Assessment of Above-Ground Biomass of Borneo Forests through a New Data-Fusion Approach Combining Two Pan-Tropical Biomass Maps

Andreas Langner,Christelle Vancutsem,Kanehiro Kitayama,Frédéric Achard,Jean-Francois Pekel,Dario Simonetti,Giacomo Grassi,Mikiyasu Nakayama

doi:10.3390/land4030656

Andreas Langner, Christelle Vancutsem + Show 6 more

Open Access

https://doi.org/10.3390/land4030656

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Abstract

This study investigates how two existing pan-tropical above-ground biomass (AGB) maps (Saatchi 2011, Baccini 2012) can be combined to derive forest ecosystem specific carbon estimates. Several data-fusion models which combine these AGB maps according to their local correlations with independent datasets such as the spectral bands of SPOT VEGETATION imagery are analyzed. Indeed these spectral bands convey information about vegetation type and structure which can be related to biomass values. Our study area is the island of Borneo. The data-fusion models are evaluated against a reference AGB map available for two forest concessions in Sabah. The highest accuracy was achieved by a model which combines the AGB maps according to the mean of the local correlation coefficients calculated over different kernel sizes. Combining the resulting AGB map with a new Borneo land cover map (whose overall accuracy has been estimated at 86.5%) leads to average AGB estimates of 279.8 t/ha and 233.1 t/ha for forests and degraded forests respectively. Lowland dipterocarp and mangrove forests have the highest and lowest AGB values (305.8 t/ha and 136.5 t/ha respectively). The AGB of all natural forests amounts to 10.8 Gt mainly stemming from lowland dipterocarp (66.4%), upper dipterocarp (10.9%) and peat swamp forests (10.2%). Degraded forests account for another 2.1 Gt of AGB. One main advantage of our approach is that, once the best fitting data-fusion model is selected, no further AGB reference dataset is required for implementing the data-fusion process. Furthermore, the local harmonization of AGB datasets leads to more spatially precise maps. This approach can easily be extended to other areas in Southeast Asia which are dominated by lowland dipterocarp forest, and can be repeated when newer or more accurate AGB maps become available.

Highlights

Many developing countries lack the capacities to undertake their own forest carbon monitoring
In order to participate in REDD+ (Reducing Emissions from Deforestation and forest Degradation) these countries can either use default biomass values as stated in the IPCC guidelines, which are characterized by high uncertainties in combination with low transparency, or refer to other existing data sources such as local or pan-tropical above-ground biomass (AGB) maps
The best fitting model is selected from comparisons with a Landsat-based reference AGB map available over a restricted area in the

Summary

Introduction

Many developing countries lack the capacities to undertake their own forest carbon monitoring. In order to participate in REDD+ (Reducing Emissions from Deforestation and forest Degradation) these countries can either use default biomass values as stated in the IPCC guidelines, which are characterized by high uncertainties in combination with low transparency, or refer to other existing data sources such as local or pan-tropical above-ground biomass (AGB) maps. The most prominent pan-tropical sources are the benchmark maps of Saatchi [1] and Baccini [2] with 1 km or 500 m spatial resolution respectively. A previous analysis of these pan-tropical maps showed different AGB patterns at local scale and observed less pronounced differences when deriving average biomass values over larger areas [3]. The lack of a statistically valid sample of AGB reference values prevented a pan-tropical accuracy assessment of these maps [3]

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