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Multitemporal imaging is the acquisition of remotely sensed data from more than one time period. Acquiring multiple views of natural or built features through time provides an enhanced ability to accurately identify these features, significant information about how our world is changing, and unique insight into the relationships between the environment and the impact of human activities. Multitemporal images and analysis techniques provide the tools to monitor land use and land cover change (LUCC) and have been instrumental in providing an understanding of global environmental change. Our understanding of the impacts of global change would not have been possible without multitemporal imaging of diverse phenomena such as snow and ice extent, sea surface temperature, deforestation, drought, water resources, wildfire, desertification, and more.

Imaging Systems for Multitemporal Studies

Multitemporal imaging began with the earliest applications of aerial photography, in which government agencies in the United States regularly acquired aerial photos to monitor agricultural production and soils. These photographs are available from the 1930s onward and are still being used in studies of long-term landscape change. A wide range of Earth-observing satellite systems are now available for multitemporal studies. These systems have varying spatial (size of the smallest resolvable feature), spectral (wavelengths sensed), and temporal (repeat cycle of image acquisition) resolutions. The goal of any multitemporal study will dictate the best combination of spatial, spectral, and temporal resolutions to use.

Moderate-spatial-resolution (∼20- to 30-m [meter] pixel size) satellites such as Landsat (first launched in 1972) generally provide repeat coverage of the same spot every 2 to 3 weeks. We therefore have more than 35 years of data from the Landsat satellites for multitemporal studies. Data from Landsat and subsequently launched moderate-spatial-resolution systems such as France's SPOT (Satellite Pour l'Observation de la Terre, first launched in 1986) and the Indian Remote Sensing (IRS) satellite (first launched in 1988) provide multitemporal data across the globe.

Satellite systems that can provide daily coverage of every spot on Earth have a relatively coarse spatial resolution (∼1-kilometer pixel size).

Nevertheless, these systems have been ideal for global monitoring of the atmosphere, vegetation, glacial extent, sea surface temperature, and other more ephemeral phenomena. The National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) began collecting data in 1979, and subsequent AVHRR sensors have provided continuous daily coverage of the Earth. France's SPOT VEGETATION system, first launched in 1998, also provides daily coverage for multitemporal studies of vegetation condition. The United States first launched a more advanced version of the AVHRR system in 2000, called the Moderate Resolution Imaging Spectrometer (MODIS). MODIS was specifically designed to provide long-term multitemporal data to improve our knowledge of global dynamics. Acquiring spectral data in the visible, near-infrared, and thermal-infrared regions of the electromagnetic spectrum, MODIS provides daily data to study the atmosphere, ocean, and land surface at spatial resolutions of 250 to 1,000 m.

More recently, commercial satellite systems have become available that provide very high-spatial-resolution data (0.6–3 m) on an on-demand basis. High-resolution data from these systems can be reacquired in 1- to 5-day time periods, and their high spatial resolution can be an invaluable asset in disaster management and urban planning.

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