Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.1 Monitoring activity data for forests using remote sensing Module developers: Frédéric Achard, European Commission (EC) - Joint Research Centre (JRC) Jukka Miettinen, EC - JRC Brice Mora, Wageningen University Yosio Shimabukuro, Instituto Nacional de Pesquisas Espaciais & EC - JRC Country Examples: 1.Brazil 2.India 3.Democratic Republic of the Congo Sourcebook (2014) Box V1, March 2015 Creative Commons License

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 2 Country examples  The following slides will illustrate the main points of three different country level approaches for forest cover monitoring  More details can be found in the Sourcebook (2014) section 3.2  The country examples highlighted here include: ● Brazil – (PRODES deforestation monitoring program) ● India – (FSI - The Forest Survey of India) ● Democratic Republic of the Congo (DRC) – (JRC-FAO Systematic sampling)

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 3 1. Brazil: PRODES monitoring program  The Brazilian National Institute for Space Research (INPE) assesses forest cover annually over the entire Brazilian Amazon (~5 million km 2 ) in the PRODES monitoring program  The first assessment was undertaken in 1978, while annual assessments have been conducted since 1988  Landsat, DMC and CBERS satellite data (20-30 m resolution) acquired around August every year are used  Open source software TerrAmazon by INPE for pre-processing and assimilation of remotely sensed data  The mapping is performed by visual interpretation and manual digitization of deforested areas (MMU 6.25 ha)  Spatially explicit results are published yearly around December and are available at

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 4 Brazil: PRODES yearly deforestation mapping Landsat satellite mosaic of year 2006 and deforestation map period of the entire Amazon in Brazil Source: INPE, PRODES project, / Green – Forest Violet – non-forest Yellow-Orange-Red – deforestation from (~3,400 km x 2,200 km)

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 5 2. India: FSI (Forest Survey of India)  Remote sensing has been used in the biennial Forest Survey of India (FSI) since early 1980’s  Currently, 23.5 m resolution IRS P6 satellite is used, with data acquired in October-December (to enable deciduous forest discrimination); Minimum mapping unit is 1 ha  Unsupervised clustering followed by visual on-screen class assignment is used to produce the initial results  Extensive six months ground verification follows; Necessary corrections (e.g. canopy density) are incorporated  Extensive accuracy assessment using field plots and 6 m resolution images (nearly 6000 plots) is finally conducted  The entire assessment cycle takes almost two years

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 6 India: Forest cover map Source: Forest Survey of India website, Forest cover map of India (FSI, 2013) Very dense forest Mod dense forest Open forest Scrub Legend

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 7 India: Forest cover map A detail of the forest cover map of India Very dense forest Mod dense forest Open forest Scrub Non-forest Legend Source: Forest Survey of India website,

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 8 3. Democratic Republic of the Congo (DRC): JRC-FAO Systematic sampling  A systematic sampling approach with 267 (20 × 20 km 2 ) sampling sites distributed at every 0.5° was used  30 m resolution Landsat data for 1990, 2000 and 2005 was obtained for all sampling sites  The satellite imagery was analyzed with object-based (multi- date segmentation) approach using land cover signature database and subsequent visual validation  The results are represented by a change matrix for every sample site and allow derivation of nation-wide deforestation rate at high statistical accuracy (e.g annual deforestation rate 0.32% ± 0.05%)

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 9 Defining degraded forest Sourcebook (2014) Box Example of results of interpretation for a sample in Congo Basin

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 10 Democratic Republic of the Congo (DRC): deforestation results Source: JRC, Mayaux et al, 2013 DRC

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 11 Recommended modules as follow up  Module 2.2 to proceed with monitoring activity data for forests remaining forests (incl. forest degradation)  Module 2.8 for overview and status of evolving technologies, including e.g. Radar data  Module 3 to learn more about REDD+ assessment and reporting