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#*#MFWWET_eqv 639816 17185 #*#MGV0001_eqv 639885 17189 #*#MGV0002_eqv 639949 17193 #*#MGV0003_eqv 640013 17197 #*#MGV0004_eqv 640077 17201 #*#MGV0005_eqv 640141 17205 #*#MGV0006_eqv 640205 17209 #*#MGV0007_eqv 640269 17213 #*#MGV0008_eqv 640333 17217 #*#MGV0009_eqv 640397 17221 #*#MGV0010_eqv 640461 17225 #*#MGV0011_eqv 640525 17229 #*#MGV0012_eqv 640589 17233 #*#MGV8504_eqv 640653 17237 #*#MGV8505_eqv 640717 17241 #*#MGV8506_eqv 640781 17245 #*#MGV8507_eqv 640845 17249 #*#MGV8508_eqv 640909 17253 #*#MGV8509_eqv 640973 17257 #*#MGV8510_eqv 641037 17261 #*#MGV8511_eqv 641101 17265 #*#MGV8512_eqv 641165 17269 #*#MGV8601_eqv 641229 17273 #*#MGV8602_eqv 641293 17277 #*#MGV8603_eqv 641357 17281 #*#MGV8604_eqv 641421 17285 #*#MGV8605_eqv 641485 17289 #*#MGV8606_eqv 641549 17293 #*#MGV8607_eqv 641613 17297 #*#MGV8608_eqv 641677 17301 #*#MGV8609_eqv 641741 17305 #*#MGV8610_eqv 641805 17309 #*#MGV8611_eqv 641869 17313 #*#MGV8612_eqv 641933 17317 #*#MGV8701_eqv 641997 17321 #*#MGV8702_eqv 642061 17325 #*#MGV8703_eqv 642125 17329 #*#MGV8704_eqv 642189 17333 #*#MGV8705_eqv 642253 17337 #*#MGV8706_eqv 642317 17341 #*#MGV8707_eqv 642381 17345 #*#MGV8708_eqv 642445 17349 #*#MGV8709_eqv 642509 17353 #*#MGV8710_eqv 642573 17357 #*#MGV8711_eqv 642637 17361 #*#MGV8712_eqv 642701 17365 #*#MGV8801_eqv 642765 17369 #*#MGV8802_eqv 642829 17373 #*#MGV8803_eqv 642893 17377 #*#MGV8804_eqv 642957 17381 #*#MGV8805_eqv 643021 17385 #*#MGV8806_eqv 643085 17389 #*#MGV8807_eqv 643149 17393 #*#MGV8808_eqv 643213 17397 #*#MGV8809_eqv 643277 17401 #*#MGV8810_eqv 643341 17405 #*#MGV8811_eqv 643405 17409 #*#MGV8812_eqv 643469 17413 #*#MGVC186_eqv 643533 17417 #*#MGVC187_eqv 643597 17421 #*#MGVC188_eqv 643661 17425 #*#MGVC286_eqv 643725 17429 #*#MGVC287_eqv 643789 17433 #*#MGVC288_eqv 643853 17437 #*#MGVC386_eqv 643917 17441 #*#MGVC387_eqv 643981 17445 #*#MGVC388_eqv 644045 17449 #*#MGVC486_eqv 644109 17453 #*#MGVC487_eqv 644173 17457 #*#MGVC488_eqv 644237 17461 #*#OWE14D_eqv 644301 17465 #*#OWE14DR_eqv 644370 17469 #*#SRZAREA_eqv 644440 17473 #*#SRZCODE_eqv 644510 17477 #*#SRZPHAS_eqv 644580 17481 #*#SRZSLOP_eqv 644650 17485 #*#SRZSOIL_eqv 644720 17489 #*#SRZSUBS_eqv 644790 17493 #*#SRZTEXT_eqv 644860 17497 #*#WHCOV1_eqv 644930 17501 #*#WHCOV2_eqv 644999 17505 #*#WHSOIL_eqv 645068 17509 #*#WRCONT_eqv 645137 17513 #*#WRZSOIL_eqv 645206 17517 #*#GVI8827_eqv 645276 17521 #*#GVI8828_eqv 645340 17525 #*#GVI8829_eqv 645404 17529 #*#GVI8830_eqv 645468 17533 #*#GVI8831_eqv 645532 17537 #*#OWE14_eqv 645596 17541 END_MENU_INDEX *CD_MENU_NAME Global View - Ecosystem *VOLUME_ID GVIEWGED *SLIDE SHOW TRUE *ANIMATION TRUE *INTRODUCTION GLOBAL ECOSYSTEMS DATABASE Menu File Version 1.0 EPA Global Climate Research Program NOAA/NGDC Global Change Database Program This CD-ROM contains environmental and ecological data in a common format nested global geographic grid structure, compatible with raster GIS. This is a compilation of selected data sets from a series of CD- ROM's to develop a geographic database for modeling terrestrial climate- biosphere interactions in support of EPA's Global Climate Research Program and NOAA's Climate and Global Change Program. For more information please contact: David A. Hastings John J. Kineman David C. Schoolcraft Mark A. Ohrenschall Remote Sensing Ecosystems Documentation and Technical and Data and Global Geo-reference Data Coordinator Integration Change (303) 497-6729 (303) 497-6900 (303) 497-6125 (303) 497-6124 fax: (303) 497-6513 *default_eqv begin constant ff_format_fmt char *idrisi_fmt data_type char image image_type char bsq header_type char header_separated_varied header_file_ext char .doc palette char RAINBOW _distance short 14 delimiter_item char \n end constant begin name_equiv $file_title file%title $data_representation data%type char uchar char byte char short char integer char float char real $number_of_rows rows $number_of_columns columns $minimum_value min.%value $maximum_value max.%value $data_unit value%units $right_map_x max.%X $left_map_x min.%X $upper_map_y max.%Y $lower_map_y min.%Y $map_projection ref.%system char lat/lon char lat/long $grid_unit ref.%units $grid_size resolution end name_equiv *#\GLGEO\RASTER_eqv begin constant header_file_path char \GLGEO\META end constant 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Disc's A and B for Global View Preliminary Menu Version 1: REH 2/9/94 Last modified by Mark A. Ohrenschall on 7/29/94 11:53 AM *MAIN MENU NULL Global (Geographic -- lat/long) Raster Data-Sets #*GLOBAL (GEOGRAPHIC -- LAT/LONG) RASTER DATA-SETS US Regional (Albers Equal Area Conic) #*US REGIONAL (ALBERS EQUAL AREA CONIC) Asia Regional (Geographic -- lat/long) #*ASIA REGIONAL (GEOGRAPHIC -- LAT/LONG) *GLOBAL (GEOGRAPHIC -- LAT/LONG) RASTER DATA-SETS_help GLOBAL (GEOGRAPHIC -- LAT/LONG) RASTER DATA-SETS A01 NGDC Monthly Generalized Global Vegetation Index from NOAA-9 (APR 1985 - DEC 1988) A03 Leemans and Cramer IIASA Mean Monthly Values of Temperature, Precipitation, and Cloudiness on a Global Grid A04 Legates and Willmott Average Monthly Surface Air Temperature and Precipitation (re-gridded) A05 Olson World Ecosystems A06 Leemans Holdridge Life Zone Classifications A07 Matthews Vegetation, Land Use, and Seasonal Albedo A08 Lerner, Matthews, and Fung Methane Emissions from Animals A09 Matthews and Fung Global Distribution, Characteristics and Methane Emissions of Natural Wetlands A10 Wilson and Henderson-Sellers Global Land Cover and Soils Data for GCMs A11 Staub and Rosensweig Zobler Soil Type, Soil Texture, Surface Slope, and Other properties A12 Webb, Rosenzweig, and Levine Global Soil Particle Size Properties B01 Tateishi Monthly Maximum Global Vegetation Index and Land Cover Classifications from NOAA-9 (JAN 1986 - DEC 1989) B03 Chang Monthly Nimbus-7 SMMR Derived Global Snow Cover and Snow Depth Data Set (1978-1987) B04 Bailey Ecoregions of the Continents (reprojected) B05 Hastings and Amano Improved FNOC Percentage of Urban Development *GLOBAL (GEOGRAPHIC -- LAT/LONG) RASTER DATA-SETS MAIN MENU NGDC Generalized Vegetation Index #*NGDC GENERALIZED VEGETATION INDEX Leemans and Cramer IIASA Climate #*LEEMANS AND CRAMER IIASA CLIMATE Legates and Willmott Climate#*LEGATES AND WILLMOTT CLIMATE Olson World Ecosystems #*OLSON WORLD ECOSYSTEMS Leemans Holdridge Life Zone Classes #*LEEMANS HOLDRIDGE LIFE ZONE CLASSES Matthews Vegetation, Land Use, and Albedo #*MATTHEWS VEGETATION, LAND USE, AND ALBEDO Lerner et al Animal Methane #*LERNER ET AL ANIMAL METHANE Matthews and Fung Wetlands Methane #*MATTHEWS AND FUNG WETLANDS METHANE Wilson, Henderson-Sellers Land Cover and Soils #*WILSON, HENDERSON-SELLERS LAND COVER AND SOILS Staub and Rosensweig Soil Type, Texture, Slope #*STAUB AND ROSENSWEIG SOIL TYPE, TEXTURE, SLOPE Webb et al Soil Particle Size Properties #*WEBB ET AL SOIL PARTICLE SIZE PROPERTIES Tateishi Vegetation Index #*TATEISHI VEGETATION INDEX Chang Derived Snow Depth #*CHANG DERIVED SNOW DEPTH Bailey Ecoregions of the Continents (reprojected) #*BAILEY ECOREGIONS OF THE CONTINENTS (REPROJECTED) Improved FNOC Urban Development #*IMPROVED FNOC URBAN DEVELOPMENT *US REGIONAL (ALBERS EQUAL AREA CONIC)_help US Regional (Albers Equal Area Conic): B08 EPA-Corvallis Climate Database and 2xCO2 Predictions -- Marks Regional Water Balance Model; Daly, Nielson, and Phillips PRISM model Precipitation B09 EPA-Corvallis Kuchler Potential Natural Vegetation of the Conterminous United States *US REGIONAL (ALBERS EQUAL AREA CONIC) MAIN MENU EPA-Corvallis Climate Database #*EPA-CORVALLIS CLIMATE DATABASE EPA-Corvallis Kuchler US Vegetation #*EPA-CORVALLIS KUCHLER US VEGETATION *ASIA REGIONAL (GEOGRAPHIC -- LAT/LONG)_help Asia Regional (Geographic -- lat/long): B11 EPA-Corvallis Rice Climatology -- Bachelet Calculated UV-B Irradiance for Southern and Eastern Asia B12 EPA-Corvallis Rice Climatology -- Huke Agroclimatology for South, Southeast, and East Asia *ASIA REGIONAL (GEOGRAPHIC -- LAT/LONG) MAIN MENU EPA-C Rice Climatology -- UV-B Irradiance #*EPA-C RICE CLIMATOLOGY -- BACHELET UV-B IRRADIANCE EPA-C Rice Climatology -- Agroclimatology #*EPA-C RICE CLIMATOLOGY -- HUKE AGROCLIMATOLOGY *NGDC GENERALIZED VEGETATION INDEX_help A01 NGDC Monthly Generalized Global Vegetation Index from NESDIS NOAA- 9 Weekly GVI Data (APR 1985--DEC 1988) Monthly Generalized GVI (April 1985 - Dec. 1988) Characteristic Month Averages from the Monthly Generalized GVI (1986-1988) Annual Principal Components of the Monthly Generalized GVI for 1986, 1987, and 1988 SOURCE EXAMPLE: NOAA/NCDC Weekly Plate Carreé Global Vegetation Index from NOAA-9 (Samples for July 1988) DATA-SET DESCRIPTION Data-Set Name: NGDC Monthly Generalized Global Vegetation Index from NESDIS NOAA-9 Weekly GVI Data (April 1985 - December 1988) Principal Investigator: NOAA National Environmental Satellite, Data, and Information Service (NESDIS) Scientific Reference: (* reprint on CD-ROM) PRINCIPAL COMPONENTS ANALYSIS: + Eastman, J.R. 1992. Time series map analysis using standardized principal components. Proceedings, ASPRS/ACSM/RT'92 Convention: Mapping and Monitoring Global Change. Bethesda: ASPRS/ACSM. NOTE: The examples in this paper refer to GVI data-sets for Africa that were distributed as part of the IGBP Global Change Database Pilot Project for Africa. These data are identical to the data described here except for their geographic coverage. GVI SOURCE DATA: + Kidwell, K.B (ed.). 1990. Global Vegetation Index User's Guide. Washington: USDOC/NOAA National Climatic Data Center, Satellite Data Services Division. 45p. NOTE: This paper refers to source tapes of weekly GVI used to produce the data represented in the GED database. It also refers to other forms of the data and other products available from SDSD, which are not represented in the current database. The document is reproduced in its entirety, for completeness. SOURCE Source Data Citation: NCDC Satellite Data Services Division. 1985-1988. Weekly Plate Carreé (uncalibrated) Global Vegetation Index Product from NOAA-9 (APR 1985 - DEC 1988). Digital Raster Data on a Geographic (lat/long) 904x2500 grid. Washington DC: NOAA National Climatic Data Center. 199 files on five 9-track tapes, 425MB. Contributor: National Climatic Data Center (NCDC) Satellite Data Services Division (SDSD) National Environmental Satellite, Data, and Information Service SDSD, World Weather Building, Rm. 100 Washington, DC 20233, USA (301) 763-8400 Distributor: SDSD Vintage: 1985-1988 (switched to NOAA-11 in 1989, continuous operational products) Lineage: (1) NOAA-9 Satellite, AVHRR sensor array and on-board storage (2) Global Plate Carreé weekly GVI product NOAA/NESDIS/NCDC Satellite Data Services Division Washington, DC ORIGINAL DESIGN Variables: Scaled, Uncalibrated Weekly Maximum Normalized Difference Vegetation Index (cloud effects screened by 7-day maximizing procedure, but no other corrections for atmospheric effects or pixel-to-pixel variation in look and sun angles). Origin: NOAA-9 Polar Orbiting Satellite, Advanced Very High Resolution Radiometer "Global Area Coverage" (AVHRR/GAC) (see Primary Documentation) Geographic Reference: Plate Carreé (Latitude/Longitude) Geographic Coverage: Maximum Latitude : +75 degrees (N) Minimum Latitude : -55 degrees (S) Maximum Longitude : +180 degrees (E) Minimum Longitude : -180 degrees (W) Geographic Sampling: Last ("random") element of each 4x4 array of GAC (4km) values, mapped onto a 904x2500 Global Plate Carre (lat/long) grid. GAC values are 1x4km averages (along scan- line) of sampled values within each 4x4 array of 1km cells. Look-angle varies between pixels due to temporal compositing. Time Period: April 1985 - December 1988 Temporal Sampling: 7-day weekly maximum of daily values. Time of day varies between pixels. INTEGRATED DATA-SET Data-Set Citation: NGDC. 1992. Monthly Generalized Global Vegetation Index from NESDIS NOAA-9 Weekly GVI Data (APR 1985 - DEC 1988). Digital Raster Data on a 10-minute Geographic (lat/long) 1080x2160 grid. In: Global Ecosystems Database Version 1.0: Disc A. Boulder, CO: NOAA National Geophysical Data Center. 45 independent and 24 derived single-attribute spatial layers on CD-ROM, 190MB. Analyst: John J. Kineman and David A. Hastings Projection: Geographic (lat/long), GED window (see User's Guide). Spatial Representation: 10-minute grid aggregating 2-4 (weekly) GVI Plate Carre values (see original sampling), and interpolating from 8.6 minute to 10-minute grid cells by area-weighted average. Temporal Representation: Monthly RMS averages of 2-4 weekly samples Data Representation: Uncalibrated single-byte integer (0 to 255) values, representing an RMS average of median weekly GVI values, with spatial smoothing (high=>vegetation). The averaging procedure screens random "noise" and reduces environmental and instrumental variations inherent in the GVI data. It also provides uniform coverage (i.e., no masking), but does not eliminate consistent environmental phenomena (such as persistent clouds). Layers and Attributes: 45 independent and 24 derived single-attribute spatial layers. Compressed Data Volume: 47,445,363 bytes ADDITIONAL REFERENCES BIBLIOGRAPHY COMPILED FROM SEVERAL SOURCES: Asrar, G., Fuchs, M., Kanemasu, E. T., and Hatfield, J. L. 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflections in wheat. Agron. J., V. 76, pp. 300-306. Bartlett, D. S., Hardisky, M. A., Johnson, R. W., Gross, M. F., Klemes, V., and Hartman J. M. 1988. Continental scale variability in vegetation reflectance and its relationship to canopy morphology. International Journal of Remote Sensing, 9(7), 1223-1241. Choudhury, B. J., and Golus, R. E. 1988. Estimating soil wetness using satellite data. International Journal of Remote Sensing, 9(7), 1251-1257. Cihlar, J., St.-Laurent, L., and Dyer, J. A. 1991. Relation between the normalized difference vegetation index and ecological variables. Remote Sensing of Environment, v. 35, pp. 279-298. Deering, D.W. and T. F. Eck, 1987. Atmospheric optical depth effects on angular anisotropy of plant canopy reflectance. International Journal of Remote Sensing, 8(6):893-916. Dedieu, G., 1990. Land surface reflectances and vegetation index derived from NOAA/AVHRR. Workshop on the "Use of satellite-derived vegetation indices in weather and climate prediction models", Camp Springs, MD, Feb. 26- 27, 1990. D'Iorio, M., 1990. Corrections and improvements to NDVI procedures. Workshop on the "Use of Satellite-derived vegetation indices in weather and climate prediction models", Camp Springs, MD, Feb. 26-27, 1990. Di, L., 1991. Regional-scale soil moisture monitoring using NOAA/ AVHRR data, Ph.D. Dissertation, Department of Geography, University of Nebraska-Lincoln. Di, L., Rundquist, D., and Han, L. 1991. A mathematical model for predicting NDVI using daily precipitation. Manuscript to be published. Dijik, A., S.L. Callis, C.M. Sakamoto and W.L. Decker. 1987: Smoothing vegetation index profiles: an alternative method for reducing radiometric disturbance in NOAA/AVHRR data. Photogrammetry Engineering Remote Sensing, 53:1059-1067. Duggin, M.J., D. Piwiniski, V. Whitehead and G. Ryland. 1982: Evaluation of NOAA-AVHRR data for crop assessment. Applied Optics, 21(11):1873-1875. Duggin, M.J. and R.W. Saunders, 1984: Problems encountered in remote sensing of land and ocean surface features, Satellite Sensing of a Cloudy Atmosphere, A. Henderson- Sellers, ed. Taylor and Francis Publishers, Philadelphia. Gallo, K. P., 1990. Satellite-derived vegetation indices: a new climatic variable? Proceedings, Symposium on global change systems, February 5-9, 1990, Anaheim, California. American Meteorological Society, Boston, Massachusetts. Gallo, K. P., and Heddinghaus, T. R. 1989. The use of satellite-derived vegetation indices as indicators of climatic variability. Proceedings, Sixth Conference on Applied Climatology, March 7-10, 1989, Charleston, SD. American Meteorological Society, Boston, Massachusetts. Gallo, K. P., and Brown, J. F. 1990. Satellite-derived indices for monitoring global phytoclimatology. Proceedings, 10th International Geoscience and Remote Sensing Symposium, May, 1990, Washington, DC. Gallo, K. P., and Brown, J. F. 1990b. Evaluation of data reduction and compositing of the NOAA Global Vegetation Index product: A cast study. Washington, DC, NOAA Technical Report NESDIS 54 Gallo, K. P., Daughtry, C. S. T., and Bauer, M. E. 1985. Spectral estimation of absorbed photosynthetically active radiation in corn canopies. Remote Sensing of Environment, V. 17, pp. 221-232. Gatlin, J. A., Sullivan R. J., Tucker, C. J. 1984. Consideration of and improvements to large-scale vegetation monitoring. IEEE Trans. on Geoscience and Remote Sensing, GE-22(6), 496-502. Goward, S. N. 1990. Experiences and perspective in compiling long-term remote sensing data sets on landscapes and biospheric processes. GeoJournal, v. 20, pp. 107-114. Goward, S. N., Dye, D., Kerber, A., and Kalb, V. 1987. Comparison of North and South American biomass from AVHRR observations. Geocarto International, v. 1, pp. 27-39. Goward, S.N., D.J. Dye, W. Dulaney and J. Yang. 1990: Critical assessment of NOAA Global Vegetative Index data product. International Journal of Remote Sensing, in press. Goward, S. N., Markham, B., Dye, D. G., Dulaney, W., and Yang, J. 1991. Derivation of quantitative normalized difference vegetation index measurements from Advanced Very High Resolution Radiometer observations. Remote Sensing of Environment, v. 35, pp. 257-277. Goward, S.N., C.J. Tucker, and D.J. Dye. 1985. North American Vegetation Patterns Observed with the NOAA-7 Advanced Very High Resolution Radiometer, Vegetation, 64:3-14. Gray, T. I., and McCrary, D. G. 1981. The environmental vegetation index, a tool potentially useful for arid land management. AgRISTAR Report No. EW-N-1- 04076, Johnson Space Center, Houston, Texas 17132. Gutman, G., 1987. The derivation of vegetation indices from AVHRR data. International Journal or Remote Sensing, 8:1235-1242. Gutman, G., 1989. On the relationship between monthly mean and maximum-value composite vegetation indices. International Journal of Remote Sensing, 10(8):1317- 1325. Gutman, G. Garik, 1991. Vegetation indices from AVHRR: An update and future prospects. Remote Sensing of Environment, v. 35, pp. 121-136. Gutman, G. Garik, and Liu, William T. 1991. Bio-climates of South America as derived from multispectral AVHRR data. 24th International Symposium on Remote Sensing of Environment, Rio de Janeiro, May 1991. Ann Arbor, Michigan, Environmental Research Institute of Michigan. Summary pp. 19-20; Proceedings in press. Hastings, D.A., and W.J. Emery. 1992. The Advanced Very High Resolution Radiometer (AVHRR): A brief reference guide. Photogrammetric Engineering and Remote Sensing, 58(8):1183-1188. Holben, B.N. and R.S. Fraser. 1984. Red and near-infrared sensor response to off-nadir viewing, International Journal of Remote Sensing, 5:145-160. Holben, B.N.. 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7:1417-1434. Holben, B.N., Y.J. Kaufman, and J.D. Kendall. 1990. NOAA-11 AVHRR visible and near-IR inflight calibration. Int. J. Remote Sensing. 11(8):1511-1519. Holben, B.N., D. Kimes and R.S. Fraser. 1986. Directional reflectance response in AVHRR Red and near-IR bands for three cover types and varying atmospheric conditions. Remote Sensing of Environment, 19:215-256. Huete, A.R., 1988: A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25:295-309. Justice, C.O. (ed.). 1986. Monitoring the Grasslands of Semi- arid Africa using NOAA-AVHRR Data. Special Issue: International J. of Remote Sensing, 7(11) November, 1986. Justice, C.O., J.R.G. Townshend, B.M. Holben, and C.J. Tucker. 1985. Analysis of the Phenology of Global Vegetation using Meteorological Satellite Data, International Journal of Remote Sensing, 6(8):1271- 1318. Kaufman, Y.J. and B.N. Holben. 1990. Calibration of the AVHRR visible and near-IR bands by atmospheric scattering, ocean glint and desert reflection. Journal of Applied Meteorology, in press. Kogan, F. 1991. Remote sensing of weather impacts on vegetation in non- homogeneous areas. International Journal of Remote Sensing, in press. Koomanoff, V. A. 1989. Analysis of global vegetation patterns: a comparison between remotely sensed data and a conventional map. Biogeography Research Series, Report #890201, Department of Geography, University of Maryland, College Park, 111p. Odajima, T., K. Kajiwara, and R. Tateishi. 1990. Global land cover classification by NOAA AVHRR Data. Proceedings of the 11th Asian Conference on Remote Sensing, p. S-3-1. Ohring, G., Gallo, K., Gruber, A., Planet, W., Stowe, L., and Tarpley, J. D. 1989. Climate and global change: Characteristics of NOAA satellite data. EOS Transac tions of the American Geophysical Union, v. 70, pp. 889,891,894,901. Peters, A. J. 1989. Coarse Spatial Resolution Satellite Remote Sensing of Drought Conditions in Nebraska: 1985-1988. PhD Dissertation, Department of Geography, University of Nebraska, Lincoln. Price, J.C., 1988. An update on visible and near infrared calibration of satellite instruments. Remote Sensing of Environment, 24:419-422. Prince, S.D., and C.O. Justice (eds.). 1991. 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Simulation of solar zenith angle effect on global vegetation index (GVI) data. International Journal of Remote Sensing, 9: 237-248. Singh, S.M. 1988b. Lowest order correction for solar zenith angle to Global Vegetation Index (GVI) data. International Journal of Remote Sensing, 9: 1565-1572. Smith, E.A., W.L. Crosson, H.J. Cooper and W. Heng-yi. 1990. Heat and moisture flux modeling of the FIFE grassland canopy aided by satellite derived canopy variables. Proceedings of the AMS Symposium on FIFE, Anaheim, CA, Feb. 7-9, 1990. 154-162. Tarpley, J. D., Schneider, S. R., and Money, R. L. 1984. Global vegetation indices from the NOAA-7 meteorological satellite. Journal of Climate and Applied Meteorology. v. 23, pp. 491-494. Tateishi, R., and K. Kajiwara. 1991. Land cover monitoring in Asia by NOAA GVI data. Vol. 6, No. 4 Geocarto International, pp. 53-64. Tateishi, R., K. Kajiwara and T. Odajima. 1991. Global land cover classification by phenological methods using NOAA GVI data. Asian-Pacific Remote Sensing Journal. Vol.4, No.1. pp. 41-50. Tateishi, R. and K. Kajiwara. 1992. Global land cover monitoring by NOAA GVI data. IGARSS'92. Houston: May 26-29. Taylor, B.F., P.W. Dini and J.W. Kidson. 1985. Determination of seasonal and interannual variation in New Zealand pasture growth from NOAA-7 data. Remote Sensing of Environment, 18:177-192. Teillet, P.M., P.N. Slater, Y. Ding, R.P. Santer, R.D. Jackson, and M.S. Moran. 1990. Three Methods for the Absolute Calibration of the NOAA AVHRR Sensors In-Flight. Remote Sens. Environ. 31:105-120. Thomas, G. and A. Henderson-Sellers, 1987: Evaluation of satellite derived land cover characteristics for global climate modelling. Climate Change, 11:313-347. Townshend, J. R. G., Goff, T. E., and Tucker, C. J. 1985. Multitemporal dimensionality of images of normalized difference vegetation index at continental scales. IEEE Transactions, Geoscience and Remote Sensing, v. 23, pp. 888-895. Townshend, J. R. G., Justice, C. O., and Kalb, V. T. 1987. Characterization and classification of South American land cover types using satellite data. International Journal of Remote Sensing. v. 8, pp. 1189-1207. Townshend, J. R. G., Justice, C. O., Choudhury, B. J., Tucker, C. J., Kalb, V. T., and Goff, T. E. 1989. A comparison of SMMR and AVHRR data for continental land cover characterization. International Journal of Remote Sensing. v. 10, pp. 1633-1642. Tucker, C. J., and T. A. Gatlin. 1984. Monitoring vegetation in the Nile Delta with NOAA-6 and NOAA-7 AVHRR imagery. Photogrammetric Engineering and Remote Sensing, 50(1), 53-61. Tucker, C. J., Hielkema, J. U., and Roffey, j. 1985a. The potential of satellite remote sensing of ecological conditions for survey and forecasting desert-locust activity. International Journal of Remote Sensing, 6(1), 127-138. Tucker, C.J., J.R.G. Townshend, and T.E. Goff. 1985. African land cover classification using satellite data, Science, 227(4685):369-375. Tucker, C. J., Vanpraet, C. L., Sharman., M. J., and van Ittersum, G. 1985b. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980-1984. Remote Sensing of Environment, 17, 233-249. Tucker, C. J., Fung, I. Y., Keeling, C. D., and Gammon, R. H. 1986. Relationship between atmospheric CO2 variations and a satellite-derived vegetation index. Nature, v. 319, pp. 195-199. Tueller, P.T. and S.G. Oleson. 1989. Diurnal radiance and shadow fluctuations in a cold desert shrub plant community. Remote Sensing of Environment, 29:1-13. Walsh, S. J. 1987. Comparison of NOAA-AVHRR data to meteorological drought indices. Photogrammetric Engineering and Remote Sensing, 53(8), 1069-1074.Wiegand, C. L., Gerbermann, A. H., Gallo, K. P., Blad, B. L., and Dusek, D., 1990. Multisite analyses of spectral-biophysical data for corn. Remote Sensing of Environment, v. 33, pp. 1-16. Wiegand, C.L., Gerbermann, A.H., Gallo, K.P., Blad, B.L. and Dusek, D. 1990. Multisite analyses of spectral- biophysical data for corn. Remote Sensing of Environment, v. 33, pp. 1-16. DATA-SET FILES Location Name Number Total Size Spatial Data: \GLGEO\RASTER\ MGV8504.IMG to MGV8812.IMG 45 files 104,976,000 MGV0001.IMG to MGV0012.IMG 12 files 27,993,600 MGVC186.IMG to MGVC188.IMG 3 files 13,996,800 MGVC286.IMG to MGVC288.IMG 3 files 13,996,800 MGVC386.IMG to MGVC388.IMG 3 files 13,996,800 MGVC486.IMG to MGVC488.IMG 3 files 13,996,800 Headers: \GLGEO\META\ MGV8504.DOC to MGV8812.DOC 45 files 23,091 MGV0001.DOC to MGV0012.DOC 12 files 7,432 MGVC186.DOC to MGVC188.DOC 3 files 1,494 MGVC286.DOC to MGVC288.DOC 3 files 1,491 MGVC386.DOC to MGVC388.DOC 3 files 1,491 MGVC486.DOC to MGVC488.DOC 3 files 1,491 Palettes: \GLGEO\META\ MGV8.PAL 1 file 4,352 MGV4.PAL 1 file 272 MGVC8.PAL 1 file 4,352 Time Series: \GLGEO\META\ MGV.TS 1 file 411 MGV00.TS 1 file 114 Volume on Disk: 143 files 189,002,791 REPRINT FILES Location Name Number Total Size \DOCUMENT\A01\ MGV1_01.PCX to MGV1_53.PCX 53 files 1,253,256 MGV1_17X.PCX 1 files 93,708 MGV2_01.PCX to MGV2_10.PCX 10 files 517,211 MGV2_##X.PCX 5 files 778,338 Volume on Disk: 69 files 2,642,513 SOURCE EXAMPLE FILES Location Name Number Total Size Spatial Data: \SOURCE\RASTER\ GVI8827.IMG to GVI8831.IMG 5 files 11,300,000 Headers: \SOURCE\META\ GVI8827.DOC to GVI8831.DOC 5 files 2,593 Volume on Disk: 10 files 11,302,593 TECHNICAL REPORT John J. Kineman and David A. Hastings NOAA National Geophysical Data Center Boulder, CO 80303, USA MONTHLY GENERALIZED GLOBAL VEGETATION INDEX (APRIL 1985 - DECEMBER 1988) Advanced Very High Resolution Radiometer (AVHRR) data from NOAA Polar Orbiting Environmental Satellites were obtained from the National Environmental Satellite, Data and Information's (NESDIS) Satellite Data Services Division. The data were acquired in NOAA's operational Normalized Difference Vegetation Index (NDVI) "Plate Carreé" (latitude/longitude) weekly image format, and were subsequently converted at the National Geophysical Data Center into 10-minute grids, composited monthly. This new data-set is called the Monthly Generalized Global Vegetation Index (MG-GVI). The AVHRR spectral bands used for vegetation monitoring are Channel 1, a visible band (0.58 to 0.68m) and Channel 2, a near infrared band (0.73 to 1.0m). Since the spectral reflectance of vegetation is more than three times greater in the reflected infrared than in the visible portion of the spectrum due to leaf structure and chlorophyll absorption in the visible red (CH 1). The difference between the value for Channel 2 and Channel 1 is an indication of the degree to which the sensor "footprint" includes green vegetation. Various mathematical combinations of Channel 1 and 2 data have been found to be sensitive indicators of the presence of green vegetation and are referred to as vegetation indices. Because of the high dependence of these indices on the differential scattering and absorption of red and near IR bands, they are also dependent on leaf, plant, and canopy structure to a significant degree. Stratified analysis using ancillary land-cover data (along with other empirical calibrations) may thus improve interpretation. It is also known that changes of local time of observation (caused by variation in the satellite orbits), and thus solar azimuth and zenith, cause significant in-homogeneities in the vegetation index, which may be compounded by the weekly compositing procedure (see literature by Gutman, and by Tateishi and Kajiwara, in references above). This phenomena may be somewhat reduced by the averaging process employed for these generalized monthly images, but the resulting variability has not been quantified. The basic index used by NOAA is the Unscaled Normalized Difference Vegetation Index (XVI), defined by the equation: XVI = (CH2 - Ch1) / (Ch2 + Ch1) For vegetation, the NDVIs range from 0.1m to 0.6m, the higher values being associated with greater density and greenness of the plant canopy. Atmospheric effects, such a scattering and sub-pixel- sized clouds, all act to increase the value of Ch1 with respect to Ch2 and reduce the values of the computed vegetation indices. Maximum values compositing can thus be used as a method for cloud screening over a suitable series of observations. The normalized index has another advantage for global vegetation monitoring, for it partially compensates for changing illumination conditions, surface slope, and viewing aspect. Clouds, water, and snow have greater reflectance in the visible than in the near infrared, so for these features NDVI values are negative. Rock and bare soil have similar reflectances in the visible and near infrared and this results in vegetation indices near zero. The data provided by SDSD were scaled as integer values from 0 to 255 according to the formula NDVI = 240-(XVI+0.05)*350 (see Global Vegetation Index User's Guide). In processing, however, the scale was inverted by subtraction from 255, so that high values in the data correspond more intuitively to high vegetation signals (it also avoids mistaking it for other GVI products). Thus the values used in averaging and re-gridding are described by the formula: NDVI = (XVI+0.05)*350 + 15 The satellite images were re-sampled from weekly to monthly averages in a series of steps. Two procedures were used to control the quality of these composite images. First, registration accuracy was ensured by alignment of recognizable geographic locations. Second, each weekly composite image was visually inspected for artifacts (i.e., scan lines, orbital swaths, and other noise). If artifacts were visible approximately at the same location in two or more images, only one of those images was used. The remaining artifacts were removed during the monthly composite procedure, which combined all weeks which overlapped the calendar month (thus providing up to one week overlap between months). In this procedure, the high and low weekly values for a given month, for each cell, were eliminated and a root-mean-square average of the remaining weekly "median" cell values was calculated. This technique eliminated random artifacts evident in the weekly data and biased the result toward higher (and presumably more reliable) median values, without forcing the monthly value to its maximum. The images were then re-gridded to a 10-minute grid using a spatially weighted average. The result is therefore a statistic that is presumed to be generally representative of the month's vegetation activity over a partially "smoothed" 10-minute pixel; however, as with any such index, it must be calibrated or classified using additional information. The values were not corrected for orbital parameters or sensor drift. PROCEDURE FOR DEVELOPING NVI MONTHLY COMPOSITE IMAGES: Data Source: Weekly Composite Images of 7-day peak values on Global 8.6 minute grids 1. Process weekly images: 2500 cols. x 904 rows (covering 75N- 55S latitude) a. Identify images for each month b. Visually inspect images for artifacts c. Choose between images in the same month if artifacts overlap d. Calculate registration offset (fractional cell offsets) 2. Produce composite monthly images: 2500 cols. x 904 rows a. Produce root-mean-square average of selected weekly images for each month, removing high and low values to eliminate remaining artifacts, and applying fractional registration offsets. 3. Re-grid to 10-minute cell size: 2160 cols. x 1080 (Covering 90N to 90S latitude) a. Resample using a cell-overlap area-weighted linear average b. Pad with zeros to the poles CALIBRATION "DRIFT" GVI is known to 'drift' over time due to orbital changes (time of passage) and sensor aging. Investigation of the monthly data-set over this time period shows the trend to be increasing linearly with time in low vegetation areas, but is not so evident in the higher vegetation signals (see above). Thus, the desert areas appear (incorrectly) to be increasing in greenness by about 3% per year, whereas highly vegetated areas show little overall change, or perhaps a slight decreasing trend (also an artifact of the drift). If one considers the calibration drift characteristics of the two channels of AVHRR data used to compute the GVI (e.g., Holben, 1990), it can be shown that the NDVI calculation results in a logarithmic curve which, due to the parameters of the linear drift in each sensor, can be closely approximated with a line. The observed drift in these monthly generalized values (plotted over the 45-month time series as an average over Bare Desert regions identified in the Olson data-set - see Chapter A05) agrees well with such prediction based on Holben's calibration drift corrections. This means that empirical correction of this drift in the GVI can be performed after production of the index as well as before, with only a slight loss of accuracy (due to the non-linearity of the NDVI drift, which can be shown to be negligible in this case). Figure 1, below, shows a time profile of MG-GVI values as described above, comparing spatial averages for the Nile Delta with those for Olson's Bare and Blowing Sand Desert. The regression line for the Nile delta is flat, whereas the long-term calibration drift is evident in the desert curve. Also evident in the 45-month desert profile of the MG-GVI, is an annual dip in these low GVI values. Preliminary research (Dr. Alex Faizoun at LERTS in Toulouse, France) indicates that this dip may be explainable by the annual cycle of atmospheric water content over desert regions, which becomes significant for low GVI values. Both phase and amplitude seem to agree with these preliminary findings for the African Sahel. Atmospheric water vapor may vary differently in different regions, however, and will not have as significant an effect on higher GVI values (simply due to the signal-to-noise ratio). Figure 5 45-month time profile of the Monthly Generalize Global Vegetation Index, averaged over two areas; the Nile Delta and Bare or Blowing Sand Desert (from Olson World Ecosystems version 1.4D) Similar analysis performed on the Monthly Experimental GVI (ME- GVI, Chapter A02) produced by Kevin Gallo indicates a similar drift in those data for the NOAA-9 series. NOAA-11 data, which became operational in 1989, exhibit a different drift trend (decreasing), which is evident in the Gallo data, even after application of pre-launch corrections. Regression analyses between the Gallo data and the MG-GVI described here indicate reasonable correlation (r=.89 for a sample month, July 1986), but emphasizes that these two data-sets represent different parameters, MG-GVI being a generalized average and ME-GVI being peak values. CHARACTERISTIC MONTH AVERAGES: Twelve "characteristic" month data files were produced by averaging the three generalized monthly data files from 1986, 1987, and 1988. 1985 data were omitted because it was an incomplete year of data. The resulting data files should reveal average phenomena for the year (including calibration drift, which may be corrected empirically). These averages are provided as a convenience to users, since they can be readily compared to the climate data from Legates and Willmott and from Leemans and Cramer, which are also characteristic months rather than true time series. Seasonal phenomena should be well represented, however the user is cautioned to evaluate the effect of known annual trends in the calibration of these data for any intended study. Estimates from plotting profiles of various control areas indicate that a calibration "drift" exists in the GVI data, and appears as an increasing trend throughout the time series in low GVI regions. It is also inversely proportional to the indicated GVI (i.e., less for more vegetated areas). This drift is primarily the result of the gradual delay in time of passage of the satellite overhead, thus affecting the sun angle. Physical structure of the land cover with respect to sun angle may therefore explain the greater effect at low GVI values. The drift was found to be less than 3% of the maximum values in the data-set per year. The averaged data may reduce atypical cloud effects, however it will certainly incorporate characteristic cloudiness effects in affected regions (e.g., the tropics). It is possible that generalized data on cloudiness can be applied empirically to improve the values in cloud-prone regions, but this idea has not been tested. ANNUAL PRINCIPAL COMPONENTS OF THE MONTHLY GENERALIZED GLOBAL VEGETATION INDEX FOR 1986, 1987, AND 1988 Principal components analysis (PCA) was used to produce 12 derived digital data files of the Global Vegetation Index using the NGDC monthly generalized data as inputs. The PCA implementation in IDRISI 4.0 was used, choosing standardized variables and the correlation matrix (IDRISI 4.0, Clark University). The results of such analysis require interpretation, and research is being done. The technique was discussed by Eastman (1992), who experimented with the NGDC data for the Africa continent. Tateishi and Kajiwara (1992) have also experimented with this use of PCA along with cluster analysis to produce land-surface classifications based on GVI. Tateishi produced a data-set of monthly-maximum calibrated GVI and land- cover classifications derived from this technique (Odajima, Kajiware, and Tateishi, 1990). The Tateishi, Kajiware, and Odajima data have been contributed for the Global Ecosystems Database, Version 1.0, Disc B. Derived annual global raster arrays were produced as follows: Input Output File Names 12 files: Jan.-Dec. 1986 PCA #1,2,3,4 for 1986 MGVC186, MGVC286, etc. 12 files: Jan.-Dec. 1987 PCA #1,2,3,4 for 1987 MGVC187, MGVC386, etc. 12 files: Jan.-Dec. 1988 PCA #1,2,3,4 for 1988 MGVC188, MGVC486, etc. The above analysis results in principal components for each year, using 12 monthly inputs. Because of the nature of PCA, the particular parameters selected for this analysis (standardized components computed on the correlation matrix), and the performance of the analysis on a full global window (excluding the "no-data" regions above 75-degrees N. and below 55-degrees South), the resulting outputs are optimized to reveal specific phenomena. The first component represents an axis of strongest combined GVI signal, essentially equal to the annual average. A comparison of the first component with a 12-month mean for 1986, re-scaled to match offset and gain, showed a maximum difference of +/- 1, probably due to rounding. In the "standardized" PCA analysis, each month's spatial variation is given equal weight. The next component represents an orthogonal axis, which, by definition, is the strongest annual anomaly. Since the analysis is performed globally, the phase of this anomaly is primarily driven by the summer/winter variation. This phase alignment is reinforced by the seasonal polar "noise" in the GVI data that varies with the solar zenith angle and is easily distinguished over the oceans (un-masked images were used in the analysis). The third component, being also an orthogonal axis, becomes phased with the spring/fall variation; and the fourth component then becomes aligned with the strongest bi-modal variation. A discussion of these results in relation to seasonal patterns for the African continent is given by Eastman (1992). To the extent that the inter-annual drift effect noted above is a linear function of GVI, without spatial significance (i.e., purely a linear offset and gain difference), it will be removed by the PCA calculation (PCA will thus also remove any such trend that is genuine, but this is an extremely unlikely occurrence for any natural phenomena within a global window). The non-linear portion of the drift curve, which is probably due to annual variation in the atmospheric water content as noted above, will affect the PCA calculation, showing a slight increase in the Spring-Autumn signal, i.e., component #3. Since component #3 already isolates an annual cycle with similar phase and period to fluctuations in atmospheric water (according to preliminary research in the Sahel region of Africa conducted by LERTS in Toulouse, France), even this effect may be corrected empirically. The integer values in the PCA data files are as produced by the IDRISI software, and have not been re-scaled for inter-annual comparison. In practice, empirical calibration and re-scaling of these images may be necessary using suitable control areas, after which inter-annual comparisons may be more meaningful, taking into account that there are still atmospheric and cloud effects represented in the data. COLOR PALETTES A color palette has been developed based on intensity levels of the scaled MG-GVI. Although such palettes are arbitrary, and do not represent detailed studies of land-cover classifications, the NGDC palette has become popular among some users (it was used, for example, by the IGBP, along with an annual average of the data-set, on the cover of IGBP Report #15: Global Change System for Analysis, Research and Training (START). Boulder, CO: UCAR Office for Interdisciplinary Earth Studies). These palettes are provided with the database in both 4 bit-plane (16-color) and 8 bit-plane (256-color) form (MGV4.PAL and MGV8.PAL, respectively). These palettes also provide the capability to visually compare the MG-GVI documented here with the Monthly Experimental GVI (ME- GVI) developed by Kevin Gallo (see Chapter A02), since corresponding palettes were developed for the ME-GVI data, with identical color-slicing according to the respective offset and gain characteristics. The table below provides the color-slicing criteria for the 256-color palettes, for both data-sets. % GVI MG-GVI ME-GVI COLOR RED GREEN BLUE 0 Blue 0 0 20 1 White 63 63 63 2 White 63 63 63 0 3 Blue 0 0 20 0 32.5 100 Brown 17 0 0 20 68 117 Yellow 63 51 0 40 104 134 Olive 20 30 0 50 122 142 Green 0 30 0 75 166 163 Br.Green 0 63 0 100 211 184 Green/White 32 63 32 255 255 White 63 63 63 The color-slicing levels and color definitions, which result from simple Red-Green-Blue intensity levels (0-63) using IBM-PC conventions, are shown in the table above. "% GVI" refers to the percent of the GVI range represented in the data-set, between XVI=0 (32.5 in the MG-GVI data and 100 in the ME-GVI data) and the mean monthly maximum for July. The 16-color palette mimics the above color scheme, assuming a linear stretch to 16 classes from 0 to the Maximum data value in the file. Since the monthly maxima vary between files, comparable displays require re-setting the maxima to a standard value for the series. This will involve different procedures for each software package. Since the Characteristic Month Averages retain similar scaling to the monthly data, the same color palettes can be used. A separate palette (MGVC8.PAL) is provided for the principal components images, which must be re-scaled from minimum to maximum to 256 levels for display (e.g., using autoscaling during display with the provided software). The MGV4.PAL palette may be used with the PCA images when re-scaling to 16 levels. NOTES 1. These data are uncalibrated values, meaning that they are based on the NOAA Weekly GVI product which used digital counts rather than albedos in the calculation of the NDVI. A new calibrated product has been introduced by NOAA starting with NOAA-11 data (1990). Applying pre-launch calibrations and converting to albedos should make it easier to combine data from different sensors, but does not correct for drift problems noted above. The data in this data-set are all from one sensor (NOAA-9), thus minimizing the importance of calibration within the data-set. However, uncalibrated values may also be difficult to compare with other GVI values in the research literature. Possibilities for empirical calibration and intercomparison exist, as noted above. 2. Although averaging produces a "generalized" data-set with relatively clean, continuous coverage that is suited for use in spatial analysis systems, it also averages many other effects, such as persistent clouds, most notably along the tropical coasts. Future work may include developing separate quality masks based on cloud data. 3. The Principal Components images are provided for experimentation. Their interpretation is a matter of research at present. Some of the un-desirable effects of PCA analysis is avoided by working at a global scale, however users should be aware that PCA analysis is highly sensitive to the geographic window (and scale) of analysis. REFERENCES (see "Additional References," Pg. A01-3) *NGDC GENERALIZED VEGETATION INDEX GLOBAL (GEOGRAPHIC -- LAT/LONG) RASTER DATA-SETS Monthly Generalized Veg Index #*MONTHLY GENERALIZED VEG INDEX Characteristic Month Avgs #*CHARACTERISTIC MONTH AVGS Annual Principal Components #*ANNUAL PRINCIPAL COMPONENTS Source Example: Weekly Plate Carree Samples #*SOURCE EXAMPLE: WEEKLY PLATE CARREE SAMPLES *MONTHLY GENERALIZED VEG INDEX_help DATA ELEMENT: Monthly Generalized GVI (April 1985 - Dec. 1988) STRUCTURE: Raster Data Files: 10-minute 1080x2160 GED grid (see User's Guide) SERIES: 45 month time-series SPATIAL META-DATA: MGV8504.DOC file title : April 1985 Generalized Global Vegetation Index data type : byte file type : binary columns : 2160 rows : 1080 ref. system : lat/long ref. units : deg unit dist. : 1.0000000 min. X : -180.0000000 max. X : 180.0000000 min. Y : -90.0000000 max. Y : 90.0000000 pos'n error : unknown resolution : 0.1666667 min. value : 0 max. value : 192 value units : uncalibrated value error : unknown flag value : none flag def'n : none legend cats : 0 File Series Parameters: File Month Year Minimum Maximum MGV8504: April 1985 0 192 MGV8505: May 1985 0 209 MGV8506: June 1985 0 216 MGV8507: July 1985 0 226 MGV8508: August 1985 0 209 MGV8509: September 1985 0 189 MGV8510: October 1985 0 197 MGV8511: November 1985 0 197 MGV8512: December 1985 0 195 MGV8601: January 1986 0 190 MGV8602: February 1986 0 191 MGV8603: March 1986 0 194 MGV8604: April 1986 0 178 MGV8605: May 1986 0 203 MGV8606: June 1986 0 219 MGV8607: July 1986 0 211 MGV8608: August 1986 0 205 MGV8609: September 1986 0 201 MGV8610: October 1986 0 194 MGV8611: November 1986 0 197 MGV8612: December 1986 0 195 MGV8701: January 1987 0 189 MGV8702: February 1987 0 200 MGV8703: March 1987 0 180 MGV8704: April 1987 0 184 MGV8705: May 1987 0 205 MGV8706: June 1987 0 211 MGV8707: July 1987 0 211 MGV8708: August 1987 0 200 MGV8709: September 1987 0 184 MGV8710: October 1987 0 181 MGV8711: November 1987 0 194 MGV8712: December 1987 0 191 MGV8801: January 1988 0 196 MGV8802: February 1988 0 200 MGV8803: March 1988 0 184 MGV8804: April 1988 0 187 MGV8805: May 1988 0 190 MGV8806: June 1988 0 211 MGV8807: July 1988 0 215 MGV8808: August 1988 0 189 MGV8809: September 1988 0 174 MGV8810: October 1988 0 166 MGV8811: November 1988 0 213 MGV8812: December 1988 0 218 ATTRIBUTE META-DATA: NONE NOTES: (1) Color palette files are provided for display only. Color assignments are arbitrary. (2) The time-series file (MGV.TS) contains a list of the 45 files for sequential display. (3) See comments in the TECHNICAL REPORT section about calibration, variability due to orbital wander, and effects of long-term sensor drift. *MONTHLY GENERALIZED VEG INDEX NGDC GENERALIZED VEGETATION INDEX NGDC GVI 1985 Data #*NGDC GVI 1985 DATA NGDC GVI 1986 Data #*NGDC GVI 1986 DATA NGDC GVI 1987 Data #*NGDC GVI 1987 DATA NGDC GVI 1988 Data #*NGDC GVI 1988 DATA *NGDC GVI 1985 DATA_help NGDC Monthly Generalized Global Vegetation Index Data for 1985 *NGDC GVI 1985 DATA MONTHLY GENERALIZED VEG INDEX April 1985 #\GLGEO\RASTER\MGV8504.IMG May 1985 #\GLGEO\RASTER\MGV8505.IMG June 1985 #\GLGEO\RASTER\MGV8506.IMG July 1985 #\GLGEO\RASTER\MGV8507.IMG August 1985 #\GLGEO\RASTER\MGV8508.IMG September 1985 #\GLGEO\RASTER\MGV8509.IMG October 1985 #\GLGEO\RASTER\MGV8510.IMG November 1985 #\GLGEO\RASTER\MGV8511.IMG December 1985 #\GLGEO\RASTER\MGV8512.IMG *NGDC GVI 1986 DATA_help NGDC Monthly Generalized Global Vegetation Index Data for 1986 *NGDC GVI 1986 DATA MONTHLY GENERALIZED VEG INDEX January 1986 #\GLGEO\RASTER\MGV8601.IMG February 1986 #\GLGEO\RASTER\MGV8602.IMG March 1986 #\GLGEO\RASTER\MGV8603.IMG April 1986 #\GLGEO\RASTER\MGV8604.IMG May 1986 #\GLGEO\RASTER\MGV8605.IMG June 1986 #\GLGEO\RASTER\MGV8606.IMG July 1986 #\GLGEO\RASTER\MGV8607.IMG August 1986 #\GLGEO\RASTER\MGV8608.IMG September 1986 #\GLGEO\RASTER\MGV8609.IMG October 1986 #\GLGEO\RASTER\MGV8610.IMG November 1986 #\GLGEO\RASTER\MGV8611.IMG December 1986 #\GLGEO\RASTER\MGV8612.IMG *NGDC GVI 1987 DATA_help NGDC Monthly Generalized Global Vegetation Index Data for 1987 *NGDC GVI 1987 DATA MONTHLY GENERALIZED VEG INDEX January 1987 #\GLGEO\RASTER\MGV8701.IMG February 1987 #\GLGEO\RASTER\MGV8702.IMG March 1987 #\GLGEO\RASTER\MGV8703.IMG April 1987 #\GLGEO\RASTER\MGV8704.IMG May 1987 #\GLGEO\RASTER\MGV8705.IMG June 1987 #\GLGEO\RASTER\MGV8706.IMG July 1987 #\GLGEO\RASTER\MGV8707.IMG August 1987 #\GLGEO\RASTER\MGV8708.IMG September 1987 #\GLGEO\RASTER\MGV8709.IMG October 1987 #\GLGEO\RASTER\MGV8710.IMG November 1987 #\GLGEO\RASTER\MGV8711.IMG December 1987 #\GLGEO\RASTER\MGV8712.IMG *NGDC GVI 1988 DATA_help NGDC Monthly Generalized Global Vegetation Index Data for 1988 *NGDC GVI 1988 DATA MONTHLY GENERALIZED VEG INDEX January 1988 #\GLGEO\RASTER\MGV8801.IMG February 1988 #\GLGEO\RASTER\MGV8802.IMG March 1988 #\GLGEO\RASTER\MGV8803.IMG April 1988 #\GLGEO\RASTER\MGV8804.IMG May 1988 #\GLGEO\RASTER\MGV8805.IMG June 1988 #\GLGEO\RASTER\MGV8806.IMG July 1988 #\GLGEO\RASTER\MGV8807.IMG August 1988 #\GLGEO\RASTER\MGV8808.IMG September 1988 #\GLGEO\RASTER\MGV8809.IMG October 1988 #\GLGEO\RASTER\MGV8810.IMG November 1988 #\GLGEO\RASTER\MGV8811.IMG December 1988 #\GLGEO\RASTER\MGV8812.IMG *CHARACTERISTIC MONTH AVGS_help DATA ELEMENT: Characteristic Month Averages from the Monthly Generalized GVI (1986-1988) STRUCTURE: Raster Data Files: 10 minute 1080x2160 GED grid (see User's Guide) SERIES: 12 characteristic month time-series SPATIAL META-DATA: MGV0001.DOC file title : Average January Generalized Global Vegetation Index data type : byte file type : binary columns : 2160 rows : 1080 ref. system : lat/long ref. units : deg unit dist. : 1.0000000 min. X : -180.0000000 max. X : 180.0000000 min. Y : -90.0000000 max. Y : 90.0000000 pos'n error : unknown resolution : 0.1666667 min. value : 0 max. value : 183 value units : uncalibrated value error : unknown flag value : none flag def'n : none legend cats : 0 lineage : Produced from the following 3 files: lineage : GVI8601.IMG, GVI8701.IMG, GVI8801.IMG File Series Parameters: File Month Minimum Maximum MGV0001: January 0 183 MGV0002: February 0 184 MGV0003: March 0 172 MGV0004: April 0 176 MGV0005: May 0 199 MGV0006: June 0 207 MGV0007: July 0 211 MGV0008: August 0 188 MGV0009: September 0 177 MGV0010: October 0 171 MGV0011: November 0 193 MGV0012: December 0 193 ATTRIBUTE META-DATA: NONE NOTES: (1) Produced by taking the mean of three years for each month *CHARACTERISTIC MONTH AVGS NGDC GENERALIZED VEGETATION INDEX January #\GLGEO\RASTER\MGV0001.IMG February #\GLGEO\RASTER\MGV0002.IMG March #\GLGEO\RASTER\MGV0003.IMG April #\GLGEO\RASTER\MGV0004.IMG May #\GLGEO\RASTER\MGV0005.IMG June #\GLGEO\RASTER\MGV0006.IMG July #\GLGEO\RASTER\MGV0007.IMG August #\GLGEO\RASTER\MGV0008.IMG September #\GLGEO\RASTER\MGV0009.IMG October #\GLGEO\RASTER\MGV0010.IMG November #\GLGEO\RASTER\MGV0011.IMG December #\GLGEO\RASTER\MGV0012.IMG *ANNUAL PRINCIPAL COMPONENTS_help DATA ELEMENT: Annual Principal Components of the Monthly Generalized GVI for 1986, 1987, and 1988 STRUCTURE: Raster Data Files: 10 minute 1080x2160 GED grid (see User's Guide) SERIES: 3 year time-series, series of 4 Principal Components for each year SPATIAL META-DATA: MGVC186.DOC file title : 1986 MGV PCA Component 1 data type : integer file type : binary columns : 2160 rows : 1080 ref. system : lat/lon ref. units : deg unit dist. : 1.0000000 min. X : -180.0000000 max. X : 180.0000000 min. Y : -90.0000000 max. Y : 90.0000000 pos'n error : unknown resolution : 0.1666667 min. value : -331 max. value : 1341 value units : uncalibrated value error : unknown flag value : none flag def'n : none legend cats : 0 File Series Parameters: File Component Year Minimum Maximum MGVC186 1 1986 -331 1341 MGVC187 1 1987 -495 1404 MGVC188 1 1988 -589 1363 MGVC286 2 1986 -591 554 MGVC287 2 1987 -639 618 MGVC288 2 1988 -700 669 MGVC386 3 1986 -433 345 MGVC387 3 1987 -540 414 MGVC388 3 1988 -605 663 MGVC486 4 1986 -332 400 MGVC487 4 1987 -419 458 MGVC488 4 1988 -513 360 ATTRIBUTE META-DATA: NONE NOTES: (1) Produced using IDRISI's Standardized Principal Components Analysis, on a circum-global window between 55 deg. South and 75 deg. North Latitude. *ANNUAL PRINCIPAL COMPONENTS NGDC GENERALIZED VEGETATION INDEX Component 1 1986 #\GLGEO\RASTER\MGVC186.IMG Component 1 1987 #\GLGEO\RASTER\MGVC187.IMG Component 1 1988 #\GLGEO\RASTER\MGVC188.IMG Component 2 1986 #\GLGEO\RASTER\MGVC286.IMG Component 2 1987 #\GLGEO\RASTER\MGVC287.IMG Component 2 1988 #\GLGEO\RASTER\MGVC288.IMG Component 3 1986 #\GLGEO\RASTER\MGVC386.IMG Component 3 1987 #\GLGEO\RASTER\MGVC387.IMG Component 3 1988 #\GLGEO\RASTER\MGVC388.IMG Component 4 1986 #\GLGEO\RASTER\MGVC486.IMG Component 4 1987 #\GLGEO\RASTER\MGVC487.IMG Component 4 1988 #\GLGEO\RASTER\MGVC488.IMG *SOURCE EXAMPLE: WEEKLY PLATE CARREE SAMPLES_help DATA ELEMENT: SOURCE EXAMPLE: NOAA/NCDC Weekly Plate Carreé Global Vegetation Index from NOAA-9 (Samples for July 1988) STRUCTURE: Raster Data Files: 8.6 minute Plate Carreé 904x2500 grid (non-nested, see User's Guide) SERIES: 5 week time-series for July SPATIAL META-DATA: GVI8827.DOC file title : June 27 - July 3, 1988 Weekly Global Vegetation Index data type : byte file type : binary columns : 2500 rows : 904 ref. system : lat/long ref. units : deg unit dist. : 1.0000000 min. X : -180.0000000 max. X : 180.0000000 min. Y : -55.0000000 max. Y : 75.0000000 pos'n error : unknown resolution : 0.1440000 min. value : 0 max. value : 255 value units : uncalibrated value error : unknown flag value : none flag def'n : none legend cats : 0 File Series Parameters: File Year Week Minimum Maximum GVI8827 1988 27 0 255 GVI8828 1988 28 0 255 GVI8829 1988 29 0 255 GVI8830 1988 30 0 255 GVI8831 1988 31 0 255 ATTRIBUTE META-DATA: NONE NOTES: (1) These source files show data artifacts and minor registration problems that were removed in the monthly compositing (see Technical Report). *SOURCE EXAMPLE: WEEKLY PLATE CARREE SAMPLES NGDC GENERALIZED VEGETATION INDEX June 27 - July 3, 1988 #\SOURCE\RASTER\GVI8827.IMG July 4 - July 10, 1988 #\SOURCE\RASTER\GVI8828.IMG July 11 - July 17, 1988 #\SOURCE\RASTER\GVI8829.IMG July 18 - July 24, 1988 #\SOURCE\RASTER\GVI8830.IMG July 25 - July 31, 1988 #\SOURCE\RASTER\GVI8831.IMG *LEEMANS AND CRAMER IIASA CLIMATE_help A03 Leemans and Cramer IIASA Mean Monthly Values of Temperature, Precipitation, and Cloudiness on a Global Grid Average Month Surface Air Temperature Average Month Precipitation (uncorrected) Average Month "Cloudiness" (% sunshine) DATA-SET DESCRIPTION Data-Set Name: Leemans and Cramer IIASA Mean Monthly Values of Temperature, Precipitation, and Cloudiness on a Global Grid Principal Investigator: Rik Leemans and Wolfgang P. Cramer International Institute for Applied Systems Analysis Scientific Reference: (* reprint on CD-ROM) + Leemans, R. and W.P. Cramer, 1991. The IIASA database for mean monthly values of temperature, precipitation and cloudiness of a global terrestrial grid. Research Report RR-91-18 November 1991, International Institute of Applied Systems Analyses, Laxenburg. 61pp. SOURCE Source Data Citation: Leemans, R., and W.P. Cramer. 1991. The IIASA Database for Mean Monthly Values of Temperature, Precipitation, and Cloudiness on a Global Terrestrial Grid. Digital Raster Data on a 30 minute Geographic (lat/long) 360x720 grid. Laxenburg, Austria: IIASA. 9-track tape, 10.3 MB Contributor: Dr. Rik Leemans National Institute of Public Health and Environmental Protection, RIVM P.O. Box 1 NL-3720 BA Bilthoven, The Netherlands (31)30-749111 Distributor: IIASA and RIVM Vintage: circa 1990 Lineage: (1) Published records from 1931 to 1960 (see ORIGIN) (2) Data integrated from multiple sources at IIASA (Leemans and Cramer) ORIGINAL DESIGN Variables: (1) Average Monthly Surface Temperature, converted to C (precision=.1C) (2) Monthly Average Precipitation (interpolation of measured values), uncorrected for rain-gauge bias. (3) "Cloudiness," expressed as percentage sunshine hours of potential hours per month at the land surface. Origin: Weather records from the following sources (see Scientific Reference): 1) World Weather Records, U.S. Weather Bureau. 2) The Climate Atlas of Walter and Lieth 3) Müller: Selected Climatic Data for Vegetation Science, based on: a) UK Meteorological Office records b) World Survey of Climatology (Landsberg) 4) Bradley: Precipitation and Temperature Data for the Northern Hemisphere 5) Selected weather data for Europe from the UK Meteorological Office 6) Thornthwait and Mather's Temperature and Precipitation data. 7) Soviet Temperature and Precipitation data (Siberia) 8) Chinese Temperature and Precipitation data (NE China) Geographic Reference: latitude/longitude Geographic Coverage: Global Maximum Latitude: +90 degrees (N) Minimum Latitude: -90 degrees (S) Maximum Longitude: +180 degrees (E) Minimum Longitude: -180 degrees (W) Geographic Sampling: 30-minute cell values interpolated from station observations using spatial model (see Leemans and Cramer, 1992; pgs. 13- 14). Time Period: "current climate" (or "normal climate") as characterized from 1931-1960 Temporal Sampling: long-term means for each month composited from available records. INTEGRATED DATA-SET Data-Set Citation: Leemans, R., and W.P. Cramer. 1992. IIASA Database for Mean Monthly Values of Temperature, Precipitation, and Cloudiness on a Global Terrestrial Grid. Digital Raster Data on a 30 minute Geographic (lat/long) 360x720 grid. In: Global Ecosystems Database Version 1.0: Disc A. Boulder, CO: NOAA National Geophysical Data Center. 36 independent single-attribute spatial layers on CD- ROM, 15.6MB. [first published in 1991] Analyst: Mark A. Ohrenschall Projection: Geographic (lat/long), GED window (see User's Guide). Spatial Representation: Characteristic values for 30-minute cells, from a spatial model based on irregularly located station data. Temporal Representation: Characteristic months of average climate for 1931-1960 (a relatively stable period). Data Representation: 1) Temperature: 2-byte integers, representing surface air temperature in 1/10th degrees Celsius (or degrees x 10). 2) Precipitation: 2-byte integers, representing average monthly precipitation in millimeters (uncorrected) 3) Cloudiness: 1-byte integers, representing percentage sunshine hours of potential hours per month (0-100). Layers and Attributes: 36 independent single-attribute spatial layers Compressed Data Volume: 2,260,638 bytes ADDITIONAL REFERENCES Monserud, R.A. and Leemans, R. 1992. The comparison of global vegetation maps. Ecol. Modelling (in press). Prentice, I.C., Cramer, W., Harrison, S.P, Leemans, R., Monserud, R.A. & Solomon, A.M. 1992. A global biome model based on plant physiology and dominance, soil properties and climate. J. Biogeography (in press). Solomon, A.M. and R. Leemans. 1990. Climatic change and landscape-ecological response: Issues and analyses. In: Boer, M.M. and de Groot, R.S. (eds.), Landscape Landscape Ecological Impact of Climatic Change. IOS Press, Amsterdam. pp. 293-316 (ISBN 90 5199 023 5). DATA-SET FILES Location Name Number Total Size Spatial Data: \GLGEO\RASTER\ LCCLD01.IMG to LCCLD12.IMG12 files 3,110,400 LCPRC01.IMG to LCPRC12.IMG12 files 6,220,800 LCTMP01.IMG to LCTMP12.IMG12 files 6,220,800 Headers: \GLGEO\META\ LCCLD01.DOC to LCCLD12.DOC12 files 6,976 LCPRC01.DOC to LCPRC12.DOC12 files 6,619 LCTMP01.DOC to LCTMP12.DOC12 files 6,536 Palettes: \GLGEO\META\ LCCLD8.PAL 1 file 4,352 Time Series: \GLGEO\META\ LCCLD.TS 1 file 114 LCPRC.TS 1 file 114 LCTMP.TS 1 file 114 ------------------------- Volume on Disk: 76 files 15,576,825 REPRINT FILES Location Name Number Total Size \DOCUMENT\A03\ LC1_01.PCX to LC1_28.PCX 28 files 1,011,402 LC1_##X.PCX 3 files 240,738 LC2_01.PCX to LC2_27.PCX 27 files 1,556,067 LC2_##X.PCX 5 files 530,152 ------------------------- Volume on Disk: 63 files 3,368,359 SOURCE EXAMPLE FILES NONE TECHNICAL REPORT Mark A. Ohrenschall NOAA National Geophysical Data Center Boulder, Colorado The source data were in lat/long projection at 0.5-degree resolution. The source files were in a ASCII record format, with ocean cells omitted. Each data file had a header line containing two different FORTRAN format statements, followed by fixed-length data records containing latitude and longitude in tenths of degrees, referencing the south-west corner of the grid cell, followed by that cell's twelve monthly values. A raster data file was created for each month for each parameter, setting the background to a no-data flag and a program was written to read in grid values from the source files. Results were checked by spot-checking individual grid points. The original data structure was compatible with the GED grid conventions, and no changes were made in the original data values, numerical type, or precision. The data were inspected to verify that there were no obvious artifacts and to spot check the final integrated data against the original source. Some comparisons were made with other data-sets in the database, e.g., the Legates and Willmott data, finding some discrepancies. In particular, comparison with local patterns (e.g., near Mexico) indicated potentially large differences due to variable surface conditions. Otherwise, the data appear to be representative of broad-scale patterns, and reviewers noted that it may provide better resolution than the Legates and Willmott data. *LEEMANS AND CRAMER IIASA CLIMATE GLOBAL (GEOGRAPHIC -- LAT/LONG) RASTER DATA-SETS Avg Month Air Temp #*AVG MONTH AIR TEMP Avg Month Precip #*AVG MONTH PRECIP Avg Month "Cloudiness" #*AVG MONTH "CLOUDINESS" *AVG MONTH AIR TEMP_help DATA ELEMENT: Average Month Surface Air Temperature STRUCTURE: Raster Data Files: 0.5-degree 360x720 GED grid (see User's Guide) SERIES: series of 12 characteristic months SPATIAL META-DATA: LCTMP01.DOC file title : Leemans and Cramer January Temperature (0.1C) data type : integer file type : binary columns : 720 rows : 360 ref. system : lat/long ref. units : deg unit dist. : 1.0000000 min. X : -180.0000000 max. X : 180.0000000 min. Y : -90.0000000 max. Y : 90.0000000 pos'n error : unknown resolution : 0.5000000 min. value : -583 max. value : 406 value units : 0.1 degrees Celsius value error : unknown flag value : -999 flag def'n : flag value -999 indicates no data legend cats : 0 File Series Parameters: File Month Minimum Maximum LCTMP01: January -583 406 LCTMP02: February -546 413 LCTMP03: March -512 423 LCTMP04: April -430 432 LCTMP05: May -284 434 LCTMP06: June -223 429 LCTMP07: July -222 441 LCTMP08: August -214 423 LCTMP09: September -272 426 LCTMP10: October -371 423 LCTMP11: November -445 420 LCTMP12: December -533 417 ATTRIBUTE META-DATA: NONE NOTES: (1) units are in 1/10th degrees Celsius *AVG MONTH AIR TEMP LEEMANS AND CRAMER IIASA CLIMATE January #\GLGEO\RASTER\LCTMP01.IMG February #\GLGEO\RASTER\LCTMP02.IMG March #\GLGEO\RASTER\LCTMP03.IMG April #\GLGEO\RASTER\LCTMP04.IMG May #\GLGEO\RASTER\LCTMP05.IMG June #\GLGEO\RASTER\LCTMP06.IMG July #\GLGEO\RASTER\LCTMP07.IMG August #\GLGEO\RASTER\LCTMP08.IMG September #\GLGEO\RASTER\LCTMP09.IMG October #\GLGEO\RASTER\LCTMP10.IMG November #\GLGEO\RASTER\LCTMP11.IMG December #\GLGEO\RASTER\LCTMP12.IMG *AVG MONTH PRECIP_help DATA ELEMENT: Average Month Precipitation (uncorrected) STRUCTURE: Raster Data File: .5-degree 360x720 GED grid (see User's Guide) SERIES: series of 12 characteristic months SPATIAL META-DATA: LCPRC01.DOC file title : Leemans and Cramer January Precipitation (mm/month) data type : integer file type : binary columns : 720 rows : 360 ref. system : lat/long ref. units : deg unit dist. : 1.0000000 min. X : -180.0000000 max. X : 180.0000000 min. Y : -90.0000000 max. Y : 90.0000000 pos'n error : unknown resolution : 0.5000000 min. value : 0 max. value : 942 value units : millimeters/month value error : unknown flag value : -999 flag def'n : flag value -999 indicates no data legend cats : 0 File Series Parameters: File Month Minimum Maximum LCPRC01: January 0 942 LCPRC02: February 0 652 LCPRC03: March 0 830 LCPRC04: April 0 676 LCPRC05: May 0 1280 LCPRC06: June 0 2695 LCPRC07: July 0 2774 LCPRC08: August 0 1950 LCPRC09: September 0 1106 LCPRC10: October 0 863 LCPRC11: November 0 914 LCPRC12: December 0 743 ATTRIBUTE META-DATA: NONE NOTES: *AVG MONTH PRECIP LEEMANS AND CRAMER IIASA CLIMATE January #\GLGEO\RASTER\LCPRC01.IMG February #\GLGEO\RASTER\LCPRC02.IMG March #\GLGEO\RASTER\LCPRC03.IMG April #\GLGEO\RASTER\LCPRC04.IMG May #\GLGEO\RASTER\LCPRC05.IMG June #\GLGEO\RASTER\LCPRC06.IMG July #\GLGEO\RASTER\LCPRC07.IMG August #\GLGEO\RASTER\LCPRC08.IMG September #\GLGEO\RASTER\LCPRC09.IMG October #\GLGEO\RASTER\LCPRC10.IMG November #\GLGEO\RASTER\LCPRC11.IMG December #\GLGEO\RASTER\LCPRC12.IMG *AVG MONTH "CLOUDINESS"_help DATA ELEMENT: Average Month "Cloudiness" (% sunshine) STRUCTURE: Raster Data File: 0.5-degree 360x720 GED grid (see User's Guide) SERIES: series of 12 characteristic months SPATIAL META-DATA: LCCLD01.DOC file title : Leemans and Cramer January Cloudiness (% Sunshine) data type : byte file type : binary columns : 720 rows : 360 ref. system : lat/long ref. units : deg unit dist. : 1.0000000 min. X : -180.0000000 max. X : 180.0000000 min. Y : -90.0000000 max. Y : 90.0000000 pos'n error : unknown resolution : 0.5000000 min. value : 0 max. value : 95 value units : percentage sunshine hours of potential hours per month value error : unknown flag value : 254 flag def'n : flag value 254 indicates no data legend cats : 0 File Series Parameters: File Month Minimum Maximum LCCLD01: January 0 95 LCCLD02: February 4 94 LCCLD03: March 9 88 LCCLD04: April 2 92 LCCLD05: May 2 95 LCCLD06: June 0 98 LCCLD07: July 0 100 LCCLD08: August 0 98 LCCLD09: September 0 98 LCCLD10: October 0 99 LCCLD11: November 0 96 LCCLD12: December 0 100 ATTRIBUTE META-DATA: NONE NOTES: (1) Regional discrepancies with the FAO climatic database have been noted (e.g., Vietnam). *AVG MONTH "CLOUDINESS" LEEMANS AND CRAMER IIASA CLIMATE January #\GLGEO\RASTER\LCCLD01.IMG February #\GLGEO\RASTER\LCCLD02.IMG March #\GLGEO\RASTER\LCCLD03.IMG April #\GLGEO\RASTER\LCCLD04.IMG May #\GLGEO\RASTER\LCCLD05.IMG June #\GLGEO\RASTER\LCCLD06.IMG July #\GLGEO\RASTER\LCCLD07.IMG August #\GLGEO\RASTER\LCCLD08.IMG September #\GLGEO\RASTER\LCCLD09.IMG October #\GLGEO\RASTER\LCCLD10.IMG November #\GLGEO\RASTER\LCCLD11.IMG December #\GLGEO\RASTER\LCCLD12.IMG *LEGATES AND WILLMOTT CLIMATE_help A04 Legates and Willmott Average Monthly Surface Air Temperature and Precipitation (re-gridded) Gauge Corrected Precipitation (re-gridded) Standard Error for Gauge Corrected Precipitation (re-gridded) Measured Precipitation (re-gridded) Surface Air Temperature (re-gridded) SOURCE EXAMPLE: Average Monthly Air Temperature and Precipitation (Source Examples) DATA-SET DESCRIPTION Data-Set Name: Legates and Willmott Average Monthly Surface Air Temperature and Precipitation (re-gridded) Principal Investigator: David R. Legates and Cort J. Willmott Scientific Reference: (* reprint on CD-ROM) + Legates, David R. 1989. "A high-resolution climatology of gage-corrected global precipitation." In: Precipitation Measurement, B. Sevruk (ed.), Proceedings of the WMO/IAHS/ETH International Workshop on Precipitation Measurement, St. Moritz, Switzerland, Dec. 3-7, 1989. Zurich: Swiss Federal Institute of Technology, pp. 519-526. + Legates, David R. and Cort J. Willmott. 1990. "Mean seasonal and spatial variability in gauge-corrected global precipitation." International Journal of Climatology, vol. 10. pp. 111-127. + Legates, David R. and Cort J. Willmott. 1990. "Mean seasonal and spatial variability in global surface air temperature." Theoretical and Applied Climatology, vol. 41, pp. 11-21. SOURCE Source Data Citation: Legates, D.R. and C.J. Willmott, 1989. Average Monthly Surface Air Temperature and Precipitation. Digital Raster Data on a .5-degree Geographic (lat/long) 361x721 grid (centroid-registered on .5 degree meridians). Boulder CO: National Center for Atmospheric Research. 4 files on 9-track tape. 83MB. Contributor: Dr. David R. Legates and Dr. Cort J. Willmott Department of Geography Center for Climatic Research College of Geosciences Department of Geography University of Oklahoma University of Delaware Norman, OK 73019 USA Newark, DE 19716 USA (405) 325-6547 (302) 451-8998 Distributor: NCAR Vintage: circa 1980's Lineage: (1) Principal Investigators: David R. Legates and Cort J. Willmott (2) Archived and Distributed by: Roy Jenne National Center for Atmospheric Research Boulder, CO ORIGINAL DESIGN Variables: VARIABLE UNITS PRECISION (1) Measured precipitation mm/month 1mm (2) Gauge corrected precipitation mm/month 1mm (3) Standard error of corrected precipitation mm/month 1mm (4) Surface Air temperature degrees Celsius 0.1 C Origin: 24,941 independent surface air temperature and 26,858 independent precipitation stations, and oceanic grid point estimates from a variety of sources (see Primary Documentation). Geographic Reference: latitude/longitude Centroid-registered grid cells on 30-minute lat/long meridians. Original grid (361x721) extends from pole to pole and originates at the International Date Line. Geographic Coverage: Global Maximum Latitude: +90 degrees (N) Minimum Latitude: -90 degrees (S) Maximum Longitude: +180 degrees (E) Minimum Longitude: -180 degrees (W) Geographic Sampling: Weighted (using a spherically-based interpolation algorithm) 30-minute cell averages of station data and oceanic trackline samples, on a centroid-registered 30-minute grid. Time Period: Modern "average" climate, from records mostly between 1920 and 1980. Temporal Sampling: 12 characteristic months and characteristic years for each variable, representing long-term (approx. 60 year) monthly and annual means. INTEGRATED DATA-SET Data-Set Citation: Legates, D.R. and C.J. Willmott. 1992. Monthly Average Surface Air Temperature and Precipitation. Digital Raster Data on a 30 minute Geographic (lat/long) 360x720 grid. In: Global Ecosystems Database Version 1.0: Disc A. Boulder, CO: NOAA National Geophysical Data Center. 48 independent and 4 derived single- attribute spatial layers on CD-ROM, 47.2MB. [first published in 1989] Analyst: John Kineman and Mark Ohrenschall Projection: Geographic (lat/long), GED window (see User's Guide). Spatial Representation: 30-minute cell values interpolated from the 4 overlapping quadrant values of the original grid, which contained values interpolated from irregularly spaced point observations. Temporal Representation: 12 characteristic months and characteristic years for each variable, representing long-term (approx. 60 year) means. Data Representation: 2-byte integers, representing: VARIABLE UNITS PRECISION 1) Measured precipitation mm/month 1mm 2) Gauge corrected precipitation mm/month 1mm 3) Surface Air temperature C x 10 .1 C 4) Standard deviation (expressed in the same units and precision as above) of the interpolated cell values for each measurement (precipitation, corrected precipitation, and temperature) are provided as separate layers as an estimate of uncertainty introduced by the re-gridding process -- these three standard deviation ("SD") files were not part of the original data-set. 5) RMS Std. error of corrected precip. mm/month 1mm Note that this variable was re-gridded by a different method than the first three: The re-gridding method employed a root-mean-square average to combine the 4 quadrant values into the newly registered grid cell for the GED. Layers and Attributes: 52 independent and 39 derived single-attribute spatial layers Compressed Data Volume: 15,707,536 bytes ADDITIONAL REFERENCES Legates, David R. 1987. A Climatology of Global Precipitation. Pub. Climatol., 40(1): 103 p. Sevruk, B. 1989. "Reliability of precipitation measurement." In: Precipitation Measurement, B. Sevruk (ed.), Proceedings of the WMO/IAHS/ETH International Workshop on Precipitation Measurement, St. Moritz, Switzerland, Dec. 3-7, 1989. Zurich: Swiss Federal Institute of Technology, pp. 519-526 Shepard, D. 1968. "A two-dimensional interpolation function for irregularly-spaded data." In: Proceedings of 23rd National Conference of the Association for Computing Machinery. ACM Pub. P-68. Princeton, NJ: Brandon/Systems Press, Inc. Willmott, C.J., C.M. Rowe, and W.D. Philpot. 1985. "Small- scale climate maps: a sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. The American Cartographer, 12(1): 5-16. DATA-SET FILES Location Name Number Total Size Spatial Data: \GLGEO\RASTER\ LWCPR00.IMG to LWCPR12.IMG13 files 6,739,200 LWCSD00.IMG to LWCSD12.IMG13 files 6,739,200 LWERR00.IMG to LWERR12.IMG13 files 6,739,200 LWMPR00.IMG to LWMPR12.IMG13 files 6,739,200 LWMSD00.IMG to LWMSD12.IMG13 files 6,739,200 LWTMP00.IMG to LWTMP12.IMG13 files 6,739,200 LWTSD00.IMG to LWTSD12.IMG13 files 6,739,200 Headers: \GLGEO\META\ LWCPR00.DOC to LWCPR12.DOC13 files 6,921 LWCSD00.DOC to LWCSD12.DOC13 files 6,799 LWERR00.DOC to LWERR12.DOC13 files 6,908 LWMPR00.DOC to LWMPR12.DOC13 files 6,775 LWMSD00.DOC to LWMSD12.DOC13 files 6,944 LWTMP00.DOC to LWTMP12.DOC13 files 6,931 LWTSD00.DOC to LWTSD12.DOC13 files 6,814 Palettes: none Time Series: \GLGEO\META\ LWCPR.TS 1 file 123 LWMPR.TS 1 file 123 LWTMP.TS 1 file 123 ------------------------- Volume on Disk: 185 files 47,222,861 REPRINT FILES Location Name Number Total Size \DOCUMENT\A04\ LW1_01.PCX to LC1_17.PCX 17 files 864,882 LW1_##X.PCX 16 files 2,871,460 LW2_1.PCX to LC1_8.PCX 8 files 695,424 LW2_#X.PCX 6 files 921,810 LW3_01.PCX to LC1_11.PCX 11 files 577,957 LW3_##X.PCX 7 files 1,263,135 ------------------------- Volume on Disk: 65 files 7,194,668 SOURCE EXAMPLE FILES Location Name Number Total Size Spatial Data: \SOURCE\RASTER\ LWSCP07.IMG 1 file 520,562 LWSER07.IMG 1 file 520,562 LWSMP07.IMG 1 file 520,562 LWSTM07.IMG 1 file 520,562 Headers: \SOURCE\RASTER\ LWSCP07.DOC 1 file 538 LWSER07.DOC 1 file 528 LWSMP07.DOC 1 file 537 LWSTM07.DOC 1 file 531 ------------------------- Volume on Disk: 8 files 2,084,382