Vol 1, No 1 (2023): Indonesian Journal of Remote Sensing and Applications (June 2023)

The Panti District area which is located in the highlands often experiences landslides, on the other hand the population growth of the Panti District has also experienced an increase in population. This of course can provide potential land damage, disruption of ecosystem balance, and loss of land cover vegetation. This study aims to determine changes in land use due to landslides in the Panti District. This study uses a quantitative descriptive research method with data collection techniques sourced from secondary data. While the analysis technique uses the supervised maximum likelihood classification method and accuracy test. This accuracy test is carried out to determine the accuracy of the classified image with data in the field. The results of the Landsat 8 imagery accuracy test in Panti District in 2018 and 2022 using Google Earth obtained the same results, namely 86.66% of the 30 sample points. Meanwhile, the classification is determined into 4 classes, namely, forest, sawan, settlements, and gardens. Where from the results of the classification and accuracy test, it was obtained data that land use in the Panti District between 2018 and 2022 there was an almost complete change in land use on all of its land with the use of paddy fields having the most changes, namelyincrease to 8,421 Ha in 2022 which causes degradation and deforestation of forest lands, thus triggering an increase in landslide disasters. Therefore, it is necessary to reduce development near steep slopes and minimize development near areas with less stable slopes, as well as reduce the conversion of forest land to rice fields

Landslides that have occurred in Sadu Village, Soreang Subdistric, Bandung Regency, West Java resulted change density of vegetation low on the last 3 years, that is on year 2019-2022. Analysis change land use employ Landsat 8-Oli imagery done for identify wide change land use occurring in the region cases study based on multitemporal data as well as land area data obtained. This research aims for analyze change in vegetation density to change land use in Sadu Village on 2019-2022 seen from extraction image landsat 8-OIL. As for this research using method supervised maximum likelihood classification. Test accuracy image in this study used for look accuracy classified image and field data. The results that is, research Accuracy Test Landsat image classification land use obtain results by 90 % (2019) and 86.6 % (2022) and study related to the results of changes in land use in 2019 and 2022 which showed that there was a change in vegetation to non-vegetation in the form of built-up land of 16 Ha, then the results of changes in vegetation density in the results for 2019 and 2022 show that the density of quite dense vegetation has increased by 78 Ha and the dense vegetation category has decreased by 88 Ha. It shows that exists change use occurring land on three year final form land area forest to agricultural land and built-up land, apart from that there was an avalanche disaster which reduced the denser vegetation area because it was eroded by the avalanche.

Keywords : Landsat Imagery, Land Use, Vegetation Density

East Java Province is one of the most populous provinces in Indonesia with the second most populous position in Indonesia, the number of regencies and cities in East Java is 28 regions. Meanwhile, Bojonegoro Regency is the tenth most populous area in East Java. Population growth every year, of course, greatly impacts land use in Bojonegoro Regency, including one of its sub-districts, namely the Kadewan Sub-district. The increasing land use that occurs in Kedewan District is not only caused by increasingly dense population growth. However, this is caused by the fact that Kedewan District has an energy source in the form of oil, whose land is used for mining. If mining expands, it cannot be ruled out that vegetation, which is the main source producing oxygen for humans, will decrease. Therefore, it is important to re-analyze the vegetation index in the region. The method used in this study is a quantitative approach located in Kedewan Sub-district, Bojonegoro Regency. Data collection using mothed observation techniques (non-participant observation)  and document study techniques. The results of the study showed that NDVI and SAVI calculations had been carried out, and it was found that the overall density conditions in Kedewan District were classified as having a moderate level of vegetation density. Apart from that, from the calculations and analysis that have been done, the SAVI method is superior to using the NDVI method. 

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Vegetation has an important in human life. Studies related to vegetation can include its
density or distribution in the area. To analyze the vegetation density in an area by using
Remote Sensing System technology. This study aims to analyze vegetation density in flood
disaster using Landsat 8 imagery in the urban area of Jember in 2010-2019 and 2020-2022.
The research method used is a quantitative research method. This type of research was chosen
because there will be data that needs to be processed using remote sensing techniques, which
are also related to numbers. The research object to be studied is vegetation density using
Landsat 8-OLI imagery and field survey data. The data collection technique in this study uses
the observation method to compare the results of imagery processing on the Envi 4.5
application with direct conditions in the field, then field documentation is carried out. Then
the vegetation index method used is the NDVI method (Normalized Difference Vegetation
Index). The results of the research it is known that several sub-districts in the center of
Jember Regency, such as Patrang, Sumbersari, and Kaliwates sub-districts, have land cover
for vegetation that varies from medium to dense vegetation.

One type of spatial information that can be extracted based on remote sensing satellite data is land cover information. This information is created and used for various types of purposes, so that many different versions are found according to the intended use. This study aims to identify the vegetation density index in the Sukokulon rubber plantation area in 2009 and 2022. This research was conducted using a descriptive approach in which the researcher explained the results of field identification. Data analysis techniques were carried out by means of descriptive analysis by describing the situation based on observations made and secondary data on Landsat 7 and Landsat 9 images to determine differences in vegetation density, as well as by analysis through literature studies. Vegetation density in 2009–2022, shows an increase in the value of vegetation density in the Semboro District area, but in the northern region it is still with high vegetation density.