The sections that follow discuss the considerations that led to the panel’s conclusions and selection of mission priorities.

ROLE OF SATELLITES IN UNDERSTANDING ECOSYSTEMS

Among the major scientific advances of the last few decades is quantitative understanding of the role of terrestrial and marine life in regulating climate, protecting watersheds, providing a diversity of species for crops and medicines, maintaining healthy environments, and performing many other services fundamental to human economies. Ecosystems regulate the amount of atmospheric CO2 by storing carbon and cycling it among the land, ocean, and atmosphere. Biota also cycles nitrogen and other nutrients essential for plant growth but detrimental in excess when they cause algal blooms harmful to coastal fisheries. In addition to the cycling of carbon and nutrients, plants cycle water among the soil, atmosphere, and water bodies. Vegetation mitigates floods and drought by buffering the flow of water to streams and rivers and enhancing the recharging of groundwater. The diversity of life found in ecosystems benefits human society in many ways. Crop varieties depend on genetic diversity found in wild species, and the diversity of species maintains functioning ecosystems in the face of disease, climate change, or catastrophic events. These are a few examples of the essential role of ecosystems in maintaining food production, water supplies, and the healthy living environments that underpin the human enterprise, in addition to the intrinsic and recreational value that many people place on healthy ecosystems.

Satellite observations of ecosystems have played a key role in developing the scientific understanding described above. One example is the Advanced Very High Resolution Radiometer (AVHRR), originally designed for meteorological applications, not for observing ecosystems. Its daily measurements of the red and infrared (IR) reflectances from Earth’s surface, however, have enabled a multidecade time series of vegetation greenness against which changes in productivity from climate variability or other disturbances can be assessed (Figure 7.1). That capability has enabled such applications as the Famine Early Warning System (NRC, 2006) to identify locations susceptible to impending crop failure in Africa and weekly drought monitoring for the United States based partially on satellite observations of vegetation health (http://www.drought.unl.edu/dm/monitor.html). Landsat observations since the early 1970s have also been used in myriad scientific and practical applications, among them the ability to quantify tropical deforestation, identify where people are vulnerable to fire and floods, and assess crop yields.

Optical, multispectral sensors have been the mainstay of remote sensing for ecosystems over the last two decades. Scientific advances in applications of hyperspectral and active radar and lidar sensors hold promise for considerably enhancing the capabilities to observe and understand ecosystems, including invasive species, air quality, harmful algal blooms, and a host of other issues (e.g., Asner et al., 2004; Treuhaft et al., 2004). The ability to observe a full array of ecosystem dynamics is required to anticipate responses of ecosystems as land-use and climate change accelerate in the future.

Globally, nearly all ecosystems are under pressure from two trends. The first is pervasive land-use change and exploitation of land and ocean resources that are affecting most ecosystems, even in regions considered remote. The second is climate change, which is increasingly evident in many regions. Some of the environmental issues that result from these two trends are widespread (e.g., greenhouse-gas emissions to the atmosphere), and some are specific to local conditions (e.g., loss of habitat of endangered species). Addressing these issues requires approaches that couple the global trend (climate change, land-use and ocean-use change, pollution, and so on) with the local particulars of soil, topography, and socioeconomic circumstances. Space-based observations have exactly this character: they provide a global picture, but they are spatially-resolved and so provide local particulars.



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