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Aim: The general goal of this study is to investigate and analyze patterns of ecophysiological leaf traits and spectral response among life forms (trees, shrubs and lianas) in the Cerrado ecosystem. In this study, we first tested whether life forms are discriminated through leaf level functional traits. We then explored the correlation between leaf-level plant functional traits and spectral reflectance. Location: Serra do Cipo National Park, Minas Gerais, Brazil. Methods: Six ecophysiological leaf traits were selected to best characterize differences between life forms in the woody plant community of the Cerrado. Results were compared to spectral vegetation indices to determine if plant groups provide means to separate leaf spectral responses. Results: Values obtained from leaf traits were similar to results reported from other tropical dry sites. Trees and shrubs significantly differed from lianas in terms of the percentage of leaf water content and Specific Leaf Area. Spectral indices were insufficient to capture the differences of these key traits between groups, though indices were still adequately correlated to overall trait variation. Conclusion: The importance of life forms as biochemical and structurally distinctive groups is a significant finding for future remote sensing studies of vegetation, especially in arid and semi-arid environments. The traits we found as indicative of these groups (SLA and water content) are good candidates for spectral characterization. Future studies need to use the full wavelength (400 nm–2500 nm) in order to capture the potential response of these traits. The ecological linkage to water balance and life strategies encourages these traits as starting points for modeling plant communities using hyperspectral remote sensing. [ABSTRACT FROM AUTHOR]

Abstract: Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA). Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R 2: 0.51–0.82, p <0.0001). The best robustness (R 2 =0.74, RMSE=18.97g/m2 and Bias=0.12g/m2) was obtained using a combination of two low-scale features (1639nm, scale 4) and (2133nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the NDLMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or spaceborne imaging spectroscopy data for mapping canopy foliar dry biomass. [Copyright &y& Elsevier]

Abstract: Leaf water content is an important variable for understanding plant physiological properties. This study evaluates a spectral analysis approach, continuous wavelet analysis (CWA), for the spectroscopic estimation of leaf gravimetric water content (GWC, %) and determines robust spectral indicators of GWC across a wide range of plant species from different ecosystems. CWA is both applied to the Leaf Optical Properties Experiment (LOPEX) data set and a synthetic data set consisting of leaf reflectance spectra simulated using the leaf optical properties spectra (PROSPECT) model. The results for the two data sets, including wavelet feature selection and GWC prediction derived using those features, are compared to the results obtained from a previous study for leaf samples collected in the Republic of Panamá (PANAMA), to assess the predictive capabilities and robustness of CWA across species. Furthermore, predictive models of GWC using wavelet features derived from PROSPECT simulations are examined to assess their applicability to measured data. The two measured data sets (LOPEX and PANAMA) reveal five common wavelet feature regions that correlate well with leaf GWC. All three data sets display common wavelet features in three wavelength regions that span 1732–1736nm at scale 4, 1874–1878nm at scale 6, and 1338–1341nm at scale 7 and produce accurate estimates of leaf GWC. This confirms the applicability of the wavelet-based methodology for estimating leaf GWC for leaves representative of various ecosystems. The PROSPECT-derived predictive models perform well on the LOPEX data set but are less successful on the PANAMA data set. The selection of high-scale and low-scale features emphasizes significant changes in both overall amplitude over broad spectral regions and local spectral shape over narrower regions in response to changes in leaf GWC. The wavelet-based spectral analysis tool adds a new dimension to the modeling of plant physiological properties with spectroscopy data. [Copyright &y& Elsevier]

Abstract: This study evaluates the use of waveform LIDAR data for the characterization of secondary forests stages in a Tropical Dry Forest (TDF) area of Guanacaste, Costa Rica. A secondary forest succession is defined here as regrowth of woody vegetation following a complete or heavy forest clearance for pasture, agriculture, or other human activity. We first compare the known spatial distribution of three main TDF successional stages (Early, Intermediate and Late successional stages) as constrained by published field observations of tree height with that obtained from a three-class classification of the LIDAR data. In doing so we explicitly assess the possibility of using LIDAR data to map the distribution of the three main TDFs stages. We demonstrates that changes in the forest vertical structure (such as height) associated with principal successional stages (Early, Intermediate and Late) of TDF secondary growth can be effectively identified from LIDAR data. The successional sequence observed is related to changes in the vertical distribution of woody components that occur when forest patches evolve from an Early Stage which is dominated by sparse trees and grass, to initial stages of intermediate secondary succession characterized by small canopies and a high density of understory regeneration, and then to more complex and developed stages of intermediate and late stage secondary succession which share some attributes of Old growth forests. We then focus on the Intermediate successional Stage since it is of highest interest to policy makers dealing with programs implementing payments for environmental services. We show that an analysis of the LIDAR data can identify successional three subclasses within the Intermediate Stage providing further insights in the development of secondary forest growth. A validation of the three new forest classes is provided using field observations showing a mean tree height and standard deviation of 6.16±0.87, 7.82±0.31 and 8.62±1.22. m. [Copyright &y& Elsevier]

This research examined the spectral response of poplar (Populus deltoides, Populus trichocarpa), wheat (Triticum aestivum), and canola (Brassica napus) leaves subjected to fumigation with gaseous phase toxic industrial chemical gases (TICs). The gases include ammonia (NH3), sulphur dioxide (SO2), hydrogen sulphide (H2S), chlorine (Cl2), and hydrogen cyanide (HCN). This study aimed to determine if: 1) vegetation subjected to TICs could be distinguished from background vegetation during varying growth stages and environmental stresses; and, 2) different TICs could be distinguished based on the spectral response of vegetation. The results showed that both environmental and TICs induced similar spectral features inherent to plants, which are related primarily to chlorophyll and water loss. These features include pigments in the visible and cellulose, lignin, lipids starches, and sugars in the SWIR. Although no specific spectral features could be tied to individual TICs an analysis of the data using vegetation indices showed that the TICs and environmental stresses result in diagnostic trends from healthy mature to highly stressed leaves. In addition combinations of specific indices could be used to distinguish the effects of NH3, SO2, Cl2 and their effect from that of other treatments of the study. The continued goal for this research program is to develop a remote detection capability for hazardous events such as a toxic gas leak. Our findings at the leaf level suggest that damage can be detected within 48 hrs and should last for an extended period. Thus, the next experimental step is to test if the results shown here at the leaf level can also be detected with airborne and satellites systems. [ABSTRACT FROM PUBLISHER]

Species identification and characterization in tropical environments is an emerging field in tropical remote sensing. Significant efforts are currently aimed at the detection of tree species, of levels of forest successional stages, and the extent of liana occurrence at the top of canopies. In this paper we describe our use of high resolution imagery from the Quickbird Satellite to estimate the flowering population of Tabebuia guayacan trees at Barro Colorado Island (BCI), in Panama. The imagery was acquired on 29 April 2002 and 21 March 2004. Spectral Angle Mapping via a One-Class Support Vector machine was used to detect the presence of 422 and 557 flowering tress in the April 2002 and March 2004 imagery. Of these, 273 flowering trees are common to both dates. This study presents a new perspective on the effectiveness of high resolution remote sensing for monitoring a phenological response and its use as a tool for potential conservation and management of natural resources in tropical environments. [ABSTRACT FROM AUTHOR]

Fractional abundances predicted for a given pixel using spectral mixture analysis (SMA) are most accurate when only the endmembers that comprise it are used, with larger errors occurring if inappropriate endmembers are included in the unmixing process. This paper presents an iterative implementation of SMA (ISMA) to determine optimal per-pixel endmember sets from the image endmember set using two steps: 1) an iterative unconstrained unmixing, which removes one endmember per iteration based on minimum abundance and 2) analysis of the root-mean-square error as a function of iteration to locate the critical iteration defining the optimal endmember set. The ISMA was tested using simulated data at various signal-to-noise ratios (SNRs), and the results were compared with those of published unmixing methods. The ISMA method correctly selected the optimal endmember set 96% of the time for SNR of 100 : 1. As a result, per-pixel errors in fractional abundances were lower than for unmixing each pixel using the full endmember set. ISMA was also applied to Airborne Visible/Infrared Imaging Spectrometer hyperspectral data of Cuprite, NV. Results show that the ISMA is effective in obtaining abundance fractions that are physically realistic (sum close to one and nonnegative) and is more effective at selecting endmembers that occur within a pixel as opposed to those that are simply used to improve the goodness of fit of the model but not part of the mixture. [ABSTRACT FROM AUTHOR]

Abstract: Hyperspectral remote sensing provides great potential to monitor and study biodiversity of tropical forests through species identification and mapping. In this study, five species were selected to examine crown-level spectral variation within and between species using HYperspectral Digital Collection Experiment (HYDICE) data collected over La Selva, Costa Rica. Spectral angle was used to evaluate the spectral variation in reflectance, first derivative and wavelet-transformed spectral domains. Results indicated that intra-crown spectral variation does not always follow a normal distribution and can vary from crown to crown, therefore presenting challenges to statistically define the spectral variation within species using conventional classification approaches that assume normal distributions. Although derivative analysis has been used extensively in hyperspectral remote sensing of vegetation, our results suggest that it might not be optimal for species identification in tropical forestry using airborne hyperspectral data. The wavelet-transformed spectra, however, were useful for the identification of tree species. The wavelet coefficients at coarse spectral scales and the wavelet energy feature are more capable of reducing variation within crowns/species and capturing spectral differences between species. The implications of this examination of intra- and inter-specific variability at crown-level were: (1) the wavelet transform is a robust tool for the identification of tree species using hyperspectral data because it can provide a systematic view of the spectra at multiple scales; and (2) it may be impractical to identify every species using only hyperspectral data, given that spectral similarity may exist between species and that within-crown/species variability may be influenced by many factors. [Copyright &y& Elsevier]

Initiated in 1984, the Committee Earth Observing Satellites' Working Group on Calibration and Validation (CEOS WGCV) pursues activities to coordinate, standardize and advance calibration and validation of civilian satellites and their data. One subgroup of CEOS WGCV, Land Product Validation (LPV), was established in 2000 to define standard validation guidelines and protocols and to foster data and information exchange relevant to the validation of land products. Since then, a number of leaf area index (LAI) products have become available to the science community at both global and regional extents. Having multiple global LAI products and multiple, disparate validation activities related to these products presents the opportunity to realize efficiency through international collaboration. So the LPV subgroup established an international LAI intercomparison validation activity. This paper describes the main components of this international validation effort. The paper documents the current participants, their ground LAI measurements and scaling techniques, and the metadata and infrastructure established to share data. The paper concludes by describing plans for sharing both field data and high-resolution LAI products from each site. Many considerations of this global LAI intercomparison can apply to other products, and this paper presents a framework for such collaboration. [ABSTRACT FROM AUTHOR]

Copyright of Biotropica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Biotropica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spectral mixture analysis (SMA) has been used extensively in the hyperspectral remote sensing community for the subpixel abundance estimation of targets. However, the task of defining every end member can be difficult, as evident from the importance attributed to the topic in the recent literature. The effectiveness of SMA can be compromised when the required spectral endmembers are not well constrained in terms of their spectral magnitude and shape. The spectral magnitude of the endmembers is more difficult to obtain than their spectral shape, in part because the effects of the atmosphere and topography are difficult to constrain. This paper presents a derivative spectral unmixing (DSU) model, which is an extension of the spectral mixture analysis and derivative analysis. Using a DSU approach, it is possible to estimate the fraction of an endmember characterized by one or more diagnostic absorption features despite having only a general knowledge of the spectral shapes of the remaining endmembers. The DSU is assessed using spectral data acquired for a lichen-covered rock sample, and the estimated fractions of lichen and rock are assessed against that obtained from a high spatial resolution digital photograph. The results of the laboratory experiments suggests that the DSU is a promising algorithm for the quantitative analysis of hyperspectral data, but experiments on airborne/spaceborne imagery are now required to assess its value for geological mapping. [ABSTRACT FROM AUTHOR]

The Tiangong-1 Hyperspectral Imager (HSI) is a relatively new spaceborne hyperspectral remote sensing system that was launched by the Chinese government on September 29th 2011. The system has 64 shortwave infrared (SWIR) spectral bands (1000–2500 nm) and imagery is at a spatial resolution of 20 m. This study represents an evaluation of Tiangong-1 data for the production of alteration mineral maps. Alteration mineral maps resulting from the analysis of Tiangong-1 HSI data and airborne SASI (Shortwave infrared Airborne Spectrographic Imager) data are compared for the Jintanzi area, Beishan, Gansu province, northwest China where gold bearing veins are documented. The results illustrate the detection of muscovite, kaolinite, chlorite, epidote, calcite and dolomite from Tiangong-1 HSI data and most anomalies seen in the airborne SASI data are captured. The Tiangong-1 data appears to be well suited for the detection of surface mineralogy in support of regional mapping and exploration. The data complements that which will be offered by the Chinese GF-5 Hyperspectral Imager and the German EnMAP system, both scheduled for launch in 2018. [ABSTRACT FROM AUTHOR]

Spectral decorrelation (transformations) methods have long been used in remote sensing. Transformation of the image data onto eigenvectors that comprise physically meaningful spectral properties (signal) can be used to reduce the dimensionality of hyperspectral images as the number of spectrally distinct signal sources composing a given hyperspectral scene is generally much less than the number of spectral bands. Determining eigenvectors dominated by signal variance as opposed to noise is a difficult task. Problems also arise in using these transformations on large images, multiple flight-line surveys, or temporal data sets as computational burden becomes significant. In this paper we present a spatial–spectral approach to deriving high signal quality eigenvectors for image transformations which possess an inherently ability to reduce the effects of noise. The approach applies a spatial and spectral subsampling to the data, which is accomplished by deriving a limited set of eigenvectors for spatially contiguous subsets. These subset eigenvectors are compiled together to form a new noise reduced data set, which is subsequently used to derive a set of global orthogonal eigenvectors. Data from two hyperspectral surveys are used to demonstrate that the approach can significantly speed up eigenvector derivation, successfully be applied to multiple flight-line surveys or multi-temporal data sets, derive a representative eigenvector set for the full image data set, and lastly, improve the separation of those eigenvectors representing signal as opposed to noise. [ABSTRACT FROM AUTHOR]

Abstract: Increasing dominance of lianas in many tropical forests is considered a fingerprint of global environmental change. Despite the key role they play in ecosystem functioning, lianas remain one of the least studied life forms in tropical environments. This paper contrasts leaf traits and spectral properties (400–1100 nm) of liana and tree communities from a tropical dry forest and a tropical rainforest in Panama, Central America. Differences between lianas and tree leaf traits were analyzed using spectroscopy, leaf histology and pigment extractions. Results from this study indicate that many of the biochemical, structural, and optical properties of lianas and trees are different in the dry forest site but not in rainforest sites. In the dry forest site, liana leaves exhibited significantly lower chlorophyll and carotenoid contents and were thinner than the leaves of their host trees. Specific leaf area, dry to fresh mass ratio, and mean water content of liana leaves were significantly higher when compared with tree leaves. The differences observed in the tropical dry forest site indicate that lianas may have a higher rate of resource acquisition and usage, whereas trees tend to conserve acquired resources. We suggest that our results may be indicative of the presence of a liana syndrome related to water availability and thus best exhibited in tropical dry forests. Our findings have important implications for using remote sensing to accurately map the distribution of liana communities at regional scales and for the continued expansion of lianas in tropical environments as a result of global change. [Copyright &y& Elsevier]

Abstract: A number of recent studies have focused on estimating gross primary production (GPP) using vegetation indices (VIs). In this paper, GPP is retrieved as a product of incident light use efficiency (LUE), defined as GPP/PAR, and the photosynthetically active radiation (PAR). As a good correlation is found between canopy chlorophyll content and incident LUE for six types of wheat canopy (R 2 =0.87, n =24), indices aimed for chlorophyll assessment can be used as an indicator of incident LUE and the product of chlorophyll indices and PAR will be a proxy of GPP. In a field experiment, we investigated four canopy chlorophyll content related indices (Red edge Normalized Difference Vegetation Index [Red Edge NDVI], modified Chlorophyll Absorption Ratio Index [MCARI710], Red Edge Chlorophyll Index [CIred edge] and the MERIS Terrestrial Chlorophyll Index [MTCI]) for GPP estimation during the growth cycle of wheat. These indices are validated for leaf and canopy chlorophyll estimation with ground truth data of canopy chlorophyll content. With ground truth data, a strong correlation is observed for canopy chlorophyll estimation with correlation coefficients R 2 of 0.79, 0.84, 0.85 and 0.87 for Red Edge NDVI, MCARI710, CIred edge and MTCI, respectively (n =24). As evidence of the existence of a relationship between canopy chlorophyll and GPP/PAR, these indices are shown to be a good proxy of GPP/PAR with R 2 ranging from 0.70 for Red Edge NDVI and 0.75 for MTCI (n =240). Remote estimation of GPP from canopy chlorophyll content×PAR is proved to be relatively successful (R 2 of 0.47, 0.53, 0.65 and 0.66 for Red edge NDVI, MCARI710, CIred edge and MTCI respectively, n =240). These results open up a new possibility to estimate GPP and should inspire new models for remote sensing of GPP. [Copyright &y& Elsevier]