Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh
Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh
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DOI：
10.1016/j.ejrs.2016.12.005
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发表时间：
2018-04-01
影响因子：
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通讯作者：
Nath, Tapan Kumar
中科院分区：
文献类型：
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作者：
Islam, Kamrul;Jashimuddin, Mohammed;Nath, Tapan Kumar
自20世纪80年代以来，退化的Chunati野生动物保护区（CWS）经历了各种土地利用变化。本研究利用Landsat TM和Landsat 8 OLI/TIRS影像，对2005 - 2015年CWS的土地利用变化进行了评估。利用ArcGIS v10.1和ERDAS Imagine v14对卫星影像进行处理，并对定量数据进行评估，用于研究区土地利用变化评估。采用最大似然分类算法推导有监督的土地利用分类。结果表明，2005-2015年10年间，退化森林面积增加约256 ha，年变化率为25.56%。另有159公顷的自然林地转为其他土地利用，年变化率为15.88%。总体的监督分类准确率2015年为92.16%，2010年为86.15%，2005年为83.96%，2015年、2010年和2005年的Kappa值分别为0.89、0.82和0.81，比较满意。研究结果将有助于规划和实施重要的管理决策，以保护楚纳提野生动物保护区丰富的生物多样性。(C) 2016年国家遥感与空间科学管理局。制作和托管由爱思唯尔B.V.
The degraded Chunati wildlife sanctuary (CWS) has undergone various land use changes since 1980s. In this study, land use changes of CWS were assessed from 2005 to 2015 by using Landsat TM and Landsat 8 OLI/TIRS images. The ArcGIS v10.1 and ERDAS Imagine v14 were used to process satellite imageries and assessed quantitative data for land use change assessment of this study area. Maximum likelihood classification algorithm was used in order to derive supervised land use classification. It was found that about 256 ha of degraded forest area had been increased within 10 years (2005-2015) and the annual rate of change was 25.56%. Another 159 ha of naturally forested land had been changed to other land uses having an (-) annual rate of change of 15.88%. The overall supervised classification accuracy was found 92.16% for 2015, 86.15% for 2010, and 83.96% for 2005 with Kappa values of 0.89, 0.82, and 0.81 for 2015, 2010, and 2005, respectively and these were fairly satisfactory. The results of this study would be helpful to plan and implement important management decisions in order to conserve the rich biodiversity of Chunati wildlife sanctuary. (C) 2016 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V.