Indonesian Journal of Remote Sensing and Applications https://journal.unej.ac.id/IJRSA <p><span class="spaced"><strong>Current Issue : <a href="https://journal.unej.ac.id/IJRSA/issue/view/21">Vol. 1 No. 1, June 2023</a></strong></span></p> <p><span class="spaced"><strong>Indonesian Journal of Remote Sensing and Applications </strong></span>Published by Published by Department of Geography Education, University of Jember, Indonesia. The Journal's aim is to publish novel / improved methods / approaches and / or algorithms of remote sensing to benefit the community, open to everyone in need of them. <span class="spaced">Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by:</span></p> <ul> <li>Multi-spectral and hyperspectral remote sensing</li> <li>Active and passive microwave remote sensing</li> <li>Lidar and laser scanning</li> <li>Data fusion and data assimilation</li> <li>Spaceborne, airborne and terrestrial platforms</li> <li>Remote sensing applications</li> </ul> <p> </p> <p> </p> <div class="announcements-introduction"> <p> </p> <p> </p> <p> </p> <p> </p> </div> en-US Indonesian Journal of Remote Sensing and Applications ANALYSIS OF LAND USE CHANGES DUE TO LANDSLIDES IN PANTI DISTRICT USING LANDSAT 8 IMAGERY https://journal.unej.ac.id/IJRSA/article/view/230 <p>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</p> ZUBAIDAH ANIS WATIN FAIZAH ANNATASYA PUTRI DIANTI PRAKASTIWI ERA ISWARA PANGASTUTI ANA SUSIATI Copyright (c) 2023 ZUBAIDAH, ANIS WATIN FAIZAH, ANNATASYA PUTRI, DIANTI PRAKASTIWI https://creativecommons.org/licenses/by-nc-sa/4.0 2023-06-05 2023-06-05 1 1 1 9 Vegetation Density Change Due to Landslides in Sadu Village, Soreang Subdistrict, Bandung Regency https://journal.unej.ac.id/IJRSA/article/view/232 <p><em>Landslides that have occurred </em><em>in </em><em>Sadu Village, Soreang</em><em> Subdistric</em><em>, Bandung Regency, West Java </em><em>resulted change density of </em><em>vegetation</em> <em>low </em><em>on the last </em><em>3 years</em><em>, that is on year </em><em>2019</em><em>-</em><em>202</em><em>2</em><em>. 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 </em><em>area </em><em>data obtained. This research aims for </em><em>analyze </em><em>change in vegetation density </em><em>to change land use in </em><em>Sadu Village </em><em>on 201</em><em>9</em><em>-202</em><em>2</em><em> 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 </em><em>Test</em><em> Landsat image classification land use obtain results by 90 </em><em>% (2019) and 86.6 </em><em>% </em><em>(2022)</em><em> and </em><em>study related to the results of changes in land</em> <em>use in 2019 and 2022 which showed that there was a change in vegetation to non-vegetation in the form of built</em><em>-</em><em>up land of 16 Ha, then the results of changes in vegetation density in </em><em>t</em><em>he 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</em><em>. It shows that exists change use occurring land on </em><em>three</em> <em>year final form land </em><em>area</em> <em>forest</em><em> to agricultural </em><em>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.</em></p> <p><em>Keywords : Landsat </em><em>Imagery, </em><em>Land</em><em> U</em><em>se</em><em>, </em><em>Vegetation Density</em></p> <p><em>&nbsp;</em></p> Abhiseka D. Imanjaya Arsyta Zhafira Mukhtar Syintia Bella Yollan Aditya Amanda ERA ISWARA PANGASTUTI FAHMI ARIF KURNIANTO Copyright (c) 2023 Abhiseka D. Imanjaya https://creativecommons.org/licenses/by-nc-sa/4.0 2023-06-05 2023-06-05 1 1 10 21 Integration of Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) to Identify Vegetation Covers on an Oil-Producing Landscape in Kedewan, Bojonegoro Regency https://journal.unej.ac.id/IJRSA/article/view/233 <p>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. </p> <p><strong>References</strong></p> <p>Ariani, D., Y. Prasetyo, dan B. Sasmito. 2020. Estimasi tingkat produktivitas padi berdasarkan algoritma ndvi, evi dan savi menggunakan citra sentinel-2 multitemporal (studi kasus: kabupaten pekalongan, jawa tengah). Jurnal Geodesi Undip. 9(1):207–216. https://ejournal3.undip.ac.id/index.php/geodesi/article/view/26165</p> <p>Arimbawa, I. K. 2010. Kajian Berbagai Macam Citra Satelit Terhadap Skala Peta (Planimetris). Geoid. 5(1):055–058.</p> <p>Asma, N. (2018). Analisa Perubahan Lahan Tambak Menggunakan Metode Maximum Likelihood (Studi Kasus: Kota Banda Aceh). Tugas Akhir Universitas Syiah Kuala. Banda Aceh, 59.</p> <p>Hidayati, N. 2010. Sistem penginderaan jauh satelit ldcm (landsat-8). Kajian Pemanfaatan Satelit Masa Depan. 11(2):47–58.</p> <p>Imburi, C. S. 2020. Perbandingan algoritma pada metode klasifikasi supervised berbasis citra alos avnir-2 untuk pemetaan mangrove. Jurnal Kehutanan Papuasia. 6(1):1–9. https://doi.org/10.46703/jurnalpapuasia.Vol6.Iss1.177</p> <p>Kalinda, I. O. P., B. Sasmito, dan A. Sukmono. 2018. Analisis Pengaruh Koreksi Atmosfer Terhadap Deteksi Land Surface Temperature Menggunakan Citra Landsat 8 Di Kota Semarang. Jurnal Geodesi Undip. 7(3):66–76. https://ejournal3.undip.ac.id/index.php/geodesi/article/view/21217</p> <p>Kristanto, Y., T. Agustin, dan F. Rizki Muhammad. 2017. Pendugaan Karakteristik Awan Berdasarkan Data Spektral Citra Satelit Resolusi Spasial Menengah Landsat 8 Oli/Tirs (Studi Kasus: Provinsi Dki Jakarta). Jurnal Meteorologi Klimatologi Dan Geofisika. 4(2):42–51. https://jurnal.stmkg.ac.id/index.php/jmkg/article/view/46</p> <p>Nuraini, N. F., I. W. G. A. Karang, dan I. N. G. Putra. 2022. Estimasi Stok Karbon Di Atas Permukaan Menggunakan Citra Sentinel-1a Di Hutan Mangrove Karang Sewu, Bali. Journal of Marine Research and Technology. 5(1):21. https://doi.org/10.24843/JMRT.2022.v05.i01.p05</p> <p>Que, V. K. S., S. Y. J. Prasetyo, dan C. Fibriani. 2019. Analisis Perbedaan Indeks Vegetasi Normalized Difference Vegtation Index (Ndvi) Dan Normalized Burn Ratio (Nbr) Kabupaten Pelalawan Menggunakan Citra Satelit Landsat 8. Indonesian Journal OF Computing AND Modeling. 1(1):1–7. https://ejournal.uksw.edu/icm/article/view/2534</p> <p>Purnia, D. S., &amp; Alawiyah, T. (2020). Metode Penelitian: Strategi Menyusun Tugas Akhir. Yogyakarta: Graha Ilmu.</p> <p>Saputra, J., M. Kamal, dan P. Wicaksono. 2018. Pengaruh Resolusi Spasial Citra Terhadap Hasil Pemetaan Kandungan Hara Nitrogen Perkebunan Karet. Jurnal Penelitian Karet. (July):13–24. https://doi.org/10.22302/ppk.jpk.v36i1.545</p> <p>Simarmata, N., K. Wikantika, T. A. Tarigan, M. Aldyansyah, R. K. Tohir, A. Fauziah, dan Y. Purnama. 2021. Analisis transformasi indeks ndvi, ndwi dan savi untuk identifikasi kerapatan vegetasi mangrove menggunakan citra sentinel di pesisir timur provinsi lampung. JURNAL GEOGRAFI Geografi Dan Pengajarannya. 19(2):69–79. https://doi.org/10.26740/jggp.v19n2.p69-79</p> <p>Sinaga, S. H., A. Suprayogi, dan Haniah. 2018. Analisis ketersediaan ruang terbuka hijau dengan metode normalized difference vegetation index dan soil adjusted vegetation index menggunakan citra satelit sentinel-2a (studi kasus : kabupaten demak). Jurnal Geodesi Undip. 7(1):202–211. https://ejournal3.undip.ac.id/index.php/geodesi/article/view/19329</p> <p>Widiyatmoko, W., S. Sudibyakto, dan E. Nurjani. 2018. Analisis Kerentanan Tanaman Terhadap Ancaman Kekeringan Pertanian Menggunakan Pendekatan Multi-Temporal Di Das Progo Hulu. Geomedia: Majalah Ilmiah Dan Informasi Kegeografian. 15(2). https://doi.org/10.21831/gm.v15i2.19553</p> <p>Wijaya, S. F. A., K. Koredianto, dan S. Saidah. 2022. Analisis Perbandingan K-Nearest Neighbor Dan Support Vector Machine Pada Klasifikasi Jenis Sapi Dengan Metode Gray Level Coocurrence Matrix. Jurnal Ilmu Komputer Dan Informatika. 2(2):93–102. https://doi.org/10.54082/jiki.27</p> <p>Wulansari, H. (2017). Uji Akurasi Klasifikasi Penggunaan Lahan Dengan Menggunakan Metode Defuzzifikasi Maximum Likelihood Berbasis Citra Alos Avnir-2. BHUMI: Jurnal Agraria Dan Pertanahan, 3(1), 98-110. https://doi.org/10.31292/jb.v3i1.233</p> Saffina Eka Rahma Wati Arini Dwi Kusmaningayu Ida Khodijjah Huni Farida ERA ISWARA PANGASTUTI ELAN ARTONO NURDIN Copyright (c) 2023 Saffina Eka Rahma Wati https://creativecommons.org/licenses/by-nc-sa/4.0 2023-06-05 2023-06-05 1 1 22 32 ANALYSIS OF VEGETATION DENSITY IN FLOOD DISASTER USINGLANDSAT 8 IMAGERY IN JEMBER URBAN AREA 2010-2019 AND 2020-2022 https://journal.unej.ac.id/IJRSA/article/view/235 <p>Vegetation has an important in human life. Studies related to vegetation can include its <br>density or distribution in the area. To analyze the vegetation density in an area by using <br>Remote Sensing System technology. This study aims to analyze vegetation density in flood <br>disaster using Landsat 8 imagery in the urban area of Jember in 2010-2019 and 2020-2022. <br>The research method used is a quantitative research method. This type of research was chosen <br>because there will be data that needs to be processed using remote sensing techniques, which <br>are also related to numbers. The research object to be studied is vegetation density using <br>Landsat 8-OLI imagery and field survey data. The data collection technique in this study uses<br>the observation method to compare the results of imagery processing on the Envi 4.5 <br>application with direct conditions in the field, then field documentation is carried out. Then <br>the vegetation index method used is the NDVI method (Normalized Difference Vegetation <br>Index). The results of the research it is known that several sub-districts in the center of <br>Jember Regency, such as Patrang, Sumbersari, and Kaliwates sub-districts, have land cover <br>for vegetation that varies from medium to dense vegetation.</p> Muhammad Jiddan Amrullah Sofia Anggita Syafira Alifah Oktivia Puteri Ully Amalia Tobing Era Iswara Pangastuti Muhammad Asyroful Mujib Copyright (c) 2023 Syafira Alifah Oktivia Puteri https://creativecommons.org/licenses/by-nc-sa/4.0 2023-06-05 2023-06-05 1 1 33 41 Analysis of Differences In Vegetation Development In 2009 and 2022 In Semboro Sub-District, Jember Regency https://journal.unej.ac.id/IJRSA/article/view/262 <p>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.</p> Agum Taufan Lukman Hakim Taufiq Hidayatul Warid Era Iswara Pangastuti Bejo Apriyanto Sri Astutik Copyright (c) 2023 Agum Taufan https://creativecommons.org/licenses/by-nc-sa/4.0 2023-06-05 2023-06-05 1 1 42 48