Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data

Galvão, Lênio Soares; Formaggio, Antônio Roberto; Tisot, Daniela Arnold

doi:10.1016/j.rse.2004.11.012

Cited by 229 publications

(89 citation statements)

References 32 publications

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“…VIs are widely used to classify vegetation and environmental changes, determine crop and forage yield, monitor droughts, etc. After many years of research on narrow-band hyperspectral spectra, incomplete statistics show that dozens of VIs exist that can be used to estimate biophysical parameters [13,.…”

Section: Selection Of Vegetation Indexesmentioning

confidence: 99%

“…Traditional AGB estimates are based on destructive measurements, which are not only time and labor consuming, but more importantly, are difficult to apply over large areas [6]. In recent years, Hyperspectral remote-sensing data acquired from the ground [7,8], unmanned aerial vehicles [9], airborne platforms [10][11][12], and satellite platforms [13] have been able to capture crop canopy spectra in narrow bands and thereby provide information on the biochemical composition of the canopy. Crop physiology research shows that spectral absorption by plant leaves is mainly due to the leaf pigments, especially chlorophyll content (Chl) [14].…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

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Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R 2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models.

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Section: Selection Of Vegetation Indexesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

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“…In spite of Ponzoni and Gonçalves (1999) having recommended the usage of larger spectral bands, it should be interesting to consider the possibility to obtain orbital hyperspectral (Hyperion data, for instance) from mangrove areas. Considering the greater amount of mangroves spectra that could be extracted from the hyperspectral images, statistical procedures like those utilized by Galvão et al (2005), for example, could be applied in the entire spectral range data in order to identify differences or similarities between mangroves canopies.…”

mentioning

confidence: 99%

Spectral characterization of mangrove leaves in the Brazilian Amazonian Coast: Turiaçu Bay, Maranhão State

Rebelo-Mochel

Ponzoni

2007

An. Acad. Bras. Ciênc.

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Mangrove communities are tropical systems which have fewer species than tropical forests, especially in Latin America and display a single architecture, usually lacking the various strata commonly found in other forest ecosystems. The identification of mangrove communities by orbital data is not a difficult task but the most interesting challenge is to identify themselves by the dominant species. The first step toward that floristic identification is the spectral characterization of detached leaves. Leaves from four species of mangrove trees were spectrally characterized considering the Directional Hemispherical Reflectance Factor (DHRF) determined through radiometric measurements using an integrating sphere LICOR 1800 attached to a spectroradiometer SPECTRON SE-590. In the visible bands (0.45-0.69 µm) the button-shaped mangrove Conocarpus erectus was brighter and the red mangrove Rhizophora mangle was darker than the other two species which shows very close DHRF values. Otherwise the black mangrove Avicennia germinans and the white mangrove Laguncularia racemosa can be distinguished from one another in the Near Infra Red (NIR) region (0.76-0.90 µm and in this region of the spectrum the DHRF of C. erectus and R. mangle become very close.

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“…10 In this context, the main objective of this study is to produce a land cover map for east Suez Canal region using hyperspectral data acquired by the EO-1 Hyperion instrument with the Support Vector Machine (SVM) classification techniques. Another goal is to use Land Cover Classification System (LCCS) to define the land cover classes.…”

Section: Introductionmentioning

confidence: 99%

Land-Cover Classification for East Suez Canal Region Using Hyperspectral Eo-1 Data

Afify¹,

Sheta²,

Arafat³

et al. 2017

ECB

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Earth Observation (EO-1) data provides a highest spectral resolution to get spectral information of Earth's Surface targets within 242 spectral bands at 30 m spatial resolution. In this context, the main objective of this paper is to produce a land cover map using hyperspectral data acquired by EO-1 Hyperion instrument over one test site. Atmospheric correction on the hyperspectral data was performed using ENVI's Fast Line-of-sight Atmospheric Analysis of Spectral Hyper-cubes (FLAASH) module. Support Vector Machine (SVM) classification was implemented on the dominant elements to produce a land cover map for test site. SVM is carried out in this research to deal with the multi-class issue of Hyperion data. Classification using the kernel functions in classification made the classifier robust against the outliers. The Land Cover Classification System (LCCS) was used to know the land cover classes. The result showed high accuracy for land cover map with machine learning classifier like SVM using hyperspectral remote sensing data. The overall classification accuracy obtained was 97.85.

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Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data

Cited by 229 publications

References 32 publications

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

Spectral characterization of mangrove leaves in the Brazilian Amazonian Coast: Turiaçu Bay, Maranhão State

Land-Cover Classification for East Suez Canal Region Using Hyperspectral Eo-1 Data

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