Progress Of The Training On Disaggregation Of Activity Data And Development Of Regional Forest Reference Level May 2017 Addis Ababa

Outline of the Presentation Objective Approach of the training Results -General -Results of each of the three regions Future Improvements and Recommendation

1. Objectives To introduce Geospatial tools of Forest Monitoring To train standard procedures of Disaggregation of National Activity Data (AD) To prepare the Forest Reference Emission Levels of the three Regional States

2. Approach of the Training Theoretical lectures on : Status of National Measurement, Reporting and Verification (MRV) Basics of GIS and Remote Sensing , Practical hands on training using tailored manual and tutorial Data Topics covered were helpful to prepare the AD and FRL, they were Introduction of QGIS Creation of vector data and investigation of raster data Vector and raster data analysis Disaggregation of AD Preparation of FRL

2.2 Disaggregation and FRL preparation Disaggregation of National AD using same procedure used for preparing National AD Extraction of national AD by Regional state boundaries Determination of reference sample size using R-shiny application ( FAO, 2014) Generation of Response design based on the forest definition of Ethiopia Collection òf reference Data Multi-stage rechecking of the reference samples Analysis ( generation of confusion matrix, accuracies , corrected area estimates and Confidence Intervals)

2.3 Preparation of Regional FRLs Counting pixels of AD classes per regions and Biome, FRL sheet log that integrates the pixel counts, corrected area estimates , confidence intervals and Emission Factors (EFs) Integration of AD pixel count, CAE, CI and EFS to prepare the regional FRLs, forest and non forest area estimates. Interpretation of the results to the actual context of a particular regions

3. Results RRCU officers of Amhara, SNNPRs and Tigray instilled with the basic skills on :- Preparation of maps Regional or project level field forest inventory sample preparation Preparation and analysis of raster data Manipulation of Collect Earth and interpretation of forest changes from Google Earth Disaggregation of AD, accuracy assessment and interpretation of confusion matrix

Afforestation and deforestation iii. Carbon pools 3.1. FRL of Regions i. Scale Regional ii. Scope Afforestation and deforestation iii. Carbon pools (AGB, BGB, DW,lt) iv. Gases in FRL CO2

3.2 Emission Factors (EFs) Biome EF ( t CO2/ha) Acacia-Commiphora 33.2 Combretum-Terminalia 39.6 Dry Afromontane 68.5 Moist Afromontane 118.9

3.3. Estimation of Corrected Areas – Amhara Change Class ( Reference) 3.3.1 Estimates of Change Classes (a) Forest/Non Forest Map of Amhara (b) Confusion matrix Change Class ( Reference) Total Change Class (Map) Loss SF Gain SNF 17 14 54 85 2 55 43 102 83 6 9 98 11 73 3 812 899 30 225 918 1184 UA (%) 56.7 24.4 54.5 88.5 890 Over all accuracy (%) 75.2

Area of Change Classes (2000-2013)(ha) Corrected Area Estimates 3.4 Estimates of corrected Areas – Amhara Reginal States (a) Areas of change classes Change Class Area of Change Classes (2000-2013)(ha) Corrected Area Estimates CI Lower Upper Loss 209649 112184.19 97464 321833 Stable Forest 2242535 316018.62 1926517 2558554 Gain 131172 89006.4 42165 220178 Stable Non forest 13375840 333903.42 13041937 13709743

= -32,038,421/13yrs (tCO2) 3.5 Proposed Emission and Removal of Amhara (a) Emission = -32,038,421/13yrs (tCO2) Emission/yr 2,464,494 tons of Co2e

= 19,118,123/13yrs (tCO2) . . . Proposed (b) Removal Removal/yr 1,470,625 tons of Co2e

3.6. Estimation of Corrected Areas 3.6.1 Estimates of Change Classes in SNNPRS (a) Forest/Non Forest Map of SNNPRS (b) Confusion matrix Change Class ( Reference) Total Change Class (Map) Loss SF Gain SNF 13 10 19 42 3 258 81 345 37 9 49 16 70 2 598 686 32 375 8 707 1122 UA 40.6 68.8 37.5 84.6 Over all accuracy 77.7

Area of Change Classes (2000-2013)(ha) 3.7 Estimates of corrected Areas - SNNPRS (a) Areas of change classes (b) Areas of loss and gain Change Class Area of Change Classes (2000-2013)(ha) Corrected Area Estimates CI Lower Upper Loss 213,694.98 89991 123704 303686 Stable Forest 3,542,982.43 237493 3305489 3780476 Gain 53,860.89 46889 6972 100750 Stable Non forest 7,220,504.48 246963 6973542 7467467

= -53,059,915/13(tCO2) 3.8 Proposed Emission and Removal of SNNPRS (a) Emission = -53,059,915/13(tCO2)

. . . Proposed (b) Removal = 16,590,740/13(tCO2)

3.9. Estimation of Corrected Areas – Tigray Change Class ( Reference) 3.9.1 Estimates of Change Classes (a) Forest/Non Forest Map of Tigray (b) Confusion matrix Change Class ( Reference) Total Change Class (Map) Loss SF Gain SNF 18 7 2 17 44 54 73 5 37 6 1 49 21 127 553 703 225 12 588 869 UA 41% 24% 50% 94% 631 Over all accuracy 72.6

Area of Change Classes (2000-2013)(ha) Corrected Area Estimates 3.10 Estimates of corrected Areas – Tigray (a) Areas of change classes Change Class Area of Change Classes (2000-2013)(ha) Corrected Area Estimates CI Lower Upper Loss 148192.4 62234.79782 85957.6 234150.0 Stable Forest 1266962.5 168356.0186 1098606.5 2365569.0 Gain 28107.9 27208.68259 899.2 29007.1 Stable Non forest 4005251.4 173518.2579 3831733.1 7836984.5

= -22, 600,570/13 (tCO2) 3.11 Proposed Emission and Removal of Tigray (a) Emission = -22, 600,570/13 (tCO2)

. . . Proposed (b) Removal = 6,854,171/13(tCO2)

4. Feature Improvements and Recommendations Rechecking more reference points by the officers Fine tuning the EFs of each regions Include the degradation if new methodological tools are devised with the context of country (Biomes)

4.2 Recommendations 4.2.1 Capacity Building Activities No Gaps and Proposed Traning Topics 1 Forest Inventory 1.1 NFI/RFI/project level FI design 1.2 NFI Field Inventory 1.3 Data Analysis 1.4 R-for Forest Analysis Anlaysis ( OF-Calc) 2 Remote Sesning and GIS 2.1 Basics of Remote Sensing and GIS 2.2 RS and GIS for Forest Monitoring ( change detection) Using (OS) 2.3 Software and applications QGIS GEE-API R-Shiny

Acknowledgements No Name of Participants Region/Institution Email 1   No Name of Participants Region/Institution Email 1 Alixander Sibehatu Amhara/REDD+ ledetx2005@gmail.com 2 Mesfin Admassu atalel.mengistu1@gmail.com 3 Demess Lemma SNNP/ REDD+ demesslemma@gmail.com 4 Wasihun Regu wasihunr@gmail.com 5 Haftom Derbie Tigray/ REDD+ haftamuder@gmail.com 6 Ademnur Bereh sebrin.2020@gmail.com 7 Sewnet Enyew MEFCC sewnetenyew@gmail.com 8 Daniel Belay dbelay88@yahoo.com 9 Samson Tsegaye samson.tsegaye26@gmail.com 10 Assaye Nigussie asayenig@gmail.com 11 Melakeneh Gelet FAO Melakeneh.gelet@fao.org

Thank you