LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNOLOGY IN BARISHAL DISTRICT, BANGLADESH

Md Abdullah Salman, Md Saleh Shakeel Nomaan, Saifullah Sayed, Ayon Saha, Muhammad Risalat Rafiq
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引用次数: 3

Abstract

Barishal has recently gone through intense land use and land cover changes (LULC). This study aims to assess the changes of land use of Barishal, which were surveyed from 2000 to 2020 by utilizing Landsat TM, ETM + & OLI-TIRS imageries. The ArcGIS-10.4 & the ERDAS-14 Imagine software were used to deal with satellite images and surveyed measurable data for land cover change evaluation of the study area. Both pre- and post-classification change detection scenarios and NDVI analysis were observed to assess the change result from 2000 to 2020. Maximum likelihood classification was utilized to create unsupervised land cover category (water body, urban, fallow, agriculture, vegetation and lowland). After ensuring acceptable value for each classified image (82.16% for 2020, 76.15% for 2010 & 70.96% for 2000 with Kappa values of 0.64, 0.62 & 0.62 for 2020, 2010 and 2000), a change detection study was performed. This study discovered that the highest growth 69.22% of urban area has been improved within 20 years followed by 49.75% and 21.74% of water bodies, fallow lands; whereas the annual change rate was 14.95%, 7.91% and 10.31% respectively. In contrast, 16.28%, 10.48% and 37.20% of vegetation, agriculture and lowland had been reduced and an (-) annual change rate of 16.03%, 7.15% and 9.99% respectively. In addition, NDVI analysis was also observed a decreasing trend of the vegetation and agricultural lands. The results of this assessment could be supportive to design and appliance significant managing appraisals to protect the agricultural degradation, fruitless urbanization of Barishal district.
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利用遥感和地理信息系统技术探测孟加拉国巴里沙尔地区的土地利用和土地覆盖变化
巴里沙尔最近经历了激烈的土地利用和土地覆盖变化(LULC)。利用Landsat TM、ETM +和OLI-TIRS影像对2000 - 2020年巴里沙尔地区的土地利用变化进行了研究。利用ArcGIS-10.4和ERDAS-14 Imagine软件对卫星影像和实测数据进行处理,对研究区土地覆盖变化进行评价。通过分类前和分类后的变化检测情景和NDVI分析来评估2000 - 2020年的变化结果。利用最大似然分类法创建无监督土地覆盖类别(水体、城市、休耕、农业、植被和低地)。在确保每个分类图像的可接受值(2020年为82.16%,2010年为76.15%,2000年为70.96%,2020年,2010年和2000年的Kappa值分别为0.64,0.62和0.62)后,进行变化检测研究。研究发现,20年内城市面积得到改善的比例最高,为69.22%,水体、休耕地分别为49.75%和21.74%;年变化率分别为14.95%、7.91%和10.31%。植被、农业和低地减少了16.28%、10.48%和37.20%,年变化率分别为16.03%、7.15%和9.99%。此外,NDVI分析还发现植被和农用地呈减少趋势。该评价结果可为设计和实施重要的管理评价提供支持,以保护巴里沙尔地区的农业退化和无果的城市化。
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