Estimation of Woody Biomass of Pine Savanna Woodlands From ALOS PALSAR Imagery

We present an adapted woody biomass retrieval approach for tropical savanna areas appropriate for use with satellite acquired L-band SAR imagery. We use the semiempirical water cloud model to describe the interaction between the SAR signal and vegetation and re-arrange the model to predict biomass. Estimations are made using dual polarization SAR imagery collected by ALOS PALSAR during 2008 in combination with community woodland inventory data from pine savanna areas in Belize. Estimation accuracy is assessed internally by the fit of the model to the ground training data, and then validated against an independent external dataset, quality controlled using Worldview II imagery. The internal validation shows a biomass estimation with an RMSE of 25 t/ha and a coefficient of determination R2 of 0.70, while the external validation indicates an RMSE of 13 t/ha with R2 of 0.53. This approach to biomass estimation appears to be most influenced by the plots with higher tree numbers and where the trees were more homogeneous. The existence of many similar sized individuals in those plots influence the SAR backscatter and is predicted to be the cause the elevated level of saturation found in this study (>100t/ha) with complete saturation predicted as a result of number density increases, and concurrently increasing basal area, both not exclusively dependent on biomass.

[1]  Manabu Watanabe,et al.  Forest Structure Dependency of the Relation Between L-Band$sigma^0$and Biophysical Parameters , 2006, IEEE Transactions on Geoscience and Remote Sensing.

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

[3]  I. Woodhouse.  Introduction to Microwave Remote Sensing , 2005 .

[4]  Iain H. Woodhouse,et al.  A "Matchstick Model" of microwave backscatter from a forest , 2012 .

[5]  Pascale C. Dubois,et al.  Measuring soil moisture with imaging radars , 1995, IEEE Trans. Geosci. Remote. Sens..

[6]  Manabu Watanabe,et al.  Tight correlations between forest parameters and backscattering coefficient derived by the L-band airborne SAR (PiSAR) , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

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

[8]  Guoqing Sun,et al.  Retrieval of Forest Biomass From ALOS PALSAR Data Using a Lookup Table Method , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[10]  Richard M. Lucas,et al.  ALOS PALSAR for characterizing wooded savannas in Northern Australia , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Sassan Saatchi,et al.  A novel application of satellite radar data: measuring carbon sequestration and detecting degradation in a community forestry project in Mozambique , 2013 .

[12]  Neil Stuart,et al.  A checklist of the vascular plants of the lowland savannas of Belize, Central America , 2013 .

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

[14]  Lars M. H. Ulander,et al.  Estimation of forest stem volume using ALOS PALSAR satellite images , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[15]  Neil Stuart,et al.  The Savanna Ecosystems Map of Belize 2011: Technical Report , 2011 .

[16]  M. S. Johnson,et al.  An Inventory of the Southern Coastal Plain Pine Forests, Belize , 1974 .

[17]  Neil Stuart,et al.  Visual interpretation of synthetic aperture radar data for assessing land cover in tropical savannahs , 2006, Geoinformatics.

[18]  I. Woodhouse,et al.  Radar backscatter is not a \'direct measure\' of forest biomass , 2012 .

[19]  R. Harris,et al.  Extracting biophysical parameters from remotely sensed radar data: a review of the water cloud model , 2003 .

[20]  I. Woodhouse,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 .

[21]  Marc L. Imhoff,et al.  Radar backscatter and biomass saturation: ramifications for global biomass inventory , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[22]  P. Furley,et al.  Tropical savannas: Biomass, plant ecology, and the role of fire and soil on vegetation , 2010 .

[23]  Edson Eyji Sano,et al.  The use of ALOS PALSAR imagery for Cerrado's land use and land cover mapping , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[24]  Kamal Sarabandi,et al.  An empirical model and an inversion technique for radar scattering from bare soil surfaces , 1992, IEEE Trans. Geosci. Remote. Sens..

[25]  Dimitrios G. Michelakis,et al.  Establishing the sensitivity of ALOS PALSAR to above ground woody biomass: A case study in the pine savannas of Belize, Central America , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[26]  Edward T. A. Mitchard,et al.  The use of ALOS PALSAR for supporting sustainable forest use in southern Africa: A case study in Malawi , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[27]  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.

[28]  Christiane Schmullius,et al.  Polarimetric analysis over African savanna woodland using ALOS/PALSAR , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[29]  L. Prévot,et al.  Estimating the characteristics of vegetation canopies with airborne radar measurements , 1993 .

[30]  G. Sadowy,et al.  UAVSAR: a new NASA airborne SAR system for science and technology research , 2006, 2006 IEEE Conference on Radar.

[31]  Sandra A. Brown,et al.  CREATING A VIRTUAL TROPICAL FOREST FROM THREE-DIMENSIONAL AERIAL IMAGERY TO ESTIMATE CARBON STOCKS , 2005 .

[32]  Maurizio Santoro,et al.  Estimation of forest stem volume using ALOS-2 PALSAR-2 satellite images , 2007, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[33]  I. Woodhouse,et al.  Quantifying small‐scale deforestation and forest degradation in African woodlands using radar imagery , 2012 .

[34]  R. Lucas,et al.  A review of remote sensing technology in support of the Kyoto Protocol , 2003 .

[35]  Peter Bunting,et al.  Pre-processing and geocoding of ALOS PALSAR data over Queensland , Australia , 2008 .

[36]  D. O. Hall,et al.  The global carbon sink: a grassland perspective , 1998 .

[37]  Roberta E. Martin,et al.  Topo-edaphic controls over woody plant biomass in South African savannas , 2012 .

[38]  Maurizio Santoro,et al.  Multitemporal repeat pass SAR interferometry of boreal forests , 2005, IEEE Trans. Geosci. Remote. Sens..

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

[40]  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..

[41]  Manabu Watanabe,et al.  ALOS PALSAR: A Pathfinder Mission for Global-Scale Monitoring of the Environment , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Maycira Costa,et al.  Using ALOS/PALSAR and RADARSAT-2 to Map Land Cover and Seasonal Inundation in the Brazilian Pantanal , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[43]  Iain H. Woodhouse,et al.  Predicting backscatter-biomass and height-biomass trends using a macroecology model , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[44]  R Linares,et al.  Sustainable Forest Management Plan Deep River Forest Reserve , 2009 .

[45]  J. M. Rey Benayas,et al.  Remote sensing and the future of landscape ecology , 2009 .

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

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

[48]  S. Saatchi,et al.  Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass , 2011 .

[49]  Franz Rubel,et al.  Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification , 2010 .

[50]  Urs Wegmüller,et al.  Retrieval of vegetation parameters with SAR interferometry , 1997, IEEE Trans. Geosci. Remote. Sens..

[51]  Maurizio Santoro,et al.  Multitemporal repeat pass SAR interferometry of boreal forests , 2003, IEEE Transactions on Geoscience and Remote Sensing.

[52]  Maxim Neumann,et al.  Impacts of Spatial Variability on Aboveground Biomass Estimation from L-Band Radar in a Temperate Forest , 2013, Remote. Sens..

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

[54]  F. Ulaby,et al.  Vegetation modeled as a water cloud , 1978 .

[55]  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.

[56]  Peter A. Furley,et al.  VEGETATION CLASSIFICATION AND FLORISTICS OF THE SAVANNAS AND ASSOCIATED WETLANDS OF THE RIO BRAVO CONSERVATION AND MANAGEMENT AREA, BELIZE , 2002 .

[57]  Shih-tseng Wu,et al.  Potential Application of Multipolarization SAR for Pine-Plantation Biomass Estimation , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[58]  Yukihiro Chiba,et al.  Architectural analysis of relationship between biomass and basal area based on pipe model theory , 1998 .

[59]  Armando Marino,et al.  Backscatter and interferometry for estimating above-ground biomass of sparse woodland: A case study in Belize , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.