Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use

Abstract In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (> 100 t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was r ² = 0.53 with an RMSE of 79 t/ha. The model is valid up to 307 t/ha with an accuracy requirement of 50 t/ha and up to 614 t/ha with an accuracy requirement of 100 t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types.

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

[2]  F. Achard,et al.  Determination of Deforestation Rates of the World's Humid Tropical Forests , 2002, Science.

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

[4]  S. Popescu,et al.  Lidar remote sensing of forest biomass : A scale-invariant estimation approach using airborne lasers , 2009 .

[5]  Yong Wang,et al.  Santa Barbara microwave backscattering model for woodlands , 1993 .

[6]  J. Terborgh,et al.  The above‐ground coarse wood productivity of 104 Neotropical forest plots , 2004 .

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

[8]  Rodel D. Lasco,et al.  Forest carbon budgets in Southeast Asia following harvesting and land cover change , 2002 .

[9]  Masanobu Shimada,et al.  PALSAR Radiometric and Geometric Calibration , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[10]  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.

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

[12]  Corinne Le Quéré,et al.  Trends in the sources and sinks of carbon dioxide , 2009 .

[13]  Terje Gobakken,et al.  Estimating spruce and pine biomass with interferometric X-band SAR , 2010 .

[14]  Alan H. Strahler,et al.  A radar backscatter model for discontinuous coniferous forests , 1991, IEEE Trans. Geosci. Remote. Sens..

[15]  P. Atkinson,et al.  Relating SAR image texture to the biomass of regenerating tropical forests , 2005 .

[16]  J. Chambers,et al.  Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.

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

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

[19]  R. B. Jackson,et al.  CO 2 emissions from forest loss , 2009 .

[20]  Frédéric Baup,et al.  Radar Signatures of Sahelian Surfaces in Mali Using ENVISAT-ASAR Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Florian Siegert,et al.  Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Florian Siegert,et al.  Determination of the amount of carbon stored in Indonesian peatlands. , 2008 .

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

[24]  Christopher J. Banks,et al.  Global and regional importance of the tropical peatland carbon pool , 2011 .

[25]  Yadvinder Malhi,et al.  The role of land carbon sinks in mitigating global climate change , 2001 .

[26]  M.C. Dobson,et al.  Seasonal change in radar backscatter from mixed conifer and hardwood frorests in northern Michigan , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

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

[28]  T. O. Kvålseth.  Cautionary Note about R 2 , 1985 .

[29]  João Roberto dos Santos,et al.  TROPICAL FOREST BIOMASS AND ITS RELATIONSHIP WITH P - BAND SAR DATA , 2006 .

[30]  V. K. Dadhwal,et al.  Potential of Envisat ASAR data for woody biomass assessment , 2010 .

[31]  Mark A. Cochrane,et al.  Tropical Fire Ecology: Climate Change, Land Use and Ecosystem Dynamics , 2009 .

[32]  Michael A. Wulder,et al.  Estimating forest canopy height and terrain relief from GLAS waveform metrics , 2010 .

[33]  Marc L. Imhoff,et al.  Radar backscatter and biomass saturation: ramifications for global biomass inventory , 1995 .

[34]  A. Lugo,et al.  Estimating biomass and biomass change of tropical forests , 1997 .

[35]  P. Townsend.  Principles and Applications of Imaging Radar: Manual of Remote Sensing , 2000 .

[36]  F. Siegert,et al.  Monitoring the effect of restoration measures in Indonesian peatlands by radar satellite imagery. , 2011, Journal of environmental management.

[37]  S. Goetz,et al.  Carbon Balance and Management , 2009 .

[38]  F. Siegert,et al.  Spatiotemporal fire occurrence in Borneo over a period of 10 years , 2009 .

[39]  Kathy MacKinnon,et al.  The ecology of Kalimantan , 1996 .

[40]  Catherine Ticehurst,et al.  The potential of L‐band SAR for quantifying mangrove characteristics and change: case studies from the tropics , 2007 .

[41]  Shaun Quegan,et al.  Retrieval of Bio- and Geo-Physical Parameters from SAR Data for Land Applications. , 2002 .

[42]  Thuy Le Toan,et al.  On the relationships between radar measurements and forest structure and biomass , 2002 .

[43]  S. Romshoo.  Radar remote sensing for monitoring of dynamic ecosystem processes related to biogeochemical exchanges in tropical peatlands , 2004 .

[44]  Simone R. Freitas,et al.  Relationships between forest structure and vegetation indices in Atlantic Rainforest , 2005 .

[45]  G. Sánchez‐Azofeifa,et al.  Monitoring secondary tropical forests using space-borne data: Implications for Central America , 2003 .