scholarly journals DIFFERENT STATISTICAL METHODS FOR THE DISCRIMINATION OF TROPICAL MANGROVE SPECIES USING IN-SITU HYPERSPECTRAL DATA

2019 ◽  
Vol 9 (2) ◽  
pp. 49
Author(s):  
Tanumi Kumar ◽  
Dibyendu Dutta ◽  
Diya Chatterjee ◽  
K Chandrasekar ◽  
Goru Srinivasa Rao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Region ◽  
Hyperspectral Data ◽  
Feature Reduction ◽  
Reflectance Spectra ◽  
Stepwise Discriminant Analysis ◽  
Species Discrimination ◽  
Mangrove Species ◽  
Domain Specific ◽  
Continuum Removal

The study highlights the hyperspectral characteristics of canopies of 14 tropical mangrove species, belonging to nine families found in the tidal forests of the Indian Sundarbans. Hyperspectral observations were recorded using a field spectroradiometer, pre-processed and subjected to derivative analysis and continuum removal. Mann-Whitney U tests were applied on the spectral data in four spectral forms: (i) Reflectance Spectra (ii) First Derivative, (iii) Second Derivative and (iv) Continuum Removal Reflectance Spectra. Factor analysis was applied in each of the spectral forms for feature reduction and identification of the important wavelengths for species discrimination. Stepwise discriminant analysis was used on the feature reduced reflectance spectra to obtain optimal bands for computation of Jeffries–Matusita distance. The Mann-Whitney U test could be satisfactorily used for determining the significant (separable) bands for discriminating the species. In general, the red region, red edge domain, specific near infrared bands (including 759, 919, 934, 940, 948, 1152, 1156, 1159 and 1212 nm) and shortwave infrared region (1503–1766 nm) played major roles in spectral separability. Overall, hyperspectral data showed potential for discriminating between mangrove canopies of different species and the results of the study also indicated the usefulness of the applied statistical tools for discrimination.

scholarly journals GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong

2020 ◽  
Vol 12 (4) ◽  
pp. 656 ◽  
Author(s):  
Luoma Wan ◽  
Yinyi Lin ◽  
Hongsheng Zhang ◽  
Feng Wang ◽  
Mingfeng Liu ◽  
...  
Keyword(s):  
Hong Kong ◽  
Spectral Resolution ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Species Discrimination ◽  
Infrared Range ◽  
Landsat 8 ◽  
Mangrove Species ◽  
Spectral Bands ◽  
Species Mapping

Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management.

Tropical mangrove species discrimination using hyperspectral data: A laboratory study

2005 ◽  
Vol 65 (1-2) ◽  
pp. 371-379 ◽  
Author(s):  
Chaichoke Vaiphasa ◽  
Suwit Ongsomwang ◽  
Tanasak Vaiphasa ◽  
Andrew K. Skidmore
Keyword(s):  
Laboratory Study ◽  
Hyperspectral Data ◽  
Species Discrimination ◽  
Mangrove Species

scholarly journals AVIRIS-NG Data for Geological Applications in Southeastern Parts of Aravalli Fold Belt, Rajasthan

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 16
Author(s):  
Sameeksha Mishra ◽  
Shovan L. Chattoraj ◽  
Alen Benny ◽  
Richa U. Sharma ◽  
P. K. Champati Ray
Keyword(s):  
High Resolution ◽  
Near Infrared ◽  
Country Rock ◽  
Satellite Image ◽  
Infrared Region ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Fold Belt ◽  
Spectral Angle Mapper ◽  
Iron Bearing

Advanced techniques using high resolution hyperspectral remote sensing data has recently evolved as an emerging tool with potential to aid mineral exploration. In this study, pertinently, five mosaicked scenes of Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral data of southeastern parts of the Aravalli Fold belt in Jahazpur area, Rajasthan, were processed. The exposed Proterozoic rocks in this area is of immense economic and scientific interest because of richness of poly-metallic mineral resources and their unique metallogenesis. Analysis of high resolution multispectral satellite image reveals that there are many prominent lineaments which acted as potential conduits of hydrothermal fluid emanation, some of which resulted in altering the country rock. This study takes cues from studying those altered minerals to enrich our knowledge base on mineralized zones. In this imaging spectroscopic study we have identified different hydrothermally altered minerals consisting of hydroxyl, carbonate and iron-bearing species. Spectral signatures (image based) of minerals such as Kaosmec, Talc, Kaolinite, Dolomite, and Montmorillonite were derived in SWIR (Short wave infrared) region while Iron bearing minerals such as Goethite and Limonite were identified in the VNIR (Visible and Near Infrared) region of electromagnetic spectrum. Validation of the target minerals was done by subsequent ground truthing and X-ray diffractogram (XRD) analysis. The altered end members were further mapped by Spectral Angle Mapper (SAM) and Adaptive Coherence Estimator (ACE) techniques to detect target minerals. Accuracy assessment was reported to be 86.82% and 77.75% for SAM and ACE respectively. This study confirms that the AVIRIS-NG hyperspectral data provides better solution for identification of endmember minerals.

scholarly journals Characterization and Separation of Traditional and Bio-Plastics by Hyperspectral Devices

2020 ◽  
Vol 10 (8) ◽  
pp. 2800
Author(s):  
Monica Moroni ◽  
Alessandro Mei
Keyword(s):  
Near Infrared ◽  
New Technologies ◽  
Raw Materials ◽  
Plastic Material ◽  
Infrared Region ◽  
Industrial Applications ◽  
Hyperspectral Data ◽  
Data Detection ◽  
Mechanical Recycling ◽  
Infrared Wavelength

Nowadays, bio-plastics can contaminate conventional plastics sent to recycling. Furthermore, the low volume of bio-plastics currently in use has discourage the development of new technologies for their identification and separation. Technologies based on hyperspectral data detection may be profitably employed to separate the bio-plastics from traditional ones and to increase the quality of recycled products. In fact, sensing devices make it possible to accomplish the essential requirement of a mechanical recycling technology, i.e., end products which comply with specific standards determined by industrial applications. This paper presents the results of the hyperspectral analysis conducted on two different plastic polymers (PolyEthylene Terephthalate and PolyStyrene) and one bio-based and biodegradable plastic material (PolyLactic Acid) in different phases of their life cycle (primary raw materials and urban waste). The reflectance analysis is focused on the near-infrared region (900–1700 nm) and data are detected with a linear-spectrometer apparatus and a spectroradiometer. A rapid and reliable identification of three investigated polymers is achieved by using simple two near-infrared wavelength operators employing key wavelengths.

scholarly journals Mangrove Species Discrimination and Health Assessment using AVIRIS-NG Hyperspectral Data

2019 ◽  
Vol 116 (7) ◽  
pp. 1136 ◽  
Author(s):  
Nilima R. Chaube ◽  
Nikhil Lele ◽  
Arundhati Misra ◽  
T. V. R. Murthy ◽  
Sudip Manna ◽  
...  
Keyword(s):  
Health Assessment ◽  
Hyperspectral Data ◽  
Species Discrimination ◽  
Mangrove Species

scholarly journals A solar optical hyperspectral library of rare-earth-bearing minerals, rare-earth oxide powders, copper-bearing minerals and Apliki mine surface samples

2021 ◽  
Vol 13 (3) ◽  
pp. 923-942
Author(s):  
Friederike Koerting ◽  
Nicole Koellner ◽  
Agnieszka Kuras ◽  
Nina Kristin Boesche ◽  
Christian Rogass ◽  
...  
Keyword(s):  
Rare Earth ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Reflectance Spectra ◽  
High Tech ◽  
Oxide Powders ◽  
Surface Samples ◽  
Copper Gold ◽  
Spectral Libraries

Abstract. Mineral resource exploration and mining is an essential part of today's high-tech industry. Elements such as rare-earth elements (REEs) and copper are, therefore, in high demand. Modern exploration techniques from multiple platforms (e.g., spaceborne and airborne), to detect and map the spectral characteristics of the materials of interest, require spectral libraries as an essential reference. They include field and laboratory spectral information in combination with geochemical analyses for validation. Here, we present a collection of REE- and copper-related hyperspectral spectra with associated geochemical information. The libraries contain reflectance spectra from rare-earth element oxides, REE-bearing minerals, copper-bearing minerals and mine surface samples from the Apliki copper–gold–pyrite mine in the Republic of Cyprus. The samples were measured with the HySpex imaging spectrometers in the visible and near infrared (VNIR) and shortwave infrared (SWIR) range (400–2500 nm). The geochemical validation of each sample is provided with the reflectance spectra. The spectral libraries are openly available to assist future mineral mapping campaigns and laboratory spectroscopic analyses. The spectral libraries and corresponding geochemistry are published via GFZ Data Services with the following DOIs: https://doi.org/10.5880/GFZ.1.4.2019.004 (13 REE-bearing minerals and 16 oxide powders, Koerting et al., 2019a), https://doi.org/10.5880/GFZ.1.4.2019.003 (20 copper-bearing minerals, Koellner et al., 2019), and https://doi.org/10.5880/GFZ.1.4.2019.005 (37 copper-bearing surface material samples from the Apliki copper–gold–pyrite mine in Cyprus, Koerting et al., 2019b). All spectral libraries are united and comparable by the internally consistent method of hyperspectral data acquisition in the laboratory.

scholarly journals Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques

2019 ◽  
Vol 12 (1) ◽  
pp. 63
Author(s):  
Mozhgan Abbasi ◽  
Jochem Verrelst ◽  
Mohsen Mirzaei ◽  
Safar Marofi ◽  
Hamid Reza Riyahi Bakhtiari
Keyword(s):  
Precision Agriculture ◽  
Tree Species ◽  
Near Infrared ◽  
Principal Component ◽  
Field Measurements ◽  
Hyperspectral Data ◽  
Visible Range ◽  
Species Discrimination ◽  
Multivariate Statistical ◽  
Significant Difference

Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination with field measurements requires the development of optimized band selection strategies to separate tree species. In this study, field spectroscopy (350 to 2500 nm) was performed through scanning 165 spectral leaf samples of dominant orchard tree species (almond, walnut, and grape) in Chaharmahal va Bakhtiyari province, Iran. Two multivariable methods were employed to identify the optimum wavelengths: the first includes three-step approach ANOVA, random forest classifier (RFC) and principal component analysis (PCA), and the second employs partial least squares (PLS). For both methods we determined whether tree species can be spectrally separated using discriminant analysis (DA) and then the optimal wavelengths were identified for this purpose. Results indicate that all species express distinct spectral behaviors at the beginning of the visible range (from 350 to 439 nm), the red edge and the near infrared wavelengths (from 701 to 1405 nm). The ANOVA test was able to reduce primary wavelengths (2151) to 792, which had a significant difference (99% confidence level), then the RFC further reduced the wavelengths to 118. By removing the overlapping wavelengths, the PCA represented five components (99.87% of variance) which extracted optimal wavelengths were: 363, 423, 721, 1064, and 1388 nm. The optimal wavelengths for the species discrimination using the best PLS-DA model (100% accuracy) were at 397, 515, 647, 1386, and 1919 nm.

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