Mapping Salt Marshes in Jamaica Bay and Terrestrial Vegetation in Fire Island National Seashore Using QuickBird Satellite Data

Wang, Yeqiao; Christiano, Mark; Traber, Michael

doi:10.1201/9781420094428-c9

Cited by 5 publications

(3 citation statements)

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“…These kinds of assessments need to be rapid and be easily repeatable so that potential problem areas can be identified and mitigation strategies developed in a timely manner [11]. Remote sensing provides a means by which hurricane impacts can be assessed over large areas quickly, accurately, and repeatedly [2,5,6].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Change Image Analysis for Assateague Island National Seashore Following Hurricane Sandy

Grybas

Congalton

2015

J. Imaging

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The assessment of storm damages is critically important if resource managers are to understand the impacts of weather pattern changes and sea level rise on their lands and develop management strategies to mitigate its effects. This study was performed to detect land cover change on Assateague Island as a result of Hurricane Sandy. Several single-date classifications were performed on the pre and post hurricane imagery utilized using both a pixel-based and object-based approach with the Random Forest classifier. Univariate image differencing and a post classification comparison were used to conduct the change detection. This study found that the addition of the coastal blue band to the Landsat 8 sensor did not improve classification accuracy and there was also no statistically significant improvement in classification accuracy using Landsat 8 compared to Landsat 5. Furthermore, there was no significant difference found between object-based and pixel-based classification. Change totals were estimated on Assateague Island following Hurricane Sandy and were found to be minimal, occurring predominately in the most active sections of the island in terms of land cover change, however, the post classification detected significantly more change, mainly due to classification errors in the single-date maps used.

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Section: Introductionmentioning

confidence: 99%

Land Cover Change Image Analysis for Assateague Island National Seashore Following Hurricane Sandy

Grybas

Congalton

2015

J. Imaging

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“…Random Forest is a non-parametric ensemble learning algorithm that has been demonstrated to achieve appropriate classification accuracy in a variety of landscapes [27][28][29]. The 9 classes used in this study included 6 from a previous study of the Bay [8]. These classes included water, mudflat, sand, high marsh, patchy S. alterniflora, and S. alterniflora (>50% vegetation cover).…”

Section: Segmentationmentioning

confidence: 99%

“…Remote sensing monitoring of the salt marsh landscape can be used to determine vegetation trends for the entire bay and individual islands, facilitating an assessment of restoration impacts. Remote sensing is an important tool for furthering our understanding of how Jamaica Bay's salt marshes are affected by anthropogenic and natural factors [8,9]. This study used imagery data spanning a decade and two high resolution sensor systems.…”

Section: Introductionmentioning

confidence: 99%

Salt Marsh Monitoring in Jamaica Bay, New York from 2003 to 2013: A Decade of Change from Restoration to Hurricane Sandy

et al. 2017

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This study used Quickbird-2 and Worldview-2, high resolution satellite imagery, in a multi-temporal salt marsh mapping and change analysis of Jamaica Bay, New York. An object-based image analysis methodology was employed. The study seeks to understand both natural and anthropogenic changes caused by Hurricane Sandy and salt marsh restoration, respectively. The objectives of this study were to: (1)

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Advances in coastal wetland remote sensing

Klemas

2014

2014 IEEE/OES Baltic International Symposium (BALTIC)

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Mapping Salt Marshes in Jamaica Bay and Terrestrial Vegetation in Fire Island National Seashore Using QuickBird Satellite Data

Cited by 5 publications

References 1 publication

Land Cover Change Image Analysis for Assateague Island National Seashore Following Hurricane Sandy

Land Cover Change Image Analysis for Assateague Island National Seashore Following Hurricane Sandy

Salt Marsh Monitoring in Jamaica Bay, New York from 2003 to 2013: A Decade of Change from Restoration to Hurricane Sandy

Advances in coastal wetland remote sensing

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