The Pacific GIS/RS User Conference Suva, Fiji Island, November 2012 Sharon R. Boe, SPC/GIZ-SOPAC ) SPC/GIZ Regional REDD+ Project: Forest Carbon Inventory In Vanuatu Proposed Forest Mapping In Vanuatu

ABBREVIATIONS SPC – Secretariat of the Pacific Community GIZ – Deutsche Gesellschaft fur Internationale Zusammenarbeit REDD – Reducing Emissions from Deforestation and forest Degradation FCPF – Forest Carbon Partnership Facility MRV – Measuring, Reporting and Verifying REL – Reference Emission Level GHG – Green House Gas VANRIS – Vanuatu Resource Information System

PROJECT OVERVIEW  SPC/GIZ Climate Protection through Forest Conservation in Pacific Island Countries Project >> Aim: mitigate greenhouse gas emissions from forestry and receive compensation payments.  Vanuatu: REDD readiness process mainly supported by: >> SPC/GIZ – development of system for MRV carbon level and REL >> FCPF of World Bank – policy process  Ongoing Project until 2015

PILOT ISLANDS  The islands of Vanuatu are located approx. 2,200km east of Australia and comprises of 82 islands.  The program is focusing on 4 pilot islands: 1.Santo 2.Malekula 3.Efate 4.Erromango

MAPPING ACTIVITIES 1)Delineation of coconut stands on Santo )Deforestation processing Santo )Cloud correction Santo )Deforestation processing Santo )Accuracy assessment Santo 2010

Forest stratification (GHG Inventory, 2011)

AVAILABLE IMAGERY

WORLD VIEW 2 DATA  8 band bundle data (Santo) -Multispectral: 2m -Panchromatic: 0.5m  Projection: UTM/WGS84, zone 58S  Pre-processed (atmospheric correction, pan-sharpened (NC & FC), image enhancement, backdrop)  Digital Terrain Model (DTM): 25m

ALOS PALSAR DATA  ALOS (Advanced Land Observation Satellite) PALSAR (The Phased Array type L-band Synthetic Aperture Radar)  25m Mosaic dual polarization  Data was ordered at Level 1.5 format  Dual temporal change detection and forest cover  Adv: Cloud-free, Day & Night and All weather land observation  Training: October 2012  SAR Image Preprocessing, Enhancement, Classification (Erdas 9.1)  Support from Jaxa

HH Polarization Very low backscatter of water High backscatter in Urban Areas High backscatter over vegetation and non-vegetation HV Polarization High backscatter over vegetation areas Low backscatter over non-vegetation areas HH/HV Ratio Forest/Dense Vegetation (bright) Non-Vegetation Areas (dark) + + = RGB R: HH G: HV B:HH/HV ALOS PALSAR RGB COMPOSITE

Visible on most of Western Part of Santo: high altitude areas Layover Effects CHALLENGES WITH CURRENT RADAR DATA

PROHIBITS IMAGE ANALYSIS Spatial Modeler, Erdas 9.1  HH/HV Ratio Calculations  Classification  Conversion of amplitude data (DN) to radar cross-section (dB)  Above Ground Biomas (AGB) Calculations

Semi-Automatic Forest Classification  Move into the use of semi-automatic classification for forest mapping: -Vanuatu Study (2007) conducted by Martin Herold and his team Supervised Classification -Maximum Likelihood Algorithm Challenges -Trial Image Segmentation E.g. Imagine Objective ext. for Erdas

DELIVERABLES The project envisions the following results for Vanuatu: Forest cover based on an approved national forest definition Carbon density map for 5 generic forest strata (medium height forest [open/closed], low forest [open/closed], mangroves) Validated forest cover map using very high resolution imageries

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