Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

Popescu, Dan; Ichim, Loretta; Stoican, Florin

doi:10.3390/s17030446

Cited by 49 publications

(23 citation statements)

References 40 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of UAS is also becoming common in flood control; natural hazard management and infrastructural changes (Galarreta, Kerle, & Gerke, 2015;Giordan, Manconi, Remondino, & Nex, 2017;Perks, Russell, & Large, 2016). Studies show that UAS were used to identify disaster damages and serve humanitarian response services during disaster events in flood-affected Asian countries like Taiwan and China (Feng, Liu, & Gong, 2015;Popescu, Ichim, & Stoican, 2017;UNOCHA, 2014).…”

Section: General Applications Of Uasmentioning

confidence: 99%

Application of unmanned aerial vehicles in earth resources monitoring: focus on evaluating potentials for forest monitoring in Ethiopia

Taddese

Burud

2018

European Journal of Remote Sensing

View full text Add to dashboard Cite

Application of unmanned aerial systems was limited to the military until the last decade when we see dramatic growth of interest by civilian users. Among the many fields of application of unmanned aerial vehicles (UAVs), forestry has diverse uses ranging from forest cover assessment to species classification and real-time forest fire monitoring. Inspired by the potential uses of the technology, this study is a review of literature on the types and uses of UAVs, the challenges and opportunities, current experiences and the future prospects of using UAVs for forest resources monitoring in Ethiopia. The study has identified potential uses of UAVs for forestry applications. It has also shown that there is perceived need for accurate, demand-based and cost-effective tools for forest resources monitoring in developing countries including Ethiopia. Hence, the use of small UAVs in the forestry sector in Ethiopia is believed to be a supplementary method to the existing methods of spatial data capture for filling the gap of information and improving the quality of forest information that is needed to comply with international standards. The results of this study indicate that Ethiopia can make use of the technology and improve its forest information system. However, while doing so, rules and regulations must be put in place to avoid the challenges that come along with introducing the technology. If properly used, the technology will enhance the forest management decision-support system of the country.

show abstract

Section: General Applications Of Uasmentioning

confidence: 99%

Application of unmanned aerial vehicles in earth resources monitoring: focus on evaluating potentials for forest monitoring in Ethiopia

Taddese

Burud

2018

European Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…This approach was then used to assess overall risk of storm surges. Another example includes the use of UAVs to estimate the extent of coastal flooding and assess damages in critical areas demonstrating innovative EO capacities in the support of coastal hazard risk management activities (Popescu et al 2017).…”

Section: Application 3 Complementary Multi-platform Coastal Bathymetrymentioning

confidence: 99%

Earth observation applications for coastal sustainability: potential and challenges for implementation

Politi¹,

Paterson

Scarrott

et al. 2019

Anthropocene Coasts

View full text Add to dashboard Cite

The coast is home to unique ecosystems, where complex ecological processes take place through the interaction of terrestrial, aquatic, atmospheric, and human landscapes. However, there are considerable knowledge and data gaps in achieving effective and future change-proof sustainable management of coastal zones around the world due to both technical and social barriers, as well as governance challenges. Currently, the role of Earth observation (EO) in addressing many of the recognised information gaps is small and under-utilised. While EO can provide much of the spatiotemporal information required for historical analysis and current status mapping, and offers the advantage of global coverage; its uptake can be limited by technical and methodological challenges associated mostly with lack of capacity and infrastructure, product accuracy and accessibility, costs, and institutional acceptance. While new initiatives and recent technological progress in the EO and information technology arena aim to tackle some of these issues so that EO products can be more easily used by non-EO experts, uptake is still limited. This paper discusses how EO can potentially inform transformative practices of planning in the coastal water zone, by using examples to demonstrate the EO potential in providing information relevant to decision-making framed by international agreements, such as the United Nations Agenda 2030, the Convention on Biological Diversity, and the Sendai Framework for Risk Reduction. By presenting evidence for how EO can contribute to innovative opportunities and data synergies at scale, the paper discusses opportunities and challenges for a more solution-led approach to sustainable coastal management.

show abstract

“…[23] presented an UAV based framework for the identification of hydromorphological features in high resolution RGB (Red Green Blue) aerial images using an artificial neural network classifier. The same classifier was used in Reference [24] for the remote detection of flooded areas in UAV data. In the work of Casado et al [1], aerial images of various resolutions were tested in a similar task.…”

Section: Introductionmentioning

confidence: 99%

Identification of Water Body Extent Based on Remote Sensing Data Collected with Unmanned Aerial Vehicle

Tymków

Jóźków

Walicka

et al. 2019

Water

View full text Add to dashboard Cite

The paper presents an efficient methodology of water body extent estimation based on remotely sensed data collected with UAV (Unmanned Aerial Vehicle). The methodology includes the data collection with selected sensors and processing of remotely sensed data to obtain accurate geospatial products that are finally used to estimate water body extent. Three sensors were investigated: RGB (Red Green Blue) camera, thermal infrared camera, and laser scanner. The platform used to carry each of these sensors was an Aibot X6—a multirotor type of UAV. Test data was collected at 6 sites containing different types of water bodies, including 4 river sections, an old river bed, and a part of a lake shore. The processing of collected data resulted in 2.5-D and 2-D geospatial products that were used subsequently for water body extent estimation. Depending on the type of used sensor, the created geospatial product, and the type of the water body and the land cover, three strategies employing image processing tools were developed to estimate water body range. The obtained results were assessed in terms of classification accuracy (distinguishing the water body from the land) and geometrical planar accuracy of the water body extent. The product identified as the most suitable in water body detection was four bands RGB+TIR (Thermal InfraRed) ortho mosaic. It allowed to achieve the average kappa coefficient of the water body identification above 0.9. The planar accuracy of water body extent varied depending on the type of the sensor, the geospatial product, and the test site conditions, but it was comparable with results obtained in similar studies.

show abstract

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

Cited by 49 publications

References 40 publications

Application of unmanned aerial vehicles in earth resources monitoring: focus on evaluating potentials for forest monitoring in Ethiopia

Application of unmanned aerial vehicles in earth resources monitoring: focus on evaluating potentials for forest monitoring in Ethiopia

Earth observation applications for coastal sustainability: potential and challenges for implementation

Identification of Water Body Extent Based on Remote Sensing Data Collected with Unmanned Aerial Vehicle

Contact Info

Product

Resources

About