Estimating aboveground carbon density and its uncertainty in Borneo's structurally complex tropical forests using airborne laser scanning

Abstract. Borneo contains some of the world's most biodiverse and carbon-dense tropical forest, but this 750 000 km2 island has lost 62 % of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognizing the ecosystem services they provide, including their ability to store and sequester carbon. Airborne laser scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into statewide assessments of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and compositionally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rainforests of Sabah on the island of Borneo, we develop a simple yet general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbon stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalized and effective approach for mapping forest carbon stocks in Borneo and underpins ongoing efforts to safeguard and facilitate the restoration of its unique tropical forests.

[1]  Michael J. Crawley,et al.  The R book , 2022 .

[2]  Md. Danesh Miah.  Reducing Emissions from Deforestation and Forest Degradation (REDD+) , 2020, Encyclopedia of the UN Sustainable Development Goals.

[3]  M. Keller,et al.  Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes , 2018, Biogeosciences.

[4]  Michele Dalponte,et al.  Topography shapes the structure, composition and function of tropical forest landscapes , 2018, Ecology letters.

[5]  M. Herold,et al.  Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR , 2017 .

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

[7]  Alexis Achim,et al.  Removing bias from LiDAR-based estimates of canopy height: Accounting for the effects of pulse density and footprint size , 2017 .

[8]  David A. Coomes,et al.  Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches , 2017, Remote. Sens..

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

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

[11]  Pablo Pacheco,et al.  Rapid conversions and avoided deforestation: examining four decades of industrial plantation expansion in Borneo , 2016, Scientific Reports.

[12]  J. Ghazoul.  Dipterocarp Biology, Ecology, and Conservation , 2016 .

[13]  D. Coomes,et al.  Incorporating Canopy Cover for Airborne-Derived Assessments of Forest Biomass in the Tropical Forests of Cambodia , 2016, PloS one.

[14]  R. Nilus,et al.  Mapping the structure of Borneo's tropical forests across a degradation gradient , 2016 .

[15]  Arief Wijaya,et al.  An integrated pan‐tropical biomass map using multiple reference datasets , 2016, Global change biology.

[16]  Liviu Theodor Ene,et al.  Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass , 2016 .

[17]  R. Houghton,et al.  A role for tropical forests in stabilizing atmospheric CO 2 , 2015 .

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

[19]  Geoffrey G. Parker,et al.  The importance of spatial detail: Assessing the utility of individual crown information and scaling approaches for lidar-based biomass density estimation , 2015 .

[20]  H. Beeckman,et al.  Seeing Central African forests through their largest trees , 2015, Scientific Reports.

[21]  R. Valentini,et al.  Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels , 2015 .

[22]  M. Herold,et al.  Nondestructive estimates of above‐ground biomass using terrestrial laser scanning , 2015 .

[23]  Norman A. Bourg,et al.  CTFS‐ForestGEO: a worldwide network monitoring forests in an era of global change , 2015, Global change biology.

[24]  Terje Gobakken,et al.  Modeling Aboveground Biomass in Dense Tropical Submontane Rainforest Using Airborne Laser Scanner Data , 2015, Remote. Sens..

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

[26]  Roberta E. Martin,et al.  Targeted carbon conservation at national scales with high-resolution monitoring , 2014, Proceedings of the National Academy of Sciences.

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

[28]  Hideki Saito,et al.  Estimating above-ground biomass of tropical rainforest of different degradation levels in Northern Borneo using airborne LiDAR , 2014 .

[29]  D. Sheil,et al.  Four Decades of Forest Persistence, Clearance and Logging on Borneo , 2014, PloS one.

[30]  R. Valentini,et al.  Biodiversity Mapping in a Tropical West African Forest with Airborne Hyperspectral Data , 2014, PloS one.

[31]  S. Hubbell,et al.  Improving estimates of biomass change in buttressed trees using tree taper models , 2014 .

[32]  Txomin Hermosilla,et al.  Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates , 2014 .

[33]  Mark C. Vanderwel,et al.  Methods to estimate aboveground wood productivity from long-term forest inventory plots , 2014 .

[34]  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.

[35]  D. Sabatier,et al.  Revisiting a universal airborne light detection and ranging approach for tropical forest carbon mapping: scaling-up from tree to stand to landscape , 2014, Oecologia.

[36]  M. Struebig,et al.  Quantifying the Biodiversity Value of Repeatedly Logged Rainforests: Gradient and Comparative Approaches from Borneo , 2013 .

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

[38]  Pete Watt,et al.  The influence of LiDAR pulse density and plot size on the accuracy of New Zealand plantation stand volume equations , 2013, New Zealand Journal of Forestry Science.

[39]  Louis V. Verchot,et al.  Generic allometric models including height best estimate forest biomass and carbon stocks in Indonesia , 2013 .

[40]  Gregory P. Asner,et al.  Carbon emissions from forest conversion by Kalimantan oil palm plantations , 2013 .

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

[42]  Terje Gobakken,et al.  Inference for lidar-assisted estimation of forest growing stock volume , 2013 .

[43]  Zhenyuan Lu,et al.  The taxonomic name resolution service: an online tool for automated standardization of plant names , 2013, BMC Bioinformatics.

[44]  D. A. King,et al.  What controls tropical forest architecture: testing environmental, structural and floristic drivers , 2012 .

[45]  M. d'Oliveira,et al.  Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon , 2012 .

[46]  Roberta E. Martin,et al.  Carnegie Airborne Observatory-2: Increasing science data dimensionality via high-fidelity multi-sensor fusion , 2012 .

[47]  Juilson Jubanski,et al.  Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR , 2012 .

[48]  M. Phua,et al.  Monitoring of Deforestation Rate and Trend in Sabah between 1990 and 2008 Using Multitemporal Landsat Data , 2012 .

[49]  Göran Ståhl,et al.  Assessing the accuracy of regional LiDAR-based biomass estimation using a simulation approach , 2012 .

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

[51]  J. Terborgh,et al.  Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate , 2012 .

[52]  Joanne C. White,et al.  Lidar sampling for large-area forest characterization: A review , 2012 .

[53]  Roberta E. Martin,et al.  Topo-edaphic controls over woody plant biomass in South African savannas , 2012 .

[54]  Kyle G. Dexter,et al.  Using functional traits and phylogenetic trees to examine the assembly of tropical tree communities , 2012 .

[55]  Deborah Lawrence,et al.  Committed carbon emissions, deforestation, and community land conversion from oil palm plantation expansion in West Kalimantan, Indonesia , 2012, Proceedings of the National Academy of Sciences.

[56]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

[57]  Lenore Fahrig,et al.  A large-scale forest fragmentation experiment: the Stability of Altered Forest Ecosystems Project , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[58]  S. Popescu,et al.  Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level , 2011 .

[59]  D. Roberts,et al.  Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors , 2011 .

[60]  Jorge Nocedal,et al.  Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization” , 2011, TOMS.

[61]  Arun Agrawal,et al.  Reducing Emissions from Deforestation and Forest Degradation , 2011 .

[62]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[63]  Sean C. Thomas,et al.  A Reassessment of Carbon Content in Tropical Trees , 2011, PloS one.

[64]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.

[65]  D. Burslem,et al.  Nutrient limitation of tree seedling growth in three soil types found in Sabah. , 2011 .

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

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

[68]  M. Lefsky,et al.  Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California , 2010 .

[69]  E. Rastetter,et al.  Estimating Uncertainty in Ecosystem Budget Calculations , 2010, Ecosystems.

[70]  Alan H. Strahler,et al.  Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from lidar remote sensing , 2009 .

[71]  R. DeFries,et al.  A Contemporary Assessment of Change in Humid Tropical Forests , 2009, Conservation biology : the journal of the Society for Conservation Biology.

[72]  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 .

[73]  J. Slik,et al.  Wood Density as a Conservation Tool: Quantification of Disturbance and Identification of Conservation‐Priority Areas in Tropical Forests , 2008, Conservation biology : the journal of the Society for Conservation Biology.

[74]  Terje Gobakken,et al.  Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data , 2008 .

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

[76]  D. Burslem,et al.  Nutrient fluxes via litterfall and leaf litter decomposition vary across a gradient of soil nutrient supply in a lowland tropical rain forest , 2006, Plant and Soil.

[77]  D. Burslem,et al.  Liana habitat associations and community structure in a Bornean lowland tropical forest , 2006, Plant Ecology.

[78]  P. Erskine,et al.  Restoration of Degraded Tropical Forest Landscapes , 2005, Science.

[79]  Richard Condit,et al.  Error propagation and scaling for tropical forest biomass estimates. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[80]  Y. Malhi,et al.  Spatial patterns and recent trends in the climate of tropical rainforest regions. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[81]  R. Dubayah,et al.  Estimation of tropical forest structural characteristics using large-footprint lidar , 2002 .

[82]  W. Cohen,et al.  Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests , 1999 .

[83]  R. Walsh,et al.  The ecoclimatology of Danum, Sabah, in the context of the world's rainforest regions, with particular reference to dry periods and their impact. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[84]  Phillips,et al.  Changes in the carbon balance of tropical forests: evidence from long-term plots , 1998, Science.

[85]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

[86]  Carl-Erik Särndal,et al.  Model Assisted Survey Sampling , 1997 .

[87]  Ross Nelson,et al.  Estimating forest biomass and volume using airborne laser data , 1988 .

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

[89]  Roberta E. Martin,et al.  Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo , 2018 .

[90]  B. Vira,et al.  Forests, Trees and Landscapes for Food Security and Nutrition: A Global Assessment Report , 2015 .

[91]  T. Sunderland,et al.  1. Forests, Trees and Landscapes for Food Security and Nutrition , 2015 .

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

[93]  Damaris Zurell,et al.  Collinearity: a review of methods to deal with it and a simulation study evaluating their performance , 2013 .

[94]  Charles H. Cannon,et al.  Environmental correlates of tree biomass, basal area, wood specific gravity and stem density gradients in Borneo's tropical forests , 2010 .

[95]  David A. Coomes,et al.  Global wood density database , 2009 .

[96]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[97]  Sabah. Forest Dept,et al.  A handbook to Kabili-Sepilok Forest reserve , 1973 .