Multi-site evaluation of IKONOS data for classification of tropical coral reef environments

Andréfouët, Serge; Kramer, Philip; Torres-Pulliza, Damaris; Joyce, Karen E.; Hochberg, Eric J.; Garza-Pérez, Joaquín Rodrigo; Mumby, Peter J.; Riegl, Bernhard; Yamano, Hiroya; White, W. H.; Zubia, Mayalen; Brock, John C.; Phinn, Stuart R.; Naseer, Abdulla; Hatcher, Bruce G.; Muller‐Karger, Frank E.

doi:10.1016/j.rse.2003.04.005

Cited by 299 publications

(159 citation statements)

References 39 publications

Supporting

Mentioning

145

Contrasting

Unclassified

Order By: Relevance

“…Figure 10 illustrates that the number of optically separable classes increases as the clustering accuracy decreases for a constant set of water column optical properties at HICO wavebands. These results are similar to those produced by Andrefouet et al [1] where the thematic accuracy decreased with increasing number of habitat classes used in the benthic classification scheme. Although thematic and clustering accuracy are applied in different contexts, Figure 8 does imply that if a benthic habitat map at a constant classification accuracy is required, then the number of classes used in the classification scheme must change with the range of depths and water column optical properties within an image.…”

Section: Implications To Shallow Water Habitat Mappingsupporting

confidence: 89%

“…Previous studies with multispectral imagery have shown an inverse relationship between the number of benthic classes and the classification accuracy [1,3,8]. This same relationship has also been shown for hyperspectral imagery, see [21,22].…”

Section: Introductionsupporting

confidence: 59%

“…A fundamental issue with benthic classification of remotely imaged shallow water environments is determining the appropriate definition and number of benthic classes that: (i) optimizes the classification accuracy and precision [1]; and (ii) standardizes classification maps for ease of inter-comparisons [2]. The selection of classes and their descriptive resolution, i.e., biological detail [3], for a spectral library or training dataset, along with the image classification method, have typically been scene and sensor specific [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping

Garcia

Hedley²,

Tín

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos' natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae. OPEN ACCESSRemote Sens. 2015, 7 13158

show abstract

Section: Implications To Shallow Water Habitat Mappingsupporting

confidence: 89%

Section: Introductionsupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping

Garcia

Hedley²,

Tín

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Additional details of the hard-bottom habitats can be found in Mumby and Harborne (1999). These habitats represent communities that can typically be mapped through the use of IKONOS satellite imagery that is increasingly popular for remote-sensing applications in tropical coastal habitats (Maeder et al 2002, Mumby and Edwards 2002, Andre´foue¨t et al 2003. Not all habitats were surveyed around each island (Table 1).…”

Section: Study Sites and Sampling Designmentioning

confidence: 99%

Tropical Coastal Habitats as Surrogates of Fish Community Structure, Grazing, and Fisheries Value

Harborne

Mumby

Kappel

et al. 2008

Ecological Applications

View full text Add to dashboard Cite

Abstract. Habitat maps are frequently invoked as surrogates of biodiversity to aid the design of networks of marine reserves. Maps are used to maximize habitat heterogeneity in reserves because this is likely to maximize the number of species protected. However, the technique's efficacy is limited by intra-habitat variability in the species present and their abundances. Although communities are expected to vary among patches of the same habitat, this variability is poorly documented and rarely incorporated into reserve planning. To examine intra-habitat variability in coral-reef fishes, we generated a data set from eight tropical coastal habitats and six islands in the Bahamian archipelago using underwater visual censuses. Firstly, we provide further support for habitat heterogeneity as a surrogate of biodiversity as each predefined habitat type supported a distinct assemblage of fishes. Intrahabitat variability in fish community structure at scales of hundreds of kilometers (among islands) was significant in at least 75% of the habitats studied, depending on whether presence/absence, density, or biomass data were used. Intra-habitat variability was positively correlated with the mean number of species in that habitat when density and biomass data were used. Such relationships provide a proxy for the assessment of intra-habitat variability when detailed quantitative data are scarce. Intra-habitat variability was examined in more detail for one habitat (forereefs visually dominated by Montastraea corals). Variability in community structure among islands was driven by small, demersal families (e.g., territorial pomacentrid and labrid fishes). Finally, we examined the ecological and economic significance of intra-habitat variability in fish assemblages on Montastraea reefs by identifying how this variability affects the composition and abundances of fishes in different functional groups, the key ecosystem process of parrotfish grazing, and the ecosystem service of value of commercially important finfish. There were significant differences in a range of functional groups and grazing, but not fisheries value. Variability at the scale of tens of kilometers (among reefs around an island) was less than that among islands. Caribbean marine reserves should be replicated at scales of hundreds of kilometers, particularly for species-rich habitats, to capture important intra-habitat variability in community structure, function, and an ecosystem process.

show abstract

“…Pengaruh kolom perairan dihilangkan dari rasio pasangan band untuk meningkatkan hasil interpretasi visual dan akurasi klasifikasi substrat terumbu karang (Mumby et al, 1998;Andréfouët et al, 2003;Benfield et al, 2007). Koreksi kolom perairan diekstrak dari obyek yang diidentifikasi pada area citra untuk jenis substrat yang sama dengan kedalaman yang berbeda, selanjutnya citra ditransformasi ke nilai logaritma untuk band-band berbeda dan menghitung nilai regresi pasangan band.…”

Section: Koreksi Kolom Perairanunclassified

Change Detection of Coral Reef Habitat Using Landsat Imagery in Morotai Island North Maluku Province

Wahiddin¹,

Siregar²,

Nababan³

et al. 2015

J. Ilmu dan Teknologi Kelautan Tropis

View full text Add to dashboard Cite

Scientific information on coral reef habitat changes of Morotai island is very limited to none. This study aimed to detect the change of coral reef habitats between 1996 and 2013, using -14.1%, -14.9%, and -16.6%, respectively. In 2002-2013, mixed-habitat, sand ABSTRAKInformasi ilmiah terkait perubahan habitat terumbu karang di pulau Morotai sangat terbatas bahkan belum ada sampai saat ini. Penelitian ini bertujuan untuk mendeteksi perubahan habitat terumbu karang dalam kurun waktu 17 tahun (1996-2013) menggunakan seri citra Landsat yang diintegrasikan dengan data pengamatan lapangan tahun 2012. Penelitian ini dilaksanakan di kawasan terumbu karang pulau Morotai Propinsi Maluku Utara menggunakan data Landsat 5TM (1996), Landsat 7ETM+ (2002) dan Landsat 8 OLI (2013). Analisis deteksi perubahan menggunakan teknik klasifikasi terbimbing dan normalisasi band transformasi depth invariant index (DII) untuk lima kelas habitat yaitu kelas campuran, karang hidup, lamun, pasir, dan pecahan karang. Deteksi perubahan dideskripsikan berdasarkan perubahan habitat antar waktu maupun perubahan luas habitat antara tahun 1996-2002 dan 2002-2013. Analisis deteksi perubahan menunjukan bahwa dalam kurun waktu 1996-2002 luas kelas campuran dan pecahan karang meningkat sebesar 11,3% dan 32,5%, luas kelas pasir, lamun, dan karang hidup berkurang masing-masing sebesar -14,1%, -14,9% dan -16,6%. Sedangkan tahun 2002-2013, luas kelas campuran, pasir, dan lamun meningkat masing-masing sebesar 1,1%, 13,3% dan 24,7%, luas kelas pecahan karang dan karang hidup berkurang sebesar -22,7% dan -27,0%. Dalam kurun waktu 17 tahun, kematian terumbu karang sekitar 43,6%. Hal ini mungkin disebabkan peningkatan suhu air laut dan meningkatnya intensitas kegiatan manusia.

show abstract

Multi-site evaluation of IKONOS data for classification of tropical coral reef environments

Cited by 299 publications

References 39 publications

A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping

A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping

Tropical Coastal Habitats as Surrogates of Fish Community Structure, Grazing, and Fisheries Value

Change Detection of Coral Reef Habitat Using Landsat Imagery in Morotai Island North Maluku Province

Contact Info

Product

Resources

About