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Monitoring Mangrove forest landcover changes in the coastline of Bangladesh from 1976 to 2015

Islam, Md Monirul ; Borgqvist, Helena LU and Kumar, Lalit (2019) In Geocarto International 34(13). p.1458-1476
Abstract

This study used multi-date Landsat images to quantify mangrove cover changes in the whole of Bangladesh from 1976 to 2015. Images were pre-processed with an atmospheric correction using Dark Object Subtraction (DOS) and Relative Radiometric Normalization (RRN) using Pseudo-Invariant Features (PIFs). Land Use/Land Cover (LU/LC) classification map was generated using Maximum Likelihood (MaxLike) algorithm, indicating the areal extent of mangroves increased by 3.1% between 1976 and 2015, where 1.79% of this increase occurred between 2000 and 2015. Though mangrove areas remained almost constant in the Sundarbans, Chakaria Sundarbans has almost disappeared between 1976 and 1989. The overall accuracy of Landsat MSS, TM, ETM+, and L8 OLI... (More)

This study used multi-date Landsat images to quantify mangrove cover changes in the whole of Bangladesh from 1976 to 2015. Images were pre-processed with an atmospheric correction using Dark Object Subtraction (DOS) and Relative Radiometric Normalization (RRN) using Pseudo-Invariant Features (PIFs). Land Use/Land Cover (LU/LC) classification map was generated using Maximum Likelihood (MaxLike) algorithm, indicating the areal extent of mangroves increased by 3.1% between 1976 and 2015, where 1.79% of this increase occurred between 2000 and 2015. Though mangrove areas remained almost constant in the Sundarbans, Chakaria Sundarbans has almost disappeared between 1976 and 1989. The overall accuracy of Landsat MSS, TM, ETM+, and L8 OLI classified images were 80%, 80%, 87%, and 97% respectively. The study also found deforestation, shrimp & salt farm, coastal erosion and sedimentation, and mangrove plantation could be responsible for mangrove changes in Bangladesh.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bangladesh, landsat, Mangrove, RRN, supervised classification
in
Geocarto International
volume
34
issue
13
pages
20 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85053441676
ISSN
1010-6049
DOI
10.1080/10106049.2018.1489423
language
English
LU publication?
yes
id
23f8268a-94cb-458a-aa51-3a41757fdca8
date added to LUP
2018-10-24 08:40:44
date last changed
2022-04-25 18:04:27
@article{23f8268a-94cb-458a-aa51-3a41757fdca8,
  abstract     = {{<p>This study used multi-date Landsat images to quantify mangrove cover changes in the whole of Bangladesh from 1976 to 2015. Images were pre-processed with an atmospheric correction using Dark Object Subtraction (DOS) and Relative Radiometric Normalization (RRN) using Pseudo-Invariant Features (PIFs). Land Use/Land Cover (LU/LC) classification map was generated using Maximum Likelihood (MaxLike) algorithm, indicating the areal extent of mangroves increased by 3.1% between 1976 and 2015, where 1.79% of this increase occurred between 2000 and 2015. Though mangrove areas remained almost constant in the Sundarbans, Chakaria Sundarbans has almost disappeared between 1976 and 1989. The overall accuracy of Landsat MSS, TM, ETM+, and L8 OLI classified images were 80%, 80%, 87%, and 97% respectively. The study also found deforestation, shrimp &amp; salt farm, coastal erosion and sedimentation, and mangrove plantation could be responsible for mangrove changes in Bangladesh.</p>}},
  author       = {{Islam, Md Monirul and Borgqvist, Helena and Kumar, Lalit}},
  issn         = {{1010-6049}},
  keywords     = {{Bangladesh; landsat; Mangrove; RRN; supervised classification}},
  language     = {{eng}},
  number       = {{13}},
  pages        = {{1458--1476}},
  publisher    = {{Taylor & Francis}},
  series       = {{Geocarto International}},
  title        = {{Monitoring Mangrove forest landcover changes in the coastline of Bangladesh from 1976 to 2015}},
  url          = {{http://dx.doi.org/10.1080/10106049.2018.1489423}},
  doi          = {{10.1080/10106049.2018.1489423}},
  volume       = {{34}},
  year         = {{2019}},
}