Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets

Landslides ◽  
2010 ◽  
Vol 7 (3) ◽  
pp. 317-324 ◽  
Author(s):  
Zonghu Liao ◽  
Yang Hong ◽  
Jun Wang ◽  
Hiroshi Fukuoka ◽  
Kyoji Sassa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Early Warning System ◽  
Satellite Remote Sensing ◽  
Warning System ◽  
Geospatial Datasets

scholarly journals Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1425 ◽  
Author(s):  
Adriaan L. van Natijne ◽  
Roderik C. Lindenbergh ◽  
Thom A. Bogaard
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Early Warning ◽  
Satellite Remote Sensing ◽  
Regional Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Machine Learning Algorithms ◽  
Catchment Scale ◽  
Physical Constraints

Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for both local and regional monitoring of deep-seated landslide deformation and associated processes. Here, we list the key variables of the landslide process and the associated satellite remote sensing products, as well as the available machine learning algorithms and their current use in the field. Furthermore, we discuss both the challenges for the integration in an early warning system, and the risks and opportunities arising from the limited physical constraints in machine learning. This review shows that data products and algorithms are available, and that the technology is ready to be tested for regional applications.

Machine Learning: potential for local and regional deep-seated landslide nowcasting

2020 ◽  
Author(s):  
Adriaan van Natijne ◽  
Roderik Lindenbergh ◽  
Thom Bogaard
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Early Warning ◽  
Satellite Remote Sensing ◽  
Regional Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Landslide Hazard ◽  
Landslide Risk ◽  
Warning Systems

<p>Where landslide hazard mitigation is impossible, Early Warning Systems are a valuable alternative to reduce landslide risk. To this extent nowcasting and Early Warning Systems for landslide hazard have been implemented mostly at local scale. Unfortunately, such systems are often difficult to implement at regional scale or in remote areas due to dependency on local sensors. However, in recent years various studies have demonstrated the effective application of Machine Learning for deformation forecasting of slow-moving, deep-seated landslides. Machine Learning, combined with satellite Remote Sensing products offers new opportunities for both local and regional monitoring of deep-seated landslides and associated processes.</p><p>Working from the key variables of the landslide process we selected the available satellite Remote Sensing products, the necessary assumptions for a satellite only application and evaluated the potential benefit of local information. In the absence of continuous, satellite deformation measurements, nowcasting of the system state will provide a short term deformation prediction. We demonstrate the opportunities of Machine Learning on multi-sensor monitored Austrian landslide and anticipate on the integration in an Early Warning System. Furthermore, we highlight the risks and opportunities arising from the limited physics constraints in Machine Learning.</p>

scholarly journals KAJIAN POLA PENGGUNAAN LAHAN DAN STRATEGI PENGELOLAAN WILAYAH KEPESISIRAN PURWOREJO

2020 ◽  
Vol 8 (2) ◽  
pp. 99
Author(s):  
Gaby Nanda Kharisma ◽  
Rizky Adriadi Ghiffari ◽  
Surono Surono ◽  
Zulfitrani Busrah ◽  
Zulhan Effendy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Early Warning ◽  
Coastal Area ◽  
Early Warning System ◽  
Warning System ◽  
Disaster Risk ◽  
Population Pressure ◽  
Land Use Patterns ◽  
Central Java

AbstractPurworejo is a regency on the southern coast of Java has a potential resource but due to the aspect of disaster risk, this regency is also classified in the disaster risk assessment index class starting from the low, middle and high classes. The research objective is to examine the physical characteristics and land use patterns of coastal areas in Purworejo Regency, Central Java to then determine the coastal area strategy in the area. The location of the study is Munggangsari Beach, Grabag District, Purworejo Regency, Central Java Province. The methods used in this study include literature studies, remote sensing, field surveys (observation and measurement), and interviews. From the results of data and image analysis, there was an increase in livestock and pond fisheries activities in the Purworejo Coastal area from 2006 to 2014. The existence of these activities has the potential to harm the surrounding environment, one of which is groundwater quality. Whereas land use in the form of vacant and physical land generally decreases in area. This shows that from 2006 - 2016 there was high population pressure. Policy, technical capacity (early warning system procurement) is needed for the institution and strong emergency response mechanism in its application.Keywords: Purworejo, Coastal Region, Disaster risk, Remote Sensing, Early Warning System AbstrakPurworejo sebagai kabupaten yang berada di wilayah kepesisiran selatan Pulau Jawa memiliki potensi sumberdaya tetapi bila ditinjau dari aspek risiko bencana kabupaten ini juga terkasifikasi pada kelas indeks kajian risiko bencana mulai dari kelas rendah, menengah, dan tinggi. Tujuan penelitian untuk mengkaji karakteristik fisik dan pola penggunaan lahan wilayah kepesisiran Kabupaten Purworejo, Jawa Tengah untuk kemudian menentukan strategi wilayah kepesisiran pada daerah tersebut. Lokasi kajian yakni Pantai Munggangsari, Kecamatan Grabag, Kabupaten Purworejo, Provinsi Jawa Tengah. Metode yang digunakan dalam penelitian ini meliputi studi literatur, penginderaan jauh, survei lapangan (pengamatan dan pengukuran langsung), dan wawancara. Dari hasil analisis data dan citra terjadi peningkatan aktivitas peternakan dan perikanan tambak di wilayah Pesisir Purworejo dari tahun 2006 hingga 2014. Adanya aktivitas kegiatan tersebut berpotensi memberikan dampak negatif terhadap lingkungan sekitar salah satunya terhadap kualitas airtanah. Sedangkan untuk penggunaan lahan berupa tanah kosong dan gisik pada umumnya terjadi penurunan luasan. Hal tersebut menunjukkan dari tahun 2006 – 2016 terjadi tekanan penduduk yang tinggi. Dibutuhkan kebijakan, kapasitas teknis (pengadaan sistem peringatan dini) pada lembaga serta mekanisme penanganan darurat bencana yang kuat dalam pengaplikasiannya.Kata Kunci: Purworejo, Wilayah Kepesisiran, Risiko bencana, Penginderaan Jauh, EWS

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