Environmental remote sensing data used at VTT. Tuomas Häme

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1 Environmental remote sensing data used at VTT Tuomas Häme

universities and research institutes VTT CREATES new technology and science-based innovations in cooperation with domestic and foreign

2 VTT Technical Research Centre of Finland VTT IS the biggest multi-technological applied research organisation in Northern Europe VTT HAS multidisciplinary R&D from electronics to building technology clients and partners: industrial and business enterprises, public sector, universities and research institutes VTT CREATES new technology and science-based innovations in cooperation with domestic and foreign partners Turnover 232 M Personnel 2,740 76% with higher academic degree 5,730 customers Established 1942 VTT has been granted ISO9001:2000 certificate. 2

Environmental remote sensing data use in the nutshell Which data All kind of imagery from Earth's surface From ground-based cameras to AVHRR Optical

industry, consultants, European Space Agency, Finnish and foreign governmental agencies and foreign private sector Applications Sustainable use of

3 Environmental remote sensing data use in the nutshell Which data All kind of imagery from Earth's surface From ground-based cameras to AVHRR Optical & radar Use Value adding chain development including Preprocessing methods Analysis methods Software development System development For whom Forest industry, consultants, European Space Agency, Finnish and foreign governmental agencies and foreign private sector Applications Sustainable use of natural resources, mitigation of environmental change impact and environmental disasters. Cost savings, more effective operation, timely information. 3

4 Connecting people and data Satellite and airborne data In situ measurements Data providers Environment Environment Environment Environment monitoring monitoring Environment monitoring monitoring service service Monitoring service service Service Analysis Processing Product Tailoring Models Biomass Weather Ice Smoke Oil GIS DBs Media Content Context 4

5 Calibrated mosaic from Europe in one-kilometer resolution using 63 weather satellite images 5

6 6

7 Location of forest types in red / NIR spectral domain Figure 7. Target variable values in the spectral space in the Temperate and Boreal 7 stratum.

Modis 1Blevel data from summers 2000 and 2001 27 images Ground resolution

reflectance green channel = near-infrared reflectance VTT TECHNICAL

Suonenjoki and stem volume values from Suonenjoki plot data Landsat ETM+

8 Modis 1Blevel data from summers 2000 and images Ground resolution 250 meters Size of the mosaic about 1400 km by 1900 km red channel = red reflectance green channel = near-infrared reflectance VTT TECHNICAL RESEARCH CENTRE OF FINLAND Landsat ETM+ image from 6 th of July 2001 from Suonenjoki and stem volume values from Suonenjoki plot data Landsat ETM+ image from 10 th of June 2000 from Puumala and stem volume 8 values from Puumala plot data

9 Stem volume models estimated from the Suonenjoki data sets Coniferous trees Broad-leaved trees 9

10 Total stem volume in Finnish forestry districts Estimate Statistics mill. m S-Both S-Savo Häme-Uus Central Fin Kainuu Kymi SW Fin Lapland N-Karelia N-Both N-Savo Pirkanmaa Coast Åland 10

11 JERS radar image mosaic (preliminary) from Eurasia Global Boreal Forest Mapping Project - JAXA 11

12 Effect of topographic normalization SAR image without (left) and with (right) utilization of the DEM in radiometric and geometric correction 12

Esimerkki orto-oikaistusta Polarimetrisesta tutkakuvamosaiikista Osa mosaiikista perspektiivikuvana Korkeutta liioiteltu 5-kertaisesti

13 Esimerkki orto-oikaistusta Polarimetrisesta tutkakuvamosaiikista Osa mosaiikista perspektiivikuvana Korkeutta liioiteltu 5-kertaisesti Perspektiivikuva tehty GRASS-GIS ohjelmistolla Lokan tekoaltaan itäpäästä kohti Sokostin seudun tuntureita Kuvat huhtikuulta 2007 ALOS/Palsar data JAXA, METI

14 Dual-Pol /Heinavesi: Stem Volume Correlation/HV Whole dataset: r = m 3 /ha: r = 0.93 Saturation around 150 m 3 /ha One obvious clear-cut stand (harvested March 2007, ground March 2007) 14

15 Example of classification result May: PolSARpro supervised ALOS/Palsar data JAXA, METI

Yellow = cut 28.7.-12.9. Red = cut 12.6.

16 ALOS/Palsar in the detection of clear cuts R= G= B= Yellow = cut Red = cut ALOS/Palsar data JAXA, METI

17 Forest cover change in French Guiana using ESA radar 17

18 Natural disaster monitoring workstation Web application infrastructure Satellite imagery analysis Other information Multi-user tool with different user roles 18

19 Growing stock volume Ikonos false color image of Suonenjoki test area estimate plotted on Ikonos PAN channel 287 m3/ha 0 m3/ha 19

20 Stem diameter Ikonos false color image of Suonenjoki test area estimate plotted on Ikonos PAN channel 28 cm 0 cm 20

21 Stem number / ha Ikonos false color image of Suonenjoki test area estimate plotted on Ikonos PAN channel 2941 stems/ha 0 stems/ha 21

22 Proportion of broadleaved trees from stem volume Ikonos false color image of Suonenjoki test area estimate plotted on Ikonos PAN channel 100 % 0 % 22

23 Accuracy at plot level 500 Estimated stem vol vs. gnd data, FS1-BL Estimated stem diameter vs. gnd data, FS1-VOL a R = R = 0.90 stem vol estimate [m3/ha] stem diameter estimate [cm] stem volume [m3/ha] stem diameter [cm] 23

24 Tree species study - tree detection Detected trees on Plot No: m Spruce (validation data) Pine (validation data) Birch (validation data) Trees located on Ikonos image a) on aerial image b) on IKONOS PAN channel 24

25 Forest Monitoring Tree Species Map Tree species proportions calculated for a 1 km x 1 km Ikonos-2 image Average segment size 1 ha Pine-% Spruce -% Deciduous-% 25

28 1998 VTT TECHNICAL RESEARCH CENTRE OF FINLAND 28

29 1999 VTT TECHNICAL RESEARCH CENTRE OF FINLAND 29

30 2000 VTT TECHNICAL RESEARCH CENTRE OF FINLAND 30

38 Forest Monitoring - Segmentation Hierarchical Pyramidas segmentation result The mean size of the segments is in the three segmentations: White borders: 1 ha White and yellow borders: 0.5 ha White, yellow and green borders: 0.3 ha Ikonos-2 image, 1.4 km x 1.0 km 38

39 VTT TECHNICAL RESEARCH CENTRE OF FINLAND Web camera images Image series from Enontekiö Daily images recorded at high noon Challenges Illumination intensity variation especially sunny images Incident light colour balance variations + trend Camera colour balance variations Target movement (wind) 39

40 ForSe - Forest and Season Monitoring Web Cameras In Automatic Autumn Colour Monitoring A time series of web camera images and corresponding feature images from Enontekiö autumn

SCI comparison with reference data/oulanka site The leaves' colour development for Oulanka site 9 ROIs (1 ref + 9 for classification) classifier training with Enontekiö site data (see slide 13) no

41 SCI comparison with reference data/oulanka site The leaves' colour development for Oulanka site 9 ROIs (1 ref + 9 for classification) classifier training with Enontekiö site data (see slide 13) no instances in class 'red' (not plotted) Phenology dates (Metla) marked with blue arrows Season colour development/oulanka- Autumn 2007 green light green yellow brown fallen SCI DOY Yellowing starts Falling starts Leaves fallen (> 50%) The ROIs shown on Oulanka site image taken Leaves yellowed (> 50%) 41

42 Monitoring of seasonal changes Autumn coloring in September-October 2005 Kartan värien selitykset Lehdet täysikasvuisia Kellastuminen alkaa Variseminen alkaa Lehdet kellastuneet Lehdet varisseet 42

43 Kioto+ (post-kyoto) Satellite Mission in Brief Proposed near-polar orbiting satellite monitoring system Super high-resolution (0.5 m) optical images Statistical sampling principle => conceptually comparable to field measurements Individual trees visible Land cover class/class change can be defined without any uncertainty The images are permanent documents about the situation during image acquisition Three major uses: 1. Statistical data on global, regional and national forest and land cover with confidence interval (target area km 2 and larger) 2. Training and validation data for "wall-to-wall" satellite imagery 3. Augmentation of field measurements 43

44 15 km 4 km Sampling principle of Kioto+ QuickBird-satellite image from Southern Finland Resolution 60 cm Digitalglobe 44

Spectral Response for DigitalGlobe Earth Imaging Instruments

Spectral Response for DigitalGlobe Earth Imaging Instruments IKONOS The IKONOS satellite carries a high resolution panchromatic band covering most of the silicon response and four lower resolution spectral