In this study, the interferometric coherence calculated from 12-day Sentinel-1 image pairs was analysed in relation to mowing events on agricultural grasslands. Results showed that after a mowing event, median VH (vertical transmit, horizontal receive) and VV (vertical transmit, vertical receive) polarisation coherence values were statistically significantly higher than those from before the event. The shorter the time interval after the mowing event and the first interferometric acquisition, the higher the coherence. The coherence tended to stay higher, even 24 to 36 days after a mowing event. Precipitation caused the coherence to decrease, impeding the detection of a mowing event. Given the three analysed acquisition geometries, it was concluded that afternoon acquisitions and steeper incidence angles were more useful in the context of this study. In the case of morning acquisitions, dew might have caused a decrease of coherence for mowed and unmowed grasslands. Additionally, an increase of coherence after a mowing event was not evident during the rapid growth phase, due to the 12-day separation between the interferometric acquisitions. In future studies, six-day pairs utilising Sentinel-1A and 1B acquisitions should be considered.

Relating Sentinel-1 Interferometric Coherence to Mowing Events on Grasslands

As urbanization process has been and will be happening in an unprecedented scale worldwide, strong requirements from academic research and practical fields for smart management and intelligent planning of cities are pressing to handle increasing demands of infrastructure and potential risks of inhabitants’ agglomeration in disaster management. Geospatial data and geographic information systems (GISs) are essential components for building smart cities in a basic way that maps the physical world into virtual environment as a referencing framework. On higher level, GIS has been becoming very important in smart cities on different sectors. In the digital city era, digital maps and geospatial databases have long been integrated in workflows in land management, urban planning and transportation in government. People have anticipated GIS to be more powerful not only as an archival and data management tool but also as spatial models for supporting decision-making in intelligent cities. Successful applications hav...

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2013.774108

Interdisciplinary urban GIS for smart cities: advancements and opportunities

Regional variation in incidence for smoking and alcohol related cancers in Belgium.

http://fhs.diva-portal.org/smash/get/diva2:844035/FULLTEXT01

Military utility : A proposed concept to support decision-making

Product development, especially in aerospace, has become more and more interconnected with its operational environment. In a constant changing world, the operational environment will be subjected to changes during the life cycle of the product. The operational environment will be affected by not only technical and non-technical perturbations, but also economical, managerial and regulatory decisions, thus requiring a more global product development approach. One way to try tackling such complex and intertwined problem advocates studying the envisioned product or system in the context of system of systems (SoS) engineering. SoSs are all around us, probably in any field of engineering, ranging from integrated transport systems, public infrastructure systems to modern homes equipped with sensors and smart appliances; from cities filling with autonomous vehicle to defence systems.Since also aerospace systems are certainly affected, this work will present a holistic approach to aerospace product development that tries spanning from needs to technology assessment. The proposed approach will be presented and analysed and key enablers and future research directions will be highlighted from an interdisciplinary point of view. Consideration of the surrounding world will require to look beyond classical engineering disciplines.

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A holistic engineering approach to aeronautical product development

Weather has a significant influence on navigation processes. Driving during a heavy rain, for example, is slower and due to poor visibility more dangerous than driving in perfect weather conditions. Thus from time management and safety perspective including weather information is beneficial. Weather, especially rain may also be critical for transportation tasks since some commodities like straw or sand should not get wet. In the last years, the quality of weather information and weather forecast has improved and could be used to improve route planning.The paper discusses how weather information can be included in route planning algorithms. A first approximating algorithm to incorporate weather forecast data is presented. Some examples showing the impact on route planning conclude the paper.

Using Weather Information to Improve Route Planning

The goal of this paper is to identify the main vegetation factors in the terrain, which are important for the analysis of forest structure. Such an analysis is important for forestry, rescue operations management during crises situations and disasters such as fires, storms, earthquakes and military analysis (transportation, cover, concealment, etc.). For the forest structure determination, both LIDAR and the forest growth prediction analysis were used. As main results, the vegetation height, tree spacing and stem diameters were determined

https://iopscience.iop.org/article/10.1088/1755-1315/37/1/012031/pdf

Vegetation structure determination using LIDAR data and the forest growth parameters

Geography and health : a Nordic outlook

Three technology forecast reports from the Fraunhofer Institute and four reports on literature studies (sometimes called scanning reports) from the Swedish Defence Research Institute (FOI) have bee 

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Technology Forecast 2017 - Military Utility of Future Technologies : A Report from Seminars at the Swedish Defence University’s (SEDU) Military-Technology Division

Mapping of forest areas and other landscapes as to combine information about ground structures, topography as well as other natural and man-made features can be made with help of LiDAR (Elmqvist, M. 2001). The result can be used for planning military and civil missions and analysis of the possibility to drive though areas with bad or no roads (Sivertun & Gumos 2006) as well as for management of natural recourses and for example in physical planning. By combining LiDAR and other remotely sensed data it is possible to make use of the different advantages the different sensors provides. In this article based on a test in Linkoping municipality, Sweden, we have employed the LiDAR based SingleTree™ detection model (Ahlberg at al 2008) and hyper spectral image data as to improve the classification of the trees and the ground surface under the trees. This method differs from similar models like in Beland et al. (2014) and Cote et al (2011) that uses terrestrial TLiDAR sensors to identify the species of trees.By detecting returns of laser beams that passed through the vegetation and are reflected back to the sensor, it is possible to detect ditches, stones, logs and other obstacles to passing through the area. The data from modern LiDAR sensors can have very high spatial resolution, in many cases 50 points/m2 or more. By filtering the LiDAR data it is also possible to detect vehicles and man-made objects that are hidden under the vegetation, especially if the LIDAR uptake is compared with an earlier registration, movements and differences can be detected.LiDAR registrations are today made by the forest industry in order to obtain better and more accurate information about the vegetation and improve their activities. Observation of the health of plants or trees becomes more important as a consequence from global warming and increased pressure from insects and diseases. There is also an increasing demand on forests and crops as to fill the demands from a growing and partly wealthier world (Kamaruzaman J. and Kasawani I., 2009). In forestry the LiDAR data are used to plan for harvest, building forest roads and timber transports. Another important source of data is Hyper Spectral Scenes (HSS) where the reflected solar light is analysed to identify anomalies in the spectral response and get a hint about the health of the canopy (Hyperspectral Imaging 2011). The difference from using multispectral images in comparison with other remotely sensed data is that the hyper spectral sensor delivers response in several hundred small and well-defined spectral wavelength bands. Those are supposed to indicate the biomass and water content as well as the difference between the absorption and the reflectance band for chlorophyll and many other conditions. A system can be used to identify the spectral signature in a certain area in order to decide what material and colours that should be used for camouflage. The data can be combined with LiDAR and used in the classification of forests, soils and other landscape features in Geographic Information Systems (GIS). Modern development of sensors and platforms makes it possible to use for example Unmanned Air Vehicles (UAVs) like helicopters to collect LiDAR and HSS data.

https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1086&context=iemssconference

Åke Sivertun

Papers

Using Weather Information to Improve Route Planning

Vegetation structure determination using LIDAR data and the forest growth parameters

Geography and health : a Nordic outlook

Technology Forecast 2017 - Military Utility of Future Technologies : A Report from Seminars at the Swedish Defence University’s (SEDU) Military-Technology Division

LiDAR and Hyperspectral data for Landscape and Vegetation Classification and Monitoring