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Biogeosciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 31 Jul 2019

Submitted as: research article | 31 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

Forest aboveground biomass stock and resilience in a tropical landscape of Thailand

Nidhi Jha1,2, Nitin Kumar Tripathi1, Wirong Chanthorn3, Warren Brockelman4, Anuttara Nathalang4,5, Raphaël Pélissier2, Siriruk Pimmasarn1, Pierre Ploton5, Nophea Sasaki6, Salvatore G. P. Virdis1, and Maxime Réjou-Méchain2 Nidhi Jha et al.
  • 1Field of Remote Sensing and GIS (RSGIS), Department of Information & Communication Technologies, Asian Institute of Technology, Thailand
  • 2AMAP IRD, CNRS, CIRAD, INRA, Univ Montpellier, Montpellier, France
  • 3Faculty of Environment, Kasetsart University, Thailand
  • 4National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand
  • 5National Biobank of Thailand (NBT), Pathum Thani, Thailand
  • 6Departmentof Development and Sustainability, Asian Institute of Technology, Thailand

Abstract. Half of Asian tropical forests were disturbed in the last century resulting in the dominance of secondary forests in Southeast Asia. However, the rate at which biomass accumulates during the recovery process in these forests is poorly understood. We studied a forest landscape located in Khao Yai National Park (Thailand) that experienced strong disturbances in the last century due to clearance by swidden farmers. Combining recent field and airborne laser scanning (ALS) data, we first built a high-resolution aboveground biomass (AGB) map over 60 km2 of the forest landscape. We then used the random forest algorithm and Landsat time-series (LTS) data to classify landscape patches as non-forested versus forested on an almost annual basis from 1972 to 2017. The resulting chronosequence was then used in combination with the AGB map to estimate forest carbon recovery rates in secondary forest patches during the first 42 years of succession. The ALS-AGB model predicted AGB with an error of 14 % at 0.5-ha resolution (RMSE = 45 Mg ha−1) using the mean top-of-canopy height as a single predictor. The mean AGB over the landscape was of 291 Mg ha−1 showing a high level of carbon storage despite past disturbance history. We found that AGB recovery varies non-linearly in the first 42 years of the succession, with an increasing rate of accumulation through time. We predicted a mean AGB recovery rate of 6.9 Mg ha−1 yr−1, with a mean AGB gain of 143 and 273 Mg ha−1 after 20 and 40 years, respectively. These estimates are within the range of those reported for the well-studied Latin American secondary forests under similar climatic conditions. This study illustrates the potential of ALS data not only for scaling up field AGB measurements but also for predicting AGB recovery dynamics when combined with long-term satellite data. It also illustrates that tropical forest landscapes that were disturbed in the past are of utmost importance for the regional carbon budget and thus for implementing international programs such as REDD+.

Nidhi Jha et al.
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Nidhi Jha et al.
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Short summary
Carbon stocks and dynamics are both uncertain in tropical forests, especially in Asia. We here quantify the carbon stock and recovery rate of a Thai landscape using airborne LiDAR and four decades of Landsat data. We show that the landscape has a high carbon stock despite its disturbance history and that secondary forests are accumulating carbon at high rate. Our study shows the potential synergy of remote sensing and field data to characterize the carbon dynamics of tropical forests.
Carbon stocks and dynamics are both uncertain in tropical forests, especially in Asia. We here...