Climate Impacts on Sustainable Natural Resource Management. Группа авторов

book. We believe that the book would be read by people with a common interest in sustainable development and other diverse backgrounds within earth observation.

      The scientific quality of the book was ensured by a rigorous review process where leading researchers from Australia, Canada, India, Indonesia, Japan, Malaysia, Sri Lanka, and the USA participated to provide constructive comments to improve the chapters. Due to the confidentiality of the review process, we are unable to provide their names; however, we are deeply indebted and thankful for their voluntary support. On behalf of the team of authors, we express our gratitude to the entire crew of Wiley for all kind of assistance to make this a successful endeavor.

       Pavan Kumar

       Ram Kumar Singh

       Manoj Kumar

       Meenu Rani

       Pardeep Sharma

Section 1 Sustainable Natural Resource Management

1 Impact of Local REDD+ Intervention on Greenhouse Gas Emissions in East Kalimantan Province, Indonesia

       Kiswanto1, Martiwi Diah Setiawati2,3, and Satoshi Tsuyuki4

       1 Faculty of Forestry, Mulawarman University, Indonesia, Campus of Gunung Kelua, Penajam Street, Samarinda, East Kalimantan 75116 Indonesia

       2 Institute for Future Initiatives, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654 Japan

       3 Research Center for Oceanography, Indonesian Institute of Sciences, Jl. PasirPutih I, East Ancol, Jakarta 14430 Indonesia

       4 Graduate School of Agricultural and Life Sciences, TheUniversity of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657 Japan

1.1 Introduction

      1.1.1 Tropical Deforestation

      Initially, carbon was stored in the forests (Houghton 2012), but once the forests were logged and cleared, carbon (i.e. both above and below ground) was released into the atmosphere (Baccini et al. 2012) mostly in the for of carbon dioxide (CO₂). However, the decomposition or burning of the forest may also release small amounts of methane (CH₄) and carbon monoxide (CO) (Achard et al. 2014; Rosa et al. 2016; Bebber and Butt 2017; Brinck et al. 2017; Pearson et al. 2017). Thus deforestation received high attention from the scientific community (Rosa et al. 2016; Sierra et al. 2012; Zarin 2012) on carbon emissions (Numata et al. 2011; Houghton 2012; Le Quéré et al. 2015), especially in the tropics, where deforestation is responsible for 17%–25% of carbon dioxide (CO₂) emissions into the atmosphere (Le Quéré et al. 2015). So tropical deforestation is one of the leading causes of global carbon emissions and biodiversity loss (Brun et al. 2015). Therefore, understanding its drivers is crucial for improving policies and measuring current forest trends toward a more climate‐ and biodiversity‐friendly outcome (Hosonuma et al. 2012). Also, the Forest Resources Assessment (FRA) of the Food and Agriculture Organization of the United Nations (FAO) provides a complete measurement of above‐ground carbon stocks for tropical forests (FAO 2006, 2010, 2015).

      1.1.2 REDD+

Reducing emissions from deforestation and forest degradation (REDD+) now constitutes the international convention for mitigating climate change, particularly in forest‐rich developing countries (Gullison et al. 2007; Arima et al. 2014). The “+” after REDD comes from more recent dialogs that have broadened the mechanism's scope to recognize carbon benefits of forest conservation, sustainable forest management (SFM), and the sequestration potential of afforestation and reforestation (Venter and Koh 2012). The REDD+ mechanism is the most prominent of recent attempts to mitigate climate change (Agrawal et al. 2011). Furthermore, leakage policies of REDD+ should be monitored, measured, and mitigated to guarantee their effectiveness (Atmadja and Verchot 2012).

      Reducing emissions from deforestation by 2020 could bring the international community nearer to the goal of less than 2 degree increase in global average temperature change (Zarin et al. 2016). Furthermore, more than 180 governments, private companies, indigenous peoples, and non‐governmental organizations have signed the New York Declaration on Forests (NYDF) in September 2014 (UN Climate Summit 2014). Within the REDD+ policy framework, developing countries might develop national systems for carbon accounting (Angelsen 2009; Logan‐Hines et al. 2012).

      Developing countries are encouraged to develop national strategies and action plans for REDD+ by identifying the drivers of deforestation (Hosonuma et al. 2012). In the past three decades, satellite‐based observations of forest cover change provide an alternative to estimate deforestation rates regularly across space and time (Zhuravleva et al. 2013; Kuenzer et al. 2014; Kamaruddin et al. 2015). At continental to global scales, forest cover maps and change in cover are increasingly being generated from various satellite data sources (Potapov et al. 2012; Kim et al. 2014; Margono et al. 2014). In the latest development, Landsat images have been used to determine tropical deforestation rates (Broich et al. 2011; FAO 2011; Lehmann et al. 2014; Estavillo et al. 2013; Zhuravleva et al. 2013; Potapov et al. 2012). Also, previous studies of forest cover change datasets have been integrated with satellite‐based forest biomass information to quantify 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