Identifikasi Sebaran Spasial dan Kerapatan Mangrove Gili Lawang menggunakan Citra Landsat 9 OLI-2/TIRS-2

A. Prasetyo, Niechi Valentino, Muhammad Anwar Hadi
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Abstract

Mangrove ecosystems have a great influence on the sustainability of human life and the environment. The high level of vulnerability of mangrove ecosystems has implications for the importance of quality planning. This study aims to identify the spatial distribution and density of mangrove forests in Gili Lawang using Landsat 9 OLI-2/TIRS-2 satellite imagery. Data processing is done with the help of the QGIS 3.30 application. Data processing consists of band combinations, image classification with the SVM algorithm, classification results accuracy test, NDVI value extract, and reclass NDVI. The results showed that the use of band 564 in Landsat 9 imagery visually resulted in an increase in sharpness in identifying mangrove ecosystems. Classification of objects with the SVM algorithm has overall accuracy and kappa accuracy > 80%. The identified area of Gili Lawang is 432.72 ha, consisting of 37.89 ha of mangroves, 58.11 ha of non-mangrove and 3.75 ha of water bodies. NDVI values at the study sites ranged from 0.068 to 0.87. The maximum NDVI value is found in mangrove objects, while the minimum NDVI value is found in water body objects. Mangrove density in Gili Lawang is dominated by high and very high density. The use of Landsat 9 OLI-2/TIRS-2 imagery in the future is expected to provide positive benefits in providing data and information related to natural resources.  
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红树林生态系统对人类生活和环境的可持续性具有重大影响。红树林生态系统的高度脆弱性意味着质量规划的重要性。利用Landsat 9 OLI-2/TIRS-2卫星影像对吉利拉旺红树林的空间分布和密度进行了研究。数据处理在QGIS 3.30应用程序的帮助下完成。数据处理包括波段组合、SVM算法图像分类、分类结果精度检验、NDVI值提取、NDVI重分类。结果表明,在Landsat 9图像中使用波段564在视觉上增加了识别红树林生态系统的清晰度。使用SVM算法对目标进行分类,总体准确率和kappa准确率均> 80%。Gili Lawang的确定面积为432.72 ha,其中红树林37.89 ha,非红树林58.11 ha,水体3.75 ha。NDVI值在0.068 ~ 0.87之间。NDVI值最大的是红树林对象,最小的是水体对象。吉利拉旺红树林密度以高密度和超高密度为主。未来使用Landsat 9 OLI-2/TIRS-2图像预计将在提供与自然资源有关的数据和信息方面带来积极的好处。
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