Applications in remote sensing—anthropogenic activities

Abstract This chapter aims at presenting thematic applications of hyperspectral imagery related to human activities. This chapter is a companion of Chapter 3.1 in which four applications related to monitoring of natural resources or inspection of scenes were presented.

[1]  Rodolphe Marion,et al.  Estimation of aerosol type from airborne hyperspectral data: a new technique designed for industrial plume characterization , 2012, Remote Sensing.

[2]  L. K. Sørensen,et al.  Determination of Clay and Other Soil Properties by Near Infrared Spectroscopy , 2005 .

[3]  L. Hoffmann,et al.  Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy , 2010 .

[4]  Stefano Pignatti,et al.  A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing , 2013 .

[5]  Michael Vohland,et al.  Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms , 2017, Remote. Sens..

[6]  M. Herold,et al.  Spectral characteristics of asphalt road aging and deterioration: implications for remote-sensing applications. , 2005, Applied optics.

[7]  Fang Huang,et al.  [Retrieval model for subtle variation of contamination stressed maize chlorophyll using hyperspectral data]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.

[8]  H. Ramon,et al.  Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy , 2010 .

[9]  Rodolphe Marion,et al.  Anthropogenic aerosol emissions mapping and characterization by imaging spectroscopy – application to a metallurgical industry and a petrochemical complex , 2018, International Journal of Remote Sensing.

[10]  J. L. Gabriel,et al.  Airborne and ground level sensors for monitoring nitrogen status in a maize crop , 2017 .

[11]  Bo-Hui Tang,et al.  Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Jack S. Margolis,et al.  Mapping methane emissions from a marine geological seep source using imaging spectrometry , 2010 .

[13]  K. Shepherd,et al.  Development of Reflectance Spectral Libraries for Characterization of Soil Properties , 2002 .

[14]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[15]  Shiv O. Prasher,et al.  ESTIMATION OF CROP BIOPHYSICAL PARAMETERS THROUGH AIRBORNE AND FIELD HYPERSPECTRAL REMOTE SENSING , 2003 .

[16]  Viacheslav I. Adamchuk,et al.  A global spectral library to characterize the world’s soil , 2016 .

[17]  Ira Leifer,et al.  Detection of marine methane emissions with AVIRIS band ratios , 2011 .

[18]  Jan F. Humplík,et al.  Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses – a review , 2015, Plant Methods.

[19]  Sabine Chabrillat,et al.  Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database , 2018, Remote. Sens..

[20]  Dar A. Roberts,et al.  Point source emissions mapping using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 2012, Defense + Commercial Sensing.

[21]  Shiv O. Prasher,et al.  Development of field-scale soil organic matter content estimation models in eastern Canada using airborne hyperspectral imagery , 2005 .

[22]  James B. Reeves,et al.  Near Infrared Reflectance Spectroscopy for the Determination of Biological Activity in Agricultural Soils , 2000 .

[23]  Moon S. Kim,et al.  Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves , 1992 .

[24]  C. Daughtry,et al.  Remote- and Ground-Based Sensor Techniques to Map Soil Properties , 2003 .

[25]  Guofeng Wu,et al.  Visible and near-infrared reflectance spectroscopy-an alternative for monitoring soil contamination by heavy metals. , 2014, Journal of hazardous materials.

[26]  C. Hurburgh,et al.  Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties , 2001 .

[27]  Shunlin Liang,et al.  Earth system science related imaging spectroscopy — an assessment , 2009 .

[28]  Suresh Kumar,et al.  Hyperspectral remote sensing data derived spectral indices in characterizing salt-affected soils: a case study of Indo-Gangetic plains of India , 2015, Environmental Earth Sciences.

[29]  Yoram J. Kaufman,et al.  Remote sensing of water vapor in the near IR from EOS/MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..

[30]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[31]  Shattri Mansor,et al.  Hyperspectral Remote Sensing of Urban Areas: An Overview of Techniques and Applications , 2012 .

[32]  Sabine Chabrillat,et al.  Evaluating the detection limit of organic matter using point and imaging spectroscopy , 2018, Geoderma.

[33]  D. Roberts,et al.  Remote sensing of soils in the Santa Monica mountains : II. Hierarchical foreground and background analysis , 1999 .

[34]  Loredana Pompilio,et al.  Exponential Gaussian approach for spectral modelling: The EGO algorithm II. Band asymmetry , 2010 .

[35]  P. Gong,et al.  The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. , 1990 .

[36]  G. Taylor,et al.  Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization , 2002 .

[37]  A. Thorpe.  Mapping methane concentrations from a controlled release experiment using the next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRISng) , 2014 .

[38]  Xavier Briottet,et al.  Object-based fusion for urban tree species classification from hyperspectral, panchromatic and nDSM data , 2019, International Journal of Remote Sensing.

[39]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[40]  W. Parton,et al.  Soil Security: Solving the Global Soil Crisis , 2013 .

[41]  J. Araus,et al.  Field high-throughput phenotyping: the new crop breeding frontier. , 2014, Trends in plant science.

[42]  C. Greensill,et al.  Comparison of two NIR systems for quantifying kaolinite in Weipa bauxites , 2007 .

[43]  Simon J. Hook,et al.  High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) , 2016 .

[44]  C. Small,et al.  Cities from space: potential applications of remote sensing in urban environmental research and policy , 2003 .

[45]  A. Gitelson,et al.  Quantitative estimation of chlorophyll-a using reflectance spectra : experiments with autumn chestnut and maple leaves , 1994 .

[46]  Loredana Pompilio,et al.  Exponential Gaussian approach for spectral modeling: The EGO algorithm I. Band saturation , 2009 .

[47]  Qin Zhang,et al.  A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.

[48]  J. Baerdemaeker,et al.  Vis/NIR spectroscopic measurement of selected soil fertility parameters of Cuban agricultural Cambisols , 2014 .

[49]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

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

[51]  Bernard Barthès,et al.  Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VNIRS) in the field , 2016 .

[52]  C. Hurburgh,et al.  INFLUENCE OF SOIL MOISTURE ON NEAR-INFRARED REFLECTANCE SPECTROSCOPIC MEASUREMENT OF SOIL PROPERTIES , 2005 .

[53]  P. Zarco-Tejada,et al.  Seasonal stability of chlorophyll fluorescence quantified from airborne hyperspectral imagery as an indicator of net photosynthesis in the context of precision agriculture , 2016 .

[54]  B. Minasny,et al.  Using soil knowledge for the evaluation of mid‐infrared diffuse reflectance spectroscopy for predicting soil physical and mechanical properties , 2008 .

[55]  Timo Stuffler,et al.  EnMAP A Hyperspectral Sensor for Environmental Mapping and Analysis , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[56]  Philippe Lagacherie,et al.  Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements , 2008 .

[57]  Rodolphe Marion,et al.  Simultaneous retrieval of CO2 and aerosols in a plume from hyperspectral imagery: application to the characterization of forest fire smoke using AVIRIS data , 2013 .

[58]  Qun Ma,et al.  Optimization of Parameter Selection for Partial Least Squares Model Development , 2015, Scientific Reports.

[59]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[60]  Daniel Schläpfer,et al.  Atmospheric Precorrected Differential Absorption Technique to Retrieve Columnar Water Vapor , 1998 .

[61]  D. B. Myers,et al.  Modelling soil carbon fractions with visible near-infrared (VNIR) and mid-infrared (MIR) spectroscopy , 2015 .

[62]  Alexandre Alakian,et al.  Retrieval of microphysical and optical properties in aerosol plumes with hyperspectral imagery: L-APOM method , 2009 .

[63]  G. Hunt.  Spectroscopic Properties of Rocks and Minerals , 2017 .

[64]  W. Oechel,et al.  Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species , 2002, Oecologia.

[65]  M. A. Pizarro,et al.  Variations in Reflectance of Tropical Soils: Spectral-Chemical Composition Relationships from AVIRIS data , 2001 .

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

[67]  Jon Atli Benediktsson,et al.  Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods , 2013, IEEE Signal Processing Magazine.

[68]  A. Goetz,et al.  Software for the derivation of scaled surface reflectances from AVIRIS data , 1992 .

[69]  James Theiler,et al.  Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[70]  Robert Frouin,et al.  Determination from Space of Atmospheric Total Water Vapor Amounts by Differential Absorption near 940 nm: Theory and Airborne Verification , 1990 .

[71]  Lorraine Remer,et al.  The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol , 1997, IEEE Trans. Geosci. Remote. Sens..

[72]  M. V. Genuchten,et al.  A Comparative Study of Multiple Approaches for Predicting the Soil–Water Retention Curve: Hyperspectral Information vs. Basic Soil Properties , 2015 .

[73]  Ira Leifer,et al.  Airborne visualization and quantification of discrete methane sources in the environment , 2014 .

[74]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[75]  Robert O. Green,et al.  High spatial resolution mapping of elevated atmospheric carbon dioxide using airborne imaging spectroscopy: Radiative transfer modeling and power plant plume detection , 2013 .

[76]  A. Saleh,et al.  Application of near-infrared reflectance for quantitative assessment of soil properties , 2017 .

[77]  Lutgarde M. C. Buydens,et al.  The potential of field spectroscopy for the assessment of sediment properties in river floodplains , 2003 .

[78]  Weimin Ju,et al.  A new spectral similarity water index for the estimation of leaf water content from hyperspectral data of leaves , 2017 .

[79]  Alexandre Alakian,et al.  Remote sensing of aerosol plumes: a semianalytical model. , 2008, Applied optics.

[80]  P. Lagacherie,et al.  Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements , 2008 .

[81]  William R DeTar,et al.  Detection of Soil Properties with Airborne Hyperspectral Measurements of Bare Fields , 2008 .

[82]  Rodolphe Marion,et al.  Atmospheric correction of hyperspectral data over dark surfaces via simulated annealing , 2005, IEEE Transactions on Geoscience and Remote Sensing.

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

[84]  J. V. Stafford,et al.  Implementing precision agriculture in the 21st century. , 2000 .

[85]  S. Ustin,et al.  Predicting water content using Gaussian model on soil spectra , 2004 .

[86]  Rodolphe Marion,et al.  Measuring trace gases in plumes from hyperspectral remotely sensed data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[87]  L. West,et al.  Field-Scale Mapping of Surface Soil Organic Carbon Using Remotely Sensed Imagery , 2000 .

[88]  D Chvedov,et al.  Surface properties of red mud particles from potentiometric titration , 2001 .

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

[90]  Grégoire Maillet,et al.  Description de la campagne aéroportée UMBRA : étude de l'impact anthropique sur les écosystèmes urbains et naturels avec des images THR multispectrales et hyperspectrales , 2014 .

[91]  Brian Cairns,et al.  Atmospheric correction and its application to an analysis of Hyperion data , 2003, IEEE Trans. Geosci. Remote. Sens..

[92]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[93]  Martin Brossard,et al.  Deconvolution of SWIR reflectance spectra for automatic mineral identification in hyperspectral imaging , 2016 .

[94]  Eyal Ben-Dor,et al.  Near-Infrared Analysis as a Rapid Method to Simultaneously Evaluate Several Soil Properties , 1995 .

[95]  Jean-Michel Roger,et al.  Predictive ability of soil properties to spectral degradation from laboratory Vis-NIR spectroscopy data , 2017 .

[96]  Annamaria Castrignanò,et al.  Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content , 2015 .

[97]  Andrew Rawson,et al.  Rapid Prediction of Soil Water Retention using Mid Infrared Spectroscopy , 2007 .

[98]  E. Cloutis,et al.  Review Article Hyperspectral geological remote sensing: evaluation of analytical techniques , 1996 .

[99]  M. V. Roozendael,et al.  High-resolution NO 2 remote sensing from the Airborne Prism EXperiment (APEX) imaging spectrometer , 2012 .

[100]  Meiling Liu,et al.  Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices , 2010 .

[101]  Xavier Briottet,et al.  Estimation of Soil Moisture Content from the Spectral Reflectance of Bare Soils in the 0.4–2.5 μm Domain , 2015, Sensors.

[102]  Lammert Kooistra,et al.  Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[103]  André Carnieletto Dotto,et al.  Two preprocessing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy , 2017 .

[104]  Thomas Scholten,et al.  The spectrum-based learner: A new local approach for modeling soil vis–NIR spectra of complex datasets , 2013 .

[105]  Jay Gao,et al.  Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges , 2018 .

[106]  A. McBratney,et al.  The dimensions of soil security , 2014 .

[107]  M. S. Moran,et al.  Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence , 2014, Proceedings of the National Academy of Sciences.

[108]  S. Palmer,et al.  Characterisation of bauxite and seawater neutralised bauxite residue using XRD and vibrational spectroscopic techniques , 2009 .

[109]  M. Wiesmeier,et al.  Soil structure as an indicator of soil functions: A review , 2018 .

[110]  Pablo J. Zarco-Tejada,et al.  High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices , 2013 .

[111]  Peter Bergamaschi,et al.  Three decades of global methane sources and sinks , 2013 .

[112]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[113]  Frédéric Baret,et al.  Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands , 1992 .

[114]  Carlos Roberto de Souza Filho,et al.  A review on spectral processing methods for geological remote sensing , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[115]  Sabine Grunwald,et al.  Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra , 2008 .

[116]  Laerte Guimarães Ferreira,et al.  Predicting Soil Albedo from Soil Color and Spectral Reflectance Data , 2000 .

[117]  D. Thompson,et al.  Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region , 2016, Proceedings of the National Academy of Sciences.

[118]  Xiangnan Liu,et al.  Hyperspectral analysis of leaf copper accumulation in agronomic crop based on artificial neural network , 2008, 2008 International Workshop on Earth Observation and Remote Sensing Applications.

[119]  D. Thompson,et al.  Optimal estimation for imaging spectrometer atmospheric correction , 2018, Remote Sensing of Environment.

[120]  B. Rivard,et al.  High resolution measurement of soil organic carbon and total nitrogen with laboratory imaging spectroscopy , 2018 .

[121]  U. Schmidhalter,et al.  High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures , 2006 .

[122]  A. Stevens.  Changes in soil organic carbon at regional scales : strategies to cope with spatial variability , 2008 .

[123]  Patrick Hostert,et al.  Surface soil moisture quantification and validation based on hyperspectral data and field measurements , 2008 .

[124]  Suresh K. Bhargava,et al.  The surface chemistry of Bayer process solids: a review , 1999 .

[125]  A. Karnieli,et al.  Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel , 2002 .

[126]  R. V. Rossel,et al.  Using data mining to model and interpret soil diffuse reflectance spectra. , 2010 .

[127]  R. Lal,et al.  Effects of soil erosion on crop productivity , 1987 .

[128]  Lawrence S. Bernstein,et al.  Quick atmospheric correction code: algorithm description and recent upgrades , 2012 .

[129]  D. Quattrochi,et al.  Urban Remote Sensing , 2006 .

[130]  Yukio Kosugi,et al.  Characterization of Rice Paddies by a UAV-Mounted Miniature Hyperspectral Sensor System , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[131]  T. Oke,et al.  Thermal remote sensing of urban climates , 2003 .

[132]  Véronique Carrère,et al.  Carbon dioxide of Pu`u`O`o volcanic plume at Kilauea retrieved by AVIRIS hyperspectral data , 2008 .

[133]  Rodolphe Marion,et al.  Mineral Mapping Using the Automatized Gaussian Model (AGM) - Application to Two Industrial French Sites at Gardanne and Thann , 2018, Remote. Sens..

[134]  Ira Leifer,et al.  High resolution mapping of methane emissions from marine and terrestrial sources using a Cluster-Tuned Matched Filter technique and imaging spectrometry , 2013 .

[135]  David R. Thompson,et al.  Space‐based remote imaging spectroscopy of the Aliso Canyon CH4 superemitter , 2016 .

[136]  M. Fauvel,et al.  Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[137]  David R. Thompson,et al.  Retrieval of Atmospheric Parameters and Surface Reflectance from Visible and Shortwave Infrared Imaging Spectroscopy Data , 2018, Surveys in Geophysics.

[138]  M. Denk,et al.  Prediction of soil parameters using the spectral range between 350 and 15,000 nm: A case study based on the Permanent Soil Monitoring Program in Saxony, Germany , 2018 .

[139]  D. Thompson,et al.  Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign , 2015 .

[140]  Thomas Cudahy,et al.  Hydrothermal mineral alteration patterns in the Mount Isa Inlier revealed by airborne hyperspectral data , 2011 .

[141]  J. Aval.  Automatic mapping of urban tree species based on multi-source remotely sensed data , 2018 .

[142]  H. Bovensmann,et al.  Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane , 2015 .

[143]  F. Maupas,et al.  Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping , 2017 .

[144]  Raghavendra B. Jana.  Scaling Characteristics of Soil Hydraulic Parameters at Varying Spatial Resolutions , 2010 .

[145]  R. Sahoo,et al.  Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave-infrared (VIS-NIR-SWIR) region , 2009 .

[146]  J. Hill,et al.  Using Imaging Spectroscopy to study soil properties , 2009 .

[147]  Carle M. Pieters,et al.  Deconvolution of mineral absorption bands: An improved approach , 1990 .

[148]  Yukio Kosugi,et al.  Development of a Low-Cost Hyperspectral Whiskbroom Imager Using an Optical Fiber Bundle, a Swing Mirror, and Compact Spectrometers , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[149]  Raphael A. Viscarra Rossel,et al.  Spectral libraries for quantitative analyses of tropical Brazilian soils: Comparing vis–NIR and mid-IR reflectance data , 2015 .

[150]  Peter Strobl,et al.  Monitoring of gas pipelines - a civil UAV application , 2005 .

[151]  M. V. Genuchten,et al.  Soil moisture prediction of bare soil profiles using diffuse spectral reflectance information and vadose zone flow modeling , 2016 .

[152]  Vsevolod A. Mymrin,et al.  Red mud of aluminium production waste as basic component of new construction materials , 2001, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[153]  J. Peñuelas,et al.  The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .

[154]  Stefano Pignatti,et al.  Using imaging spectroscopy to map red mud dust waste: The Podgorica Aluminum Complex case study , 2012 .

[156]  J. Conel,et al.  Recovery of atmospheric water vapor total column abundance from imaging spectrometer data around 940 nm - Sensitivity analysis and application to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data , 1993 .

[157]  Zheng Qu,et al.  The High Accuracy Atmospheric Correction for Hyperspectral Data (HATCH) model , 2003, IEEE Trans. Geosci. Remote. Sens..

[158]  Graciela Metternicht,et al.  Remote sensing of soil salinity: potentials and constraints , 2003 .

[159]  R. B. Jackson,et al.  THE VERTICAL DISTRIBUTION OF SOIL ORGANIC CARBON AND ITS RELATION TO CLIMATE AND VEGETATION , 2000 .