The bio‐geophysical approach to remote sensing of vegetation in coupled human‐environment systems – societal benefits and global context

Hyperspectral remote sensing has promised a new era in quantitative measurement of key properties of terrestrial systems. The high information content, mechanistic relationships between reflectance spectra and canopy, leaf and molecular properties, and combination of computing power, algorithm maturity and highly quantitative methodology provides the basis for delivery of key information into new international research and observation frameworks seeking to provide societal benefits. This paper describes current capacity of global biophysical remote sensing and defines products that could be delivered by a new sensor. New products could be particularly useful in description of ecosystem services.

[1]  R. Jackson,et al.  Suitability of spectral indices for evaluating vegetation characteristics on arid rangelands , 1987 .

[2]  A. Goetz,et al.  Column atmospheric water vapor and vegetation liquid water retrievals from Airborne Imaging Spectrometer data , 1990 .

[3]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[4]  Alfredo Huete,et al.  Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices , 1996 .

[5]  S. Ustin,et al.  Estimating leaf biochemistry using the PROSPECT leaf optical properties model , 1996 .

[6]  B. D. Campbell,et al.  25 – Interspecific Variation in the Growth Response of Plants to Elevated CO2: A Search for Functional Types , 1996 .

[7]  Robert O. Green,et al.  Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS , 1997 .

[8]  Carol A. Wessman,et al.  DETECTING FIRE AND GRAZING PATTERNS IN TALLGRASS PRAIRIE USING SPECTRAL MIXTURE ANALYSIS , 1997 .

[9]  Mary E. Martin,et al.  HIGH SPECTRAL RESOLUTION REMOTE SENSING OF FOREST CANOPY LIGNIN, NITROGEN, AND ECOSYSTEM PROCESSES , 1997 .

[10]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[11]  J. Gamon,et al.  The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels , 1997, Oecologia.

[12]  S. Running,et al.  Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active , 1998 .

[13]  B. Holben,et al.  Smoke, Clouds, and Radiation-Brazil (SCAR-B) Experiment , 1998 .

[14]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[15]  Nicholas C. Coops,et al.  Assessing forest productivity in Australia and New Zealand using a physiologically-based model driven with averaged monthly weather data and satellite-derived estimates of canopy photosynthetic capacity , 1998 .

[16]  Claudia M. Castaneda,et al.  Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods , 1998 .

[17]  R. Clark,et al.  Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .

[18]  George Alan Blackburn,et al.  Relationships between Spectral Reflectance and Pigment Concentrations in Stacks of Deciduous Broadleaves , 1999 .

[19]  Philip J. Howarth,et al.  Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems , 1999 .

[20]  D. Roberts,et al.  Deriving Water Content of Chaparral Vegetation from AVIRIS Data , 2000 .

[21]  S. Running,et al.  Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System , 2000 .

[22]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[23]  R. B. Jackson,et al.  Global patterns of root turnover for terrestrial ecosystems , 2000 .

[24]  Y. H. Kerr,et al.  Monitoring vegetation cover across semi-arid regions: Comparison of remote observations from various scales , 2000 .

[25]  D. Lobell,et al.  A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .

[26]  Limin Yang,et al.  COMPLETION OF THE 1990S NATIONAL LAND COVER DATA SET FOR THE CONTERMINOUS UNITED STATES FROM LANDSAT THEMATIC MAPPER DATA AND ANCILLARY DATA SOURCES , 2001 .

[27]  John R. Miller,et al.  Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..

[28]  J. N. Sweet,et al.  An evaluation of atmospheric correction techniques using the spectral similarity scale , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[29]  J. Dungan,et al.  Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies , 2001 .

[30]  Xiaoliang Wu,et al.  A BRDF-corrected Landsat 7 mosaic of the Australian continent , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[31]  D. Roy,et al.  Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach , 2002 .

[32]  S. Ollinger,et al.  Regional variation in foliar chemistry and n cycling among forests of diverse history and composition , 2002 .

[33]  J. Townshend,et al.  Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data , 2002 .

[34]  Jane R. Foster,et al.  Comparison of EO-1 Hyperion to AVIRIS for mapping forest composition in the Appalachian Mountains, USA , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[35]  D. Roy,et al.  The MODIS fire products , 2002 .

[36]  J. Townshend,et al.  Development of a MODIS tree cover validation data set for Western Province, Zambia , 2002 .

[37]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[38]  Emilio Chuvieco,et al.  Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains , 2002 .

[39]  Gregory P. Asner,et al.  Forest leaf area density profiles from the quantitative fusion of radar and hyperspectral data , 2002 .

[40]  P. Angelstam,et al.  Long-term differences in the dynamics within a natural forest landscape—consequences for management , 2002 .

[41]  William J. Foley,et al.  Spectrometric prediction of secondary metabolites and nitrogen in fresh Eucalyptus foliage: towards remote sensing of the nutritional quality of foliage for leaf-eating marsupials , 2002 .

[42]  S. Ollinger,et al.  DIRECT ESTIMATION OF ABOVEGROUND FOREST PRODUCTIVITY THROUGH HYPERSPECTRAL REMOTE SENSING OF CANOPY NITROGEN , 2002 .

[43]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[44]  A. Gitelson,et al.  Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy¶ , 2002, Photochemistry and photobiology.

[45]  N. Kamata,et al.  Outbreaks of forest defoliating insects in Japan, 1950–2000 , 2002, Bulletin of Entomological Research.

[46]  B. Law,et al.  Structure‐based forest biomass from fusion of radar and hyperspectral observations , 2003 .

[47]  A. Gitelson,et al.  Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit , 2003 .

[48]  Marie-Louise Smith,et al.  Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor , 2003, IEEE Trans. Geosci. Remote. Sens..

[49]  Susan L. Ustin,et al.  Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible/Infrared Imaging Spectrometer , 2003, IEEE Trans. Geosci. Remote. Sens..

[50]  G. Asner,et al.  Net changes in regional woody vegetation cover and carbon storage in Texas Drylands, 1937–1999 , 2003 .

[51]  M. Friedl,et al.  Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors , 2003 .

[52]  S. Ustin,et al.  Water content estimation in vegetation with MODIS reflectance data and model inversion methods , 2003 .

[53]  G. Asner,et al.  Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[54]  Christopher B. Field,et al.  Assessing photosynthetic downregulation in sunflower stands with an optically-based model , 2004, Photosynthesis Research.

[55]  Christopher B. Field,et al.  Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies , 1990, Oecologia.

[56]  Jose L. Silvan-Cardenas,et al.  Monitoring short-term changes in biophysical variables of forests with Landsat ETM , 2004, SPIE Remote Sensing.

[57]  D. Lobell,et al.  Cropland distributions from temporal unmixing of MODIS data , 2004 .

[58]  D. Roberts,et al.  Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales , 2004, Ecosystems.

[59]  D. Roberts,et al.  Using Imaging Spectroscopy to Study Ecosystem Processes and Properties , 2004 .

[60]  C. Field,et al.  Allocating leaf nitrogen for the maximization of carbon gain: Leaf age as a control on the allocation program , 1983, Oecologia.

[61]  Limin Yang,et al.  Development of a 2001 National land-cover database for the United States , 2004 .

[62]  Kyung-Soo Han,et al.  A land cover classification product over France at 1 km resolution using SPOT4/VEGETATION data , 2004 .

[63]  Hans Peter Schmid,et al.  Potential of MODIS ocean bands for estimating CO2 flux from terrestrial vegetation: A novel approach , 2004 .

[64]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[65]  J. Aber,et al.  A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems , 1992, Oecologia.

[66]  F. J. Barnes,et al.  Interrelationships between plant functional types and soil moisture heterogeneity for semiarid landscapes within the grassland/forest continuum: a unified conceptual model , 2004, Landscape Ecology.

[67]  S. I. Pogosyan,et al.  Application of Reflectance Spectroscopy for Analysis of Higher Plant Pigments , 2003, Russian Journal of Plant Physiology.

[68]  T. A. Black,et al.  A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest , 2005 .

[69]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[70]  R. D. Ramsey,et al.  Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south‐western USA , 2005 .

[71]  R. Green,et al.  The Flora Mission for Ecosystem Composition, Disturbance and Productivity , 2005 .

[72]  S. Ollinger,et al.  Net Primary Production and Canopy Nitrogen in a Temperate Forest Landscape: An Analysis Using Imaging Spectroscopy, Modeling and Field Data , 2005, Ecosystems.

[73]  D. Roy,et al.  Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data , 2005 .

[74]  Chandra Giri,et al.  A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .

[75]  Tim R. McVicar,et al.  Assessment of the MODIS LAI product for Australian ecosystems , 2006 .

[76]  David P. Roy,et al.  Remote sensing of fire severity: assessing the performance of the normalized burn ratio , 2006, IEEE Geoscience and Remote Sensing Letters.

[77]  A. Huete,et al.  Amazon rainforests green‐up with sunlight in dry season , 2006 .