Multi-decadal land cover evolution in the Sundarban, the largest mangrove forest in the world

Quader, Mohammad Abdul; Agrawal, Shefali; Kervyn, Matthieu

doi:10.1016/j.ocecoaman.2017.02.008

Cited by 46 publications

(38 citation statements)

References 26 publications

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“…We found that the annual deforestation rate varies with region. For example, using a hierarchical classification approach on Landsat imageries, Quader et al (2017) found that the Sundarbans mangrove forest, the largest contiguous mangrove forest in the world sized around 10,000 square kilometers, was encountering 0.38% annual loss in forest cover over the period of 1975-2010 [27]. However, Ghosh et al (2016) used a supervised maximum likelihood classification (MLC) approach and reported that the Sundarbans was experiencing a 0.81% annual loss in the period 1977-2015 [28].…”

Section: Sdg 151-forestsmentioning

confidence: 99%

“…However, Ghosh et al (2016) used a supervised maximum likelihood classification (MLC) approach and reported that the Sundarbans was experiencing a 0.81% annual loss in the period 1977-2015 [28]. We think that considering the dynamic nature of the intertidal environment [26,27] and the image classification accuracy [29], the deforestation rate in the Sundarbans may not be worrisome.…”

Section: Sdg 151-forestsmentioning

confidence: 99%

“…Forest health assessment, habitat degradation, species composition related studies used various approaches, in addition to supervised MLC, in their analysis including OLS Figure 5. Forest cover loss/gain estimation by different studies [24,27,[30][31][32][33][34].…”

Section: Sdg 151-forestsmentioning

confidence: 99%

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Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review

et al. 2020

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The Sustainable Development Goals (SDGs) have been in effect since 2015 to continue the progress of the Millennium Development Goals. Some of the SDGs are expected to be achieved by 2020, while others by 2030. Among the 17 SDGs, SDG 15 is particularly dedicated to environmental resources (e.g., forest, wetland, land). These resources are gravely threatened by human-induced climate change and intense anthropogenic activities. In Bangladesh, one of the most climate-vulnerable countries, climate change and human interventions are taking a heavy toll on environmental resources. Ensuring the sustainability of these resources requires regular monitoring and evaluation to identify challenges, concerns, and progress of environmental management. Remote sensing has been used as an effective tool to monitor and evaluate these resources. As such, many studies on Bangladesh used various remote-sensing approaches to conduct research on the issues related to SDG 15, particularly on forest, wetland, erosion, and landslides. However, we lack a comprehensive view of the progress, challenges, concerns, and future outlook of the goal and its targets. In this study, we sought to systematically review the remote-sensing studies related to SDG 15 (targets 15.1–15.3) to present developments, analyze trends and limitations, and provide future directions to ensure sustainability. We developed several search keywords and finally selected 53 articles for review. We discussed the topical and methodological trends of current remote-sensing works. In addition, limitations were identified and future research directions were provided.

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Section: Sdg 151-forestsmentioning

confidence: 99%

Section: Sdg 151-forestsmentioning

confidence: 99%

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Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review

et al. 2020

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“…Table 1 lists the pertinent datasets (or subsets of datasets) that were not acquired as part of this process, providing author details and the names and status of their Approximately 5.8% of the world's mangroves are found specifically in the S Asia sub-region [33], distributed primarily in sporadic coastal pockets [19]. This sub-region includes the world's largest mangrove ecosystem, the Sundarbans, covering approximately 1,000,000 ha at the India-Bangladesh interface [65]. Throughout this sub-region, loss is attributed to land-cover conversion, pollution, over-harvesting for timber and natural drivers including cyclones, tsunamis and coastal erosion [66].…”

Section: Inventory Acquisition and Description Of Datasetsmentioning

confidence: 99%

“…The borders of countries/territories were geographically defined by combining political boundaries with corresponding exclusive economic zones (EEZ). Country polygons were sourced from the Global Administrative Boundaries database (v3.6, www.gadm.org) [65] and EEZs from Marine Regions (v10 World EEZ, www.marineregions. org) [70].…”

Section: Single-date Mangrove Distributionmentioning

confidence: 99%

Identifying Mangrove Deforestation Hotspots in South Asia, Southeast Asia and Asia-Pacific

Gandhi

Jones

2019

Remote Sensing

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Mangroves inhabit highly productive inter-tidal ecosystems in >120 countries in the tropics and subtropics providing critical goods and services to coastal communities and contributing to global climate change mitigation owing to substantial carbon stocks. Despite their importance, global mangrove distribution continues to decline primarily due to anthropogenic drivers which vary by region/country. South Asia, Southeast Asia and Asia-Pacific contain approximately 46% of the world’s mangrove ecosystems, including the most biodiverse mangrove forests. This region also exhibits the highest global rates of mangrove loss. Remotely sensed data provides timely and accurate information on mangrove distribution and dynamics critical for targeting loss hotspots and guiding intervention. This report inventories, describes and compares all known single- and multi-date remotely sensed datasets with regional coverage and provides areal mangrove extents by country. Multi-date datasets were used to estimate dynamics and identify loss hotspots (i.e., countries that exhibit greatest proportional loss). Results indicate Myanmar is the primary mangrove loss hotspot, exhibiting 35% loss from 1975–2005 and 28% between 2000–2014. Rates of loss in Myanmar were four times the global average from 2000–2012. The Philippines is additionally identified as a loss hotspot, with secondary hotspots including Malaysia, Cambodia and Indonesia. This information helps inform and guide mangrove conservation, restoration and managed-use within the region.

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A Remote Sensing Assessment of Spatio-Temporal Dynamics of Coastal Ecosystem: Evidence from Indian Sundarbans

Mandal

Nayak

2022

Transforming Coastal Zone for Sustainable Food and Income Security

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Multi-decadal land cover evolution in the Sundarban, the largest mangrove forest in the world

Cited by 46 publications

References 26 publications

Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review

Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review

Identifying Mangrove Deforestation Hotspots in South Asia, Southeast Asia and Asia-Pacific

A Remote Sensing Assessment of Spatio-Temporal Dynamics of Coastal Ecosystem: Evidence from Indian Sundarbans

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