High-resolution ground verification, cluster analysis and optical model of reef substrate coverage on Landsat TM imagery (Red Sea, Egypt)

Purkis, Samuel J.; Kenter, J.A.M.; Oikonomou, Emmanouil; Robinson, I. S.

doi:10.1080/01431160110047722

Cited by 43 publications

(19 citation statements)

References 30 publications

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“…Finally, because of the use of various different habitat classification methods and mapping techniques, it is difficult to make comparisons between approaches, and this makes each mapping of coral reef habitats unique (i.e. Mumby and Edwards 2002;Purkis et al 2002;Andrefoüet et al 2003). Thus, it is not surprising if the purposes of the production of a map of major coastal habitats are as a background for further investigation, and for management, Mumby and Green (2000) assumed that a relatively low overall map accuracy (60%) is deemed appropriate.…”

Section: Resultsmentioning

confidence: 99%

Variation of leaflet shape from several soybean genotypes and its relation to morphological characters

Krisnawati¹,

Adie²

2017

Biodiversitas

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Abstract. Sulistyo B. 2017. The accuracy of the outer boundary delineation of coral reef area derived from the analysis of various vegetation indices of satellite . This research aimed at determining the accuracy of the outer boundary delineation of coral reef area derived from the analysis of various vegetation indices of satellite Landsat Thematic Mapper in Enggano Island, the District of North Bengkulu, Bengkulu Province. The vegetation indices of Landsat Thematic Mapper analyzed were the Ratio Vegetation Index, the Normalized Difference Vegetation Index, the Soil Adjusted Vegetation Index, the Transformed Soil Adjusted Vegetation Index, the Modified Soil Adjusted Vegetation Index and the Transformed Vegetation Index. The result of every analysis of vegetation index (index map) was then used as a base for on-screen digitization of the points assumed to be the outer boundary of coral reefs in order to get digital data. Digitization of shoreline was also done to create an inner boundary of coral reefs. When it is combined with its outer boundary then the calculation of the area is possible. The accuracy of the result was validated by overlaying it onto Map of Coral Reefs derived from a topographic map of Joint Operation Group. To justify whether the accuracy is good (or accepted) or not good (or not accepted), a threshold value proposed by Justice and Townshend was chosen, those are Poor (when the accuracy is < 70%), Moderate (when the accuracy is between 70% and 85%), and Good (when the accuracy is ≥ 85%). The results of the research showed that the accuracy of RVI and NDVI values were Poor, those are 67.11% and 66.42%, respectively, so it could not be used for further analysis. The other results were Moderate, those are 73.33% for SAVI, 74.22% for TSAVI, 77.32 % for MSAVI and 77.33% for TVI, and may be used as a quick alternative when the map is absent.

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Section: Resultsmentioning

confidence: 99%

Variation of leaflet shape from several soybean genotypes and its relation to morphological characters

Krisnawati¹,

Adie²

2017

Biodiversitas

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“…For a scene with patchy or heterogeneous benthos or that was imaged with moderate to low spatial resolution, the spectral library could contain classes pertaining to merged endmembers (i.e., mixed benthic assemblages) and low descriptive resolution [3,8,9]. Due to the spectral characteristics of the sensors used (e.g., SPOT, Landsat 5-7, IKONOS; QuickBird), the above studies mostly utilized supervised classification schemes with or without water column correction to produce benthic habitat maps (additional examples include [10][11][12]). …”

Section: Introductionmentioning

confidence: 99%

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

Garcia

Hedley²,

Tín

et al. 2015

Remote Sensing

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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

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“…Remote-sensing is a proven tool to quantify spatial extent of shallow benthic habitats (Mumby et al 1997, Purkis et al 2002. Recent studies identified the IKONOS sensor as an appropriate platform to resolve reef facies due to its appropriate spectral and spatial resolution characteristics (Maeder et al 2002, Mumby & Edwards 2002, Andréfouët et al 2003.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal dynamics of Arabian Gulf coral assemblages quantified from remote-sensing and in situ monitoring data

Purkis¹,

Riegl²

2005

Mar. Ecol. Prog. Ser.

120

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High-resolution ground verification, cluster analysis and optical model of reef substrate coverage on Landsat TM imagery (Red Sea, Egypt)

Cited by 43 publications

References 30 publications

Variation of leaflet shape from several soybean genotypes and its relation to morphological characters

Variation of leaflet shape from several soybean genotypes and its relation to morphological characters

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

Spatial and temporal dynamics of Arabian Gulf coral assemblages quantified from remote-sensing and in situ monitoring data

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