scholarly journals Unmanned Aerial Vehicle surveys for monitoring and managing river system: a case study in Valsassina (Northern Italy)

Author(s):  
Alessio Cislaghi ◽  
Alessio Moscaritoli ◽  
Paolo Fogliata ◽  
Paolo Sala ◽  
Emanuele Morlotti ◽  
...  

<p>Hundreds of thousands of people live and work in areas at risk of flooding, especially into deep valleys over the Italian territory. Floods cause fatalities and considerable economic damages to infrastructures and to private and public properties, besides impacting on fluvial-geomorphic landforms. During the last decade, these extreme events are occourring more frequently, contributing to increase the public awareness on the potential damaging consequences, and on the demand of monitoring and post-event assessment procedures. However, an efficient, systematic and accurate framework of post-event actions aiming to document the impacts of such disasters in terms of flooded areas, meteorological controls, geomorphological and vegetation change, is rare.</p><p>On this background, the role of the post-event surveys is fundamental to provide information/data and to increase knowledge for improving forecasting and designing the countermeasures. Flood events documentation consists in a series of field- and desk-based activities that request considerable consuming resources (time and human) and a high level of technical expertise. The post-event analyses, then, should correctly balance the different activities and efforts to reduce time and costs and then become a part routine post-event procedure.</p><p>The present study shows the results of a field campaign carried out after a flash flood occurred on June 12th 2019 along a 2 km stretch of Pioverna torrent in Valsassina (Lombardy, Italy). The survey consisted in collecting meteorological data, and video and pictures taken by inhabitants and rescuers for reconstructing field evidences of flood and the peak discharge. Few weeks after the flood, an Unmanned Aerial Vehicle (UAV) captured multiple images that were processed by Structure from Motion (SfM) photogrammetric algorithms, together with permanent Ground Control Points (GCPs) positioned on the riverbed and the streambanks, in order to obtain a high-resolution topography data. The methodology is likely to be truly effective if a pre-event photogrammetric survey is available for the same stretch, as in the present case.</p><p>The UAV photogrammetric surveys expected to be able to detect: (i) the geomorphological changes including streambank erosion, sediment deposition and the general stream evolution; (ii) the flood-damaged areas including buildings and roads (useful for estimating economic losses) and hydraulic structures (useful for giving a priority to the restoration works); (iii) the change in vegetation patterns that strongly influence the fluvial geomorphological processes.</p><p>In such a perspective, a simple methodology has been developed and applied to obtain a good balance between accuracy, time-consuming, efforts and collected data. In addition, it has been showed how the post-flood campaign has a strategic significance for a wide spectrum of multidisciplinary aspects (damage assessment, hydraulics, and ecology) and allows to rapidly reconstruct the flood event and its consequences. Standardizing such procedure should be extremely important to collect similar data, useful to improve specific guidelines and post-emergency management plans.</p>

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6540
Author(s):  
Qian Pan ◽  
Maofang Gao ◽  
Pingbo Wu ◽  
Jingwen Yan ◽  
Shilei Li

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.


2020 ◽  
Author(s):  
Yuan-Fong Su ◽  
Yan-Ting Lin ◽  
Jiun-Huei Jang ◽  
Jen-Yu Han

Abstract. Sophisticated flood simulation in urban areas is a challenging task due to the difficulties in data acquisition and model verification. This study incorporates three rapid-growing technologies, i.e. volunteered geographic information (VGI), unmanned aerial vehicle (UAV), and computational flood simulation (CFS) to reconstruct the flash flood event occurred in 14 June 2015, GongGuan, Taipei. The high-resolution digital elevation model (DEM) generated by a UAV and the real-time VGI photos acquired from social network are served to establish and validate the CFS model, respectively. The DEM data are resampled based on two grid sizes to evaluate the influence of terrain resolution on flood simulations. The results show that flood scenario can be more accurately modelled as DEM resolution increases with better agreement between simulation and observation in terms of flood occurrence time and water depth. The incorporation of UAV and VGI lower the barrier of sophisticated CFS and shows great potential in flood impact and loss assessment in urban areas.


Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1566
Author(s):  
Alessandro Matese ◽  
Andrea Berton ◽  
Valentina Chiarello ◽  
Riccardo Dainelli ◽  
Carla Nati ◽  
...  

The need to rely on accurate information about the wood biomass available in riparian zones under management, inspired the land reclamation authority of southern Tuscany to develop a research based on the new remote sensing technologies. With this aim, a series of unmanned aerial vehicle (UAV) flight campaigns flanked by ground-data collection were carried out on 5 zones and 15 stream reaches belonging to 3 rivers and 7 creeks, being representative of the whole area under treatment, characterized by a heterogeneous spatial distribution of trees and shrubs of different sizes and ages, whose species’ mix is typical of this climatic belt. A careful preliminary analysis of the zones under investigation, based on the available local orthophotos, followed by a quick pilot inspection of the riverbank segments selected for trials, was crucial for choosing the test sites. The analysis of a dataset composed of both measured and remotely sensed acquired parameters allowed a system of four allometric models to be built for estimating the trees’ biomass. All four developed models showed good results, with the highest correlation found in the fourth model (Model 4, R2 = 0.63), which also presented the lowest RMSE (0.09 Mg). The biomass values calculated with Model 4 were in line with those provided by the land reclamation authority for selective thinning, ranging from 38.9 to 70.9 Mg ha−1. Conversely, Model 2 widely overestimated the actual data, while Model 1 and Model 3 offered intermediate results. The proposed methodology based on these new technologies enabled an accurate estimation of the wood biomass in a riverbank environment, overcoming the limits of a traditional ground monitoring and improving management strategies to benefit the river system and its ecosystems.


Author(s):  
Ahmed Refaat Ragab ◽  
Mohammad Sadeq Ale Isaac ◽  
Marco A. Luna ◽  
Pablo Flores Peña

In Europe, fire represents an important issue for a lot of researchers due to economic losses, environmental disasters, and human death. In the last decade, the European parliament sheds light upon this problem by dealing with the community project” Forest Focus”. Thus, researchers and scientific research departments of European companies begin to work on solving and creating different techniques to deal with such a problem, these research centers found that the most attractive and accurate way of solving such a problem was using an Unmanned Aerial Vehicle (UAV). In this paper, the research center at Drone Hopper Company analysis the deficiencies for forest fire fighting systems, in order to start designing its new prototype of a special drone named WILD HOPPER, solving all the shortcomings of similar systems. This paper is the first of a group of research papers that will take place during designing and producing our WILD-HOPPER system.


2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  

2018 ◽  
pp. 7-13
Author(s):  
Anton M. Mishchenko ◽  
Sergei S. Rachkovsky ◽  
Vladimir A. Smolin ◽  
Igor V . Yakimenko

Results of experimental studying radiation spatial structure of atmosphere background nonuniformities and of an unmanned aerial vehicle being the detection object are presented. The question on a possibility of its detection using optoelectronic systems against the background of a cloudy field in the near IR wavelength range is also considered.


Author(s):  
Amir Birjandi ◽  
◽  
Valentin Guerry ◽  
Eric Bibeau ◽  
Hamidreza Bolandhemmat ◽  
...  

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