Assessment, monitoring, and early warning of droughts: the potential for satellite remote sensing and beyond

2019 ◽  
pp. 115-131 ◽  
Author(s):  
Valerie Graw ◽  
Olena Dubovyk ◽  
Moses Duguru ◽  
Paul Heid ◽  
Gohar Ghazaryan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Satellite Remote Sensing ◽  
Monitoring And Early Warning

scholarly journals Monitoring and Early Warning of Transmission Line External Breakage Based on Satellite-Ground Coordination

2021 ◽  
Vol 2078 (1) ◽  
pp. 012071
Author(s):  
Zhi Yang ◽  
Yuanjing Deng ◽  
Mengxuan Li ◽  
Yi Liu ◽  
Binbin Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Transmission Line ◽  
Transmission Lines ◽  
Early Warning ◽  
Satellite Remote Sensing ◽  
Construction Work ◽  
Remote Sensing Images ◽  
Registration Method ◽  
Monitoring And Early Warning ◽  
External Damage

Abstract This article first proposes a high-precision spatio-temporal registration method between satellite remote sensing images and ground sensors. Then, using satellite remote sensing images, an intelligent identification model for typical external damage hidden dangers of transmission lines based on satellite remote sensing is established to realize intelligent identification of transmission line construction work areas and mining affected areas. Aiming at the results of intelligent identification of construction work areas and mining-affected areas, the proposed YOLOv4-based external damage identification algorithm for transmission lines is used to detect external damage hidden dangers. Through the method in this paper, it is possible to realize a regular general survey of hidden dangers of external damage (construction work area, mining affected area) with full coverage of transmission channels, and carry out targeted 24-hour monitoring on the ground. The test results show that the satellite-ground coordinated transmission line external damage monitoring and early warning in this paper. The method timely and accurately realizes the monitoring and early warning of the external breakage of the transmission line.

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>

Landslide mapping, monitoring and early warning by using optical remote sensing, InSAR and ground-based sensors: case study of the Qingpo landslide (Wenchuan, China)

2020 ◽  
Author(s):  
Weihua Zhao ◽  
Mingli Xie ◽  
Nengpan Ju
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Real Time ◽  
Early Warning ◽  
Deformation Monitoring ◽  
Monitoring Methods ◽  
Real Time Monitoring ◽  
Deformation Velocity ◽  
Monitoring And Early Warning

<p> Studies of landslide evolution that improve the knowledge of ground movements are essential to understand the mechanism of deformation for landslide-prone territories to mitigate the associated risk. The large Qingpo landslide, with a volume of about 200,0000 m<sup>3</sup>, is located in a mega ancient landslide (with a width of 1300 m and a height difference about 400 meters), and a pylon is just located on the boundary of Qingpo landslide. How to accurately judge the historical evolution process, current evolution stage and the future evolution trend of the large landslides is very important for landslide and pylon monitoring and early warning. In this study, on the basis of a detailed on-site investigation, a total of 114 Sentinel-1A Images over five years with Level-1 Single Look Complex (SLC) mode and Interferometric Wide (IW) acquisition mode were downloaded from Copernicus Open Access Hub and were preprocessed by time series InSAR model, which allow us to produce deformation time series and mean deformation velocity maps. An automatic monitoring and warning scheme was designed, 10 sets of ground-based sensors, containing self-adapting crack meter, rain gauge, strain gauge and dip meter were installed, followed by real-time monitoring within one month. Ultimately, the temporal and spatial evolution characteristics of the landslide were comprehensively analyzed through on-site deformation investigation, long-term deformation monitoring by InSAR and ground-based real-time monitoring. The applicability of long-term remote sensing monitoring and real-time monitoring methods and how to use them together have also been verified. This study may can also provide a typical case for the comprehensive use of multi-source data.</p>

scholarly journals Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Nicola Casagli ◽  
William Frodella ◽  
Stefano Morelli ◽  
Veronica Tofani ◽  
Andrea Ciampalini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Landslide Mapping ◽  
Remote Sensing Techniques ◽  
Monitoring And Early Warning
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