Landscape fires show large variability in the amount of biomass or fuel consumed per unit area burned. Fuel consumption (FC) depends on the biomass available to burn and the fraction of the biomass that is actually combusted, and can be combined with estimates of area burned to assess emissions. While burned area can be detected from space and estimates are becoming more reliable due to improved algorithms and sensors, FC is usually modeled or taken selectively from the literature. We compiled the peer-reviewed literature on FC for various biomes and fuel categories to understand FC and its variability better, and to provide a database that can be used to constrain biogeochemical models with fire modules. We compiled in total 77 studies covering 11 biomes including savanna (15 studies, average FC of 4.6 t DM (dry matter) ha−1 with a standard deviation of 2.2), tropical forest (n = 19, FC = 126 ± 77), temperate forest (n = 12, FC = 58 ± 72), boreal forest (n = 16, FC = 35 ± 24), pasture (n = 4, FC = 28 ± 9.3), shifting cultivation (n = 2, FC = 23, with a range of 4.0–43), crop residue (n = 4, FC = 6.5 ± 9.0), chaparral (n = 3, FC = 27 ± 19), tropical peatland (n = 4, FC = 314 ± 196), boreal peatland (n = 2, FC = 42 [42–43]), and tundra (n = 1, FC = 40). Within biomes the regional variability in the number of measurements was sometimes large, with e.g. only three measurement locations in boreal Russia and 35 sites in North America. Substantial regional differences in FC were found within the defined biomes: for example, FC of temperate pine forests in the USA was 37% lower than Australian forests dominated by eucalypt trees. Besides showing the differences between biomes, FC estimates were also grouped into different fuel classes. Our results highlight the large variability in FC, not only between biomes but also within biomes and fuel classes. This implies that substantial uncertainties are associated with using biome-averaged values to represent FC for whole biomes. Comparing the compiled FC values with co-located Global Fire Emissions Database version 3 (GFED3) FC indicates that modeling studies that aim to represent variability in FC also within biomes, still require improvements as they have difficulty in representing the dynamics governing FC.

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 Unpublished academic theses contributed the largest share of the biomass equations.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.

Currently, the common and feasible way to estimate the most accurate forest biomass requires ground measurements and allometric models. Previous studies have been conducted on allometric equations development for estimating tree aboveground biomass (AGB) of tropical dipterocarp forests (TDFs) in Kalimantan (Indonesian Borneo). However, before the use of existing equations, a validation for the selection of the best allometric equation is required to assess the model bias and precision. This study aims at evaluating the validity of local and pantropical equations; developing new allometric equations for estimating tree AGB in TDFs of Kalimantan; and validating the new equations using independent datasets. We used 108 tree samples from destructive sampling to develop the allometric equations, with maximum tree diameter of 175 cm and another 109 samples from previous studies for validating our equations. We performed ordinary least squares linear regression to explore the relationship between the AGB and the predictor variables in the natural logarithmic form. This study found that most of the existing local equations tended to be biased and imprecise, with mean relative error and mean absolute relative error more than 0.1 and 0.3, respectively. We developed new allometric equations for tree AGB estimation in the TDFs of Kalimantan. Through a validation using an independent dataset, we found that our equations were reliable in estimating tree AGB in TDF. The pantropical equation, which includes tree diameter, wood density and total height as predictor variables performed only slightly worse than our new models. Our equations improve the precision and reduce the bias of AGB estimates of TDFs. Local models developed from small samples tend to systematically bias. A validation of existing AGB models is essential before the use of the models.