Principal Investigators:

Summary:

Primary References:

* Olson, J.S., J.A. Watts, and L.J. Allison, 1983. Carbon in Live Vegetation of Major World Ecosystems, Report ORNL­5862, Oak Ridge Laboratory, Oak Ridge, Tennessee. (Incorporated in NDP-017, cited above)

Olson World Ecosystems
 

• Integrated dataset
• Design
• Source
• Additional references
• File lists

Dataset Description

INTEGRATED DATA­SET

Data­Set Citation:

Projection:

Spatial Representation:

Temporal Representation:

Data Representation:

Layers and Attributes:

Dataset Description

DESIGN

Variables:

(1) Major Groups at 30-minute resolution (version 1.3)
(2) Detailed Categories at mixed 30-minute and 10-minute resolution (version 1.4)
(3) Resolution codes for (version 1.4)

Origin:

Geographic Reference:

Geographic Coverage: Global

Geographic Sampling:

Time Period:

Modern

Temporal Sampling:

Dataset Description

SOURCE

Source Data Citation:

Contributor:

Distributor:

Date of Production:

Lineage & Contacts:

(2) Reprocessed and updated by:
Jerry S. Olson, Lee Stanley, Jeff Colby, and Mark Ohrenschall
NOAA/NGDC, Boulder, CO 80303 

Dataset Description

ADDITIONAL REFERENCES

* Olson, J.S., Watts, J.A., and Allison, L.J. 1985. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: A Database. NDP-017. Carbon Dioxide Information Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee.

Olson, R.J., Goff, F.G., and Olson, J.S. 1976. Development and applications of regional data resources in energy­related assessment and planning. Advancements in Retrieval Technology as Related to Information Systems. AGARD­CP­201. pp. 12.1­12.7, Technical Information Panel, AGARD, NATO, Washington D.C.

* Olson, J.S., 1982. "Earth's Vegetation and Atmospheric Carbon Dioxide," in Carbon Dioxide Review: 1982. Ed. by W.C. Clark, Oxford University Press, New York, pp. 388­398. (Incorporated in NDP-017, cited above)

Olson, J.S., and Watts, J.A. 1982. Map of Major World Ecosystem Complexes Ranked According to Carbon in Live Vegetation, 1982. Map insert in: W. C. Clark (ed.), Carbon Dioxide Review: 1982, Oxford University Press, New York, (Also in Olson et al. 1983)

Olson, J.S. and Watts, J.A. 1982. "Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation." Oak Ridge National Laboratory, Oak Ridge, Tennessee (map).

Olson, J.S., Watts, J.A. and Allison, L.J. 1985. Major world ecosystem complexes ranked by carbon in live vegetation: A Database. NDP­017, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee.

References used in updating from WE1.2 (CDIAC Data Package NDP-017) to WE1.4:

Barth, H. ----. Mangroves. In: D.N. Sen and K.S. Rajpurohit (eds.), Contributions to the Ecology of Halophytes. Dr. W. Junk, Publishers, The Hague (in press).

Bazilevich, N.I. 1974. Energy flow and biogeochemical regularities of the main world ecosystems. pp. 182-186. In: Structure, Functioning and Management of Ecosystems. Centre for Agricultural Publishing and Documentation, Wageningen, The Netherlands.

Bazilevich, N.I., and Rodin, L.Ye. 1967. Maps of productivity and the biological cycle in the Earth's principal terrestrial vegetation types. Izv. Vses. Geogr. Obschestva. 999(3):190-194.

Bazilevich, N.I., and Rodin, L.Ye. 1971. Geographical regularities in productivity and the circulation of chemical elements in the Earth's main vegetation types. Sov. Geogr.: Rev. and Transl. 12:24-53.

Bazilevich, N.I., and Titlyanova, A.A. 1980. Comparative studies of ecosystem function. pp. 713-758. In: A.I. Breymeyer and G.M. Van Dyne (eds.), Grasslands, Systems Analysis and Man. Cambridge University Press, Cambridge, United Kingdom.

Bazilevich, N.I., Gordeeva, T.K., Zalensky, O.V., Rodin, L.Ye.,  and Ross, J.K. 1969. Obschchie Teoreticheskie Problemi Biologicheskoi Produktivnosti. Nauka, Leningrad.

Bazilevich, N.I. Pers. comm. 1968-1978. (1968 Symposium on Roots Systems and Rhizosphere Organisms, Moscow, Leningrad, Dushanbe; 1974 World Soils Congress, Moscow; and her 1978 paper read by J. Olson to International Ecological congress, the Hague, Netherlands.

Brown, S., and Lugo, A.E. 1981. The role of the terrestrial biota in the global CO2 cycle. Preprints 26:1019-1025. In: Report of the Symposium on the Carbon Dioxide Issue. American Chemical Society, Division of Petroleum Chemistry, New York.

Brünig, E.F. 1969. On the limits of vegetable productivity in the tropical rain forest and the boreal coniferous forest. J. Indian Bot. Soc. 46:314-322.

Duvigneaud, P. (ed.). 1971. Productivity of Forest Ecosystems. UNESCO, Paris.

Gerasimov, E.P., et al. (eds.). 1964. Fiziko-geograficheskii Atlas Mira (Physical-Geographic Atlas of the World). USSR Academy of Science, Moscow. (also cited as Fillipov)

Goward, S. N., Tucker, C.J., and Dye, D.G. 1985. North American vegetation patterns observed with the NOAA­7 advanced very high resolution radiometer. Vegetatio 64: 3­64.

Grubb, P.J. 1977. Control of forest growth and distribution on wet tropical mountains. Ann. Rev. Ecol. Syst. 8:83-107.

Henderson­Sellers, A., Wilson, M.F., Thomas, G., and Dickinson, R.E. 1986. Current Global Land­Surface Data Sets for Use in Climate­Related Studies. NCAR Technical Notes, NCAR/TN­272+STR, National Center for Atmospheric Research, Boulder, Colorado

Hobbs, R., and Mooney, H. (eds.). 1990. Remote Sensing and Biosphere Functioning. Ecological Studies. Springer­Verlag, New York.

Koomanoff, V.A. 1988. Analysis of Global Vegetation patterns: A Comparison Between Remotely Sensed Data and a Conventional Map. Report 890201 of Laboratory for Global Remote Sensing Studies, Geography, University of Maryland, College Park MD.

Küchler, A.W. 1978. Natural vegetation map. pp. 16-17. In: E.B. Espenshade, Jr., and J.L. Morrison (eds.), Goode's World Atlas, 15th Edition. Rand McNally & Company, Chicago.

Loveland, T. R., Merchant, J.W., Ohlen, D.O., and Brown, J.F. 1991. Development of a land­cover characteristics database for the conterminous United States. Photogram. Engineering and Remote Sensing 57: 1453­1463.

Müller, J.­F. 1992. Geographical distribution and seasonal variation of surface emissions and deposition velocities of atmospheric trace gases. J. Geophysical Research 97: 3787­3804.

Rodin, L.Ye, and Bazilevich, N.I. 1967. Production and Mineral Cycling in Terrestrial Vegetation. Oliver and Boyd, Edinburgh. [Translated from L. Ye Rodin and N.I. Bazilevich, 1965. Dynamics of the Organic Matter and Biological Turnover of Ash Elements and Nitrogen in the Main Types of the World Vegetation. Nauka, Moscow-Leningrad (in Russian).]

Rodin, L.Ye, and Bazilevich, N.I. 1968. World distribution of plant biomass. pp. 45-52. In: F.E. Eckardt (ed.), Functioning of Terrestrial Ecosystems at the Primary Production Level. UNESCO, Paris.

Rowe, J.S. 1972. Forests of Canada. Canadian Forestry Service, Ottawa.

Schmithüsen, J. 1976. Atlas zur Biogeographie. Meyers Grosser Physischer Weltatlas, Band 3, Bibliographisches Institute, Manheim/Wien/Zurich, Switzerland.

Sollins, P., Reichle, D.E., and Olson, J.S. 1973. Organic matter budget and model for a southern Appalachian Liriodendron forest. EDFB/IBP-73/2. Oak Ridge National Laboratory, Oak Ridge, Tennessee.

Specht, R.L. 1981. Structural attributes -- foliage projective cover and standing biomass. In: A.N. Gillison and D.J. Anderson (eds.), Vegetation Classification in the Australian Region. Australian National University Press, CSIRO, Canberra.

Dataset Description

FILE LISTS

• Dataset Files
• Reprint Files
• Source Files(examples)

Olson World Ecosystems
 

• World Ecosystems (version 1.3)
• World Ecosystems (version 1.4d)
• World Ecosystems (version 1.4dr resolution codes)
• SOURCE ELEMENT: World Ecosystems (WE1.4)

World Ecosystems (version 1.3)
 

Description:

 Structure:

 Series:

 System Files:

 Notes:

 World Ecosystems (version 1.4d)

Description:

Structure:

Series:

System Files:

 Notes:

World Ecosystems (version 1.4dr resolution codes) 
 

Description:

 Structure:

 Series:

 System Files:

 Notes:

 SOURCE ELEMENT: World Ecosystems (WE1.4) 
 

Description:

 Structure:

 Series:

 System Files:

 Notes:

Olson World Ecosystems
 

John J. Kineman, Mark A. Ohrenschall, and Jeffrey D. Colby
NOAA National Geophysical Data Center
Boulder, Colorado

PREFACE (Jerry Olson, April 22, 1992)

Several years spent before and after my 1985 early retirement from Oak Ridge National Laboratory (ORNL) in Tennessee brought together ideas and data on global patterns of ecological systems (ecosystems). Patterns previously mapped by large computers (Olson et al. 1983, 1985) are now made available, with improvements, for personal computers (PCs).

Parts of 3 years in Europe (1985­88) and in Western States and the Pacific (1988­91) helped improve my World Ecosystems database. It was licensed for the National Center for Atmospheric Research (NCAR), in Boulder, Colorado, to help set the parameters for calculating air­landscape interaction in a new Community Climate Model (CCM2, in NCAR's Climate and Global Dynamics Division, CGD). Soon NCAR's Atmospheric Chemistry Division (ACD) started using the 1989 version for estimating chemical contributions from plants or fires to air (Müller 1992).

In Boulder, I also started refining the half­degree resolution of my previous worldwide grid (WE1.2: 720 columns x 360 rows of picture elements or pixels to 10­minute resolution, initially for a pilot project chosen for Africa by the World Data Center­A). WDC­A's host, the National Geophysical Data Center of the National Oceanic and Atmospheric Administration (NGDC/NOAA), meanwhile was distributing other data, using the larger capacity of compact disks.

During the beta test (outside checking) of NOAA's Global Ecosystem Database (GED) in late 1991 I compared my mapping with the index of greenness estimated from NOAA's meteorological satellites and with GIS layers contributed by others. The combined features WE1.4D show more than any data layer could alone about global patterns. But they also emphasize a problem we face repeatedly­­striving for breadth of global coverage while working toward depth of data layers, and eventually of understanding, and of actions to improve our world. The grouping of types in WE1.3A (section 2 below) is a step toward handling such breadth and depth together.

Acknowledgment: I thank Lee Stanley of GPC and Mark Ohrenschall, John Kineman, and David Hastings of NGDC/NOAA and WDC­A. We used Idrisi Geographic Information System (GIS) software version 3.0 from Ron Eastman, Clark University, Worcester, Massachusetts, 01610; version 4.0 was not available during this work.

World Ecosystems WE is a Trademark and Global Patterns trade name of Global Patterns Company of Roane County, Tennessee. Please contact the author for further information or advanced or trial versions of WE or further documentation. These working versions of the World Ecosystems data-set are released for public testing by GPC and for educational uses, with the understanding that it is incomplete. Improvements by users may be offered to GPC for "in kind" trade as part of the license fee for later versions that are not released to the public domain, i.e. for research and monitoring groups for whom the most current or tested version is important for their work.

INTRODUCTION

This report explains how the World Ecosystem data-set version 1.4D (WE1.4D), and version 1.3A (WE1.3A) were produced from previous versions, and how they describe land parts of Earth's sphere of life, or Biosphere.

Detailed ecosystem types (0-73) in WE1.4D, at both 10-minute and 30-minute resolution, are related to the broader Main Groups of ecosystem types (0-29) in WE1.3A, at 30-minute resolution. The land Groups include forests of conifer, broadleaved, mixed, and mostly tropical moist broadleaved (mostly evergreen) types. Other mixtures include: grass­shrub, shrub­tree, semidesert and tundra, field/woods and savanna, northern taiga, forest/field and dry evergreen woods, wetlands, desert, succulent/thorn woods, crop and settlements, and other (ice and fringe) types.

Since WE1.4D incorporates data at mixed resolutions (10 and 30 minutes lat/long), a separate data element (OWE14DR) is provided with resolution codes for the main file (OWE14D). A special palette file (OWE13A.PAL) is provided for the WE1.3A data, mostly for convenience in recognition of some conventional color assignments.

HISTORY OF THE OLSON DATA SET

The original version (1.0) of the Olson Ecosystems data set was produced for the DOE Carbon Dioxide Information and Analysis Center in Oak Ridge Tennessee by Jerry S. Olson, Julia A. Watts, and Linda J. Allison. Reprints of the Primary Documentation from this work are included on disc (see Reprint Files, above), and should be consulted for detailed information on the creation of these data and their use of estimating carbon content.

DATA UPDATING PROCEDURES FOR THE 1991 PROTOTYPE (WE1.4)

During the summer of 1989, Jerry Olson, Lee Stanley and research assistants from the National Geophysical Data Center updated the data set, "Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: A Database." The results of this update have been incorporated into Olson World Ecosystem WE1.4.

Data were revised at both 30 and 10 minute grid cells. Changes were first made for the 30 minute data between 20 degrees West and 70 degrees East. In addition, modifications were made to limited portions of the United States data. Revisions were made in the following ecological classes:

(1) tropical forest (type 33)
(2) polar deserts (type 69)
(3) ice (type 70)
(4) a few areas of mangrove/tropical swamp forest (type 72)

Updates for the United States were quite limited and affected mostly the islands and coastlines of Alaska. To a lesser extent changes were made along the western and eastern coastlines of the lower 48 states.

Modifications at 10 minute resolution were numerous, but were confined to the African continent and some coastlines. They included changes in tropical montane complexes (type 28), broadleaved evergreen forest types at high altitudes with cool climates despite tropical latitudes (type 6), for example Cameroon, Ethiopia, and other areas of East Africa and some in the Atlas Mountains; mangrove tropical swamp forests (type 72), and coastlines. Salt/soda flats (type 71) were not systematically reviewed but received sample editing.

SUBSEQUENT EDITING FOR WE1.3A AND WE1.4D

Greenness indices from Advanced Very High Resolution Radiometer (AVHRR) satellite data were used in certain desert and coastal ecosystems. WE1.4D is a first step toward global resolution at 10 arc­minutes, replacing a test version called GOLSON in GED Prototype (Version 0.1). Most of the main land cover complexes are still effectively mapped at 30' (half­degree) scale in WE1.4D. Ten arc­minute (10') improvements are mostly limited to areas with low greenness indices from NOAA satellites and, in Africa, to mangrove (type #72) and mountain complexes (#6, 28). Conditions of high and intermediate greenness are important, but have quite different meanings in different parts of the biosphere.

The quality of basic geographic data is very uneven from various parts of the world. The computer media becoming available from the NOAA National Geophysical Data Center/World Data Center­A (WDC­A) and from Global Patterns Company (GPC) provide worldwide coverage of many features that can be quantified for interpreting changes of climate and atmospheric chemistry and many feedbacks on life.

Ten­minute mapping of certain landscape types was donated in 1989 for NGDC's Africa Pilot Project for the IGBP, as one testing step of GPC. Yet comparable refinement to 10' has not yet been done evenly on any continent. The main patterns of the Olson World Database for the NOAA CD­ROM in 1992 (GED) still have the same half­degree resolution as the 1982 printed map of Olson and Watts (~75 x 150 cm: enclosed with and documented by Olson et al. 1983, and the 1985 re­release of the same report with computer­readable data by Oak Ridge National Laboratory, ORNL). ORNL's Carbon Dioxide Information and Analysis Center (CDIAC contact: Tom Boden) will continue to distribute such maps, and the Olson et al. (1985) Numeric Data Set 017, which is called WE1.2 in the Global Patterns numbering series.

MAIN LANDSCAPE GROUPS AND ECOSYSTEM TYPES

The documentation file OWE14D.DOC gives the detailed category legend for WE1.4D, modified slightly from that of the test version (GOLSON) prepared in 1989 for testing in 1990­91. Closely related types from WE1.4D are put in GROUPS, with Group numbers, in the two sections below (A-main types, and B-selected "fringe" types). These MAJOR GROUPS and numbers were used to create a new 30­minute data file (WE1.3A).

The narrower type numbers of WE1.4D are listed below each Major Group description. Brackets [] enclose those legend numbers that are still applied very unevenly. Braces {} foretell more subdivisions, not yet used or even explained here. This list is expanded slightly from previous ORNL reports (Olson et al. 1983, 1985), with additions between 0 and 19, and above 71. Readers should consult GPC and references just cited for more explanation. Readers should be forewarned that the Canadian Centre for Remote Sensing (CCRS) compact disk will use an intermediate version, renumbered to omit certain numbers that were deliberately skipped here. Despite potential confusion in numbering sequence between CCRS and other releases, the three­LETTER mnemonic codes given below between old (ORNL/GPC/NOAA) numbers and titles should clarify the match with the ISY Global Change Encyclopedia (GeoScope).

The Group sequence below is arranged to take advantage of a standard IBM color palette for either the Enhanced Graphic Adapter (EGA), or Video Graphics Adapter (VGA). Thus, some color conventions (e.g. purple for tropical moist forest) follow the UNESCO vegetation committee suggestions or our older ORNL printed map (Olson and Watts 1982). In most printing, black on the video screen (0) is replaced with pale background for the ocean (e.g. pale cyan). A 16-color IDRISI color palette is provided that retains the IBM color scheme with this minor change to the background (OWE13A.PAL).
 

MAJOR GROUP DESCRIPTION

(OWE13A)

A. Main LAND GROUPS of Ecosystem Complexes (1-14):

(0) SEAS Oceans, Mediterranean Sea, Black Sea
FORESTS:
(1) CONIFOR (CONIfer FORest) here stands for all complexes dominated by coniferous trees (evergreen or deciduous, in snowy climate or not), except for the coastal fringe below (i.e. group 15 type 20 in section 2B):

WE1.4D classes grouped here:
#21 MBC Main Boreal Conifer forest, closed or open;
#22 SNB Snowy Non­Boreal conifer forest;
#27 NSC Non­Snowy Conifer forest.

(2) BRODLFOR (BROADLeaf FORest) of temperate and seasonally dry ~subtropical (rain­green or partly drought­deciduous) groups (#32 for the latter).

WE1.4D classes grouped here:
#25 SDF Snowy Deciduous Forest, i.e. summergreen (= cold­deciduous) types;
#26 TBF Temperate Broadleaf Forest: deciduous, semideciduous, and some temperate­subtropical broadleaf evergreen types that are least active in winter. (The latter could be shifted to type #6 and perhaps to group 5 later, in order to get more broadleaf evergreen types together.)
[#6] TBE Temperate/Tropical­montane Broadleaf Evergreen covers warm temperate or montane broadleaf evergreen forest, so far mostly in Africa where our pilot test for 10' started.
#32 RGD Rain­green (Drought­deciduous) or very seasonal dry evergreen forests to open woodlands, very frequently burned.

(3) MIXEDFOR MIXED FORest here includes not only deciduous­conifer mixtures, within stands and as mosaics over the landscape, but many gradations toward broadleaved evergreen. Conifers are common, but often uneven; native and/or planted.

WE1.4D classes grouped here:
#23 CDF Conifer/Deciduous Forest: snow persisting in winter;
#24 TBC Temperate Broadleaf/Conifer forest: with deciduous and/or evergreen hardwood trees;
[#54] TER Temperate Evergreen Rainforests, e.g. in Chile.

For simplicity, southern Boreal (= taiga in Russian) deciduous mixtures with aspen, birch, and/or larch as well as evergreen conifers are included here:

#60 SDT Southern Dry Taiga, or similar aspen/birch with northern and/or mountain conifers;

#61 LT Larch Taiga with deciduous conifer.

(4) GRASSHRB GRASS­SHRUB­HERB complexes vary widely in structure, precipitation and temperature. Few trees (mostly sparse, planted, or streamside/ravine patches, if any) break the open horizon. Cropland, especially dryland cereal grains or local irrigation, can be important economically, but is a minor fraction of total land cover/use in most years.

WE1.4D classes grouped here:

[#2] SSG Short or Sparse Grass/shrub of semiarid climates;

#40 CGS Cool Grass/Shrub, snowy in most years

#41 MGS Mild/warm/hot Grass/Shrub,

#42 CSM Cold Steppe/Meadow, +/­ larch woods (in Siberia), scrub (Bering sea) or tundra (Tibetan highland). (This class might be regrouped with the tundra margin group, especially when better defined at 10' resolution.)

(5) TROPICFR = TROPICAL/subtropical moist or Broadleaf Humid FORest. Most are evergreen but deciduous forms increase in the subtropics, especially with extreme monsoon droughts.

WE1.4D classes grouped here:

#28 TMC Tropical Montane Complex, typically evergreen, including dwarfed ("elfin") forest, opening to grass, or tall or short forbs (puna, paramo) above timberline;

#29 TBS Tropical Broadleaf Seasonal, with dry or cool season;

#33 TRF Tropical RainForest.

(6) SCRUBWDS SCRUB­WOODS Complexes, often with grass or crops locally, tend to have a dry season and/or pronounced fire. Trees are not always rare, but may be short or open­grown. The bigger ones tend to cluster on favorable substrate or terrain, or near places where fire starts or spreads less often.

WE1.4D classes grouped here:

{#16} BES Broadleaf Evergreen Scrub, commonly with the following

#46 MES Mediterranean­type Evergreen (mostly) broadleaved Scrub and forest relics;

#47 DHS Dry or Highland Scrub or open woodland.

(7) SEMIDTUN SEMIDesert or TUNdra. Open shrub or shrub­steppe (low grass) of very dry regions may grade into the preceding groups 5 and 6. Dwarf­shrub or grass­like (graminoid) tundra tends to occur above the altitudes or latitudes of local tree line (see groups 9 and 27 below).

WE1.4D classes grouped here:

#51 SDS SemiDesert/Desert Scrub/succulent/sparse grass;

#52 CSS Cool/cold Shrub Semidesert/steppe;

#53 TUN Tundra (polar, alpine).

(8) FLDWDSAV FIELD/WOODS mosaic or SAVANNA. Tall grass or crops together often cover more area than forest or woodland in Field/woods:

WE1.4D classes grouped here:

#55 SFW Snowy Field/Woods complex;

#58 FWG Field/Woods with Grass and/or Cropland;

#43 SGW Savanna/Grass, Seasonal Woods: Trees or shrubs above grass groundcover may be interspersed on many scales in savanna belts of varying drought duration and high fire frequency.

(9) NORTAIGA NORThern TAIGA or SUBALPINE narrow­crowned sparse conifer and/or dwarf deciduous tree/scrub/meadow/wetland mosaics

WE1.4D classes grouped here:

#62 NMT Northern or Maritime Tiaga typifies a wide latitude belt or a narrow altitude belt above denser forest or woodland.

{#62a} or a new number might distinguish the subalpine mosaics at lower latitudes.

(10) FORFDREV FOREst/FIELD or DRy EVergreen mixtures commonly have much broadleaf tree and tall shrub, but conifers pure or mixed (as in group 3) may be important. Nonwooded land is also interspersed, especially where low forest or open woodland is cleared and burned for crops or grazing, and where drought or seasonally wet soil limits establishment or the mature height and density of trees.

WE1.4D classes grouped here:

#56 FFR Forest/Field complex with Regrowth after disturbances, mixed with crops and/or other non­wooded lands;

#57 SFF Snowy Forest/Field, commonly openings are pasture and/or mires;

#48 DEW Dry Evergreen Woodland or low forest, mapped mostly in interior Australia and South America.

(11) WETLAND Mires, Marshes, or Swamps.

WE1.4D classes grouped here:

#44 MBF Mires include peaty Bogs and Fens (mostly in high latitudes;

#45 MOS Marsh or Other Swampy wetlands include various transitions to or mixtures with trees.

(Also see group 19 below for #72 mangrove, digitized first for Africa in GED.)

(12) DESERTS ~Mostly Bare, Sandy, or Salt­Soda deserts grade into semideserts (group 7); both have patches interspersed within the other and within dry grassland.

WE1.4D classes grouped here:

#8 DMB Desert, Mostly Bare stone, clay or sand;

#50 SDB Sand Deserts, partly Blowing Dunes;

#71 SSF Salt/Soda Flats: desert playas, occasionally with intermittent lakes.

(13) SHRBTRST SHRUB­TRee, Succulent or Thorn thickets are alternatives to the tree/grass life form strategy response to tropical droughts. Two droughts per year may occur near the equator as rain belts shift north or south; or droughts may persist with little or no relief as in eastern-most Brazil.

WE1.4D classes grouped here:

#59 STW Succulent and Thorn Woods or scrub is widespread;

#49 HVI Hot Volcanic Islands presently is used in the Galapogos Islands, which have local denser forest on some older lava flows but wide areas of sparse cover on recent lavas.

(14) CROPSETL CROP/SETTLement/Commercial Complexes include rice and other irrigated cropland (#36; 37­39) and other cropland, with interspersed villages, cities, or industrial areas (#30, 31).

WE1.4D classes grouped here:

#30 CFS Cool Farmland and Settlements, more or less snowy;

#31 MFS Mild­hot Farmland and Settlements;

#36 PRA Paddy Rice and Associated land mosaics;

#37 WCI Warm­hot Cropland, Irrigated extensively;

#38 CCI Cool Cropland with Irrigation of variable extent;

#39 CCP Cold Cropland and Pasture, irrigated locally.

B. Ice, Land­water and Other FRINGES (15­29):

Glacier ice and the following mostly narrow fringe types can be distinguished on a separate video display (pagedown image with Idrisi software using the same color palette as for 0­14). Or a palette with more colors can be defined by the user.

(15) CONIFERFC CONIFER RAINForest FRinge Coast is here applied to snowy conifer rainforest in a narrow band from the southern Alaska to coastal Washington and Oregon:

WE1.4D classes grouped here:

#20 SRC Snowy, Rainy Coastal Conifer

(16­18) temporarily reserved for later uses.

(19) MANGROVE is separately digitized only for Africa.

WE1.4D classes grouped here:

#72 MSM Mangrove and non­saline Swamps and tidal Mudflats; may also be common within group 5, 7 or 10.

(20) WALANCST WAter/LANd mixtures & COASTal SYSTems. Previously mapped mostly as Coast/hinterland complexes:

WE1.4D classes grouped here:

#65 CNW Coastal: NorthWest quadrant near most land;

#66 CNE Coastal: NorthEast quadrant near most land;

#67 CSE Coastal: Southeast quadrant near most land;

#68 CSW Coastal: Southwest quadrant near most land;

{80} CWL Coastal Water/Land (~51­90% water) besides those oriented per #65­68: beach and various dunes, cliff/rock/fjord, and delta complexes as well as inland types are common. Locating such kinds of coastal ecosystems and landscapes are refinements for the future.

(21­22) reserved for later use.

(23) ISLFRING ISLand­shore water FRINGes really just mean >90% water. But in practice this applies mostly to small islands. Edges of islands or mainland may occur, with near­shore ocean or inland water bodies:

WE1.4D classes grouped here:

#73 ISL Islands and shore waters in oceans and/or lakes.

(24) LANDWATR LAND­WATER combinations, with water ~31­50%, include not only additional coastal pixels with more land but also many 10' land pixels with small lakes or wide rivers or reservoirs.

WE1.4D classes grouped here:

{#74­76} in GPC's extended legend are expected to include complexes with lake and wetland mixtures, alluvial wildlands, floodplain and/or shoreline farms and settlements or ports.

(25) ICE is mostly in Antarctica (#17, or new #12 when the long legend is revised) or Greenland, or in smaller glaciers (#70).

WE1.4D classes grouped here:

#17 ICE Antarctic glacial cap [may be #12 in future versions];

#70 GLA Glaciers in polar or alpine complex, with rock fringes.

(26) POLARDES POLAR "DESert" spans small but somewhat diverse areas where precipitation is low and/or rarely melted as water.

WE1.4D classes grouped here:

#69 PDL Polar "Desert" with rock Lichens, locally abundant or productive (even between mineral grains) but provide little food. Animals depend on nearby waters, and import some residues from their food chains for localized humus.

(27) WTNDMHTH Wooded TUNDra or Moorland­HEATH types

WE1.4D classes grouped here:

#63 WTM Wooded Tundra Margin or mountain scrub/meadow

#64 HMW Heath and Moorland, Wild or artificially managed, as by burning and/or grazing. Moorland conventionally includes wetland (#44­45) interspersed with drier heath, with dwarfed or taller, commonly dense scrub on peat or sand.

(28) reserved for later use.

(29) INLDWATR INLAND WATER here refers to specific lake body pixels in which land is negligible. Otherwise not distinguished from #0 in long legend.

SOURCES

Improved mapping of the main ecosystem groups described above, depends mainly on sources noted in this report and Olson et al. (1983, 1985). Data from these sources helped to improve and sometimes combine current information about global patterns in ecological and landscape systems. WE1.3A and WE1.4D represent examples of doing that using the sources and history outlined below.

A. Maps and Source Data for Numeric Data Package­17 (NDP­017 = WE1.2)

1) Hummel­Reck Database (1978­79).

To aid studies of carbon cycling and climate at ORNL, Hummel and Reck (1979) contributed a computer­readable data-set from General Motors Laboratory. They had digitized a land­use map from Oxford Economic Atlas (Jones 1972; also Cohen 1973). Their main refinement was to add snow duration in one or two quarters of the year (respectively "cool" and "cold") because it strongly affected albedo, or reflectivity of the regional surface affecting their climate models.

2) ORNL Map and Database (1978­85)

The Olson and Watts (1982) map resampled the modified Gall projection of the Oxford Economic Atlas maps to half­degrees in both latitude and longitude. Fig. 1 of Olson et al. (1983) shows our splitting of several categories, especially "grazing lands" and certain forests--especially Boreal (= taiga in Russian) and mosaics of wooded/non­wooded types (see below). The Russian­language Physical­Geographic Atlas of the World was cited by Olson et al. (1983) after Gerasimov, Committee Chairman; Fillipov, the operational Editor is cited as author of the same atlas in NCAR's library in Boulder. Global vegetation map plates were used previously; continental and USSR maps in some of the revisions. Unpublished maps of the former Soviet Union from Natalia Bazilivich are being digitized for NOAA databases by Dmitri Varlyguin at Clark University.

Tropical/subtropical broadleaf humid forest includes extreme "rainforest" and other somewhat seasonal (but not necessarily deciduous) forms. In printing Olson and Watts (1982), blue stipples were inserted over the purple to separate the former type (#33) from the latter (#29). Hand­controlled (red line) printing for mangroves on Olson and Watts (1972) used information from any source. Yet half­degree pixels seem too coarse for digitizing such a "fringe" type of the tropical/subtropical land­saltwater margin. Its type (#72) was added for the ICSU/UNEP project in 1989 when finer 10' pixels were first incorporated.

B. Early revisions by Olson (Global Patterns Company)

1) Europe (1985­88)

In October­November 1985, visits to the European Space Agency (ESA) found much remote sensing information at laboratories in Frascati and Ispra, Italy that was useful for ecosystem mapping. From late May 1986 to 1987, cross­checking of maps in Sweden, Belgium, Netherlands, Germany, Denmark, Norway, and Iceland showed more possible refinements than have been used so far. In July 1987, botanical fieldwork included Greece.

In April and June­July, 1988, two trips to Austria were added to different routes in Sweden and Germany. At the International Institute for Applied Systems Analysis (IIASA, at Laxenburg, near Vienna), a data-set organized for analysis of acid deposition quantified percentages of forest and total land, but for grid cells of 0.5 degrees latitude x 1 degree longitude.

2) USA and Pacific (especially 1987­91)

Browsing in libraries and research laboratories in Asia, Australia and New Zealand and the USA as well as Europe, showed many more maps or articles than can be cited. Observations were recorded on diverse base maps, many of which have schematic overlays of green showing forest or tree cover. Some provide much finer resolution than maps at a national to global level and also suggest mosaic combinations: Forest/field has most continuity between the main wooded parts of a patchwork. Field/woods has more continuity between croplands, grasslands or other non­wooded land categories than between forest/woodland patches (woodlots, plantations, regrowing forests--commonly degraded by thinning, grazing, or fire).

The triangle diagram shown below as Figure 1 (from Olson et al. 1983) ideally suggests 60 per cent nonwoods component for separating Field/woods (above) from more evenly divided Forest/field (40­60% nonwoods parts of the mosaic). Below that level Olson divides broadleaved (or more concisely broadleaf) forest or woodland (>75% of the woods stand area), from broadleaf/conifer mixed woods (25­50% conifer), and a wider band called conifer (>50% conifer).

Broadleaf gradations above or below 25% may be recognized but are less commonly mapped. This tradition reflects common attribution of more economic value to "softwood" forest products or of more ecological/biophysical indicator value to percent conifer than "hardwood" when in mixtures.

Where trees are sparse and/or dwarfed by cold, drought or other stress, tall or dense scrub may be included in green overlay: e.g. my groups 6 and 13. Olson's use of the term "woods," like "the bush" in Australia, is a broad grouping, ranging from such shrubby low growth to closed or open forest, of tall, medium, or low stature.

The Cairns and Brisbane­Cooloola areas of Queensland, were visited in September, 1990, and southeastern New South Wales in October­December. As in Australia, New Zealand broadleaf woods are mostly evergreens (southern beech, Nothofagus, and many others). These could be separated in new types when the Olson legend is being extended instead of being simplified as in this report. {Type #77 will be for southern conifers (Podocarpus, etc.) and/or planted Pinus alternating with broadleaved forest, fjords, and glaciated peaks; #78 for forest that is tall (>30 m) and/or dense (>70% foliage cover); #79 for other Eucalyptus forest (30­70%.}

In Japan, several agencies are active in developing imagery for sample localities. The Institute of Agro­environmental Research in the science city Tsukuba (northeast of Tokyo) has data files directly relevant to grazing and some tree crops as well as to farming. Forestry records also have potential, and a national land digital database (of land use and elevation) for planning may be even more helpful, with pixels at a level of 10 km.

Much finer resolution is available in most countries of the world but many questions remain about how to shift from "thinking locally" to mapping and thinking globally.
 
 

Figure 1 Approximate relations of tree cover, regional percent of non-tree formations, and major kinds of forest, interrupted woods, and non-woods systems. (from ORNL­DWG­81­9450, Oak Ridge National Laboratory)

METHODS

Showing just one land type or group for all nine 10' subcells in one half­degree cell is commonly a simplification of the real world. Yet heterogeneous regions may have one main category taken as representative: a "winner" among competing nominee types. The "runner­up" candidates may be winners in other cells or blocks. The hope is that suitable proportions of all types used in modeling the environment of a wider surrounding area, or the whole planet, will average out. But wetland, mountain summit and some other types often occur only in minor proportions. These may be under­represented by weighting schemes in which "winner takes all."

Shifts toward finer resolution (e.g. from the 30' grid called WE1.3A to 10' pixels of WE1.4D) are partly justified to overcome or avoid such bias, as well as to refine boundary shapes. An attempt was made to compensate by including "representatives­at­large" among the mapped pixels--located in places where the minority type is relatively important but not necessarily the single commonest type or group.

During processing, a 30' cell may be temporarily flagged with a minor type instead of losing the latter's identity and approximate location completely. Later editing ought to show which 10' subcell(s) deserve the less common labels when most become reassigned like their commoner neighbors.

Techniques of map or database improvement include digitizing from paper map sources or adapting global database information that is already computer­readable. Both approaches are essential, but the emphasis may shift as work progresses. Early editing of data has used only a fairly small fraction of the possible refinements. WE1.4D illustrates using data already digitized from satellites.

In 1989 the new digitizing of shore types (especially mangrove, #72) and montane tropical complexes (#28 and/or #6) used the ocean mask and altitude files from the FNOC Terrain data-set [Chapter A13 in this volume], as improved by Roy Jenne and Dennis Joseph of NCAR and John Kineman of NOAA. For Africa, the proper elevation for montane rainforest was found to be only adjacent to where it had been marked on Olson and Watts (1982). Altitude data, first at 30' and then 10' resolution also clarified where the Olson data had correctly included the peak or where it had originally lacked enough location control to do so. The real refinements at 10' are mostly limited to the few types just mentioned and others which happen to have low greenness.

A. Editing from previous maps and diverse sources

For Africa, earlier 30' type locations were first refined manually where available maps or references allowed. Then the 30' grid was expanded 3­fold in both coordinates. That meant fewer 10' pixels needed to be fixed along the edges or within a 3x3 array of identical values--compared with editing all 9 values independently. However, pending such follow­up checking at 10', mountain labels may be left temporarily pinned on some pixels in a valley or on a plain or plateau below altitudes defining the real peak(s).

Much of the 1989 editing was done with Wordstar­2000, convenient (though tedious) for dealing with single cells or data strings in large files. Within Idrisi, substituting of new values for old ones was also applied not only to points, rows, or columns, but to rectangular arrays (commonly 3, 6, or 9 10' pixels wide, e.g. in case 1, 2, or 3 of the half­minute pixels required large­area correction).

However, the difficulty of mislocating points or boundaries, relative to a sparse printed latitude­longitude net or landmarks, had to be diminished first. This was accomplished by using reference data already digitized in the GED Prototype disk.

B. Associating vegetation and greenness indexes

Indices of vegetation greenness from weather satellites of NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) could be used in several ways. At NASA's Goddard Space Flight Center (GSFC) and the University of Maryland, Koomanoff (1988) used the old Olson et al. (1985: WE1.2) database to show almost normally distributed variations of greenness for some type groups (e.g. her Figs. 4.1 and 4.3). Skewness for other groups (her Figs. 4.2­4.6) and more diversity for others (even distinct peaks of greenness for her Figs. 4.7­4.16) indicated that the lumped groups were heterogeneous, and individual cover types (as originally mapped or refined later) may be significantly more homogeneous.

Those and other Maryland analyses used early AVHRR indices averaged over one whole year. Ignoring medium­ and long­ infrared channels (#3­5), channel signals 1 and 2 (0.58­0.68 m visible; 0.73­1.10 m near­infrared) are commonly expressed as ratios of difference/sum of reflectances Ch1 and Ch2: i.e.

XVI = (Ch2 ­ Ch1)/(Ch2 + Ch1) (1)

This unscaled index ranges from ­0.1 for water, cloud or ice or +/­ 0.1 for nearly bare rock or soil to ~0.6 or 0.7 for dense, vigorous vegetation. NOAA then derives integer values rescaled to

NDVI = (XVI+0.05)*350 + 15. (2)

Composite sampling and regridding discards values that are low artificially (due to clouds, haze or dust). Extra bias toward exceptionally green values by this procedure was decreased by saving the last physically acceptable weekly value.

Second generation NOAA products include a weekly summary of equal latitude ­ longitude grid cells (~16 km at the equator) for latitudes 75N to 55S. Further NOAA quality control in developing the NGDC monthly GED grids used here included (1) checking registration accuracy against prominent geographic features and (2) inspection for artifacts (e.g. bad scan lines and system noise) and selection of images without co-located artifacts. Then (3), both the low and high values were discarded from the remaining weekly pixel values within each month, to eliminate random noise evident in the weekly files. (4) A root­mean­square average of the remaining "median" weekly values within each month was computed for each pixel. (5) The images were then re-gridded to a 10-minute grid using a spatially weighted average. (6) The images used to process the World Ecosystems data-set were multi-year averages of these monthly "generalized" images [provided in Chapter A01 of this volume].

The GED compact disk also has corrections from Kevin Gallo for pre­launch calibration, drift of the satellite or instrument, and refinements related to solar zenith declination angles. Gallo's file of weekly data masked the unusually low values of NDVI that may be clouds in some places but glaciers or bare desert in others. Monthly summary files from Gallo were not ready when the work took place from December 1985 through November 1988 average 10' NDVI and GVI intervals.

Another kind of monthly AVHRR summary from Japan [Tateishi and Kajiwara - see reference in Chapter A01 of this volume] deliberately selects the greenest values for each month, and therefore has least risk of cloud contamination of the image. However, that advantage is traded off against highest exaggeration of locally high selection (e.g. irrigated cropland; dense, tall forest) or of seasonal trend favoring the summer end of spring (or monsoon) season or autumn transition months. Neither the Gallo or Japanese file is yet likely to be as representative as Kineman's 3 year average used here.

After exploring data for particular months, annually averaged 3­month seasonal images (December­February, March­May, June­August, September­November) and a 3­year average for all 12 months were produced. Another simplification was to establish categories for the Monthly Generalized Global Vegetation Index (MGV), each step matching 11 levels of the scaled MGV values (which range from 0 to about 211). Our first questions concerned lower intervals, i.e.,

low GVI Levels 0, 1, 2, 3: for MGV = 0­11, 12­22, 23­33, 34­44, respectively

Briefer inspection and analysis included:

medium GVI Levels 4, 5, 6: for MGV = 45­55, 56­66, 67­77; and

high GVI Levels 7-11: for MGV > 77.

Clearly the latter deserve more attention. Lands in the high range have most green foliage generating organic production, nutrient recycling, and evaporation.

RESULTS

ECOSYSTEMS, VEGETATION, AND LANDSCAPES

1) Desert, cold, and water: low GVI landscapes

Investigation was made to determine the match between low greenness index (MG-GVI, Chapter A01) and sand desert areas (type 50 on my legend), e.g. the Ergs of the Sahara. The lowest GVI categories (Categories 1 and 2; MGV = 12­33) were associated with salt flats or intermittent playa lakes (type #71), except for some matches with water that were used to refine shore delineation. Especially at the 10' pixel resolution, pixels or clusters of pixels showed up in the depressions located from altitude files (some below sea­level) or shown in many atlases.

A few very low index values also appeared on the highest Himalaya Mountains and ranges on or west of the Tibetan Plateau. These may represent glaciers (#70) or other very snowy landscapes that may be missed or mislabelled #71, or else mixed with #8 as bare "alpine" desert, along with the following true desert types.

The next lower level (Level 3; MGV = 34­44) in desert areas was also not mainly in areas where the working database or extra maps showed most dunes. A "mostly bare" desert category, already defined as #8 was only sporadically used before 1991. It includes some sand but commonly also rocky and fine­soil deserts with little or no green cover. Independent cartography confirms many such landscapes, but more checking is required, especially outside Africa. In the Nubian, eastern and western Arabian, and some Iranian deserts, areas originally mapped as grass­shrub (Group 4, type #41, because of their designation as grazing lands on the Oxford Economic Atlas) effectively belong in this bare or true desert category in most years. Nomadic economies depend on distant herd migrations to find the exceptional times and places where grazers can be kept alive, even if not in thriving condition.

2) Gradients (Ecotones) of Intermediate Greenness (GVI Levels 4­6)

In the Sahara and elsewhere, some dune regions showed higher index values for greenness (Levels 4 and 5) than the landscape types of the preceding paragraphs. Seasonal or sporadic variation in the index suggested ephemeral vegetation where occasional rains occur. Alternatively, plants on the inter­dune depressions could be sub­irrigated from rains previously intercepted along the truly bare dune ridges. For part of the Kalahari desert, GVI Level 6 (MGV = 67-77) overlapped areas of Kalahari sand that was mapped as #50. It has not been confirmed whether this and a few other areas represent just the upper range of a pixel variations for sand desert and semidesert type, or if a "sand hill grass/shrub" category should be created (#87 reserved), or if it should be coded with existing shrub-grass types #50 or 51.

Oases (irrigated agriculture #37, Group 14) are likely to occur more often as higher­resolution pixels are provided for. Most seem too localized to show even at 10'­­even when associated with persistent marshy vegetation from natural seepage or drainage (#45, Group 11).

In the USA, higher­resolution (~1.1 km) AVHRR imagery has been treated in more detail by Loveland et al. (1991). They map somewhat wider areas of effectively "bare" landscape and infer (pers. com.) that these match deserts with only a few per cent of green cover. Desert pixels of 10' and especially 30' typically include mosaics of such nearly bare land plus shrub or shrub­steppe or irrigated cover, and so are less likely to be considered bare in the aggregate.

At high latitudes, the low sun angle, especially in winter, limits use of AVHRR. Data are not even retained above 75 N latitude. Nevertheless, a reasonable distinction between high Arctic tundra (annual mean GVI Level 3) and low Arctic tundra (GVI Level 4 or lower range of 5). The Wooded Tundra fringe (mapped, illustrated and discussed in detail by Larsen 1989, for North America) conversely has GVI mostly 4, less often (or less clearly) 3. It needs to be much better defined at the 10' resolution than the initial 30' mapping.

Within the Northern Taiga and other Boreal forest belts, there is also an orderly progression of the GVI (from the MGV data-set), despite numerous inclusions of locally lower GVI. The inclusions of land­water mixtures (see section 4C) accounts for much of the seeming "noise" that is related to lakes and mires but these are not yet provided for in WE1.4D. Analyzing how the whole complex can be resolved, with better resolution from Local Area Coverage (LAC) AVHRR and still finer satellite imagery, will be a major contribution of the planned BOREAS project of NASA and other sponsors.

RESOLUTION CODES AND 10-MINUTE UPDATES FOR WE1.4D

The resolution code overlay (OWE14DR) was produced by first tagging all pixels on the WE1.4D 10-minute grid that differed from their 30-minute mode (for the standard 30-minute grid registration). These cells were coded 1 against a 0 background. Known classes that were edited at 10-minutes were then over-written onto the grid. The resulting map thus provides the following codes:

EXPLANATION OF RESOLUTION CODES

0 unaltered cells representing 30-minute spatial dominance (expanded to a 10-minute grid)

1 edited 10-minute cells

2 residual, or "orphaned" cells from 30-minute regions within which other 10-minute updates were made. These cells are presumed to represent spatial dominance at less than 15-minutes (only coded if such cells cover less than half of the original 30-minute cell), because the other cells in the 30-minute region have been changed.

The following table describes the specific 10-minute updates made to the data-set, on a background of 30-minute values:

TABLE OF 10-MINUTE EDITS IN OWE14D

OLSON14D DESCRIPTION

CLASS

0 Water, coastline edits for Africa and some other coasts

6 Montane forest, edits in Africa only

8 Bare desert, updated globally using average GVI.

28 Tropical montane, edits for Africa only

65­68 Coastline, mostly Africa but other areas as well.

71 Salt/soda flats, updated globally using average GVI.

72 Mangroves, Africa only

73 Island/Coastal (Elba Island only)

REFERENCES

ORIGINAL REFERENCES

Extensive references for the original work are given in Olson et al. (1983) and its excerpt (scanned images of this reference are provided on the CD-ROM -- see Primary Documentation, above). All references related to the present work are listed under Additional References, above.

REFERENCES FOR RECENT UPDATES AND COMPARISONS

Cohen, S. (ed.). 1973. The Oxford World Atlas. Oxford University Press, London.

Goward, S. N., C. J. Tucker and D. G. Dye. 1985. North American vegetation patterns observed with the NOAA­7 advanced very high resolution radiometer. Vegetatio 64: 3­64.

Henderson­Sellers, A., M. F. Wilson, G. Thomas, and R. E. Dickinson. 1986. Current Global Land­Surface Data Sets for Use in Climate­Related Studies. NCAR Technical Notes, NCAR/TN­272+STR, National Center for Atmospheric Research, Boulder, Colorado

Hobbs, R., and H. Mooney (eds.). 1990. Remote Sensing and Biosphere Functioning. Ecological Studies. Springer­Verlag, New York.

Hummel, J.R., and R.A. Reck. 1979. A global surface albedo model. J. Appl. Meteorol. 18:239-253.

Jones, D.B. (ed.). 1972. Oxford Economic Atlas of the World, 4th Ed. Oxford University Press, London.

Koomanoff, V. A. 1988. Analysis of Global Vegetation patterns: A Comparison Between Remotely Sensed Data and a Conventional Map. Report 890201 of Laboratory for Global Remote Sensing Studies, Geography, Univ. of Maryland, College Park MD.

Larson, J.A. 1989. The northern forest border in Canada and Alaska: Biotic communities and ecological relationships. Ecological Studies 70. Springer-Verlag, New York.

Loveland, T. R., J. W. Merchant, D. O. Ohlen, and J. F. Brown. 1991. Development of a land­cover characteristics database for the conterminous United States. Photogram. Engineering and Remote Sensing 57:1453­1463.

Olson, J.S. 1992. Global changes and resource management. ASPRS/ACSM/RY92 Technical Papers, Volume 1. P. 32-42.