Mapping forest canopy height globally with spaceborne lidar

[1] Data from spaceborne light detection and ranging (lidar) opens the possibility to map forest vertical structure globally. We present a wall-to-wall, global map of canopy height at 1-km spatial resolution, using 2005 data from the Geoscience Laser Altimeter System (GLAS) aboard ICESat (Ice, Cloud, and land Elevation Satellite). A challenge in the use of GLAS data for global vegetation studies is the sparse coverage of lidar shots (mean = 121 data points/degree2 for the L3C campaign). However, GLAS-derived canopy height (RH100) values were highly correlated with other, more spatially dense, ancillary variables available globally, which allowed us to model global RH100 from forest type, tree cover, elevation, and climatology maps. The difference between the model predicted RH100 and footprint level lidar-derived RH100 values showed that error increased in closed broadleaved forests such as the Amazon, underscoring the challenges in mapping tall (>40 m) canopies. The resulting map was validated with field measurements from 66 FLUXNET sites. The modeled RH100 versus in situ canopy height error (RMSE = 6.1 m, R2 = 0.5; or, RMSE = 4.4 m, R2 = 0.7 without 7 outliers) is conservative as it also includes measurement uncertainty and sub pixel variability within the 1-km pixels. Our results were compared against a recently published canopy height map. We found our values to be in general taller and more strongly correlated with FLUXNET data. Our map reveals a global latitudinal gradient in canopy height, increasing towards the equator, as well as coarse forest disturbance patterns.

[1]  C. Kummerow,et al.  The Tropical Rainfall Measuring Mission (TRMM) Sensor Package , 1998 .

[2]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[3]  G. Powell,et al.  Terrestrial Ecoregions of the World: A New Map of Life on Earth , 2001 .

[4]  W. Cohen,et al.  Lidar remote sensing of above‐ground biomass in three biomes , 2002 .

[5]  Thomas A. Hennig,et al.  The Shuttle Radar Topography Mission , 2001, Digital Earth Moving.

[6]  R. Dubayah,et al.  Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest , 2002 .

[7]  H. Zwally,et al.  Overview of ICESat's Laser Measurements of Polar Ice, Atmosphere, Ocean, and Land , 2002 .

[8]  H. Zwally,et al.  Derivation of Range and Range Distributions From Laser Pulse Waveform Analysis for Surface Elevations, Roughness, Slope, and Vegetation Heights , 2012 .

[9]  J. Townshend,et al.  Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm , 2003 .

[10]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[11]  W. Cohen,et al.  Estimates of forest canopy height and aboveground biomass using ICESat , 2005 .

[12]  Michael A. Lefsky,et al.  Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity , 2005 .

[13]  D. Harding,et al.  ICESat waveform measurements of within‐footprint topographic relief and vegetation vertical structure , 2005 .

[14]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[15]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[16]  K. Ranson,et al.  Predicting lidar measured forest vertical structure from multi-angle spectral data , 2006 .

[17]  Robert G. Knox,et al.  The use of waveform lidar to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire , 2006 .

[18]  S. Goetz,et al.  Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA , 2006 .

[19]  R. Nelson,et al.  Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. , 2008 .

[20]  S. Goetz,et al.  Reply to Comment on ‘A first map of tropical Africa’s above-ground biomass derived from satellite imagery’ , 2008, Environmental Research Letters.

[21]  K. Ranson,et al.  Forest vertical structure from GLAS : An evaluation using LVIS and SRTM data , 2008 .

[22]  George C. Hurtt,et al.  Using lidar data and a height-structured ecosystem model to estimate forest carbon stocks and fluxes over mountainous terrain , 2008 .

[23]  D. Baldocchi ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems , 2008 .

[24]  Peter R. J. North,et al.  Vegetation height estimates for a mixed temperate forest using satellite laser altimetry , 2008 .

[25]  Characterizing Vegetation 3D structure Globally using Spaceborne Lidar and Radar. , 2008 .

[26]  Mahta Moghaddam,et al.  Mapping vegetated wetlands of Alaska using L-band radar satellite imagery , 2009 .

[27]  David B. Lindenmayer,et al.  Re-evaluation of forest biomass carbon stocks and lessons from the world's most carbon-dense forests , 2009, Proceedings of the National Academy of Sciences.

[28]  O. Hagollea,et al.  Quality assessment and improvement of temporally composited products of remotely sensed imagery by combination of VEGETATION 1 and 2 images , 2009 .

[29]  Michael A. Wulder,et al.  Estimating forest canopy height and terrain relief from GLAS waveform metrics , 2010 .

[30]  Kenneth B. Pierce,et al.  Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches , 2010 .

[31]  Qi Chen Retrieving vegetation height of forests and woodlands over mountainous areas in the Pacific Coast region using satellite laser altimetry , 2010 .

[32]  M. Lefsky A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System , 2010 .

[33]  Wenze Yang,et al.  Physically based vertical vegetation structure retrieval from ICESat data: Validation using LVIS in White Mountain National Forest, New Hampshire, USA , 2011 .

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

[35]  Wenze Yang,et al.  Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model , 2011 .