Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana

LiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution. In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data. The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of RF regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset.

[1]  Michael A. Lefsky,et al.  Modeling canopy height in a savanna ecosystem using spaceborne lidar waveforms , 2014 .

[2]  Christiane Schmullius,et al.  Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape , 2012, Remote. Sens..

[3]  R. Houghton,et al.  Characterizing 3D vegetation structure from space: Mission requirements , 2011 .

[4]  Michael A. Wulder,et al.  Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia , 2014, Remote. Sens..

[5]  Johannes Heinzel,et al.  Accuracy of vegetation height and terrain elevation derived from ICESat/GLAS in forested areas , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Valéry Gond,et al.  Broad-scale spatial pattern of forest landscape types in the Guiana Shield , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[7]  W. Cohen,et al.  Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height , 2002 .

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

[9]  David J. Harding,et al.  SRTM C-band and ICESat Laser Altimetry Elevation Comparisons as a Function of Tree Cover and Relief , 2006 .

[10]  A. McBratney,et al.  Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging , 1995 .

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

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

[13]  Birgit Peterson,et al.  Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR , 2014, Remote. Sens..

[14]  Jean-Michel Poggi,et al.  Variable selection using random forests , 2010, Pattern Recognit. Lett..

[15]  Nicolas Baghdadi,et al.  Canopy Height Estimation in French Guiana with LiDAR ICESat/GLAS Data Using Principal Component Analysis and Random Forest Regressions , 2014, Remote. Sens..

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

[17]  Graham D. Farquhar,et al.  Determinants of maximum tree height in Eucalyptus species along a rainfall gradient in Victoria, Australia , 2014 .

[18]  J. Chambers,et al.  Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.

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

[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]  Nicolas Baghdadi,et al.  External Geophysics, Climate and Environment Assessment of C-band SRTM DEM in a dense equatorial forest zone , 2005 .

[22]  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 .

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

[24]  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 .

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

[26]  Simone R. Freitas,et al.  Relationships between forest structure and vegetation indices in Atlantic Rainforest , 2005 .

[27]  S. Goetz,et al.  Importance of biomass in the global carbon cycle , 2009 .

[28]  Nicolas Baghdadi,et al.  Testing Different Methods of Forest Height and Aboveground Biomass Estimations From ICESat/GLAS Data in Eucalyptus Plantations in Brazil , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[30]  Michael A. Lefsky,et al.  Validation of the ICEsat vegetation product using crown-area-weighted mean height derived using crown delineation with discrete return lidar data , 2008 .

[31]  Guoqing Sun,et al.  The Uncertainty of Plot-Scale Forest Height Estimates from Complementary Spaceborne Observations in the Taiga-Tundra Ecotone , 2014, Remote. Sens..

[32]  A. Baccini,et al.  Mapping forest canopy height globally with spaceborne lidar , 2011 .

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

[34]  Warren B. Cohen,et al.  Relationship between LiDAR-derived forest canopy height and Landsat images , 2010 .

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

[36]  R. Dubayah,et al.  Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships , 2003 .

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

[38]  Budiman Minasny,et al.  Analysis and prediction of soil properties using local regression-kriging , 2012 .

[39]  R. Olea.  Geostatistics for Natural Resources Evaluation By Pierre Goovaerts, Oxford University Press, Applied Geostatistics Series, 1997, 483 p., hardcover, $65 (U.S.), ISBN 0-19-511538-4 , 1999 .

[40]  K. Moffett,et al.  Remote Sens , 2015 .

[41]  G. Heuvelink,et al.  A generic framework for spatial prediction of soil variables based on regression-kriging , 2004 .

[42]  Mehrez Zribi,et al.  Evaluation of ALOS/PALSAR L-Band Data for the Estimation of Eucalyptus Plantations Aboveground Biomass in Brazil , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[43]  M. Lefsky,et al.  Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park, Gabon: Overcoming problems of high biomass and persistent cloud , 2012 .