Suitability of Global Forest Change data to report forest cover estimates at national level in Gabon

Transcription

1 Systèmes d Information à Référence Spatiale Suitability of Global Forest Change data to report forest cover estimates at national level in Gabon Expert workshop on using global datasets for national REDD+ measuring and monitoring Christophe Sannier, Ron McRoberts & Louis-Vincent Fichet 9-10 November 2015, Wageningen, The Netherlands Dr Christophe SANNIER Head of Research & Innovation christophe.sannier@sirs-fr.com Parc de la Cimaise Immeuble I 27 rue du Carrousel VILLENEUVE D ASCQ info@sirs-fr.com - Site Internet : S.A.S. au capital de RCS LILLE B APE 6311 Z - N d identification FR SIRET

2 Context National Forest Cover and Forest Cover Change Mapping for Gabon To develop the precursor of an operational national Forest monitoring system for Gabon Project funded by and coordinated by is coordinating Gabon activities in collaboration with AGEOS Complete Coverage of Gabon (267,000km²) in 1990, 2000, 2010 and now 2015 with production in Gabon by AGEOS Setting up wall to wall mapping requires substantial efforts Can global data sets be used at national level? Forest definition: Minimum 1ha area, 30% Crown cover and 5m height at maturity

3 Objectives to assess whether the UMD GFC dataset can be processed to match the selected national forest definition for Gabon to determine the degree to which estimates of forest cover and forest cover change and their associated uncertainties are enhanced using the UMD GFC dataset relative to using reference data alone to quantify the loss, if any, of accuracy and precision resulting from using UMD GFC maps rather than a nationally produced map to determine the level of additional effort in terms of increased sample size and/or post-processing of the UMD GFC data that would be required to obtain comparable estimates with respect to accuracy and precision to estimates based on a nationally produced map to develop guidelines on how to use global products for national reporting

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6 Accuracy assessment Unaligned systematic area frame sample Independant Interpretation of sample sites

7 Comparaison SPOT 5 Landsat 7

8 Forest Cover Map 2010 accuracy Fichet LV, Sannier C, Massard K. Makaga E., Seyler F (2014) Assessing the Accuracy of Forest Cover Map for 1990, 2000 and 2010 at National Scale in Gabon. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing. 7,

9 Uncertainty estimates Forest cover and forest cover change estimates can be produced based on samples alone (Direct estimate) Observations from reference samples and the map can be combined to improve the precision of estimates (Model Assisted Regression): MAR map ˆ = µ ˆ Bîas µ µ Vâr MAR ( µ ˆ ) = 1 m m ( 1) map ( ˆ ) m ( i ) i= 1 Sannier C, McRoberts R A, Fichet LV and Massard K. Makaga R. (2014) Using the regression estimator with Landsat data to estimate proportion forest cover and net proportion deforestation in Gabon. ForestSat 2012 Special Issue. Remote Sensing of Environment. 2

10 Gabon Forest Cover Estimates (%) Gabon Total Forest Area 23.6 million ha Gabon Total Area = 26.7 million ha Direct Expansion Estimate Map area statistics Regression Estimate

11 Gabon Forest Cover Change Estimates (1) 140,000 Gross Deforestation between 0.31 and 0.52% 120, ,000 80,000 60,000 40,000 20, Direct Expansion Estimate Map area statistics Regression Estimate

12 Gabon Forest Cover Change Estimates (2) 80,000 Regeneration between 0.16 and 0.22% 70,000 60,000 50,000 40,000 30,000 20,000 10, Direct Expansion Estimate Map area statistics Regression Estimate

13 Gabon Forest Cover Change Estimates (3) 140,000 Net Change between 0.10 and 0.34% 120, ,000 80,000 60,000 40,000 20, , Direct Expansion Estimate Map area statistics Regression Estimate

14 Processing of UMD GFC percent tree cover and loss/gain data for Gabon Global coverage 30m Spatial resolution Percent Tree Cover product for 2000 Yearly loss/gain of forest cover at 30m resolution GFC data tiles downloaded, mosaicked re-projected and clipped to Gabon map projection and boundaries GFC data processed to match selected Forest definition for Gabon Sannier C, McRoberts R A and Fichet LV (2015) Suitability of Global Forest Change data to report forest cover estimates at national level in Gabon. ForestSat 2014 Special Issue. Remote Sensing of Environment. UMD GFC Data TCD 30% Threshold 1ha MMU

15 Comparison between National and Global Forest Change Data for 2000 Reference Forest Non-Forest Total User's accuracy National Forest 28, , Classification Non-Forest 312 3,616 3, Total 29,296 3,954 33,250 Producer's accuracy Overall accuracy GFC 30% 1ha MMU Forest 29,222 1,298 30, Classification Non-Forest 74 2,656 2, Total 29,296 3,954 33,250 Producer's accuracy Overall accuracy GFC 70% 1 ha MMU Forest 28, , Classification Non-Forest 494 3,418 2, Total 29,296 3,954 33,250 Producer's accuracy Overall accuracy

16 Comparison between National and Global Forest Change Data for 2000 Extract for each PSU proportion of area covered by forest Reference Map SIRS UMD GFC 30% UMD GFC 70%

17 Comparison between National and Global Forest Change Data for 2000 Study area (ha) Direct Estimate (ha) (%) Direct Estimate at 95% (ha) ± Confidence Interval (%) ±2.00 SIRS GFC TCD30% MMU 1ha GFC TCD30% No MMU GFC TCD70% MMU 1ha GFC TCD70% No MMU Map area statistics (ha) (%) Bias MAR Estimate (ha) (%) MAR Estimate at 95% (ha) ± ± ± ± ± Confidence Interval (%) ±0.26 ±0.80 ±0.80 ±0.51 ±0.51 Relative Efficiency

18 Comparison between National and Global Forest Change Data for net change Study area (ha) Direct Estimate (ha) (%) 0.09 Direct Estimate at 95% (ha) ± Confidence Interval (%) ±0.10 SIRS GFC TCD30% MMU 1ha GFC TCD30% No MMU GFC TCD70% MMU 1ha GFC TCD70% No MMU (ha) Map area statistics (%) Bias (ha) MAR Estimate (%) MAR Estimate at 95% (ha) ± ± ± ± ± Confidence Interval (%) ±0.09 ±0.13 ±0.15 ±0.20 ±0.15

19 UMD GFC 2000 Percent Tree Cover and Landsat WRS2 scene boundaries % Tree Cover

20 Further improvement to adjust for local conditions: example for year 2000 Eastern part of Gabon UMD GFC 30% UMD GFC 70% SIRS UMD GFC 50%

21 Comparison of selected UMD GFC percent tree cover thresholds and SIRS map with reference data for 2 selected PSUs within WRS2 183/061 Scene East West Reference Data SIRS UMD GFC 30% UMD GFC 70% UMD GFC 50%

22 Conclusions UMD GFC data set can be processed for producing national forest cover estimates Accuracy of estimates likely to be lower than that from nationally produced data sets 95% Confidence Intervals larger by a factor of 2-3 compared with that of national data for forest cover area Appropriate Level of precision to be related to ratio between additional costs and benefits generated by the increased precision from a performance based payment scheme Cost of processing UMD GFC data likely to be substantially lower than national map Comparison of change estimates do not provide any substantial improvement compared with direct estimates due to small magnitude of change: estimates are not significantly different from 0 Bias and 95%CI for UMD GFC data can be further reduced if adjusted for local conditions Further case studies are necessary