Abstract

Abstract Key message We compiled 2,458 biomass equations from 168 destructive sampling studies in Indonesia. Unpublished academic theses contributed the largest share of the biomass equations. The availability of the biomass equations was skewed to certain regions, forest types, and species. Further research is necessary to fill the data gaps in emission factors and to enhance the implementation of climate change mitigation projects and programs. Context Locally derived allometric equations contribute to reducing the uncertainty in the estimation of biomass, which may be useful in the implementation of climate change mitigation projects and programs in the forestry sector. Many regional and global efforts are underway to compile allometric equations. Aims The present study compiles the available allometric equations in Indonesia and evaluates their adequacy in estimating biomass in the different types of forest across the archipelago. Methods A systematic survey of the scientific literature was conducted to compile the biomass equations, including ISI publications, national journals, conference proceedings, scientific reports, and academic theses. The data collected were overlaid on a land use/land cover map to assess the spatial distribution with respect to different regions and land cover types. The validation of the equations for selected forest types was carried out using independent destructive sampling data. Results A total of 2,458 biomass equations from 168 destructive sampling studies were compiled. Unpublished academic theses contributed the majority of the biomass equations. Twenty-one habitat types and 65 species were studied in detail. Diameter was the most widely used single predictor in all allometric equations. The cumulative number of individual trees cut was 5,207. The islands of Java, Kalimantan, and Sumatra were the most studied, while other regions were underexplored or unexplored. More than half of the biomass equations were for just seven species. The majority of the studies were carried out in plantation forests and secondary forests, while primary forests remain largely understudied. Validation using independent data showed that the allometric models for peat swamp forest had lower error departure, while the models for lowland dipterocarp forest had higher error departure. Conclusion Although biomass studies are a major research activity in Indonesia due to its high forest cover, the majority of such activities are limited to certain regions, forest types, and species. More research is required to cover underrepresented regions, forest types, particular growth forms, and very large tree diameter classes.

Highlights

  • Tropical forests store more than 25 % of carbon in the terrestrial biosphere (Bonan 2008)

  • We focus only on aboveground biomass (AGB), belowground biomass (BGB), and total biomass (TB) equations as these equations refer to the major biomass pools, which reduced the number of equations from 511 to 131

  • We found that secondary lowland dipterocarp and peat swamp forests were the most studied forest types in Indonesia

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Summary

Introduction

Tropical forests store more than 25 % of carbon in the terrestrial biosphere (Bonan 2008). The forests in the tropics are being lost at the rate of 2,100 km year−1 (Hansen et al 2013), adding significantly to global net greenhouse gas (GHG) emissions (Intergovernmental Panel on Climate Change (IPCC) 2013). Emission reduction strategies such as the REDD+ mechanism under the auspices of the United Nations Framework Convention on Climate Change (UNFCCC) depend on countries being able to produce accurate and precise estimates of standing biomass stocks and changes in these stocks. The FAO FRA (2010) showed that most countries use global IPCC default values for estimating the biomass and carbon stocks in their forests, and they do not have estimates based on locally appropriate data. One of the main limitations to improving emissions factors is the lack of appropriate biomass equations for converting plot scale measurements collected in a traditional forest inventory into biomass estimates and subsequently into carbon numbers (IPCC 2006; Verchot et al 2012; Joseph et al 2013)

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