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Land Cover Change Analysis Around the Sundarbans Mangrove Forest of Bangladesh Using Remote Sensing and GIS Application

M M Rahman
Environmental Science Discipline, Khulna University, Bangladesh

S Begum
Environmental Science Discipline, Khulna University, Bangladesh

Abstract/ Executive Summary:

Land cover change is a widespread and prominent feature in Bangladesh. It is more energetic in the South-west coastal region such as Khulna and Satkhira region. The research was completed by 30 years (1980 to 2009) by using four Landsat satellite images (1980, 1989, 2002 and 2009). During research it was originated that the main classes of changes were homestead, fallow land, water bodies and mangrove vegetation in the study area. The most imperative change was observed in fallow lands (the types of cultivable lands with or without any standing crops and also uncultivable lands). Fallow land was decreased, concerning 2,259 sq km in 2009 which land was 3,867 sq km during in 1980. It was dropped 49% to 29% of total study area between the years of 1980 to 2009. The water bodies were increased about 10% in those 30 years, regarding 14% to 24%. The total water bodies covered 1140 sq km at 1980 which was increased 1851 sq km during 2009 in the study area. The sea-level rise, increasing salinity and availability of less fresh water are responsible for inland intrusion of saline water and creating favorable environment for shrimp farming in the region. As a result farmers are converting fallow lands to shrimp farming which is economically very beneficial. Important agricultural and non-agricultural lands also converted by homestead intended for increasing population of Bangladesh. There was only 398 sq km (only 5%) area covered by homestead during 1980. But the homestead covered area was increased about 1490 sq km (19%) at 2009. So, compared to the last 30 years 14% homesteads land cover areas are increased. There was not any astonishing changed in mangrove vegetation. Some mangroves areas were shattered and some other mangroves were fullfledged again in delta areas. The harmful land cover changes are not expectable for the natural environment. But it transpires by many natural and anthropogenic tricks now a day. So, the authority should seize initiatives to protect this category of catastrophe.

Keywords: Sundarban, Mangrove, Forest

Location: Khulna and Satkhira region

Start Date: 00-00-1980

End Date: 00-00-2009

Program Area: Resource Development and Management

Non-commodity: Remote sensing

Objectives:

This research focuses only on the spatial and temporal dynamics of land cover and its impact on the ecosystem in Khulna and Satkhira districts in Bangladesh where majority of the Bangladesh’s portion of the mangrove forest is located. Moreover, it is extremely difficult to get into vast swamps of the mangrove forests and conducting field inventory is time consuming and costly.

Methodology:

Data collection: The research generally based on secondary data, satellite image and remote sensing and GIS software’s. There has been used Landsat images to derived historical land cover maps of 30-year. Landsat image spatial and spectral characteristics were suitable to derive land cover at local scale and provide longest historical records at free of cost. Images acquired in the months of January and February were selected considering vegetation phenology, cropping pattern in the region and less cloudy time periods of the year. Actual study interval period was compromised depending on availability of cloud free quality images. Data from three sensors of Landsat, Multi Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Plus (ETM+) were used to derive land cover maps in satellite image. GIS and remote sensing softwares use In this study some modern software and systems were used. The image classification and recoded techniques employed in this study were conducted using Erdas Imagine 8.4 software which is remote sensing software. The next steps, such as convert classified recoded image into grid, joint the grid study areas in same years and at last clipping the study area to get actual results were conducted by Arc view 3.2a with extension of Imagine image support, Spatial Analyst and Image Analyst software. Data Processing and preparation of maps Classification scheme: A five-class Landsat data suitable, site specific, objective oriented classification scheme was developed through careful visual interpretation of all the images. The classes along with their descriptions are provided in Table 2. Recoding classified image: Recoded are the next steps after image classification. Classified images have converted into recoded image to identify one class by one fixed color. There were five classes are recoded in each image. Then recoding has been done and different land feature classes were assigned after studying the spectral profile. Converting recoded images into GRID: The Arc view with the help of loading Arc view extensions Imagine image support, Image analyst and Spatial analyst can be converted each recoded image into GRID. So the eight recoded images have converted into GRID. Joining same years’ GRID: After converting classified recoded image into GRID, the same year two GRID image have jointed together to formulate one year’s one GRID image to cover total study area. Converting shape file of study area into GRID: The study area was present as shape file. The shape file of study area has converted same way into GRID. Clipping the study area from the joint GRID: After joining two GRID of same year, the GRID study area has loaded upon the Joint GRID classified image and then clipped the actual classified GRID study area. In this way, there were four years classified GRID study areas’ maps have been gotten. Calculation of different land covers areas: From the each year’s classified GRID maps, the different land covers areas have been calculated. Then it has compared the five classified fields with each others for the research results preparation. GPS based ground survey: In this step, areas belonging to different class categories have been selected from the classified image and have been taken as a basis for field investigation. A global positioning system (GPS) has been used to locate each of these selected areas with a good precision. Then detailed observations on the locations have been made over each of these selected areas. The information on homestead, fallow lands, water bodies and mangroves have also been collected from some selected areas. Such a step based on a limited number of ground observations provided an interpretation key to classify the study.

Source of Information: J. Sci. Foundation, 9(1&2): 95-107, June-December 2011 ISSN 1728-7855

Article Link (Online Journal):

Funding Source:

Total Budget (Tk.) :

Achievement/Findings:

The land covers in the region are changing, the most noticeable change was conversion of fallow (agricultural or non-agricultural) land to shrimp farming and homestead between 1989 and 2002. The salinity intrusions in the region aided the growth in shrimp farming following a low to high gradient from east to west. Loss of agricultural land and increased water areas have amplified the salinity imbalance in the region through reduced surface runoff and loss of vegetation coverage, leading to decrease in ecosystem productivity following the same low to high gradient from east to west in the study areas. The homestead areas had increased and fallow lands had decreased a large quantity. The water bodies had noticeably increased in the last 30 years. There was not any remarkable changed in mangrove vegetation. Some mangroves areas were destroyed and some other mangroves were grown up again in delta areas. The estuarine new born islands, deltas were responsible for mangroves increased. So above discussion and the previous maps, it has cleared and reached the decision of that the homestead were increased, fallow lands were decreased and water bodies were increased by many ways continuously in last 30 years. The study was conducted on a very small part of the country and differences in the characteristics of data are substantial considering rapidly developing satellite technology. So, it is difficult to reach a decisive conclusion based on such materials along.

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