A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique

Giri, Sandip; Mukhopadhyay, Anirban; Hazra, Sugata; Mukherjee, Sandip; Roy, Deborupa; Ghosh, Suchismita; Ghosh, Tuhin; Mitra, Debashis

doi:10.1007/s11852-014-0322-3

Cited by 84 publications

(50 citation statements)

References 19 publications

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“…7). Previous studies indicate that Heritiera prefers to grow in oligohaline regions, whereas Ceriops, Sonneratia and Xylocarpusare generally found in mesohaline regions and species like Avicennia and Excoecaria grow in polyhaline regions (Giri et al 2014;Naskar and Guha Bakshi 1987;Bhattacharjee et al 2013;Clough 2013). However, some researchers observed that Avicennia and Excoecaria can thrive over a wide salinity range and Excoecaria was found to grow well even in low Fig.…”

Section: Distribution and Predicted Loss Of Total Carbon From Differementioning

confidence: 99%

Blue Carbon Stock of the Bangladesh Sundarban Mangroves: What could Be the Scenario after a Century?

et al. 2016

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The total blue carbon stock of the Bangladesh Sundarban mangroves was evaluated and the probable future status after a century was predicted based on the recent trend of changes in the last 30 years and implementing a hybrid model of Markov Chain and Cellular automata. At present 36.24 Tg C and 54.95 Tg C are stored in the above-ground and below-ground compartments respectively resulting in total blue carbon stock of 91.19 Tg C. According to the prediction 15.88 Tg C would be lost from this region by the year 2115. The low saline species composition classes dominated mainly by Heritiera spp. accounts for the major portion of the carbon sock at present (45.60 Tg C), while the highly saline regions stores only 14.90 Tg C. The prediction shows that after a hundred years almost 22.42 Tg C would be lost from the low saline regions accompanied by an increase of 8.20 Tg C in the high saline regions dominated mainly by Excoecaria sp. and Avicennia spp. The net carbon loss would be due to both mangrove area loss (~510 km 2 ) and change in species composition leading to 58.28 Tg of potential CO 2 emission within the year 2115.

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Section: Distribution and Predicted Loss Of Total Carbon From Differementioning

confidence: 99%

Blue Carbon Stock of the Bangladesh Sundarban Mangroves: What could Be the Scenario after a Century?

et al. 2016

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“…For mapping and change detection purposes, a number of studies conducted in the Sundarbans have applied remote sensing tools (for example [15,[33][34][35][36][37][38]); however, most of the studies have kept their focus only on determining spatial change. Giri and co-authors [39] identified mangrove composition and their changes at the species level in the Sundarbans, but this was only for the Indian Territory. Therefore, plant species and their distribution and dynamics remain unrecorded for the entire Sundarbans mangrove forest comprising both countries.…”

Section: Introductionmentioning

confidence: 99%

Mapping Long-Term Changes in Mangrove Species Composition and Distribution in the Sundarbans

Ghosh

Kumar

Roy

2016

Forests

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Abstract:The Sundarbans mangrove forest is an important resource for the people of the Ganges Delta. It plays an important role in the local as well as global ecosystem by absorbing carbon dioxide and other pollutants from air and water, offering protection to millions of people in the Ganges Delta against cyclone and water surges, stabilizing the shore line, trapping sediment and nutrients, purifying water, and providing services for human beings, such as fuel wood, medicine, food, and construction materials. However, this mangrove ecosystem is under threat, mainly due to climate change and anthropogenic factors. Anthropogenic and climate change-induced degradation, such as over-exploitation of timber and pollution, sea level rise, coastal erosion, increasing salinity, effects of increasing number of cyclones and higher levels of storm surges function as recurrent threats to mangroves in the Sundarbans. In this situation, regular and detailed information on mangrove species composition, their spatial distribution and the changes taking place over time is very important for a thorough understanding of mangrove biodiversity, and this information can also lead to the adoption of management practices designed for the maximum sustainable yield of the Sundarbans forest resources. We employed a maximum likelihood classifier technique to classify images recorded by the Landsat satellite series and used post classification comparison techniques to detect changes at the species level. The image classification resulted in overall accuracies of 72%, 83%, 79% and 89% for the images of 1977, 1989, 2000 and 2015, respectively. We identified five major mangrove species and detected changes over the 38-year (1977-2015) study period. During this period, both Heritiera fomes and Excoecaria agallocha decreased by 9.9%, while Ceriops decandra, Sonneratia apelatala, and Xylocarpus mekongensis increased by 12.9%, 380.4% and 57.3%, respectively.

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“…The SMRI can be calculated from multi-tide, high-resolution images for use in recognizing submerged mangroves. In turn, the normalized difference vegetation index (NDVI), which is a spectral index widely used to separate vegetation from non-vegetation [49][50][51], can also be calculated as a spectral feature from one of the images (i.e., for low or high tide) to identify high-stand mangroves ( Figure 1). Thus, these two indices can be applied in an advanced framework of object-based RS image classification via machine learning (e.g., SVM) for mapping mangrove forests.…”

Section: Basic Ideamentioning

confidence: 99%

Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery

Xia

Qin

et al. 2018

Remote Sensing

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Abstract:Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery.

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A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique

Cited by 84 publications

References 19 publications

Blue Carbon Stock of the Bangladesh Sundarban Mangroves: What could Be the Scenario after a Century?

Blue Carbon Stock of the Bangladesh Sundarban Mangroves: What could Be the Scenario after a Century?

Mapping Long-Term Changes in Mangrove Species Composition and Distribution in the Sundarbans

Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery

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