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 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+.

[1]  S. McMahon,et al.  The importance and challenges of detecting changes in forest mortality rates , 2019, Ecosphere.

[2]  J. Chave,et al.  Estimating the aboveground biomass in an old secondary forest on limestone in the Moluccas, Indonesia: Comparing locally developed versus existing allometric models , 2017 .

[3]  F. Bongers,et al.  Biomass is the main driver of changes in ecosystem process rates during tropical forest succession. , 2015, Ecology.

[4]  G. B. Williamson,et al.  Demographic Drivers of Aboveground Biomass Dynamics During Secondary Succession in Neotropical Dry and Wet Forests , 2017, Ecosystems.

[5]  Florian Hartig,et al.  Structure and community composition in a tropical forest suggest a change of ecological processes during stand development , 2017 .

[6]  Harold S. J. Zald,et al.  Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure , 2014 .

[7]  Francis E. Putz,et al.  Critical need for new definitions of “forest” and “forest degradation” in global climate change agreements , 2009 .

[8]  Virpi Junttila,et al.  Linear Models for Airborne-Laser-Scanning-Based Operational Forest Inventory With Small Field Sample Size and Highly Correlated LiDAR Data , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[9]  G. Asner,et al.  Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric , 2014 .

[10]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[11]  S. Popescu,et al.  Lidar remote sensing of forest biomass : A scale-invariant estimation approach using airborne lasers , 2009 .

[12]  J. Chave,et al.  Towards a Worldwide Wood Economics Spectrum 2 . L E a D I N G D I M E N S I O N S I N W O O D F U N C T I O N , 2022 .

[13]  David Kenfack,et al.  Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks , 2014 .

[14]  Juha Hyyppä,et al.  Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data , 2005 .

[15]  F. Achard,et al.  Determination of tropical deforestation rates and related carbon losses from 1990 to 2010 , 2014, Global change biology.

[16]  Sassan Saatchi,et al.  Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them , 2019, Surveys in Geophysics.

[17]  R. Chazdon,et al.  Demographic drivers of tree biomass change during secondary succession in northeastern Costa Rica. , 2015, Ecological applications : a publication of the Ecological Society of America.

[18]  Michael A. Lefsky,et al.  Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System , 2009 .

[19]  Susan G. Letcher,et al.  Rates of change in tree communities of secondary Neotropical forests following major disturbances , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[20]  G. Hurtt,et al.  Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica , 2009 .

[21]  S. Goetz,et al.  Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps , 2013, Carbon Balance and Management.

[22]  J. Terborgh,et al.  Tree height integrated into pantropical forest biomass estimates , 2012 .

[23]  Susan G. Letcher,et al.  Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics , 2016, Science Advances.

[24]  S. Goetz,et al.  A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing , 2013 .

[25]  Arief Wijaya,et al.  Carbon storage potential in degraded forests of Kalimantan, Indonesia , 2018, Environmental Research Letters.

[26]  Douglas K. Bolton,et al.  Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using Landsat time-series and airborne lidar data , 2015 .

[27]  M. Toledo,et al.  Secondary Succession and Indigenous Management in Semideciduous Forest Fallows of the Amazon Basin 1 , 2006 .

[28]  Juha Hyyppä,et al.  Confirmation of post-harvest spectral recovery from Landsat time series using measures of forest cover and height derived from airborne laser scanning data , 2018, Remote Sensing of Environment.

[29]  Nicholas C. Coops,et al.  Tree species classification in subtropical forests using small-footprint full-waveform LiDAR data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[30]  B. Hérault,et al.  Drivers of biomass recovery in a secondary forested landscape of West Africa , 2019, Forest Ecology and Management.

[31]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[32]  Ariel E. Lugo,et al.  Tropical forests as sinks of atmospheric carbon , 1992 .

[33]  Maxim Neumann,et al.  Detecting tropical forest biomass dynamics from repeated airborne lidar measurements , 2013 .

[34]  M. Jepsen.  Above-ground carbon stocks in tropical fallows, Sarawak, Malaysia , 2006 .

[35]  B. Brown,et al.  Seed Ecology of Woody Species in a Caribbean Dry Forest , 1994 .

[36]  Yasushi Morikawa,et al.  Biomass recovery of naturally regenerated vegetation after the 1998 forest fire in East Kalimantan, Indonesia , 2006 .

[37]  R. Chazdon.  Second growth : the promise of tropical forest regeneration in an age of deforestation , 2014 .

[38]  David O. Wallin,et al.  Two Decades of Carbon Flux from Forests of the Pacific Northwest , 1996 .

[39]  A. Lugo,et al.  Tropical secondary forests , 1990, Journal of Tropical Ecology.

[40]  P. Leimgruber,et al.  Using Relative Abundance Indices from Camera-Trapping to Test Wildlife Conservation Hypotheses – An Example from Khao Yai National Park, Thailand , 2011 .

[41]  Frédéric Achard,et al.  Change in tropical forest cover of Southeast Asia from 1990 to 2010 , 2013 .

[42]  C. Favier,et al.  Viewing tropical forest succession as a three-dimensional dynamical system , 2016, Theoretical Ecology.

[43]  Howard Rogers.  Tropical Forest Diversity and Dynamism , 2005 .

[44]  T. Yumoto,et al.  Characteristics of hornbill-dispersed fruits in a tropical seasonal forest in Thailand , 2004, Bird Conservation International.

[45]  G. B. Williamson,et al.  Contrasting community compensatory trends in alternative successional pathways in central Amazonia , 2011 .

[46]  G. Baskerville.  Use of Logarithmic Regression in the Estimation of Plant Biomass , 1972 .

[47]  Nicholas C. Coops,et al.  Lidar plots — a new large-area data collection option: context, concepts, and case study , 2012 .

[48]  R. Chazdon,et al.  Vegetation Structure, Composition, and Species Richness Across a 56‐year Chronosequence of Dry Tropical Forest on Providencia Island, Colombia 1 , 2005 .

[49]  Klaus Scipal,et al.  In Situ Reference Datasets From the TropiSAR and AfriSAR Campaigns in Support of Upcoming Spaceborne Biomass Missions , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[50]  J. Terborgh,et al.  Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites , 2014, Global ecology and biogeography : a journal of macroecology.

[51]  D. A. King,et al.  Height-diameter allometry of tropical forest trees , 2010 .

[52]  Michele Dalponte,et al.  Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data , 2017 .

[53]  J. Chave,et al.  biomass: an r package for estimating above‐ground biomass and its uncertainty in tropical forests , 2017 .

[54]  Susan G. Letcher,et al.  Biomass resilience of Neotropical secondary forests , 2016, Nature.

[55]  Florian Siegert,et al.  Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[56]  Craig C. Brelsford,et al.  Estimating aboveground carbon density and its uncertainty in Borneo's structurally complex tropical forests using airborne laser scanning , 2018, Biogeosciences.

[57]  G. B. Williamson,et al.  Successional dynamics in Neotropical forests are as uncertain as they are predictable , 2015, Proceedings of the National Academy of Sciences.

[58]  Ariel E. Lugo,et al.  The Potential for Carbon Sequestration Through Reforestation of Abandoned Tropical Agricultural and Pasture Lands , 2000 .

[59]  F. Putz,et al.  Effect of disturbance intensity on regeneration mechanisms in a tropical dry forest , 2002 .

[60]  Sandra A. Brown,et al.  Monitoring and estimating tropical forest carbon stocks: making REDD a reality , 2007 .

[61]  B. Nelson,et al.  Improved allometric models to estimate the aboveground biomass of tropical trees , 2014, Global change biology.

[62]  J. Chave,et al.  Slow rate of secondary forest carbon accumulation in the Guianas compared with the rest of the Neotropics. , 2019, Ecological applications : a publication of the Ecological Society of America.

[63]  Sean C. Thomas,et al.  Diversity and carbon storage across the tropical forest biome , 2017, Scientific Reports.

[64]  Jeffrey G. Masek,et al.  Estimating forest carbon fluxes in a disturbed southeastern landscape: Integration of remote sensing, forest inventory, and biogeochemical modeling , 2006 .

[65]  M. Herold,et al.  Estimating aboveground net biomass change for tropical and subtropical forests: Refinement of IPCC default rates using forest plot data , 2019, Global change biology.

[66]  G. Asner,et al.  A universal airborne LiDAR approach for tropical forest carbon mapping , 2011, Oecologia.

[67]  O. Phillips,et al.  Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest , 2015 .

[68]  W. Salas,et al.  Baseline Map of Carbon Emissions from Deforestation in Tropical Regions , 2012, Science.

[69]  E. Leigh,et al.  Tropical forest diversity and dynamism : findings from a large-scale plot network , 2004 .

[70]  W. Cohen,et al.  Using Landsat-derived disturbance history (1972-2010) to predict current forest structure , 2012 .

[71]  Sassan Saatchi,et al.  Forest degradation and biomass loss along the Chocó region of Colombia , 2019, Carbon Balance and Management.

[72]  Sean C. Thomas,et al.  Carbon Content of Tree Tissues: A Synthesis , 2012 .

[73]  E. Næsset.  Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data , 2002 .

[74]  J. Saldarriaga,et al.  LONG-TERM CHRONOSEQUENCE OF FOREST SUCCESSION IN THE UPPER RIO NEGRO OF COLOMBIA AND VENEZUELA , 1988 .

[75]  W. Cohen,et al.  Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics , 2014 .

[76]  S. Goetz,et al.  Carbon Balance and Management , 2009 .

[77]  G. Powell,et al.  High-resolution forest carbon stocks and emissions in the Amazon , 2010, Proceedings of the National Academy of Sciences.

[78]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[79]  T. Feldpausch,et al.  Secondary forest growth deviation from chronosequence predictions in central Amazonia , 2007 .

[80]  Susan G. Letcher,et al.  Rapid Recovery of Biomass, Species Richness, and Species Composition in a Forest Chronosequence in Northeastern Costa Rica , 2009 .

[81]  Thomas R. Crow,et al.  Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA , 2004 .

[82]  Nicholas C. Coops,et al.  Forest recovery trends derived from Landsat time series for North American boreal forests , 2016 .