Mangrove species mapping using Sentinel-1 and Sentinel-2 Data in North Vietnam

Tien Dat Pham; Junshi Xia; Gerald Baier; Nga Nhu Le; Naoto Yokoya

doi:10.1109/igarss.2019.8898987

Mangrove species mapping using Sentinel-1 and Sentinel-2 Data in North Vietnam

Tien Dat Pham, Junshi Xia, Gerald Baier, Nga Nhu Le, Naoto Yokoya

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

11 Citations (Scopus)

Abstract

This study employed Sentinel-1A C-band and Sentinel-2A multispectral data combined with the decision tree ensemble algorithms to map the spatial distribution of five mangrove communities in a coastal area in North Vietnam. The results show that the rotation forests (RoFs) model achieved better overall accuracy and kappa coefficient in mapping mangrove species than those of the canonical correlation forests (CCFs) and the random forests (RFs) models. This research demonstrates the potential of using optical and SAR data together with machine learning techniques to map mangrove species in tropical areas.

Original language	English
Title of host publication	2019 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publication	proceedings
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	6102-6105
Number of pages	4
ISBN (Electronic)	9781538691540, 9781538691533
ISBN (Print)	9781538691557
DOIs	https://doi.org/10.1109/igarss.2019.8898987
Publication status	Published - 2019
Externally published	Yes
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Publication series

Name
ISSN (Print)	2153-6996
ISSN (Electronic)	2153-7003

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

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10.1109/igarss.2019.8898987

Cite this

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title = "Mangrove species mapping using Sentinel-1 and Sentinel-2 Data in North Vietnam",

abstract = "This study employed Sentinel-1A C-band and Sentinel-2A multispectral data combined with the decision tree ensemble algorithms to map the spatial distribution of five mangrove communities in a coastal area in North Vietnam. The results show that the rotation forests (RoFs) model achieved better overall accuracy and kappa coefficient in mapping mangrove species than those of the canonical correlation forests (CCFs) and the random forests (RFs) models. This research demonstrates the potential of using optical and SAR data together with machine learning techniques to map mangrove species in tropical areas.",

author = "Pham, {Tien Dat} and Junshi Xia and Gerald Baier and Le, {Nga Nhu} and Naoto Yokoya",

year = "2019",

doi = "10.1109/igarss.2019.8898987",

language = "English",

isbn = "9781538691557",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "6102--6105",

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address = "United States",

note = "39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

}

Pham, TD, Xia, J, Baier, G, Le, NN & Yokoya, N 2019, Mangrove species mapping using Sentinel-1 and Sentinel-2 Data in North Vietnam. in 2019 IEEE International Geoscience and Remote Sensing Symposium: proceedings. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 6102-6105, 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/igarss.2019.8898987

Mangrove species mapping using Sentinel-1 and Sentinel-2 Data in North Vietnam. / Pham, Tien Dat; Xia, Junshi; Baier, Gerald et al.

2019 IEEE International Geoscience and Remote Sensing Symposium: proceedings. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 6102-6105.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Mangrove species mapping using Sentinel-1 and Sentinel-2 Data in North Vietnam

AU - Pham, Tien Dat

AU - Xia, Junshi

AU - Baier, Gerald

AU - Le, Nga Nhu

AU - Yokoya, Naoto

PY - 2019

Y1 - 2019

N2 - This study employed Sentinel-1A C-band and Sentinel-2A multispectral data combined with the decision tree ensemble algorithms to map the spatial distribution of five mangrove communities in a coastal area in North Vietnam. The results show that the rotation forests (RoFs) model achieved better overall accuracy and kappa coefficient in mapping mangrove species than those of the canonical correlation forests (CCFs) and the random forests (RFs) models. This research demonstrates the potential of using optical and SAR data together with machine learning techniques to map mangrove species in tropical areas.

AB - This study employed Sentinel-1A C-band and Sentinel-2A multispectral data combined with the decision tree ensemble algorithms to map the spatial distribution of five mangrove communities in a coastal area in North Vietnam. The results show that the rotation forests (RoFs) model achieved better overall accuracy and kappa coefficient in mapping mangrove species than those of the canonical correlation forests (CCFs) and the random forests (RFs) models. This research demonstrates the potential of using optical and SAR data together with machine learning techniques to map mangrove species in tropical areas.

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U2 - 10.1109/igarss.2019.8898987

DO - 10.1109/igarss.2019.8898987

M3 - Conference proceeding contribution

SN - 9781538691557

SP - 6102

EP - 6105

BT - 2019 IEEE International Geoscience and Remote Sensing Symposium

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019

Y2 - 28 July 2019 through 2 August 2019

ER -

Mangrove species mapping using Sentinel-1 and Sentinel-2 Data in North Vietnam

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