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Assessment of Chalan Beel Ecosystem Diversity Through Remote Sensing and Geographical Information Systems

Md. Abu Sayeed
Department of Fisheries Technology and Quality Control, Sylhet Agricultural University, Sylhet-3100, Bangladesh

M. A. Wahab
Department of Fisheries Management, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

Mostafa Ali Reza Hossain
Department of Fisheries Biology and Genetics, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

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

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

Abstract/ Executive Summary:

Chalan beel, the largest natural depression in Bangladesh is under threat due to population pressure, over exploitation, change of water courses, pollution and siltation. Therefore, a study was conducted to assess the habitat and biodiversity status in dry and wet seasons through MODIS AQUA 250 m images and Geographical Information Systems supported by field data from July 2010 to June 2012. The MODIS data of 2002 and 2008 were collected and analyzed to perceive the water availability, extend of water area and variety of fishes found round the year. The False Colour Composite images of blue, near infra-red and mid infra-red bands and Normalized Difference Vegetation Index of 2002 and 2008 were prepared to perform the land use and land cover changes. The data interpretation showed the trend of water receding tendency from wet to dry season in 2002 and 2008 images respectively. The occurrence of moderate and high vegetation was comparatively higher in 2002 images than in 2008. Moreover, very small water area was observed in March and April in both year images due to over use of surface and underground water for irrigation that is the threat to the natural resources in the region. Hence, steps need to be taken by the Government and stakeholders to stop fishing via dewatering and maintain the minimum water level in the Chalan beel for safe guard the aquatic biodiversity.

Keywords: MODIS AQUA image, Biodiversity, Natural depression, Land use change, Irrigation.

Location: Pabna, Sirajgonj and Natore.

Start Date: 00-07-2010

End Date: 00-06-2012

Program Area: Conservation and Biodiversity

Non-commodity: GIS

Objectives:

To provide an overview on the present status of ecosystem diversity through Remote Sensing (RS) images and Geographical Information Systems (GIS) tools
To investigate the real scenario of water area and depth in different seasons for the aquatic organisms
To identify the scope and opportunities to develop fisheries management strategies with a focus on biodiversity conservation.

Methodology:

THE STUDY AREA: The Chalan beel is situated between 24.35^o and 24.70^o North latitude and 89.10^o and 89.35^o East longitudes. Historically ?Chalan beel was spread over the districts of Rajshahi, Pabna, Sirajgang, Natore, Naogoan and Bogra. At present, the beel has been compressed in the districts of Pabna, Sirajgonj and Natore due to crisscross roads, embankments and other infrastructural expansion. Therefore, the research was conducted in three representative cluster sites under the above districts. For data collection, ten upazilas such as Singra, Gurudaspur and Boraigram of Natore district, Chatmohar, Bhangura and Faridpur of Pabna and Shahjadpur, Ullapara, Tarash and Raygonj of Sirajganj district were selected. DATA SOURCES: The MODIS AQUA images were collected from the University of Stirling, Scotland, UK. The images of different dates of dry and wet seasons were collected and analyzed to see the land use changes if any of the ecosystems in the beels which caused the biodiversity degradation in the beel. The methodology of the study was based on Remote Sensing (RS) image interpretation which is supported by the secondary data collected from a range of sources like field visit, literature review, internet search and consultation with the relevant reports. IMAGE ANALYSIS TECHNIQUE: MODIS AQUA data range from the bands Red, NIR, MIR and Blue were used in this study. The spatial resolution of image was 250 m x 250 m which used in the study. Image enhancement False colour composite (FCC) for visual interpretation, NDVI (Normalized Dense Vegetation Index) preparation, unsupervised image classification, etc. were also done. IMAGE ENHANCEMENTS: The images were subjected to preliminary digital enhancements in order to enable their visual interpretation. The FCC images provided a clear visual discrimination of vegetation, water and water-bare land boundaries. The image processing methodology is presented in Figure 2. An associated contrast stretch of 5% was applied to have better visual discrimination among the features. NORMALIZED DIFFERENCE VEGETATION INDEX: The Normalized Difference Vegetation Index (NDVI) [13] was applied to calculate the state of vegetation on the land surface for each image from the RED and MIR bands of the satellite data. The NDVI expresses the vegetated ground and the vegetation condition and is closely related to the leaf area index (LAI). The land use changes between the years were studied by NDVI images. The dense vegetation showed up very strongly in the imagery and the areas with little or no vegetation were also clearly identified. It also identified the water and the bare lands clearly. The NDVI took values between -1 to +1 where the value of 0.5 indicating dense vegetation and value <0 indicating no vegetation. Produced NDVI maps were reclassified into six land use categories depending on the digital number (DN) as deep water, shallow water, bare land, very low vegetation, moderate vegetation and high vegetation etc. UNSUPERVISED IMAGE CLASSIFICATION: The logic by which unsupervised classification works is known as cluster analysis and this was done by IDRISI Andes through ISOCLUSTER module. This module groups the features together with similar reflectance patterns. The unsupervised classification was done using the image bands of infrared, mid infrared and near infrared, and which provided the number of spectral classes in the raw data. This was conducted in several steps. First of all, the ISOCLUSTER module was used with the option of user selecting number of classes for the final image. The module then classified the image into discrete categories. This process was conducted using 15 fine clusters. Following the cluster, a 3 x 3 mode FILTER was applied out to eliminate minor clusters less than 9 pixels. The clusters were then identified and further reclassified into five land use and land cover classes based on the colour composite images and field visit data. STATISTICAL ANALYSIS: Land use type data was evolved through ISOCLUSTER and NDVI analysis. Land and water were compared by two-way ANOVA considering years and seasons as fixed factors. The percentage data were arc-sine transformed. If the means were significantly different, then the differences among the treatments were tested with Duncan’s Multiple Range Test (DMRT). All statistical tests were carried out at 5% significance level using SPSS (version 12.0).

Source of Information: International Journal of Innovation and Applied Studies ISSN 2028-9324 Vol. 7 No. 1 July 2014, pp. 353-365

Article Link (Online Journal):

Funding Source:

Total Budget (Tk.) :

Achievement/Findings:

The land and water area did not differ significantly in the year and seasons but differed significantly among the months. The higher water area observed in the month of January in 2002 image which might be presence of surface water by rain before the satellite pass over, which increased the mean value of dry season. However, the actual situation is that very low amount of water was observed in the month of March and April in both the years images, which is the real threat to the fish and aquatic diversity of the Chalan beel. The situation was deteriorated further through the over use of surface and underground water for irrigation. Therefore, the authority should take necessary steps for maintaining aquatic biodiversity with special emphasis on fish biodiversity to have a sustainable fish production from this renowned beel in Northwestern Bangladesh.

Knowledge Management: Journal