IGAD Biodiversity Management Program Regional Biodiversity Data Base and RRIS Nairobi November. FAO DDNS Division FAO - Rome

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1 IGAD Biodiversity Management Program Regional Biodiversity Data Base and RRIS Nairobi November FAO- DDNS and Global Land Cover Network(GLCN) overview on, standards, tools and products Antonio Di Gregorio (FAO) FAO DDNS Division FAO - Rome

2 Global Spa)al Datasets at FAO FAO Core datasets Grouped into the following categories: ADMINISTRATIVE AND POLITICAL BOUNDARIES AGRICULTURE AND LIVESTOCK APPLIED ECOLOGY BIOLOGICAL AND ECOLOGICAL RESOURCES CLIMATE AND AGROCLIMATOLOGY FISHERIES FORESTRY HYDROLOGY AND WATER RESOURCES LAND COVER AND LAND USE POPULATION AND SOCIO- ECONOMIC INDICATORS SOILS AND SOIL RESOURCES TOPOGRAPHY

3 Informa)on systems at FAO FAOSTAT FAMILY COUNTRYSTAT FishSTAT (FIGIS) Food Security StaJsJcs PriceSTAT ProdSTAT ResourcesSTAT TradeSTAT FIVIMS (Food Insecurity and Vulnerability InformaJon and Mapping Systems) GIEWS WorkstaJon (Global InformaJon and Early Warning System) GLIPHA (Global Livestock ProducJon and Health Atlas) FRA - Global Forest Resources Assessment GLCN (Global Land Cover Network) AgroMaps AQUASTAT TEMS Terrestrial Ecosystem Monitoring Sites WORLD SOTER GeoNetwork

4 Global Land Cover Network (GLCN) Main Objec)ves: To improve linkages between global, regional and najonal studies on land cover and the environment To improve standardizajon, homogenizajon, compajbility and efficiency of informajon provided by different applicajons To provide informajon that improves design and efficiency of sampling for validajon of land cover products at global, regional and najonal levels. To increase use and sharing of remote sensing data and its derived datasets To provide comparable products at global, regional, and najonal and lower levels To undertake capacity development and insjtujon strengthening to maximize benefits for developing countries To support operajonal development and use by najonal stakeholders of products emanajng from the programme multi-date landsat imagery

5 Global Land Cover Network (GLCN) methodological approach Main characteris)cs: Mapping acjvijes at najonal, regional and global level At najonal level full involvement of local experts NaJonal data sets done to serve many different applicajons at najonal and sub- najonal level, therefore with an high level of themajc details. NaJonal data base realizajon using a visual and semi automajc approach using specific GLCN interpretajon sozware tool box. Vector data base based on automajc image segmentajon algorithms. Legends with a very high number of classes tailored for each country specific inherent characterisjcs and needs, but harmonized at regional and global level using the GLCN ontological standards. multi-date landsat imagery

6 Global Land Cover Network (GLCN) ONTOLOGICAL STANDARDS Major objec)ve: Ensure the semanjc interoperability of land cover classificajons, i.e. the ability of systems to exchange land cover data with unambiguous and shared meaning The basic idea is that: instead to try to standardize category (legend classes) it is more important and efficient to standardize the WAY HOW A SPECIFIC CATEGORY HAS BEEN CREATED. THIS IMPLIES THE CREATION OF A SPECIFIC L.C. META- LANGUAGE. The FAO LCCS is a common protocol of rules to describe real world features that will guarantee complete flexibility in the creajon of specific categories and in the same Jme will assure semanjc interoperability between them. data sets done to serve many different applicajons at najonal and sub- najonal level, therefore with an high level of themajc details.

7 A rich formal narra<ve and an open dynamic syntax to create seman<cally rich class descrip<ons a long work in progress THE DIFFERENT STEPS OF FAO LCCS/LCML IMPLEMENTATION First LCCS concept developed in First LCCS soiware test interface developed in 1997 First LCCS v.1 release in MigraQon from LCCSv.1 to LCCSv.2 in 2005 (not substanqal modificaqons of concept/ and soiware fundamentals LCML ISO standard in 2012 aier 5 years of process LCCS v.3 a tool to apply the rules of the Meta- Language FAO (GLCN) ONTOLOGICAL STANDARDS

8 Land Cover Meta Language LCML/LCCS v.3 THE LCML/LCCS BASIC IDEA A FULL OBJECT ORIENTED META - LANGUAGE THE EMPHASIS IS NO LONGER ON THE TRADITIONAL CLASS NAME OR CLASS DESCRIPTION BUT ON THE ELEMENTS OF THE META- LANGUAGE USED TO CREATE THE CLASS ONTOLOGY THE COMPLEX RELATIONSHIP BETWEEN THE LANGUAGE ELEMENTS AS THE SYNTAX RULES USED TO REGULATE/ORGANIZE THEM ARE EXPRESSED IN A STANDARD MODERN MODELLING LANGUAGE - THE UNIFIED MODELLING LANGUAGE (UML). THE META- LANGUAGE CAN ALSO ASSURE MIGRATION FROM THE HUMAN LANGUAGE TO A MACHINE REPRESENTATION OF THE RULES AND CONDITIONS WITH WHICH THE FORMALIZATION OF THE MEANING OF A GIVEN CATEGORY HAS BEEN CREATED. LCML IS NOT ONLY A SYSTEM TO HARMONIZE CLASSIFICATION SYSTEM BUT AN ADVANCED APPROACH TO FORMALIZE THE MEANING OF LAND FEATURES {An LCML_Stratum may have an LCML_VerticalRelations hip only with all the next LCML_Stratum in the ordered list of LCML_Stratum that belongs to the same LCML_LandCoverClass} { Two LCML_Element(s) in one LC_Stratum must be of the same class unless there is an LCML_SequentialTemp oralrelationship between them or unless they are of type LCML_Gramineae or LCML_Forbs. LCML_SequentialTemporalRelationship - length[0..1] : LCML_PermittedPositiveRealValues - type[1] : LCML_SequentialTemporalRelationshipTypes <<enumeration>> LCML_SequentialTemporalRelationshipTypes (from LCML_LandCoverClassStructure) + sequentialsameyear + sequentialotheryear LCML_VerticalRelationship LCML_LandCoverClassificationSystem +system systemcollection +systemclasses 1..* LCML_LandCoverClass (from LCML_LandCoverClassStructure) +composedby classcomposition 1..* +composedof LCML_HorizPattern (from LCML_LandCoverClassStructure) + PatternType : CharacterString + PatternCoverPercentage : LC_PermittedPercentageValues + PatternOccurrence : LC_PermittedPercentageValues 0..1 composition LCML_Stratum (from LCML_LandCoverClassStructure) LCML_SequentialTemporalRelationship 0..1 layered +composedby 1..* +composedof {ordered} +layer 0..* +layerelement LCML_Element (from LCML_LandCoverClassStructure) +describedby Characteristic {An LCML_Element may have an LCML_SequentialTemporalRelationship only with the next LCML_Element in the ordered list of LCML_Elements which belong to the same LCML_Stratum} +describe LCML_ClassCharacteristic (from LCML_ClassCharacteristics)

9 Example of LCML/LCCS derived class schema Complete the passage from a classification system to a reference Meta-Language. Develop a full Object oriented syntax to support a (possible use of) relational data base Instantiate the formalization of the meaning using a standard modelling language. Improve the capacity of the system to be extended with a large series of application related attributes A NEW WAY TO REPRESENT THE COMPLEXITY OF THE REAL WORLD A MODEL INSTEAD OF A CLASS NAME AND TEXT DESCRIPTION

10 LCML flux of information Capture variety and complexity of real world information with LCML semantic protocol Use the structured amount of information to built ad hoc categories to develop specific application THE GRANULARITY OF INFORMATION OF THE REAL WORLD REPRESENTING THE REAL ITY WITH LCML X 2 Y 1 Y X 1 X 2 F 1 Z X 1 X 2 2 Y 1 Y X 1 X 2 F 1 Z X 1 X 2 2 Y 1 Y X 1 X 2 X 1 F 1 X 2 Z Y 1 2Y X 1 X 2 F 1 LCML SEMANTIC ELEMENTS BASIC OBJECTS, PROPERTIES and CHARACTERISTICS DYNAMIC DATABASE REPRESENTED BY OBJECTS NOT CATEGORIES USER 1 USER 2 USER 3 USER n CATEGORIES CATEGORIES CATEGORIES APPLICATION 1 Carbon flux estimation APPLICATION 2 Agriculture assessment APPLICATION 3 Rangeland Assessment APPLICATION 4 Ecosystem accounting

11 GLCN Toolbox

12 Global Land Cover Network (GLCN) NATIONAL L.C.DATA- SETS IN IGAD AREA

13 AFRICOVER DATA SETS

14 Kenya Land Cover Update 2011 sponsored by WRI in collaboration with DRSRS based on Aster ( ) National legend 70 LC classes

15 Kenya Land Cover Change Assessment Forest areas changes Agricultural areas changes: 1970 s 2000 Areas of Agricultural encroachment Agricultural areas Kenya Provinces Closed forest coverage Kenya Provinces

16 Land Cover Change Agricultural Areas Bare Areas Nat Veg Herbaceous / Shrub Natural Vegetation Trees Forest Plantation Urban Areas Aggregated Land Cover Area Ha Area Ha Agricultural Areas 45, ,078 Bare Areas 2, Nat Veg: Herbaceous / Shrub 64,884 64,311 Natural VegetaJon: Trees 205,840 68,388 Forest PlantaJon 21,626 12,034 Urban Areas 1,240 7,824

17 South Sudan 2010 based on SPOT ( ) LandSat ( )

18 Sudan based on SPOT ( ) IRS (2007) Aster (2010)

19 Somalia L.C. Mapping of Owdweyne and Burao

20 Somalia L.C. Mapping by dots The analysis of Northern and Central Somalia s agricultural areas, was carried out from June 2013 to February 2014 applying the same dot-grid methodology utilized to map Southern Somalia in The methodology used (Rapid Land Cover Mapping) was developed by USGS ( consists in the visual interpretations of the land cover features intersecting a regularly spaced grid of sample dots. The dot-grid technique is a fast methodology (in terms of time/efficiency) to perform an accurate estimation of preselected land cover features The grid spacing chosen for this analysis was 500 meters. The output is a statistical estimation of the main LC features from which is derived a land cover map.

21 Content of the Dot-grid attribute table Each dot of the grid contains the following information: User Label (2013) 7 Classes Original FAO Africover (1999) Class Aggregated Africover (1999) Class Landsat NDVI value MODIS NDVI value

22 Ethiopia Data used Primary: Spot5 imagery , 5m res., Field surveys (example of points taken in Tigray) Ancillary Data Landsat Etm imagery year 2000, 30m res. Google Heart high resolution imagery Environmental data from local institutions (e.g. irrigation areas from Water Department)

23 GLCN Global Product GLC- SHARE ini)a)ve New global land cover database created by FAO Land and Water Division in partnership and with contributions from various partners and institutions; First global product created using the ISO standard for land cover classification ISO TC LMCL (Land Cover Meta Language); It provides a set of eleven major thematic land cover layers (FAO SEEA LCML legend); Resulted by a combination of best available high resolution national, regional and/or subnational land cover databases; The database is produced with a resolution of 30 arc-second (~1sqkm).

24 Global Land Cover (GLC)- SHARE Land Cover Mapping: national.regional.global Methodology and datasets published in 2014 in

25 GeoNetwork FAO metadata catalogue

26 Land evalua)on and planning The Agro-Ecological Zones (AEZ) process is the main system for assessing agricultural resources and potential Can be applied at global, regional and national levels for better planning, management and monitoring of land resources FAO involved in methodology development since 1978 Used as SO-2 indicator (area with improved agricultural productivity and crop suitability

27 GAEZ Data Portal Brief summary of content: 5 thematic areas (Land and water resources; Agro-climatic resources; Suitability and potential yields; Actual yields and production; Yield and production gaps) > 300,000 global datasets at mainly 5 arcminutes, also core layers at 30 arc-seconds 11 crop groups, 49 crops, 92 crop types and 280 Crop/LUTs yield and production gap analysis for 17 crops/ commodities 5 water supply types 4 Input levels (High, Intermediate, Low, Mixed) Historical , 30 year average ( ) and Future, 2020s, 2050s, 2080s)

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