Monitoring Mangrove forest landcover changes in the coastline of Bangladesh from 1976 to 2015
(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.
(Less)
- author
- Islam, Md Monirul ; Borgqvist, Helena LU and Kumar, Lalit
- organization
- publishing date
- 2019
- 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 & 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}}, }