Quantifying empirical relations between planted species mixtures and canopy reflectance with PROTEST

Abstract Mapping plant species composition of mixed vegetation stands with remote sensing is a complicated task. Uncertainties may arise from similar spectral signatures of different plant species as well as from variable influences of prevailing plant states (e.g., growth stages, vigor, or stress levels). Despite these uncertainties, empirical approaches may often be able to take up the challenge. However, their performance is likely to be affected by the temporal variability of empirical relations between reflectance and plant species composition. To assess some aspects of this temporal variability, we performed a greenhouse study. Three mixed stands of grassland species were planted with defined spatial variation in species proportions. The canopy reflectance of these mixed stands was measured with a field spectrometer over a period of three months. Confounding external influences on plant states apart from maturation were minimized. The suitability of canopy reflectance and derivative reflectance to draw conclusions on differences in qualitative species mixtures between the stands was tested with a classification approach (Spectral Angle Mapper, SAM). Procrustean randomization test (PROTEST), which is to our knowledge new to the field of remote sensing, was applied in combination with Isometric Feature Mapping to quantify the spectral variation caused by within-stand spatial variation in species proportions. Model fits in both analyses increased with progressing plant development; further, utilization of derivative reflectance improved the model fits. Regardless of the within-stand variation, SAM enabled a successful discrimination of the three stands with an average overall accuracy of 85% (reflectance) and 92% (derivative reflectance). In PROTEST analysis, spatial variation in reflectance was successfully related to within-stand variation in species proportions. However, observed influences of variable growth stages and health states on these relations were considerable. The temporal variation of these relations ( r  = 0.27–0.73 for reflectance and 0.48–0.73 for derivative reflectance) was quantified for the first time under controlled conditions.

[1]  Kalle Ruokolainen,et al.  Floristic patterns along a 43‐km long transect in an Amazonian rain forest , 2003 .

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

[3]  H. Gleason The individualistic concept of the plant association , 1926 .

[4]  Thomas L. Ainsworth,et al.  Improved Manifold Coordinate Representations of Large-Scale Hyperspectral Scenes , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Sebastian Schmidtlein,et al.  Mapping the floristic continuum : Ordination space position estimated from imaging spectroscopy , 2007 .

[6]  S. Sarkar,et al.  Systematic conservation planning , 2000, Nature.

[7]  J. E. Sanger,et al.  Quantitative Investigations of Leaf Pigments From Their Inception in Buds Through Autumn Coloration to Decomposition in Falling Leaves , 1971 .

[8]  Ben Somers,et al.  Hyperspectral canopy measurements under artificial illumination , 2008 .

[9]  H. Ellenberg,et al.  Vegetation Mitteleuropas mit den Alpen , 1984 .

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

[11]  Donald A. Jackson PROTEST: A PROcrustean Randomization TEST of community environment concordance , 1995 .

[12]  Tian Han,et al.  Investigation of Nonlinearity in Hyperspectral Imagery Using Surrogate Data Methods , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[13]  S. Schmidtlein,et al.  Mapping of continuous floristic gradients in grasslands using hyperspectral imagery , 2004 .

[14]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[15]  Kalle Ruokolainen,et al.  LINKING FLORISTIC PATTERNS WITH SOIL HETEROGENEITY AND SATELLITE IMAGERY IN ECUADORIAN AMAZONIA , 2003 .

[16]  J. Kerr,et al.  From space to species: ecological applications for remote sensing , 2003 .

[17]  G. Carter,et al.  Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. , 2001, American journal of botany.

[18]  Henry Clifton Sorby,et al.  XXII. On comparative vegetable Chromatology , 1873, Proceedings of the Royal Society of London.

[19]  A. Skidmore,et al.  Smoothing vegetation spectra with wavelets , 2004 .

[20]  J. Gower Generalized procrustes analysis , 1975 .

[21]  Richard P. Armitage,et al.  Identification of the spectral characteristics of British semi-natural upland vegetation using direct ordination: a case study from Dartmoor, UK , 2004 .

[22]  H. Gausman,et al.  Evaluation of factors causing reflectance differences between Sun and Shade Leaves , 1984 .

[23]  Fuan Tsai,et al.  Derivative Analysis of Hyperspectral Data , 1998 .

[24]  J. Ditomaso Invasive weeds in rangelands: Species, impacts, and management , 2000, Weed Science.

[25]  D. Roberts,et al.  Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data , 1993 .

[26]  Susan L. Ustin,et al.  Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem , 2008 .

[27]  Milo E. Richmond,et al.  Field Determination of Optimal Dates for the Discrimination of Invasive Wetland Plant Species Using Derivative Spectral Analysis , 2005 .

[28]  Marguerite Madden,et al.  Hyperspectral image data for mapping wetland vegetation , 2003, Wetlands.

[29]  Gregory A. Carter,et al.  Responses of leaf spectral reflectance to plant stress. , 1993 .

[30]  Michael D. Steven,et al.  High resolution derivative spectra in remote sensing , 1990 .

[31]  Masayuki Tamura,et al.  Hyperspectral identification of grassland vegetation in Xilinhot, Inner Mongolia, China , 2003 .

[32]  D. Roberts,et al.  Using Imaging Spectroscopy to Study Ecosystem Processes and Properties , 2004 .

[33]  P. Treitz,et al.  Image classification of a northern peatland complex using spectral and plant community data , 2003 .

[34]  Lalit Kumar,et al.  Imaging Spectrometry and Vegetation Science , 2001 .

[35]  N. Mantel The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[36]  Thomas L. Ainsworth,et al.  Exploiting manifold geometry in hyperspectral imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

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

[38]  A. V. Humboldt,et al.  Essai sur la géographie des plantes , 1805 .

[39]  R. Wisskirchen,et al.  Standardliste der Farn- und Blütenpflanzen Deutschlands , 1998 .

[40]  Liquan Zhang,et al.  Multi-seasonal spectral characteristics analysis of coastal salt marsh vegetation in Shanghai, China , 2006 .

[41]  Peng Gong,et al.  Hyperspectral Characteristics of Canopy Components and Structure for Phenological Assessment of an Invasive Weed , 2006, Environmental monitoring and assessment.

[42]  Roberta E. Martin,et al.  Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .

[43]  W. Cohen,et al.  MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data , 2007 .

[44]  S. Silvestri,et al.  Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing , 2006 .

[45]  Caz M Taylor,et al.  The spatial spread of invasions: new developments in theory and evidence , 2004 .

[46]  A. Hastings,et al.  Mapping marshland vegetation of San Francisco Bay, California, using hyperspectral data , 2005 .

[47]  Jincheng Gao,et al.  The effect of solar illumination angle and sensor view angle on observed patterns of spatial structure in tallgrass prairie , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[48]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

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

[50]  S. Steinberg,et al.  Mapping salt marsh vegetation using aerial hyperspectral imagery and linear unmixing in Humboldt Bay, California , 2007, Wetlands.

[51]  S. Ustin,et al.  The role of environmental context in mapping invasive plants with hyperspectral image data , 2008 .

[52]  Kate S. He,et al.  Linking variability in species composition and MODIS NDVI based on beta diversity measurements , 2009 .