Now, I’ll give you our real contribution in human welfare. Let’s begin…



Washington, D.C.

Book Cover
Book Cover

Coming decades will see major changes in the numbers, distribution, and lifestyles of human populations; climate and other environmental conditions; and land use in response to both economic demands and altered environments. These vast transformations challenge the scientific community to understand complex linkages between environmental conditions and human access to food, water, healthy living conditions, and other aspects of human welfare—and to transfer this understanding into usable information. Remote sensing data offer one piece in this multifaceted puzzle. Combined with other data, remote sensing reveals interactions over space and time that simply cannot be observed from the ground.

Human welfare is defined in this report as the health and well-being of all humans. Factors affecting human welfare include human and ecosystem health, resource availability, and social and economic stability. Understanding the linkages between human welfare and land cover is progressing at a rapid pace. For example, the relation between land surface characteristics, habitat, and disease vectors at multiple spatial scales has advanced over the last decade. Responses of land productivity to land use and climate variability are revealing insights into the vulnerabilities of human populations to food insecurity. As scientific understanding progresses, so does the potential for applying land remote sensing in operational systems to support decision making about human welfare. Considerable scientific and institutional obstacles must first be addressed, however. Integration of remote sensing with other environmental and socioeconomic data is one such obstacle.

Land Remote Sensing Applications for Agricultural Support

Chris J. Johannsen, Purdue University

Precision agriculture enables farmers to predict and maximize crop yields and determine the extent of damage from storms or other events. The basic premise of precision farming is that identified variations within an agricultural field can be considered individual management units. Potential applications of remotely sensed data depend on the types and values of crops, geographic features such as soils of the area being farmed, farmer’s use of fertilizer, irrigation and other types of management, the remote sensing expertise available to the farmer, and the timeliness of remotely sensed data. Cost is a large barrier to the application of precision farming. Most applications occur when appropriate data are available at the right time, at an affordable cost, and when there is access to expertise to use the data. Progress is steadily being made with improvements in data quality, user-friendliness of software, and access and affordability of remotely sensed data. To expand the application of precision farming, systems have to become more automated, data and software formats must become more uniform to allow data sharing, and continued sources of application funding will also be required.

Maize crop conditions for the 2005/06 growing season
Southern Africa: Maize crop conditions for the 2005/06 growing season

Opportunities and Challenges in Using Land Remote Sensing: A Case Study in Forecasting the Spread and Risk of Infectious Disease

Terry L. Yates, University of New Mexico

Hantaviruses are a group of negative-stranded RNA viruses, some of which are known to be highly pathogenic for humans. Diseases caused by hantaviruses were thought to be largely restricted to Europe and Asia until 1993 when an outbreak of hantavirus pulmonary syndrome (HPS) caused by a previously unknown hantavirus, Sin Nombre virus (SNV), occurred in the southwestern United States. Initially, there was a fatal outcome in more than 50 percent of human cases of the new virus. The deer mouse, Peromyscus maniculatus, was found to be the virus’s primary reservoir (Nichol et al., 1993). Since the discovery of SNV, some 27 additional hantaviruses have been described in the New World (Schmaljohn and Hjelle, 1997; Peters et al., 1999). While the cause of the outbreak in 1993 may be speculative, more than 10 years of ecological monitoring in the American Southwest and the results of retrospective serosurveys for SNV using archived rodent samples suggest a climate-driven trophic cascade model for SNV outbreaks in North America. It appears that increased late winter and spring precipitation in the southwestern United States driven by the El Niño-Southern Oscillation was responsible for an increase in plant primary productivity, which in turn resulted in increased rodent population densities. A direct but delayed correlation exists between increases in deer mouse population densities, increases in density of infected rodents, and increased incidence of HPS. Furthermore, retrospective data show that SNV and other New World hantaviruses have been present, essentially in their current form, in the Western Hemisphere for at least decades and probably have been coevolving with their rodent hosts in the New World for approximately 20 million years. An understanding of the relationship between climate change, ecology, and hantaviruses may enable development of improved predictive models for the prevention of human infection and improve the understanding of biocomplexity on a rapidly changing planet. A complex trophic cascade, in which impacts on one trophic level permeate through other levels, triggered by climate fluctuation can be a model for predicting HPS risk to humans. In addition, data from studies in North and South America suggest that certain human land use patterns that result in a reduction of biological diversity favor reservoir species for hantavirus and significantly increase human risk for HPS. These data make it clear that understanding the ecology of infectious diseases will require a long-term, multidisciplinary effort that is essential to public health efforts of the future. Although on a broad regional scale there is an increased risk to humans from the trophic cascade triggered by increased precipitation input into the environment, the actual risk to humans is highly localized and depends on a complex series of variables. Other factors, such as landscape heterogeneity, microclimatic differences, rodent disease, local food abundance, and competition, may be involved as well, and such complexity will have to be taken into account before a predictive model of HPS risk can be developed on a fine-grained scale. Understanding the biological complexity of natural and human-dominated ecosystems will be required before ecological and evolutionary forecasting can be employed on the scale needed to safeguard the public health against hantaviral and other zoonotic disease outbreaks. Large-scale, long-term, multidisciplinary studies also will be needed to determine if foreign or genetically modified pathogens are being introduced into our ecosystems. Near-real-time forecasting of risks of these types of diseases will be possible only if remote and other types of sensing become utilized on a continental or global scale.

Ecology and Epidemiology of Cholera: A Paradigm for Waterborne Diarrheal Diseases

Rita Colwell, University of Maryland College Park andJohns Hopkins University

Diarrheal diseases are among the leading global causes of death by infectious disease, third only to acute respiratory infections and AIDS, and particularly acute among children under 5 (WHO, 1999). Cholera is a diarrheal disease caused by the bacterium Vibrio cholerae that infects the intestine, and is transmitted through ingestion of water or food that is contaminated by the cholera bacterium. Pathogens such as V. cholerae can exist in a viable yet inactive state, like many other gram-negative bacteria that also enter dormancy when faced with adversity. Direct fluorescent and molecular genetic assays of water samples collected from the Chesapeake Bay and off the coast of Maryland and Delaware demonstrated that vibrios are present year-round, yet their levels were hard to determine with traditional methods of culturing. Similar results were obtained in the Bay of Bengal and the rivers and ponds of Bangladesh. With remote sensing, however, data can be gathered to supplement existing information that would be useful across multiple disciplines. For instance, it is known that the zooplankton and phytoplankton populations are highly correlated, since Zooplankton consume phytoplankton. Phytoplankton blooms are strongly correlated with seasonal above-average temperatures at the surface of the sea. Sea surface temperature (SST) can be monitored with remote sensing instruments, and these SST measurements can be used to estimate phytoplankton and zooplankton blooms. Ocean temperature and height patterns were found to be linked to cholera outbreak patterns in Bangladesh, India, and South America. During the El Niño years, the associated warm water patterns were correlated with new cholera outbreaks during 1991-1992 on the South American coast of Peru. Using remote sensing, research has shown that copepod and Vibrio populations are coupled to salinity, temperature, and sea height and hence to both seasonal and interannual climactic patterns in a complex, nonlinear manner. Simply stated, there is a positive correlation between the seasonal increased sea surface temperature and sea surface height and subsequent outbreaks of cholera that occur in the late spring and fall months in Bangladesh. Thus, remote sensing has the potential to contribute to a global warning system for increased plankton production and associated cholera outbreaks.

Challenges and Potential for Applying Land Remote Sensing to Human Welfare Resume:

(1) Need for integration of spatial data on environmental conditions derived from remote sensing with socioeconomic data;

(2) Need communication between remote sensing scientists and decision makers to determine the effective use of land remote sensing data for human welfare issues; and

(3) Need acquisition and access to long-term environmental data and development of the capacity to interpret these data.

Table1. Environmental Conditions and Change Requiring Monitoring by Multiple Types of Remote Sensing (e.g., optical, radar, microwave)




Examples of

Remote Sensing


and Existing



• Water

quality (e.g.,


oxygen content)

• Water availability

• Water locations

and types (e.g.,

wetlands, lakes)

• Rainfall

Monitor conditions

conducive to waterborne

disease growth

or migration (worms,

flu, meningitis, cholera,

malaria, West Nile virus,

AIDS); wetland mapping

Radar, multispectral



Air and


• Ozone


• Particulates

• Heat and


• UV


• Wind dynamics

• Dust movements

Air quality, atmospheric

chemistry, climate

change—monitoring these

conditions allows for

indirect measurement of

diseases such as asthma




Soil and


• Soil moisture

• Vegetation types

• Vegetation


Habitats for disease vectors






Land use

and land


• Land cover

• Livestock


• Cropland extent

Soil, water, and livestock

interactions; land-sea

interface; detection of

floodplains, ice cover







• Roads and


• Water access

• Sewers

• Communications

• Waste disposal

• Urban population

distributions at

high resolution

Disease vector tracking;

improving health service

response in times of

emergency; developing GIS

data layers for modeling

housing, land cover, etc.;

high-resolution population

distributions that can assist

in health issues associated

with infrastructure

(i.e., obesity as related

to infrastructure);

understanding of


Very high





MOPITT: Measurement of Pollution in the Troposphere; instrument on Terra spacecraft measuring CO and CH4 in the troposphere

MODIS: Moderate Resolution Imaging Spectroradiometer

IKONOS: Commercial earth observation satellite collecting high-resolution multispectral and panchromatic imagery

SPOT: Systeme Pour l’Observation de la Terre


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