Understanding the dynamics of landscape of greater Sundarban area using multi-layer perceptron Markov chain and landscape statistics approach
Sardar, Purnendu LU
and Samadder, Sukha Ranjan
(2021)
In Ecological Indicators
121.
- Abstract
- Sundarban mangrove is the largest continuous mangrove forest in the world and is the habitat of an incredible amount of biodiversity. Biodiversity loss and degradation of this highly ecologically productive mangrove forest due to climate change will have a tremendous impact on the livelihood of the people living in the deltaic region. The present study was aimed to find out the dynamics of change in different land cover for the greater Sundarban region. The multi-temporal Landsat images were used to understand the dynamics in three decades from 1998 to 2018. The analysis revealed that Sundarban mangrove forest changed very insignificantly. The change between 1998 and 2008 was approximately 1.2%, i.e. around 26.2 km2, and the... (More)
- Sundarban mangrove is the largest continuous mangrove forest in the world and is the habitat of an incredible amount of biodiversity. Biodiversity loss and degradation of this highly ecologically productive mangrove forest due to climate change will have a tremendous impact on the livelihood of the people living in the deltaic region. The present study was aimed to find out the dynamics of change in different land cover for the greater Sundarban region. The multi-temporal Landsat images were used to understand the dynamics in three decades from 1998 to 2018. The analysis revealed that Sundarban mangrove forest changed very insignificantly. The change between 1998 and 2008 was approximately 1.2%, i.e. around 26.2 km2, and the change between the year 2008 and 2018 was approximately 1.27%, which consists of 28 km2. However, the dynamics of open mangrove forest and dense mangrove forest were found very high. The human habitat and aquaculture were found as the most rapidly expanding land-use type in this landscape. Area under human habitat has increased approximately 80%, i.e. nearly 654 km2 between 1998 and 2018; area under aquaculture has increased approximately 116% (i.e. around 231 km2) between 1998 and 2008. The MLP-Markov Chain based modelling of land-use has shown an overwhelming increase in human habitat in near future. However, being situated in the close vicinity of dense human population of the city Kolkata, the mangrove habitat remained intact, which is largely due to great amount of conservation efforts that have been exercised by the Govt. and the local forest department. The landscape statistical analysis for understanding the ecological dynamics of every land cover types showed that in 2018, the human habitat became second dominant category in this study area. The aquaculture practice for high economic value has pushed down the native agricultural practice in the region. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/aeb3b0aa-d7ff-4582-9a0c-832c70cda871
- author
- Sardar, Purnendu
LU
and Samadder, Sukha Ranjan
- publishing date
- 2021-02-01
- type
- Contribution to journal
- publication status
- published
- keywords
- Sundarban, Mangrove, Remote Sensing, Landcover Change, multilayer perceptron (MLP), Landscape Statistics
- in
- Ecological Indicators
- volume
- 121
- article number
- 106914
- pages
- 12 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85092152608
- ISSN
- 1470-160X
- DOI
- 10.1016/j.ecolind.2020.106914
- language
- English
- LU publication?
- no
- id
- aeb3b0aa-d7ff-4582-9a0c-832c70cda871
- date added to LUP
- 2025-06-18 11:27:15
- date last changed
- 2025-06-19 04:01:31
@article{aeb3b0aa-d7ff-4582-9a0c-832c70cda871,
abstract = {{Sundarban mangrove is the largest continuous mangrove forest in the world and is the habitat of an incredible amount of biodiversity. Biodiversity loss and degradation of this highly ecologically productive mangrove forest due to climate change will have a tremendous impact on the livelihood of the people living in the deltaic region. The present study was aimed to find out the dynamics of change in different land cover for the greater Sundarban region. The multi-temporal Landsat images were used to understand the dynamics in three decades from 1998 to 2018. The analysis revealed that Sundarban mangrove forest changed very insignificantly. The change between 1998 and 2008 was approximately 1.2%, i.e. around 26.2 km<sup>2</sup>, and the change between the year 2008 and 2018 was approximately 1.27%, which consists of 28 km<sup>2</sup>. However, the dynamics of open mangrove forest and dense mangrove forest were found very high. The human habitat and aquaculture were found as the most rapidly expanding land-use type in this landscape. Area under human habitat has increased approximately 80%, i.e. nearly 654 km<sup>2</sup> between 1998 and 2018; area under aquaculture has increased approximately 116% (i.e. around 231 km<sup>2</sup>) between 1998 and 2008. The MLP-Markov Chain based modelling of land-use has shown an overwhelming increase in human habitat in near future. However, being situated in the close vicinity of dense human population of the city Kolkata, the mangrove habitat remained intact, which is largely due to great amount of conservation efforts that have been exercised by the Govt. and the local forest department. The landscape statistical analysis for understanding the ecological dynamics of every land cover types showed that in 2018, the human habitat became second dominant category in this study area. The aquaculture practice for high economic value has pushed down the native agricultural practice in the region.}},
author = {{Sardar, Purnendu and Samadder, Sukha Ranjan}},
issn = {{1470-160X}},
keywords = {{Sundarban; Mangrove; Remote Sensing; Landcover Change; multilayer perceptron (MLP); Landscape Statistics}},
language = {{eng}},
month = {{02}},
publisher = {{Elsevier}},
series = {{Ecological Indicators}},
title = {{Understanding the dynamics of landscape of greater Sundarban area using multi-layer perceptron Markov chain and landscape statistics approach}},
url = {{http://dx.doi.org/10.1016/j.ecolind.2020.106914}},
doi = {{10.1016/j.ecolind.2020.106914}},
volume = {{121}},
year = {{2021}},
}