changes in forest carbon stocks (Baccini et al. 2012; Achard et al. 2014; Tyukavina et al. 2015). However, they might show diverse results due to their different methods for mapping and analyzing.
1.1.3 REDD+ in Indonesia
At the COP 15–2009 of the United Nations Framework Convention on Climate Change (UNFCCC), Indonesia voluntarily agreed to reduce emissions by 26% and up to 41% with international support by 2020. This commitment was submitted as Indonesia's Nationally Appropriate Mitigation Actions (NAMA) in 2010 (Indonesia 2013). Since the commitment, Indonesia made some policies, including Presidential Regulation No. 61 of 2011 (Indonesia 2011b) on the national action plan of REDD+ and Presidential Regulation No. 71 of 2011 on the implementation of the National GHG inventory (Indonesia 2011c). Those regulations mandate different government bodies to provide national, local, and corporate GHG inventories annually. Based on its nationally determined contribution (NDC) submitted to the UNFCCC on September 24, 2015 (Indonesia 2016), Indonesia committed to reducing GHG emissions by 29% under BAU (business as usual) scenario by 2030 unconditionally, and up to 41% conditionally. To meet the objective, Indonesia recognizes the requirement for consolidating both methods and data sources to guarantee a high degree of precision.
The study area, East Kalimantan, is one of the target provinces for REDD+ initiatives in Indonesia. This provincial government has also developed an action plan for reducing emissions (East Kalimantan 2013), REDD+ strategy (East Kalimantan 2012), and part of Forest Carbon Partnership Facility (FCPF). FCPF is a global partnership of governments, businesses, civil society, and indigenous peoples (IP), focused on reducing GHG emissions from deforestation and forest degradation, forest carbon stock conservation, SFM, and the enhancement of forest carbon stocks in developing countries (FCPF 2017). Furthermore, this province is also working through close support from civil society and the private sector, which have joined with the government to launch a Green Growth Compact (GGC) by the end of 2017. This initiative has two interrelated targets: to reduce deforestation by at least 80% by 2025 and to increase economic growth by 8% by 2030 (TNC 2016). Also, East Kalimantan was hosting some REDD+ demonstration projects managed by international NGOs and donors. However, further effort should be integrated within official government climate mitigation measures (East Kalimantan 2011a).
This study therefore aimed to estimate annual GHG emissions in East Kalimantan based on the yearly land cover maps derived from satellite data between 2000 and 2016, to determine the historical (2000–2010) and the REDD+ progress (2010–2016) baseline of GHG emissions, and to predict the future trajectories of GHG emissions for 2020 and 2030. Furthermore, 2010 was chosen as the base year for comparing emissions before and after the REDD+ commitment. Also, Indonesia's NDC target in 2030 was selected as the end period of future trajectories.
1.2 Materials and Methods
1.2.1 Spatial Dataset
Annual land cover maps in East Kalimantan from 2000 to 2016 from Landsat satellite images were used to estimate GHG emissions in each land cover map. The detailed data set and methodology used in this paper were explained in Kiswanto et al. 2018. These spatial datasets were used for calculating the total changed areas in each period using the transition matrix (Appendix 1.A) and multiplying with the carbon stock changes in each period.
1.2.2 Carbon Stock in Each Land Cover Class
The emission factor for land cover changes is defined as the stock difference in carbon between two land cover classes (Santosa et al. 2014). The reference for carbon stock estimation for each land cover class was required to calculate carbon stock differences and GHG emissions from the land‐based sector at a specific location. For each land cover class, the reference was generated from research related to above‐ground biomass for specific sites. For forest cover classes, data references were developed from the average of above‐ground biomass in the forest areas (Hairiah et al. 2011). For cropland and agricultural land covered with regular cycles of planting and harvesting, carbon stock references were developed from the time average of above‐ground biomass (Agus et al. 2013a).
The provincial and district are supported to develop the highest carbon stock estimation that accurately illustrated the circumstances. If reference data is available at the province level, the carbon stock in the various districts within the province can be used to represent the emission factor. If it is not available, data can be used from the national level (Santosa et al. 2014). Table 1.1 shows land cover classes and their carbon stocks for estimating GHG emissions that were used for devising the local action plan for REDD+ in East Kalimantan.
Table 1.1 Land cover classes and their carbon stocks for estimating carbon emissions from land‐based sectors.
| Land cover type | Carbon stock (tC ha–1) | Reference; remarks | ||
|---|---|---|---|---|
| Forest | Dryland | Primary | 195 | MoF 2008; Agus et al. 2013b |
| Secondary | 169 | |||
| Mangrove | Primary | 170 | MoF 2008; Agus et al. 2013b; Krisnawati et al. 2014 | |
| Secondary | 120 | |||
| Swamp | Primary | 196 | MoF 2008, Agus et al. 2013a,b | |
| Secondary | 155 | |||
| Artificial/Plantation forest | 64 | Agus et al. 2013b; MoF 2008; Verstegen et al. 2019 | ||
| Non‐forest | Agriculture | Pure dry | 8 | East Kalimantan 2013 |
| Mixed dry | 10 | East Kalimantan 2013 | ||
| Rice field | 5 | Rahayu et al. 2005; East Kalimantan 2013, | ||
| Estate cropland | 63 | Agus et al. 2013b; Verstegen et al. 2019 | ||
| Aquaculture | 0 | Agus et al. 2013b | ||
| Shrubland | Dry | 15 | Prasetyo and Saito 2000; East Kalimantan 2013 | |
| Wet | 15 | Prasetyo and Saito 2000; East Kalimantan 2013 | ||
| Savanna and grasses | 4.5 | Rahayu et al. 2005; Agus et al. 2013b | ||
| Open swamp |
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