REDDAF. Infosheet. Content. Objective and Concept. November 2012

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1 November 2012 REDDAF Infosheet Content Objective and Concept Objective and Concept 1 User Requirements 2 Methods Development 2 Service Development 5 Validation and Proof of Concept 6 Capacity Building/Training 7 Benefits to the Users 7 Please visit for further information The objective of the REDDAF project is to develop, test and provide improved methodologies for deforestation/degradation assessment and direct biomass estimation using a combination of optical, radar and in-situ data. The REDDAF project results will contribute to enhance the operational monitoring of forest cover and biomass changes in two tropical countries actively involved in the REDD process Cameroon and Central African Republic (CAR). The REDDAF project is conceptualised around five main tasks. These include the User Requirements which provides the national context for project implementation; Methods Development to offer cost-effective methods with known certainty levels for products and services development; Service Development and Integration to develop customised products and services that are compliant to international standards and norms and can be easily integrated into the working cycles of the Users; Validation and Proof of Concept to demonstrate and provide evidence that the developed services and products are fit for purpose and lastly Dissemination and Training to ensure that the methods and techniques developed are transferred to the User and to a wider external audience through seminars, workshops, publications etc. The REDDAF project is organised into two Phases constituting 18 months each. The research leading to these results has received funding from the European Community s Seventh Framework Programme (FP7/ ) under grant agreement n

2 User Requirements The technical needs and standards for implementation of a national REDD+ MRV (Measurement, Reporting and Verification system) were assessed in both countries to elaborate the technical specifications of the REDDAF services. The existing work practices, current geospatial capacities and the range of standards and protocols on information exchange/ dissemination that the REDDAF service portfolio must comply with in both countries were also analysed. A training needs assessment was conducted in both countries as basis for the establishment of training modules. REDD Policy Developments User Requirements IPR 1. Improving EO-based Forest Cover Mapping The continuous impairment of optical satellite sensor signals by clouds in the tropics necessitates the use of multi-sensor, multi-temporal datasets for effective wallto-wall coverage of tropical countries and robust, costeffective pre-processing techniques. Three main preprocessing issues were investigated and techniques developed during Phase 1: a cloud shadow algorithm to radiometrically adjust cloud shadow areas and increase the amount of interpretable pixels, which otherwise would be discarded (see Fig. 3); a blockbased radiometric adjustment tool, which improves the spectral consistency between neighbouring images since multi-sensor, multi-temporal images are required for production; and the development of a robust and fully automatic matching procedure to ensure geometrical congruence between optical and SAR data sets (see Fig. 2). In order to assess the operationality of the methods, the developed tools will be further tested on prototype areas in Phase 2. Technical Requirements R&D Reddaf Services Figure 1: Key components of REDDAF Services Based on this information, Service Level Agreements (SLA) for the intended services and products were signed between the REDDAF Service Providers and the Users in Cameroon and the Central African Republic. Methods Development The REDDAF service portfolio comprises the following products: Forest Cover maps Forest Cover Change maps IPCC- compliant land cover/use maps Forest Degradation maps Biomass maps of Low Carbon Forest The overarching objective of Research and Development (R&D) in REDDAF is to improve the processing chains for the development of the mentioned products. Three key research areas are under investigation in the REDDAF project. Figure 2: Automatic image matching of PALSAR and Landsat images - upper image: before matching; lower image: after matching with RMSE < 10m in X and Y direction. 2

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4 Figure 3: Cloud shadow compensation on Landsat image subset - left image: Landat input image with cloud shadow examples (clouds masked in black); right image: with cloud shadow compensation. 2. Forest Degradation Assessment Direct approaches for assessing forest degradation with EO-based methods were tested during Phase 1: the first approach is comparing NDVI values derived from RapidEye data from 2009 to 2011 in order to detect disturbed areas in a forest concession. The thematic processing includes the calculation of NDVI for the different RapidEye data mosaics, histogram matching of each NDVI, re-classification and aggregation. Preliminary results are demonstrated in Figure 4. less operational potential but future developments in sensors such as the TerraSAR Tandem-X mission might change this situation in future. Figure 5: Testing Options for 3D Mapping. Left image: Test area for 3D disturbance mapping, Rapideye data from December Red: areas affected by degradation; blue: extent of 3D model from CSK data. Right image: Difference model from CSK 3D generated with the SRTM difference approach. Phase 2 will focus on analysing the efficacy of the tested methods on an operational scale and developing tools to distinguish anthropogenic from non-anthropogenic changes which is primordial in REDD+. The second method is based on the use of Cosmo- Skymed stereo radar data to compute 3D models. A comparison of the developed model with the SRTM model shows a better correlation with canopy gaps and logging roads. The results represent a clear indicator for areas of disturbance found in the test site. Due to data costs, this approach currently has 4 Figure 4: An illustration of disturbance based on NDVI, derived from multi-temporal RapidEye data from 2009 to The results show different degrees of disturbance and regeneration calculated in 1 ha units (Eastern province of Cameroon). 3. Direct EO- based Biomass Assessment SAR data is being applied to develop a transferrable methodology for direct above-ground biomass estimation. The approach focuses on improving existing image processing and biomass inversion methods to fully exploit the potential of the currently available data (ALOS PALSAR L-band). The work in Phase 1 was based on testing existing methods using in-situ biomass data from Cameroon for validation and calibration. The improvement of the methods beyond the state of the art in biomass mapping

5 Figure 6: Biomass mapping results from inversion of dualpolarized ALOS PALSAR data for the Adamawa region, Cameroon. includes: improvements of the spatial resolution of the biomass map based on an optimised use of multi-temporal and polarimetric data, reduction of the uncertainties in biomass values beyond tons/ha by taking into account the perturbing effects which mask the sensitivity of the backscatter to biomass (e.g. topographic and temporal variation effects); and the use of a limited number of in-situ data for model calibration. A Bayesian inversion method is also used to improve the retrieval performance. Changes in biomass are observed in more details with ALOS PALSAR data from 2007 to The map in Figure 6 shows the dense humid forests of the Adamawa region (Mbam and Djerem National Park) with biomass values of less than 150 tons/ha, and the gallery forests in the savanna, with biomass values lower than 100 tons/ha. It should be noted that large areas of gallery forests and transitional forests (with biomass values reaching tons/ha) contain a large amount of carbon stocks, which is often neglected in carbon estimates. Service Development The objective of the service development is to integrate the R&D findings in the development of products and services on an operational scale. The focus of Phase 1 was to develop forest cover and forest cover change maps ( ) for Cameroon and 5

6 CAR. The service area for Cameroon is the Centre administrative region ( sqkm) while for CAR it comprises the regions Ombella Mpoko, Lobaye, and Sangha Mbaéré ( sqkm). Optical datasets (Landsat TM/ETM, RapidEye) constituted the basis of the Phase 1 analyses, acquired close to the base year to create cloud-free mosaics using the best (cloud-, cloud shadow- and haze free) pixels from all images. A multi-temporal composite approach based on segments is used to interpret the images and create the forest cover and forest cover change maps for the corresponding periods. An accuracy assessment has been conducted based on area frame sampling with overall accuracies of the forest maps generally above 85%. Figure 8: Accuracy Assessment Approach for Forest Cover Maps using Area Frame Sampling. RADAR images will be used to compensate the cloud gaps during Phase 2 and to produce an IPCCcompliant land cover/use map for both countries for The direct biomass and forest degradation mapping methods will be tested on an operational scale. Figure 9: Forest Cover Change between 2000 and 2010 for Centre Province, Cameroon, based on the corresponding Forest Cover Maps. Validation and Proof of Concept Figure 7: Forest Cover Maps for the epoch 2010 for the demonstration areas in CAR (upper image) and Cameroon (lower image) based on HR optical satellite data (Landsat TM4/ TM5/ETM, RapidEye, Deimos). Reporting requirements for REDD+ will be guided by the general principles of transparency, consistency, comparability, completeness and accuracy. To assure the quality of the REDDAF products and services, a validation plan which provides the organisational 6

7 structure of the validation process as well as reference to specific guideline documents, templates and information on the validation reference information has been elaborated. A three-stage process is foreseen, comprising: (i) a First-Party assessment, checking the conformity of the achieved results with the previously agreed specifications in a self-assessment step by the Service Providers; (ii) a Second-Party assessment undertaken by the Users (iii) a Third-Party assessment performed by a body that is independent from both supplier and customer organisations. The independent assessment of the REDDAF products and services is foreseen at the end of Phase 2. Figure 10 and 11: GPS measurements during the Remote Sensing and GIS Training workshop in CAR in February Capacity Building/Training The Capacity Building/Training ensures that the methods/techniques developed are transferred to the counterparts in Cameroon and CAR. The trainings are organised in the form of theoretical courses as well as practical exercises, based on the technical concepts used in the project and adapted to the needs, knowledge and skills of Users. The training material developed in Phase 1 of the REDDAF project includes case studies and exercises to build experience in service portfolio utilisation as well as demonstrations and information sessions on service chain operations, validation processes and data access conditions. Training modules include: concepts of remote sensing & GIS, training on image processing for forest cover mapping in REDD+, field surveys for the acquisition of ground truth information, and REDD Sensitisation and MRV workshops. Open source software applications are used to ensure sustainability. Benefits to the Users The REDDAF project has so far achieved technical results in the application of optical and SAR EO data for the monitoring of forest cover, forest cover changes and biomass measurements. Key aspects of the research have already supported the methods successfully used to produce the forest cover and forest cover change products in Cameroon and CAR. Important achievements in the REDDAF project have been the formalisation of the partnerships with relevant Ministries and corresponding national institutions, ensuring the uptake of methods and products in both countries. Additionally, capacity building is being undertaken using open source software to ensure sustainability. The overall benefit for the countries will therefore be an expanded knowledge base of the users through capacity building, the establishment of a framework for the elaboration of national MRV systems in both countries, and the provision of information related to activity data and emission factors. Phase 2 will provide an opportunity for further developments in research which will support the production process in order to achieve a pre-operational to fully operational stage. This will be implemented via testing research methods in prototype areas as a precursor to fully operational production. Further training and dissemination of project results will be provided to the user community. Finally a third-party validation of the REDDAF products and services will ensure the credibility of the project activities. 7

8 Consortium lead Arnulfstraße Munich Germany Phone: +49 (0) Fax: +49 (0) URL: Project Consortium Service Provider Research Partner Expert Consultants GAF AG (Germany), SIRS (France) CESBIO (France), Joanneum Research (Austria), LaCCEG - University of Banguy (CAR) Geospatial Technology Group SARL (Cameroon), MesaConsult (Germany) Project funded by the European Community s Seventh Framework Programme (FP7/ ) under grant agreement n Further Information forestry@gaf.de website: