Land cover change detection of Hatiya Island, Bangladesh, using remote sensing techniques

Kumar, Lalit; Ghosh, Manoj Kumer

doi:10.1117/1.jrs.6.063608

Cited by 28 publications

(15 citation statements)

References 4 publications

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“…Afterwards, extracted spectral signatures were used for image classification and identification of mangrove species. In addition to supplementary information obtained from various sources, the author's prior knowledge was also used to document characteristics of different mangrove species, as supported by Kumar and Ghosh [58]. There are 27 mangrove species in the Sundarbans [15], but many of these are present in small quantities, spread over wide areas in small patches and thus unable to be detected using medium-resolution satellite data.…”

Section: Image Classificationmentioning

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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Section: Image Classificationmentioning

confidence: 99%

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

Ghosh

Kumar

Roy

2016

Forests

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“…During the period 1989-2014, the urban area in Rangpur sub-district increased 20% [42]. Urban areas in the southern cities have also increased over time [72,73]. The urban area in Kushtia city increased the most, 56.71% followed by Jessore city at 23.28%, and Satkhira city at 10.03% between 1989 and 2010 [74].…”

Section: Urban Landmentioning

confidence: 99%

A Synthesis of Studies on Land Use and Land Cover Dynamics during 1930–2015 in Bangladesh

et al. 2017

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Land use and land cover (LULC) is dynamic and changes in it have important environmental and socio-economic consequences. The pathways and pace of change vary with space and time and are related to the interaction between human activities and biophysical conditions in an area. This study provides a systematic review of the changing status, patterns, and compositions of LULC in Bangladesh on national, regional, and local scales over the past 85 years. The primary LULC classes in Bangladesh are agricultural land, urban and built-up area, forest and vegetation, water bodies, and wetlands. Most of the country is covered with agricultural land, followed by urban areas; the latter has been expanding rapidly in the area surrounding the capital city, Dhaka, especially the southern capital area. Forest cover is mostly concentrated in southeast Bangladesh, the Chittagong district, and the mangrove forests are predominantly located in the southwest, with the Gangetic delta. High population growth, rapid urbanization, and infrastructure development have been directly associated with changing patterns of land use across the country. In recent decades, urban areas and water bodies have been increasing, to the detriment of both forests and agricultural land. Most of the studies reviewed here describe a general trend involving agricultural and forested land being transformed into urban areas.

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“…Table 2. Land use/land cover (LULC) classes delineated based on supervised classification (based on [43,[50][51][52][53]). , and field visit were used to recognize and confirm the different features [42].…”

Section: Image Classificationmentioning

confidence: 99%

“…Maximum likelihood (ML) [43][44][45], random forest (RF) [46], decision tree (DT) [47], support vector machine (SVM) [48], and neural network (NN) [49] classifiers are some of the conventional supervised land cover classification methods. Widely used image classification methods, such as ML [43,[50][51][52][53][54][55], work on a uniscale pixel-by-pixel basis and ignore multiscale information within the image and spatial information surrounding the pixels [56]. ML classification techniques often fail to differentiate between different forest types, agriculture, and grassland [57].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Shrimp Farming in the Southwestern Coastal Districts of Bangladesh Using a Shrimp Yield Dataset (SYD) and Landsat Satellite Archives

Karim

Zhang

2019

Sustainability

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The shrimp-farming area and shrimp yield are continuously changing in the southwestern coastal districts of Bangladesh. The three southwestern coastal districts, Bagerhat, Satkhira, and Khulna, along with Rampal, a subdistrict of Bagerhat, contribute 75% of the total shrimp yield of Bangladesh. However, the shrimp yield and farming area have declined in Bagerhat district, and the cause of this decline is uncertain. In this research, the differences in the shrimp yield were quantified using a shrimp yield dataset (SYD) and k-means classification. A supervised image classification approach was applied to quantify the spatiotemporal changes and identify the influencing factors behind the declining shrimp-farming area and yield in Rampal, Bagerhat district, using Landsat satellite archives. K-means classification reveals that, between 2015 and 2017, the shrimp yield in Bagerhat district declined significantly compared to Satkhira and Khulna. The satellite-based monitoring results affirm that the shrimp-farming area of Rampal also decreased rapidly, from 21.82% in 2013 to 6.52% in 2018. This research estimates that approximately 70% of the shrimp-farming area was lost in Rampal since December 2013. Hence, the findings of this research might motivate the responsible bodies to declare the shrimp-farming coastal area as a "shrimp zone" and implement an active policy to protect the vulnerable shrimp-farming industry and shrimp farmers, considering it is the second-largest export earning source in Bangladesh after ready-made garments.

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Land cover change detection of Hatiya Island, Bangladesh, using remote sensing techniques

Cited by 28 publications

References 4 publications

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

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

A Synthesis of Studies on Land Use and Land Cover Dynamics during 1930–2015 in Bangladesh

Dynamics of Shrimp Farming in the Southwestern Coastal Districts of Bangladesh Using a Shrimp Yield Dataset (SYD) and Landsat Satellite Archives

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