Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery

Zhang, Jinkai; Rivard, Benoît; Sánchez‐Azofeifa, Arturo; Castro-Esau, Karen L.

doi:10.1016/j.rse.2006.06.010

Cited by 189 publications

(145 citation statements)

References 63 publications

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“…To conclude, differences in leaf and reproductive phenology, canopy structure, and contribution of other components (e.g. epiphylls, bark, background) are as such additional sources of spectral variation among forest sites that contribute to the within-species variability (and spectral variation; Zhang, Rivard, Sanchez-Azofeifa, & Castro-Essau, 2006) thereby attenuating the direct link with tree species richness.…”

Section: Discussionmentioning

confidence: 99%

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Somers

Asner

Martin

et al. 2015

Remote Sensing of Environment

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Section: Discussionmentioning

confidence: 99%

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Somers

Asner

Martin

et al. 2015

Remote Sensing of Environment

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“…If soil signatures were a large part of the spectral footprints (i.e., for small-leaved species), these differences in soil signatures in the plots and fields may contribute to the spectral differences in plots and fields. Depending on vegetation cover, differences in soil moisture and soil texture can influence spectral data [16,49,50]. Differences in soil characteristics such as soil nutrients, soil biota, and soil pH could also directly and indirectly influence differences observed among communities [51].…”

Section: Other Considerationsmentioning

confidence: 99%

Distinguishing Early Successional Plant Communities Using Ground-Level Hyperspectral Data

Aneece

Epstein

2015

Remote Sensing

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Abandoned agricultural fields have recently become more abundant in the U.S. and remain susceptible to species invasions after cultivation disturbance. As invasive species become more widespread with increases in anthropogenic activities, we need more effective ways to use limited resources for conservation of native ecosystems. Remote sensing can help us monitor the spread and effects of invasive species, and thus determine the species and locations to target for conservation. To examine this potential, we studied plant communities dominated by exotic invasive plant species in secondary successional fields in northern Virginia using ground-level hyperspectral data. Within these communities, ordination analyses of vegetation surveys revealed differences in species compositions among plots and fields. These differences among communities were also observed in the spectral data. Stepwise multiple linear regression analyses to determine which species influenced the ordination axes revealed that many of the influential species are considered invasive, again underscoring the influence of invasive species on community properties. Stepwise regression analyses also revealed that the most influential wavelengths for discrimination were distributed along the spectral profile from the visible to the near-infrared regions. A discriminant analysis using wavelengths selected with a principal components analysis demonstrated that different plant communities were separable using spectral data. These spectrally observable differences suggest that we can use hyperspectral data to distinguish among invasive-dominated successional plant communities in this region.

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“…We chose the Haar mother wavelet as it is the simplest of all available mother wavelets, and recent investigations have illustrated its effectiveness in hyperspectral data analysis [33,36,39]. The decomposition level is determined by the type of wavelet and the original signal length.…”

Section: Calculation Of Discrete Wavelet Coefficientsmentioning

confidence: 99%

“…The discrete wavelet transform (DWT) has been increasingly used in recent years in the processing of hyperspectral images for a variety of purposes [33][34][35][36][37][38][39]. These studies have shown its importance as an effective tool for reducing the dimensionality of hyperspectral data while maintaining information content for a variety of applications.…”

Section: Introductionmentioning

confidence: 99%

“…First, it transforms the correlated set of spectral features into wavelet features at different scales; the information carried at different scales is less correlated because of the orthogonal nature of transformation [39]. Second, most of the wavelet coefficients contain very low values which can be discarded by applying an appropriate threshold without losing significant information, thus affording dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI

et al. 2013

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Abstract:The need for an efficient and standard technique for optimal spectral sampling of hyperspectral data during the inversion of canopy reflectance models has been the subject of many studies. The objective of this study was to investigate the utility of the discrete wavelet transform (DWT) for extracting useful features from hyperspectral data with which forest LAI can be estimated through inversion of a three dimensional radiative transfer model, the Discrete Anisotropy Radiative Transfer (DART) model. DART, coupled with the leaf optical properties model PROSPECT, was inverted with AVIRIS data using a look-up-table (LUT)-based inversion approach. We used AVIRIS data and in situ LAI measurements from two different hardwood forested sites in Wisconsin, USA. Prior to inversion, model-simulated and AVIRIS hyperspectral data were transformed into discrete wavelet coefficients using Haar wavelets. The LUT inversion was performed with three different datasets, the original reflectance bands, the full set of wavelet extracted features, and two wavelet subsets containing 99.99% and 99.0% of the cumulative energy The results indicate that the discrete wavelet transform can increase the accuracy of LAI estimates by improving the LUT-based inversion of DART (and, potentially, by implication, other terrestrial radiative transfer models) using hyperspectral data. The improvement in accuracy of LAI estimates is potentially due to different properties of wavelet analysis such as multi-scale representation, dimensionality reduction, and noise removal.

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Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery

Cited by 189 publications

References 63 publications

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Distinguishing Early Successional Plant Communities Using Ground-Level Hyperspectral Data

Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI

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