Reprint of: Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area

Abstract Remote sensing is a valuable tool for estimating forest biomass in remote areas. This study explores retrieval of forest above-ground biomass (AGB) over a cold and arid region in Northwest China, using two different methods (non-parametric and parametric), field data, and three different remote sensing data: a SPOT-5 HRG image, multi-temporal dual-polarization ALOS PALSAR and airborne LiDAR data. The non-parametric method was applied in 300 different configurations, varying both the mathematical formulation and the data input (SPOT-5 and ALOS PALSAR), and the quality of the performance of each configuration was evaluated by Leave One Out (LOO) cross-validation against ground measurements. For the parametric method (the multivariate linear regression), the same remote sensing data were used, but in one additional configuration the airborne LiDAR data were used for stepwise multiple regression. The result of the best performing non-parametric configuration was satisfactory ( R  = 0.69 and RMSE = 20.7 tons/ha). The results for the parametric method were notoriously inaccurate, except for the case where airborne LiDAR data were included. The regression method with airborne low density LiDAR point cloud data was the best of all tested methods ( R  = 0.84 and RMSE = 15.2 tons/ha). A cross comparison of the two best results showed that the non-parametric method performs nearly as well as the parametric method with LiDAR data, except for some areas where forests have a very heterogeneous structure. It is concluded that the non-parametric method with SPOT data is able to map forest AGB operatively over the cold and arid region as an alternative to the more expensive airborne LiDAR data.

[1]  J. Hyyppä,et al.  Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions , 2004 .

[2]  E. Tomppo,et al.  Selecting estimation parameters for the Finnish multisource National Forest Inventory , 2001 .

[3]  Piermaria Corona,et al.  Estimation of Mediterranean forest attributes by the application of k‐NN procedures to multitemporal Landsat ETM+ images , 2005 .

[4]  Sylvain G. Leblanc,et al.  Biomass measurements and relationships with Landsat‐7/ETM+ and JERS‐1/SAR data over Canada's western sub‐arctic and low arctic , 2009 .

[5]  E. Chuvieco,et al.  Biomass Burning Emissions: A Review of Models Using Remote-Sensing Data , 2005, Environmental monitoring and assessment.

[6]  Paul J. Curran,et al.  JERS-1/SAR backscatter and its relationship with biomass of regenerating forests , 2000 .

[7]  E. Næsset.  Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia , 2007 .

[8]  K. Lim,et al.  Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators , 2004 .

[9]  Martti Hallikainen,et al.  Retrieval of biomass in boreal forests from multitemporal ERS-1 and JERS-1 SAR images , 1999, IEEE Trans. Geosci. Remote. Sens..

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

[11]  R. Hall,et al.  Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume , 2006 .

[12]  M. Steininger.  Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia , 2000 .

[13]  Piermaria Corona,et al.  Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems , 2008 .

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

[15]  The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates , 2001 .

[16]  Peter Bunting,et al.  Retrieving forest biomass through integration of CASI and LiDAR data , 2008 .

[17]  Sakari Tuominen,et al.  Performance of different spectral and textural aerial photograph features in multi-source forest inventory , 2005 .

[18]  Erkki Tomppo,et al.  Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: a genetic algorithm approach , 2004 .

[19]  N. Coops,et al.  Estimating fragmentation effects on simulated forest net primary productivity derived from satellite imagery , 2004 .

[20]  B. Parresol.  Assessing Tree and Stand Biomass: A Review with Examples and Critical Comparisons , 1999, Forest Science.

[21]  R. Sun,et al.  Observation and simulation of net primary productivity in Qilian Mountain, western China. , 2007, Journal of environmental management.

[22]  Josaphat Tetuko Sri Sumantyo,et al.  Employing a Method on SAR and Optical Images for Forest Biomass Estimation , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[23]  S. Popescu,et al.  Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height , 2004 .

[24]  M. Nilsson,et al.  Regional forest biomass and wood volume estimation using satellite data and ancillary data , 1999 .

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

[26]  Shilong Piao,et al.  Satellite-based estimation of biomass carbon stocks for northeast China's forests between 1982 and 1999 , 2007 .

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

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

[29]  João Roberto dos Santos,et al.  Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data , 2002 .

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

[31]  R. Hall,et al.  Biomass mapping using forest type and structure derived from Landsat TM imagery , 2006 .

[32]  E. Næsset,et al.  Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser , 2008 .

[33]  M. Bauer,et al.  Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method , 2001 .

[34]  João Roberto dos Santos,et al.  Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest , 2003 .

[35]  T. Dawson,et al.  Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol , 2005 .

[36]  Xin Li,et al.  Preface “Observing and modeling the catchment scale water cycle” , 2011 .

[37]  R. Houghton,et al.  Aboveground Forest Biomass and the Global Carbon Balance , 2005 .

[38]  Robert B. Waide,et al.  Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data , 1989 .

[39]  S. M. Jong,et al.  Above‐ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: the DAIS Peyne experiment , 2003 .

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

[41]  B. Nelson,et al.  Allometric regressions for improved estimate of secondary forest biomass in the central Amazon , 1999 .

[42]  Guoqing Sun,et al.  Mapping biomass of a northern forest using multifrequency SAR data , 1994, IEEE Trans. Geosci. Remote. Sens..

[43]  Fabio Maselli,et al.  Evaluation of statistical methods to estimate forest volume in a mediterranean region , 2006, IEEE Transactions on Geoscience and Remote Sensing.

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

[45]  Craig A. Coburn,et al.  SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Barbara Koch,et al.  Satellite estimation of forest carbon using regression models , 2008 .

[47]  S. Reutebuch,et al.  Light detection and ranging (LIDAR): an emerging tool for multiple resource inventory. , 2005 .

[48]  Adrian Luckman,et al.  A study of the relationship between radar backscatter and regenerating tropical forest biomass for spaceborne SAR instruments , 1997 .

[49]  M. Batistella,et al.  Satellite estimation of aboveground biomass and impacts of forest stand structure , 2005 .

[50]  Scott L. Powell,et al.  Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972–2006 , 2007 .

[51]  Göran Ståhl,et al.  Simultaneous Estimations of Forest Parameters using Aerial Photograph Interpreted Data and the k Nearest Neighbour Method , 2001 .

[52]  A. Beaudoin,et al.  A shadow fraction method for mapping biomass of northern boreal black spruce forests using QuickBird imagery , 2007 .

[53]  A. Skidmore,et al.  Narrow band vegetation indices overcome the saturation problem in biomass estimation , 2004 .

[54]  M. Maltamo,et al.  Forest stand characteristics estimation using a most similar neighbor approach and image spatial structure information , 2001 .

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

[56]  C. Kleinn,et al.  Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory , 2009 .

[57]  Thomas R. Crow,et al.  Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA , 2004 .

[58]  R. Houghton,et al.  Annual fluxes of carbon from deforestation and regrowth in the Brazilian Amazon , 2000, Nature.

[59]  R. Fournier,et al.  A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland , 2006 .

[60]  B. Griscom,et al.  Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data , 2004 .

[61]  Paul J. Curran,et al.  Factors affecting the remotely sensed response of coniferous forest plantations , 1993 .

[62]  Sandra A. Brown.  Measuring carbon in forests: current status and future challenges. , 2002, Environmental pollution.

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

[64]  Ronald E. McRoberts,et al.  Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery , 2009 .

[65]  Andrew O. Finley,et al.  Applying an Efficient k-Nearest Neighbor Search to Forest Attribute Imputation , 2006, Forest Science.

[66]  Linda S. Heath,et al.  Carbon sequestration in the U.S. forest sector from 1990 to 2010 , 2007 .

[67]  Giles M. Foody,et al.  Mapping the biomass of Bornean tropical rain forest from remotely sensed data , 2001 .

[68]  F. Maselli,et al.  Extension of environmental parameters over the land surface by improved fuzzy classification of remotely sensed data , 2001 .

[69]  S. Magnussen,et al.  Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators , 1998 .

[70]  F. M. Danson,et al.  Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data , 2010 .

[71]  Richard A. Fournier,et al.  Mapping aboveground tree biomass at the stand level from inventory information: test cases in Newfoundland and Quebec , 2003 .

[72]  Guoqing Sun,et al.  Radiometric slope correction for forest biomass estimation from SAR data in the Western Sayani Mountains, Siberia , 2002 .

[73]  Liviu Theodor Ene,et al.  Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models , 2010 .

[74]  J. Fransson,et al.  Combining remotely sensed optical and radar data in kNN-estimation of forest variables , 2003 .

[75]  E. Næsset.  Practical large-scale forest stand inventory using a small-footprint airborne scanning laser , 2004 .

[76]  R. Lawrence,et al.  Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape : Mount St. Helens, Washington , 1998 .

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

[78]  Richard G. Oderwald,et al.  Technical note: Canopy height models and airborne lasers to estimate forest biomass: Two problems , 2000 .

[79]  W. Salas,et al.  Secondary Forest Age and Tropical Forest Biomass Estimation Using Thematic Mapper Imagery , 2000 .

[80]  Z. Niu,et al.  Watershed Allied Telemetry Experimental Research , 2009 .

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

[82]  R. Birdsey,et al.  Biomass Estimation for Temperate Broadleaf Forests of the United States Using Inventory Data , 1997, Forest Science.

[83]  T. Dawson,et al.  Quantifying forest above ground carbon content using LiDAR remote sensing , 2004 .

[84]  Giles M. Foody,et al.  Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development , 2003 .

[85]  Thuy Le Toan,et al.  Relating forest biomass to SAR data , 1992, IEEE Trans. Geosci. Remote. Sens..

[86]  G. A. Blackburn,et al.  Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LIDAR and multi‐spectral remotely sensed data , 2005 .

[87]  P. S. Roy,et al.  Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest , 1996, Journal of Biosciences.