Discrimination of Seagrass Species and Cover Classes with<i>in situ</i>Hyperspectral Data

Pu, Ruiliang; Bell, Susan S.; Baggett, Lesley P.; Meyer, Cynthia; Zhao, Yongchao

doi:10.2112/jcoastres-d-11-00229.1

Cited by 29 publications

(20 citation statements)

References 34 publications

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“…Therefore, while the related VIs for seagrass species S were significantly different from those for the other two seagrass species, T and H (Table 5), such differences might reflect the influence of sediment type. However, in an earlier paper we found that spectral signatures of S measured in controlled laboratory setting were indeed distinct from T and H (Pu et al, 2012b). More samples of all seagrass species over different sediment types (if possible) may be needed to interpret better the results reported here.…”

Section: Discussionmentioning

confidence: 57%

“…As a result, bottom (e.g., seagrass, sediment, etc.) spectra may be modified, thereby limiting the use of remote sensing (Dierssen et al, 2010;Holden and LeDrew, 2002;Pu et al, 2012b).…”

Section: Introductionmentioning

confidence: 99%

“…Barillé et al (2010) used field-measured hyperspectral data from seagrass and algae to produce a biomass distribution map based upon quantitative relationships between the field-measured hyperspectral data and biomass (dry weight). More recently, Pu et al (2012b) reported on hyperspectral data collected from a combined laboratory and field-based study. They examined seagrass spectral characteristics and evaluated whether seagrass species and levels of seagrass cover could be distinguished using true in situ hyperspectral data collected by a spectrometer overlying seagrass-dominated vegetation in a shallow water setting in the central west coast of Florida.…”

Section: Introductionmentioning

confidence: 99%

“…Data on spectral features of different vegetation types may be linked to the absorption properties of photosynthetic and accessory pigments contained in plant structural components (e.g., blades and thalli), but whether these spectral differences can be similarly detected when seagrass is embedded in sediments and an overlying water column is present remains poorly studied. Given that measurements for aquatic plant species and coverage represent just one of the many possible sources of reflectance acquired by airborne and satellite sensors, analysis of bottom reflectance retrieved from true in situ water surface spectral measurements can improve our understanding of the modifications of remote sensing data caused by the water column (Pu et al, 2012b).…”

Section: Introductionmentioning

confidence: 99%

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Developing Hyperspectral Vegetation Indices for Identifying Seagrass Species and Cover Classes

Bell

English

2015

Journal of Coastal Research

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Pu, R.; Bell, S., and English, D., 2015. Developing hyperspectral vegetation indices for identifying seagrass species and cover classes. Journal of Coastal Research, 31(3), 595-615. Coconut Creek (Florida), ISSN 0749-0208.Seagrass habitats are characteristic features of shallow waters worldwide and provide a variety of ecosystem functions.To date, few studies have evaluated the efficiency of spectral vegetation indices (VIs) for characterizing aquatic plants.Here we evaluate the use of in situ hyperspectral data and hyperspectral VIs for distinguishing among seagrass species and levels of percentage submerged aquatic vegetation (%SAV) cover in a subtropical shallow water setting. Analysis procedures include (1) retrieving bottom reflectance, (2) calculating correlation matrices of VIs with %SAV cover and F value matrices from analysis of variance among species, (3) testing the difference of VIs between levels of %SAV cover and between species, and (4) discriminating levels of %SAV cover and species by using linear discriminant analysis (LDA) and classification and regression trees (CART) classifiers with selected VIs as input. The experimental results indicated that (1) the best VIs for discriminating the four levels of %SAV cover were simple ratio (SR) VI, normalized difference VI (NDVI), modified simple ratio VI, and NDVI 3 SR, whereas the best VIs for distinguishing the three seagrass species included the weighted difference VI, soil-adjusted VI (SAVI), SAVI 3 SR and transformed SAVI; (2) the optimal central wavelengths for constructing the best VIs were 460, 500, 610, 640, 660, and 690 nm with spectral regions ranging from 3 to 20 nm at band width 3 nm, most of which were associated with absorption bands by photosynthetic and other accessory pigments in the visible spectral range. Compared with LDA, CART performed better in discriminating the four levels of %SAV cover and identifying the three seagrass species.ADDITIONAL INDEX WORDS: Seagrass, hyperspectral vegetation index, bottom reflectance retrieval, submerged aquatic vegetation (SAV), hyperspectral remote sensing.

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Section: Discussionmentioning

confidence: 57%

“…As a result, bottom (e.g., seagrass, sediment, etc.) spectra may be modified, thereby limiting the use of remote sensing (Dierssen et al, 2010;Holden and LeDrew, 2002;Pu et al, 2012b).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Developing Hyperspectral Vegetation Indices for Identifying Seagrass Species and Cover Classes

Bell

English

2015

Journal of Coastal Research

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“…Perbandingan sebaran rata-rata reflektansi spektral lamun menunjukkan pola yang memiliki dua puncak pada panjang gelombang sinar tampak yaitu hijau-kuning (500-650 nm) dan panjang gelombang merah (700-750 nm). Hal tersebut sesuai dengan penelitian Pu et al (2012) dan Fyfe (2003) yang mengukur pola reflektansi spektral lamun dan menunjukkan adanya puncak di panjang gelombang 500-650 nm dan 700-800 nm. Adanya kesamaan pola reflektansi tidak menunjukkan lima jenis lamun memiliki nilai yang sama karena pada dasarnya menurut Indarto (2014) reflektansi spektral setiap objek di permukaan bumi umumnya unik (bersifat khusus atau khas yang mencirikan objek) sehingga memiliki nilai yang berbeda dengan cirinya masingmasing.…”

Section: Pengukuran Reflektansi Spektralunclassified

Analisis Reflektansi Spektral Lamun Menggunakan Spektrometer Di Pulau Tunda Serang, Banten

Aziizah

Siregar

Agus

2016

JTPK

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Research of object spectral reflectance has develop and giving data or information about spectral pattern. Objects that are used in this study was seagrass, which plant with ability to adapt with high salinity water. The problem arrise was how extent object spectral that observed was able to giving an overview of different characteristics of the object in the field. The experiment was conducted in seagrass Tunda Island Serang District in August 2014 and resumed in March 2015. The aim of this research was to determine seagrases spectral reflectance curve, spectral characteristics, and identifier wavelength which gave different characteristics from each type of seagrass. In situ reflectance measurements by using USB4000 spectrometer which generate intensity value and analysis using reflectance algorithms to generate the curve. Reflectance curve has two peaks at wavelengths 500-650 nm and 700-750 nm with a highest value was 22%) in the first peak and 14% in the second peak. The result from statistic analysis showed the reflectance values of five species of seagrass in Tunda Island is significantly different. Based on the reflectance values of the five species of seagrass and Tukey test has been done, it is known wavelength identifier for five species of seagrasses have significant differences that wavelenght of green, yellow, red edge, and NIR-2. ABSTRAKAnalisis reflektansi spektral suatu objek mulai banyak berkembang dan memberikan data atau informasi tentang pola spektral. Objek dalam penelitian ini adalah lamun, yang merupakan tanaman memiliki kemampuan untuk beradaptasi hidup di perairan dengan salinitas tinggi. Permasalahan yang muncul adalah sejauh mana pola spektral objek mampu memberikan gambaran yang sesuai dengan karakteristik objek di lapangan. Penelitian dilaksanakan di ekosistem lamun Pulau Tunda Kabupaten Serang pada bulan Agustus 2014 dan dilanjutkan bulan Maret 2015. Tujuan dari penelitian ini adalah mendapatkan kurva reflektansi spektral lamun, karakteristik spektral, dan identifikasi panjang gelombang yang memberikan karakteristik berbeda dari setiap jenis lamun. Pengukuran reflektansi insitu menggunakan spektrometer USB4000 menghasilkan nilai intensitas dan dianalisis menggunakan algoritma reflektansi untuk menghasilkan kurva. Kurva reflektansi memiliki dua puncak pada panjang gelombang 500-650 nm dan 700-750 nm dengan nilai tertinggi adalah 22% di puncak pertama dan 14% di puncak kedua. Hasil dari analisis statistik menunjukkan nilai reflektansi dari lima spesies lamun di Pulau Tunda berbeda signifikan. Berdasarkan nilai reflektansi dari lima jenis lamun dan uji Tukey yang telah dilakukan, dapat diketahui panjang gelombang penciri karena lima jenis lamun memiliki perbedaan signifikan yaitu pada panjang gelombang hijau, kuning, merah tepi, dan NIR-2.

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Remote Sensing of Coastal Ecosystems and Environments

Klemas

2014

Remote Sensing and Modeling

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Discrimination of Seagrass Species and Cover Classes within situHyperspectral Data

Cited by 29 publications

References 34 publications

Developing Hyperspectral Vegetation Indices for Identifying Seagrass Species and Cover Classes

Developing Hyperspectral Vegetation Indices for Identifying Seagrass Species and Cover Classes

Analisis Reflektansi Spektral Lamun Menggunakan Spektrometer Di Pulau Tunda Serang, Banten

Remote Sensing of Coastal Ecosystems and Environments

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