Mapping and assessing seagrass along the western coast of Florida using Landsat TM and EO-1 ALI/Hyperion imagery

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

doi:10.1016/j.ecss.2012.09.006

Cited by 87 publications

(42 citation statements)

References 36 publications

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“…The overall accuracy for each validation year of 67% in 2007 and 76% in 2008 was within the range of accuracies achieved from other studies employing supervised classification of Landsat data to map seagrass [16,17,30]. Substantial differences in the classification accuracy by cover class are apparent, with the Low cover class performing the worst and all other cover classes exceeding 75% producer's accuracy in either validation year (Table 3).…”

Section: Image Classificationsupporting

confidence: 55%

“…The study basins are particularly interesting for seagrass dynamics as a bay-wide investigation into decadal changes in seagrass (1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994) found turtle grass decline was not homogeneous throughout the Bay, with the largest reductions in shoot density and biomass occurring in central and western Florida Bay [41]. Statistical analysis of the study area's in-situ seagrass cover data from 1995 to 2012 showed seasonal variation to be low and annual variation to be incremental, as would be expected, since tropical seagrass beds are typically stable over the temporal scale of years, even in the advent of severe storms [17]. …”

Section: Study Sitementioning

confidence: 91%

“…The sparse cover class was defined to represent areas that are predominantly bare sediment. Specification of low and dense classes was based on typical definitions for these classes from the literature [13][14][15][16][17]. Spectral evaluation of the four reference classes revealed that medium and dense training pixels were difficult to distinguish from one another, so they were merged to define a single medium-dense spectral class.…”

Section: Seagrass Classificationmentioning

confidence: 99%

“…Wabnitz et al [16] tested the feasibility of achieving large-scale seagrass mapping for the Wider Caribbean region with limited ground truth data, obtaining an average overall accuracy of 68% across sites for the three-class scheme. Pu et al [17] mapped seagrass along the western coast of Florida using Landsat 5 data to calculate depth-invariant bands and achieved a 93% and 66% overall accuracy for their three-class and five-class schemes, respectively. However, the applicability of the pixel-based classifier in producing a time series of benthic map products was not tested in these studies.…”

Section: Introductionmentioning

confidence: 99%

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Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

2015

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Abstract:We tested a supervised classification approach with Landsat 5 Thematic Mapper (TM) data for time-series mapping of seagrass in a subtropical lagoon. Seagrass meadows are an integral link between marine and inland ecosystems and are at risk from upstream processes such as runoff and erosion. Despite the prevalence of image-specific approaches, the classification accuracies we achieved show that pixel-based spectral classes may be generalized and applied to a time series of images that were not included in the classifier training. We employed in-situ data on seagrass abundance from 2007 to 2011 to train and validate a classification model. We created depth-invariant bands from TM bands 1, 2, and 3 to correct for variations in water column depth prior to building the classification model. In-situ data showed mean total seagrass cover remained relatively stable over the study area and period, with seagrass cover generally denser in the west than the east. Our approach achieved mapping accuracies (67% and 76% for two validation years) comparable with those attained using spectral libraries, but was simpler to implement. We produced a series of annual maps illustrating inter-annual variability in seagrass occurrence. Accuracies may be improved in future work by better addressing the spatial mismatch between pixel size of remotely sensed data and footprint of field data and by employing atmospheric correction techniques that normalize reflectances across images. OPEN ACCESSRemote Sens. 2015, 7 5099

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Section: Image Classificationsupporting

confidence: 55%

Section: Study Sitementioning

confidence: 91%

Section: Seagrass Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

2015

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“…A variety of indices can be used to define the spectral similarity between two classes including root mean square error, spectral angle mapper [29] or spectral information divergence [30]. Though clustering is a common procedure in unsupervised classification [31][32][33][34], it is seldom performed on the endmember spectra that constitute the spectral library (for exceptions see [35][36][37]). Particularly on benthic spectra that are further modulated by the physical processes occurring in an optically variable water column.…”

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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High Resolution Satellite Image Based Seagrass Detection Using Generalized Regression Neural Network

Upadhyay¹,

Gupta²,

Tiwari³

et al. 2019

Communications in Computer and Information Science

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Mapping and assessing seagrass along the western coast of Florida using Landsat TM and EO-1 ALI/Hyperion imagery

Cited by 87 publications

References 36 publications

Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

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

High Resolution Satellite Image Based Seagrass Detection Using Generalized Regression Neural Network

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