Mapping Aboveground Biomass using Texture Indices from Aerial Photos in a Temperate Forest of Northeastern China

Optical remote sensing data have been considered to display signal saturation phenomena in regions of high aboveground biomass (AGB) and multi-storied forest canopies. However, some recent studies using texture indices derived from optical remote sensing data via the Fourier-based textural ordination (FOTO) approach have provided promising results without saturation problems for some tropical forests, which tend to underestimate AGB predictions. This study was applied to the temperate mixed forest of the Liangshui National Nature Reserve in Northeastern China and demonstrated the capability of FOTO texture indices to obtain a higher prediction quality of forest AGB. Based on high spatial resolution aerial photos (1.0 m spatial resolution) acquired in September 2009, the relationship between FOTO texture indices and field-derived biomass measurements was calibrated using a support vector regression (SVR) algorithm. Ten-fold cross-validation was used to construct a robust prediction model, which avoided the over-fitting problem. By further comparison the performance of the model estimates for greater coverage, the predicted results were compared with a reference biomass map derived from LiDAR metrics. This study showed that the FOTO indices accounted for 88.3% of the variance in ground-based AGB; the root mean square error (RMSE) was 34.35 t/ha, and RMSE normalized by the mean value of the estimates was 22.31%. This novel texture-based method has great potential for forest AGB estimation in other temperate regions.

[1]  Jérôme Feret,et al.  Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar , 2015, Remote. Sens..

[2]  C. Proisy,et al.  The variation of apparent crown size and canopy heterogeneity across lowland Amazonian forests , 2010 .

[3]  Yadvinder Malhi,et al.  Biomass estimation of mixed forest landscape using a Fourier transform texture-based approach on very-high-resolution optical satellite imagery , 2014 .

[4]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[5]  P. Couteron,et al.  Predicting tropical forest stand structure parameters from Fourier transform of very high‐resolution remotely sensed canopy images , 2005 .

[6]  Maxim Neumann,et al.  Assessing Performance of L- and P-Band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[7]  J. Nichol,et al.  Improved forest biomass estimates using ALOS AVNIR-2 texture indices , 2011 .

[8]  Yong Pang,et al.  Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China: Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China , 2013 .

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Aurélie Shapiro,et al.  Erratum: Aboveground biomass mapping of African forest mosaics using canopy texture analysis: Toward a regional approach (Ecological Applications (2014) 24:8 (1984-2001)) , 2015 .

[11]  Pierre Couteron,et al.  Quantifying change in patterned semi-arid vegetation by Fourier analysis of digitized aerial photographs , 2002 .

[12]  Chor-Pang Lo,et al.  PC-based two-dimensional discrete Fourier transform programs for terrain analysis , 1996 .

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

[14]  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.

[15]  Liu Lijuan,et al.  Fused airborne LiDAR and hyperspectral data for tree species identification in a natural temperate forest , 2013, National Remote Sensing Bulletin.

[16]  Sandra Eckert,et al.  Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data , 2012, Remote. Sens..

[17]  Pang Yong,et al.  Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China , 2012 .

[18]  Nicolas Barbier,et al.  Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach. , 2014, Ecological applications : a publication of the Ecological Society of America.

[19]  Barry Turner.  United Nations Framework Convention on Climate Change , 2009 .

[20]  C. Proisy,et al.  Biomass Prediction in Tropical Forests: The Canopy Grain Approach , 2012 .

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

[22]  Minerva Singh,et al.  Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth , 2015, Remote. Sens..

[23]  F. Achard,et al.  A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals associated with deforestation, gains and losses of carbon stocks in forests remaining forests, and forestation , 2012 .

[24]  Fabian Ewald Fassnacht,et al.  Modeling forest biomass using Very-High-Resolution data—Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images , 2015 .

[25]  Richard Simon,et al.  Bias in error estimation when using cross-validation for model selection , 2006, BMC Bioinformatics.

[26]  Alicia Troncoso Lora,et al.  A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables , 2015, Neurocomputing.

[27]  Daniel A. Friess,et al.  Mangrove biomass estimation in Southwest Thailand using machine learning , 2013 .

[28]  Yong Pang,et al.  Characterizing forest canopy structure with lidar composite metrics and machine learning , 2011 .

[29]  Nicolas Barbier,et al.  Monitoring canopy grain of tropical forest using Fourier-based textural ordination (FOTO) of very high resolution images , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[30]  M. Batistella,et al.  Exploring TM Image Texture and its Relationships with Biomass Estimation in Rondônia, Brazilian Amazon. , 2005 .

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

[32]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[33]  Chuankuan Wang,et al.  Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests , 2006 .

[34]  Audrey Robert,et al.  Simulation of the effect of topography and tree falls on stand dynamics and stand structure of tropical forests , 2003 .

[35]  R. J. Scholes,et al.  African greenhouse gas emission inventories and mitigation options: Forestry, land-use change, and agriculture , 1995, Environmental monitoring and assessment.

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

[37]  M. Nilsson.  Estimation of tree heights and stand volume using an airborne lidar system , 1996 .

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

[39]  William W. Hsieh,et al.  Seasonal prediction of winter extreme precipitation over Canada by support vector regression , 2010 .

[40]  C. Proisy,et al.  Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images , 2007 .

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

[42]  W. Cohen,et al.  Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA , 1999 .

[43]  Nicholas C. Coops,et al.  Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots , 2013 .

[44]  C. Proisy,et al.  Assessing aboveground tropical forest biomass using Google Earth canopy images. , 2012, Ecological applications : a publication of the Ecological Society of America.

[45]  Jian Zhang,et al.  Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data , 2013 .