Hyperspectral shape-based unmixing to improve intra- and interclass variability for forest and agro-ecosystem monitoring

Abstract The monitoring of forests and agro-ecosystems often requires the use of a spectral mixture model to provide detailed information on spatial and temporal variations in vegetation cover. Two key issues in the mapping of vegetation cover in complex ecosystems are the high spectral similarity (i.e. low interclass variability) between and the high spectral variability among different vegetation species (i.e. high intraclass variability), as they impede the performance of the Root Mean Square Error (RMSE) criterion, traditionally used in Spectral Mixture Analysis (SMA) to optimise the fit between modelled and measured mixed signal. Shape-based objective functions have been proposed as an alternative. Experiments, based on ray-tracing simulations, indeed demonstrated the added value of implementing shape-based error metrics in unmixing of vegetation in (i) reducing the effects of intra-class endmember variability (Δ f RMSE  ≈ 0.21 vs Δ f SAM  ≈ 0.10) and (ii) highlighting the subtle spectral differences among similar endmembers (Δ f RMSE  ≈ 0.61 vs Δ f SAM  ≈ 0.43). Shape-based unmixing as such has the potential to become an alternative to the traditional but CPU intensive MESMA approach. Simulated data results presented in this study show a significant increase in fraction estimate accuracy for shape-based unmixing over MESMA (Δ f MESMA , RMSE  ≈ 0.15 vs Δ f sSMA , SAM  ≈ 0.10) and subsequently confirmed using three different real hyperspectral data sets.

[1]  D. Roberts,et al.  A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper , 2004 .

[2]  Andrew V. Bradley,et al.  Scale dependence in multitemporal mapping of forest fragmentation in Bolivia: implications for explaining temporal trends in landscape ecology and applications to biodiversity conservation , 2003 .

[3]  P. Zarco-Tejada,et al.  Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices , 2012 .

[4]  M. Batistella,et al.  Linear mixture model applied to Amazonian vegetation classification , 2003 .

[5]  Pol Coppin,et al.  Endmember variability in Spectral Mixture Analysis: A review , 2011 .

[6]  Jessica A. Faust,et al.  Aviris Radiometric Laboratory Calibration, Inflight Validation and a Focused Sensitivity Analysis in 1998 , 2000 .

[7]  Jan Verbesselt,et al.  Magnitude- and Shape-Related Feature Integration in Hyperspectral Mixture Analysis to Monitor Weeds in Citrus Orchards , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Gregory Asner,et al.  Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis , 2000, IEEE Trans. Geosci. Remote. Sens..

[9]  W. Verstraeten,et al.  Nonlinear Hyperspectral Mixture Analysis for tree cover estimates in orchards , 2009 .

[10]  Kenji Omasa,et al.  Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data , 2007 .

[11]  Changshan Wu,et al.  Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery , 2004 .

[12]  John R. Miller,et al.  Forest canopy closure from classification and spectral unmixing of scene components-multisensor evaluation of an open canopy , 1994, IEEE Trans. Geosci. Remote. Sens..

[13]  Benoit Rivard,et al.  Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery , 2006 .

[14]  P. Vitousek,et al.  Remote analysis of biological invasion and biogeochemical change. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[15]  H. Poilvé,et al.  Hyperspectral Imaging and Stress Mapping in Agriculture , 1998 .

[16]  M. S. Moran,et al.  Remote Sensing for Crop Management , 2003 .

[17]  Nicolas H. Younan,et al.  Hyperspectral pixel unmixing using singular value decomposition , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[18]  N. Coops,et al.  Assessing plantation canopy condition from airborne imagery using spectral mixture analysis and fractional abundances , 2005 .

[19]  Derek M. Rogge,et al.  Iterative Spectral Unmixing for Optimizing Per-Pixel Endmember Sets , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Jin Chen,et al.  Generalization of Subpixel Analysis for Hyperspectral Data With Flexibility in Spectral Similarity Measures , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[21]  W. Verstraeten,et al.  The impact of common assumptions on canopy radiative transfer simulations: A case study in Citrus orchards , 2009 .

[22]  A. Skidmore,et al.  A hyperspectral band selector for plant species discrimination , 2007 .

[23]  Gregory Asner,et al.  Imaging spectroscopy for desertification studies: comparing AVIRIS and EO-1 Hyperion in Argentina drylands , 2003, IEEE Trans. Geosci. Remote. Sens..

[24]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[25]  Dimitry Van der Zande Mathematical modeling of 3D canopy structure in forest stands using ground-based LiDAR , 2008 .

[26]  S. Delalieux,et al.  An automated waveband selection technique for optimized hyperspectral mixture analysis , 2010 .

[27]  Alessandro Mecocci,et al.  Theoretical and experimental assessment of noise effects on least-squares spectral unmixing of hyperspectral images , 2005 .

[28]  Jin Chen,et al.  A Quantitative Analysis of Virtual Endmembers' Increased Impact on the Collinearity Effect in Spectral Unmixing , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[29]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[30]  W. Verstraeten,et al.  Modelling moisture‐induced soil reflectance changes in cultivated sandy soils: a case study in citrus orchards , 2010 .

[31]  Greg Humphreys,et al.  Physically Based Rendering: From Theory to Implementation , 2004 .

[32]  S. Garman,et al.  Comparison of five canopy cover estimation techniques in the western Oregon Cascades , 2006 .

[33]  Jeff Settle,et al.  On the effect of variable endmember spectra in the linear mixture model , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Roberta E. Martin,et al.  Remote sensing of native and invasive species in Hawaiian forests , 2008 .

[35]  Laurent Tits,et al.  The Potential and Limitations of a Clustering Approach for the Improved Efficiency of Multiple Endmember Spectral Mixture Analysis in Plant Production System Monitoring , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Peter M. Vitousek,et al.  Biological invasion by Myrica faya in Hawai'i: plant demography, nitrogen fixation, ecosystem effects , 1989 .

[37]  P. Thenkabail,et al.  Spectral Matching Techniques to Determine Historical Land-use/Land-cover (LULC) and Irrigated Areas Using Time-series 0.1-degree AVHRR Pathfinder Datasets , 2007 .

[38]  Chein-I Chang,et al.  An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis , 2000, IEEE Trans. Inf. Theory.

[39]  D. Lobell,et al.  A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .

[40]  J. Sweet,et al.  The spectral similarity scale and its application to the classification of hyperspectral remote sensing data , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[41]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[42]  Qian Du Optimal linear unmixing for hyperspectral image analysis , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[43]  Benoit Rivard,et al.  Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and rock , 2004, IEEE Transactions on Geoscience and Remote Sensing.