Monitoring archaeological sites in a changing landscape–using multitemporal satellite remote sensing as an ‘early warning’ method for detecting regrowth processes

2007 ◽  
Vol 14 (4) ◽  
pp. 231-244 ◽  
Author(s):  
Stine Barlindhaug ◽  
Inger Marie Holm-Olsen ◽  
Hans Tømmervik
2021 ◽  
Vol 2078 (1) ◽  
pp. 012071
Author(s):  
Zhi Yang ◽  
Yuanjing Deng ◽  
Mengxuan Li ◽  
Yi Liu ◽  
Binbin Zhao ◽  
...  

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.


Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1425 ◽  
Author(s):  
Adriaan L. van Natijne ◽  
Roderik C. Lindenbergh ◽  
Thom A. Bogaard

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.


2020 ◽  
Author(s):  
Adriaan van Natijne ◽  
Roderik Lindenbergh ◽  
Thom Bogaard

<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>


2020 ◽  
Vol 12 (12) ◽  
pp. 2003 ◽  
Author(s):  
Iulia Dana Negula ◽  
Cristian Moise ◽  
Andi Mihai Lazăr ◽  
Nicolae Cătălin Rișcuța ◽  
Cătălin Cristescu ◽  
...  

The capabilities of satellite remote sensing technologies and their derived data for the analysis of archaeological sites have been demonstrated in a large variety of studies over the last decades. Likewise, the Earth Observation (EO) data contribute to the disaster management process through the provision of updated information for areas under investigation. In addition, long term studies may be performed for the in–depth analysis of the disaster–prone areas using archive satellite imagery and other cartographic materials. Hence, satellite remote sensing represents an essential tool for the study of hazards in cultural heritage sites and landscapes. Depending on the size of the archaeological sites and considering the fact that some parts of the site might be covered, the main concern regards the suitability of satellite data in terms of spatial and spectral resolution. Using a multi–temporal Sentinel–2 dataset between 2016 and 2019, the present study focuses on the hazard risk identification for the Micia and Germisara archaeological sites in Romania as they are endangered by industrialisation and major infrastructure works and soil erosion, respectively. Furthermore, the study includes a performance assessment of remote sensing vegetation indices for the detection of buried structures. The results clearly indicate that Sentinel–2 imagery proved to be fundamental in meeting the objectives of the study, particularly due to the extensive archaeological knowledge that was available for the cultural heritage sites. The main conclusion to be drawn is that satellite–derived products may be enhanced by integrating valuable archaeological context, especially when the resolution of satellite data is not ideally fitting the peculiarities (e.g., in terms of size, underground structures, type of coverage) of the investigated cultural heritage sites.


Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management


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