Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data

Schlerf, Martin; Atzberger, Clement

doi:10.1016/j.rse.2005.10.006

Cited by 253 publications

(258 citation statements)

References 54 publications

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“…Such high correlation artificially carries exaggerated weight and causes inversion bias [30][31]. The irrelevant bands not sensitive to the model parameters are also more likely to introduce noise instead of useful information and thus adversely affect the inversion accuracy [23]. Darvishzadeh et al [13] did not find any improvement in accuracies by using spectral subsets over all wavebands in estimating grassland LAI from hyperspectral data by inversion of PROSAIL.…”

Section: Discussionmentioning

confidence: 98%

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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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Section: Discussionmentioning

confidence: 98%

“…Several model inversion techniques are available [16,22,23]. Traditional inversion methods like optimization techniques iteratively adjust model parameters until the modeled reflectance 'fits' or matches measured reflectance.…”

Section: Introductionmentioning

confidence: 99%

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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“…Palace et al [67] used panchromatic IKONOS imagery to analyze tree crowns in a Brazilian forest, accurately determining field measured crown widths. Schlerf and Atzberger [68] model canopy structure variables in Norway spruce stands using airborne HyMap data building a link between HyMap hyperspectral and Landsat through a model called INFORM.…”

Section: Discussionmentioning

confidence: 99%

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Chávez

Tullis

2013

Remote Sensing

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Coverage and frequency of remotely sensed forest structural information would benefit from single orbital platforms designed to collect sufficient data. We evaluated forest structural information content using single-date Hyperion hyperspectral imagery collected over full-canopy oak-hickory forests in the Ozark National Forest, Arkansas, USA. Hyperion spectral derivatives were used to develop machine learning regression tree rule sets for predicting forest neighborhood percentile heights generated from near-coincident Leica Geosystems ALS50 small footprint light detection and ranging (LIDAR). The most successful spectral predictors of LIDAR-derived forest structure were also tested with basal area measured in situ. Based on the machine learning regression trees developed, Hyperion spectral derivatives were utilized to predict LIDAR forest neighborhood percentile heights with accuracies between 2.1 and 3.7 m RMSE. Understory predictions consistently resulted in the highest accuracy of 2.1 m RMSE. In contrast, hyperspectral prediction of basal area measured in situ was only found to be 6.5 m 2 /ha RMSE when the average basal area across the study area was ~12 m 2 /ha. The results suggest, at a spatial resolution of 30 × 30 m, that orbital hyperspectral imagery alone can provide useful structural information related to vegetation height. Rapidly calibrated biophysical remote sensing techniques will facilitate timely assessment of regional forest conditions.

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“…Schlerf and Atzberger (2006) argued that, where quantitative approaches are used involving use of remote sensing data in vegetation studies, the application of empirical or physical models can be explored. Empirical models usually relate Bidirectional Refl ectance Factors (FRB) or Vegetation Indices (IVs) to one or more biophysical variables by defi ning statistical regression models.…”

Section: Introductionmentioning

confidence: 99%

“…Grounded on physical laws, it is possible to establish correlations of biochemical and/or biophysical variables with canopy reflectance. These correlations are said to be 'direct' when spectral properties (for instance, FRB) are defi ned as a function of biochemical and/or biophysical parameters, and said to be 'inverse' when biophysical and/or biochemical parameters are defined as a function of observed spectral values (SCHLERF; ATZBERGER, 2006).…”

Section: Introductionmentioning

confidence: 99%

Structural characterization of canopies of Eucalyptus spp. using radiometric data from TM/Landsat 5

Pacheco¹,

Ponzoni²,

Santos³

et al. 2012

CERNE

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Empirical approaches and, more recently, physical approaches, have grounded the establishment of logical connections between radiometric variables derived from remote data and biophysical variables derived from vegetation cover. This study was aimed at evaluating correlations of dendrometric and density data from canopies of Eucalyptus spp., as collected in Capão Bonito forest unit, with radiometric data from imagery acquired by the TM/Landsat-5 sensor on two orbital passages over the study site (dates close to fi eld data collection). Results indicate that stronger correlations were identifi ed between crown dimensions and canopy height with near-infrared spectral band data (ρ s 4), irrespective of the satellite passage date. Estimates of spatial distribution of dendrometric data and canopy density (D) using spectral characterization were consistent with the spatial distribution of tree ages during the study period. Statistical tests were applied to evaluate performance disparities of empirical models depending on which date data were acquired. Results indicated a signifi cant difference between models based on distinct data acquisition dates.Key words: Spectral characterization, estimate of biophysical data, refl ectância de dosséis. CARACTERIZAÇÃO ESTRUTURAL DE DOSSÉIS DE Eucalyptus spp. MEDIANTE DADOS RADIOMÉTRICOS TM/Landsat 5 RESUMO: Abordagens empíricas e, mais recentemente, abordagens físicas têm fundamentado o estabelecimento de conexões lógicas entre variáveis radiométricas provenientes de dados remotamente coletados e outras de caráter biofísico provenientes da cobertura vegetal. Nesse sentido, foram avaliadas correlações entre dados dendrométricos e de densidade de dosséis de Eucalyptus spp. coletados na Unidade Florestal de Capão Bonito da empresa FIBRIA S/A e dados radiométricos extraídos de imagens do sensor TM/Landsat 5, provenientes de duas passagens (datas próximas à coleta de dados em campo) sobre a área de estudo. Os resultados indicaram que as maiores correlações foram identifi cadas nas relações estabelecidas entre as dimensões da copa e a altura dos dosséis com dados da banda espectral do infravermelho próximo (ρ s 4), independentemente da data de passagem do satélite. As estimativas da distribuição espacial de dados dendrométricos e de densidade do dossel (D) fundamentadas na caracterização espectral foram consistentes em relação à distribuição espacial das idades dos plantios para

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Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data

Cited by 253 publications

References 54 publications

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

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

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Structural characterization of canopies of Eucalyptus spp. using radiometric data from TM/Landsat 5

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