A review of radar remote sensing for biomass estimation

Forest plays a vital role in regulating climate through carbon sequestration in its biomass. Biomass reflects the health and environmental conditions of a forest ecosystem. In context to the climate change mitigation mechanisms like REDD (reducing emissions from deforestation and forest degradation), an extensive forest monitoring campaign is especially important. Remote sensing of forest structure and biomass with synthetic aperture radar (SAR) bears significant potential for mapping and understanding forest ecological processes. Limitations of the conventional forest inventory procedures, like the extensive cost, labor and time, can be overcome through integrated geospatial techniques. Optical sensor or SAR data are suitable for extracting information about simple and homogeneous forest stand sites. However, optical sensors face serious limitations, specifically in tropical regions, like the cloud cover that SAR can overcome along with targeting saturation and penetration aspects. Simultaneous use of spectral information and image texture parameters improves the biomass assessment over undulating terrain and in radical conditions. Also, synergic use of multi-sensor optical and SAR has better potential than single sensor. Interferometric (InSAR) and polarimetric (PolSAR) SAR or a combination of the both (PolInSAR) serves as effective alternatives. These techniques could serve as valuable methods for biomass assessment of heterogeneous complex biophysical environments. However, SAR data have its own limitations and complexities. Identifying, understanding and solving major uncertainties in different stages of the biomass estimation procedure are critical. In this regard, the current study provides a review of radar remote sensing-based studies in forest biomass estimation.

[1]  Jie Liang,et al.  Bayesian approach to quantify parameter uncertainty and impacts on predictive flow and mass transport in heterogeneous aquifer , 2013, International Journal of Environmental Science and Technology.

[2]  F. Hall,et al.  Importance of structure and its measurement in quantifying function of forest ecosystems , 2010 .

[3]  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.

[4]  Harifidy Rakoto Ratsimba,et al.  Combined biomass inventory in the scope of REDD (Reducing Emissions from Deforestation and Forest Degradation , 2010 .

[5]  João Roberto dos Santos,et al.  Eucalyptus Biomass and Volume Estimation Using Interferometric and Polarimetric SAR Data , 2010, Remote. Sens..

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

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

[8]  Corinne Le Quéré,et al.  Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks , 2007, Proceedings of the National Academy of Sciences.

[9]  Irena Hajnsek,et al.  Biomass estimation from polarimetric SAR interferometry over heterogeneous forest terrain , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

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

[11]  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..

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

[13]  Rangaswamy Madugundu,et al.  Estimation of above ground biomass in Indian tropical forested area using multi-frequency DLR-ESAR data. , 2010 .

[14]  Oleg Antropov,et al.  Stand-Level Stem Volume of Boreal Forests From Spaceborne SAR Imagery at L-Band , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  C. Wiley Synthetic Aperture Radars , 1985, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Jennifer L. Dungan,et al.  Toward a Comprehensive View of Uncertainty in Remote Sensing Analysis , 2006 .

[17]  Laurent Ferro-Famil,et al.  Remote sensing of vegetation using multi-baseline polarimetric SAR interferometry (theoretical modeling and physical parameter retrieval) , 2009 .

[18]  Temilola Fatoyinbo,et al.  Remote Characterization of Biomass Measurements: Case Study of Mangrove Forests , 2010 .

[19]  Kazuo Ouchi,et al.  Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics , 2013, Remote. Sens..

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

[21]  Chengquan Huang,et al.  Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges , 2012, Int. J. Digit. Earth.

[22]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[23]  Lei Chen,et al.  Uncertainty analysis for nonpoint source pollution modeling: implications for watershed models , 2015, International Journal of Environmental Science and Technology.

[24]  Yadvinder Malhi,et al.  Forests, carbon and global climate , 2002, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[25]  D. Lu Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon , 2005 .

[26]  C. Schmullius,et al.  Assessment of stand‐wise stem volume retrieval in boreal forest from JERS‐1 L‐band SAR backscatter , 2006 .

[27]  Aboveground Forest Biomass and the Global Carbon , 2005 .

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

[29]  A. Mather,et al.  Global Forest Resources Assessment 2000 Main Report: FAO Forestry Paper 140, FAO, Rome, 2001, xxvii+479pp, price $40.00, ISBN 92 5 104642-5, ISSN 0258-6150 , 2003 .

[30]  K. A. Rahman,et al.  REMOTELY SENSED L-BAND SAR DATA FOR TROPICAL FOREST BIOMASS ESTIMATION , 2011 .

[31]  Tropical Dry Deciduous Forest Stand Variable Estimation Using SAR Data , 2011 .

[32]  M. Keller,et al.  Biomass estimation in the Tapajos National Forest, Brazil: Examination of sampling and allometric uncertainties , 2001 .

[33]  K. S. Jayappa,et al.  Shoreline change rate estimation and its forecast: remote sensing, geographical information system and statistics-based approach , 2014, International Journal of Environmental Science and Technology.

[34]  N. Nakicenovic,et al.  Climate change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Summary for Policymakers. , 2007 .

[35]  Y. Yamagata,et al.  The use of ALOS/PALSAR backscatter to estimate above-ground forest biomass: A case study in Western Siberia , 2013 .

[36]  Sandra A. Brown,et al.  Monitoring and estimating tropical forest carbon stocks: making REDD a reality , 2007 .

[37]  Yrjö Rauste,et al.  Techniques for wide-area mapping of forest biomass using radar data , 2006 .

[38]  Y. Ban Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops , 2003 .

[39]  Masanobu Shimada,et al.  An Evaluation of the ALOS PALSAR L-Band Backscatter—Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[40]  F. Rocca,et al.  The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle , 2011 .

[41]  Ranga B. Myneni,et al.  Regional distribution of forest height and biomass from multisensor data fusion , 2010 .

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

[43]  C. Loehle Forest ecotone response to climate change: sensitivity to temperature response functional forms , 2000 .

[44]  Patrick Johnson,et al.  BioSARTM: an inexpensive airborne VHF multiband SAR system for vegetation biomass measurement , 2000, IEEE Trans. Geosci. Remote. Sens..

[45]  W. Walker,et al.  Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy , 2006 .

[46]  L. S. Araujo,et al.  TROPICAL FOREST BIOMASS MAPPING FROM DUAL FREQUENCY SAR INTERFEROMETRY ( X AND P-BANDS ) , 2004 .

[47]  Philippe Ciais,et al.  Weak Northern and Strong Tropical Land Carbon Uptake from Vertical Profiles of Atmospheric CO2 , 2007, Science.

[48]  J. B. M. Sambatti,et al.  Assessing Forest Biomass and Exploration in the Brazilian Amazon with Airborne InSAR: an Alternative for REDD , 2012 .

[49]  Sandra Englhart,et al.  Modeling Aboveground Biomass in Tropical Forests Using Multi-Frequency SAR Data—A Comparison of Methods , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[50]  Mahmod Reza Sahebi,et al.  Biomass Estimation of a Temperate Deciduous Forest Using Wavelet Analysis , 2013, IEEE Transactions on Geoscience and Remote Sensing.

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

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

[53]  J. Kong,et al.  Retrieval of forest biomass from SAR data , 1994 .

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

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

[56]  Lars M. H. Ulander,et al.  Topographic correction for biomass retrieval from P-band SAR data in boreal forests , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[57]  M. Sahebi,et al.  A review on biomass estimation methods using synthetic aperture radar data. , 2011 .

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

[59]  Martti Hallikainen,et al.  Feasibility of multi-temporal interferometric SAR data for stand-level estimation of boreal forest stem volume , 2003 .

[60]  Alex C. Lee,et al.  Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia , 2006 .

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

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

[63]  Q. Ketterings,et al.  Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests , 2001 .

[64]  Joanna Isobel House,et al.  Reconciling apparent inconsistencies in estimates of terrestrial CO2 sources and sinks , 2003 .

[65]  P. Atkinson,et al.  Uncertainty in remote sensing and GIS , 2002 .

[66]  João Manuel de Brito Carreiras,et al.  Estimating the Above-Ground Biomass in Miombo Savanna Woodlands (Mozambique, East Africa) Using L-Band Synthetic Aperture Radar Data , 2013, Remote. Sens..

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

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

[69]  Oleg Antropov,et al.  Improved Mapping of Tropical Forests With Optical and SAR Imagery, Part II: Above Ground Biomass Estimation , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[70]  Chandra Shekhar Jha,et al.  Estimation of forest biomass using Envisat-ASAR data , 2006, SPIE Asia-Pacific Remote Sensing.

[71]  Johan E. S. Fransson,et al.  Stem volume estimation in boreal forests using ERS-1/2 coherence and SPOT XS optical data , 2001 .

[72]  Maurizio Santoro,et al.  TREE HEIGHT ESTIMATION FROM MULTI-TEMPORAL ERS SAR INTERFEROMETRIC PHASE , 2004 .

[73]  D. Leckie,et al.  Estimating boreal forest species type with airborne polarimetric synthetic aperture radar , 2011 .

[74]  Brendan Mackey,et al.  Estimating forest biomass using satellite radar: an exploratory study in a temperate Australian Eucalyptus forest , 2003 .

[75]  D.H. Hoekman,et al.  Land cover type and forest biomass assessment in the Colombian Amazon , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[76]  B. Law,et al.  Structure‐based forest biomass from fusion of radar and hyperspectral observations , 2003 .

[77]  L. Sharma,et al.  Top-down and bottom-up inventory approach for above ground forest biomass and carbon monitoring in REDD framework using multi-resolution satellite data , 2013, Environmental Monitoring and Assessment.

[78]  Pavan Kumar,et al.  Geospatial Strategy for Tropical Forest-Wildlife Reserve Biomass Estimation , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.