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

Abstract

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.

Analysis of Vegetative Cover Vulnerability in Rohingya Refugee Camps of Bangladesh Utilizing Landsat and Per Capita Greening Area (PCGA) Datasets

“…Although LULCC analysis depicts the relationship between the human and physical environment, the use of diverse datasets (e.g., satellite data and socioeconomic data) with clustering algorithms can produce detailed information and facts. For example, hierarchical clustering [29], K-means [30][31][32], and Gaussian mixture model [33] are a few benchmark clustering techniques for change analysis. The hierarchical clustering method is preferable with larger datasets, but k-means clustering performs better both with the large and medium datasets [34].…”

“…The hierarchical clustering method is preferable with larger datasets, but k-means clustering performs better both with the large and medium datasets [34]. Karim et al (2019) used k-means classification to quantify the changing pattern of shrimp yield in three coastal districts of Bangladesh from 2002 to 2017 [30]. For water quality analysis, Zou et al (2015) utilized the k-means classification technique and took the Heihe River in China as a study area [32].…”

“…Remote Sens. 2021, 13, x 4 of 29 [30][31][32], and Gaussian mixture model [33] are a few benchmark clustering techniques for change analysis. The hierarchical clustering method is preferable with larger datasets, but k-means clustering performs better both with the large and medium datasets [34].…”

Time-Series Remote Sensing Study to Detect Surface Water Seasonality and Local Water Management at Upper Reaches of Southwestern Bengal Delta from 1972 to 2020

“…For example, ten or less than ten Landsat observations between November and April have been used to estimate eight to forty-two years of LULC and surface water change [6,18]. Furthermore, five or less than five observations between November and March have been considered for thirteen to thirty-six years of land use change detection including surface water [7,10,11,13,14,16,17] which are insufficient to produce conclusive results, especially for SWB where frequent human activity changes land classes rapidly [34]. The studies, thus, could not fulfill the need to detect and determine the extent and dynamics of surface water exclusively to observe its long-term persistence around the region.…”

Amplicon sequencing reveals significantly increased Vibrio abundance and associated gene functions in vibriosis‐infected black tiger shrimp (Penaeus monodon)

“…In consequences, the global shrimp market of Bangladesh has been declined in recent years (Debnath et al., 2016; Hannan et al., 2019). Vibriosis is one of the leading cause of shrimp industries' shrinkage in Bangladesh caused by different species of Vibrio— a gram‐negative, rod‐shaped, motile bacterium from Vibrionaceae family (Hannan et al., 2019; Karim et al., 2019).…”