Monitoring of deforestation and forest degradation using remote sensing techniques for REDD policy implementation

Transcription

1 25-27 June 2008, UNFCCC REDD Workshop in Tokyo Monitoring of deforestation and forest degradation using remote sensing techniques for REDD policy implementation Forestry and Forest Products Research Institute Yasumasa Hirata

2 Outlines Forest monitoring using remote sensing Forest degradation in developing countries New remote sensing technologies Technical issues Conclusions

3 Outlines Forest monitoring using remote sensing Forest degradation in developing countries New remote sensing technologies Technical issues Conclusions

4 Forest monitoring using satellite remote sensing No leakage in the area. Coat is large. Difficulty of acquiring cloud-free data. Applicability for local policy is large. Accuracy for sampling rate. Coat is effective. Acquiring cloud-free data is relatively easy. Applicability for local policy? Ref. FRA2010 Remote Sensing Survey Task Force

5 Understanding of land-cover from remotely sensed data

6 Interpretation of satellite images Appropriate segmentation of ambiguous domain Requirement of interpretation technique Different outcomes by interpreter

7 Pixel-based classification Mangrove forest Natural forest Grass with trees Human activity area Mangrove forest Natural forest Secondary forest Some vegetation Water Agricultural land

8 Object-oriented classification Classification results that is similar to human interpretation Advantage of handling by object (segment) Ref. FRA2010 Remote Sensing Survey Task Force

9 Field survey and Database Importance of ground-based data Necessity of geo-reference for the data

10 The challenges of forest monitoring Deforestation (Area) Forest vs. Non-forest Deforestation (Carbon stock) Degradation Classification of forest types Incremental change Crown extraction by high resolution satellite More challenging!

11 Role of forest monitoring using remote sensing For clarifying historic trend of forest change For planning and implementing certain actions after assessment of forest change

12 Outlines Forest monitoring using remote sensing Forest degradation in developing countries New remote sensing technologies Technical issues Conclusions

13 Shifting cultivation Remote area (whole mountain or overall slope ( ha) Shortening of rotation and enlargement of cultivation area 2km Urban forest area (ownership is clear and patch distribution, ha) Conversion to rubber plantation after shifting cultivation

14 Monitoring of sifting cultivation by ASTER images 2002/2/9 2003/3/ /2/1 2006/3/8 Image pre-processing Object-oriented classification 6 years - shifting cultivation distribution map Monitoring of sifting cultivation for six years

15 Forest degradation due to selective logging

16 Forest fire Type of fire Fire up to canopy Surface fire ex. Tropical seasonal forest in dry season Fire in peat of underground Intensity of fire Development vs. restoration

17 Fire of peat Forest is damaged by the fire gradually and continuously. on 4 September 2002 In East Kalimantan, Indonesia

18 Degradation of mangrove forest caused by shrimp farm Land use change (deforestation) and consequently degradation DEFORESTATION 4 Feb April 1997 DEGRADATION

19 Outlines Forest monitoring using remote sensing Forest degradation in developing countries New remote sensing technologies Technical issues Conclusions

20 Comparability between SAR and optical sensor ALOS PALSAR data ALOS AVNIR II data (optical)

21 ALOS Kyoto & Carbon Initiative Offered by Dr. M. Shimada (JAXA) 21

22 Offered by Dr. M. Shimada (JAXA) 22

23 Estimating biomass using high resolution satellite data crown diameter height DBH Source: Hirata (2008) Journal of Forest Research 14 Stand density volume obtained from QuickBird from data satellite (m 3 /ha) R = volume derived from field survey (m 3 /ha) Stand volume from field survey

24 3-D D forest measurement with LiDAR A part of the laser beam reflects on canopy. The rest goes through canopy and reflects on the ground. IMU IMU GPS GPS One shot of laser Intensity Measurement of ground and canopy surface 1200 Time Time 1000 木 木 Time First Pulse Last pulse 現地調査による林分材積 (m 3 ) R = 0.93 Source: Hirata et al (2008) Journal of Forest Planning Stand DCMvolume から得られた林分材積 from (m LiDAR 3 )

25 Outlines Forest monitoring using remote sensing Forest degradation in developing countries New remote sensing technologies Technical issues Conclusions

26 0.0 Locality and seasonality of data acquisition Cloud20% Cloud10% Cloud0% 月 2 月 3 月 4 月 5 月 6 月 7 月 8 月 9 月 10 月 11 月 12 月 total Cloud20% Cloud10% Cloud0% Cloud20% Cloud10% Cloud 0% Cloud20% 50.0 Cloud10% Cloud0% 月 2 月 3 月 4 月 5 月 6 月 7 月 8 月 9 月 10 月 11 月 12 月 total 月 2 月 3 月 4 月 5 月 6 月 7 月 8 月 9 月 10 月 11 月 12 月 total 月 2 月 3 月 4 月 5 月 6 月 7 月 8 月 9 月 10 月 11 月 12 月 total Cloud20% 50.0 Cloud10% Cloud0% 月 2 月 3 月 4 月 5 月 6 月 7 月 8 月 9 月 10 月 11 月 12 月 total Cloud20% Cloud10% Cloud0% Cloud20% Cloud10% Cloud0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Dry Rainy Dry Cloud20% Cloud10% Cloud0% 月 2 月 3 月 4 月 5 月 6 月 7 月 8 月 9 月 10 月 11 月 12 月 total % 80% 月 2 月 3 月 4 月 5 月 6 月 7 月 8 月 9 月 10 月 11 月 12 月 total 月 2 月 3 月 4 月 5 月 6 月 7 月 8 月 9 月 10 月 11 月 12 月 total Cloud20% Cloud10% Cloud0%

27 Topographic effect Forest remains in mountainous area. Effect of topography on both SAR and optical sensor data Legend 0-5deg. 5-10deg deg deg. 30- deg.

28 Outlines Forest monitoring using remote sensing Forest degradation in developing countries New remote sensing technologies Technical issues Conclusions

29 Conclusions Consistency of satellite data and the results Determining methodology Issue of definition Importance of field survey There is much grand-based data, which was collected by different organizations, for different factors with different formats, without georeference Established methods and further challenging studies

30 Evergreen Mixed forest Deciduous forest Grass land Yasumasa Hirata affrc.go..go.jp Rubber Plantation