Forest aboveground biomass stock and resilience in a tropical landscape of 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 of over 60 km 2 of 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 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. This rate estimate is about 50 % larger than the rate prescribed for young secondary Asian tropical rainforests in the 2019 refinement of the 2006 IPCC guidelines for national greenhouse gas inventories. Our study hence suggests that the new IPCC rates, which were based on limited data from Asian tropical rainforests, strongly underestimate the carbon potential of forest regrowth in tropical Asia. Our recovery estimates are also 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+.Published by Copernicus Publications on behalf of the European Geosciences Union.

Spatial Assessment of Para Rubber (Hevea brasiliensis) above Ground Biomass Potentials in Songkhla Province, Southern Thailand

“…As a result, it can be hypothesized that the density and structural complexity of certain forest types can have a significant effect on its AGB potential. Similar spatial variability in AGB has been reported by Jha et al [48], at the landscape scale assessment of Khao Yai National Park in central Thailand. They discovered a high degree of spatial variability in AGB, with a noticeable bias in favor of low AGB values, attributing this to human activity in this region prior to the park's creation.…”

Different responses of soil respiration to environmental factors across forest stages in a Southeast Asian forest

“…Nevertheless, these findings are still preliminary and call for detailed investigations on SR and its variations with environmental factors in Southeast Asian tropical forests where patches of successional stages dominate. KYNP contains mostly old-growth (primary) forest with scattered patches of secondary forest at various stages, which have regenerated from old fields within the past 50 years (Jha et al, 2020).…”

Applying LiDAR to Quantify the Plant Area Index Along a Successional Gradient in a Tropical Forest of Thailand

“…The age of secondary forest pixels in the study area was obtained from Jha et al [30], whose study was based on LiDAR and a Landsat time-series dataset (LTS). Jha et al [30] employed a random forest algorithm to classify the Landsat time-series (1972 to 2017) dataset using training pixels derived from the mean height of the Canopy height Model (CHM) from LiDAR (2017) at a 60-m resolution.…”

“…There are many studies that investigated the combination of high temporal resolution satellite images (e.g., Landsat) and high spatial resolution LiDAR data for estimating the dynamics of aboveground forest [28,29]. Recently, this approach has also been successfully applied to the assessment of forest aboveground biomass and its resilience in Thailand [30]. Until recently, however, there have not been any studies examining the long-term vertical PAI in successional gradients of tropical forests.…”