DYNAMIC OF CHANGING AREA OF SUSPENDED SOLID BY UTILIZING LANDSAT 8 OIL IMAGES IN LAKE SINGKARAK, WEST SUMATRA PROVINCE, 2017 and 2022

I. Kurniawan, D. Arief
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Abstract

TSS is suspended materials (diameter > 1 µm) retained on a millipore filter with a pore diameter of 0.45 µm. TSS consists of silt and fine sand and micro-organisms. The main cause of TSS in waterways is soil erosion or soil erosion that is carried into water bodies. If the TSS concentration is too high, it will inhibit the penetration of light into the water and result in disruption of the photosynthesis process (Effendi in Lestari, 2009:4). Many activities cause turbidity that affects the penetration of sunlight into water bodies, so it can hinder the process of photosynthesis and primary production of waters. Turbidity usually consists of an organic particle originating from watershed erosion and resuspension from the lake bottom. Keywords : Normalized Difference Vegetation Index, Normalized Burn Ratio, Landsat 8, Severity Level of Forest and Land Fires. Based on the results of the study, researchers have obtained TSS values ​​in 2017 and 2022 at Lake Singkarak with the Landsat 8 image data processing method using the Syarif Budiman algorithm with several stages, namely first combining image data bands from band 1 to band 7 then cropping which serves to determine the area to examine then performs masking which functions to separate land from water and then enter the Syarif Budiman algorithm formula then classify the TSS values ​​in Lake Singkarak. It can be seen that the predicted TSS concentration has not been too much different from the TSS concentration in the field. researchers have obtained TSS values ​​in 2017 and 2022 at Lake Singkarak with the Landsat 8 image data processing method using the Syarif Budiman algorithm with several stages, namely first combining image data bands from band 1 to band 7 then cropping which functions to determine the area which will be examined then do masking which functions to separate land from water and then enter the Syarif Budiman algorithm formula then classify the TSS values ​​in Lake Singkarak. It can be seen that the predicted TSS concentration has not had too much difference in the concentration in the field. researchers have obtained TSS values ​​in 2017 and 2022 at Lake Singkarak with the Landsat 8 image data processing method using the Syarif Budiman algorithm with several stages, namely first combining image data bands from band 1 to band 7 then cropping which functions to determine the area which will be examined then do masking which functions to separate land from water and then enter the Syarif Budiman algorithm formula then classify the TSS values ​​in Lake Singkarak. It can be seen that the predicted TSS concentration has not to have o much difference with wififrameS concentration in the field.
基于2017年和2022年西苏门答腊省辛喀拉克湖LANDSAT 8 OIL影像的悬浮物面积变化动态
TSS是悬浮材料(直径>.1µm)保留在孔径为0.45µm的毫孔过滤器上。TSS由泥沙、细沙和微生物组成。水道中TSS的主要原因是土壤侵蚀或土壤侵蚀进入水体。如果TSS浓度过高,则会抑制光进入水中,导致光合作用过程中断(Effendi in Lestari, 2009:4)。许多活动会导致浑浊,影响阳光对水体的渗透,从而阻碍光合作用和水的初级生产过程。浊度通常由来自流域侵蚀和湖底再悬浮的有机颗粒组成。关键词:归一化植被指数,归一化燃烧比,Landsat 8,森林和土地火灾严重程度基于研究结果,研究人员利用分阶段的Syarif Budiman算法对Landsat 8图像数据进行处理,获得了Singkarak湖2017年和2022年的TSS值。即首先将1到7波段的图像数据波段进行组合,然后进行裁剪,确定要检查的区域,然后进行掩蔽,将土地与水分开,然后输入Syarif Budiman算法公式,然后对Singkarak湖的TSS值进行分类。可以看出,预测的TSS浓度与现场TSS浓度没有太大的差异。研究人员使用Syarif Budiman算法对Landsat 8图像数据进行处理,获得了Singkarak湖2017年和2022年的TSS值,该方法分为几个阶段,即首先将图像数据波段从1波段到7波段进行组合,然后对其进行裁剪以确定要检查的区域,然后对其进行掩蔽以实现水陆分离,然后输入Syarif Budiman算法公式,然后对Singkarak湖的TSS值进行分类。可以看出,预测的TSS浓度在田间没有太大的差异。研究人员使用Syarif Budiman算法对Landsat 8图像数据进行处理,获得了Singkarak湖2017年和2022年的TSS值,该方法分为几个阶段,即首先将图像数据波段从1波段到7波段进行组合,然后对其进行裁剪以确定要检查的区域,然后对其进行掩蔽以实现水陆分离,然后输入Syarif Budiman算法公式,然后对Singkarak湖的TSS值进行分类。可以看出,预测的TSS浓度与现场的wififrameS浓度没有太大的差异。
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