Object Oriented data models / Feature data models applied to Land Cover databases Guillermo VILLA IGN Spain
EIONET EIONETWorkshop. OODMWGEEA Meeting. Copenhagen, Madrid 23 10-11 & 24 December April 2009 2009
Outline
1. Land Cover information: the concept 2. Basic concepts about Object Oriented Land Cover databases 3. Some examples 4. FAQs about OO Land Cover databases
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Land Cover Traditional definition (INSPIRE, 2007): “Physical and biological cover of the earth's surface including artificial surfaces, agricultural areas, forests, (semi-)natural areas, wetlands, water bodies”. Problems of this definition: - It is not specyphic enough - It can cause confusion with topographic information and some other geographic themes
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Land Cover
Proposed definition: “Land Cover is a geospatial information “theme” (a way of looking at the world) whose objective is to show (and allow studying) how the different natural and artificial objects present in a region “compete” between them for a scarce resource: the Land”
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Outline
1. Land Cover information: the concept 2. Basic concepts about Object Oriented Land Cover databases 3. Some examples 4. FAQs about OO Land Cover databases
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Many different names for the same concept:
- Object Oriented Data Model - Object based Data Model - Parametric Data Model - Parametric Object Oriented Data Model - Feature Data Model - Feature based Data Model - Multi-parameter Data Model - Application Schema (ISO 19109) - …….
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What is a Data Model ? •
A data model is the description of what we are storing in a database and how.
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It is the “link” between reality and the Database
World
Data Model
Database
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What is a Object Orientation ?
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“Object Orientation” is the standard in Computer Science today
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Parametric Object Oriented Data Models (POODM) are used extensively in every type of databases and Information Systems (airports, hospitals, production facilities,..)…. ….including “some” GIS databases
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UML (Unified Modeling Language)
UML is the standard for designing Object Oriented data Models UML is used by ISO to define its standards
Class Building -height : Double -numberOfFloors : Integer -name : String +calculateFloorHeight() : Double
Object EmpireStateBuilding : Building height : Double = 381.0 numberOfFloors : Integer = 102 name : String = Empire State Building
Attributes Methods
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Principal relationships between classes: Inheritance (Generalization / Specialization): A class inherits (or specializes) the state and behavior of another class Aggregation: allows to capture the structural relationships among entities in the real world (part-of) Association: allows to capture other kinds any of relationships among entities in the real world
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Basic Principles
1) The working area must be divided in a set of closed polygons, each one containing a surface that is as homogeneous as possible. 2) Our aim is not to classify each polygon. Our aim is to “describe” each polygon as exactly as possible.
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Basic Principles
3) The description of each polygon is made associating “Land Covers”, “Land Cover Elements” and “attributes” to it. 4) “Land Cover Elements” are the “real world objects” (e.g.: buildings, trees, rocks, sand, etc…) found in the terrain.
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Basic Principles
5) Many Land Cover Elements are mentioned in the definition of Corine classes
LEVEL 1
LEVEL 2
LEVEL 3
LEVEL 4
1. ARTIFICIAL AREAS
1.1. Urban fabric:
1.1.1. Continuous urban fabric: Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.1.1 Residential continuous dense urban fabric. Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories. 1.1.1.2 Residential continuous medium dense urban fabric. Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories. 1.1.1.3 Informal settlements
1.1.2 Discontinuous urban fabric Most of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
1.1.2.1 Residential discontinuous dense urban fabric. Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit. 1.1.2.2 Residential discontinuous sparse urban fabric. Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture. 1.1.2.3 Residential urban blocks 1.1.2.4 Informal discontinuous residential structures
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Basic Principles
6) “Land Covers” are “mixtures” or “associations” of LCE with a “special personality”. E.g.: “urban fabric” is a special mixture of buildings, streets, etc..
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7) Corine Land Cover classes are “Land Covers”. CLC nomenclature is a good taxonomy of “Land Covers”. (need to review some of them)
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8) Moland nomenclature contains many “threshold classes” that should be modeled with % or attributes
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9) “Land Covers” (LC) and “Land Cover Element” (LCE) have attributes:
Attributes are observable characteristics (biophysical or socio-economic) that describe LC or LCE in more detail (or “qualify” it). These attributes take different values in each “instance” (appearance of the LC or LCE). Eg: tree height
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Two instances of the class Coniferous tree
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One instance of the class Deciduous tree
One instance of the class Coniferous tree 20 20
One instance of the class Deciduous tree
One instance of the class Coniferous tree 21 21
One instance of the class Deciduous tree One instance of the class Coniferous tree
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One instance of the class Coniferous tree
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One instance of the class Coniferous tree
One instance of the class Coniferous tree One instance of the class Shrub
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10) Attributes are of different types: - Some attributes are simple variables of the adequate type (e.g.: number of floors: integer). - Other attributes are “Controlled lists” (e.g.: “vegetation distribution geometry”). Controlled lists are defined as “enumerations” in UML. - Other attributes are more complex (e.g.: “vegetation state”) and are represented as “UML classes” (rectangles in the UML diagram).
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11) Homogeneous polygons (at the scale of the database) have one Land Cover.
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12) When homogeneous surfaces have an area smaller than the Minimum Mapping Unit, the photointerpreter must draw a nonhomogeneous polygon that encloses areas with different characteristics. In this case, the photointerpreter must measure (or estimate), and store in the database, the percentage of surface in which each “Land Cover” is present in the polygon. The sum of all percentages of each polygon must be 100 %.
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13) For each “Land Cover” found in a polygon, the photointerpreter must measure or estimate: - The values for each of the attributes in this “Land Cover” - The “Land Cover Elements” present in this “Land Cover”, and the percentage of the surface that each occupies. - The exact values for each of the attributes in each “Land Cover Element”. (e.g.: tree height = 17 m; Number of floors of buildings = 4)
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12) All the information of each polygon (percentage of surface of each LC present, average values of each parameter affecting each LC or LCE,…) is stored in an Relational Database (RDB).
13) This database has to be designed in such a way to “materialize” the classes, attributes and relationships of the Object Oriented Data Model represented in the UML diagram.
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14) From the UML diagram an expert in Relational Databases can easily derive a “Physical Implementation” in an RDBMS (Relational Data Base Management System).
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15) There is no need to use any “special kind” of database. You can use any standard Relational Data Base Management System (Access, Oracle, SQL Server…).
16) This RDB stores all the objects and attributes in a structured and robust way, and allows to interact with all these data through: - SQL queries - Different databases “crossing” - Interaction with an specific “application”
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17) The UML diagram allows for unlimited extensions of the model, making it easy to add more classes, parameters, conditions, etc…
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In Land Cover Classifications definitions we find:
LEVEL 1
LEVEL 2
LEVEL 3
LEVEL 4
1. ARTIFICIAL AREAS
1.1. Urban fabric:
1.1.1. Continuous urban fabric: Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.1.1 Residential continuous dense urban fabric. Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories. 1.1.1.2 Residential continuous medium dense urban fabric. Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories. 1.1.1.3 Informal settlements
1.1.2 Discontinuous urban fabric Most of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
1.1.2.1 Residential discontinuous dense urban fabric. Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit. 1.1.2.2 Residential discontinuous sparse urban fabric. Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture. 1.1.2.3 Residential urban blocks 1.1.2.4 Informal discontinuous residential structures
Density thresholds Land Cover Elements Attributes Percentage of polygon occupation 33 33
LEVEL 1
LEVEL 2
LEVEL 3
LEVEL 4
1. ARTIFICIAL AREAS
1.1. Urban fabric:
1.1.1. Continuous urban fabric: Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.1.1 Residential continuous dense urban fabric. Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories. 1.1.1.2 Residential continuous medium dense urban fabric. Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories. 1.1.1.3 Informal settlements
1.1.2 Discontinuous urban fabric Most of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
1.1.2.1 Residential discontinuous dense urban fabric. Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit. 1.1.2.2 Residential discontinuous sparse urban fabric. Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture. 1.1.2.3 Residential urban blocks 1.1.2.4 Informal discontinuous residential structures
Density thresholds:
- somewhat “arbitrary” - induce class proliferation
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LEVEL 1
LEVEL 2
LEVEL 3
LEVEL 4
1. ARTIFICIAL AREAS
1.1. Urban fabric:
1.1.1. Continuous urban fabric: Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.1.1 Residential continuous dense urban fabric. Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories. 1.1.1.2 Residential continuous medium dense urban fabric. Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories. 1.1.1.3 Informal settlements
1.1.2 Discontinuous urban fabric Most of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
1.1.2.1 Residential discontinuous dense urban fabric. Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit. 1.1.2.2 Residential discontinuous sparse urban fabric. Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture. 1.1.2.3 Residential urban blocks 1.1.2.4 Informal discontinuous residential structures
Simple components: - not structured
- not explicit (“hidden” in de definitions of the classes) - incomplete - not “extensible” (one can not subdivide a component in different types) 35 35
LEVEL 1
LEVEL 2
LEVEL 3
LEVEL 4
1. ARTIFICIAL AREAS
1.1. Urban fabric:
1.1.1. Continuous urban fabric: Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.1.1 Residential continuous dense urban fabric. Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories. 1.1.1.2 Residential continuous medium dense urban fabric. Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories. 1.1.1.3 Informal settlements
1.1.2 Discontinuous urban fabric Most of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
1.1.2.1 Residential discontinuous dense urban fabric. Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit. 1.1.2.2 Residential discontinuous sparse urban fabric. Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture. 1.1.2.3 Residential urban blocks 1.1.2.4 Informal discontinuous residential structures
Attributes: - not structured - not explicit in the database (“hidden” in de definitions of the classes) - incomplete
- actual values not stored (only “ranges”) - not “extensible” (one can not add new attributes) 36 36
LEVEL 1
LEVEL 2
LEVEL 3
LEVEL 4
1. ARTIFICIAL AREAS
1.1. Urban fabric:
1.1.1. Continuous urban fabric: Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.1.1 Residential continuous dense urban fabric. Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories. 1.1.1.2 Residential continuous medium dense urban fabric. Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories. 1.1.1.3 Informal settlements
1.1.2 Discontinuous urban fabric Most of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
1.1.2.1 Residential discontinuous dense urban fabric. Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit. 1.1.2.2 Residential discontinuous sparse urban fabric. Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture. 1.1.2.3 Residential urban blocks 1.1.2.4 Informal discontinuous residential structures
Percentage of polygon occupation: - sometimes expressed vaguely (e.g: “predominant”) - actual values not stored in database - not explicit (“hidden” in the definition of the classes)
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Object oriented data model (OODM)
Land Cover elements: - complete - structured - explicit - extensible
Attributes: - complete - structured - explicit - exact values measured and stored in database
Percentage of polygon occupation: - explicit - expressed rigorously: (type: real, integer, boolean, list,…) - exact values measured and stored in database
- extensible
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The use of OODM for Land Cover Information has been developed, tested and is working in the Spanish SIOSE Project, which is in advanced production phase (finishing by end of 2009)
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- Spain has proposed an “evolution” of SIOSE Data Model, “Corine Land Cover Object Oriented Data Model” (CLC-OODM) - CLC-OODM has been designed to allow for: Maximum compatibility with standard Corine Land Cover Databases Maximum reuse of existing information in Europe Easy migration path from CLC classifications to Parametric databases 40 40
Corine Land Cover –Object Oriented Data Model
Land Cover Elements: features of other Inspire themes
Land Covers = Standard CLC Nomenclature, in UML 41 41
Land Cover Elements (only 1st level presented)
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Outline
1. Land Cover information: the concept 2. Basic concepts about Object Oriented Land Cover databases 3. Some examples 4. FAQs about OO Land Cover databases
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Example 1: artificial area
1 homogeneous polygon: Land cover 1.1.2: Artificial areas. Urban fabric. Discontinuous urban fabric (100 % of polygon’s surface) Land Cover Elements in it: • Buildings (50 %) • Roads (15 %) • Trees (deciduous) (20 %) • Herbaceous vegetation (10 %) • Swimming pools (5 %) 44 44
Example 2: agricultural area
1 non-homogeneous polygon: Land cover 2.1.1: Agricultural Areas. Arable Land. Non-irrigated arable land (72 % of polygon’s surface) Land Cover Elements in it: • Soil (60 %) • Herbaceous Vegetation (crop) (40 %) Land cover 2.2.2: Agricultural Areas. Permanent crops. Fruit trees (28 % of polygon’s surface) Land Cover Elements in it: • Soil (12 %) • Fruit Trees (80 %) • Greenhouses (2 %) • Irrigation reservoirs (6 %)
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Example 3: natural area
1 non-homogeneous polygon: Land cover 3.1.1: Forest and seminatural Areas. Forest. Broad-leaved forest (55 % of polygon’s surface) Land Cover Elements in it: • Soil (10 %) • Trees (deciduous) (90 %) Land cover 3.1.2: Forest and seminatural Areas. Forest. Coniferous forest (45 % of polygon’s surface) Land Cover Elements in it: • Soil (5 %) • Trees (coniferous) (95 %)
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Outline
1. Land Cover information: the concept 2. Basic concepts about Object Oriented Land Cover databases 3. Some examples 4. FAQs about OO Land Cover databases
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FAQ 1: ¿How do you draw the polygons?
- In the same way as in a traditional classification database: you draw polygons enclosing areas as homogeneous as possible, but at the same time preserving the “Minimum Mapping Unit”: - In order to obtain a database of a reasonable and “usable” size, and also to limit the production budget, it is obliged to define a MMU. MMU is the minimum surface permitted for any polygon. - The MMU forces the photointerpreter, in many cases, to draw polygons that contain areas with different Land Covers 48 48
FAQ 2: If you are describing Land Cover Elements (LCE) don’t you need to draw each individual LCE (eg: each tree, each building,…) ?
- No. In a Land Cover database we are not interested in individual LCE. We only describe the LCEs present in a polygon by: a) The % of the surface of each Land Cover (LC) occupied by these LCE b) The attributes of these LCE (e.g: tree height)
- Note: each collection of “similar” LCE is described as one instance: e.g: a group of similar buildings are only one “instance” of the class “Building”.
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FAQ: What is a data model?
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A data model is the description of what we are storing in a database and how.
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It is the “link” between reality and the Database
World
Data Model
Database
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FAQ: What is an object?
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An object is an “instance” (an “appearance” in reality) of a UML Class:
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FAQ: What is the difference between a data model, a Classification and a Nomenclature?
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A Classification is a very simple data model, in which we use a database with only 1 “table” and 1 field: the class label.
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A Classification can be described as a “partition of the attribute space”.
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A Nomenclature (or legend) is the “list of rules” for making this partition
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FAQ: I have read you use “classes”, but you say you don’t want to classify. Isn’t this a contradiction?
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A UML diagram represents a data model using: • • •
“UML classes” Attributes Relationships
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A “UML class” is an abstraction of similar “real world objects”. E.g: The UML class called “Building” is an abstraction of all real world buildings.
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In Land Cover OODM we use different UML Classes, attributes and relationships to “describe” each polygon. We don’t classify polygons.
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FAQ: OODM looks complicated compared to normal Classifications. Why to use them?
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Classifications have lots of drawbacks and shortcomings (see other presentations). The only way to overcome them is to use a more sophisticated data model: an OODM.
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OODM have many advantages over classifications, that fully compensate the “complicated” appearance of the database.
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OODM is mandatory in Inspire
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FAQ: It seems it will take a lot of work to fill in all the fields for each polygon.
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Not so much. The most difficult parts of the photo interpretation process are:
1) Drawing the polygons 2) Getting to know what is there in the polygon And: 1) It is made in the same way in OODB and in Classifications 2) In a classification, you also need to know everything about the polygon to be able to apply the “classification rules” - The only difference is that, in an OODB, once you know what is there in a polygon, you have to take a little time to fill a small number of fields (1 to 10 normally), instead of only 1.
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FAQ: How do you measure the exact % of each LCE and LC present in a polygon?
There are several methods: 1) If the polygon has a “repetitive pattern” (eg. Urban area), you can measure very precisely (using orthophotos) one, or several, small (and “representative”) parts of the polygon and “extrapolate” it to all the polygon. .
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FAQ: How do you measure the exact % of each LCE and LC present in a polygon? 2) In the case of trees, you can measure the “average” distance between trees and diameter of the crown, and apply a very simple formula to calculate de % of crown coverage. 3) Sometimes you have a very big scale database (eg: cadastral, LPIS,…) that contains all the information needed. You only have to “cross” this big scale database with your polygons, and make the appropriate SQL queries to get the exact % and attribute values.
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FAQ: how do you measure the exact % of each LCE and LC present in a polygon? (continued)
4) You can use some “digital image processing” techniques. E.g: - automatic classification of multispectral images and/or high resolution orthophotos - artificial vision on orthophotos to count point objects - special algorithms on Lidar data to “Classify” - etc…
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Development of productive methodologies of segmentation and automatic classification of images for mapping land cover/land use purposes in the projects SIOSE (Land Cover/ Use Information System of Spain) and Corine Land Cover
Área de Fotogrametría y Teledetección Servicio de Ocupación del Suelo
INFOESPACIAL
DATA •High Resolution images (ortophos, satellite) with NIR band •Cartographic, cadastral and agricultural parcels Data Bases
Object definition and extraction using cadastral parcels and polygons
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INFOESPACIAL Incorporating LIDAR data for: • Improving efficiency when classifying by adding 3D data (buildings, vegetation) •Obtain complementary parameters (e.g: % tree coverage)
DEM extraction Elevation correction Model of tree height
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Eg: semi-automated determination of “LC Elements”
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Eg: semi-automated determination of “LC Elements”
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FAQ: how do you measure the exact % of each LCE and LC present in a polygon? (continued)
5) A good photointerpreter can estimate quite well %, based on the experience of “training areas” and “sample areas”. 6) Sometimes, field work to make “sampling measures” can be needed if you want a very detailed and precise database. 7) It should be possible to “ask the owners” (of parcels, buildings,…) through well designed Internet enquiries. E.g: Spain LPIS (SIGPAC), and “summarize” this detailed information in the LC database.
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INFOESPACIAL
Partners:
•Valencia Politecnic University •Valencia Cartographic Institute
Objectives:
•Assessment of methodologies for segmentation and classification currently available in a specific area for simple classes and attributes of the SIOSE and Corine Land Cover. •Automatic classification from the data collected. •Evaluating accuracy and precision, both semantic and geometric, of the results obtained.
Phases:
•Collection of background information, pilot areas and data •Defining classes. •Pre-assessment tests & Analysis of results. •Development and adaptation of algorithms. •Assessment, conclusions and future proposals.
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Conclusions…
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Land Cover is an extremely important GI theme: - It is an essential input to monitor critical environmental and economical processes as: climate change, desertification, deforestation, urban sprawl, soil sealing, agriculture, etc.. - Also, land cover has overlaps with almost every other GI theme (buildings, forestry, agriculture, transport, vegetation,…). - For this reasons, land cover should be modeled using the latest “state of the art” technology, that is ISO 19109 Feature Data Models.
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LU/LC, as any other G.I. theme can and should be modeled in UML using an ISO 19109 “Application Schema” (Feature Data Model)
LCCS (FAO’s Land Cover Classification System) is not an acceptable solution
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International Geographic Standards:
Object-Oriented, Feature Data Models are mandatory in ISO 19109 (called “Application Schemas”)
They are also mandatory in Inspire INSPIRE “Generic Conceptual Model” (Document 2.5 v3.1)
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The use of FDM to model every geospatial theme is the only way to assure compatibility between them, and to make it possible to develop a “Consolidated Data Model” (CDM) of all Geospatial Information (GI) themes. - The development of this CDM is a must if we want GI to achieve the goals (interoperability, coherency, multi-resolution, captured only once, …) needed in 21st century GI.
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European´s INSPIRE directive is going to develop a CDM to integrate the different GI themes (in Annexes 1 to 3)
Theme 1 Theme 2
Theme n
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Land Use
Proposed definition:
“Land Use is a geospatial information “theme” (a way of looking at the world) whose objective is to show (and allow studying) how the different human and nature activities taking place in a region “compete” between them for a scarce resource: the Land”
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Thank you for your attention !
[email protected]
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Extra slides…
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Can OOLCDB solve the Classification problems ?
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Land Cover Classifications Generalization problem:
3.2.4
?
3.1.1
3.1.1. Broad leaved forest (Trees ≥ 30 %) 3.2.4. Transitional woodland (Trees < 30 %)
The merged polygon is of unknown class.
We have to repeat the Photo interpretation 80 80
Object-Oriented Land Cover Databases Generalization: e.g: CLC-OO Databases
3.2.4 3.2.4 3.1.1
3.1.1. Broad leaved forest (Trees = 48 %) Polygon surface = 15 Ha 3.2.4. Transitional woodland (Trees = 14 %) (Polygon surface = 60 Ha
Merged Polygon surface = 15 + 60 = 75 Ha % Trees = (15*48 + 60*14)/ 75= 20.8 %
→
3.2.4. Transitional woodland with Trees = 20.8 % 81 81
Land Cover Classifications mosaicking: Region 2: Nomenclature 1 e.g. Forest = trees ≥ 50 %
Region 1: Land Cover Nomenclature 1 e.g. Forest = trees ≥ 30 %
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? ? ?
?
The polygons of the database with a different nomenclature are of unknown class We have to repeat the Photo interpretation 83 83
2 Object-Oriented Land Cover Databases: Region 2: OODM / “Profile 2”
Trees = 47 % Trees = 28 %
Trees = 34 %
Trees = 48 %
Region 1: OODM / “Profile 1”
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We can merge the common part of both profiles
Trees = 47 % Trees = 28 %
Trees = 34 %
Trees = 48 %
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Land Cover Classifications MMU problem: Inconsistencies between high resolution databases and generalized version
High resolution (national) classification database:
Low resolution (european) classification database:
10 % of region: 1.1.1. Continuous urban fabric
0 % of region 1.1.1. in generalized data. (Polygons assigned to dominant classes)
But all polygons (< 25 Ha)
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Object Oriented Land Cover databases Regardless of MMU: statistical Consistency between high resolution databases and generalized versions
High resolution (national) classification database:
Low resolution (european) classification database:
10 % of region: 1.1.1. Continuous urban fabric
10 % of region is 1.1.1. in generalized data. (Polygons can have different land covers, and the % of each one is stored) 87 87
All polygons (< 25 Ha)
Requirements for Inspire Land Cover Theme and GMES
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Adequacy to INSPIRE and GMES ?
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Needs 1. Spatial generalization
2. Multirresolution coherence 3. Mosaicking of different existing Land Cover databases possible
Land Cover/Use classifications
Object Oriented Data Models
NO
YES
When we aggregate polygons into bigger ones, there is no way to automatically derive the class of the resultant polygon
NO
YES
Classes in polygons smaller than MMU disappear during generalization
NO
YES
There is no way to know de label of a polygon in a DB with a different Nomenclature
One can merge existing OOLCDB to a common POODB 90 90
Needs
Land Cover/Use classifications
Object Oriented Data Models
4. Integration with remote sensing automatically derived parameters (TopDown approach)
NO
YES
Each polygon has a single attribute: the class label
The mean of continuous value variables for each polygon´s area can then be input and stored in the OODM database as a parameter that qualifies each polygon
NO
YES
- Proliferation of unusable classes, due to the multiple “crossings” of several classification criteria - It is impossible to add external information from a specialized field to a HC database
One can add new features and attributes as needed
NO
YES
5. Indefinite Extensibility
6. Allow for different update periods
Each polygon has a single attribute: the class label. Is has to be updated at once
One can update certain “Land covers”, Land Cover Elements or Attributes of an Object Oriented database more frequently. E.g: - urban fabric: 1 year - forest trees: 5 years
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Needs 7. Perfect change detection
8. ISO 19109 compliance 9. INSPIRE “Generic Conceptual Model” (Document 2.5 v3.1)
compliance
Land Cover/Use classifications
Object Oriented Data Models
NO
YES
Changes in polygons not registered unless they cross the “Class limit” values
NO They are not Feature Data Models (Application Schemas) (The same for LCCS)
Any variation in the composition or attributes of a polygon can be registered
YES An OO-LCDM can easily be an ISO19109 FDM (Application Schema)
NO
YES
They are not ISO 19109 compliant (The same for LCCS)
An OO-LCDM can easily be ISO19xxx compliant (Application Schema). You need to use UML 2.1
92 92
Cost - benefit
Cost for producing an Object Oriented LU/LC database is estimated compared to traditional Classification Databases: only 15 – 20 % more
Great increase in usefulness and reusability of information
Migration path for existing classification databases possible
Object Oriented approach make less critical to improve MMU, because statistics will be correct even with coarse MMU
93 93
Ontologies
Ontologies: sometimes, they are being used in a wrong way: to describe classifications or legends
Instead, they should be used to make more rigorous “taxonomies” in Parametric Feature Data Models
So Parametric Feature Data Models and Ontologies are complementary, not alternatives
94 94
Land Cover overlaps with most other GI Themes
Buildings
Vegetation
Other artificial elements
Geology Soils
Transport Industrial Energy Mineral Utilities
Natural Agriculture areas: Aquaculture Seas Biotopes Hydrography
95 95
Open issues (1)
What is the best way to model land use: Completely separate new data model? complex attributes to land cover classes? Use of UML “methods”,…?
More “levels” in the data model (e.g: polygon > land cover > patch/stand > population/set > individual > part > material)
Hierarchical polygons ?
Attributes values by mean + standard deviation of the population/set?
Temporal existence/presence of elements?
“Multiple fuzzy membership”?
CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
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Open issues (2) Model and measure “mobile” elements presence ?: Artificial: cars, trains,..
Natural: Animals: Wild
Cattle
People
CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
97 97
Spain is proposing international institutions with responsibility in LU/LC information (EEA, Inspire, GMES, ISO, FAO…) to adopt a similar philosophy for future LU/LC databases and offers its collaboration for building a common LC Feature Based Object Oriented Parametric Data Model.
98 98
