skip to main content
research-article

Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires

Published: 01 January 2020 Publication History

Abstract

The primary sources for ecological degradation currently are the Forest Fires (FF). The present observation frameworks for FF absence need supporting in constant checking of each purpose of the location at all time and prime location of the fire dangers. This approach gives works on preparing UAV (Unmanned Aerial Vehicle) aeronautical picture information as indicated by the prerequisites of ranger service territory application on a UAV stage. It provides a continuous and remote watch on a flame in forests and mountains, all the while the UAV is flying and getting the elevated information, helping clients maintain the number and area of flame focuses. Observing programming spreads capacities, including Fire: source identification, area, choice estimation, and LCD module. This paper proposed includes (1) Color Code Identification, (2) Smoke Motion Recognition, and (3) Fire Classification algorithms. Strikingly, the use of a helicopter with visual cameras portrayed. The paper introduces the strategies utilized for flame division invisible cameras, and the systems to meld the information acquired the following: Correctly, the current FF location stays testing, given profoundly convoluted and non-organized conditions of the forest, smoke hindering the flame, the movement of cameras mounted on UAVs, and analogs of fire attributes. These unfavorable impacts can truly purpose either false alert. This work focuses on the improvement of trustworthy and exact FF recognition algorithms which apply to UAVs. To effectively execute missions and meet their relating execution criteria examinations on the best way to diminish false caution rates, increment the possibility of profitable recognition, and upgrade versatile abilities to different conditions are firmly requested to improve the unwavering quality and precision of FF location framework.

References

[1]
N. Von Wahl, S. Heinen, R. Tobera, D. Nüßler, R. Brauns, M. Schröder, P. Knott, W. Krüll, I. Willms, Intermediate Report Internationale Waldbrandbekämpfungi WBB, FHR-Report Nr. 134, FGAN Research Institute for High-Frequency Physics and Radar Techniques, Wachtberg, Germany, 2009.
[2]
M. Henrichs, Armored and Tracked Vehicle for Rescue/Extinguish/Defend Missions, in: 14th International Conference on Automatic Fire Detection, AUBE ’09, Duisburg, Germany, 2009.
[3]
Yuan C., Zhang Y.M., Liu Z.X., A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques, Can. J. Forest Res. 45 (7) (2015) 783–792.
[4]
Ambrosia V.G., Wegener S., Zajkowski T., Sullivan D.V., Buechel S., Enomoto F., Lobitz B., Johan S., Brass J., Hinkley E., The Ikhana unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006–2010), Geocarto Int. 26 (2) (2011) 85–101.
[5]
Ambrosia V.G., Zajkowski T., Selection of appropriate class UAS/sensors to support fire monitoring: experiences in the United States, in: Handbook of Unmanned Aerial Vehicles, Springer Netherlands, 2015, pp. 2723–2754.
[6]
C. Wilson, J.B. Davis, Forest fire laboratory at riverside and fire research in California: past present, and future, in General Technical Report PSW-105 (Pacific Southwest Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture, 1988.
[7]
M. Tranchitella, S. Fujikawa, T.L. Ng, D. Yoel, D. Tatum, P. Roy, C. Mazel, S. Herwitz, E. Hinkley, Using tactical unmanned aerial systems to monitor and map wildfires, in: Proceedings of AIAA Infotech@Aerospace Conference, 2007.
[8]
Ambrosia V.G., Wegener S., Zajkowski T., Sullivan D.V., Buechel S., Enomoto F., Lobitz B., Johan S., Brass J., Hinkley E., The ikhana unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006–2010), Geocarto Int. 26 (2) (2011) 85–101.
[9]
V. Ambrosia, Remotely piloted vehicles as fire imaging platforms: the future is here, Available from http://geo.arc.nasa.gov/sge/UAVFiRE/completeddemos.html, 2002.
[10]
Martinez-de Dios J.R., Merino L., Ollero A., Ribeiro L.M., Viegas X., Multi-UAV experiments: application to forest fires, Springer Tracts Adv. Robot. 37 (2007) 207–228.
[11]
Pastor E., Barrado C., Royo P., Santamaria E., Lopez J., Salami E., Architecture for a helicopter-based unmanned aerial systems wildfire surveillance system, Geocarto Int. 26 (2) (2011) 113–131.
[12]
D.W. Casbeer, R.W. Beard, T.W. McLain, S.M. Li, R. Mehra, Forest fire monitoring with multiple small UAVs, in: Proceedings of American Control Conference, 2005, pp. 3530–3535.
[13]
C. Yuan, K.A. Ghamry, Z.X. Liu, Y.M. Zhang, Unmanned aerial vehicle-based forest fire monitoring and detection using image processing technique, in: The IEEE Chinese Guidance, Navigation and Control Conference, 2016.
[14]
Li M., Xu W., Xu K., Fan J., Hou D., Review of fire detection technologies based on the video image, J. Theor. Appl. Inf. Technol. 49 (2) (2013) 700–707.
[15]
Cetin A.E., Dimitropoulos K., Gouverneur B., Grammalidis N., Gunay O., Habiboglu Y.H., Verstockt: Video fire detection: Review, Digit. Signal Process. 23 (6) (2013) 1827–1843.
[16]
Toreyin B.U., Verstockt S., Video fire detection–review, Digit. Signal Process. 23 (6) (2013) 1827–1843.
[17]
T. Celik, H. Ozkaramanlt, H. Demirel, Fire pixel classification using fuzzy logic and statistical color model, in: IEEE International Conference on Acoustics, 2007, pp. 1205–1208.
[18]
Yuan F., A fast accumulative motion orientation model, based on the integral image for video smoke detection, Pattern Recognit. Lett. 29 (7) (2008) 925–932.
[19]
C.C. Ho, T.H. Kuo, Real-time video-based fire smoke detection system, in: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2009, pp. 1845–1850.
[20]
S. Surit, W. Chatwiriya, Forest fire smoke detection in video based on digital image processing approach with static and dynamic characteristic analysis, in: Proceedings of the 1st ACIS/JNU International Conference on Computers, Networks, Systems, and Industrial Engineering, 2011, pp. 35–39.
[21]
Bosch I., Serrano A., Vergara L., Multisensor network system for wildfire detection using infrared image processing, Sci. World J. (2013).
[22]
Pastor E., Agueda A., Andrade-Cetto J., Munoz M., Perez Y., Planas E., Computing the rate of spread of linear flame fronts by thermal image processing, Fire Saf. J. 41 (8) (2006) 569–579.
[23]
Ononye A.E., Vodacek A., Saber E., Automated extraction of fire line parameters from multispectral infrared images, Remote Sens. Environ. 108 (2) (2007) 179–188.
[24]
Al-Sa’dab Mohammad F., Al-Ali Abdulla, Mohamed Amr, Khattab Tamer, Erbada Aiman, RF-based drone detection and identification using deep learning approaches: An initiative towards a large open-source drone database, Future Gener. Comput. Syst. 100 (2019) 86–97.
[25]
Fabra Francisco, Zamoraa Willian, Masaneta Joan, Calafate Carlos T., Canoa Juan-Carlos, Manzonia Pietro, Automatic system supporting multicopter swarms with manual guidance, Comput. Electr. Eng. 74 (2019) 413–428.
[26]
Zhang R., Shen J., Wei F., Li X., Sangaiah A.K., Medical image classi_cation based on multi-scale non-negative sparse coding, Artif. Intell. Med. 83 (2017) 44–51.
[27]
Ullah A., Ahmad J., Muhammad K., Sajjad M., Baik S.W., Action recognition in video sequences using deep Bi-directional LSTM with CNN features, IEEE Access 6 (2017) 1155–1166.
[28]
Muhammad K., Ahmad J., Baik S.W., Early redetection using convolutional neural networks during surveillance for effective disaster management, Neurocomputing 288 (2018) 30–42.
[29]
Zhang R., Shen J., Wei F., Li X., Sangaiah A.K., Medical image classification based on multi-scale non-negative sparse coding, Artif. Intell. Med. 83 (2017) 44–51.

Cited By

View all

Index Terms

  1. Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires
            Index terms have been assigned to the content through auto-classification.

            Recommendations

            Comments

            Information & Contributors

            Information

            Published In

            cover image Computer Communications
            Computer Communications  Volume 149, Issue C
            Jan 2020
            393 pages

            Publisher

            Elsevier Science Publishers B. V.

            Netherlands

            Publication History

            Published: 01 January 2020

            Author Tags

            1. Forest Fire Detection
            2. UAV Aerial
            3. Smoke detection
            4. Autonomous vehicles
            5. Fire forecast

            Qualifiers

            • Research-article

            Contributors

            Other Metrics

            Bibliometrics & Citations

            Bibliometrics

            Article Metrics

            • Downloads (Last 12 months)0
            • Downloads (Last 6 weeks)0
            Reflects downloads up to 14 Jan 2025

            Other Metrics

            Citations

            Cited By

            View all

            View Options

            View options

            Media

            Figures

            Other

            Tables

            Share

            Share

            Share this Publication link

            Share on social media