Quantifying empirical relations between planted species mixtures and canopy reflectance with PROTEST
Introduction
Detailed maps of plant species and plant species assemblages are required for various tasks in nature conservation (Margules & Pressey, 2000), rangeland management (DiTomaso, 2000), or ecological research (Hastings et al., 2005). Ever since sensors with high spectral and spatial resolution became available, remote sensing has aimed for the rapid and efficient mapping of species assemblages (Kerr & Ostrovsky, 2003).
Even though the mechanistic relations between reflectance and vegetation properties are still not completely understood, reflectance has been successfully used as proxy for mapping patterns of species assemblages on an empirical basis (e.g., Belluco et al., 2006, Judd et al., 2007, Rosso et al., 2005). So far, such studies rely on supervised approaches that quantify empirical relations between punctual vegetation samples and corresponding reflectance with statistical modeling techniques. These relationships are subsequently spatially extrapolated. Still, empirical modeling of species assemblages at the site level with reflectance data remains a difficult task as can be seen from studies featuring either partial success (Armitage et al., 2004) or poor relations (Thomas et al., 2002).
Approaches that target the full spatial variation in species composition of plant assemblages instead of single species distributions appear to be promising. This may be attributable to the fact that the problem of frequently reported missing unique spectral signatures of species is eluded (reports in, e.g., Castro-Esau et al., 2006, Price, 1994). These approaches take into account that co-occurring species show similar ecological demands. Consequently, species occurrences in natural ecosystems are not random but related to environmental gradients. The dominant reflectance pattern is hence determined by these gradients, while individual contributions of species are highly redundant.
The emerging patterns of co-occurring species can be either described categorically as plant communities (von Humboldt, 1807) or as gradual transitions of species proportions (Gleason, 1926). Both concepts can be addressed with supervised remote-sensing approaches. Maps of plant communities (Hirano et al., 2003) or gradients in species proportions (Schmidtlein and Sassin, 2004, Schmidtlein et al., 2007) do thus not require unique spectral signatures of individual species, as long as the floristic patterns cause spatial variation in reflectance. For this reason an adequate spatial resolution with respect to the floristic patterns is required.
Canopy cover fractions of structural and biochemical stand properties that account for canopy reflectance of mixed stands (Asner and Martin, 2008, Kumar et al., 2001, Ustin et al., 2004) are however only partly influenced by species proportions. Manifold enduring changes result from prevailing growth stages as well as from health status of plants and abiotic stress conditions (Carter, 1993, Carter and Knapp, 2001, Gausman, 1984, Sanger, 1971, Sorby, 1873). These short-term influences may enhance or weaken the empirical relationship between species assemblages and reflectance. Since such effects are omnipresent in the field and hard to quantify, it is difficult to assess the proportion of variation in reflectance that is exclusively related to variation in species proportions of a mixed stand. We thus performed a study under controlled conditions to empirically relate canopy reflectance measured with a field spectrometer to planted variation in species composition. Species composition was defined in this context as species proportions within a mixed stand. Spatial variation in species composition was generated quantitatively by varying proportions per area or qualitatively by replacing one species by another. To simulate conditions prevalent in natural stands, the variation in species composition involved multiple redundant species occurrences. Analyses of the empirical relations between reflectance and species composition were carried out during the maturation of the stands by means of a statistical approach, which is to our knowledge new to the field of remote sensing. The study had two major objectives. First, we aimed to determine the proportion of qualitative and quantitative variation in grassland species composition at the site level predictable by reflectance. For this purpose, confounding differences in plant states caused by environmental factors were minimized. Second, we assessed the temporal stability of the empirical relation between species composition and reflectance depending on the growth stage of plants. We focused on the question (a) to what degree overall species composition is expressed in reflectance. For a better understanding, we assessed (b) how individual proportions of variation in species composition contribute to the empirical relation.
Section snippets
Establishment of target plant stands
The study was performed in a greenhouse environment. In contrast to analyses in natural environments, greenhouse studies offer the possibility to control species composition and to obtain reflectance data with constant illumination geometry and intensity by applying a standardized measurement protocol (Delalieux et al., 2008). It is further possible to keep effects from soil properties, litter, and external stressors at a minimum. In our study, this was achieved by the use of homogenized
Empirical relations between species composition and reflectance
Pronounced empirical relations between overall species composition and reflectance as targeted in objective (a) were found in both quantitative and qualitative analyses. As for the within-set analyses, PROTEST showed matching patterns of reflectance and quantitative species composition (Fig. 4). In between-set analyses, qualitative differences were also accurately assessed with SAM classification (Fig. 5).
For quantitative variation in set 1, the Procrustes r exceeded 0.6 for all time steps and
Discussion of methods
Comparison of matrices with PROTEST analysis is to our knowledge new to the field of remote sensing, whereas Procrustes rotation has been tested successfully on reflectance patterns (Lobser & Cohen, 2007). The Mantel test (Mantel, 1967) is more common to match two multivariate datasets. In Mantel test analyses, resemblance matrices describing mutual similarities of the samples within the datasets are correlated directly. Several studies (e.g., He et al., 2009, Tuomisto, Poulsen, et al., 2003,
Conclusions and outlook
The results of this study confirmed the existence of empirical relations between grassland species composition and reflectance. Influence of growth stages on these relations was obvious. The results further suggested influences of plant states and morphological trait combinations. While a broad trait spectrum strengthened the relations, effects of prevailing plant states were dependent on spatially homogeneous distributions.
It can be expected that the relation between species composition and
Acknowledgements
H.F. is funded by the German Research Foundation (DFG) grant SCHM 2153/2-1 “Entkopplung von räumlichen Bezügen zwischen Reflexion und Artenzusammensetzung der Vegetation.” We gratefully acknowledge the practical advice and help of the staff of INRES, University of Bonn, U. Steiner, J. Bauer, L. Fricke, F. Jonas, and A.K. Mahlein, in various aspects of this study. Further invaluable support came from the DFG's graduate school 722 “Use of information technologies for precision crop protection.”
References (55)
- et al.
The role of environmental context in mapping invasive plants with hyperspectral image data
Remote Sensing of Environment
(2008) - et al.
Remote sensing of native and invasive species in Hawaiian forests
Remote Sensing of Environment
(2008) - et al.
Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels
Remote Sensing of Environment
(2008) - et al.
Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing
Remote Sensing of Environment
(2006) - et al.
High resolution derivative spectra in remote sensing
Remote Sensing of Environment
(1990) - et al.
Multi-seasonal spectral characteristics analysis of coastal salt marsh vegetation in Shanghai, China
Estuarine, Coastal and Shelf Science
(2006) Evaluation of factors causing reflectance differences between sun and shade leaves
Remote Sensing of Environment
(1984)- et al.
Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem
Remote Sensing of Environment
(2008) - et al.
From space to species: Ecological applications for remote sensing
Trends in Ecology & Evolution
(2003) - et al.
The spectral image processing system (SIPS) — Interactive visualization and analysis of spectrometer data
Remote Sensing of Environment
(1993)
How unique are spectral signatures?
Remote Sensing of Environment
Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data
Remote Sensing of Environment
Mapping of continuous floristic gradients in grasslands using hyperspectral imagery
Remote Sensing of Environment
Derivative analysis of hyperspectral data
Remote Sensing of Environment
Identification of the spectral characteristics of British semi-natural upland vegetation using direct ordination: A case study from Dartmoor, UK
International Journal of Remote Sensing
Exploiting manifold geometry in hyperspectral imagery
IEEE Transactions on Geoscience and Remote Sensing
Improved manifold coordinate representations of large-scale hyperspectral scenes
IEEE Transactions on Geoscience and Remote Sensing
Responses of leaf spectral reflectance to plant stress
American Journal of Botany
Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration
American Journal of Botany
Variability in leaf optical properties of Mesoamerican trees and the potential for species classification
American Journal of Botany
Hyperspectral canopy measurements under artificial illumination
International Journal of Remote Sensing
Invasive weeds in rangelands: Species, impacts, and management
Weed Science
Vegetation Mitteleuropas mit den Alpen
Hyperspectral characterization of canopy components and structure for phenological assessment of an invasive weed
Environmental Monitoring and Assessment
The individualistic concept of the plant association
Bulletin of the Torrey Club
The effect of solar illumination angle and sensor view angle on observed patterns of spatial structure in tallgrass prairie
IEEE Transactions on Geoscience and Remote Sensing
Generalized Procrustes analysis
Psychometrika
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