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Aquaculture Planning Through Remote Sensing Image Analysis and GIS Tools in Northeast Region, Bangladesh

Shaharior Hashem
Department of Aquaculture, Bangladesh Agricultural University, Mymensingh-2202

Md. Tawheed Hasan
Department of Aquaculture, Sylhet Agricultural University, Bangladesh

M. A. Salam
Department of Aquaculture, Bangladesh Agricultural University, Mymensingh-2202

Taslima Akter
Department of Aquaculture, Sylhet Agricultural University, Bangladesh

Abstract/ Executive Summary:

The study was conducted to detect the land use changes using Remote Sensing (RS) images and Geographical Information Systems (GIS) tools for aquaculture planning in Trishal Upazila, northeast region of Bangladesh. Landsat TM image of 1999 and Google image of 2012 were used in the study. Normalized Difference Vegetation Index (NDVI) and Cluster image was prepared for discriminating the vegetation and for unsupervised image classification respectively. The unsupervised Landsat TM image of 1999 interpretation showed that total bare land and water body were 212 km² and 8 km² respectively. However, the bare land has decreased to 115 km² and the water body increased to 74 km² in Google image 2012. By contrast, the vegetation has increased 10% in NDVI Google image of 2012 than the Landsat TM image of 1999. Therefore, it is evident that 66 km² (28%) of water body has increased in 2012 Google image than the 1999 Landsat TM image, that means land use has changed tremendously in the study area over the fourteen years. It is also noted that vegetation has increased in the area due to water availability round the year in the region as fish ponds are everywhere. However, the conversion will only be permitted when follow the planning rules and integration of fish with agriculture is done.

Keywords: Land use change, Satellite image, Unsupervised image classification, Aquaculture planning.

Location: Trishal Upazila, Mymensingh

Start Date:

End Date:

Program Area: Knowledge Management

Commodity: Land management

Objectives:

To detect the land use changes using Remote Sensing (RS) images and Geographical Information Systems (GIS) tools for aquaculture planning in Trishal Upazila, northeast region of Bangladesh.

Methodology:

The Study Area: The study area Trishal Upazila is located in the northern part of the country near to Indian boarder Meghalaya in Mymensingh district which covers an area of 338.98 sq km. It lies between 24°35' and 24°58' north latitude and 90° 24' and 90°40' east longitude. The Old Bhramaputra river pass through the area and other rivers are Khiru, Sutia, Meduari, Nageshwari, Pagria and Barera; lots of notable depressions are present in the study area. The weather patterns in the area governed mostly by the Southwest and the Northwest monsoon. The dry winter season starts from December to February when rainfall is infrequent, the climate of the area is warm and humid; but moderately cold and dry weather prevails in winter. The rainy season begins from mid April and continues till mid of September; while winter begins from the 1st of November and continues till mid of March. The humidity gradually decreases during this season. The sky remains generally clear.Hardware, Software and image enhancement: The hardware components used in this study include units that are common to any computerized database management system. The GIS software IDRISI KILIMANJARO used in the study, developed by the Graduate school of Geography at Clark University, Worcester, MA, USA. The Microsoft Excel used for data analysis and trimming before being imported into IDRISI. The Google images of 2000 and Landsat TM. Images of 1999 were used in the study, which supported by hard copy maps of the Local Government Engineering Department (LGED) and supported by secondary data collected from various sources, field visit and consultation with the relevant stakeholders. Image Analysis Technique: The image enhancements with 5% stretch done for clear visual discrimination of vegetation, water and water-bare land boundary . The overall image processing methodology for this study is presented. Normalized Difference Vegetation Index: The Normalized Difference Vegetation Index (NDVI) applied to calculate the state of vegetation on the land surface with RED and mid infrared (MIR) bands of the satellite data through equation 1. Dense vegetation appears strongly in NDVI image, and areas with little or no vegetation are also clearly visible. NDVI technique is successful to identify water and bare lands clearly. It took values between -1 to 1, the value 0.5 indicates dense vegetation and value <0 indicates no vegetation. Produced NDVI image later reclassified in to five land use categories depending on the digital number (DN) as water, bare land, low vegetation, moderate vegetation and high vegetation types. Unsupervised image classification: This module groups the features together with similar reflectance patterns. The module usually applied in the unsupervised image classification. The logic by which unsupervised classification works are known as cluster analysis and this provided in IDRISI KILIMANJARO by the ISOCLUSTER classification from the image bands of infrared, mid infrared and near infrared, and this provides an interpretation of spectral number in the raw data. This is done in several steps. Iso-cluster used with the option of user selected number of classes for the final image. The module then classified the images into discrete categories. This process was first conducted with 15 fine clusters which then finally classified into five classes. Following the fine cluster and reclassification, a 3 x 3 mode FILTER was carried out to eliminate all the small clusters less than 9 pixels. Figure 3 and 4 shows the Landsat thematic image 1999 and Google image 2012. Data analysis: All the collected data were input into Microsoft Excel first and after sorting out it imported to KILIMANJARO GIS software and image analysis done. After image analysis, area calculation and cross tabulation done in GIS environment and exported into Excel again. The data refurbish in excel, graphical and tabular output done in Excel for report writing.

Source of Information: International Journal of Fisheries and Aquatic Studies 2014; 1(5): 134-143

Article Link (Online Journal):

Funding Source:

Total Budget (Tk.) :

Achievement/Findings:

There are 22% area were suitable for aquaculture in Mymensingh district. These findings support the present finding. In the extended 66 km² areas, we can culture fish without spending a penny for water installation. Therefore, fish species which are first growing and become harvestable size in a short period like tilapia, SIS, shingh, magur, koi, etc. can culture. On the other hand, by using balanced feed and environmental management different carp species can be cultured in this area all the year round. In this way, we can utilize these water areas properly without any interruption.

Knowledge Management: Journal