1. 0105-5-10 Height Growth [m/3yr] An example: Stand biomass estimation by LiDAR

2. Stand biomass by LiDAR & Stand Density Chart RMSE = 53.0 m 3 /ha Bias = -20.9 m 3 /ha Ratio of contribution = 0.94 Stand Height Stand Density Biomass

3. LiDAR Large footprint Harding, D.J., Lefsky, M.A., Parker, G.G., Blair, J.B., 2001. Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests. Remote Sensing of Environment 76, 283-297.

4. Lefsky, M.A et al. 1999. Remote Sensing of Environment 70, 339-361.

5. Do we actually tell you the truth by RS? Need the accuracy control How can we disseminate the developed techniques to the real forest management?

6. Real world vs. satellite view Real land cover Satellite map vs.

7. Real world vs. satellite view Error Matrix (real) Real land coverSatellite mapvs. K statistics Overall Accuracy Error Matrix

8. Real world vs. satellite view Error Matrix (random sample) Real land coverSatellite mapvs. Error Matrix / Multinominal Sampling K statistics Overall Accuracy

9. Real world vs. satellite view Error Matrix (random sample) Real land coverSatellite mapvs. Error Matrix / Multinominal Sampling Area Estimation

10. Real world vs. satellite view Error Matrix (stratified sample) Real land coverSatellite mapvs. Error Matrix / Stratified Sampling (Sat map) Overall Accuracy

11. Real world vs. satellite view Error Matrix (stratified sample) Real land coverSatellite mapvs. Error Matrix / Stratified Sampling (Sat map) Area Estimation K statistics

12. Accuracy evaluation is really awful So many samples to make the result statistically relevant On the field, we can never be ‘random’ Many researches using RS skipped/ignored/pretended this process Sometimes they induced problematic aftermath How can we make them meaningful?

13. Activities in Forested Landscapes How can we disseminate RS to the real forest management? FM/LM SFM/SLM Research MeasurementC&I RS

14. But… Remote sensing has not yet succeeded in the practical world – the world of application. Franklin (2001) “Remote “Remote Sensing for Sustainable Forest Management” Why? How can we overcome?

15. Why RS is successful in researches, but not in real SFM? No universal answers Result here cannot be applied to there What are the universal tendency of forested landscapes? Ground truths expensive Pitfall of ‘Professional’ monitoring How to re-use the existing knowledge? Credibility/accuracy Khat syndrome, but actually makes sense? Minimum guideline for assessable result User’s capacity Colorful Landsat image cannot intuitively be understood High resolution image interpretation – intuitive & interesting

16. For real use of RS, simple methods have been longed for Less skill/equipment demanding More intuitive analyses Simpler procedures More talks between the users and the RS specialists

17. Conclusion RS has been expected to contribute to the forest measurement and monitoring Many methods have been developed There are new sophisticated sensors and analysis techniques Appropriate use will contribute to solve the mysteries in forests To make RS work in the real world, we should also consider the impact of RS on the users/decision makers

18. Thank you for your attention For more discussion, contact G.Takao@cgiar.org

19. Chambers, J.Q et al., 2007. Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. TRENDS in Ecology and Evolution 22, 414-423.