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1 Global Observation of Forest and Land Cover Dynamics UN Land Cover Classification System needs-concepts-implementation Martin Herold (1) & Antonio Di Gregorio (2) (1) ESA GOFC-GOLD Land Cover Project Office FSU Jena, Germany (2) FAO & Overview Content: 1. Land cover mapping status quo 2. Harmonization and standardization 3. UN Land Cover Classification System 4. Implementation activities Purpose: Introduction of evolving initiatives (global) Invitation for scientific community to participate Open discussion forum for European experts GEO(SS), UNFCCC, UNCBD, MEA, International Sponsors of GTOS: FAO, UNEP, ICSU, UNESCO, WMO GCOS GOOS Data producer Global Terrestrial Observing System (GTOS) Technical panel GOFC-GOLD Science IGOS Partnership Associates of CEOS Data users Collaborative Projects REQUIREMENTS Committee on Earth Observation Satellites (CEOS) incl. Cal-Val S T R A TE G Y IMPLEMENTATION Background Land cover is the most important element for description and study of the environment Land cover is the easiest detectable indicator of human interventions on the land (proxy for processes) Land cover is a critical parameter for (multi-purpose) environmental databases Land cover can be considered the key explicit feature which other disciplines may use as geographical reference Land observations are not operational in a weather forecasting sense Dealing with heterogeneity: Land surface itself, mapping standards, users of land information Growing RS data bases and evolving technologies International drivers International initiatives outline land observation requirements, e.g. GEOSS reference plan: Land cover is important for all areas of societal benefit Terrestrial domain is least developed GEO work plan and Forest Community of Practice UNFCCC-GCOS impl. Plan tasks GTOS and GOFC-GOLD: Establish international standards for land-cover characterization Develop an in situ reference network and apply validation protocols Generate products documenting global land-cover characteristics Reliable and accepted land (or carbon) change estimates assume international consensus on approaches Ongoing/planned global mapping efforts: GLOBCOVER, UN GLCN, FAO/FRA 2010, UNFCCC-REDD Mapping Tradition Land cover mapping the derived legends too discipline specific (good detail for the some specific theme poor for others) Each discipline producing is own land cover data base Derived consequences the same geographic area mapped several time (at different scales, for different purposes, in different times, whit different type of data, whit different accuracy etc.) Rangeland map 1a 1b 1c 1d 1e 1f 1g 1h 1i 1l 1m 2a 2b 3a 3b 3c 3d Agriculture map 1a 1b 1c 1d 1e 1f 1g 1h 1i 1l 1m 2a 2b 3a 3b Forestry map 1a 1b 1c 1d 1e 1f 1g 1h 2a 2b 3a Rangeland Agriculture Forestry Natural vegetation classes Agricultural classes Forestry classes 1

2 Land cover mapping Each discipline producing is own land cover data base In the past (more than 15 years ago) not a severe constraint spatial analysis (GIS capabilities) very limited different disciplines were building up their own data set Presently, and especially in the future, it is a severe limiting factor spatial analysis (GIS capabilities) very powerful and diffuse at any level Earth observation and land cover mapping technology evolving exchange and/or use of environmental data between disciplines, organizations and countries an essential step to enhance any management of natural resources It seems straightforward to produce land cover datasets! World rich of data and advanced algorithms Age of GIS and spatial analysis has vastly enhanced the users for land cover information National/international land mapping initiatives: reflect different interests, requirements and methodologies Current maps exist as independent datasets: limited compatibility, validation, and no or irregular updates Low efficiency and applications (change, scaling) Technology has not solved problem of standardization Land cover dataset heterogeneity Vision for integrated & operational observations Seek compatibility and comparability: For multi-temporal analysis and updates Within and between countries Within and between applications, disciplines and agencies From local to global scales Syntactic heterogeneity: e.g. logical data models (e.g. raster/vector) Schematic heterogeneity: e.g. database models/spatial reference system Semantic heterogeneity: Naming : trees woodland forest Cognitive (conceptualizations/categories) high Spatial detail low Land type/ Phenology Effort for frequent update high MODIS/MERIS (intra-)annual IN-SITU (+ IKONOS type) periodically (usually 1-10 yrs) Detailed physionomy Floristics and species distribution Crop type and rotation etc. LANDSAT/SPOT type inter-annual (1-5 yrs) Thematic detail Vegetation physionomy high Harmonization and Standardization Harmonization - Bottom up process : from an existing divergence to a state of comparability/compatibility union of similarities in existing definitions does not necessarily eliminate all inconsistencies Standardization - Top down process : introduces a new, common definition or standard application of standards should eliminate all inconsistencies Developments for land cover harmonization Harmonization of soil maps/legends through FAO Originally strong push for single lc/lu legend Too much standardization reduces application relevance Standardizing terminology rather than categories Consensus on criteria and thresholds Workshops: FAO/UNEP 1994, LUCC 2002, GOFC-GOLD 2004 Rationales: Separation of land cover and use Common language (LCCS) Need for case studies and research Impact on operational lc/lu data collection Implementation e.g. through GOFC-GOLD/GTOS, UN Global Land Cover Network (GLCN), research networks (LUCC, GLP) 2

3 Overview of classification systems: LEVEL I Mapping scale 1: URBAN OR BUILT UP LAND Agricultural land Rangeland Forest land Water Barren land Tundra Perennial snow or ce LEVEL II Mapping scale 1: Resdential Commercial and service Industrial Transportation, communication utilities Industrial and commercial complexes Mixed urban or built up land Other urban or built up land Cropland and pasture Orchards, grves, vineyards, nurseries, etc. Confined feeding operations Other agriculture land Herbaceous rangeland Shrub and brush rangeland Mixed rangeland Decidous forest land Evergreen forest land Mixed forest land Streams and canals Lakes Reservoirs Bays and estuaries Dry salts flat Beaches Sandy areas other than beaches Bare exposed rocks Strip mines, quarries, and gravel pits Transitional areas Mixed baren land Shrub and brush tundra Herbaceous tundra Bare ground tundra Wet tundra Mixed tundra Perennial snowfields Glaciers The USGS Land Cover Classification System (Anderson e al. 1972/1976) Mixing L.C.with L.U. terms or use of others (geographic etc.) Absence of imp. veg. physionomic param ( Canopy cover, Leaf type) Absence of imp. Veg. aspects: Savannas,, Woodlands Inproper use of mixed classes Imbalance between classes ( Tundra vs other veg.) etc. Unclear classes, inproper class names, absence of imp. L. C. classes. Common problem of legends Confusion between classification system and legend Improper thematic definitions e.g. spectral classes resulting from interpretation process Mix of terminology: land use/cover terms Regional specifics names, ecology, floristics Internal unbalance/inconsistency: thematic overlaps and gaps Difference between Classification and Legend Classification is defined as the ordering ordering or arrangement of objects into groups or sets on the basis of their relationships (Sokal, 1974). It is an abstract Abstract representation of of the situation in the field using well-defined a classification diagnostic consisting criteria. of a continuum with two gradients: circles and triangles in red and white (Küchler and Zonneveld, 1988). Concrete situation in the field in a particular area (Küchler and Zonneveld, 1988). One should note that not all possible combinations of circles and triangles in black and white here represented occur in the left side (abstract representation). UN Land cover classification system (LCCS) Classification system to describe land cover features worldwide at any scale or level of detail High level of flexibility with absolute level of standardization of definitions Harmonized and standardized collection of land cover data A system that allows a dynamic creation of classes without to oblige the user to relate to a pre-defined list of names Comparison and correlation of land cover classes Endorsed by UN, GEOSS, UNFCCC, GTOS Submitted to ISO, widely approved 06, some edits trees closed? open broadleaved? shrubs herbaceous? sparse evergreen Reference Classification System >50m 30m >5m 100% Basic concept of a land cover class (the idea) Defined geographic area LCCS 2 Software LCCS method (the language) 10% Legend development in LCCS code (the concept expression) Interpretation process (the map product) Trees A3 Closed A10 Height 14-7m B6 Needeleaved D2 Evergreen E1 =A3+A10+B6+D2+E1 Mapping units End users 3

4 Mixed unit concept in LCCS LCCS initial structure Understanding of classifiers Concept of Language: The problem is to find the right combination of words to describe things = correct combination of classifiers to describe a certain land cover feature Users can easily refer to classifiers Common LCCS classifiers: Vegetation life form (trees, shrubs, herbaceous vegetation, lichen and mosses, non-vegetated) Leaf type (needle-leaf, broad-leaf) and leaf longevity (deciduous, evergreen) Non-vegetated covers (bare soil/rock, built up, snow, ice, water) Density of each land category in percent cover Additional options: Terrestrial / aquatic or regularly flooded Cultivated and managed / (semi-natural) User defined attributes Link tool Common ground for land characterization Existing global land cover datasets Common land cover classifiers (LCCS) Cover type/ life form Trees Shrubs Herbaceous Bare Snow & Ice Artificial Leaf longevity Leaf type Evergreen Needle-leaved Deciduous Broadleaved Cultivated and managed/ (semi-)natural Cultivated/ managed Terrestrial / aquatic+ regularly flooded Aquatic/ flooded The future - LCCS 3 From LCCS 2 to 3 LCCS syntax in mathematical formulization LCCS 2 concept and software LCCS 3 mathematical formulation PROLOG implementation DLL???? JAVA Other 4

5 Harmonization mechanisms Harmonization resources: Capacity building and web-based resources (LCCS) Raise awareness and foster use of harmonized products Harmonization experiences for existing datasets: Develop legend translation protocols/case studies Translated legends: IGBP/CORINE/GLC2000/Anderson/IPCC Compatibility/Comparability of datasets Synergy among land cover products Harmonization in future mapping products: Impact on future projects and operational programs Standardized legend generation (e.g. MERIS products) Consider inconsistencies in previous maps Harmonization and validation are parallel efforts Heterogeneity of forest estimates in global land cover maps Forest definitions: IGBP legend : percent tree cover >60% / tree height >2m GLC2000 legend : percent tree cover >15% / tree height >3m Global forest observations from satellite data GLC2000 translated to IPCC Legend GLC2000 Translator LCCS IPCC land use guidance for carbon/greenhouse gas inventory calc. 0% tree cover threshold 100% Credit: M. Hansen/University of Maryland Harmonized map Synergy of existing global land cover maps for carbon cycle modeling Trees Trees & Shrubs Trees & Grasses Trees & Crops Shrubs Shrubs & Grasses Shrubs & Barren Grasses Crops Barren Snow & Ice The SYNMAP data set (life form assemblages) compiled using synergy of IGBP DIS, GLC2000, MODIS land cover, and MODIS/AVHRR VCF to provide best estimate for land cover characteristics. Leaf attributes of trees (evergreen, deciduous, needle, broad) are not shown for reasons of visibility but are defined for each class that has a tree component (Jung et al., 2006, RSE). UN Global land cover network (GLCN) Direction, focus and guidance for harmonized land cover classification and mapping strategy Implementation through resources (data, mapping strategies ), coordination, and capacity building Developed LCCS resources and experiences Several regional capacity building workshops: West Africa Nov 2003 South-East Asia 1-6 Dec 2003 South America Nov 2004 Southern Africa Dec 2004 India National Feb 2005 North Africa/Middle East Mar 2005 RP of China National Apr 2005 Central America 5-9 Dec 2005 Asia Pacific 4-8 Dec 2006 NIS/CIS states

6 Africover Burundi Congo D.R. Asiacover Cambodia Egypt Eritrea Kenya Rwanda China (Yunnan) Somalia Sudan Lao PDR Tanzania Uganda Malaysia Myanmar Thailand Viet Nam Regional Program Regional Program New Full Country Mapping - ongoing Albania SADC Angola Iraq Botswana Libyan A.J. - ong. Lesotho Malawi Moldova R. Mozambique Romania Namibia Yemen South Africa Oman - ong. Swaziland Zambia Zimbabwe Regional Program proposed new harmonisation project Sahel Senegal Official Translation Afghanistan Gambia Lebanon Guinea New Zealand Bissau South Africa Mauritania Mali Burkina Faso Niger Chad Regional Program proposed new harmonisation project 6

7 Translation in Progress Burkina Faso Cambodia Cameroon Yunnan (China) Ghana Guinea Java (Indonesia) Iran Isl.R. Lao P'sD.R. Malaysia Mali Mongolia Myanmar Nepal Niger Nigeria Senegal Thailand Togo Viet Nam Partial Country Mapping Brazil Bulgaria Chile Indonesia Madagascar Mexico Tunisia Harmonization/Validation framework GLOBCOVER In-situ global Existing Primary global LC validation products Legend translations Design based sample of reference sites Comparative validation LCCS-based interpretation Updated valid./change Validation of new products Updated interpretations Product synergy Reference database: statistically robust, consistent, harmonized, updated, and accessible Degree of comparability and harmonization Global land cover using ENVISAT/MERIS (300 m resol.) ESA, JRC, industry consortium (Medias France ) Link to international initiatives and mapping programs Built upon previous experiences (GLC2000, LCCS) Attempts to link with CORINE program Global MERIS mosaic Linking GLOBCOVER and CORINE? Translating CORINE legend Interest by both ESA and EEA Further involvements: GOFC-GOLD GLOBCOVER consortium GEOVILLE info systems (DUE Innovator: Cartochange ) Possible avenues to link global and regional land cover mapping efforts 2005 land characterization Integration on data/product level? GOFC-GOLD has provided: CORINE LCCS legend translation and evaluation Dataset comparison and assessment of inconsistencies LCCS is classification system / CORINE is legend CORINE is internally consistent (for very most part) Translation OK, but sometimes hard to find a common ground with a consistent land cover description Threshold differences: Many CLC vegetation classes contain no cover density information Some qualitative ( dense ) Some quantitative (forests, urban = 30%) Terminology: Land cover and use Processes: Transitional woodland-shrub and Construction sites Environmental events: Burnt areas Cultural practices: Permanently irrigated land and Pastures 7

8 Translating CORINE legend Mixing of definition criteria: Vegetation physiognomy versus floristic terms: Broad-leaved versus coniferous forests Sclerophyllous vegetation 1st-level class Agricultural areas contains level 2 subclasses Arable land and Permanent crops Permanent crops also among Arable land (inside of class 212 Permanently irrigated land ) Mixed unit categories with complex land cover: Heterogeneous agricultural areas Moors and heathland Transitional woodland-shrub Problem of some synthetic internal consistency: Extension: pastures up to 50% trees Vision for integrated & operational observations high Spatial detail low Land type/ Phenology Effort for frequent update high MODIS/MERIS (intra-)annual IN-SITU (+ IKONOS type) periodically (usually 1-10 yrs) Detailed physionomy Floristics and species distribution Crop type and rotation etc. LANDSAT/SPOT type inter-annual (1-5 yrs) Thematic detail Vegetation physionomy high Translation conclusions Translation as process of assessing inconsistencies and compatibility GLOBAL: CORINE level II shows fair amount of agreement for classes in global LC data Known inconsistencies In situ - experiences from CORINE validation: Classes being most subjective LUCAS in situ data interpretations also highlighted in LCCS translation Subjectivity index: agriculture with significant amount of natural vegetation (42.3 %) transitional woodland, shrub (36.1 %) complex cultivation patterns (34.0 %) Translation conclusions CORINE translation conclusions Special attention should be paid to the less accurate classes which means that there is a need for improvement of the definition of mapping rules Importance for the decomposition of CLC mixed classes into pure land cover classes Efforts in the context of GLOBCOVER GEO task AG for global land cover towards high resolution land cover change dataset Towards more standardized land cover observations Discussion/adoption of evolving standards: e.g. common land cover language LCCS Assessment of user requirements and development of specifications and international consensus Link with existing mapping programs: National and international Cooperation and willingness of actors Assess interoperability and inconsistencies (legend harmonization) Link with in situ data and validation protocols Possible tradeoffs: Between quality - application requirements level of standardization 8

9 Summary Global activities: Evolve LCCS and related resources and products Implementation activities Evolving standards in other areas: Land cover validation (CEOS protocols) Land Use Classification System Invitation to community to participate feedback to be expected North American Land Cover Summit (last week) Web resources GTOS: GLCN and LCCS: GOFC-GOLD: GOFC-GOLD land cover project office: Land cover IT newsletter: 9