Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: A regional‐scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets

[1] A method is presented to characterize forest stand heights in a 110,000 km2 region in the eastern United States surrounding the Chesapeake Bay area, driven by a statistical fusion model solely based on remote sensing data. The predicted map was tested against ground survey data from the Forest Inventory and Analysis (FIA) plot network. Input data to the model were 2003 medium footprint lidar data from the Laser Vegetation Imaging Sensor (LVIS) sensor, interferometric radar data from the 2000 Shuttle Radar Topography Mission (SRTM), 1999–2001 Landsat ETM+ data, and ancillary data sets of land cover and canopy density developed for the 2001 National Land Cover Database. In the presented approach, the interferometric synthetic aperture radar (InSAR), optical, and ancillary data sets were masked to the forested areas of the study region and used to segment the raster data stack. The generated image objects closely represented quasi-homogenous forest stands. For a small region in the study area covered by an LVIS acquisition, LVIS lidar data were then used within the established segments to extract lidar-based mean forest stand heights. Subsequently these LVIS mean stand heights were used as the response variable to the statistical prediction model (randomForest) which had segment-based metrics like mean InSAR height (derived from SRTM minus ground digital elevation model data from the National Elevation Data set), mean optical reflectance (derived from Landsat ETM+ Tassled Cap Data), and ancillary metrics as predictive variables. The model developed over the area where LVIS data were available was then applied to map the entire study region. Independent validation of the model was performed in two ways. First, splitting of the model data stack into training and independent testing populations, i.e., testing on LVIS data. This test was deemed to describe the model performance within the LVIS swath. Second, predicted heights were compared to plot height metrics derived from FIA data in the entire study region, thus testing the validity of the model across the larger study area. Results, which are somewhat tampered by the time disconnect between the various data collections, showed the validity and usefulness of this approach. Independent LVIS testing resulted in a correlation coefficient r = 0.83 with an RMSE of 3.0 m (9% error), independent FIA data tested with r = 0.71 with an RMSE of 4.4 m (13% error).

[1]  Ranga B. Myneni,et al.  Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks , 2003 .

[2]  Keqi Zhang,et al.  Mapping Height and Biomass of Mangrove Forests in Everglades National Park with SRTM Elevation Data , 2006 .

[3]  K. Ranson,et al.  Mapping of boreal forest biomass using SAR , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[4]  M. Dobson,et al.  The use of Imaging radars for ecological applications : A review , 1997 .

[5]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[6]  Fawwaz T. Ulaby,et al.  Using Mimics To Model L-band Multiangle and Multitemporal Backscatter From A Walnut Orchard , 1990 .

[7]  J. V. Soares,et al.  Distribution of aboveground live biomass in the Amazon basin , 2007 .

[8]  W. Cohen,et al.  Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA , 1999 .

[9]  Irena Hajnsek,et al.  Height biomass allometry in temperate forests , 2003 .

[10]  Irena Hajnsek,et al.  Forest biomass estimation using polarimetric SAR interferometry , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[11]  Josef Kellndorfer,et al.  Quality assessment of SRTM C- and X-band interferometric data: Implications for the retrieval of vegetation canopy height , 2007 .

[12]  Rick L. Lawrence,et al.  Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest) , 2006 .

[13]  John D. Vona,et al.  Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets , 2004 .

[14]  Scott J. Goetz,et al.  Observed and predicted responses of plant growth to climate across Canada , 2005 .

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

[16]  M. Moghaddam,et al.  Vegetation characteristics and underlying topography from interferometric radar , 1996 .

[17]  J. Townshend,et al.  Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data , 2008, Proceedings of the National Academy of Sciences.

[18]  Thuy Le Toan,et al.  Dependence of radar backscatter on coniferous forest biomass , 1992, IEEE Trans. Geosci. Remote. Sens..

[19]  Limin Yang,et al.  Development of a 2001 National land-cover database for the United States , 2004 .

[20]  Kamal Sarabandi,et al.  Estimation of forest biophysical characteristics in Northern Michigan with SIR-C/X-SAR , 1995, IEEE Trans. Geosci. Remote. Sens..

[21]  Charles Gordon Brown.  Tree height estimation using Shuttle Radar Topography Mission and ancillary data , 2003 .

[22]  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.

[23]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

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

[25]  W. Walker,et al.  An empirical InSAR-optical fusion approach to mapping vegetation canopy height , 2007 .

[26]  W. Walker,et al.  Evaluation of the Horizontal Resolution of SRTM Elevation Data , 2006 .

[27]  Ralph Dubayah,et al.  Validation of Vegetation Canopy Lidar sub-canopy topography measurements for a dense tropical forest , 2002 .

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

[29]  W. Walker,et al.  Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems , 2005 .

[30]  B. Law,et al.  Forest Attributes from Radar Interferometric Structure and Its Fusion with Optical Remote Sensing , 2004 .

[31]  Kamal Sarabandi,et al.  An evaluation of the JPL TOPSAR for extracting tree heights , 2000, IEEE Trans. Geosci. Remote. Sens..

[32]  Irena Hajnsek,et al.  Applying a common allometric equation to convert forest height from Pol-InSAR data to forest biomass , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[33]  Limin Yang,et al.  A STRATEGY FOR ESTIMATING TREE CANOPY DENSITY USING LANDSAT 7 ETM+ AND HIGH RESOLUTION IMAGES OVER LARGE AREAS , 2001 .

[34]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[35]  Heiko Balzter,et al.  Deriving forest characteristics using polarimetric InSAR measurements and models , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[36]  C. Tucker,et al.  A large carbon sink in the woody biomass of Northern forests , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[37]  J. Blair,et al.  The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography , 1999 .

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

[39]  Guoqing Sun,et al.  Mapping of boreal forest biomass from spaceborne synthetic aperture radar , 1997 .

[40]  I. Hajnsek,et al.  Height-biomass allometry in temperate forests performance accuracy of height-biomass allometry , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[41]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[42]  Jennifer L. Dungan,et al.  Forest variable estimation from fusion of SAR and multispectral optical data , 2002, IEEE Trans. Geosci. Remote. Sens..

[43]  S. Goetz,et al.  Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[44]  M. D. Nelson,et al.  Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information , 2008 .

[45]  N. Cabrol,et al.  The High‐Lakes Project , 2009 .

[46]  R. Dubayah,et al.  Lidar Remote Sensing for Forestry , 2000, Journal of Forestry.

[47]  Fawwaz Ulaby,et al.  Relating Polaization Phase Difference of SAR Signals to Scene Properties , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[48]  Kamal Sarabandi,et al.  Estimation of red pine tree height using Shuttle Radar Topography Mission and ancillary data , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

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

[50]  M. Craig Dobson,et al.  Vegetation height derivation from Shuttle Radar Topography Mission data in southeast Georgia, USA , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.