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Most plant species feature similar biochemical compositions and thus similar spectral signals. Still, empirical evidence suggests that the spectral discrimination of species and plant assemblages is possible. Success depends on the presence or absence of faint but detectable differences in biochemical (e.g., pigments, leaf water and dry matter content) and structural properties (e.g., leaf area, angle, and leaf structure), i.e., optical traits. A systematic analysis of the contributions and spatio-temporal variability of optical traits for the remote sensing of organismic vegetation patterns has not yet been conducted. We thus use time series of optical trait values retrieved from the reflectance signal using physical models (optical trait indicators, OTIs) to answer the following questions: How are optical traits related among patterns of floristic composition and reflectance? How variable are these relations in space and time? Are OTIs suitable predictors of plant species composition? We conducted a case study of three temperate open study sites with semi-natural vegetation. The canopy reflectance of permanent vegetation plots was measured on multiple dates over the vegetation period using a field spectrometer. We recorded the cover fractions of all plant species found in the vegetation plots and extracted gradients of species composition from these data. The physical PROSAIL leaf and canopy optical properties model was inverted with random forest regression models to retrieve time series of OTIs for each plot from the reflectance spectra. We analyzed these data sets using correlation analyses. This approach allowed us to assess the distribution of optical traits across gradients of species composition. The predictive performance of OTIs was tested in relation to canopy reflectance using random forest models. OTIs showed pronounced relationships with floristic patterns in all three study sites. These relationships were subject to considerable temporal variability. Such variability was driven by short-term vegetation dynamics introduced by local resource stress. In 72% of all cases OTIs out-performed the original canopy reflectance spectra as indicators of plant species composition. OTIs are also easier to interpret in an ecological sense than spectral bands or features. We thus conclude that optical traits retrieved from reflectance data have a high indicative value for ecological research and applications. [ABSTRACT FROM AUTHOR]

Most remote sensing approaches for mapping invasive plant species focus on species in a prominent phenological stage, such as during flowering, and do not systematically evaluate the performance for mapping lower cover fractions. In this study, we used airborne imaging spectroscopy (also known as hyperspectral imaging) to detect the invasive grass Phalaris aquatica and the invasive herb Centaurea solstitialis in a pre-flowering stage in the Jasper Ridge Biological Preserve, California, and compared the performance of three different one-class classifiers: Maxent, biased support vector machines and boosted regression trees. We collected presence data for C. solstitialis and P. aquatica to calibrate each approach and additional presence-absence data to validate model performance on 3 m × 3 m plots. The imaging spectroscopy data were acquired using the Carnegie Airborne Observatory Visible-to-Shortwave Infrared (VSWIR) imaging spectrometer (400–2500 nm range) with a pixel size of 1 m × 1 m. The resulting overall accuracies were 72–74% for C. solstitialis , and 83–88% for P. aquatica . For both species, the overall performance was slightly better for Maxent and BRT than for biased SVM. The detection rates for low cover plots were considerably higher for C. solstitialis than for P. aquatica . The models relied on different areas of the reflectance spectrum, but still produced the same general pattern of predicted species occurrences. We conclude that the different one-class classifiers allow for the detection and monitoring of target species with similar success rates. [ABSTRACT FROM AUTHOR]

Aims Accurate field quantification of stem counts is time consuming and laborious, especially in ecosystems containing many small individuals where counts are immense. There are many large, long-term data sets worldwide that include various measures of woody plant abundance, such as crown cover, that are faster and easier to measure than true counts of individuals. Quantifying the number of individuals allows in-depth research into various topics including functional ecology, carbon accounting, conservation and metabolic theory. Here we test the accuracy of predicting the abundance of individual woody plants from cover and forest structure measures, and explore the implications of refined abundance measures for understanding the rarity of woody plant species across southeast Queensland. Location Southeast Queensland ( SEQ), Australia. Methods To supplement an existing data set of 1251 sites with measured crown covers for woody plants in various strata, we sampled stem counts in an additional 30 sites, forming a calibration data set. We ensured that these 30 sites covered the environmental variation in the study area. Generalized additive mixed effects models were used to predict stem counts for all woody plants in the original data set from their crown cover and forest structure values and a site-level variable accounting for water balance. We used these refined abundance data to investigate relationships between local and regional abundance for different size classes. Results Stem densities increased with crown cover in a non-linear fashion; slowing once crown cover reached ca. 5% for shrubs and ca. 50% for trees, depending on their maximum potential height. Using the predicted stem count data, we found that shrubs were more locally abundant than trees, but less regionally abundant. Larger trees (>20 m maximum potential height) showed a negative relationship between local and regional abundance; there was no significant relationship between other size classes. Conclusions Our study demonstrates how crown cover can be converted to stem counts using a calibration data set and flexible mixed-effects models. This enables further ecological analysis at a fraction of the investment required to re-survey. Analyses of abundances revealed that tree species are less locally abundant than smaller woody species, likely due to space filling constraints, but more regionally abundant. [ABSTRACT FROM AUTHOR]

Abstract: Assessing and mapping patterns of (semi-)natural vegetation types at a large spatial scale is a difficult task. The challenge increases if the floristic variation within vegetation types (i.e., subtype variation of species composition) is the target. A desirable way to deal with this task may be to address such vegetation patterns with remote-sensing approaches. In particular data from multispectral sensors are easy to obtain, globally accessible, and often provide a high temporal resolution. They hence offer a comprehensive basis for vegetation mapping. The potential of such sensors for vegetation mapping has, however, never been thoroughly investigated. In particular, a systematic test regarding the spectral capabilities of these data for an assessment of detailed floristic variation has not been implemented to date. We thus addressed in this study the question how the ability of optical sensors to map floristic variation is affected by their respective spectral coverage and number of bands. To answer this question, we simulated monotemporal and multiseasonal data of eleven multispectral sensors. These data were used to model gradual transitions in species composition (i.e., floristic gradients) within three types of spontaneous vegetation typical for Central Europe using Partial Least Squares regression. Comparison of the model fits (ranging up to R 2 =0.76 in cross-validation) illustrated the potential of multispectral data for detailed vegetation mapping. The results show that spectral coverage of the entire solar-reflective domain is the most important sensor characteristic for a successful assessment of floristic variation. Model and sensor performances as well as limitations are thoroughly discussed, and recommendations for sensor development are made based on the final conclusions of this study. [Copyright &y& Elsevier]

Abstract: Mapping requires a meaningful generalization of information. For vegetation maps, classification is frequently used to generalize the species composition of (semi-)natural plant assemblages. As an alternative to classification, ordination methods aim to extract major floristic gradients describing the prevailing compositional variation in a floristic data set as metric variables. This ability has been used previously to derive gradient maps of homogeneous landscapes that show plant species composition in continuous fields. In the present study, gradient mapping was used in a more heterogeneous landscape with intricate environmental gradients and higher variation in vegetation physiognomy. Since established ordination methods may have difficulties to cope with the highly variable plant species composition, we tested the novel method Isometric Feature Mapping (Isomap) against conventional methods (Detrended Correspondence Analysis and Nonmetric Multidimensional Scaling). The resulting floristic gradients were related to hyperspectral imagery (HyMap) using partial least squares regression (PLSR) and subsequently mapped. Prediction uncertainties are provided as additional map. Isomap was able to preserve 74% of the original variation inherent to the floristic data set in a three-dimensional solution. This was considerably more than the established techniques achieved. The PLSR models for the floristic gradients extracted with Isomap showed model fits ranging from R²=0.59 to R²=0.73 in calibration and from R²=0.55 to R²=0.69 in tenfold cross-validation. The resulting gradient map provides detailed information on compositional vegetation patterns. [Copyright &y& Elsevier]

Abstract: Developed in the field of chemometrics, Partial Least Squares Regression (PLSR) has become an established technique in vegetation remote sensing. PLSR was primarily designed for laboratory analysis of prepared material samples. Under field conditions in vegetation remote sensing, the performance of the technique may be negatively affected by differences in brightness due to amount and orientation of plant tissues in canopies or the observing conditions. To minimize these effects, we introduced brightness normalization to the PLSR approach and tested whether this modification improves the performance under changing canopy and observing conditions. This test was carried out using high-fidelity spectral data (400–2510nm) to model observed leaf chemistry. The spectral data was combined with a canopy radiative transfer model to simulate effects of varying canopy structure and viewing geometry. Brightness normalization enhanced the performance of PLSR by dampening the effects of canopy shade, thus providing a significant improvement in predictions of leaf chemistry (up to 3.6% additional explained variance in validation) compared to conventional PLSR. Little improvement was made on effects due to variable leaf area index, while minor improvement (mostly not significant) was observed for effects of variable viewing geometry. In general, brightness normalization increased the stability of model fits and regression coefficients for all canopy scenarios. Brightness-normalized PLSR is thus a promising approach for application on airborne and space-based imaging spectrometer data. [Copyright &y& Elsevier]