EventsOHOW 2023 – The 2nd International Symposium on One Health, One World
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This submission belongs to the session CCGR. Climate change and green recovery of the event OHOW 2023 – The 2nd International Symposium on One Health, One World
Published date
16 May, 2024
Academic Editor
author-avatarWataru Takeuchi
Citation
A.I OLADIPUPO, A.S.M MAKSUD KAMAL, ASHRAFUL ISLAM, Spatial-Temporal Assessment of Habitat Quality and Degradation in Sundarbans Mangrove Environment, Bangladesh, in Proceedings of OHOW 2023 – The 2nd International Symposium on One Health, One World, Dhaka University, Dhaka, 6 December–8 December 2023, MDPI: Basel, Switzerland
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Spatial-Temporal Assessment of Habitat Quality and Degradation in Sundarbans Mangrove Environment, Bangladesh

1. Department of Disaster Science and Climate Resilience, University of Dhaka, Bangladesh
2. Department of Geology, University of Dhaka, Bangladesh
Abstract

Mangrove ecosystem, such as the Sundarbans Mangrove Forest (SMF) in Bangladesh, plays a significant role in the conservation of coastal biodiversity, give protection to the communities against harsh impacts of climate change, as well as ecosystem services benefits from its diverse habitat. However, with the current trend in climate extreme events which is further exacerbate by continuous anthropogenic activities and natural disasters, this beautiful landscape faces an increasing threat to habitat degradation and change in habitat status due to these factors.

Quite a number of research has been conducted to assessed the impact of threats to the SMF, however, most of this research make use of indirect approach of ecological study which only assess single threat, habitat, and a very small spatial coverage. Therefore, the purpose of this study is to explore the use of remote sensing and Ecosystem modeling to assess habitat quality and degradation from interplay between multiple threats. Using TERRSET2020 Ecosystem Habitat Model as a modeling tool, and Google Earth Engine for Habitat classification, this study aims to determine (i) how landcover in SMF and surrounding communities changed overtime, (ii) what are the threats causing habitat degradation and their sources, (iii) what are habitats experiencing quality and degradation changes, and (iv) how is the distribution of habitat quality and degradation overtime in the SMF.

Our methodology involves four analytical steps with first three steps serving as an input for the final. Firstly, we will use Remote sensing data, and Google Earth Engine (GEE) platform to analyze Landcover as the habitat type for our model. Landsat and Sentinel-2A (2000, 2011, 2023) data will be assessed through GEE cloud and supervised classification techniques will apply to generate comprehensive Landcover history. In the second step, we will identify various threat that are causing habitat degradation from both anthropogenic activities, climate and environmental impact. Relative impact of each threat will be assessed by giving weight to each of them and then determine maximum distance to habitat. Also, all threats will be organized in a table and map representing each will be generated. Next is the analysis of habitat suitability to each threat. A range of suitability score will be assigned between 0 and 1 to each habitat with the use of Analytical Hierarchical Process for decision making and organize in table. Finally, all results including, Land cover map, threat maps and table, and habitat suitability table will be fed into the Habitat Quality Model to generate habitat degradation and habitat quality map for each selected year over the study area.

This research will provide valuable understanding of multiple threats impact to habitat quality and degradation in the Sundarbans Mangrove. The results are expected to support and inform government, stakeholder, and Non-Governmental Organization sustainable management practices, ecosystem conservation, Natural resources management, and contribute to climate change mitigation and adaptation strategies.

Keywords
Mangrove Ecosystem
Sundarbans
Habitat Degradation
Climate Change
Remote Sensing
Ecosystem Modeling
Sustainable Management
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