Identification of mangrove plant using hyperspectral remote sensing data along the Red Sea, Egypt

Basheer, Manar A.; Kafrawy, Sameh B. El; Mekawy, Amal A.

doi:10.21608/ejabf.2019.25932

Cited by 11 publications

(4 citation statements)

References 6 publications

(6 reference statements)

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“…L1C and L2A_Sen2Cor have same digital values in Band 5 through 12 but because of the atmospheric influence, there are variances in blue, green, and red band. Based on the interaction of wavelengths with mangroves, the low values for the blue and red bands are though to be caused by absorption and usage by chlorophyll for photosynthesis, reflecting the green and NIR bands, [42] [43]. The mangrove map based on random forest classification for each data level are displayed in Figure 5.…”

Section: Resultsmentioning

confidence: 99%

Comparison between top and bottom of atmosphere Sentinel-2 image for mangrove mapping in Balikpapan Bay, East Kalimantan

Ginting,

Setiawan,

Anggraini

et al. 2024

BIO Web Conf.

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Sentinel-2 is high-resolution multispectral imagery that launched by the European Space Agency on June 23, 2015 for Sentinel-2A and March 7, 2017 for Sentinel-2B. The two satellites were launched with the aim of land monitoring studies, including vegetation, soil, and water cover, as well as the observation of inland waterways and coastal areas. In 2018, Sentinel-2 produced bottom-of-atmosphere (L2A) imagery derived from top-of-atmosphere (L1C), which has been atmospherically corrected using Sen2Cor algorithm. However, there is an overcorrection effect due to inaccuracies of digital elevation model, over-detection of clouds over bright targets, and miss-classification of topographic shadows. This research aims to explore the application of Sentinel-2 imagery for mangrove mapping by comparing two levels of data, including L1C and L2A. L2A is divided into two, namely L2A atmospherically corrected using the Sen2Cor method (L2A_Sen2Cor) and dark object subtraction method (L2A_DOS). The classification scheme was built based on in-situ data containing seven objects: water, clouds, built-up, cloud shadows, bare land, mangroves, and land vegetation using random forest classification. The comparison of each level of data is analyzed based on the spectral signature and accuracy assessment using confusion matrix. The result shows that there are differences in the spectral signature between L1C and L2A data because of atmospheric impacts. L2A outperforms L1C, as shown by the higher coefficient of determination (R2). The accuracy is in the range of 93.7 – 95.4%, with the best accuracy shown by L2A_Sen2Cor.

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Section: Resultsmentioning

confidence: 99%

Comparison between top and bottom of atmosphere Sentinel-2 image for mangrove mapping in Balikpapan Bay, East Kalimantan

Ginting,

Setiawan,

Anggraini

et al. 2024

BIO Web Conf.

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“…In 1984, mangroves area was 0.16 km 2 , which ended by 0.18 km 2 in 2021. This increase may be explained by "non influenced areas by anthropogenic activities", or because of the soil properties of the area that suit this mangrove (Avicennia marina) (e.g., Sheded et al,2013;Basheer et al, 2019). Recently, six sites suitable for mangroves plantation were identified along Quseir-Marsa Alam corridor by Abd-El Monsef et al, 2017.…”

Section: Lulc Change Ofmentioning

confidence: 99%

Detection and mapping of land use land cover with Support vector machines SVM-based change monitoring using Landsat and Sentinel-2 data. The case of Quseir, Red Sea

2022

Miṣriqiyā

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Land use land cover (LULC) mapping and spatial change monitoring are essential to manage the coastal cities and its resources. This study aims to investigate LULC changes of Quseir, a typical small Red Sea coastal city for a long period from 1984 to 2021. Support vector machines (SVMs) classifier and Post classification comparison (PCC) change detection technique were used to analyse two Thematic Mappers (TM), an Enhanced Thematic Mapper plus (ETM+), an Operational Land Imager (OLI) Landsat imagery in addition to a Multi-Spectral Instrument (MSI) Sentinel-2 image cover the study period. Detection and mapping of land use land cover with Support vector machines SVM-based change monitoring using Landsat and Sentinel-2 data. The case of Quseir, Red Sea Miṣ riqiyā Vol. 2 Issue 2 (2022) 2 2021, had the highest accuracy. Through data-resampling, combining Landsat and Sentinel-2 satellite datasets for long-term monitoring studies using PCC resulted in more reliable and accurate outputs. Results obtained from this study will fill the gap of rare LULC maps and spatial change information of Quseir during the past four decades.

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“…Mangrove forests cover approximately 137,760 km2 worldwide, of which approximately half are located in Asia alone [4]. Mangrove vegetation contributes to biodiversity conservation, erosion control, and protection from storms and floods along the coast [5]. They provide shelter, coastal protection, habitat, nurseries, and breeding grounds for many fish, crustaceans, and other marine and terrestrial animals [6].…”

Section: Introductionmentioning

confidence: 99%

Identification of Egyptian Mangrove Species Based on DNA Barcoding

Said

Bahnasy²

2023

AJAHR

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Aims: Mangroves are woody trees or shrubs that grow in the intertidal zone and are distributed along tropical and subtropical coasts. These plants are resilient to various environmental challenges; they are also one of the most efficient terrestrial and coastal ecosystem for carbon fixation and storage. In recent years, mangrove reforestation has attracted much attention as a strategy to reduce the effects of climate change. In Egypt, there are two types of mangroves, Rhizophora mucronata and Avicennia marina, between 30°N and 30°S of the equator. Mangrove management presents a difficult task, particularly when it comes to managing molecular mangroves for long-term sustainability. With the impact of human activity on mangrove ecosystems increasing each year, molecular research on mangrove correlates remains to be conducted. For this reason, using DNA barcoding technology to quickly identify species, mangrove ecosystems may be protected. Methodology: In this work, the two Egyptian mangrove species were assessed through morphological, cytological, and molecular approaches. Two universal DNA barcodes, rbcL and ITS, were examined to identify their efficacy for Egyptian mangrove species identification and phylogenetic reconstruction. Results: According to pairwise alignments, the rbcL region had the highest level of variability (73.2%), whereas the ITS region was the least variable (11.96%). The selected Egyptian mangrove species can potentially be distinguished by barcoding loci rbcL and ITS due to the existence of distinctive variable sites.

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Identification of mangrove plant using hyperspectral remote sensing data along the Red Sea, Egypt

Cited by 11 publications

References 6 publications

Comparison between top and bottom of atmosphere Sentinel-2 image for mangrove mapping in Balikpapan Bay, East Kalimantan

Comparison between top and bottom of atmosphere Sentinel-2 image for mangrove mapping in Balikpapan Bay, East Kalimantan

Detection and mapping of land use land cover with Support vector machines SVM-based change monitoring using Landsat and Sentinel-2 data. The case of Quseir, Red Sea

Identification of Egyptian Mangrove Species Based on DNA Barcoding

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