Applying Remote Sensing Technology and Geographic Information  System in Batu, East Java

Herawati, Herawati; Nasikh, Nasikh

doi:10.22146/ijg.12842

Cited by 3 publications

(7 citation statements)

References 9 publications

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“…Fine textures include sandy clay loam (SCL), silty clay loam (SiCL), and clay loam (CL), and very fine texture classes, namely, sandy clay (SC), silty clay (Piñeiro et al), and clay (C). Very fine texture conditions on land have the potential for very high landslides, and this can be analyzed by remote sensing and geographical information systems (Hartono & Nasikh, 2017).…”

Section: Discussionmentioning

confidence: 99%

Classification of potential landslides using the Shuttle Radar Topography Mission imagery in the Tulis Watershed, Indonesia

Harjadi

Abdiyani²,

Sukartono

et al. 2022

Sains Tanah J. Soil Sci. Agroclimatology

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Tulis is one of the watersheds in the Mrica Reservoir Catchment Area in Indonesia. The Tulis Watershed has an area of 12,750 ha, which is dominated by hilly areas with areas below alluvial-colluvial. This study aimed to map the potential distribution of the landslides in the Tulis Watershed. As the Tulis Watershed has the potential for landslides, this study was conducted by using Shuttle Radar Topography Mission (SRTM) imagery year 2016. This study considered five aspects that affect landslides, namely: geological type, soil regolith depth, fault, slope, and soil texture. Areas in the Tulis Watershed were classified into five levels of landslide potential The following landslide classes and the area they cover were predicted after applying the formula: very low (0%), low (48%, 6,126 ha), moderate (51%, 6,548 ha), high (0.5%, 63 ha), and very high (0.1%, 13 ha). From the results of the level of potential landslides, several prevention and mitigation measures are recommended according to the level. For shallow landslide levels, it is recommended that relocation centers should be set up. In contrast, for those areas with very high landslide potential, it is necessary to mitigate and install Early Warning System (EWS) tools and prepare the community for adaptation.

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Section: Discussionmentioning

confidence: 99%

Classification of potential landslides using the Shuttle Radar Topography Mission imagery in the Tulis Watershed, Indonesia

Harjadi

Abdiyani²,

Sukartono

et al. 2022

Sains Tanah J. Soil Sci. Agroclimatology

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“…Shahabi & Hashim (2015) employed GIS-based statistical models and remote sensing data with the overlay method to create a landslide susceptibility map in Cameron Highlands area of Malaysia, while Hadmoko et al (2017) overlaid annual isohyets to establish the distribution of landslide occurrence in Java that increases in every interval class, and the highest in 2500-3000 mm. Hartono & Nasikh (2017) employed map analysis using the intersect type of overlay with scoring to produce a landslide potency map of Batu-East Java. In addition, Kurnianto et al (2018) produced a map of landslide-prone disaster zone in Jember-East Java using the application of GIS.…”

Section: Discussionmentioning

confidence: 99%

“…Most of them ended in the map result of GIS used overlay method. Hartono & Nasikh (2017) focus on producing a landslide potency map with analysis of the causative factors of landslides, while Juang et al (2019) focused on reporting previous landslide maps. Bartelletti et al (2017) focused on developing a GIS landslide map from a landslide database, statistical analysis, geomorphological and geological maps.…”

Section: Discussionmentioning

confidence: 99%

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The Compatibility of a GIS Map of Landslide-Prone Areas in Kendari City Southeast Sulawesi with Actual Site Conditions

2020

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Kendari is the capital of the Indonesian province of Southeast Sulawesi. It is located on mainly the karst hills region with high rainfall and there were numerous human activities on the karst hills. Many landslides have occurred in these areas. Natural and human factors may contribute to the landslide. The purpose of this study was to determine whether the present GIS map of landslide-prone areas was in agreement or compatible when compared to the actual site conditions in Kendari City. This research is mainly a regional survey. Data was collected through direct interview and observation at the sites. Data were analyzed quantitatively with percentages. The results showed that 87.4% of the area in Kendari City as shown in the map of landslide-prone distribution using GIS was included in the low risk or slightly vulnerable category. The category of landslide-prone areas was divided into; Very low risk, Low, Medium, High risk and Very high risk which represents the less vulnerable areas to the very vulnerable areas. The level of compatibility of landslide-prone maps in Kendari City, when compared with actual site conditions, reaches 75%. This shows that the map of the GIS spatial analysis can be used as a guide in mapping the level of landslide vulnerability in Kendari City. Landslide-prone map may be used as a guideline for engineers, designers, planners, and city officials in planning to reduce the risk of potential disaster.

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“…Lonsor adalah proses berpindahnya tanah yang dipengaruhi oleh gaya grafitasi yang bergerak secara vertical. Pada dasarnya longsor terjadi disebabkan karena tidak seimbangnya kemiringan (Hartono, 2017). Longsor merupakan bencana yang dapat menyebabkan kerugian ekonomi, sosial serta kerusakan sumber daya tanah dan air (Komac dan Auflič, 2017;Raja et al, 2017).…”

Section: Pendahuluanunclassified

Penerapan Sistem Informasi Geografis Untuk Mengidentifikasi Tingkat Bahaya Longsor Di Kec. Sabbang, Kab. Luwu Utara, Prov. Sulawesi Selatan

Thamsi¹,

Anwar²,

Bakri³

et al. 2019

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Bencana alam merupakan kejadian yang perlu dilakukan penelitian lebih mendalam sehingga dapat meminimalisir kerugian yang diakibatkan oleh bencana. Penelitian ini bertujuan untuk mengetahui luas daerah rawan bencana longsor, tingkat daerah bencana longsor dan foktor yang dapat menyebabkan terjadinya longsor. Metode penelitian dilakukan menggunakan aplikasi sistem informasi geografis (SIG) yang terdiri dari data curah hujan, penggunaan area lahan, kemiringan lereng, jenis tanah, lithologi batuan, bentuk lahan dan struktur geologi. Data tersebut kemudian dilakukan pengharkatan (scoring) untuk memperoleh area tingkat bahaya longsor. Hasil penelitian diperoleh empat zona tingkat bahaya longsor yaitu zona bahaya longsor rendah dengan luas 3.656 Ha, zona bahaya longsor sedang dengan luas 22.628 Ha, zona bahaya longsor tinggi dengan lus 42.063 Ha dan zona bahaya longsor sangat tinggi 331 Ha. Hasil yang diperoleh mengidentifiksikan bahwa potensi untama bahaya longsor disebebkan oleh curah hujan yang tinggi yaitu 3826 mm/tahun. Area kemiringan lereng 40% sampai 100% yang luas juga merupakan factor yang menyebabkan potensi terjadinya longsor.

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Applying Remote Sensing Technology and Geographic Information System in Batu, East Java

Cited by 3 publications

References 9 publications

Classification of potential landslides using the Shuttle Radar Topography Mission imagery in the Tulis Watershed, Indonesia

Classification of potential landslides using the Shuttle Radar Topography Mission imagery in the Tulis Watershed, Indonesia

The Compatibility of a GIS Map of Landslide-Prone Areas in Kendari City Southeast Sulawesi with Actual Site Conditions

Penerapan Sistem Informasi Geografis Untuk Mengidentifikasi Tingkat Bahaya Longsor Di Kec. Sabbang, Kab. Luwu Utara, Prov. Sulawesi Selatan

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