Land Cover ClassificationProf. Dr. Christiane Schmullius

Friedrich Schiller University Jena, GermanyDepartment of Geoinformatics and Remote Sensing

(P)P

P

P

3 (-5) directly retrievable from EO out of 13

(P)

Variables from GCOS Adequacy Report

Land Cover – Bodenbedeckung ... Land Use - Bodennutzung

Definition

“Land cover is the observed (bio)physical cover on the Earth’s surface.When considering land cover in a very pure and strict sense it should beconfined to describe the vegetation and the man-made features.

Consequently, areas where the surface consists of bare rock or bare soil describe land itself rather than land cover. Also water surfaces can be disputed as being real land cover. However, in practise the scientific community is used to describe those aspects under the term land cover.

Land cover is not to be confused with land use. Example: woodland or forest are land covers, but the land use may be hunting

or rubber tapping” (LCCS, UN FAO).

IGBP Land Cover

LPJ „Land Cover“

Sitch et al. 2000

DISCREPANCIES

“Land use is characterised by the arrangements, activities and inputs people undertake in a certain land cover type to

produce, change or maintain it.

Land use defined in this way establishes a direct link between land cover and the actions of people in their environment.“ (LCCS, UN FAO)

- Multispectral Systems -

Disadvantages:

- Dependent on Illumination

- Atmospheric Effects

ALBERTZ

- Radar -

Komplex dependences between backscatterand surface phenomena

NASA

Polarisation Roughness

Frequency Dielectric Const.

Incidence Angle Soil Type

Vegetation

Structure

Advantages:- penetrates clouds- illumin. independent

Additional Data necessary for- methodological development- reference data

-Additional Data -

- Statistics

- Maps

- Ground truth

- Aerial Photos

- DEMs

- Expert Knowledge

Nomenclature Requirements

- Spatial consistent

- Temporal consistent

- Sensor independent

- Scale consistent

- Completeness

- no overlaps

- rule based

- compatibel

- multi-user capabilities

Theoretischer Aufbau

Nomenclature: systematic composition of surface objects

CORINE

Comparison of Level I Categories

Anderson - CORINE

1 Urban or built-up areas

2 Agricultural Regions

3 Rangeland

4 Forested Areas

5 Water

6 Wetlands

7 Unvegetated open land

8 Tundra

9 Snow/Ice

1 Built-up Areas

2 Agricultural Regions

3 Forests and quasi-natural regions

4 Wetlands

5 Water Surfaces

IGBP Land Cover

LPJ „Land Cover“

Sitch et al. 2000

DISCREPANCIES

Southeast AsiaA12 Natural Semi Natural F Forest Hr Lowland forest < 1000 m

Vegetated F.1 Evergreen Forest Ht Submontane forestForest F.1.1. Tropical evergreen Forest 1000 m – 2000 mWoodland F.1.2. Mangrove Forest Hp Montane forest > 2000 mThicket F.1.3. Swamp Forest Hm Mangrove forestSparse Vegetation F.1.4. Caniferous Forest Hs Swamp forestLichens /Mosses F.1.5. Bamboo Forest Hti Manmade forest

F.1.6. Beach ForestF.1.7. Mossy / Montane ForestF.1.8. Decideous ForestF.1.9. Mixed ForestF.1.10.Forest PlantationF.1.11.Disturbed Forest /

Secondary ForestShurbland G Grassland Lktp Unproductive DrylandGrassland G.1. Grass

G.2. Shrubland (Bush)B16 Bare Land O Bare land / Open land

Consolidated Areas O.1. MiningUnconsolidated Areas O.2. Cleared Land

O.3. Abandoned Land / Idled LandO.4. Sand BarO.5. Mud FlatO.6. Fire Scar

FAO Classification Scheme

NFI ClassificationLCLUC Classification SchemeScheme Indonesia

EXAMPLE OF MISMATCH CLASSIFICATION

FAO – Land Cover Classification System (LCCS-2) Scheme

GOFC-GOLD Land Cover Implementation plan2-4 March Jena

Afr

ico

ver/

GL

CN

LCCS was created in response to a need for:

• a harmonized and standardized collection of land cover data;

• availability of land cover data for a wide range of applications

and users; and

• comparison and correlation of land cover classes.

LCCS is a comprehensive methodology for LCCS is a comprehensive methodology for description, characterization, classification and description, characterization, classification and

comparison of most of land cover features identified comparison of most of land cover features identified anywhere in the world, at any scale or level of anywhere in the world, at any scale or level of

detail.detail.

GOFC-GOLD Land Cover Implementation plan2-4 March Jena

Afr

ico

ver/

GL

CN

THE OBJECTIVETHE OBJECTIVETo produce a world-wide reference system for landcover able to combine an high level of flexibilityflexibility (abilityto describe land cover features all over the world at anyscale or level of detail) with an absolute level of standardizationstandardization of the class definition between different users

THE IDEATHE IDEAA system that allows a dynamic creation of classeswithout to oblige the user to relate to a pre-defined list of names

THE BASIC CONCEPTTHE BASIC CONCEPTIn LCCS the creation of a class is done by a dynamic combination of land cover diagnostic attributed diagnostic attributed calledclassifiers classifiers

Image EnhancementTechniques

Contrast Stretch

IHS TransformationALBERTZ+

CRISP

RSRG Bonn

RSRG Bonn

Visual InterpretationTechniques!

RSRG Bonn

FSU Jena � Institut für Geographie � Vorlesung Bildverarbeitung � Prof. Dr. C. Schmullius

Role of Classification

FSU Jena � Institut für Geographie � Vorlesung Bildverarbeitung � Prof. Dr. C. Schmullius

ALBERTZ

MultispectralClassification

TERRA

Instrumente:

•ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)

•CERES-2 (Cloud and Earth Radiant Energy System)

•MISR (Multi-angle Imaging Spectro Radiometer)

•MODIS (Moderate Resolution Imaging SpectroRadiometer)

•MOPITT (Measurements of Pollutants in the Troposphere)

TERRA – MODIS-Produkte

• surface temperature

• water leaving radiance

• chlorophyll fluorescence

• concentration of chlorophyll-a within 35 percent, net ocean primary productivity, other optical properties;

• vegetation/land-surface cover, conditions, and productivity;

• cloud mask containing confidence of clear sky (or the probability of cloud), shadow, fire, and heavy aerosol at 1-km resolution;

• cloud properties characterized by cloud phase, optical thickness, particle size and mass transport;

• fire occurrence, temperature, and burn scars;

• global distribution of total precipitable water;

• cirrus cloud cover.

TerraTerra--MODISMODIS--ProductsProducts

- pre-preocessed byNASA Goddard Space Center

and

- distributed via the Land Processes section of theNASA DAAC (Distributed ActiveArchive Centre)

Segment-based Classifications

ASTER RGB Channel 432 30.5.2001Multilayer segmentation ASTER RGB Channel 432 30.5.2001

à„Salt-and-Pepper“ Effect of conventional classifiers

• not usable in a GIS

Objektorientierte Bildanalyse mit eCognition - Einleitung

Result of a pixel-basedmaximum-likelihood classifier

Objektorientierte Bildanalyse mit eCognition – Grundlagen

Segmentation

Advantages:

• each image object contains besides ist spectral information

further image characteristics (Standard Deviation, Form, Texture)

and context informations (Exp.: neighbour to class A and B)

• Averaging of spectral heterogeneities over the whole object

• Object characteristics in different generalisation scales

• Reduction of classifiable units

Objektorientierte Bildanalyse mit eCognition – Grundlagen

Hierarchical Network of Image Objects

Foto

Objektorientierte Bildanalyse mit eCognition – Beispiele

Level 1

Objektorientierte Bildanalyse mit eCognition – Beispiele

Level 2

Objektorientierte Bildanalyse mit eCognition – Beispiele

Level 3

Objektorientierte Bildanalyse mit eCognition – Beispiele

Level 3: Area

Objektorientierte Bildanalyse mit eCognition – Beispiele

Level 3: Length/Width

Objektorientierte Bildanalyse mit eCognition – Beispiele

E-SAR - RGB =X-HH, C-VV, L-VV

Objektorientierte Bildanalyse mit eCognition – Klassifikation von E-SAR und HyMap™ Daten

objektorientiertes Klassifikationsergebnis (E-SAR)

Objektorientierte Bildanalyse mit eCognition – Klassifikation von E-SAR und HyMap™ Daten

pixelbasiertes Klassifikationsergebnis (E-SAR)

State of the Art• E.g. GLC 2000

• Continental or global in coverage

• Hard Classification

• Kilometre pixel size

• Temporally staticUniversity of Wales

Swansea

MODIS16-day composite

Summer 2002Days 225-241bands 6,2,1

SWIR, NIR, RED

Level 1 comparison

University of Wales

Swansea

MODIS LC (IGBP Classes) MODIS LC (UMD Classes)

LC Maps from Remote Sensing

University of Wales

Swansea

GLC2000 LC Map

(SPOT VGT S1 2000)

LC Maps from Remote Sensing

University of Wales

Swansea

Southern Cropland

SibII Lvl2 GLC2000

University of Wales

Swansea

NW Wetlands

SibII Lvl2 GLC2000

University of Wales

Swansea

FAO – Land Cover Classification System (LCCS-2) Scheme

Result: Agriculture-forest-tundra

MODIS, 500m

MERIS, 300m

GLC2000, 1km

Result: River in forest

MODIS, 500m

MERIS, 300m

GLC2000, 1km

Heterogeneity

- Mismatch in spatial scale - Scaling issues

University of Wales

Swansea

Advancing the science

MODIS Land-cover Product

- Hard classification- 1km pixel size- Updated every 3 months- IGBP, UMD, LAI/fPAR, BGC

biomes

MODIS Continuous Fields Product

- 500m pixel size- NOT hard classification- Life form, leaf type, leaf longevity

Red

=Tre

e C

ove

r, G

reen

=Her

b. C

ove

r,B

lue=

Bar

e g

rou

nd

% tree cover derived from 1992-93 1km AVHRR (DeFries et al, 2000)

Training from Landsat

MODIS 250m U.S. Tree Cover Prototype

0%

100%

Tree Cover

For details seehttp://glcf.umiacs.umd.edu

For details seehttp://glcf.umiacs.umd.edu

Hansen Defries and Townshend

Leaf type

Leaf longevity

% evergreen% deciduous

% needleleaf% broadleaf

Defries, Townshend and Hansen

Fractional Cover Modeling Fractional Cover Modeling (VGT4 image)(VGT4 image)

Fractional Cover Modeling (VGTFractional Cover Modeling (VGT--fcfc))

Source: Fernandes

GOFC-GOLD Land Cover Implementation Meeting and ESA Project OfficeInaugurationMarch 2004

Jena

What is GOFC?• An ambitious, multifaceted international strategy to bring the Earth’s

forests/land cover under continuous observation.• A vision to share data, information and knowledge, leading to

informed action.• A coordinated program of activities to ensure that earth observation

and other data are used effectively for global monitoring for natural resource management, policy and global change research.

• A network of participants implementing coordinated demonstrations of the use of geospatial information and technology for sustainable development

• An international organization of space agencies and end users working together.

• A long term process of building an improved match between data products and user needs.

Organizational Status• Started as a project of the Integrated Global Observation

Strategy proposed by CSA.• Panel of the Global Terrestrial Observing System• Two main implementation teams: Landcover, Fire• Scope: transitioning from

– GOFC (Global Observations of Forest Cover) to– GOLD (Global Observations of Landcover Dynamics)

• Principal Role of GOFC/GOLD is to act as a coordinating mechanism for national and regional activities.

• Active regional networks developing• Secretariat established with Canadian Forest Service

Roles of GOFC/GOLD

• Facilitate end-to-end international coordination mechanism: from observation to use

• Improve technical development and application of new methods of observation, especially remote sensing.

• Improve integration of satellite and in situ observations.

• Improve understanding of the causes of changes and their impacts

ESA’s GOFC-GOLD Land Cover Implementation Project Office (LC PO)

moderates…

the development of methods, tools and products for land cover measurements and monitoring using space-borne and in-situobservations;

the assessment of current needs and deficiencies for global and regional land cover monitoring to support Global Change research, national and regional forest inventories and international policy;

close cooperation with the Fire Implementation Team and regionalnetworks in Asia and Africa

…in agreement with CFS’ GOFC-GOLD Project Office and the GOFC-GOLD Executive Committee.

Land Cover ClassificationProf. Dr. Christiane Schmullius

Friedrich Schiller University Jena, GermanyDepartment of Geoinformatics and Remote Sensing