Slope-adaptive waveform metrics of large footprint lidar for estimation of forest aboveground biomass

Abstract The effect of terrain slope on large footprint lidar waveforms is still a great challenge to the accurate mapping of forest aboveground biomass (AGB), although previous studies have made significant progress for slopes lower than 15°. A new method was proposed to calculate slope-adaptive waveform metrics for forest AGB mapping over mountainous areas. First, a theoretical model was proposed to calculate lidar waveforms of bare ground with known terrain slopes. The forest waveform and its corresponding calculated bare ground waveform were then aligned by the signal ending points. Their energy quartiles were extracted separately by taking the signal ending points as references. The height differences between the corresponding energy quartiles were defined as slope-adaptive waveform metrics, which were then used as independent variables for the estimation of the forest AGB. The proposed method was examined with both simulated lidar waveforms of forest stands and real waveforms acquired by the Geoscience Laser Altimeter System (GLAS). Two typical footprint sizes (i.e., 25 m and 70 m in diameter) and 21 terrain slopes (i.e., from 0° to 40° at an interval of 2°) were used in the simulation of the lidar waveforms. The terrain slopes of the GLAS waveforms were between 5.54° and 34.08°. The results showed that the terrain stretching on the forest waveform could be compensated for by the bare ground waveform. Compared with the Gaussian decomposition and waveform parameter methods, the proposed method gave the best estimation of the forest AGB with R2 = 0.92 and RMSE = 15.95 Mg/ha using simulated waveforms with a footprint size of 70 m and R2 = 0.84 and RMSE = 34.99 Mg/ha using the GLAS waveforms. The results demonstrated that the proposed method had great potential for the estimation of forest AGB using lidar waveforms over mountainous areas.

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

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

[3]  Darrel L. Williams,et al.  Forest ecosystem dynamics: linking forest succession, soil process and radiation models , 1993 .

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

[5]  Michael A. Lefsky,et al.  Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms , 2007 .

[6]  Hong Chi,et al.  National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China , 2015, Remote. Sens..

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

[8]  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.

[9]  Christiane Schmullius,et al.  Large-scale mapping of boreal forest in Siberia , 2003 .

[10]  R. Dubayah,et al.  Lidar remote sensing for forestry. , 2000 .

[11]  Yrjö Rauste,et al.  Multi-temporal JERS SAR data in boreal forest biomass mapping , 2005 .

[12]  I. Woodhouse,et al.  Using satellite radar backscatter to predict above‐ground woody biomass: A consistent relationship across four different African landscapes , 2009 .

[13]  J. Sathaye,et al.  Mitigation of carbon emissions to the atmosphere by forest management. , 1996 .

[14]  Hong Chi,et al.  Research on tropical forest aboveground biomass estimating using ICESat/GLAS data in Xishuangbanna, Southwest China , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[15]  Juilson Jubanski,et al.  ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia , 2011, Remote. Sens..

[16]  R. K. Dixon,et al.  Carbon Pools and Flux of Global Forest Ecosystems , 1994, Science.

[17]  Zhou Fang,et al.  Estimation of Forest Canopy Height Over Mountainous Areas Using Satellite Lidar , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  M. Lefsky,et al.  Impact of footprint diameter and off-nadir pointing on the precision of canopy height estimates from spaceborne lidar , 2011 .

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

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

[21]  Wenge Ni-Meister,et al.  Modeling lidar waveforms in heterogeneous and discrete canopies , 2001, IEEE Trans. Geosci. Remote. Sens..

[22]  G. Foody,et al.  Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions , 2003 .

[23]  Craig A. Coburn,et al.  Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain , 2010 .

[24]  Ribana Roscher,et al.  Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data , 2017, Int. J. Appl. Earth Obs. Geoinformation.

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

[26]  Yang Zhang,et al.  A 3-D Joint Simulation Platform for Multiband Remote Sensing , 2017, IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens..

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

[28]  Qi Chen.  Assessment of terrain elevation derived from satellite laser altimetry over mountainous forest areas using airborne lidar data , 2010 .

[29]  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 .

[30]  Sassan Saatchi,et al.  Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests , 2016, Remote. Sens..

[31]  Terje Gobakken,et al.  Biomass and InSAR height relationship in a dense tropical forest , 2017 .

[32]  Min Cao,et al.  Geological History, Flora, and Vegetation of Xishuangbanna, Southern Yunnan, China 1 , 2006 .

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

[34]  M. Cao,et al.  Tree species diversity of tropical forest vegetation in Xishuangbanna, SW China , 1997, Biodiversity & Conservation.

[35]  Sassan Saatchi,et al.  Measuring biomass changes due to woody encroachment and deforestation/degradation in a forest-savanna boundary region of central Africa using multi-temporal L-band radar backscatter , 2011 .

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

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

[38]  Guoqing Sun,et al.  Modeling lidar returns from forest canopies , 2000, IEEE Trans. Geosci. Remote. Sens..

[39]  Chengquan Huang,et al.  Regional forest growth rates measured by combining ICESat GLAS and Landsat data , 2009 .

[40]  Kenneth J. Ranson,et al.  Forest biomass from combined ecosystem and radar backscatter modeling , 1997 .

[41]  G. Vosselman,et al.  Single and two epoch analysis of ICESat full waveform data over forested areas , 2008 .

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

[43]  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 .

[44]  J. Abshire,et al.  Geoscience Laser Altimeter System (GLAS) on the ICESat Mission: On‐orbit measurement performance , 2005 .

[45]  Guoqing Sun,et al.  Northern Forest Ecosystem Dynamics Using Coupled Models and Remote Sensing , 2001 .

[46]  Ying Yu,et al.  Estimates of forest structure parameters from GLAS data and multi-angle imaging spectrometer data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[47]  Yoshiki Yamagata,et al.  Forest canopy height estimation using ICESat/GLAS data and error factor analysis in Hokkaido, Japan , 2013 .

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

[49]  Guoqing Sun,et al.  Inversion of a lidar waveform model for forest biophysical parameter estimation , 2006, IEEE Geoscience and Remote Sensing Letters.

[50]  Göran Ståhl,et al.  Model-assisted estimation of change in forest biomass over an 11 year period in a sample survey supported by airborne LiDAR: A case study with post-stratification to provide “activity data” , 2013 .

[51]  K. Itten,et al.  Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization , 2007 .

[52]  G. Larocque,et al.  Development of the gap model ZELIG-CFS to predict the dynamics of North American mixed forest types with complex structures , 2011 .

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

[54]  Junjie Zhang,et al.  An improved method for estimating forest canopy height using ICESat-GLAS full waveform data over sloping terrain: A case study in Changbai mountains, China , 2010, Int. J. Appl. Earth Obs. Geoinformation.

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

[56]  Achim Roth,et al.  Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data , 2018 .

[57]  R. Nelson,et al.  Estimating Siberian timber volume using MODIS and ICESat/GLAS. , 2009 .

[58]  H. Zwally,et al.  Overview of the ICESat Mission , 2005 .

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