Landslide Inventory: Challenge for Landslide Hazard Assessment in Indonesia

Ngadisih,; Samodra, Guruh; Bhandary, Netra Prakash; Yatabe, Ryuichi

doi:10.1007/978-4-431-54391-6_8

Cited by 10 publications

(7 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Twenty-four months of the rainfall data are used, as shown in Figure 3. Based on the rainfall data, two characteristics of monsoon data were formed as usually as the tropical climate types, where the wet season formed since October to May and the dry season were formed since June to September [27]. The visualization was shown a similar pattern through rainfall and PS-InSAR data.…”

Section: Discussionmentioning

confidence: 96%

The Disaster Mitigation of Slow-Landslide Movement Induced By Rainfall Based on Ps-Insar Method

Bahti

Praptawati

2021

taiwanproceeding

View full text Add to dashboard Cite

Disaster management is a big issue in the past few years. Talking about the disaster, an aspect that should be focussed on is mitigation. The development and the ability of Remote sensing technology have a significant impact on disaster management and significantly contribute to disaster mitigation, such as for the disaster monitoring system. The slow-landslide movement is rarely considered in disaster mitigation, even though the acceleration can increase time by time and will be more dangerous than usual. Therefore, the observation of the remote sensing technology is needed for disaster mitigation. PS-InSAR as a space-based observation method can observe the continuous movement on a site location. Thus, this study illustrates the slow-landslide movement mechanism based on remote sensing technology using the PS-InSAR method compared with rainfall data. In this study, the Sentinel-1 images and STAMPS/MTI by Hooper (2004) successfully detect the displacement rate of the Kalibawang Village, Special Region of Yogyakarta, Indonesia, with the maximum displacement rate -23 mm/year along the Line of Sight (LoS) of the satellite. The PS-InSAR result was also compared with the rainfall data, and shows a correlation of the movement during the rainfall season. Therefore, further mitigation is needed to reduce the risk of the disaster.

show abstract

Section: Discussionmentioning

confidence: 96%

The Disaster Mitigation of Slow-Landslide Movement Induced By Rainfall Based on Ps-Insar Method

Bahti

Praptawati

2021

taiwanproceeding

View full text Add to dashboard Cite

show abstract

“…Para ahli geosains dan profesional di bidang keteknikan telah menjadikan bahaya tanah longsor sebagai bagian yang diminati untuk diteliti. Upaya untuk menilai bahaya dan resiko tanah longsor yang digambarkan secara spasial di peta telah banyak dilakukan oleh pemerintah dan lembaga penelitian di seluruh dunia (Ngadisih et al, 2017;Qiu dan Mitani, 2017). Di Indonesia tanah longsor adalah bencana alam utama yang disebabkan oleh kondisi geomorfologi yang ekstrim dan iklim tropis (Ngadisih et al, 2017).…”

Section: Pendahuluanunclassified

“…Bahaya alamiah yang utama salah satunya adalah longsor yang dapat menyebabkan terjadinya kerusakan properti yang besar. Memperkirakan potensi kerugian yang terdapat pada daerah rawan bencana longsor merupakan langkah yang dibutuhkan untuk mengembangkan mitigasi bencan longsor (Pradhan, Lee dan Buchroithner, 2009;Ngadisih et al, 2017). Daerah Kabupaten Luwu Utara merupakan salah satu dari sebelas daerah yang rawan terjadi longsor (Triutomo 2010 dalam Nasiah dan Invanni, 2014).…”

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

View full text Add to dashboard Cite

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.

show abstract

“…However, information on the state of activity is still not up-to-date (Kalia, 2018) and is hampered by several conditions. It is a major challenge to detect and map the landslides present and assess the state of activity for several reasons, for instance, the high dense tree canopy covering (Jaboyedoff et al, 2012, Van Den Eeckhaut et al, 2013, Pirasteh and Li, 2016, Mezaal et al, 2017b, Salleh et al, 2018, the undulating area (Brardinoni et al, 2003, McKean and Roering, 2004, Korup, 2005, Cigna et al, 2013, Tien Bui et al, 2018, and the rapid vegetation growth (Brardinoni et al, 2003, Musinguzi and Asiimwe, 2014, Pradhan et al, 2015, Mezaal et al, 2017b, Mezaal et al, 2017a, Samodra et al, 2017, Samodra et al, 2018. These factors remove the landslide signature and complicate the determination of a landslide boundary and state of activity.…”

Section: Introductionmentioning

confidence: 99%

Vegetation-based Approach for Rotational Landslide Activity Classification: Case Study of Kundasang, Sabah

Salleh

Rahman

Ismail

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Mapping and monitoring the state of activity of landslides is crucial for effective landslide management and risk assessment. This study presents a novel approach using vegetation anomalies indicator (VAI) derived from high-resolution remotely sensed data for landslide state of activity mapping. The study focuses on the Kundasang area in Sabah, Malaysia, known for its tectonic activity. High-resolution remotely sensed data were utilized to assist in the manual inventory process of landslide activities and to generate VAIs as input for modeling. Results The landslide inventory process identified active, dormant, and relict landslides. The resulting inventory map was divided into training (70%) and validation (30%) datasets for modeling purposes. Seven main VAIs, including canopy gap, mature woody vegetation, primary forest, Root Strength Index (RSI), and water-loving tree, were produced and used as the input for the classification process using Support Vector Machine (SVM) and Artificial Neural Network (ANN) methods. The result showed that SVM outperforms ANN for both deep-seated and shallow rotational landslides, with an overall accuracy of 68.6% and 80.7%, respectively. Furthermore, an evaluation of SVM revealed that the radial basis function (RBF) kernel yielded the highest accuracies, whereas ANN performed best with a hyperbolic tangent (tanh) activation function. Conclusion The accurate classification of landslide state of activity using VAI provides several benefits, including the ability to map and classify landslide activity in forested areas, characterize vegetation characteristics specific to each activity state, and enable continuous monitoring in areas where field monitoring is impractical. This research opens new possibilities for more accurate landslide activity mapping and monitoring, thereby improving disaster risk reduction and management in tectonically active regions.

show abstract

Landslide Inventory: Challenge for Landslide Hazard Assessment in Indonesia

Cited by 10 publications

References 52 publications

The Disaster Mitigation of Slow-Landslide Movement Induced By Rainfall Based on Ps-Insar Method

The Disaster Mitigation of Slow-Landslide Movement Induced By Rainfall Based on Ps-Insar Method

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

Vegetation-based Approach for Rotational Landslide Activity Classification: Case Study of Kundasang, Sabah

Contact Info

Product

Resources

About