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Image Databases: Search and Retrieval of Digital Imagery Edited by Vittorio Castelli, Lawrence D. Bergman Copyright  2002 John Wiley & Sons, Inc. ISBNs: 0-471-32116-8 (Hardback); 0-471-22463-4 (Electronic) 3 Satellite Imagery in Earth Science Applications H.K. RAMAPRIYAN NASA, Goddard Space Flight Center, Greenbelt, Maryland 3.1 INTRODUCTION Remote sensing is the science of measuring characteristics of interest from a distance. Our focus in this chapter is the remote sensing of Earth from instru- ments flown on aircraft or spacecraft. Imaging from remote sensors has had a long history, starting early in the twentieth century with photographs from balloons and aircraft. In recent years, there has been a proliferation of aerial and space-borne sensors that image the Earth at various resolutions. There is considerable interna- tional interest in remote sensing, both in the public and in the private sectors. The data from remote sensors come in many forms, such as images, profiles, and so on. However, images tend to dominate the archives of remotely sensed data both in volume and in variety. The applications of remote sensing are numerous in both civilian and military arenas. Examples of civilian applications include daily weather forecasting, long-term climate studies, monitoring atmospheric ozone, forecasting crops, and helping farmers with precision agriculture. A variety of data collection systems exist today to obtain image and nonimage data using remote sensing. Instruments that obtain image data are in general referred to as imagers. Measurements such as surface height obtained by altimeters and reflected radiance from the Earth at various wavelengths, obtained by radiometers, spectrometers, or spectroradiometers, are represented as images. The number of wavelength ranges (spectral bands) used by imaging instruments can vary from one (panchromatic) to hundreds (hyperspectral). A variety of mechanisms are used in sensing, including across-track and along-track scanning (Section 3.4.2), resulting in the need for algorithms modeling the sensing geometry to ensure that the images are properly mapped (registered) to a ground coordinate system. Usually, several detectors are used to obtain images from a given instrument, resulting in the need for proper cross-calibration, to ensure that the numbers obtained from the different detectors have the same physical meaning. Images are obtained at various resolutions (or pixel sizes), ranging from one meter to a few kilometers. 35 36 SATELLITE IMAGERY IN EARTH SCIENCE APPLICATIONS Generally, low-resolution images are used for frequent and global coverage of the Earth, whereas high-resolution images are used for occasional and detailedcoverage of selected areas. Remotely sensed images must be radiometrically and geometrically corrected to ensure that they provide accurate information. The corresponding processing usually includes corrections and derivation of “higher levels” of information (beyond gray levels and colors of pixels) such as chlorophyll concentration, sea surface temperature, cloud heights, ice motion, and land cover classes. In designing a database for remotely sensed image data, it is important to understand the variety of data collection systems, the types of processing performed on the data, and the various ways in which the data and the derived information are accessed. The goal of this chapter is to discuss the issues associated with managing remotely sensed image data, with a particular focus on the problems unique to such data, and to provide a few examples of how some existing systems handle those problems. Section 3.2 gives a brief history of remote sensing. Section 3.3 provides a discussion of applications with two examples of assessing human impact on the Earth’s environment. Section 3.4 covers data collection systems briefly with emphasis on defining terminology relevant to obtaining and managing image data. Section 3.5 is a discussion of the types of errors and artifacts in remotely sensed images and the corrections required before proceeding with higher levels of information extraction. Section 3.6 outlines processing steps to derive useful information from remotely sensed images. Section 3.7 addresses the implications of the variety of collection systems, processing, and usage patterns on the storage and access of remotely sensed image data. Section 3.8 gives a few examples of systems managing such data and how they address the issues identified in Section 3.7. Section 3.9 concludes the chapter with a summary of the key points. 3.2 HISTORICAL BACKGROUND AND REMOTE SENSING MISSIONS A brief history of remote sensing, with a focus on land remote sensing, can be found in Ref. [1]. An extensive survey of airborne and space-borne missions and sensors for observing the Earth is given in Ref. [2]. The latter reference describes more than 125 space-borne missions, several of which are series of multiple satellites, and more than 190 airborne sensors. Some of the interesting facts from these references are summarized in the following text to illustrate the variety of applications and the breadth of international interest in remote sensing. Remote sensing of Earth is said to have started with the first photograph from a balloon over Paris taken by Gaspard Felix Tournachon in 1895 (see http://observe.ivv.nasa.gov/nasa/exhibits/history/ ). By 1909, aerial photographs of the Earth were being taken for various applications. The first military Earth- observing satellite, called Discoverer, was launched in August 1960. The data were initially meant to support biomedical research and Earth observations. However, a few months after launch, the data were classified secret and became HISTORICAL BACKGROUND AND REMOTE SENSING MISSIONS 37 unavailable for civilian purposes. The first satellite for civilian applications was launched by the National Aeronautics and Space Administration (NASA) and the Department of Defense (DOD) in August 1960. It was the first experimental weather satellite and was called the Television Infrared Observation Satellite (TIROS-1). This led to a series of satellites that became operational in 1966 as TIROS Operational Satellites (TOS), to be renamed the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satel- lites (POES) in 1970. This was followed in 1978 with a series of NOAA weather satellites carrying the Advanced Very High-Resolution Radiometer (AVHRR) that have been used for both meteorologic applications and studies of vegetation and land use. NASA launched the first of the series of satellites dedicated to land remote sensing in July 1972. This was initially called the Earth Resources Technology Satellite (ERTS-1) and was later renamed Landsat-1. Since then, there have been several Landsat satellites. The latest in the series, Landsat-7 was launched in April 1999. While Landsat provides relatively high-resolution data (30 m), AVHRR provides coarser resolution (1.1 km) data, but more frequent observations of a given location on Earth. The series of NOAA Geostationary Operational Environmental Satellites (GOES) provide continuous observations of the Earth at an even coarser resolution. These are principally used for continuous monitoring of atmospheric phenomena and for supporting weather forecasts. The satellite series called SPOT, designed by the Centre National d’Etudes Spatiales (CNES) in France, has been providing remotely sensed land images since 1986. The latest in the series, SPOT 4, was launched in March 1998. In September 1999, Space Imaging, Inc., a private company in the United States launched IKONOS, a satellite for high-resolution (1-m panchromatic and 4-m multispectral) imaging. Since the launch of IRS-1A, in March 1988, India has had a series of remote sensing satellites, called IRS. There have been other satellites such as the Nimbus series (Nimbus-1, launched in August 1964, to Nimbus-7, launched in October 1978), SeaSat (launched in June 1978) and Sea star (with the SeaWiFS instru- ment launched in August 1997) that have been used mainly for ocean, coastal zone, and fishery applications. NASA initiated a program called the Mission to Planet Earth (MTPE) in the 1980s. This is a part of the broader U.S. Global Change Research Program (USGCRP). The MTPE is now renamed the Earth Science Enterprise. There are several partners from other agencies (e.g., NOAA, U.S. Geological Survey, Department of Energy) and countries (e.g., Australia, Brazil, Canada, Finland, France, Japan, Netherlands) in this program. The purpose of this comprehensive program is to study the Earth as an integrated and coupled system, consisting of the atmosphere, oceans, and landmasses interacting with each other over a range of spatial and temporal scales [3–6]. Phase 1 of this program consisted of many spacecraft, several of which are still operational, including NASA missions: Earth Radiation Budget Satellite (ERBS), Upper Atmospheric Research Satellite (UARS), Topography Experiment for Ocean Circulation (TOPEX/Poseidon — joint with France), Tropical Rainfall Measuring Mission (TRMM — joint with Japan). Several non-NASA missions that are 38 SATELLITE IMAGERY IN EARTH SCIENCE APPLICATIONS considered part of the Phase 1 of MTPE include NOAA-12 and NOAA-14, European Remote Sensing Satellites (ERS-1 and–2), Japanese Earth Resources Satellite (JERS-1), and Radarsat (Canada). Phase 2 of this program consists of the Earth Observing System (EOS). EOS was considered the centerpiece of the MTPE and continues to be the biggest part of NASA’s Earth Science Program. EOS consists of a series of satellites and a variety of instruments that will monitor the Earth from space. The primary purpose of EOS is to establish a long-term basis for determining the extent, causes, and consequences of global climate change. The first two EOS instruments were launched on TRMM in November 1997. The first major EOS satellite, Terra (formerly known as EOS-AM), was launched in December 1999. This is to be followed by Aqua (formerly known as EOS-PM) in 2001 and Aura (formerly known as EOS CHEM) in 2003. Complete details of all satellites and instruments constituting the EOS Program can be found in the Earth Science Enterprise/EOS Reference Handbook [6]. 3.3 APPLICATIONS OF REMOTE SENSING Remotely sensed data have many different civilian and military applications in diverse disciplines. A few examples of civilian applications are as follows: characterizing and studying variations in atmospheric ozone, quantifying and identifying causes and effects of pollution, forecasting weather, monitoring volcanic eruptions, forest fires, floods and other natural hazards, tracking sea-ice motion, mapping and studying temporal changes in global biomass productivity, studying deforestation and desertification, topographical mapping, forecasting crops, supporting precision agriculture, monitoring urban change, land use planning, and studying long-term climate change. These applications range from providing detailed information covering small areas to individuals and small organizations for commercial purposes to generating global-scale information that is relevant in formulating international policies. Examples of international policies in which remotely sensed information has played a significant role include the 1987 Montreal protocol for eliminating chlorofluorocarbons (CFCs) [7] and the emission-reducing accords from the Kyoto Climate Change Conference in December 1997 [8]. Although accuracy requirements may vary from application to application, a common theme is that decisions with individual, regional, national, or international impact are made using information derived from remote sensing data. Especially in cases in which the information is used in making a public policy that affects the lives of millions of people, it is extremely important that the quality of the data and the scientific basis of the algorithms that are used to derive conclusions from the data are well understood, documented, and preserved for posterity. Preserving all the data and related information and making them conveniently accessible to users are as important as collecting the data using remote sensing systems. Many interesting applications, images, and animations can be found at the URLs listed at the end of this chapter. We will consider two examples of appli- cations of remote sensing. The first provides an illustration of a time series of data from NASA’s Total Ozone Measuring System (TOMS) instruments used APPLICATIONS OF REMOTE SENSING 39 to observe the progression of the Antarctic ozone concentration over the past several years. The second provides an example of observing deforestation over time using Landsat data. 3.3.1 Antarctic Ozone Ozone (O 3 ) in the Earth’s atmosphere is critical to the life on the surface of the Earth [9]. Although it is a lethal pollutant at lower altitudes, it screens the ultra- violet (UV) radiation that destroys cells in plants, animals, and humans at high altitudes. Extreme exposure to UV radiation causes skin cancer. Ground-based instruments and those flown aboard balloons, aircraft, and spacecraft have been used extensively for measuring ozone concentration. The ozone concentration is measured in parts per million (the number of O 3 molecules per million molecules of air) and is typically only a few parts per million. Measurements show that about 90 percent of the ozone in the atmosphere is in the stratosphere (altitudes between 10 and 50 km). Therefore, the ozone layer is generally referred to as the stratospheric ozone layer. The “thickness” of the ozone layer is measured in “Dobson Units (DU).” To define DU, imagine that, all the ozone contained in a vertical column of atmosphere above a given ground location is brought to sea level and at room temperature. The average thickness of such a layer of ozone over the globe is 3 mm (about the thickness of two stacked pennies). This is designated as 300 DU. Despite its very low concentration, ozone is critical to the survival of life on Earth. In the 1920s, CFCs, a family of chemicals, were developed as a safe substitute for flammable substances such as ammonia for use in refrigerators and spray cans. Over subsequent decades, there was an enormous growth in the use of CFCs. Although the amount of chlorine occurring naturally in the atmosphere is very low, CFCs introduced a significant amount of chlorine into the ozone layer. Under certain conditions, chlorine has the potential for destroying large amounts of ozone. This effect of reducing ozone concentration has, in fact, been observed, especially over Antarctica. In 1985, Farman and coworkers [10] showed that ozone was disappearing over Antarctica and that the measured amounts were much less than the natural low values. This led to an intensive measurement campaign and analyses that have yielded a nearly complete characterization of the physical and chemical processes controlling Antarctic ozone. Concerns over the health of the ozone layer led, in 1987, to the international agreement, called the Montreal Protocol that restricted and ultimately phased out the production of CFCs [45]. There is a time lag of years, however, between the stopping of the production of CFCs and the reduction of their concentration in the stratosphere. As the CFCs begin to decrease, it is expected that the Antarctic ozone amounts should begin to recover. Several types of measurements have been made on a global scale to monitor the ozone layer. Remotely sensed images from a variety of sources, including space-borne instruments, aircraft, and ground-based stations, were needed for the thorough analysis of cause and effect relationships required to support a decision about CFCs. Remotely sensed images from the TOMS instruments are being 40 SATELLITE IMAGERY IN EARTH SCIENCE APPLICATIONS BUV & TOMS Total ozone Total ozone (DU) Oct. 70 Oct. 71 Oct. 72 Oct. 79 Oct. 84 Oct. 89 120 520 Oct. 91 Oct. 93 Figure 3.1. The progression of the hole in the ozone layer between 1970 and 1993, imaged by the Total Ozone Measuring System (TOMS) instruments. A color version of this figure can be downloaded from ftp://wiley.com/public/sci tech med/image databases. used for ongoing monitoring of the ozone concentration. The TOMS instruments have been flown on several spacecraft, starting with Nimubs-7 in 1978. The series of images shown in Figure 3.1 illustrates the progression of the ozone hole during the period 1970 through 1993. [Before 1978 images were obtained from the backscatter ultraviolet (BUV) instrument on Nimbus 4]. Each image shows a color representation of the thickness of the total ozone column measured in DUs. The images represent the monthly means of thickness for October of each year displayed. As shown in the color scale, the thickness is the smallest in the blue areas. Generation of these images involves several steps, starting with obtaining instrument-observed radiance values, deriving ozone concentration values, and mapping them to a standard polar projection for display. Access to image data from TOMS can be obtained through http://toms.gsfc.nasa.gov and http://daac.gsfc.nasa.gov. 3.3.2 Deforestation Reduction of forest areas around the world due to natural causes, such as forest fires, and human activities, such as logging and converting of forested regions into agricultural or urban regions, is referred to as deforestation. Deforestation has significant impact on the global carbon cycle and biodiversity. The removal of large trees and the burning of debris to clear the land increase the carbon dioxide content of the atmosphere, which may have an impact on climate. Tropical rain forests occupy about 7 percent of the land area of the Earth. However, they are home to more than half of the living plant and animal species. Thus, deforestation can lead to massive extinction of plant and animal species. The largest tropical rain forest in the world is the Amazon Rain Forest. It covers parts of Bolivia, Brazil, Colombia, Ecuador, and Peru. An example of deforestation over time is shown in Figure 3.2. This shows two Landsat images of Rondonia, Brazil. The APPLICATIONS OF REMOTE SENSING 41 June, 1975 August, 1986 Figure 3.2. Example of deforestation in Rondonia, Brazil, as it appears in Landsat TM images. A color version of this figure can be downloaded from ftp://wiley.com/public/sci tech med/image databases. left image was obtained in 1975 and the right image in 1986. Significant increase in human population occurred between 1975 and 1986, resulting in colonization of the region adjacent to the main highway and conversion of forestland to agri- cultural use. It is easy to see these areas in the 1986 image — they appear as a fish bone pattern. By accurately coregistering images such as these (i.e., over- laying them), comparing them, and using classification techniques discussed in Section 3.6, it is possible to obtain accurate estimates of the extent of defor- estation. By analyzing a large number of Landsat images such as these (it takes about 210 Landsat images to cover the Brazilian Amazon Basin), it has been shown that between 1975 and 1988, about 5.6 percent of the Brazilian Amazon Basin became deforested. The impact on biodiversity is even greater than that indicated by this estimate of deforestation, because the natural plants and animals in the forest are adversely affected by isolation of previously contiguous habitats. Contiguous areas of less than 100 km 2 are considered isolated. Greater exposure to winds and predators at the boundary between forested and deforested areas also affects the natural plants and animals. The habitat within 1 km of the forest boundary is considered to be affected in this manner. With these considerations, it is estimated that about 14.4 percent of the habitat for natural plant and animal life in Brazil was impacted [11]. Acquiring remotely sensed images on a global scale periodically and over a long period of time, and archiving them along with ancillary data (i.e., data other than the remotely sensed image data needed for analysis), metadata, and the results of analyses, will help monitor deforestation, assist in policy making, and aid in studying the impacts of changes in policy. Because of the importance of this issue, there are several global and regional scale initiatives to monitor the forests of the world over time. Examples of global initiatives are as follows: • The international Global Observations of Forest Cover (GOFC) Project. • NASA Landsat Pathfinder Humid Tropical Forest Inventory Project (HTFIP). 42 SATELLITE IMAGERY IN EARTH SCIENCE APPLICATIONS • Commission of the European Communities Topical Ecosystem Environment Observation by Satellite (TREES) Project. • High-Resolution Data Exchange Project of the Committee on Earth Obser- vation Satellites (an international affiliation of space agencies) and the International Geosphere Biosphere Program. • The multinational Global Forest Mapping Program (GFMP) led by the Earth Observation Research Center (EORC) of the National Space Development Agency of Japan (NASDA). Regional initiatives include the following: • North American Landscape Characterization (NALC) Project • Regional Multiresolution Land Characteristics (MRLC) covering the conter- minous United States; • U.S. Forest Inventory and Analysis (FIA) and the Canadian National Forestry Database Program (NFDP). More details about these and other programs can be found in Ref. [12]. 3.4 DATA COLLECTION SYSTEMS There are a large variety of systems for collecting remotely sensed data. These can be categorized in several ways according to the • type of instrument (imager, sounder, altimeter, radiometer, spectrometer, etc.), • mechanics of the instrument (push broom, whisk broom, serial cross-track, parallel cross-track, conical scan, step-staring, etc.), • sensing mechanism — passive or active, • viewing characteristics — pointable or fixed, nadir- or off-nadir-looking, single- or multiangle (mono or stereo), • spectral characteristics measured (panchromatic, multispectral, hyper- spectral), • spatial resolution (high, moderate, low), • observed wavelength range (UV, visible, near infrared, thermal, microwave, etc.), • platform — aircraft, spacecraft, and • altitude (in case of airborne sensors) or orbits (in the case of space- borne sensors) — sun-synchronous, geosynchronous, geostationary, low inclination. Some of the foregoing terms, especially the ones contributing to data management issues, are defined in the following text. Because the focus of this chapter is on image data, the discussion will be limited to the terms relating to imaging instruments. For a more complete discussion of terminology see Refs. [2,13]. DATA COLLECTION SYSTEMS 43 3.4.1. Instrument Types Instruments used in remote sensing belong to one of the following categories: • Imager. (Imaging Instrument) An instrument that has one or more sensors (also called detectors) that measure characteristics of the remote object (e.g., the Earth) and that produces measurements that can be represented as an image. Most of the imagers used in remote sensing acquire images elec- tronically by measuring the radiance incident at the sensor(s), convert the data into digital format, and transmit the results to a receiving system on the ground. • Altimeter. An instrument whose purpose is to measure altitudes. Altitudes are measured over a “reference ellipsoid” — a standard for the zero altitude surface of the Earth. The altitudes can be represented as a gray scale or color-coded two-dimensional image or as a three-dimensional surface. • Radiometer. An instrument that measures radiance values (either reflected or emitted) from the Earth’s surface in a given set of wavelength bands of the electromagnetic spectrum. • Spectrometer. An instrument that measures the spectral content of radiation incident at its sensor(s). • Spectroradiometer. An instrument that measures both the radiance values and their spectral distribution. 3.4.2 Mechanics Generally, unlike conventional cameras, an imaging instrument does not obtain an image as a “snap shot” at a single point in time. It uses a relatively small number of sensors and relies on some form of scanning mechanism and on the motion of the aircraft (or spacecraft) to measure radiance from different parts of the scene being imaged. Consider the general imaging geometry shown in Figure 3.3. Here, a detector D measures the radiance reflected from point P on Earth. S is the source of illumination, which is generally the Sun. The detector actually measures radiance from a finite region surrounding P, called Flight line (along track) Cross-track S P D N D: Detector N: Nadir point (point on earth vertically beneath D) P: Point being viewed by D S: Source of illumination (usually the sun) Figure 3.3. General imaging geometry. 44 SATELLITE IMAGERY IN EARTH SCIENCE APPLICATIONS the instantaneous field of view (IFOV). (Note that the size of an IFOV is loosely referred to as resolution. Each IFOV results in a pixel in the sensed image). As the platform (spacecraft or aircraft) moves along its path, the IFOV traces a small strip on the ground. By using an array of detectors in an instrument, the radiance values from an array of IFOVs can be measured simultaneously. Wide ground swaths are imaged using various combinations of scanning mechanisms, arrays of detectors, and platform motion. Two of the commonly used combinations and the synthetic-aperture radar instrument are discussed in the following list: • Across-Track (Whisk Broom) Scanner. Using an oscillating (or rotating) mirror, an across-track scanner traces a scan line along which the detector measures radiance values of an array of IFOVs. The continuous signal measured by the detector is sampled and converted to digital counts through an analog-to-digital conversion process. Thus, a scan line results in a row of pixels in the image. As the platform (spacecraft or aircraft) moves, succes- sive scan lines are traced. The instrument can have several detectors, so that, as the mirror oscillates, n scan lines can be traced simultaneously. The platform velocity and scanning period are matched to avoid overlaps or gaps between successive sets of n scan lines. If n = 1, then the instrument is called a serial cross-track scanner.Ifn>1, then it is called a parallel cross-track scanner. • Along-Track (Push Broom) Scanner. An along-track scanner uses a linear array of detectors arranged perpendicular to the direction of platform motion. There is no scanning mirror. Each detector measures the radiance values along a track parallel to the platform motion, thus generating a column of pixels in the resulting image. The set of detectors in the linear array generates a row of pixels at a time. • Synthetic Aperture. Radar instruments (see active sensing described in the following section) measure reflected signals from the ground as the plat- form moves and mathematically reconstruct high-resolution images, creating images as if obtained with a very large antenna. These are called synthetic- aperture radar instruments (SAR). 3.4.3 Sensing Mechanism Sensing mechanisms can be either passive or active. • Passive. A passive sensor measures emitted and/or reflected radiance values from the Earth without an active source of radiation in the sensor. The source of illumination with passive sensors is usually the sun. • Active. An active sensor provides its own source of radiation, usually in a narrow spectral band (e.g., radar or laser) and measures the reflected (echo) radiance. 3.4.4 Viewing Characteristics Depending on the acquisition direction, instruments can be categorized as follows: [...]... temporal sequences of derived global data products, such as sea surface topography (http://podaac.jpl.nasa.gov/tpssa/images/tpssa.mov?251,155), ozone concentration (http://daac.gsfc.nasa.gov/CAMPAIGN− DOCS/ATM− CHEM/graphics/October− 1991− Ozone.mov and http://toms.gsfc.nasa.gov/multi/multi.html), biosphere and a variety of other parameters (http://seawifs.gsfc.nasa.gov/SEAWIFS/IMAGES/MOVIES.html) Overlays... follows: • • • • General Description Name, size estimate, date of production, process input, ancillary data, associated browse image(s), software version used to produce the granule, pointers to software documentation, and quality measures Data Origin Campaign, platform, instrument, sensor, model, and so on Spatial Coverage One of the several ways of specifying the geographic area covered by the images... the difficulty in predicting the load on the database systems It is necessary to gather usage statistics, assess them, and be prepared to augment system capacity and capabilities as needed Clear on-line documentation, multiple layers of indexes to data, aliases to accommodate differences in terminology, and easy-to-use search and order facilities are needed Just as distributed implementation helps in... such as long-term climate studies Any data from the past that are lost can never be reacquired These considerations dictate several storage requirements At a minimum, raw data and all ancillary data, documentation, and software required to generate higher levels of products must be permanently stored “Ancillary” data include calibration parameters, prelaunch and onboard calibration data, any in situ . algorithms that are used to derive conclusions from the data are well understood, documented, and preserved for posterity. Preserving all the data and related