National Scale Forest Information Extraction from Coarse Resolution Satellite Data, Part 1

Forests provide essential economic and ecological services, and their essential role in the planetary system is being increasingly recognized. In parallel, the demand for timely and accurate information on the status and function of the forest biome, for a variety of purposes, is also increasing. While traditionally forest information was gathered in situ or through air photography, the role of satellite remote sensing is becoming central because of the need to match the spatial and temporal scales of observations to those of the key forest processes. In this Chapter, we briefly review (i) data processing issues underpinning the use of ‘coarse resolution’ optical satellite data for terrestrial studies, and (ii) an application to land cover mapping at the national level. An accompanying Chapter (13) describes the use of the processed data sets to derive information on biophysical parameters, land cover change, and carbon uptake. The discussion draws heavily on our work in Canada but the findings are applicable to similar geographic and ecoclimatic conditions elsewhere.

[1]  Aisheng Wu,et al.  Effects of land cover type and greenness on advanced very high resolution radiometer bidirectional reflectances : analysis and removal , 1995 .

[2]  G. Campbell,et al.  Simple equation to approximate the bidirectional reflectance from vegetative canopies and bare soil surfaces. , 1985, Applied optics.

[3]  R. Latifovic,et al.  Land cover from multiple thematic mapper scenes using a new enhancement-classification methodology , 1999 .

[4]  C. Rao,et al.  Post-launch calibration of the visible and near-infrared channels of the Advanced Very High Resolution Radiometer on the NOAA-14 spacecraft , 1996 .

[5]  C. Justice,et al.  A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data , 1994 .

[6]  J. Cihlar,et al.  Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors , 2002 .

[7]  Laurence S. Rothman,et al.  Reprint of: The HITRAN molecular spectroscopic database and HAWKS (HITRAN Atmospheric Workstation): 1996 edition , 1998 .

[8]  Alexander P. Trishchenko,et al.  A method for the correction of AVHRR onboard IR calibration in the event of short-term radiative contamination , 2001 .

[9]  J. Chen,et al.  GeoComp-n, an advanced system for the processing of coarse and medium resolution satellite data. Part 2: Biophysical products for Northern ecosystems , 2002 .

[10]  J. Muller,et al.  Sampling the surface bidirectional reflectance distribution function (BRDF): 1. evaluation of current and future satellite sensors , 1994 .

[11]  C. Justice,et al.  The generation of global fields of terrestrial biophysical parameters from the NDVI , 1994 .

[12]  Alan R. Gillespie,et al.  Structural stage in Pacific Northwest forests estimated using simple mixing models of multispectral images , 2002 .

[13]  Jing Chen,et al.  A comparison of BRDF models for the normalization of satellite optical data to a standard Sun-target-sensor geometry , 2003, IEEE Trans. Geosci. Remote. Sens..

[14]  Jing Chen,et al.  Impact of variable atmospheric water vapor content on AVHRR data corrections over land , 2001, IEEE Trans. Geosci. Remote. Sens..

[15]  Qinghan Xiao,et al.  Land Cover of the BOREAS Region from AVHRR and Landsat data , 1997 .

[16]  Frédéric Baret,et al.  Developments in the 'validation' of satellite sensor products for the study of the land surface , 2000 .

[17]  Josef Cihlar,et al.  On the Validation of Satellite-Derived Products for Land Applications , 1997 .

[18]  Changyong Cao,et al.  Solar contamination effects on the infrared channels of the advanced very high resolution radiometer (AVHRR) , 2001 .

[19]  Kurtis J. Thome,et al.  A generalized approach to the vicarious calibration of multiple Earth observation sensors using hyperspectral data , 2001 .

[20]  H. S. Chen Remote Sensing Calibration Systems: An Introduction , 1997 .

[21]  Zhanqing Li,et al.  The bidirectional effects of AVHRR measurements over boreal regions , 1996, IEEE Trans. Geosci. Remote. Sens..

[22]  Thomas R. Loveland,et al.  The IGBP-DIS global 1 km land cover data set , 1997 .

[23]  Sylvain G. Leblanc,et al.  A four-scale bidirectional reflectance model based on canopy architecture , 1997, IEEE Trans. Geosci. Remote. Sens..

[24]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[25]  E. Vermote,et al.  A Method to Retrieve the Reflectivity Signature at 3.75 μm from AVHRR Data , 1998 .

[26]  V. Salomonson,et al.  MODIS: advanced facility instrument for studies of the Earth as a system , 1989 .

[27]  Yoram J. Kaufman,et al.  Atmospheric correction against algorithm for NOAA-AVHRR products: theory and application , 1992, IEEE Trans. Geosci. Remote. Sens..

[28]  Qinghan Xiao,et al.  Land cover classification with AVHRR multichannel composites in northern environments , 1996 .

[29]  Rasim Latifovic,et al.  Testing Near-Real Time Detection of Contaminated Pixels in AVHRR Composites , 1999 .

[30]  J. C. Price,et al.  Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer , 1984 .

[31]  Alexei I. Lyapustin Three-dimensional effects in the remote sensing of surface albedo , 2001, IEEE Trans. Geosci. Remote. Sens..

[32]  Laurence S. Rothman,et al.  The HITRAN molecular spectroscopic database and HAWKS (HITRAN atmospheric workstation) , 1998, Defense, Security, and Sensing.

[33]  J. Chen,et al.  Seasonal AVHRR multichannel data sets and products for studies of surface‐atmosphere interactions , 1997 .

[34]  William B. Rossow,et al.  Measuring cloud properties from space: a review , 1989 .

[35]  Thomas M. Smith,et al.  Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation , 1994 .

[36]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[37]  G. Fedosejevs,et al.  GeoComp-n, an advanced system for generating products from coarse and medium resolution optical satellite data. Part 1: System characterization , 2002 .

[38]  B. N. Holben,et al.  Towards operational radiometric calibration of NOAA AVHRR imagery in the visible and near-infrared channels , 1994 .

[39]  Alexander P. Trishchenko,et al.  Atmospheric Correction of Satellite Signal in Solar Domain: Impact of Improved Molecular Spectroscopy , 2002 .

[40]  J. Cihlar,et al.  Validation of the Geocoding and Compositing System (GEOCOMP) using contextual analysis for AVHRR images , 1997 .

[41]  Philip N. Slater,et al.  Calibration of Space-Multispectral Imaging Sensors , 1999 .

[42]  K. Carder,et al.  Monte Carlo simulation of the atmospheric point-spread function with an application to correction for the adjacency effect. , 1995, Applied optics.

[43]  J. Chen,et al.  A hotspot function in a simple bidirectional reflectance model for satellite applications , 1997 .

[44]  Lorraine Remer,et al.  Detection of forests using mid-IR reflectance: an application for aerosol studies , 1994, IEEE Trans. Geosci. Remote. Sens..

[45]  A. P. Trishchenko,et al.  Removing Unwanted Fluctuations in the AVHRR Thermal Calibration Data Using Robust Techniques , 2002 .

[46]  Alan H. Strahler,et al.  Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing , 1992, IEEE Trans. Geosci. Remote. Sens..

[47]  J. Roujean,et al.  A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .

[48]  Jing M. Chen,et al.  Systematic corrections of AVHRR image composites for temporal studies , 2004 .

[49]  A. Karnieli,et al.  Progress in the remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data , 1999 .

[50]  Yong Du,et al.  Land cover dependence in the detection of contaminated pixels in satellite optical data , 2001, IEEE Trans. Geosci. Remote. Sens..

[51]  Rasim Latifovic,et al.  Thematic mapper (TM) based accuracy assessment of a land cover product for Canada derived from SPOT VEGETATION (VGT) data , 2003 .

[52]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

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

[54]  W. Rossow,et al.  Advances in understanding clouds from ISCCP , 1999 .

[55]  A. Strahler,et al.  On the derivation of kernels for kernel‐driven models of bidirectional reflectance , 1995 .

[56]  G. Dedieu,et al.  SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum , 1994 .

[57]  P. M. Teillet,et al.  Forward piecewise linear calibration model for quasi-real time processing of AVHRR data , 1995 .

[58]  J. Coakley,et al.  Updated calibration coefficients for NOAA-14 AVHRR Channels 1 and 2 , 2001 .

[59]  C. Justice,et al.  Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation , 1997 .

[60]  J. Cihlar Identification of contaminated pixels in AVHRR composite images for studies of land biosphere , 1996 .

[61]  Anthony J. Ratkowski,et al.  MODTRAN4: radiative transfer modeling for remote sensing , 1999, Remote Sensing.

[62]  Lawrence P. Giver,et al.  Visible and near-infrared H216O line intensity corrections for HITRAN-96 , 2000 .

[63]  Kuo-Nan Liou,et al.  Radiation and Cloud Processes in the Atmosphere: Theory, Observation and Modeling , 1992 .

[64]  J. Cihlar,et al.  Multitemporal, multichannel AVHRR data sets for land biosphere studies—Artifacts and corrections , 1997 .

[65]  S. Leblanc,et al.  Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements , 2002 .

[66]  N. Loeb In-flight calibration of NOAA AVHRR visible and near-IR bands over Greenland and Antarctica , 1997 .

[67]  Rasim Latifovic,et al.  Selecting Representative High Resolution Sample Images for Land Cover Studies. Part 1: Methodology , 2000 .