Use of ENVISAT Multi-Sensor Data for Global Land Cover Determination.

Dr. Hervé Trebossen(1), Dr Olivier Arino(2) , Dr Stephen Plummer(3) , Dr Frédéric Achard(4) , and Dr Federico González-Alonso,(5)

(1) ESA ESRIN, Via Galileo Galilei, Casella Postale 64, 00044 Frascati (RM), Italy (2) ESA, Via Galileo Galilei, Casella Postale 64, 00044 Frascati (RM), Italy

(3) IGBP / ESRIN, Via Galileo Galilei, Casella Postale 64, 00044 Frascati (RM) , Italy(4) JRC, CCR / TP 440, , I-21020 Ispra (VA), Italy

(5) Centro de Investigación Forestal-INIA, Crta. De la Coruña km 7, 28040 Madrid, Spain

Abstract

References

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Stephen Plummer, Olivier Arino, Freddy Fierens, Jing Chen, Gerard Dedieu, Dino Di Cola and Muriel Simon; THE GLOBCARBON INITIATIVE: MULTI-SENSOR ESTIMATION OF GLOBAL BIOPHYSICAL PRODUCTS FOR GLOBAL TERRESTRIAL CARBON STUDIES

Methodology :

Ortho-rectification:

• MERIS and AATSR data were processed into WGS84 geodetic reference using BEAM32 orthorectificationtool, DEM used is GETASSE 30.

Pre-processing

•Cloud screening : We choose data with a minimum of clouds, few ones were manually removed.

•Atmospheric correction : SMAC processor from BEAM 32 was used.

•Temporal compositing : Remaining contaminated pixels were removed by selecting pixels with the Max NDVI.

Classification : A Unsupervised classification algorithm approach was used then labelling was made considering GLC 2000 maps.

Objectives:

The principal objective of this communication is to test strategy / methodology for the production of land cover maps in the framework of ESA/DUE GLOBCOVER Project. We will use first MERIS FR data and in cases where no proper MERIS acquisitions are available (clouds, acquisitions conflicts for FR) to test and use ENVISAT ASAR WS and ENVISAT AATSR instruments data.

Two test sites and their preliminary results will be documented in this communication : (1) Senegal/Gambia and (2) Thailand / Viet-Nam.

(1) We consider that MERIS FR data acquisition is “sufficient” to produce land cover maps.

(2) In a humid tropical area, we will test first contribution of AATSR data and then for a specific application dedicated to “wetlands” we will use ASAR Wide Swath data.

Acknowledgements:

Data used are available within ESA CAT1 ID 2682 project called : « Globcover: Testing Algorithms for global land cover determination using MERIS FR, AATSR and ASAR WSM ».

The objective of the GLOBCOVER initiative is to develop a service that in first instance will produce a global land-cover map for tentatively the year 2005, using as its main source of data MERIS Full Resolution Full Swath mode, data to be acquired over the full year. In order to improve actual methodologies, we will present different study cases of the appropriateness of MERIS-FR data for land cover mapping in combination, with ASAR Wide-Swath data for cloudy areas and AATSR data especially with the short-wave infrared band.

#

#Rangoon

Mandalay

Site 2

Site 1

Location Map

II. Use of AATSR and ASAR WS data for Land Mapping in South-East Asia

I. Use of MERIS FR data in Senegal

For each MERIS FR : mask based on NDVI for temporal compositing

Test site (N15.5°; W17.5° – N13°; W15°) could be considered into two distinguished parts :

•First, north a semi_arid area.

•Second, south in Casamance region and along Gambia River a mix of arid zones and humid tropical vegetation with mangrove swamp and paddy fields.

Two datasets were used :

•Three quasi uncloudy MERIS FR images were first used in order to test methodology. Characteristics of these data are :

MERIS FR 2003 04 12 L1B

MERIS FR 2003 04 15 L1B

MERIS FR 2003 05 30 L1B

•Then a single uncloudy one (MERIS FRS 2005 03 28) was used for a comparison with both reference dataset (GLC 2000) and the previous dataset, twoyears before.

April 2003 MERIS FR composite

2005 28th March Land Cover MapApril 2003 Land Cover MapGLC 2000 Land Cover Map

Results / Discussion :

Preliminary results show a good correspondence by visual comparison between GLC 2000 and resulting maps.

Some specific land classes were not so well identified on MERIS FR data or composite : « urban areas », « irrigated croplands », « deciduous shrubland with sparse trees », « deciduous woodland » and in some cases « open grassland with sparse shrubs ».

Comparison between land cover maps produced with MERIS FR data gives results much more closer.

However, an equal reasoning approach should be conducted in order to separate North and South parts of this test site.

II.1. MERIS FR and AATSR dual use (Site 1)

Test site (N18.5°; E105° – N17°; E107°) is located in Viet-Nam. One MERIS FR and one AATSR data were use to produce a first land cover map. Characteristics of these data are :

•MERIS FR 2003 11 07 L1B

•AATSR TOA 2003 11 20

AATSR data was first geolocated using BEAM software, co-registered with MERIS ortho-rectified data in order to avoid discrepancies between the two images then re-sampled.

AATSR Band 4 (1.58-1.64 µm) was then used as additional band.

GLC 2000 Land Cover Map Land Cover Map using MERIS Land Cover Map using MERIS and AATSR

Test site (N14.5°; E99.5° – N13°; E101.5°) is located in Thailand.

In the specific case where no MERIS data are available (essentially due to cloud coverage), Possibility to retrieve specific land cover classes with ASAR data is here interesting.

One ASAR WS data were used in order to classify paddy fields along the coastline.

Characteristics of ASA WS were :

•ASA_WSM_1 2003 11 04

ASAR image was ortho-rectified using a method based on precise orbit product, geoid (EGM-96) and SRTM DEM.

II.2. Use of ASAR Wide-Swath images (Site 2)

Other result on Cameroon (dark green: tropical lowland forest, pale green: swamp forest, NE, slopes oriented

front of SAR were classified as swamp forest).

Result on Bangkok area (in Red paddy fields), on the eastern part of this image appear paddy fields with higher rice crop growth unclassified.

SAR georeferencing method.

August 2005

One month of cloudy MERIS images on Bangkok city and surroundings.

Supervised classification was conducted with an iterative program based on markovian field theory.

Results / Discussion :

On this site, classification using AATSR as additional band of MERIS data gives Land Cover Maps much more closer with GLC 2000 than without AATSR. Same as for Senegal, an equal reasoning approach should be conducted in order to separate coastal and mountainous part of this site.

Especially for AATSR data, an automatic tool is under construction in order to remove existing inhomogeneous planimetric offsets existing between AASTR and ASAR, MERIS.

Unsupervised, but also supervised classification on ASAR images gives often poor results due to speckle effect and also due to slopes effects. There is actually no perfect method able to remove these two problems (multi-temporal filter algorithm for speckle and radar-clinometry equations for slopes give quite good results).