Sentinel-2 Images and Finnish Corine Land Cover Classification

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1 Sentinel-2 Images and Finnish Corine Land Cover Classification Markus Törmä, Suvi Hatunen, Pekka Härmä, Elise Järvenpää Finnish Environment Institute SYKE

2 CORINE Land Cover (CLC) What Program to gather information about the environment of the European Union Why Determine and assess the effects of EU s environment policy Needed to have a proper information about the different features of the environment How Visual interpretation of satellite images Mapping scale 1: Minimum mapping unit 25 ha Changes 5 ha Only area elements classified Hierarchical classification nomenclature 44 third level classes 2

3 CLC evolution The main outputs of CLC2000 project 1. National and European wide satellite image mosaic for the year 2000 (IMAGE2000) 2. CORINE land cover classification for 2000 (CLC2000) 3. Database of land cover changes between 1990 and 2000 CLC Orthorectified satellite images for the reference year European mosaic based on satellite imagery called IMAGE Corine land cover changes Corine land cover classification High resolution core land cover data for built-up areas including degree of soil sealing for year High resolution core land cover data for forest areas for year 2006 CLC Satellite images and mosaics IMAGE High Resolution Layers: soil-sealing, forest, grasslands, wetlands and waters 3. Corine land cover changes Corine land cover classification

4 Finnish CORINE Land Cover Produced data must fulfill also the needs of various national end-users Resolution: 25 ha / 5 ha change MMU too large Nomenclature: national classes needed Use of data from national on-going programmes Avoid overlapping work Use of best data and expertise available National co-operation Open data policy Downloadable from Free of charge, even for commercial purposes 4

5 Finnish CORINE Land Cover Land cover by interpreting satellite images Land use from digital map databases Mainly based on interpretation of aerial images The main data sources of Finnish CLC2000 5

6 Production of CLC in Finland Preparation of Satellite Data -detection of clouds -atmospheric correction -mosaicing Preparation of Input Map Data -rasterizing, mosaicing, reclassification etc Interpretation of Satellite Data -automated -semi-automated / visual Crown cover Tree height Tree species Veget. cover Updated Land use data Soil data Data Integration LC data for national use (25 m raster) Generalisation Conversion from raster to vector LC data for EU (25 ha MMU) 6

7 Change detection for Finnish CLC

27.4.2012 CLC2006 data sources Forest Research Institute EU National Forest Inventory IMAGE2000, IMAGE2006 Finnish Environment Institute Environmental db, Manual and automatic interpretations Forests

8 CLC2006 data sources Forest Research Institute EU National Forest Inventory IMAGE2000, IMAGE2006 Finnish Environment Institute Environmental db, Manual and automatic interpretations Forests Dump sites Mineral extr. Sites Etc. Soils, roads, Arable land Build-up areas National Land Survey Basic mapping Ministry of agriculture and forestry Agricultural land parcel db Population Register Centre Building and Dwelling db

9 Finnish Corine Land Cover classification National CLC (25 m) EU CLC (25 ha) More details in poster Integration of GIS Datasets and EO Data in Land Cover Production Järvenpää, E., Teiniranta, R., Hallin-Pihlatie, L. 9

10 Images Landsat-7 ETM (IMAGE2000) Spot-4 HRVIR (IMAGE2006, 2009, 2012) IRS P6 LISS III (IMAGE2006, 2009, 2012) Sentinel-2 MSI Channels (µm) ETM1: ETM2: ETM3: ETM4: ETM5: ETM7: ETM6: PAN: XI1: XI2: XI3: XI4: PAN: MS1: MS2: MS3: MS4: B1: B2: B3: B4: B5: B6: B7: B8: B8a: B9: B10: B11: B12: Pixel size (m) 30 multispectral 15 panchromatic 20 multispectral 10 panchromatic : B2, B3, B4, B8 20: B5, B6, B7, B8a, B11, B12 60: B1, B9, B10 Quantization (bits) Swath width (km) IMAGE2006 onwards: 1. summer and 2. spiring/autumn coverages IMAGE2006: some DMC-images were used IMAGE2012: Spiring/autumn RapidEye 10

11 Images: processing Geometric orthocorrection by Metria Sweden Manual detection of clouds Atmospheric correction IMAGE2000: VTT SMAC IMAGE2006: Erdas Imagine ATCOR2 Topographic correction in Lapland IMAGE2000: Ekstrand correction IMAGE2006: Statisticalempirical correction Mosaicing 11

12 Experiences Acquisition of a cloud-free HR satellite data coverage over Finland is very challenging Acquisition of IMAGE2000 data took 4 years missing 0.55% from land area Acquisition of IMAGE2006 took 3 years missing 4.5% (summer) or 13.5% (spring/autumn) EEA requires that CLC represents the situation visible in images Extra work because have to compare GIS-datasets with images and correct them Geometric correction of images has been quite successful As pixel size decreases, more accurate DEM in needed 12

13 Experiences Atmospheric correction for different instruments IMAGE2000 with SMAC-based software developed by VTT Technical Research Centre of Finland Estimated Atmospheric Optical Depth from images IMAGE2006 using ATCOR2 of Erdas Imagine Difficulties in adjusting the correction of IMAGE2006 so that images would correspond IMAGE2000 Difficulties with calibration coefficients or other correction parameters Mosaic of 3 IMAGE2006 images With histogram matching 13

14 Experiences IMAGE2006 and metadata-image Metadata tells from which image pixel has been taken 76 km 14

15 Sentinel-2: Spatial resolution Sentinel-2: 10 m / 20 m / 60 m depending on band Possibility to differentiate smaller details than with IMAGE2000 & IMAGE2006 Finnish CLC classification 25 m raster could be changed to 10 m IMAGE2000 (25m) IMAGE2006 (20m) Sentinel-2 (10 & 20m) Simulated using RapidEye

16 Sentinel-2: Temporal resolution Temporal resolution will increase quite a lot + Swath width: 290 km + Neighboring orbital paths close to each other in North + Two satellites - Cloud cover and short growing season will decrease Consequences Image mosaicking and interpretation should be easier, less time-consuming and cost-efficient Better possibilities for hazard monitoring Use of multi-temporal data for classification Some examples in poster: Seasonality of Land Cover Types as Basis for Improved Land Cover Classification within Pan-European Area Frame Sampling Scheme Törmä, Lewiński, Aleksandrowicz, Esch, Metz, Smith, Lamb, Turlej 16

17 Sentinel-2: Cloud cover Cloud masking could be centralized Mask should include thick clouds, thin clouds, haze and shadows IMAGE2009-image and provided cloud mask Mask does not include all thick clouds and thin clouds and shadows are missing 17

18 Sentinel-2: Radiometric resolution Sentinel-2: 12 bits Possible to detect smaller differences in measured radiance Expectations Help to differentiate similar classes e.g. forest classes Increase the accuracy of the estimation of the vegetation parameters tree height and crown cover 18

19 Sentinel-2: Spectral resolution Sentinel-2: larger number and decreased width of bands New channels like red-edge (B5 and B6), vegetation analysis (B7 and B8a) atmospheric correction (B1, B9 and B10) Expectations increase the separability of land cover classes atmospheric correction easier specific software will be needed 19

20 Sentinel-2: Radiometric correction Previous IMAGE-mosaics images from several instruments consistent atmospheric correction difficult Sentinel-2: should be easier specific bands for atmospheric correction two satellites more images for same processing chain Topographic correction: as before IMAGE2006 mosaic Topographic Correction using Statistical-Empirical correction method 20

21 Sentinel-2: Geometric correction Orthocorrection Good Digital Elevation Model needed DEM pixel 1/3 from image pixel if possible Ground Control Points Preferably reference mosaic and automatic image matching E.g. Erdas Imagine Autosync The use of same reference makes image-to-image change detection easier Less erroneous changes due to image mismatch 21

22 Conclusions Problems with IMAGE2000 and IMAGE2006 Clouds Temporal resolution has been quite poor Manual masking Use of several different instruments Difficult to make good consistent atmospheric correction Images should be radiometrically comparable with each other Expectations concerning Sentinel-2 MSI Spatial, spectral and radiometric resolutions will increase will provide images with better quality and temporal resolution will make the processing of future IMAGE-mosaics easier provide better Corine Land Cover classifications 22

23 THANK YOU FOR YOUR ATTENTION!

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