Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) Module developers: Jukka Mietinnen, European Commission - Joint Research Centre Frédéric Achard, European Commission - Joint Research Centre Exercise: Intact/non-intact forest mapping using a proxy approach V1, March 2015 Creative Commons License

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 2 Intact/non-intact forest mapping using proxies  Non-intact forest mapping using proxies is typically based on a)the detection of signs of potential disturbance b)assuming fixed buffer zones around certain landscape features in forest areas (e.g. borders of the forest, roads, villages, rivers etc.)  Subsequently, the remaining forest areas where no signs of disturbance is noticed and/or which are out of the buffer zones will be considered intact

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 3 Intact/non-intact forest mapping using proxies  In this exercise we will go through a set of examples illustrating how the approach can be implemented in practice a)the first three examples illustrate different cases of visual detection of potential signs of disturbance, typically followed by manual delineation of non-intact areas b)slides illustrate an alternative approach, used in the Module 2.7 REDD+ matrix exercise, utilizing an existing forest/non-forest map and performing a buffering of forest/landscape features based on morphological spatial pattern analysis

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 4 Detecting signs of potential disturbance in Landsat data – example 1 Location N01 E099  Can you detect any potential signs of disturbance in this Landsat 7 image in Indonesia?  How would you delineate non- intact forest area? See the next slide for explanation

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 5 Detecting signs of potential disturbance in Landsat data – example 1, explanation  At least a logging area and a road passing through the image can be clearly seen  The exact delineation of non- intact areas depends on the set of criteria and buffers used in a particular exercise  The yellow lines provide a coarse example of one potential delineation in an area like this Location N01 E099 Road Logging area

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 6 Detecting signs of potential disturbance in Landsat data – example 2 Location N15 E106  Can you detect any potential signs of disturbance in this Landsat 7 image in Lao PDR?  How would you delineate non-intact forest area? See the next slide for explanation

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 7 Detecting signs of potential disturbance in Landsat data – example 2, explanation  There are numerous signs of disturbance in this image that can be used as proxies for non-intact forest areas  The exact delineation of non- intact areas depends on the set of criteria and buffers used in a particular exercise  Generally, the bottom right corner of the image is the most intact portion of this forest area River Forest edge Shifting cultivation/ Forest encroachment Clearance Location N15 E106

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 8 Detecting signs of potential disturbance in Landsat data – example 3  This Landsat image is from Papua New Guinea (see Sourcebook (2014) Figure for more details)  How would you delineate non-intact forest area? See the next slide for explanation

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 9 Detecting signs of potential disturbance in Landsat data – example 3, explanation  The hatched areas present those areas that have been considered intact in a visual image analysis  More details on the criteria used in the mapping can be found in Sourcebook (2014) section 2.2.

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 10 Detecting signs of potential disturbance in Landsat data – example 3, historical analysis (a) Papua New Guinea 26 Dec 1988 (b) Papua New Guinea 07 Oct 2002  In the same sample area, in 14 years, 51% of the existing intact forest land has been converted to non-intact forest land  At the same time, deforestation accounts for less than 1% (roads)  See more details in Sourcebook (2014) Section 2.2.

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 11 Intact/non-intact forest mapping in the Module 2.7 REDD+ matrix exercise  A simple and pragmatic way to estimate the extent of intact and non-intact forests is to use forest edges as a proxy for “boundary forests” (potentially non-intact)  The underlying assumption is that forests that are sufficiently remote from non forested areas (i.e. at a certain distance from roads, navigable waters, crops, grasslands, mines, etc.) are protected against significant anthropogenic degradation  This approach was used in the study (Bucki et al. 2012) which is the base for the REDD+ matrix in Module 2.7  See next slide for more details of the mapping approach, and explore the related calculations at Module 2.7

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 12 - Input: Binary forest/non-forest map - Treatment: Morphological Spatial Pattern Analysis (Soille and Vogt 2009) - User specified edge buffer = 500 m Black = natural forest (considered as intact) Grey = exposed (considered as non-intact) White = non forest - This approach can be further developed e.g. by including buffering of landscape features like roads, rivers and villages with the help of a GIS database Example of an automated buffering approach Intact/non-intact forest mapping in the Module 2.7 REDD+ matrix exercise Source: Bucki et al. 2012

Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 13 Recommended modules as follow up  Module 2.3 for methods to assess emission factors in order to calculate changes in forest carbon stocks  Modules 3. to learn more about REDD+ assessment and reporting