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Systèmes d’Information à Référence Spatiale

Dr Christophe SANNIER Head of Research & Innovation christophe.sannier@sirs-fr.com

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

Context

• 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?

National Forest Cover and Forest Cover Change Mapping for Gabon

Forest definition: Minimum 1ha area, 30% Crown cover and 5m height at maturity

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

Page 4

Page 5

Accuracy assessment

Unaligned systematic area frame sample

Independant Interpretation of

sample sites

Comparaison SPOT 5 Landsat 7

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, 1346 - 1356. http://dx.doi.org/10.1109/JSTARS.2013.2271845

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):

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. http://dx.doi.org/10.1016/j.rse.2013.09.015

( )mapmapMAR ˆasiBˆˆ µ−µ=µ

( ) ( ) ( )∑=

∆−∆−

=µm

1i

2i

MAR

1mm1ˆraV

Gabon Forest Cover Estimates

80818283848586878889909192939495

1990 2000 2010

(%)

Direct Expansion Estimate Map area statistics Regression Estimate

Gabon Total Forest Area ≈ 23.6 million ha Gabon Total Area = 26.7 million ha

Gabon Forest Cover Change Estimates (1)

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

1990-2000 2000-2010

Gross Deforestation between 0.31 and 0.52%

Direct Expansion Estimate Map area statistics Regression Estimate

Gabon Forest Cover Change Estimates (2)

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

1990-2000 2000-2010

Regeneration between 0.16 and 0.22%

Direct Expansion Estimate Map area statistics Regression Estimate

Gabon Forest Cover Change Estimates (3)

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

1990-2000 2000-2010

Net Change between 0.10 and 0.34%

Direct Expansion Estimate Map area statistics Regression Estimate

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

UMD GFC Data TCD 30% Threshold 1ha MMU

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. http://dx.doi.org/10.1016/j.rse.2015.10.032

Reference

Forest Non-Forest Total User's accuracy National Forest 28,984 338 29,322 0.9885

Classification Non-Forest 312 3,616 3,928 0.9206 Total 29,296 3,954 33,250

Producer's accuracy 0.9894 0.9145 Overall accuracy 0.9805

GFC 30% 1ha MMU Forest 29,222 1,298 30,520 0.9575

Classification Non-Forest 74 2,656 2,730 0.9729 Total 29,296 3,954 33,250

Producer's accuracy 0.9975 0.6717 Overall accuracy 0.9587

GFC 70% 1 ha MMU Forest 28,802 536 29,366 0.9801

Classification Non-Forest 494 3,418 2,912 0.8800 Total 29,296 3,954 33,250

Producer's accuracy 0.9861 0.8644 Overall accuracy 0.9690

Comparison between National and Global Forest Change Data for 2000

Comparison between National and Global Forest Change Data for 2000

SIRS UMD GFC 30% UMD GFC 70%

Reference Map

Extract for each PSU proportion of area covered by forest

Comparison between National and Global Forest Change Data for 2000

Study area (ha) 26 766 700

Direct Estimate (ha) 23 589 452 (%) 88.13

Direct Estimate at 95% Confidence Interval

(ha) ±534 395 (%) ±2.00

SIRS GFC

TCD30% MMU 1ha

GFC TCD30% No

MMU

GFC TCD70%

MMU 1ha

GFC TCD70% No

MMU

Map area statistics (ha) 23 663 416 24 717 461 24 726 364 23 774 718 23 677 428 (%) 88.41 92.45 92.38 88.82 88.46

Bias 11 393 978 763 964 694 15 331 -78 471

MAR Estimate (ha) 23 651 643 23 768 378 23 761 669 23 759 387 23 755 899 (%) 88.36 88.80 88.77 88.76 88.75

MAR Estimate at 95% Confidence Interval

(ha) ±70 332 ±216 512 ±213 461 ±136 830 ±137 385 (%) ±0.26 ±0.80 ±0.80 ±0.51 ±0.51

Relative Efficiency 58 6 6 15 15

Comparison between National and Global Forest Change Data for 2000-2010 net change

Study area (ha) 26 766 700

Direct Estimate (ha) 24 343 (%) 0.09

Direct Estimate at 95% Confidence Interval

(ha) ± 26 573 (%) ±0.10

SIRS GFC

TCD30% MMU 1ha

GFC TCD30% No MMU

GFC TCD70%

MMU 1ha

GFC TCD70% No MMU

Map area statistics (ha) 4142 92 444 176 117 84 492 138 869 (%) 0.02 0.35 0.66 0.32 0.52

Bias -15 950 71 166 143 429 45 851 126 205

MAR Estimate (ha) 20 092 21 277 32 688 38 641 12 664 (%) 0.08 0.08 0.12 0.14 0.05

MAR Estimate at 95% Confidence Interval

(ha) ±25 072 ±35 356 ±40 119 ±53 875 ± 40 451 (%) ±0.09 ±0.13 ±0.15 ±0.20 ±0.15

UMD GFC 2000 Percent Tree Cover and Landsat WRS2 scene boundaries

% Tree Cover

Further improvement to adjust for local conditions: example for year 2000 Eastern part of Gabon

SIRS

UMD GFC 30% UMD GFC 70%

UMD GFC 50%

Comparison of selected UMD GFC percent tree cover thresholds and SIRS map with reference data for 2 selected PSUs within WRS2 183/061 Scene

Reference Data SIRS UMD GFC 30% UMD GFC 70% UMD GFC 50%

East

West

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