Tropical mangrove species discrimination using hyperspectral data: A laboratory study

Vaiphasa, Chaichoke; Ongsomwang, Suwit; Vaiphasa, Tanasak; Skidmore, Andrew K.

doi:10.1016/j.ecss.2005.06.014

Cited by 159 publications

(133 citation statements)

References 41 publications

(62 reference statements)

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“…For each measurement, the value recorded was based on an average of 15 spectral readings. Four measurements were obtained for each sample by rotating the plate roughly 90° to avoid potential impacts from the bidirectional reflectance distribution function [27]. The final spectral reflectance number for each sample was calculated as the average of the four measurements.…”

Section: Spectral Response Measurements and Preprocessingmentioning

confidence: 99%

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Separating Mangrove Species and Conditions Using Laboratory Hyperspectral Data: A Case Study of a Degraded Mangrove Forest of the Mexican Pacific

et al. 2014

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Given the scale and rate of mangrove loss globally, it is increasingly important to map and monitor mangrove forest health in a timely fashion. This study aims to identify the conditions of mangroves in a coastal lagoon south of the city of Mazatlán, Mexico, using proximal hyperspectral remote sensing techniques. The dominant mangrove species in this area includes the red (Rhizophora mangle), the black (Avicennia germinans) and the white (Laguncularia racemosa) mangrove. Moreover, large patches of poor condition black and red mangrove and healthy dwarf black mangrove are commonly found. Mangrove leaves were collected from this forest representing all of the aforementioned species and conditions. The leaves were then transported to a laboratory for spectral measurements using an ASD plot, principal components analysis and stepwise discriminant analyses were then used to select wavebands deemed most appropriate for further mangrove classification. Specifically, the wavebands at 520, 560, 650, 710, 760, 2100 and 2230 nm were selected, which correspond to chlorophyll absorption, red edge, starch, cellulose, nitrogen and protein regions of the spectrum. The classification and validation indicate that these wavebands are capable of identifying mangrove species and mangrove conditions common to this degraded forest with an overall accuracy and Khat coefficient higher than 90% and 0.9, respectively. Although lower in accuracy, the classifications of the stressed (poor condition and dwarf) mangroves were found to be satisfactory with accuracies higher than 80%. The results of this study indicate that it could be possible to apply laboratory hyperspectral data for classifying mangroves, not only at the species level, but also according to their health conditions.

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Section: Spectral Response Measurements and Preprocessingmentioning

confidence: 99%

“…However, only a few studies have applied laboratory hyperspectral data to separate mangrove species. Vaiphasa et al [27] identified sixteen mangrove species in Thailand using analysis of variance (ANOVA) and Jeffries-Matusita distance. Kamaruzaman and Kasawani [28] separated five mangrove species by applying stepwise discriminant analysis.…”

Section: Introductionmentioning

confidence: 99%

Separating Mangrove Species and Conditions Using Laboratory Hyperspectral Data: A Case Study of a Degraded Mangrove Forest of the Mexican Pacific

et al. 2014

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“…However, the use of field spectroradiometer to discriminate species has done for many times for field and laboratory measurements (Schmidt andSkidmore, 2003 Bellucoetal, 2006;Brown, 2004;Rossoetal, 2005;Pengraetal, 2007;Kamaruzaman and Kasawani 2007;and Mutanga Adam, 2009;Vaiphasa et al, 2005;Enrica et al, 2006). In this research, the elimination of redundant data and the identification of relevant data uses discriminant analysis in a condition that the dimensional reduction does not cause loss of information related to the object of the research (Adam and Mutanga, 2009) ANOVA test result at the 95% confidence level (p <0:05) indicates that there are significant differences in spectral reflectance between all pairs of classes (V vs W, V vs. WS, and V vs DS) at n = 412 wavelengths.…”

Section: Resultsmentioning

confidence: 99%

“…Those researches include researches on mangrove mapping and monitoring using multi-spectral sensors and hyperspektrum (Demuro and Chisholm, 2003;Dimiliki et al, 2003 ;Hirano et al, 2003). Multispectral sensors on the satellite platform, including SAR, Landsat TM, and SPOT are the most popular application with the advantages in cost and effectiveness, but they are mainly confined to the district scale because of the relatively coarse space and spectral resolution (Aschbacher et al, 1995 ;Ramsey and Jensen, 1996;GAO, 1999, Green et al, 2000Sulong et al, 2002;Vaiphasa et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Mangrove Ecosystem Objects on the Visible Spectrum Using Spectroradiometer HR-1024

Arfan

2015

For. Geo.

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The study was conducted to determine whether the vegetation in the mangrove ecosystem, can be contrasted with another objectt, using Spectroradiometer HR-1024. The data used is data visible spectrum(400-700 nm) which resulted in 204 bands. The analysis used is the integrated analysis with three levels. First, using ANOVA to determine significant differences in spectral reflectance between vegetation with water, wet soil and dry soil. Second, using Step wise Canonical Discriminant Analysis to identify the most sensitive band for discrimination reflection spectrum. This analysis which resulted in six bands are considered practical to distinguish vegetation with another object namely 401.5 nm, 416.9 nm, 508.2 nm, 599.3 nm, 660.3nm and 689.2 nm. Third using the Jeffries-Matusita separability index which resulted in the separation index of mangrove vegetation, water, wet soil and dry soil is 1.414. Keywords:Mangrove ecosystem, Object, Spectroradiometer HR-1024, Stepwise Canonical Discriminant Analysis, JeffriesMatusita AbstrakPenelitian dilakukan untuk mengetahui apakah vegetasi di ekosistem mangrove dapat dibedakan dengan obyek yang lain dengan menggunakan Spectroradiometer HR-1024. Data yang digunakan adalah data spektrum tampak (400 -700 nm) yang menghasilkan 204 band. Analisis yang digunakan adalah analisis terpadu dengan tiga tingkatan. Pertama, menggunakan ANOVA untuk mengetahui perbedaan signifikan pantulan spektrum antara vegetasi dengan air, tanah basah dan tanah kering. Kedua, menggunakan Canonical Stepwise Discriminant Analysis untuk mengidentifikasi band paling sensitif untuk diskriminasi pantulan spektrum. Analisis ini menghasilkan enam band yang dianggap praktis untuk membedakan vegetasi dengan obyek yang lain yaitu 401.5 nm, 416.9 nm, 508.2 nm, 599.3 nm, 660.3 nm and 689.2 nm. Ketiga, menggunakan indeks keterpisahan Jeffries-Matusita yang menghasilkan indeks keterpisahan vegetasi mangrove dengan air, tanah basah dan tanah kering yaitu 1,414.

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“…However, the spatial and spectral information provided by this conventional equipment may not be sufficient for studying mangrove ecosystems and their diversity in details [14,23,26,31,35,[42][43][44][45][46]. As a result, new generation sensors that possess higher spatial and spectral resolutions are therefore needed for a finer level of mangrove studies [17,35,42,43,[45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data

Koedsin

Vaiphasa

2013

Remote Sensing

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Understanding the dynamics of mangroves at the species level is the key for securing sustainable conservation of mangrove forests around the globe. This study demonstrates the capability of the hyper-dimensional remote sensing data for discriminating diversely-populated tropical mangrove species. It was found that five different tropical mangrove species of Southern Thailand, including Avicennia alba, Avicennia marina, Bruguiera parviflora, Rhizophora apiculata, and Rhizophora mucronata, were correctly classified. The selected data treatment (a well-established spectral band selector) helped improve the overall accuracy from 86% to 92%, despite the remaining confusion between the two members of the Rhizophoraceae family and the pioneer species. It is therefore anticipated that the methodology presented in this study can be used as a practical guideline for detailed mangrove species mapping in other study areas. The next stage of this work will be to exploit the differences between the leaf textures of the two Rhizophoraceae mangroves in order to refine the classification outcome.

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Tropical mangrove species discrimination using hyperspectral data: A laboratory study

Cited by 159 publications

References 41 publications

Separating Mangrove Species and Conditions Using Laboratory Hyperspectral Data: A Case Study of a Degraded Mangrove Forest of the Mexican Pacific

Separating Mangrove Species and Conditions Using Laboratory Hyperspectral Data: A Case Study of a Degraded Mangrove Forest of the Mexican Pacific

Discrimination of Mangrove Ecosystem Objects on the Visible Spectrum Using Spectroradiometer HR-1024

Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data

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