A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment

This review paper evaluates the potential of hyperspectral remote sensing for assessing species diversity in homogeneous (non-tropical) and heterogeneous (tropical) forest, an increasingly urgent task. Existing studies of species distribution patterns using hyperspectral remote sensing have used different techniques to discriminate different species, in which the wavelet transforms, derivative analysis and red edge positions are the most important of them. The wavelet transform is used based on its effectiveness and determined as the most powerful technique to identify species. Furthermore, estimations of relationships between spectral values and species distributions using chemical composition of foliage, tree phenology, selection of signature training sites based on field measured canopy composition, selection of the best wavelet coefficient and waveband regions may be useful to identify different plant species. This paper presents a summary on the feasibility, operational applications and possible strategies of hyperspectral remote sensing in forestry, especially in assessing its biodiversity. The paper also reviews the processing and analysis of techniques for hyperspectral data in discriminating different forest tree species.

[1]  P. Gong,et al.  Conifer species recognition: Effects of data transformation , 2001 .

[2]  Mary E. Martin,et al.  Determining Forest Species Composition Using High Spectral Resolution Remote Sensing Data , 1998 .

[3]  A. Skidmore,et al.  Exploring spectral discrimination of grass species in African rangelands , 2001 .

[4]  C. François,et al.  Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements , 2004 .

[5]  N. H. Brogea,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2022 .

[6]  G. Daily,et al.  COUNTRYSIDE BIOGEOGRAPHY: USE OF HUMAN-DOMINATED HABITATS BY THE AVIFAUNA OF SOUTHERN COSTA RICA , 2001 .

[7]  H. Nagendra.  Using remote sensing to assess biodiversity , 2001 .

[8]  Gerardo Avalos,et al.  Seasonal Changes in Liana Cover in the Upper Canopy of a Neotropical Dry Forest 1 , 1999 .

[9]  Paul M. Mather,et al.  Wavelet Shrinkage in Noise Removal of Hyperspectral Remote Sensing Data , 2005 .

[10]  Jiang Li,et al.  Correction to "Wavelet-Based Feature Extraction for Improved Endmember Abundance Estimation in Linear Unmixing of Hyperspectral Signals" , 2004 .

[11]  J. C. Price.  How unique are spectral signatures , 1994 .

[12]  N. Myers.  The world's forests: problems and potentials , 1996, Environmental Conservation.

[13]  Gregory P. Asner,et al.  Variability in Leaf and Litter Optical Properties: Implications for BRDF Model Inversions Using AVHRR, MODIS, and MISR , 1998 .

[14]  S. Hubbell,et al.  The unified neutral theory of biodiversity and biogeography at age ten. , 2011, Trends in ecology & evolution.

[15]  W. R. Windham,et al.  Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. , 1995, Tree physiology.

[16]  Ruiliang Pu,et al.  Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index , 2003, IEEE Trans. Geosci. Remote. Sens..

[17]  Fuan Tsai,et al.  Derivative analysis of hyperspectral data , 1996, Remote Sensing.

[18]  John R. Miller,et al.  Comparative Relationships between Some Red Edge Parameters and Seasonal Leaf Chlorophyll Concentrations , 1995 .

[19]  Richard G. Oderwald,et al.  Spectral Separability among Six Southern Tree Species , 2000 .

[20]  Donald G. Leckie,et al.  Analysis of high resolution multispectral MEIS imagery for spruce budworm damage assessment on a single tree basis , 1992 .

[21]  Frans Bongers,et al.  The ecology of lianas and their role in forests , 2002 .

[22]  P. Curran,et al.  A new technique for interpolating the reflectance red edge position , 1998 .

[23]  S. Ustin,et al.  Differentiating salt marsh species using foreground/background analysis , 1996 .

[24]  D. Pérez-Salicrup,et al.  Number of Lianas per Tree and Number of Trees Climbed by Lianas at Los Tuxtlas, Mexico 1 , 2005 .

[25]  C. Justice,et al.  Land cover and global productivity: A measurement strategy for the NASA programme , 2000 .

[26]  S. Schnitzer.  A Mechanistic Explanation for Global Patterns of Liana Abundance and Distribution , 2005, The American Naturalist.

[27]  S. Paton,et al.  ARE LIANAS INCREASING IN IMPORTANCE IN TROPICAL FORESTS? A 17‐YEAR RECORD FROM PANAMA , 2004 .

[28]  Hao Chen,et al.  Processing Hyperion and ALI for forest classification , 2003, IEEE Trans. Geosci. Remote. Sens..

[29]  S. Ustin,et al.  Estimating leaf biochemistry using the PROSPECT leaf optical properties model , 1996 .

[30]  Jean-Philippe Gastellu-Etchegorry,et al.  Forest canopy chemistry with high spectral resolution remote sensing , 1996 .

[31]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[32]  S. Dobrowski,et al.  Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects , 2003 .

[33]  Andrew K. Chan,et al.  Wavelets for Sensing Technologies , 2003 .

[34]  B. Turner,et al.  Determination of Eucalypt forest landscape characteristics from high resolution hyperspectral data , 1997 .

[35]  Ruiliang Pu,et al.  Conifer species recognition: An exploratory analysis of in situ hyperspectral data , 1997 .

[36]  T. L. Coleman,et al.  Monitoring forest plantations using Landsat Thematic Mapper data. , 1990 .

[37]  Terry Caelli,et al.  Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels , 2007 .

[38]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[39]  D. Roberts,et al.  Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales , 2004, Ecosystems.

[40]  R. Busing,et al.  The Unified Neutral Theory of Biodiversity and Biogeography , 2002 .

[41]  G. Carter,et al.  Variability in leaf optical properties among 26 species from a broad range of habitats. , 1998, American journal of botany.

[42]  P. Valdes,et al.  The effect of Amazonian deforestation on the northern hemisphere circulation and climate , 2000 .

[43]  T. Subba Rao,et al.  Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB , 2004 .

[44]  Craig S. T. Daughtry,et al.  Spectral Discrimination of Cannabis sativa L. Leaves and Canopies , 1998 .

[45]  David L. Verbyla,et al.  Satellite Remote Sensing of Natural Resources , 1995 .

[46]  Luc Van Gool,et al.  Multi-spectral vision system for weed detection , 2001, Pattern Recognit. Lett..

[47]  Mary E. Martin,et al.  HIGH SPECTRAL RESOLUTION REMOTE SENSING OF FOREST CANOPY LIGNIN, NITROGEN, AND ECOSYSTEM PROCESSES , 1997 .

[48]  William F. Laurance,et al.  Reflections on the tropical deforestation crisis , 1999 .

[49]  Robert C. Harriss,et al.  Deforestation in Costa Rica: A Quantitative Analysis Using Remote Sensing Imagery1 , 2001 .

[50]  D. Pérez-Salicrup,et al.  EFFECT OF LIANA CUTTING ON TREE REGENERATION IN A LIANA FOREST IN AMAZONIAN BOLIVIA , 2001 .

[51]  Xavier Otazu,et al.  Multiresolution-based image fusion with additive wavelet decomposition , 1999, IEEE Trans. Geosci. Remote. Sens..

[52]  Lori M. Bruce,et al.  Discrimination of subtly different vegetative species via hyperspectral data , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[53]  Robert P. W. Duin,et al.  PRTools - Version 3.0 - A Matlab Toolbox for Pattern Recognition , 2000 .

[54]  Gerardo Avalos,et al.  Leaf Optical Properties of Trees and Lianas in the Outer Canopy of a Tropical Dry Forest 1 , 1999 .

[55]  A. Skidmore,et al.  Use of remote sensing and GIS for sustainable land management , 1997 .

[56]  K. McGwire,et al.  Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. , 2000 .

[57]  F. M. Danson,et al.  RED-EDGE RESPONSE TO FOREST LEAF-AREA INDEX (VOL 16, PG 183, 1995) , 1995 .

[58]  Melba M. Crawford,et al.  Analysis of forest environments - classification as a metric of hyperspectral instrument performance , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[59]  Paul Scheunders,et al.  Generic wavelet-based hyperspectral classification applied to vegetation stress detection , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[60]  Benoit Rivard,et al.  Variability in leaf optical properties of Mesoamerican trees and the potential for species classification. , 2006, American journal of botany.

[61]  S. Ustin,et al.  Mapping nonnative plants using hyperspectral imagery , 2003 .

[62]  Terry Caelli,et al.  Discrimination of lianas and trees with leaf-level hyperspectral data , 2004 .

[63]  M. Schlossberg,et al.  An evaluation of multi-spectral responses on selected turfgrass species , 2000 .

[64]  L. Johnson,et al.  Spectrometric Estimation of Total Nitrogen Concentration in Douglas-Fir Foliage , 1996 .

[65]  K. Schmidt,et al.  Hyperspectral remote sensing of vegetation species distribution in a saltmarsh , 2003 .

[66]  S. Ustin,et al.  Spectral reflectance characteristics of California subalpine marsh plant communities , 1998, Wetlands.

[67]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[68]  S. S. Iyengar,et al.  Wavelet-based feature extraction from oceanographic images , 1998, IEEE Trans. Geosci. Remote. Sens..

[69]  G. A. Blackburn,et al.  Towards the Remote Sensing of Matorral Vegetation Physiology : Relationships between Spectral Reflectance, Pigment, and Biophysical Characteristics of Semiarid Bushland Canopies. , 1999 .

[70]  H. Dietz,et al.  Determination of plant species cover by means of image analysis , 1996 .

[71]  Hao Chen,et al.  Forest information from hyperspectral sensing , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[72]  M. Ashton,et al.  Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications , 2004 .

[73]  Paul Scheunders,et al.  A band selection technique for spectral classification , 2005, IEEE Geoscience and Remote Sensing Letters.

[74]  M. Kirschbaum,et al.  Discrimination between Betula pendula , Betula pubescens , and their hybrids using near-infrared reflectance spectroscopy , 1997 .

[75]  Robert C. Harriss,et al.  Deforestation in Costa Rica: A Quantitative Analysis Using Remote Sensing Imagery 1 , 2001 .

[76]  Helmi Zulhaidi Mohd Shafri,et al.  Hyperspectral Remote Sensing of Vegetation Using Red Edge Position Techniques , 2006 .

[77]  R. Lawrence,et al.  Early Detection of Douglas-Fir Beetle Infestation with Subcanopy Resolution Hyperspectral Imagery , 2003 .

[78]  Yadvinder Malhi,et al.  Increasing dominance of large lianas in Amazonian forests , 2002, Nature.

[79]  L. Bruce,et al.  Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max) , 2003 .

[80]  John Moncrieff,et al.  Carbon Dioxide Uptake by an Undisturbed Tropical Rain Forest in Southwest Amazonia, 1992 to 1993 , 1995, Science.

[81]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[82]  M. Cochrane.  Using vegetation reflectance variability for species level classification of hyperspectral data , 2000 .

[83]  A. Skidmore,et al.  MERIS and the red-edge position , 2001 .

[84]  R. Clark,et al.  Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .

[85]  Jiang Li,et al.  Wavelets for computationally efficient hyperspectral derivative analysis , 2001, IEEE Trans. Geosci. Remote. Sens..

[86]  G. Carter.  Reflectance Wavebands and Indices for Remote Estimation of Photosynthesis and Stomatal Conductance in Pine Canopies , 1998 .

[87]  Yadvinder Malhi,et al.  Fingerprinting the impacts of global change on tropical forests. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[88]  Harini Nagendra,et al.  Satellite imagery as a tool for monitoring species diversity: an assessment , 1999 .

[89]  S. Hubbell,et al.  A unified theory of biogeography and relative species abundance and its application to tropical rain forests and coral reefs , 1997, Coral Reefs.

[90]  Sagar V. Kamarthi,et al.  Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[91]  P. Fearnside,et al.  RAIN FOREST FRAGMENTATION AND THE STRUCTURE OF AMAZONIAN LIANA COMMUNITIES , 2001 .

[92]  Donald B. Percival,et al.  The discrete wavelet transform and the scale analysis of the surface properties of sea ice , 1996, IEEE Trans. Geosci. Remote. Sens..

[93]  Susan L. Ustin,et al.  Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at Jornada, New Mexico , 2005 .

[94]  R.E.E. Jongschaap,et al.  Imaging spectrometry for agricultural applications , 2002 .

[95]  R. Fuller,et al.  Ground and airborne radiometry over intertidal surfaces: Waveband selection for cover classification , 1998 .

[96]  S. Ustin,et al.  Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data , 1996 .

[97]  Why Do Some Tropical Forests Have So Many Species of Trees? , 2004 .

[98]  K. O. Niemann,et al.  Remote sensing of forest stand age using airborne spectrometer data , 1995 .

[99]  Hao. Chen.  An advanced classification system for processing multitemporal landsat imagery and producing Kyoto Protocol products , 2004 .

[100]  R. Lunetta,et al.  A change detection experiment using vegetation indices. , 1998 .

[101]  Charles E. Olson,et al.  The significance of spatial resolution: Identifying forest cover from satellite data , 2001 .

[102]  G. A. Sanchez-Azofeifa,et al.  Canopy observations on the hyperspectral properties of a community of tropical dry forest lianas and their host trees , 2006 .

[103]  Seisuke Fukuda,et al.  A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images , 1999, IEEE Trans. Geosci. Remote. Sens..

[104]  D. M. Moss,et al.  Spectral reflectance measurements in the genus Sphagnum , 1993 .

[105]  J. A. Thomasson,et al.  Differentiating bottomland tree species with multispectral videography , 1994 .

[106]  Philip J. Howarth,et al.  Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems , 1999 .

[107]  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 .

[108]  David G. Goodenough,et al.  Pixel unmixing for hyperspectral measurement of foliar chemistry in Pacific Northwest coastal forests , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[109]  M. Cho.  Hyperspectral remote sensing of biochemical and biophysical parameters: the derivate red-edge "double-peak feature", a nuisance or an opportunity? , 2007 .

[110]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .