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9 Ecosystems and the Carbon Cycle Research on the biosphere aims to understand and pre- increasing Â(Keeling et al. 1976) and that it stemmed from fos- dict how terrestrial and marine ecosystems are changing, sil fuel burning, catalyzing an interest in obtaining a global how they are affected by human activity or through their perspective of the carbon cycle. In 1982, the National Aero- own intrinsic biological dynamics, how they respond to nautics and Space Administration (NASA) held a workshop climate variations, and in turn how they affect climate. One in Woods Hole, Massachusetts, on global change (Goody of the primary goals of ecosystem research is to determine 1982) that spurred a subsequent paper by Tilford (1984) the amount of primary production, which is most commonly presenting the scientific rationale for the Earth Observing expressed in units of carbon incorporated during photo- System (EOS). These papers called attention to how anthro- synthesis and estimates the amount of energy available for pogenic global changes might impact ecosystems. higher trophic levels. Since the discovery of the importance of carbon dioxide as a greenhouse gas, the estimation of Terrestrial Primary Productivity global carbon fixed by photosynthetic processes has become a central quest in global carbon cycle research and an integral New awareness of the relationship between microcli- part of climate models. mate and plant functions in the 1970s and 1980s spurred Before the satellite era, few scientists had attempted to the development and evolution of field-portable instruments estimate these parameters at a global scale. Instead, most to measure plant physiological processes, photosynthesis, research efforts were dedicated to understanding local and transpiration, moving these measurements from the dynamics because ecosystem processes are highly variable in laboratory to the field. Despite these newly available field response to localized environmental changes. Orbiting satel- instruments, global observations of ecosystem and larger- lites provide an ideal vantage point for viewing dynamic eco- scale processes did not become available until the advent of systems on the land and in the ocean (Box 9.1). This chapter satellite observations because the field measurements were discusses how the remarkable technological advances of the generally restricted to short-term (seconds to minutes) leaf past decades have enabled scientists to compose routinely measurements. The capability of assessing plant productivity global maps of terrestrial and marine productivity, assess the from satellite radiance measurements (Box 9.2) opened an role of the ocean in the global carbon cycle, observe long- entirely new front in ecosystem research. Because small- term ecosystem trends and atmosphere-biosphere coupling, scale point measurements did not lend themselves well to and even study plant physiology from space. interpolating and creating global maps, synoptic satellite data For the first time, remote sensing made direct global provided the first direct globally distributed measurements observations of photosynthesis, plant growth, and ecosystem of terrestrial functioning. phenology possible, leading to the evolution of a global per- The NASA EOS program brought new capabilities for spective on ecology (Boxes 9.2 and 9.3). Charles Keelingâs monitoring terrestrial productivity, with near-daily global continuous measurements of atmospheric carbon dioxide coverage of a more capable well-calibrated Moderate Reso- (CO2) concentrations at Mauna Loa, beginning in 1957, lution Imaging Spectroradiometer (MODIS) that has allowed showed a seasonal signal in the atmospheric CO2 concen- development of new biophysical measurements with less tration due to the terrestrial biosphere being a source and reliance on simple empirical indices. One of the new prod- sink for carbon during the winter and summer, respectively. ucts is the direct global measurement at 1-km resolution of Subsequent work showed that atmospheric CO2 was steadily leaf area index (LAI)âan important structural property of 73
74 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS BOX 9.1 Ecosystems as Seen from Space Satellite-based studies of the land and ocean ecosystems rely primarily on imaging sensors measuring radiance in the visible and near infrared. These spectral bands were ideally suited to monitor plant biomass and primary produc- tion because the chlorophyll a pigment, found in all marine and terrestrial photosynthetic plants, reflects green light while absorbing in the blue and red spectral regions. Because plant leaves contain no molecules with high absorption in the near infrared, they are highly reflective in this region. Therefore, the âgreennessâ of terrestrial ecosystems can be mapped by employing the ratio of red to infrared bands. However, this ratio does not work for the ocean because water is such a strong absorber in the red and infrared that little or no radiation is reflected out of the ocean at those wavelengths. Instead, the ratio of blue to green bands, after correcting for the atmosphere, has been used to quantify the chlorophyll concentration in the ocean (Box 9.3). Remote sensing techniques for mapping and studying terrestrial and marine ecosystems have evolved along differ- ent paths because of different technological requirements. Compared to the ocean, the land is a bright surface whose features have distinct spectral signatures and generally sharp boundaries. The spatial scale of such features is on the order of tens of meters, thus requiring high spatial resolution, but the features generally change slowly over seasons or longer. In contrast, the ocean is a dark surface with subtle spectral variation that requires high radiometric sensitivity. Reflectance from the atmosphere dominates the signal received by a satellite over the ocean, and this signal must be estimated and removed before the ocean signal can be analyzed. Features in the ocean have spatial scales on the order of tens of kilometers, with fluid boundaries that change on timescales of hours to days. These differences have led to different sensor and mission requirements, but the goals remain similar. Both terrestrial and marine studies have sought to quantify primary productivity and the role of the biosphere in the global carbon cycle. BOX 9.2 Converting Radiance to Plant Productivity Jordan (1969) was the first to use a ratio of near-infrared and red radiation to estimate biomass and leaf area index (leaf area/ground surface area) in a forest understory. This study was quickly followed by application of near-infrared/ red ratios to estimate biomass in rangelands (e.g., Pearson and Miller 1972; Rouse et al. 1973, 1974; Maxwell 1976) and was extended by Carneggie et al. (1974) to the Earth Resources Technology Satellite (ERTSâ1) observations of seasonal growth, which showed that the seasonal peak in the near-infrared/red ratio coincided with maximum foliage production, thus effectively tracking the phenological cycle. Rouse et al. (1974) introduced a spectral index, a normalized ratio that reduced illumination differences and other extrinsic effects by dividing the difference of the two bands by their sum, a ratio adopted as the normalized difference vegetation index (NDVI). A landmark paper by Tucker (1979) established linear relationships between vegetation spectral indices (ratios of visible and near-infrared bands) to leaf area and biomass. Following this paper, vegetation indices rapidly became an established method for analysis of plant biophysical properties using laboratory, field, airborne, and Landsat data. Today, nearly 2,000 papers have been published using the NDVI, and nearly 6,000 have used some type of vegetation index to study vegetation. These early studies established that red and near-infrared satellite bands could track changes in plant growth and development. the plant canopy used to estimate functional process rates of nological patterns among six global terrestrial biome types. energy and mass exchange, specifically to calculate rates of LAI is defined as the one-sided leaf area per unit of ground photosynthesis, evapotranspiration, and respiration (Figure area and is produced by R.B. Myneni, Boston University. 9.1). For the first time this measurement provides a con- An algorithm is used to convert red and near-infrared band sistent observational basis to estimate and monitor global reflectances to global maps of LAI with modifications for the productivity. Time series of LAI allow comparison of phe- six biome types, taking into account the directional Sun and
ECOSYSTEMS AND THE CARBON CYCLE 75 a b FIGURE 9.1â February (top panel) and August (bottom panel) 2006 global 4-km monthly composites of leaf area index, computed from the Moderate Resolution Imaging Spectroradiometer (MODIS; Mod15, collection 4). SOURCE: R.B. Myneni, Boston University, http://diveg. bu.edu. 9-1 a,b view factors and measurement uncertainties. Prior to todayâs response to climate variability and climate change. This new satellites, this key biophysical variable was painstakingly observational perspective has led ecologists to see ecosystem evaluated at the scale of small field sites by dropping a pin processes in an integrated temporal and global context. or line through the canopy and counting the number of leaves that were contacted. With the development of red and near- Marine Primary Productivity infrared indices such as normalized difference vegetation index (NDVI) in the 1980s, it became possible to correlate Approximately half of all global primary production these ground measurements with index values, allowing the occurs in the ocean, almost entirely due to microscopic extension of direct measurements to larger regions. single-cell algae known as phytoplankton. In the presence Today, with MODIS, this observation has become more of ample sunlight and nutrients, phytoplankton reproduce precise by its extension to a biophysical measurement. Sat- rapidly and biomass can double in a day. As the cells grow ellite monitoring of the dynamics of Earthâs vegetation is and reproduce, carbon dioxide dissolved in the surface ocean essential to understanding global ecosystem functioning and is converted to organic matter, which is then consumed by
76 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS BOX 9.3 Global Marine Biomass from Ocean Color Remote Sensing The ability to derive global maps of chlorophyll a concentration (milligrams per cubic meter) in the upper ocean from ocean color sensors was a groundbreaking achievement for the oceanographic community (Figure 9.2). This biomass estimate can then be related to primary productivity and the marine carbon cycle. Although clouds prevent ocean color sensors to see the entire ocean surface on each orbital pass, a global picture of the distribution of photosynthetic plant biomass emerges from averaging data over several consecutive days or weeks. The first ocean color sensor was the Coastal Zone Color Scanner (CZCS), an experimental proof-of-concept mission operating on the Nimbus 7 satellite between 1978 and 1986. The CZCS demonstrated that it is possible to detect subtle changes in the color of the ocean and relate these to the concentration of chlorophyll a, the light-harvesting pigment found in all plants. In particular, chlorophyll a concentrations are quantified by empirical algorithms relating spectral band ratios (blue to green) to the concentration of chlorophyll in the ocean (Clark 1981, Gordon and Morel 1983, OâReilly et al. 2000). A major requirement is that the spectral radiance measurements made by the satellite be corrected to remove the effect of the atmosphere, which comprises more than 90 percent of the top-of-atmosphere signal. This was a major technological breakthrough after the launch of the CZCS (Gordon et al. 1980). Contrary to its name, the sensor was better at estimating biomass in the open ocean than in the coastal zone. Phytoplankton and dissolved organic mat- ter are the primary sources of optical variability in the open ocean (so-called Case 1 waters [Morel and Prieur 1977, Gordon and Morel 1983, Siegel et al. 2002]), whereas in coastal regions, mixtures of organic and inorganic materials affect the ocean color. The problem of differentiating and quantifying in- dividual constituent concentrations in the coastal ocean remains a challenge today. The ocean color technology pioneered by the CZCS has since been improved and incorporated into modern space instruments. The first modern global ocean color sensor was Japanâs Ocean Color and Temperature Sensor (OCTS) launched in August 1996 aboard the Advanced Earth Observing Satellite (ADEOS). The U.S. Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) followed in August 1997, shortly after the ADEOS expe- rienced structural damage after only 9 months in orbit. SeaWiFS is owned by Orbital Sciences Corporation, with a guarantee from NASA to buy data for the scientific research community. Ocean color data continue to be ac- quired by the Moderate Resolution Imaging Spectroradiometers (MODIS) aboard the Terra and Aqua satellites launched in 1999 and 2002, respec- tively, and by a number of other ocean FIGURE 9.2â Map of chlorophyll a concentration (milligrams per cubic meter) in color instruments operated by other the upper Atlantic Ocean derived from data obtained by the Sea-viewing Wide countries (Table 9.1). Field-of-view Sensor (SeaWiFS). SOURCE: SeaWiFS Project, NASA Goddard Space Flight Center, and GeoEye. continued
ECOSYSTEMS AND THE CARBON CYCLE 77 BOX 9.3 Continued TABLE 9.1â Past and Present Satellite Sensors with Ocean Color Capability Spatial Resolution No. of Sensor Satellite Country Dates (m) Bands Comments CZCS Nimbus-7 United States November 1978-July 1986 825 5 Proof-of-concept MOS IRS P3 Germany-India March 1996-March 2004 523 13 Requires ground station OCTS ADEOS-1 Japan August 1996-June 1997 700 8 + Four thermal IR bands for SST SeaWiFS SeaStar United States August 1997-present 1,100 8 Commercial data, free to researchers OCI ROcSAT-1 Taiwan December 1998-July 2004 800 6 Latitude coverage 35Â° N-35Â° S OCM OceanSat-1 (IRS P4)India May 1999-present 360 8 + Scanning microwave-SST MODIS Terra Aqua United States December 1999-May 2002 1,000 9 + 27 other bands for land, atmosphere, SST MERIS Envisat EU March 2002-present 250 LAC 15 LAC data require ground 1,000 GAC station NOTE: Nations such as Japan, Taiwan, and India have invested in ocean color as a valuable source of information for their fishing fleets (Laurs et al. 1984, Butler et al. 1988). This has also met with some success in the United States where it has been argued that the satellite information makes fishing more efficient, thus saving fuel and other limiting resources. CZCS = Coastal Zone Color Scanner; GAC = Global Area Coverage; LAC = Local Area Coverage; MERIS = Medium Resolution Imaging Spectrometer; MODIS = Moderate Resolution Imaging Spectroradiometer; MOS = Maritime Observation Satellite; OCI = Ocean Color Imager; OCTS = Ocean Color and Temperature Sensor; SeaWiFS = Sea-Viewing Wide Field-of-View Sensor; IRS = Indian Remote Sensing Satellite; ADEOS = Advanced Earth Observing Satellite; SST = Sea Surface Temperature. NPP (grams C m-2 year-2) FIGURE 9.3â Global annual NPP (in grams of carbon per square meter per year) for the biosphere, calculated from the integrated CASA- VGPM (Vertically Generalized Production Model) model. Input data for ocean color from CZCS sensor are averages from 1978 to 1983. The land vegetation index from the AVHRR sensors is the average from 1982 to 1990. SOURCE: Field et al. (1998). Reprinted with permission from AAAS, copyright 1998. 9-3
78 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS zooplankton, fish, and other animals in the âfood chain.â Color Scanner (CZCS) data and models of the subsurface Because of its rapid growth and many consumers, phyto- chlorophyll distribution and Photosynthesis-irradiance (P-I) plankton biomass or chlorophyll concentration varies on relationships defined for 57 biogeochemical provinces. short timescales, yet the extent of a âpatchâ of accumulated biomass is on the order of 10-100 km. Global Marine and Terrestrial Primary Satellites have allowed scientists to routinely estimate Production phytoplankton productivity on an annual basis for the first time, enabling them to detect a trend in decreasing phyto- Net primary productivity (NPP) is influenced by Âclimate plankton productivity associated with warming of the surface and biotic controls that interact with each other. Field et ocean at mid- to low latitudes. Because a phytoplankton al. (1995) predicted global terrestrial NPP on a monthly bloom and its associated productivity are such large-scale yet time step using the Carnegie-Ames-Stanford Assimilation short-lived phenomena, there is simply no way to survey large (CASA) model, incorporating a set of ecological principles enough areas of the ocean to capture their dynamics using and satellite and surface data. Several authors have used ships to map phytoplankton biomass and productivity. satellite data to estimate global net primary production, Prior to the introduction of satellite observations, esti- combining both terrestrial and oceanic models. Within a few mates of oceanic primary production depended on relatively years they used a linked ocean-terrestrial model that com- few labor-intensive ship-based incubations using the 14C bined an 8-year Advanced Very High Resolution ÂRadiometer technique that had become the standard method for measur- (AVHRR) record and a 6-year CZCS data record with a ing primary productivity in the ocean (Steeman-Nielsen and biogeochemistry model to estimate global land and ocean Jensen 1957). To estimate global annual oceanic production NPP (Field et al. 1998, FigureÂ 9.3). This study found that (gigatons of carbon per year), the mean integral productivity the contribution of land and ocean to NPP was nearly equal was first estimated for the different oceans and depth ranges but that there was striking variability in NPP at a local level. using relatively few measurements made in each domain. Based on the spatial variability in the satellite data, their These were then multiplied by the area of the ocean domain model predicted strong differential resource limitations for and 365 days per year to derive annual oceanic primary pro- terrestrial and ocean habitats. duction. Due to the vastness of the ocean and high spatial Behrenfeld et al. (2001) used the Sea-Viewing Wide and temporal variability, ship-based global mapping was Field-of-view Sensor (SeaWiFS) data to estimate terrestrial infrequently attempted and could not realistically capture and ocean primary production during the transition between the interannual variability. Even with the development of El NiÃ±o and La NiÃ±a conditions in 1997 to 1999. They found fluorescence-based estimates of marine primary productiv- that the ocean exhibited the greatest effect, particularly in ity, which could be obtained from instruments towed behind tropical regions where El NiÃ±o-ÂSouthern Oscillation (ENSO) ships, obtaining global coverage would still require years. It impacts on upwelling and nutrient availability were great- has long been recognized that ship-based sampling methods est. Terrestrial ecosystems did not exhibit a clear ENSO suffer from significant undersampling in both space and time response, although regional changes were substantial. These (McCarthy 1999). Consequently, the best quantitative global studies clearly demonstrate the invaluable contribution satel- estimates of both biomass and productivity are derived with lite observation of NPP make to the fundamental understand- the use of satellite observations that provide the necessary ing of climate change impacts on the biosphere. frequency of global coverage. To estimate primary productivity from satellite measure- The Ocean Carbon Cycle ments, it is assumed that the productivity is proportional to the phytoplankton biomass. Consequently, measuring bio- Satellite observations afford the only means of estimat- mass is the first critical step in estimating marine primary ing and monitoring the role of ocean biomass as a sink for productivity from space. Chlorophyll a, the ubiquitous carbon. In particular, the fundamental question of whether the light-harvesting pigment found in all green plants, has long biological carbon uptake is changing in response to climate been a standard measure of phytoplankton biomass (Box 9.3, change can only be addressed with satellite measurements. It Figure 9.2, Table 9.1). This is largely because chlorophyll requires not only ocean color measurements (phytoplankton can be measured rapidly and easily owing to its fluorescent biomass and productivity) but also coincident space-based and absorption properties. observations of the physical ocean environment (circulation Early estimates of oceanic primary productivity derived and mixing) and land-ocean exchanges through rivers and using satellite data provided a relative static picture in that tidal wetlands, as well as winds, tides, and solar energy input they represented average annual productivity (Platt and to the upper ocean. Observing linkages between the physical Sathyendranath 1988, Antoine et al. 1996, Behrenfeld and and chemical environment and the biology of the ocean is a Falkowski 1997, Field et al. 1998). One of the most thorough significant achievement of observations from space. Continu- estimates was that of Longhurst el al. (1995), who estimated ity of this record is critical. Understanding the consequences global ocean net primary production using Coastal Zone of the CO2 increase and its effect on terrestrial and marine
ECOSYSTEMS AND THE CARBON CYCLE 79 ecosystems will require global-scale long-term observations Long-term Ecosystem Record Reveals from carefully calibrated satelliteborne sensors. Atmosphere-Biosphere Coupling Early carbon cycle models that were used to investi- Although early studies established that red and near- gate sources and sinks of anthropogenic CO2 ignored the infrared satellite bands could track changes in plant growth effects of marine productivity, which was thought to be in and development (Box 9.1), the large number of Landsat equilibrium on annual timescales. Since marine productivity images (~5,000) required to assemble a global database, is not limited by carbon, it was reasoned that increases in combined with computational requirements and frequent CO2 would not affect oceanic productivity. More recently, cloud cover, have prevented analysis of complete global modelers have investigated how marine productivity might or time series of Landsat data sets. Launched in 1978, the be affected indirectly by climate change through its effect on Coastal Zone Color Scanner showed that ocean productivity oceanic and atmospheric circulation patterns. could be observed using visible and near-infrared bands; Because phytoplankton life cycles are orders of mag- however, CZCS measurements were saturated over land and nitude shorter (days versus years or decades) than those thus unusable. of terrestrial plants, phytoplankton may respond to climate The Advanced Very High Resolution Radiometer on influences on ocean circulation, mixing, and the supply the National Oceanic and Atmospheric Administrationâs of nutrients and light much more quickly than plants in (NOAA) polar-orbiting weather satellites has obtained a terrestrial ecosystems. Given that oceanic primary produc- continuous record of daily global observations since 1978, tivity is estimated to be roughly half of all global primary acquiring both red and near-infrared bands. Because AVHRR p Â roductivity, the oceanic component of the carbon cycle will was not designed for observing the terrestrial biosphere and respond more quickly to climate changes. the 1- to 8-km scale of AVHRR pixels was significantly For example, there are vast areas of the Pacific and larger than theoretical understanding of ecosystem processes, Southern Oceans, where phytoplankton productivity might scientists were initially skeptical about whether biospheric be limited by iron (Martin et al. 1994). In contrast to the patterns and trends could be observed. However, scientists other limiting nutrients, which are supplied primarily by the have managed to overcome technical problems such as deep ocean, atmospheric dust deposition is one of the main maintaining calibrations, screening clouds, and adjusting sources of iron to the open ocean. Paleorecords indicate that for different observational angles. Thanks to the pioneering the Southern Ocean responded with increased productivity efforts of Compton Tucker, the daily AVHRR data set now during colder periods when iron atmospheric deposition was spans more than 25 years and is the longest continuous global enhanced due to the expansion of arid regions. This led to the record available of terrestrial productivity, phenology, and notion that these areas in the Pacific and Southern Oceans ecosystem change for monitoring biospheric responses to could be stimulated to draw down large amounts of atmo- climate change and variability. Although AVHRR was not spheric CO2 if they were provided with iron. Several experi- designed for climate monitoring, continuing improvements ments conducted in the late 1990s and early 2000s proved in calibration and reanalysis have produced a consistent conclusively that iron does limit production in these regions record for monitoring and assessing past and future bio- (Coale et al. 2004). Iron is supplied to the open ocean by spheric responses resulting from climate change and vari- atmospheric transport (dust deposition), by lateral advection ability and anthropogenic activities. of waters from the continental margins, and by upwelling of Initial studies using AVHRR followed seasonal and deep iron-rich waters. Long-term monitoring of the ocean annual trends in ecosystem production and vegetation phe- phytoplankton will reveal whether climate change will affect nology at regional and continental scales (Tucker et al. 1985, these iron supplies potentially fertilizing the Southern Ocean Townshend et al. 1985) and at the global scale (Justice et al. or the Pacific. 1985). In the early 1990s some key papers introduced the use With 10 years of continuous ocean color data (since of remote sensing data to ecology (Roughgarden et al. 1991, 1997), we now have the ability to observe year-to-year vari- Ustin et al. 1991) and stressed the need for ecologists to focus ability in global oceanic primary production and begin to on global ecological problems (Mooney 1991). These ideas assess longer-term trends in ocean carbon uptake. Behrenfeld led to the resurgence in ecosystem research and modeling et al. (2006) describe a steady climate-driven decrease in of biogeochemical processes and significant advances in oceanic NPP related to the warming of permanently stratified understanding the Earth as a system. ocean waters at mid- to low latitudes over the past 8 years. By the mid-1990s, global ecosystem and biogeoÂchemical This period of decreasing NPP followed the rise in NPP models used satellite data to establish variable vegetation between the El NiÃ±o and La NiÃ±a phases. Satellite observa- composition and abundance (e.g., Biome BioGeochemical tions afford the only means of estimating and monitoring the Cycles [BGC], Running and Hunt 1993; CASA, Potter et role of the ocean biomass as a sink for carbon. al. 1993). The concept of resource limitations as the control- ling mechanism determining NPP was established in the late 1980s (Chapin et al. 1987). This placed a premium on direct
80 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS satellite observations of vegetation conditions to provide and Figure 9.5), and monitoring the state of the biosphere more realistic estimates of NPP. Previous estimates used uni- (Anyamba et al. 2001) and other ecosystem phenomena. form rates of NPP for each land-cover type and assumed that Long-term records of NDVI have revealed its increase in NPP is proportional to reflected net shortwave radiation. response to a warming climate during the 1980s and early The relationship between vegetation indices and the 1990s, but this trend has leveled off most recently (Angert physiological processes of photosynthesis and absorbed et al. 2005). photosynthetic radiation (APAR) were formalized in theo- retical analyses by Piers Sellers (1986). These developments Studying Plant Physiology from Space led to a seminal paper by Tucker et al. (1986) in which it was shown that changes in the planetary NDVI (greenness) To estimate actual NPP in the presence of environmen- were strongly correlated with daily dynamics of terrestrial tal stressors, researchers developed methods to remotely IPAR (intercepted photosynthetically active radiation) and estimate regulatory plant biochemicals. The first advance atmospheric CO2 concentrations. There is a strong negative was the development of the âphotochemical reflectance correlation between NDVI and atmospheric CO2 such that indexâ (PRI) by John Gamon and colleagues (Gamon et al. NDVI is high when CO2 concentrations are low and low 1992) to better predict radiation use efficiency. This index when CO2 concentrations are high (Figure 9.4). This tem- has had extensive use for noninvasive studies of leaf photo- poral pattern in ecosystem photosynthesis and respiration synthesis by plant physiologists, although at the image level demonstrates the dynamic coupling between the biosphere it appears more related to carotenoid content. The PRI has and the atmosphere. led to a range of other studies to quantify plant pigments In the past decade, NDVI data from AVHRR have and develop methods for assessing them. These advances become a critical component in monitoring climate change follow increasingly specific knowledge of spectroscopy of (Fung et al. 1987, Sellers et al. 1994, Angert et al. 2005), plant properties and how this information can be retrieved assessing changing length and timing of the growing season from satellite sensors. (e.g., Justice et al. 1985, Myneni et al. 1997, 1998; Box 9.4, A radiative transfer model, developed by Jacquemoud FIGURE 9.4â Weighted NDVI data plotted against time and latitude zone. Note the highly seasonal effects in the northern latitudes, the influ- ence of deserts in the 20Â°-30Â° N latitude zone, the generally constant response in equatorial areas, and the influence of the low proportion of land area south of 30Â° S. SOURCE: Reprinted with permission from J.E. Pinzon (SSAI-NASA/GSFC) and C.J. Tucker (NASA/GSFC).
ECOSYSTEMS AND THE CARBON CYCLE 81 BOX 9.4 Increasing Growing Season Myneni et al. (1997) published a groundbreaking paper using daily satellite data over a 9-year period to show in- creases in the length of the Âgrowing season in the boreal region. They used a time series of NDVI, a measure of the photosynthetic activity of vegetation canopies, derived from the daily AVHRR satellite data, and showed an increase in length of the growing season in the boreal region (north of 45Âº) of 12 days (8 days in spring and 4 days in autumn) from 1981 to 1991. They demonstrated that this extension of the growing season and enhanced amplitude of NDVI over the summer were likely correlated with warmer spring and autumn temperatures over the region. This result partially corroborated an estimated 7-day extension of the growing season that was inferred from atmospheric CO2 measurements. Uniquely, their analysis detected significant spatial variation in the distribution of enhanced NDVI, with western and eastern Canada and southern and central Alaska having large increases in contrast with little change in other areas, such as central Canada and Siberia. Monitoring the spatially variable increase in biospheric activity over the circumpolar region was only possible because of the availability of polar-orbiting satellites. Furthermore, scatterometer data from satellites provide further evidence that the growing season has lengthened in the Arctic region over the past 20 years. Figure 9.5 shows the progression of the spring 2000 thaw in Alaska. Similar measurements made since 1988 show that the thaw in the Arctic has been advancing by almost 1 day a year. These observations could not have been made without satellites since melting occurs rapidly across the Arctic during the period of melt and the timing varies between years, depending on weather conditions. February 28 April 22 May 30 frozen thawed FIGURE 9.5â Progression of the spring thaw in Alaska during the year 2000 with snow and ice (blue), ice and slush with bare ground (yellow), and water and bare ground (red). A series of SeaWinds scatterometer measurements on the QuickScat Âsatellite, which are sensitive to water in frozen and liquid states, were used to make these images. SOURCE: fig 9-5 Kimball et al. (2006). Reprinted with permission from the American Meteorological Society, the American Geophysical Union, and the Association of American Geographers, copyright 2006. and Baret (1990), has rigorously demonstrated the potential nitrogen content (Kokaly and Clark 1999). Many of the more to retrieve several plant biochemicals from reflectance and recent advances are based on new imaging spectroscopy transmittance data and is in wide use today. As summarized technology using NASAâs Airborne Visible/Infrared Imaging by Ustin et al. (2004), the list of plant biochemicals has Spectrometer (AVIRIS), an aircraft instrument operated by become longer with studies of chlorophyll fluorescence the Jet Propulsion Laboratory since 1987. NASA has flown (Zarco-Tejeda et al. 2000a, b), canopy water content (Gao one hyperspectral imager in space, the Earth Observing-1 and Goetz 1995, Zarco-Tejeda et al. 2003), and canopy Hyperion, which was launched in 2000 as an engineering test
82 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS bed yet has continued to operate to today. This technology increased signal to noise ratios, with simultaneous higher has significant promise for continued advances in detecting spatial and spectral resolution, radiometrically stable instru- biochemical properties of interest but also for using the high ments, accurate geolocation of images due to advances in dimensionality of the data to improve land-cover and land- satellite pointing control and Global Positioning System use classifications. Several recent studies have used NASAâs (GPS), and development of atmospheric radiative transfer AVIRIS and other hyperspectral imagers to map invasive models allowing retrieval of accurate reflectance data. weeds with high specificity (Figures 9.6 and 9.7; see also Computer advances have allowed more complex analytical Box 9.5, Williams and Hunt 2002, Underwood et al. 2003, methods to be developed that better match the spatial and Asner and Vitousek 2005). spectral patterns in the data. The extensive research funded In a span of slightly more than 25 years, NASA instru- by NASA through the Earth Observing System program and ments and the research supported by the agency have evolved the scientific advances in understanding our home planet from primitive correlative studies to physically based accu- over the past two decades represent a major achievement of rate analyses. Understanding has advanced rapidly with the space program. the synergistic advent of new sensor capabilities such as FIGURE 9.6â A map of invasive species in the Hawaiian rainforest, measured using NASAâs AVIRIS data and impacts of invasive species and plant functional types on biogeochemical cycles. SOURCE: Modified from Asner and Vitousek (2005).
ECOSYSTEMS AND THE CARBON CYCLE 83 BOX 9.5 Detecting Invasive Plant Species All global ecosystems, with the possible exception of Antarctica, are impacted by invasive species that are substan- tially changing their functional and structural integrity. Invasion of natural ecosystems represents a serious threat to global biodiversity. Factors attributed to the spread of these species include climate change, land use, land conversion, resource extraction, and habitat fragmentation, combined with international transport. Substantial economic costs are associated with these changes, from loss of agricultural production and increased wildfire frequency to loss of recre- ational potential. Costs in the United States alone are estimated to exceed $120 billion per year (Pimentel et al. 2005). Recent advances in imaging spectroscopy, a technique to measure a detailed spectrum for all pixels in the image have allowed mapping of individual species and plant communities based on their spectral characteristics. Underwood et al. (2003) used this data to map invasive species in native shrublands along the central coast of California at Vanden- berg Air Force Base. Figure 9.7 shows the distribution of invasive species and native plant communities at 3-m pixel resolution for part of the base along the Pacific Coast shoreline. This information is being used by land managers to improve efficiencies in eradication and containment programs. Data of the quality required for mapping individual plant species must currently be acquired by airborne hyperspectral imagers. NASAâs suborbital sciences program has led to the development of this cutting-edge technology and has supported the research required to use it effectively, as shown in the figure. Jubata invaded chaparral Intact chaparral Intact scrub Iceplant invaded scrub chaparral Iceplant invaded chaparral Blue gum Masked Road Coastline FIGURE 9.7â Distribution of three invasive speciesâiceplant, jubata grass, and blue gumâin two native shrub eco- systemsâcoastal sage scrub and Burton Mesa chaparralâon the central coast of California. The map was produced from a mosaic of flightlines acquired from airborne NASA AVIRIS data, a 224-band imaging spectrometer measuring from the visible through the solar infrared (400-2,500 nm) and measured at a nominal 3-m pixel resolution. SOURCE: Underwood et al. (2006). Reprinted with kind permission of Springer Science and Business Media, copyright 2006. 9-7