Table A9.1. Land cover classes, national level and sub-national improvement (East Kalimantan) No National Landcover Classes Sub-National Landcover Cla

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1 Appendix 9.1. Activity Data (/Land Cover Classes) Improvement The method to improve activity (forest cover/land cover data) in sub-national East Kalimantan is the same method that used by KLHK to produce national land cover data. The term working definition of forest was used to produce land cover maps through visual interpretation of satellite images at a scale that minimum area for polygon delineation is 0.25 cm2 at 1: 50,000 of scale which equal to 6.25 ha. degraded mainly caused by human activity, either by legal logging activities, or by illegal logging and shifting cultivation. classified into three classes using canopy cover or crown cover percentage approach: high density, medium density and low density. These three classes could interpret from optical satellite images through visual interpretation using visual interpretation principle (element): tone/color, texture, pattern, shapes, size, etc. WWF employee ry Remote Sensing Expert that have deep/wide or a lot of field experience & knowledge. The expert could classify land cover of East Kalimantan into 32 classes. The detail of the classes & the comparison with the national land cover classes could see in Table 1. classes into three classes (Figure A9.1): - high density (rather closed canopy): crown cover > 70% - medium density (medium open canopy): crown cover 40% - 70% - low density (very open canopy): crown cover 10% - 40% Canopy density - Crown cover percentage: Crown Cover (cc) refers to the canopy closure or tree density within the forest cover, i.e. within a pixel or uniform polygons. From satellite images the following three, approximate classes were mapped: high density: medium density: low density: (cc 70%) (70% cc 40%) (40% cc > 10%) rather closed canopy medium open canopy very open canopy 90% 80% 60% 40% -30% 10% Figure A9.1. Canopy density and Crown cover percentage to estimate density of the Natural forest.

2 Table A9.1. Land cover classes, national level and sub-national improvement (East Kalimantan) No National Landcover Classes Sub-National Landcover Classes No 1 Hutan lahan kering primer Primary dry land forest Dry land forest high density 1 2 Hutan lahan kering sekunder Secondary dry land forest Dry land forest medium density 2 Dry land forest low density 3 3 Hutan rawa primer Primary swamp forest Swamp forest high density 4 4 Hutan rawa sekunder Secondary swamp forest Swamp forest medium density 5 Swamp forest low density 6 5 Hutan mangrove primer Primary mangrove forest Mangrove forest high density 7 6 Hutan mangrove sekunder Secondary mangrove Mangrove forest medium density 8 forest Mangrove forest low density 9 7 Hutan tanaman plantation plantation 10 Oil palm plantation 11 Monoculture rubber plantation 12 8 Perkebunan / Kebun Crop plantation Rubber plantation mix with 13 shrubs Mix garden 14 9 Semak belukar Shrubs Re-growth (Belukar tua) 15 Shrubs (Semak/Belukar Muda) Semak belukar rawa Shrubs on swamp Re-growth on Swampy 17 Shrubs on Swampy Savanna / Padang rumput Savana Grassland/Savana Pertanian lahan kering Unirrigated agriculture Unirrigated agriculture Pertanian lahan kering campur semak / kebun campur Mix unirrigated agriculture Mix unirrigated agriculture 14 Sawah Paddy field Paddy field Tambak Aquaculture pond Aquaculture pond 23 Permukiman / Lahan Settlement Settlement terbangun 17 Transmigrasi Transmigration Transmigration Lahan terbuka Open area Open area 26 Burnt scars Pertambangan Mining Mining Tubuh air Water body Water body Rawa Swamp Swamp Awan Cloud Cloud Bandara / Pelabuhan Airport/harbour Airport/harbour 32 21

3 Figure A9.1. /Land Cover Kutai Barat & Mahakam Ulu 2016 At present WWF have the 32 /Land Cover classes data at two district Kutai Barat & Mahakam Ulu for 2009, 2013, 2014, 2015, 2016 and Using the sample data that use by MoEF to do the accuracy assessment for East Kalimantan land cover data and also use to do accuracy assessment off WWF land cover for Kutai Barat & Mahakam Ulu, that the sample had clipped in these two districts. The 32 /Land Cover in Kutai Barat & Mahakam Ulu have grouped in three classes: Primary Intact, Primary Degraded & Non- to meet the classes in the sample data that share by MoEF. The accuracy assessment and area estimate calculate using Olofsson et al, The data use 2009 and 2016, the result could see in the table below:

4 Table A9.2. Sample Matrix Sample/Reference Map Deforestation Degradation Gain Non- Total (N) Deforestation Degradation Gain Non Total Table A9.3. Area proportion & Accuracy Map Deforestation Degradation Sample/Reference Gain Non- Total Deforestation Degradation Gain Non Total user accuracy (Ui) producer accuracy (Pi) overall accuracy (O) Table A9.4. Area Estimate Stratum Estimate area (ha) SE of estimated area (ha) CI (95%) Deforestation 175,927 48,825 95,696 Degradation 11,378 11,378 22,300 Gain NA NA NA 2,356,284 66, ,816 Non- 864,756 67, ,662

5 Figure A9.2. /Land Cover Changes Kutai Barat & Mahakam Ulu The method is possible to be applied or scale up at provincial level and national level given the availability of dedicated human resources, finance and infrastructure. The use of visual interpretation method requires experienced analysts with deep field experience that is still lacking on the Sub-National and National level. These improvements also could use for capacity building at Sub-National & National level. For the capacity building will conduct the training for Subnational & National on land cover analysis using visual interpretation with ground check activities by the expert of the forestry remote sensing in the first year and monitor the result on the next years.

6 On the other hand, the carbon stock data for each degradation level is also not available yet. There is a need to develop sample plots to measure the carbon stock in different degraded forests. The degradation level of the secondary forest can be categorized as lightly and heavily degraded and shrubs also into two categories as old shurbs (forest regrowth) and shrubs. In these regards, the forest regrowth can be categorized as forestland but shrubs as non-forestland as it takes many years to regenerate naturally. With this categorization, we will be able to measure emission from degradation of secondary forest. The country considers that adoption of this approach is possible in the next two years (2021) before the first verification, thus the recalculation of REL may be applied. The implication of this approach, the contribution of emission from deforestation and degradation to REL will change. The emission from fire causing degradation of forest will also change, as part of area of shrubs will be defined as forest (i.e. forest regrowth). As defined, most area affected by fire is shrub, thus the burnt of the old shrubs (forest regrowth) will be included in the calculation of emission from the degradation and this may elevate the REL. In the preparation of the adoption of the approach, the government is preparing the establishment of new permanent sampling plots in different level of degradation of the secondary forest as well as training of technical staffs.