3D-Canopy Models from Photogrammetry und Radargrammetry

Transcription

1 3D-Canopy Models from Photogrammetry und Radargrammetry Mathias Schardt JOANNEUM RESEARCH Research Group Remote Sensing and Geoinformation Graz Technical University Institute of Remote Sensing and Photogrammetry

2 2 JR Software RSG Cartographic Transformations Data Interfaces Management Modules Utilities Geometric Various Sensors Modelling Rectification Stereo Data Extraction SAR Interferometry Block Adjustment DEM Generation

3 3 Structure of Presentation Validation Photogrammetry / Radargrammetry Forest Degradation / ROC Example ESA GSE - Forest Photogrammetric Airborne Remote Sensing Solution Example Pilot Project Forest Inventory Surinam

4 4 Test and Validation Site Forestry test site Eastern Austria Available imagery: IKONOS stereo (2 epochs) ALOS PRISM (forward/backward) IRS P5 TerraSAR-X spotlight triplet (3 ascending scenes: angles: 22.2, 37.2, 48.5 Cosmo-SkyMed triplets (2 epochs) Reference data: Airborne Laserscanning (2 epochs DSM + DTM) Terrestrial Measurments CIR Aerial images

5 5 Radargrammetry TerraSAR-X / Cosmo-SkyMed Multi-angle ability Very high resolution

6 6 Radargrammetry / Photogrammetry - Workflow Multi-image mapping - 3 images with different incidence angles, Redundant matching to increase robustness / outlier detection

7 7 3D Forest Models derived Surface Model TerraSAR-X Surface Model ALOS Prism Surface Model IKONOS

8 8 Radargrammetry - Accuracy TerraSAR-X DSM accuracy w.r.t. LiDAR: bare ground forest µ[m] σ[m] µ[m] σ[m] Τ [%] asc123-c Cosmo-SkyMed DSM accuracy w.r.t. LiDAR: bare ground forest µ[m] σ[m] µ[m] σ[m] Τ [%] asc123-c

9 9 Comparison Radargrammetry - Photogrammetry LiDAR 2009 IKONOS 2004 PRISM 2008 Cosmo-SkyMed 2010 TSX 2009 sensor & acquisition time IKONOS 2004 PRISM 2006 Cosmo-SkyMed 2010 TerraSAR-X 2009 Difference of mean forest stand height to LiDAR reference m -4.47m -3.73m -5.23m

10 10 Structure of Presentation Validation Photogrammetry / Radargrammetry Forest Degradation / ROC Example ESA GSE - Forest Photogrammetric Airborne Remote Sensing Solution Example Pilot Project Forest Inventory Surinam

11 11 Logging Gap Detection in Tropical Forests red line: original DSM with full resolution 2m black: DSM filtered with window size 400

12 12 Logging Gap Detection in Tropical Forests DSM Cosmo-SkyMed filtered minus DSM Cosmo-SkyMed 2011 (2m) -> degredation along logging roads

13 13 Structure of Presentation Validation Photogrammetry / Radargrammetry Forest Degradation Example ESA GSE - Forest in ROC Photogrammetric Airborne Remote Sensing Solution Example Pilot Project Forest Inventory Surinam

14 Photogrammetric Airborne Data Acquisition and Mapping (ADAM)

15 Motivation The Problem only specially adapted and certified aircrafts can capture remote sensing data data capture in developing countries causes high aircraft transfer costs only few companies can do the job The Solution ADAM C can be mounted on any* Cessna s wing strut in 20 minutes and has all the hardware within one shell (* certified for Cessna 172,182,206,208) The result: flexibility, cost efficient, easy to use

16 Sensors georeferencing ADAM Hardware GPS phase receiver Inertial measuring unit(imu) the images can be directly geo-referenced fully automatically without ground control points Sensors image capture Mid-format digital camera (20MP) Near infrared camera Control unit rugged PC data storage Independent power supply by alternator and batteries

17 Resolution and Direct Geocoding Accuracy georeferencing type rapid mapping without ground control points (direct geo - referencing) precision mapping with ground control points height above ground / resolution 1000m / RGB: 15 cm NIR: 30 cm 2000m / RGB: 30 cm NIR 60 cm accuracy < 1m < 2m 1000m < 0.15m 2000m < 0.3m Image size: red line: 4872 x 3248

18 18 Image Exampes

19 19

20 20 Photogrammetry #408 #407 anaglyph parallax

21 21 Data Acquisition

22 22 Potential Applications of ADAM Verification of Satellite Based Application Combined Terrestrial / Aerial Photo Area Frame Sampling for Forest Inventory Wall to Wall Mapping of smaller forest areas of interest (mangrove forest, Forest areas with logging activities, certification areas, etc.)

23 23 Resumee 3D Canopy Models can be used for: Degradation based on Forest Structure / Density Forest Height (if DTM available) Biomass Modelling by 2D / 3D Image features and terrestrial measurements Combinating 3D Canopy Models and Direct Biomass Assessment (L / P Band) Biodiversity??