moderate resolution; it believes, further, that the usefulness of these data can be enhanced significantly if fine-resolution data are also available.
Why are there not more moderate-resolution remote sensing satellite systems available for use today? Many countries and private-market firms recognize that while moderate-resolution systems are of value, there is more commercial demand for finer-resolution panchromatic and multispectral data. Figure 3.1 shows that almost all major public and commercial remote sensing systems are migrating toward finer-spatial-resolution panchromatic and visible and near-infrared (VNIR)/shortwave infrared (SWIR) wavelength bands. Many important applications are not possible using only Landsat-like moderate-resolution data, driving a dramatic shift toward finer spatial resolution. Several important applications and data sources that require finer-scale data than Landsat 8 delivers include the following:
• Land use/land cover. Land cover information is categorized by the map scale at which the information is provided.3 Remote sensor data with fine spatial resolution are required to extract high-level information about “landscape metrics.”4 Many city and county agencies throughout the United States and some federal agencies require access to land cover products at a spatial resolution finer than 2 m.
• Building and property infrastructure. Almost all counties in the United States collect and store property ownership information in a digital system,5 including detailed information about each parcel’s dimensions and all building footprints (perimeters). This effort requires a tremendous amount of remote sensing data collection and processing of fine-resolution imagery throughout the United States every year. Numerous government agencies, including the U.S. Census Bureau, also require building infrastructure information. Fine-resolution imagery can be used to identify the location of new residential structures and the associated road network information. This geospatial information is then conflated with postal and other sources of geospatial data to obtain accurate address information.
• Socioeconomic characteristics. The American Community Survey is an ongoing Census Bureau statistical survey that samples a very small percentage of the population every year.6 Local and regional organizations use fine-resolution imagery to predict the spatial distribution of population between censuses to identify new developments or structures and to estimate the number of persons living in each dwelling unit based on building footprint and square footage estimates.
• Transportation and utility infrastructure. Federal and state departments of transportation rely heavily on high-resolution stereoscopic aerial photography, satellite imagery, and LiDAR data to monitor transportation infrastructure, allowing them to inventory and characterize roadways, especially to identify deteriorating infrastructure.7
• Hydrology. While moderate-resolution remote sensing data can be used to identify general stream or river centerlines, fine-resolution stereoscopic data or LiDAR data are required to precisely map drainage networks and determine the topography of floodplains for preparing digital flood insurance rate maps8 and hydrologic models.
• Vegetation assessment. Moderate-resolution imagery is useful for monitoring vegetation type (e.g., forest, rangeland, wetland, agriculture), biomass, and functional health over relatively large geographic areas. Fine spatial- and spectral-resolution imagery and LiDAR data can be used to identify vegetation structure, predict watershed runoff, model urban heat islands, and describe agriculture and forest canopy biomass. Extensive remote sensing literature addresses scientific research and applications for vegetation studies based on the use of fine-resolution remote sensing data.
• Disaster emergency response examples. The Department of Homeland Security has significant fine-resolution data requirements, such as determining the boundary of disaster areas and vulnerable structures.9 USGS
3 J.R. Anderson, E.E. Hardy, J.T. Roach, and R.E. Witmer, A Land Use and Land Cover Classification System for use with Remote Sensor Data, U.S. Geological Survey Professional Paper 964, 1976.
4 M. Herold, J. Scepan, and K.C. Clarke, The use of remote sensing and landscape metrics to describe structures and changes in urban land uses, Environment and Planning A 34:1443-1458, 2002.
5 National Research Council, National Land Parcel Data: A Vision for the Future, The National Academies Press, Washington, D.C., 2008.
8 National Research Council, Elevation Data for Floodplain Mapping, The National Academies Press, Washington, D.C., 2007.
9 U.S. Government Accountability Office, Homeland Security: Actions Needed to Improve Response to Potential Terrorist Attacks and Natural Disasters Affecting Food and Agriculture, GAO-11-652, 2011, available at http://www.gao.gov/new.items/d11652.pdf.