Agricultural Research Management Information System

Home
Research Summary
Search
About Us
- ARMIS
- Brochure
Contact Us
Report
User Request
Data Input
Help
- Operation Manual
  - PDF
  - Video
- Program Area & Commodity
We have reached 37600 number of research entries at this moment.

- Logout

Research Detail

Home
Research
Detail

Mapping Mangrove Forest Change in Nijhum Dwip Island

M. M. Rahman*
Forestry Division, Bangladesh Space Research and Remote Sensing Organization (SPARRSO), Agargaon, Sher-E-Bangla Nagar, Dhaka-1207

M. A. T. Pramanik
Forestry Division, Bangladesh Space Research and Remote Sensing Organization (SPARRSO), Agargaon, Sher-E-Bangla Nagar, Dhaka-1207

M. I. Islam
Forestry Division, Bangladesh Space Research and Remote Sensing Organization (SPARRSO), Agargaon, Sher-E-Bangla Nagar, Dhaka-1207

S. Razia
Forestry Division, Bangladesh Space Research and Remote Sensing Organization (SPARRSO), Agargaon, Sher-E-Bangla Nagar, Dhaka-1207

Abstract/ Executive Summary:

Mangroves have been planting in the coastal belt of Bangladesh to protect the inhabitants of the coastal areas from cyclones and storm surges. Nijhum Dwip is located at the southern part of Hatiya Island. Most part of the island has been planted with the mangroves in the 1970s and 1980s; while parts of the mangroves have been deforested during the past few decades. The objectives of this research were to delineate and quantify the changes in the extent of mangroves in the island. The Landsat data of 1989, 2001, 2010 and 2018 have been utilized in the study. Three major land covers, namely forest, water and other land have been interpreted and delineated by using on-screen digitizing. The quantity of mangrove forest loss in the island is estimated as 1,024 ha, while 395 ha were afforested during 1989-2018. In the decadal change analysis, it was revealed that net forest cover change was higher in 2000s compared to other two decades and it was -425 ha. The result of the study is helpful to understand the extent and pattern of forest conversion in the island and to halt further forest loss and conserve the remaining forest.

Keywords: Afforestation, Deforestation, Mangroves, Mapping, Landsat

Location: Nijhum Dwip located at the southern part of Hatiya Island

Start Date: 00-00-1989

End Date: 00-00-2018

Program Area: Resource Development and Management

Non-commodity: Remote sensing

Objectives:

The objectives of this study were to (i) prepare forest cover maps and change maps in Nijhum Dwip Region over the last thirty years (1989-2018) and (ii) made change analysis of mangrove coverage for the period of 1989-2001, 2001-2010 and 2010-2018.

Methodology:

Study area and datasets Nijhum Dwip located at the southern part of Hatiya Island belongs to the central region of the coastal belt of Bangladesh. Clusters of islands including Char Osman appeared in the early 1950s in the shallow estuary of the Bay of Bengal. Those islands were located in the south part of Noakhali. Char Osman was later called Nijhum Dwip. Time-series Landsat satellite data was used in the study. The datasets are from Landsat Thematic Mapper (TM) of 1989 and 2010, Landsat Enhanced Thematic Mapper Plus (ETM+) of 2001 and Landsat Operational Land Imager (OLI) image of 2018 (further details in. Field visits were conducted during 20172018 to check the geometric accuracy of Landsat satellite image, the interpretation of major land covers (forest, water and other land) and the status and distribution of mangroves in the island. Landsat image pre-processing Level 1 terrain corrected Landsat data was downloaded from United States Geological Survey website. Compressed data was uncompressed to individual bands. Bands were layer stacked to generate multispectral image. Geometric accuracy of Landsat image was checked on the ground with portable GPS (Garmin GPS 78s). Identifiable objects like road crossing, corner of ponds etc. were identified on satellite image and on the ground. Ground coordinate obtained from GPS was compared with satellite image coordinate and found that geometric accuracy was adequate for further processing. Generation of vector layers by on-screen digitizing Landsat satellite images were visualized on computer screen. Different band combinations were checked and finally bands 4, 5 and 3 for Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+); bands 5, 6 and 4 for Landsat 8 Operational Land Imager (OLI) were selected for visualizing in red, green and blue (rgb) color composite as optimal band combination for forest cover mapping. Other combinations like 5, 4 and 3 for Landsat 5/7 and 6, 5 and 4 for Landsat 8 were also helpful for the separation of bare land from other vegetation. Bare land was appeared as pink in this band combination. Interpretation was started from the historical image. Mangroves in the island appeared on 1989 Landsat image was visualized on computer-screen and digitized. After the completion of digitizing the mangrove layer of 1989, the layer was overlaid to 2001 satellite image. The change in the extent of forest coverage was detected and added keeping the earlier forest boundaries of 1989. After completion, the vector coverage was saved as 1989-2001 change layer. Approximately two Landsat pixels were considered as minimum mapping unit during the generation of vector layer; it means that if forest change is noticed approximately for the extent of two Landsat pixels, approximately 0.18 ha, was digitized. All polygons were interpreted both for 1989 and 2001 and stored in two columns. Areas of each polygon was computed and summarized to assess forest cover change over the period of 1989-2001. Similarly, change map was generated and change statistics was computed for 2001-2010 and 2010-2018. After the completion of above time series change maps of 1989-2001, 2001-2010 and 2010-2018, forest maps of Nijhum were extracted for 1989, 2001, 2010 and 2018. After unifying the forest cover maps of 1989 and 2018, forest cover change map of 1989-2018 was generated. This change map was overlaid to both 1989 and 2018 satellite image to check any error, sliver polygon and corrected, if necessary. From this change map, forest cover change statistics was generated for the period of 1989-2018.

Source of Information: J. Environ. Sci. & Natural Resources, 11(1&2):217–225, 2018 ISSN 1999-7361

Article Link (Online Journal):

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

Forest cover change in Nijhum Dwip region has been assessed for 1989-2018 based on forest cover mapping and change analysis. The total area of mangrove forest loss for the period of 1989-2018 was 1,024 ha. In this period, 395 ha of mangroves have been planted. The net loss of forest area was highest in 2001-2010 (change rate-47 ha per year) and lowest in 2010-2018 (change rate -5 ha per year). Forest loss is concentrated in the eastern and north-eastern part of the island. There was some forest loss in the western and northern part of the island by coastal erosion; forest was lost by tidal wave. Forest conversion primarily took place for agricultural and settlement purpose by the migrants from other regions. The result of the study will be helpful to understand the rate and pattern of forest conversion in the island for forest managers and policy makers to halt further deforestation and conserve the remaining forest. There were some limitations of the study. The study utilized visual interpretation and on-screen digitizing technique, which is time consuming. Landsat data was used for mapping the changes of forest cover and analyzing the decadal changes of forests in the study area. Landsat image has the spatial resolution of 30m×30m. The minimum mapping unit of change analysis was equivalent to two Landsat pixels. Forest conversion at a finer scale may not be represented by this dataset. Further research should be concentrated to develop automatic image processing technique like advanced digital classification and image segmentation approach for forest cover change mapping and assessment. Although using digital classification like supervised and unsupervised classification may not accurately represent forest fragmentation, object based image analysis would be helpful for representing landscape fragmentation. Further studies should be concentrated to understand the causal factors of deforestation and develop strategy to halt forest conversion in the island.

Knowledge Management: Journal