity is measured as the ratio of agricultural inputs to agricultural outputs. While individual products are usually measured by weight, their varying densities make measuring overall agricultural output difficult. using satellite data. International Journal of Remote Sensing 1996;17:917-29. (3.) Freier JW. Eastern equine encephalomyelitis Eastern equine encephalomyelitis see encephalomyelitis. . Lancet 1993;342:1281-3. (4.) Thompson DF, Malone JB, Harb M, Faris R, Huh OK, Buck AA, et al. Bancroftian filariasis bancroftian filariasis Tropical medicine Infection with Wuchereria bancrofti, which causes elephantiasis, hydrocele, and regional economic loss Treatment Diethylcarbamazine, ivermectin, albendazole. See Diethylcarbamazine, Filariasis. distribution and diurnal diurnal /di·ur·nal/ (di-er´nal) pertaining to or occurring during the daytime, or period of light. di·ur·nal adj. 1. Having a 24-hour period or cycle; daily. 2. temperature differences in the southern Nile Delta The Nile Delta (Arabic:دلتا النيل) is the delta formed in Northern Egypt where the Nile River spreads . Emerg Infect Dis 1996;2:234-5. (5.) Hassan AN, Beck LR, Dister S. Prediction of villages at risk for filariasis filariasis: see elephantiasis. transmission in the Nile Delta using remote sensing and geographic information system technologies. J Egypt Soc Parasitol 1998;28:75-87. (6.) Hassan AN, Dister S, Beck L. Spatial analysis (Data West Research Agency definition: see GIS glossary.) Analytical techniques to determine the spatial distribution of a variable, the relationship between the spatial distribution of variables, and the association of the variables of an area. of lymphatic lymphatic /lym·phat·ic/ (lim-fat´ik) 1. pertaining to lymph or to a lymphatic vessel. 2. a lymphatic vessel. lym·phat·ic adj. filariasis distribution in the Nile Delta in relation to some environmental variables using geographic information system technology. J Egypt Soc Parasitol 1998;28:119-31. (7.) Cross ER, Newcomb WW, Tucker CJ. Use of weather data and remote sensing to predict the geographic and seasonal distribution of Phlebotomus paptasi in Southwest Asia Southwest Asia or Southwestern Asia (largely overlapping with the Middle East) is the southwestern portion of Asia. The term Western Asia is sometimes used in writings about the archeology and the late prehistory of the region, and in the United States subregion . Am J Trop Med Hyg 1996;54:530-6. (8.) Dister SW, Beck LR, Wood BL, Falco R, Fish D. The use of GIS and remote sensing technologies in a landscape approach to the study of Lyme disease transmission risk. Proceedings of GIS '93: Geographic Information Systems in Forestry, Environmental and Natural Resource Management; 1993 Feb 15-18; Vancouver, B.C., Canada. 1993. (9.) Dister SW, Fish D, Bros BROS Brothers BROS Benefits and Retirement Operations Section (King County, Washington) BROS Barnes and Richmond Operatic Society (London, UK) S, Frank DH, Wood BL. Landscape characterization of peridomestic risk for Lyme disease using satellite imagery Satellite imagery consists of photographs of Earth or other planets made from artificial satellites. History The first satellite photographs of Earth were made August 14, 1959 by the US satellite Explorer 6. . Am J Trop Med Hyg 1997;57:687-92. (10.) Kitron U, Kazmierczak JJ. Spatial analysis of the distribution of Lyme disease in Wisconsin. Am J Epidemiol 1997;145:558-66. (11.) Pope KO, Rejmankova E, Savage HM, Arredondo-Jimenez JI, Rodriguez MH, Roberts DR. Remote sensing of tropical wetlands for malaria control in Chiapas, Mexico. Ecological Applications 1993;4:81-90. (12.) Rejmankova E, Roberts DR, Pawley A, Manguin S, Polanco J. Predictions of adult Anopheles Anopheles: see mosquito. albimanus densities in villages based on distance to remotely sensed larval larval 1. pertaining to larvae. 2. larvate. larval migrans see cutaneous and visceral larva migrans. habitats. Am J Trop Meg Hyg 1995;53 (5):482-488. (13.) Roberts DR, Paris JF, Manguin S, Harbach RE, Woodruff R, Rejmankova E, et al. Predictions of malaria vector distribution in Belize based on multispectral satellite data. Am J Trop Med Hyg 1996;54:304-8. (14.) Rodriguez AD, Rodriguez MH, Hernandez JE, Dister SW, Beck LR, Rejmankova E, et al. Landscape surrounding human settlements and malaria mosquito abundance in southern Chiapas, Mexico. J Med Entomol 1996;33:39-48. (15.) Thomson MC, Connor SJ, Milligan PJM PJM Pacific Journal of Mathematics PJM Project Manager PJM Puerto Jimenez, Costa Rica (Airport code) PJM Pennsylvania New Jersey Maryland Interconnection LLC (Mid-Atlantic region power pool) , Flasse SP. The ecology of malaria-as seen from Earth-observation satellites. Ann Trop Med Parasitol 1996;90:243-64. (16.) Thomson MC, Connor SJ, Milligan PJM, Flasse SP. Mapping malaria risk in Africa: what can satellite data contribute? Parsitology Today 1997;13:313-8. (17.) Beck LR, Rodriguez MH, Dister SW, Rodriguez AD, Rejmankova E, Ulloa A, et al. Remote sensing as a landscape epidemiologic tool to identify villages at high risk for malaria transmission. Am J Trop Med Hyg 1994;51:271-80. (18.) Beck LR, Rodriguez MH, Dister SW, Rodriguez AD, Washino RK, Roberts DR, et al. Assessment of a remote sensing based model for predicting malaria transmission risk in villages of Chiapas, Mexico. Am J Trop Med Hyg 1997;56:99-106. (19.) Linthicum KJ, Bailey CL, Davies FG, Tucker CJ. Detection of Rift Valley Fever Rift Valley fever An arthropod-borne (primarily mosquito), acute, febrile, viral disease of humans and numerous species of animals. Rift Valley fever is caused by a ribonucleic acid (RNA) virus in the genus Phlebovirus of the family Bunyaviridae. viral activity in Kenya by satellite remote sensing imagery. Science 1987;235:1656-9. (20.) Linthicum KJ, Bailey CL, Tucker CJ, Mitchell KD, Logan TM, Davies FG, et al. Applications of polar-orbiting, meteorological satellite meteorological satellite: see satellite, artificial; weather satellite. data to detect flooding in Rift Valley Fever virus vector mosquito habitats in Kenya. Med Vet Entomol 1990;4:433-8. (21.) Pope KO, Sheffner EJ, Linthicum KJ, Bailey CL, Logan TM, Kasischke ES, et al. Identification of central Kenyan Rift Valley Fever virus vector habitats with Landsat TM and evaluation of their flooding status with Airborne Imaging Radar Traditional radar sends directional pulses of electromagnetic energy and detects the presence, position and motion of an object (such as an aircraft) by analyzing the portion of the energy reflected from the object back to the radar station. . Remote Sensing Environment 1992;40:185-96. (22.) Linthicum KJ, Bailey CL, Tucker CJ, Gordon SW, Logan TM, Peters CJ, et al. Man-made ecological alterations of Senegal River Senegal River A river of western Africa rising in western Mali and flowing about 1,609 km (1,000 mi) generally northwest and west along the Mauritania-Senegal border to the Atlantic Ocean. basin on Rift Valley Fever transmission. Sistema Terra 1994;45-7. (23.) Malone JB, Huh OK, Fehler DP, Wilson PA, Wilensky DE, Holmes RA, et al. Temperature data from satellite images and the distribution of schistosomiasis in Egypt. Am J Trop Med Hyg 1994;50:714-22. (24.) Rogers DJ. Satellite imagery, tsetse tsetse /tset·se/ (tset´se) an African fly of the genus Glossina, which transmits trypanosomiasis. tsetse an African fly of the genus glossina, which transmits trypanosomiasis. and trypanosomiasis. Prev Vet Med 1991;11:201-20. (25.) Kitron U, Otieno LH, Hungerford LL, Odulaja A, Brigham WU, Okello OO, et al. Spatial analysis of the distribution of tsetse flies tsetse fly (tsĕt`sē), name for any of several bloodsucking African flies of the genus Glossina, and in the same family as the housefly. in the Lambwe Valley, Kenya, using Landsat TM satellite imagery and GIS. Journal of Animal Ecology 1996;65:371-80. (26.) Rogers DJ, Randolph SE. Mortality rates and population density of tsetse flies correlated with satellite imagery. Nature 1991;351:739-41. (27.) Rogers DJ, Williams BG. Monitoring trypanosomiasis in space and time. Parasitology Parasitology The scientific study of parasites and of parasitism. Parasitism is a subdivision of symbiosis and is defined as an intimate association between an organism (parasite) and another, larger species of organism (host) upon which the parasite is 1993;106 Suppl:77-92. (28.) Robinson TP, Rogers DJ, Williams B. Mapping tsetse habitat suitability in the common fly belt of Southern Africa
n a statistical approach used to evaluate multiple variables. multivariate analysis, n a set of techniques used when variation in several variables has to be studied simultaneously. of climate and remotely sensed data. Med Vet Entomol 1997;11:235-45. (29.) Committee on Earth Observation Satellites Partial list of Earth observation satellites by series/program.
(30.) Stoney ston·ey adj. Variant of stony. WE. The Pecora legacy-land observation satellites in the next century. Proceedings of the Pecora 13 Symposium; 1996 Aug 20-22; Sioux Falls, SD; Bethesda, MD: American Society for Photogrammetry and Remote Sensing The American Society for Photogrammetry and Remote Sensing, or ASPRS is the United States branch of the International Society for Photogrammetry and Remote Sensing. Founded in 1934, the society is a scientific association serving over 7,000 professional members around the world. ; 1998. p. 260-74. (31.) Dister SW, Beck LR, Wood BL, Falco R, Fish D. The use of GIS and remote sensing technologies in a landscape approach to the study of Lyme disease transmission risk. In: Proceedings of GIS '93: Geographic Information Systems in Forestry, Environmental and Natural Resource Management. Vancouver, B.C., Canada; 1993. (32.) Dister SW, Fish D, Bros S, Frank DH, Wood BL. Landscape characterization of peridomestic risk for Lyme disease using satellite imagery. Am J Trop Med Hyg 1997;57:687-92. (33.) Crist EP, Cicone RC. A physically based transformation of Thematic Mapper data--The TM Tasseled Cap. IEEE (Institute of Electrical and Electronics Engineers, New York, www.ieee.org) A membership organization that includes engineers, scientists and students in electronics and allied fields. Trans Geosciences and Remote Sensing 1984;22:256-63. (34.) Lobitz B, Beck L, Huq A, Wood B, Fuchs G, Faroque ASG ASG Assign ASG Allen Systems Group (Naples, FL) ASG Abu Sayyaf Group (terrorist group) ASG Associated Student Government ASG Area Support Group ASG Adaptive Services Grid ASG Assistant Secretary General , et al. Climate and infectious disease: use of remote sensing for detection of Vibrio cholerae by indirect measurement. Proc National Academy Sciences 2000;97:1438-43. (35.) Huq A, Colwell RR. Vibrios vibrios (vib´rēōs´), n.pl bacteria belonging to the genus Vibrio found in plaque after 1 to 2 weeks of no flossing or brushing. in the marine and estuarine es·tu·a·rine adj. 1. Of, relating to, or found in an estuary. 2. Geology Formed or deposited in an estuary. Adj. 1. estuarine - of or relating to or found in estuaries estuarial environments. J Marine Biotechnology 1995;3:60-3. (36.) NASA Jet Propulsion Laboratory “JPL” redirects here. For other uses, see JPL (disambiguation). Jet Propulsion Laboratory (JPL) is a NASA research center located in the cities of Pasadena and La Cañada Flintridge, near Los Angeles, California, USA. , Physical Oceanography Distributed Active Archive Center. Pasadena, California; 1996. Archived data available at the following URL URL in full Uniform Resource Locator Address of a resource on the Internet. The resource can be any type of file stored on a server, such as a Web page, a text file, a graphics file, or an application program. : http://podaac.jpl.nasa.gov (37.) Center for Space Research. University of Texas, Austin; 1996. Archived data available at the following URL: http://www.csr.utexas.edu (38.) NASA Goddard Distributed Active Archive Center, Greenbelt, Maryland; 1996. Archived data available at the following URL: http://seawifs.gsfc.nasa.gov/cgibrs/level3.pl Appendix A. Acronyms used in the text and tables
Acronym Mission
ADEOS II Advanced Earth Observation Satellite
ALOS Advanced Land Observing Satellite
ARIES Australian Resource Information & Environment
Satellite
CBERS China-Brazil Earth Resources Satellite
ENVISAT Environmental Satellite
EOS Earth Observation System
ERS-2 ESA (European Space Agency) Remote Sensing
IRS Indian Remote Sensing Satellite
NOAA National Oceanographic & Atmospheric Administration
SPOT Systbme Pour l'Observation de la Terre
Acronym Instrument
AATSR Advanced Along Track Scanning Radiometer
AMI-SAR Active Microwave Instrumentation Synthetic Aperture
Radar
ASAR Advanced Synthetic Aperture Radar
ASTER Advanced Spaceborne Thermal Emission & Reflection
Radiometer
AVHRR Advanced Very High Resolution Radiometer
AVNIR Advanced Visible & Near Infrared Radiometer
CCD Charged Couple Device Camera
ETM+ Enhanced Thematic Mapper Plus
GLI Global Land Imager
HRV High Resolution Visible
HRVIR High Resolution Visible & Infrared
IR-MSS Infrared-Multispectral Scanner
LISS III Linear Imaging Self-Scanning System
MODIS Moderate Resolution Imaging Spectro Radiometer
MOMS-2P Modular Optoelectronic Multispectral Scanner
MSU-E2 Multizone High-Resolution Electronic Scanner
MSU-SK Multizone Middle-Resolution Optomechanical Scanner
PAN Panchromatic
PAN Panchromatic
SAR-70 Synthetic Aperture Radar (70 cm)
SeaWiFS Sea-Viewing Wide Field-of-View Sensor
SROSM Spectroradiometer for Ocean Satellite Monitoring
TM Thematic Mapper
Acronym Miscellaneous
ESA European Space Agency
TIR Thermal Infrared
Acronym Instruments Country
ADEOS II GLI Japan
ALOS AVNIR Japan
ARIES ARIES Australia
CBERS CCD, IR/MSS China/Brazil
ENVISAT AATSR, ASAR Europe
EOS ASTER, MODIS USA
ERS-2 AMI-SAR Europe
IRS PAN, LISS India
NOAA AVHRR USA
SPOT HRV, HRVIR France
Acronym Mission Country
AATSR ENVISAT 1 ESA
AMI-SAR ERS-l, 2 ESA
ASAR ENVISAT 1 ESA
ASTER Terra ESA
AVHRR NOAA USA
AVNIR ALOS Japan
CCD CBERS China/Brazil
ETM+ Landsat-7 USA
GLI ADEOS II Japan
HRV SPOT 1, 2 France
HRVIR SPOT 4, 5 France
IR-MSS CBERS China/Brazil
LISS III IRS-1C, D India
MODIS Terra, EOS PM 1-3 USA
MOMS-2P Priroda/Mir Russia
MSU-E2 Almaz-1B Russia
MSU-SK Almaz-1B Russia
Priroda Russia
Resurs-O1, 02 Russia
PAN IRS-lC, D India
PAN Ikonos-2 Space Imaging
SAR-70 Almaz-1B Russia
SeaWiFS TOPEX/Poseidon France/USA
SROSM Almaz-1B Russia
TM Landsat USA
Acronym
ESA
TIR
Appendix B. Partial list of Internet locations that contain (or will contain) products derived from remotely sensed precipitation, moisture, relative humidity relative humidity n. The ratio of the amount of water vapor in the air at a specific temperature to the maximum amount that the air could hold at that temperature, expressed as a percentage. , and surface temperature data
Parameter Mission/sensor(a) Spatial
Precipitation TRMM/TMI 10 km
Precipitation TRMM/TMI 5 [degrees]
Precipitation Terra(c)/DAS(d) 1 [degrees]
Precipitation GOES/Sounder 10 km
Precipitation rate Terra(c)/DAS(d) 2 [degrees]
Precipitation amount TOVS/MSU 0.5 [degrees]
Moisture GOES/Imager 8 km
Relative humidity Terra(c)/DAS(d) 2 [degrees]
Surface temperature Terra(c)/MODIS 1 km
Surface temperature Terra(c)/MODIS 1 [degrees]
Surface temperature Terra(c)/DAS(d) 2 [degrees]
Surface temperature Envisat/AATSR 17 km
Surface temperature Envisat/AATSR 50 km
Surface temperature ERS/ATSR 1 km
Surface temperature Meteosat/VISSR 5 km
Surface temperature GOES/Imager 4 km
Parameter Temporal(b) Web site
Precipitation D daac.gsfc.nasa.gov/CAMPAIGN_
DOCS/hydrology/hd_main.html
Precipitation M daac.gsfc.nasa.gov/CAMPAIGN_
DOCS/hydrology/hd_main.html
Precipitation 8-day eos-am.gsfc.nasa.gov
Precipitation hr www.nndc.noaa.gov/phase3/
productaccm.htm
Precipitation rate 8/day eos-am.gsfc.nasa.gov
Precipitation amount D, M, Y ghrc.msfc.nasa.gov/ghrc/
list.html
Moisture hr www.nndc.noaa.gov/phase3/
productaccm.htm
Relative humidity 4/day eos-am.gsfc.nasa.gov
Surface temperature D eos-am.gsfc.nasa.gov
Surface temperature D, 8-day, M eos-am.gsfc.nasa.gov
Surface temperature 8/day eos-am.gsfc.nasa.gov
Surface temperature D envisat.estec.esa.nl/
envisat-welcom.html
Surface temperature D envisat.estec.esa.nl/
envisat-welcom.html
Surface temperature W earth1.esrin.esa.it/ERS
Surface temperature 48/day www.eumetsat.de/en
Surface temperature hr www.nndc.noaa.gov/phase3/
productaccm.htm
(a) See Appendix A for explanation of sensor acronyms. (b) hr, hourly; D, daily; W, weekly; M, monthly; Y, yearly. (c) Future launch. (d) Data Assimilation System. Louisa R. Beck,(*)([dagger]) Bradley M. Lobitz,([dagger]) and Byron L. Wood([dagger]) California State University, Monterey Bay External links
Address for correspondence: Louisa R. Beck, Earth Systems Science and Policy, California State University, Monterey Bay, MS 242-4, NASA Ames Research Center, Moffett Field, CA 94035-1000, USA; fax: 650-604-4680; e-mail: lrbec@gaia.arc.nasa.gov. |
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