The study area of ca. 6400 ha is part of Khao Yai National Park in central Thailand (14∘25′20.4′′ N, 101∘22′36.9′′ E; Fig. 1). Khao Yai is the first national park established in Thailand, in 1962. It is home to numerous endangered plant and animal species (Kitamura et al., 2004). The area receives approximately 2200 mm of precipitation annually, with a dry season of 5 to 6 months (precipitation below 100 mmmonth-1) from November to April (Brockelman et al., 2011; Chanthorn et al., 2016). The annual mean temperature is about 22–23 ∘C (Jenks et al., 2011), and the altitude of the study area varies from 650 to 870 m. Before establishment of the park, some areas were used for low-intensity agriculture activities that likely started at the end of the 19th century (Brockelman et al., 2011, 2017) and then naturally reforested at different times depending on when burning ceased (Chanthorn et al., 2016). As a consequence, the landscape constitutes a mosaic of secondary forests of different ages amidst old-growth forests (Chanthorn et al., 2016).

We used three sets of forest inventory plots with a total sample area of 35 ha (Fig. 1). First, a large 30 ha contiguous (500m×600m) forest dynamics plot, named Mo Singto, was established in old-growth forest after 1998 and completely censused in 2004–2005, 2010–2011 and 2016–2017. The census method follows the protocol of the Center for Tropical Forest Science (CTFS) network to which the plot belongs since 2009 (Brockelman et al., 2011). The second set of plots included eight separate 0.48 ha plots (60m×80m) that were established from March to May 2013 and re-censused from November 2017 to January 2018 (Chanthorn et al., 2017). These plots are set along a successional gradient varying from near stand initiation to old-growth forest. Lastly, a 1 ha plot (100m×100m) located near the north border of the 30 ha Mo Singto plot was established in a secondary forest in 2005 and then re-censused in 2010 and 2017. In all plots, trees ≥1 cm in diameter at breast height (dbh) were tagged, identified to species, mapped and measured for their diameter, except in the 0.48 ha plots where the minimum dbh was 4 cm. A total of 184 239 individual trees were measured across all the plots, from which 517 trees were measured for height using a pole for short trees (<5 m), a laser range finder (Nikon Forestry 550) for medium height trees (5–7 m) and a Vertex III hypsometer for tall (>7 m) trees (Chanthorn et al., 2017). In this paper, we used the 2017 census data, concomitant with the ALS campaign, to estimate AGB and multiple censuses to estimate the AGB dynamics of secondary plots. For the sake of homogeneity in tree measurements, we used only trees ≥5 cm in dbh in the whole data set.

In order to homogenize plot size, we subdivided all plots ≥1 ha into 0.5 ha subplots. This resulted in 70 plots of either 50m×100m (n=62) or 60m×80m (n=8) that we classified in three successional stages from young- to old-growth forests following the classification from Chanthorn et al. (2017): stand initiation (early) stage (SIS; n=3); stem exclusion (intermediate) stage (SES; n=5); and old-growth stage (OGS; n=62). Based on interviews of senior park rangers and using Landsat remote sensing data, Chanthorn et al. (2017) estimated that the ages were approximately 15–20 years for SIS forests, 35–40 years for SES forests and unknown but probably older than 200 years for OGS forests. This classification into successional stages followed the framework of Oliver and Larson (1996) who studied successional gradients in temperate forests. Although the original framework considered four successional stages, we did not find any area corresponding to the understory re-initiation stage in the study landscape, i.e., the stage following SES and preceding OGS. Most second-growth forests have regenerated since the Park was established about 50–60 years ago so that old second-growth forests, where understory re-initiation occurs, are very rare in this area. Note also that our study period (1972–2017; see below) cannot account for forests from the SES stage older than 40 years, e.g., that directly started regenerating at the establishment of the park in 1962, as suggested by some hand-drawn historical maps (Cumberlege and Cumberlege, 1963; Smitinand, 1968).

The airborne laser scanning (ALS) campaign was conducted on 10 April 2017 over ca. 64 km2 (Fig. 1). The Asian Aerospace Services Limited company (Bangkok) acquired the ALS data with a RIEGL LMS Q680i installed into a Diamond Aircraft “Airborne Sensors” DA-42 fixed-wing airplane. The flying altitude was about 500–600 m above ground level with a 60∘ field of view, and a pulse repetition frequency of 400 kHz, for which the aircraft maintained an average ground speed of 185 kmh-1 capturing the area of interest in 50 overlapping laser strips. We discretized the full waveform data for subsequent analyses resulting in an average point density of ca. 22 points m-2.

Post-processing of ALS data and point cloud classification into ground, vegetation or noise was done using TerraScan of Terrasolid Version 14. Points classified as ground were used to build a digital terrain model (DTM) at 1 m resolution using a k nearest-neighbor kriging approach implemented in the LidR R package (Roussel and Auty, 2017). A 1 m resolution canopy height model (CHM) was then computed from the height of the normalized vegetation points, discarding outliers classified as air or noise. Finally, we used the CHM and the normalized vegetation point cloud to derive different forest height metrics at the plot level (Table S1 in the Supplement).

We retrieved Landsat images (MSS, TM, OLI and TIRS products) for the study area from the Landsat archive (http://glovis.usgs.gov, last access: 17 April 2019) in the 1972–2017 period (WRS-1 138/50 and WRS-2 path/row: 129/50). To minimize the impact of clouds and potentially varying phenology within years, we mostly selected images acquired during the dry season, from November to March. We thus collected Landsat 1–3 MSS data (1972–1983), Landsat 4–5 TM (1984–2011) and Landsat 8 OLI & TIRS (2013–2017) data. We did not consider Landsat 7 ETM+ images due to the failure of the Scan Line Corrector, leading to data gaps. All Landsat images were already orthorectified and displayed an accurate co-registration with ALS data. Before 1984, Landsat MSS collected data at 60m×60m spatial resolution in most bands. Thus, to have consistent time series data, we aligned all the post-1983 Landsat data using a reference image from 1972 and aggregated each image to 60×60 m. Over the 44 years, we selected a total of 34 high-quality images, each representing 1 year. For the 11 missing years, we could not find cloud-free images and no image was available in 2012 since we discarded Landsat 7 ETM+ data.

We estimated tree aboveground biomass (AGB) using a pantropical allometric model (Eq. 4 from Chave et al., 2014). This model uses the diameter (D), total tree height (H) and wood density (WD) as the predictors and was shown to hold across tropical vegetation types and regions. Wood density was estimated using species- (47 % of stems), genus- (50 %) or stand-averaged (3 %) values from the global wood density database (Chave et al., 2009; Zanne et al., 2009). Tree height was estimated through locally adjusted height–diameter (H-D) models of the form given in Eq. (1): 1ln⁡H=a+b×ln⁡D+c×ln⁡D2+ε, where a and b are model parameters to be adjusted and ε is a normally distributed error with mean 0 and standard error σlogH. Tree height was subsequently estimated using the back-transformation formula including a known bias correction (Baskerville, 1972) using following Eq. (2): 2H=exp⁡0.5×σlogH2+a+b×ln⁡D+c×ln⁡D2+ε. Because the H-D relationship varies along the successional gradient (Chanthorn et al., 2017), we fitted three independent H-D models for the three different successional growth forest stages using 177 measured trees for SIS plots, 159 for SES plots and 181 for OGS plots.

AGB at the plot level was then estimated in Mgha-1 by summing individual tree AGB for all trees with dbh ≥5 cm belonging to the plot. We did all these analyses using the R BIOMASS package (Réjou-Méchain et al., 2017).

We relied on a log–log model form given in Eq. (3) to model AGB from ALS data (Asner et al., 2012; Réjou-Méchain et al., 2015): 3ln⁡AGB=a+b×ln⁡L1+c×ln⁡L2+…+ε, where L1, L2, … are the lidar metrics to be selected (see Table S1) and ε the error term assumed to be normally distributed with zero mean and residual standard error σlogL. Fitting the model with log-transformed variables allows us to model a multiplicative error and thus to account for higher model prediction error with larger AGB values (Zolkos et al., 2013). Using this model, we selected the most predictive lidar metrics from our full set of lidar metrics using a leave-one-out cross-validation (LOOCV) scheme nested within a forward selection procedure. The LOOCV consists of fitting models with all observations except one, and then using the model to predict the value of the observation held out of model calibration. The process is repeated for all observations so that model prediction accuracy, here the root mean squared error (RMSE), can be independently assessed from all observations. This LOOCV approach was repeated for different models following a forward procedure that begins by selecting the most discriminant variable according to the LOOCV-RMSE criterion. The procedure then continues by selecting the second most discriminant variable and so on. To minimize the problem of model overfitting, we only kept explanatory variables that contribute to a decrease in relative RMSE (RMSE divided by the mean observed AGB) by more than 1 %. The selected lidar-AGB model was then used to predict AGB values over the landscape at 60 m resolution, to match the resolution of Landsat images.

Field plots AGB ranged from 80 to 577 Mgha-1 (mean of 315 Mgha-1), with a mean AGB of SIS, SES and OGS plots of 87, 291 and 328 Mgha-1, respectively. Among all the lidar metrics, the mean of top-of-canopy height (TCH, defined as the maximum height of 1 m resolution pixels) was the best predictor of field AGB estimates with a relative RMSE of 14 % (RMSE=45 Mgha-1; R2=0.85) at 0.5 ha scale (Fig. 2). Adding a second predictor did not reduce the relative RMSE by more than 1 % (Table S2). We thus kept TCH as a single predictor for our analyses resulting in the Eq. (4) for the lidar-AGB model: 4AGBL=4.30×TCH1.39. Using this lidar-AGB model, we predicted AGB over the whole landscape (Fig. 3a). The distribution of AGB values over the landscape was not normally distributed due to an over-representation of pixels with low AGB values. At the landscape scale, predicted AGB ranged from 0 to 681 Mgha-1 with a mean of 291 Mgha-1 (Fig. 3b), close to the mean AGB of the field plots.

Our forest and non-forest classification through time was highly accurate, with 90 % to 99 % of well-classified validation pixels in individual classified images (Table S3, Figs. S1–S2 in the Supplement). Figure 4a illustrates the resulting spatialized time series of non-forest-to-forest shifts over the study area and showed that most (83 %) of the landscape did not experience such a shift at 60 m resolution. About 78 % and 5 % of the study area remained permanently forested and non-forested over the 42-year study period, respectively. Most of the stable non-forested areas correspond to National Park building areas, including tourist shops and guesthouses or to continuously cleared areas such as camping locations. Over the 17 % remaining pixels that experienced a shift, we concentrated our analyses on the 4 % pixels (n=550; ca. 198 ha) that passed our selection procedure and that were well distributed over the landscape (Fig. 4b).

Considering the selected pixels that experienced a shift from non-forest to forest, we found that AGB accumulated non-linearly through time during the 42 first years of the succession (Fig. 5). A simple power model led to a pseudo-R2 of 0.66 and a power exponent of greater than 1, indicating an increase in the rate of AGB accumulation with recovery time. This model predicts an AGB gain of 143 Mgha-1 after 20 years of recovery and of 273 Mgha-1 after 40 years (spatialized gain in AGB is shown in Fig. S3). Using field AGB estimates at two different census dates from eight secondary forest plots that started regenerating during the study period (see Fig. S5), we showed that the observed rate of AGB accumulation was similar to the one predicted by our model and also tended to increase with forest age (in blue dots in Fig. 5). Finally, focusing on the 17 % pixels that experienced at least one shift from non-forest to forest since 1972, we estimated that the study area has stored a minimum AGB of 455 Gg, as observed in the 2017 lidar AGB map, equivalent to 214 GgC during the study period.

Using extensive field data, we have shown that forest AGB can be predicted with an error of 14 % at a 0.5 ha resolution using a single lidar metric, the mean top-of-canopy-height (TCH), a metric previously identified as a robust predictor of AGB (Asner and Mascaro, 2014). This error typically falls within the range of expected errors at this resolution (Zolkos et al., 2013). Using a robust metric selection approach, we showed that adding any other lidar metrics did not bring any additional information and that our single predictor did not show any saturation for large AGB values. Many studies have used a combination of several lidar metrics selected through less robust approaches, i.e., not through independent validation approaches such as our LOOCV procedure, potentially generating overfitting problems (Junttila et al., 2015). We here confirm, similarly to Asner et al. (2012) and Réjou-Méchain et al. (2015), that simple parsimonious models should be preferred, at least within a given tropical forest landscape. Due to a limited number of field plots in low-biomass areas, we were, however, unable to test whether model prediction error varied with forest stand AGB.

Using this lidar model, we predicted a mean AGB over the landscape of 291 Mgha-1, corresponding to a carbon density of 137 MgCha-1 (using a ratio of biomass to carbon conversion of 0.47; Thomas and Martin, 2012). Using a large network of field plots, a recent pantropical study suggested that Southeast Asian and African forests store significantly more carbon than Amazonian forests (Sullivan et al., 2017). However, in this latter study, Southeast Asian forests were only represented by field data from Indonesia and Malaysia where trees are known to be particularly tall (Coomes et al., 2017; Feldpausch et al., 2011; Jucker et al., 2018). Here, we found that our study forests stored significantly less carbon than forests in Indonesia and Malaysia, where the mean carbon density reached ca. 200 MgCha-1 (Sullivan et al., 2017), but as much as in Amazonian forests (mean of 140 MgCha-1; Sullivan et al., 2017), even when considering only old-growth forest plots. Whether the relatively low carbon density of our study site, compared to other Southeast Asian forests, is specific to our study area or representative of other Southeast Asian forests should be further investigated.

We found a very high spatial heterogeneity of AGB at the landscape scale with an apparent over-representation of low AGB values. This is most probably the consequence of past human activities in this area up to the establishment of the park that led to the present mosaic of secondary and mature forests. This result indicates that this area is currently likely to be a net carbon sink.

Combining classified images obtained from LTS and lidar data, we quantified the recovery rate of forests after land abandonment. As expected, we showed a significant increase of AGB with recovery time. After 20 years of recovery, our model predicts an AGB accumulation of 143 Mgha-1, an estimate slightly higher than the one predicted by Poorter et al. (2016a) in Neotropical secondary forests (122 Mgha-1). However, this difference can partly be explained by the inclusion of trees between 5 and 10 cm dbh in our study, contrary to the study of Poorter et al. (2016b). AGB accumulation in our study corresponds to a net carbon uptake of 3.4 MgCha-1yr-1 for the first 20 years. This rate of carbon accumulation is close to the pantropical estimate from Silver et al. (2000) and is similar to the default continent recovery rates given by the previous 2006 IPCC guidelines for national greenhouse gas inventories (IPCC, 2006). However, the 2019 refinement of these guidelines halved the recovery rate estimate for young Asian secondary rainforest (≤20 years; Requena Suarez et al., 2019; IPCC, 2019), suggesting that young secondary forests in Asia store carbon at a much lower rate than in Latin America or in Africa. This new estimate is derived from a very limited data set (seven chronosequences) that may not be representative of Asian tropical rainforests. Besides, these data included very small field plots (≤0.01 ha in size; Hiratsuka et al., 2006; Ewel et al., 1983), potentially leading to important sampling errors (Réjou-Méchain et al., 2014). Given the serious implications of these updated IPCC default rates for Asian countries, we here call for further testing of these new IPCC rates across tropical Asia.

Our model showed that a non-linear power model with an exponent of >1 best fit our data, suggesting an increase in the rate of carbon accumulation during the first 42 years of succession. Contrary to the results found by Feldpausch et al. (2007), the rates of AGB accumulation inferred with our approach provided estimates similar to those obtained from long-term field plot surveys (Fig. 5), validating the chronosequence approach in our study area. Assuming that the carbon recovery rate rapidly decreases after 50–60 years (Brown and Lugo, 1990; Silver et al., 2000), our result suggests a sigmoid relationship of AGB accumulation with time in our study area. Previous studies have shown different models of AGB accumulation with forest age. Saldarriaga et al. (1988) showed that the AGB of Neotropical forests from the upper Rio Negro increased linearly with stand age during the 40 years, while Jepsen (2006) reported a sigmoidal increase in AGB accumulation in Sarawak, Malaysia, as is likely the case in our study area. Finally, working on 41 Neotropical sites, Poorter et al. (2016a) assumed a logarithmic trend in the AGB accumulation over time, hence a decrease of the rate of carbon accumulation through time, probably because they investigated a longer time period. Selecting the sites of Poorter et al. (2016a, b) that had at least 10 observations over the first 44 years (n=21 out of 28 sites, i.e., excluding 7 sites for which model fitting was not possible), site-specific power models revealed that two-thirds of the sites displayed a power exponent of <1 and one-third showed an exponent of >1 (Fig. S4). Thus, the accumulation of AGB with age follows different trends across sites, as already highlighted in previous studies (Kennard et al., 2002; Poorter et al., 2016a; Ray and Brown, 2006; Ruiz et al., 2005; Silver et al., 2000; Toledo and Salick, 2006). Understanding how these trends vary according to abiotic factors (e.g., soil type, rainfall), species assemblage and diversity, and prioritizing effects such as types of land use and land management existing before forest recolonization, constitute an important avenue of research (Chazdon, 2014; McMahon et al., 2019).

Our analysis was based on a forest/non-forest classification through time and our independent validation suggested high overall accuracy (90 % to 99 %), similar to that reported by other studies using Landsat data classification in boreal systems (Bolton et al., 2015; White et al., 2018). Furthermore, our estimate of forest age using this approach was highly consistent with our expectations. Indeed, using our forest plots, we found that the SES and SIS forest stages lasted on average 40 years (range 38–42) and 13 years (range 8–20), respectively, hence very close to suggestions of Chanthorn et al. (2017; Fig. S5). However, our overall approach cannot be replicated easily in human-occupied areas. Indeed, human disturbances lead to forest degradation that, in contrast to deforestation, is not captured by the Landsat signal, so that, when combined with a reference AGB map, natural carbon recovery potential could be seriously underestimated. Because our study area was protected from human disturbances during the study period, we were in very favorable conditions to estimate forest carbon recovery rates and strongly encourage researchers benefiting from similar conditions to replicate our analyses in other study sites.