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Jha N., Tripathi N. K., Chanthorn W., Brockelman W., Nathalang A., Pélissier Raphaël, Pimmasarn S., Ploton Pierre, Sasaki N., Virdis S. G. P., Réjou-Méchain Maxime. (2020). Forest aboveground biomass stock and resilience in a tropical landscape of Thailand. Biogeosciences, 17 (1), 121-134. ISSN 1726-4170

Fichier PDF disponiblehttp://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers20-01/010077772.pdf[ PDF Link ]

Lien direct chez l'éditeur doi:10.5194/bg-17-121-2020

Titre
Forest aboveground biomass stock and resilience in a tropical landscape of Thailand
Année de publication2020
Type de documentArticle référencé dans le Web of Science WOS:000507313500001
AuteursJha N., Tripathi N. K., Chanthorn W., Brockelman W., Nathalang A., Pélissier Raphaël, Pimmasarn S., Ploton Pierre, Sasaki N., Virdis S. G. P., Réjou-Méchain Maxime.
SourceBiogeosciences, 2020, 17 (1), p. 121-134. ISSN 1726-4170
RésuméHalf 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+.
Plan de classementEtudes, transformation, conservation du milieu naturel [082] ; Télédétection [126]
Descr. géo.THAILANDE ; ZONE TROPICALE
LocalisationFonds IRD [F B010077772]
Identifiant IRDfdi:010077772
Lien permanenthttp://www.documentation.ird.fr/hor/fdi:010077772

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