Comparison of Individual Sensors in the Electronic Nose for Stress Detection in Forest Stands
Language English Country Switzerland Media electronic
Document type Journal Article
Grant support
CZ.02.2.69/0.0/0.0/19_073/0016944
Ministry of Education Youth and Sports
PubMed
36850598
PubMed Central
PMC9965568
DOI
10.3390/s23042001
PII: s23042001
Knihovny.cz E-resources
- Keywords
- 3D odor mapping, early detection, electronic nose, natural disturbance, unmanned aerial vehicles,
- MeSH
- Coleoptera * MeSH
- Ecosystem * MeSH
- Electronic Nose MeSH
- Forests MeSH
- Dogs MeSH
- Trees MeSH
- Animals MeSH
- Check Tag
- Dogs MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
Forests are increasingly exposed to natural disturbances, including drought, wildfires, pest outbreaks, and windthrow events. Due to prolonged droughts in the last years in Europe, European forest stands significantly lost vitality, and their health condition deteriorated, leading to high mortality rates, especially, but not limited to, Norway spruce. This phenomenon is growing, and new regions are being affected; thus, it is necessary to identify stress in the early stages when actions can be taken to protect the forest and living trees. Current detection methods are based on field walks by forest workers or deploying remote sensing methods for coverage of the larger territory. These methods are based on changes in spectral reflectance that can detect attacks only at an advanced stage after the significant changes in the canopy. An innovative approach appears to be a method based on odor mapping, specifically detecting chemical substances which are present in the forest stands and indicate triggering of constitutive defense of stressed trees. The bark beetle attacking a tree, for example, produces a several times higher amount of defense-related volatile organic compounds. At the same time, the bark beetle has an aggregation pheromone to attract conspecifics to overcome the tree defense by mass attack. These substances can be detected using conventional chemical methods (solid-phase microextraction fibers and cartridges), and it is proven that they are detectable by dogs. The disadvantage of classic chemical analysis methods is the long sampling time in the forest, and at the same time, the results must be analyzed in the laboratory using a gas chromatograph. A potential alternative novel device appears to be an electronic nose, which is designed to detect chemical substances online (for example, dangerous gas leaks or measure concentrations above landfills, volcanic activity, etc.). We tested the possibility of early-stage stress detection in the forest stands using an electronic nose Sniffer4D and compared the individual sensors in it for detecting the presence of attacked and dead trees. Our results indicate the promising applicability of the electronic nose for stress mapping in the forest ecosystem, and more data collection could prove this approach.
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Seidl R., Thom D., Kautz M., Martin-Benito D., Peltoniemi M., Vacchiano G., Wild J., Ascoli D., Petr M., Honkaniemi J., et al. Forest Disturbances under Climate Change. Nat. Clim. Chang. 2017;7:395–402. doi: 10.1038/nclimate3303. PubMed DOI PMC
Fearnside P.M. Deforestation Soars in the Amazon. Nature. 2015;521:423. doi: 10.1038/521423b. PubMed DOI
Yuan K., Zhu Q., Zheng S., Zhao L., Chen M., Riley W.J., Cai X., Ma H., Li F., Wu H., et al. Deforestation Reshapes Land-Surface Energy-Flux Partitioning. Environ. Res. Lett. 2021;16:024014. doi: 10.1088/1748-9326/abd8f9. DOI
Bowman D.M.J.S., Balch J.K., Artaxo P., Bond W.J., Carlson J.M., Cochrane M.A., D’Antonio C.M., DeFries R.S., Doyle J.C., Harrison S.P., et al. Fire in the Earth System. Science. 2009;324:481–484. doi: 10.1126/science.1163886. PubMed DOI
Zhu Q., Li F., Riley W.J., Xu L., Zhao L., Yuan K., Wu H., Gong J., Randerson J. Building a Machine Learning Surrogate Model for Wildfire Activities within a Global Earth System Model. Geosci. Model Dev. 2022;15:1899–1911. doi: 10.5194/gmd-15-1899-2022. DOI
Kautz M., Meddens A.J.H., Hall R.J., Arneth A. Biotic Disturbances in Northern Hemisphere Forests—A Synthesis of Recent Data, Uncertainties and Implications for Forest Monitoring and Modelling: Biotic Disturbances in Northern Hemisphere Forests. Glob. Ecol. Biogeogr. 2017;26:533–552. doi: 10.1111/geb.12558. DOI
Seidl R., Donato D.C., Raffa K.F., Turner M.G. Spatial Variability in Tree Regeneration after Wildfire Delays and Dampens Future Bark Beetle Outbreaks. Proc. Natl. Acad. Sci. USA. 2016;113:13075–13080. doi: 10.1073/pnas.1615263113. PubMed DOI PMC
Stadelmann G., Bugmann H., Wermelinger B., Bigler C. Spatial Interactions between Storm Damage and Subsequent Infestations by the European Spruce Bark Beetle. For. Ecol. Manag. 2014;318:167–174. doi: 10.1016/j.foreco.2014.01.022. DOI
Chinellato F., Faccoli M., Marini L., Battisti A. Distribution of Norway Spruce Bark and Wood-Boring Beetles along Alpine Elevational Gradients: Norway Spruce Bark and Wood Beetles along Altitude. Agric. For. Entomol. 2014;16:111–118. doi: 10.1111/afe.12040. DOI
Senf C., Seidl R., Hostert P. Remote Sensing of Forest Insect Disturbances: Current State and Future Directions. Int. J. Appl. Earth Obs. Geoinf. 2017;60:49–60. doi: 10.1016/j.jag.2017.04.004. PubMed DOI PMC
Meigs G.W., Kennedy R.E., Cohen W.B. A Landsat Time Series Approach to Characterize Bark Beetle and Defoliator Impacts on Tree Mortality and Surface Fuels in Conifer Forests. Remote Sens. Environ. 2011;115:3707–3718. doi: 10.1016/j.rse.2011.09.009. DOI
Pajares G. Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs) Photogramm. Eng. Remote Sens. 2015;81:281–330. doi: 10.14358/PERS.81.4.281. DOI
Burgués J., Marco S. Environmental Chemical Sensing Using Small Drones: A Review. Sci. Total Environ. 2020;748:141172. doi: 10.1016/j.scitotenv.2020.141172. PubMed DOI
Hall R.J., Castilla G., White J.C., Cooke B.J., Skakun R.S. Remote Sensing of Forest Pest Damage: A Review and Lessons Learned from a Canadian Perspective. Can. Entomol. 2016;148:S296–S356. doi: 10.4039/tce.2016.11. DOI
Klouček T., Komárek J., Surový P., Hrach K., Janata P., Vašíček B. The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation. Remote Sens. 2019;11:1561. doi: 10.3390/rs11131561. DOI
Huo L., Persson H.J., Lindberg E. Early Detection of Forest Stress from European Spruce Bark Beetle Attack, and a New Vegetation Index: Normalized Distance Red & SWIR (NDRS) Remote Sens. Environ. 2021;255:112240. doi: 10.1016/j.rse.2020.112240. DOI
Smigaj M., Gaulton R., Suárez J.C., Barr S.L. Canopy Temperature from an Unmanned Aerial Vehicle as an Indicator of Tree Stress Associated with Red Band Needle Blight Severity. For. Ecol. Manag. 2019;433:699–708. doi: 10.1016/j.foreco.2018.11.032. DOI
Vošvrdová N., Johansson A., Turčáni M., Jakuš R., Tyšer D., Schlyter F., Modlinger R. Dogs Trained to Recognise a Bark Beetle Pheromone Locate Recently Attacked Spruces Better than Human Experts. For. Ecol. Manag. 2023;528:120626. doi: 10.1016/j.foreco.2022.120626. DOI
Fuentes S., Tongson E., Unnithan R.R., Gonzalez Viejo C. Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling. Sensors. 2021;21:5948. doi: 10.3390/s21175948. PubMed DOI PMC
Marković D., Vujičić D., Tanasković S., Đorđević B., Ranđić S., Stamenković Z. Prediction of Pest Insect Appearance Using Sensors and Machine Learning. Sensors. 2021;21:4846. doi: 10.3390/s21144846. PubMed DOI PMC
Paczkowski S., Datta P., Irion H., Paczkowska M., Habert T., Pelz S., Jaeger D. Evaluation of Early Bark Beetle Infestation Localization by Drone-Based Monoterpene Detection. Forests. 2021;12:228. doi: 10.3390/f12020228. DOI
Kuhlmann G., Henne S., Meijer Y., Brunner D. Quantifying CO2 Emissions of Power Plants With CO2 and NO2 Imaging Satellites. Front. Remote Sens. 2021;2:689838. doi: 10.3389/frsen.2021.689838. DOI
Schlyter F., Birgersson G. Individual Variation in Bark Beetle and Moth Pheromones—A Comparison and an Evolutionary Background. Ecography. 1989;12:457–465. doi: 10.1111/j.1600-0587.1989.tb00923.x. DOI
Ramakrishnan R., Hradecký J., Roy A., Kalinová B., Mendezes R.C., Synek J., Bláha J., Svatoš A., Jirošová A. Metabolomics and Transcriptomics of Pheromone Biosynthesis in an Aggressive Forest Pest Ips Typographus. Insect Biochem. Mol. Biol. 2022;140:103680. doi: 10.1016/j.ibmb.2021.103680. PubMed DOI
Pickett J.A., Wadhams L.J., Woodcock C.M. Developing Sustainable Pest Control from Chemical Ecology. Agric. Ecosyst. Environ. 1997;64:149–156. doi: 10.1016/S0167-8809(97)00033-9. DOI
Martins C.B.C., Zarbin P.H.G. Volatile Organic Compounds of Conspecific-Damaged Eucalyptus Benthamii Influence Responses of Mated Females of Thaumastocoris Peregrinus. J. Chem. Ecol. 2013;39:602–611. doi: 10.1007/s10886-013-0287-y. PubMed DOI
Paré P.W., Tumlinson J.H. Plant Volatiles as a Defense against Insect Herbivores. Plant Physiol. 1999;121:325–332. doi: 10.1104/pp.121.2.325. PubMed DOI PMC
Martins C., Vidal D., Gomes S., Zarbin P. Volatile Organic Compounds (VOCs) Emitted by Ilex Paraguariensis Plants Are Affected by the Herbivory of the Lepidopteran Thelosia Camina and the Coleopteran Hedypathes Betulinus. J. Braz. Chem. Soc. 2017;28:1204–1211. doi: 10.21577/0103-5053.20160279. DOI
Valencia-Ortiz M., Marzougui A., Zhang C., Bali S., Odubiyi S., Sathuvalli V., Bosque-Pérez N.A., Pumphrey M.O., Sankaran S. Biogenic VOCs Emission Profiles Associated with Plant-Pest Interaction for Phenotyping Applications. Sensors. 2022;22:4870. doi: 10.3390/s22134870. PubMed DOI PMC
Jaakkola E., Gärtner A., Jönsson A.M., Ljung K., Olsson P.-O., Holst T. Spruce Bark Beetle (Ips typographus) Infestation Cause up to 700 Times Higher Bark BVOC Emission Rates from Norway Spruce (Picea abies) Biogeosci. Discuss. 2022 doi: 10.5194/bg-2022-125. in review . DOI
Ghimire R.P., Kivimäenpää M., Blomqvist M., Holopainen T., Lyytikäinen-Saarenmaa P., Holopainen J.K. Effect of Bark Beetle (Ips typographus L.) Attack on Bark VOC Emissions of Norway Spruce (Picea abies Karst.) Trees. Atmos. Environ. 2016;126:145–152. doi: 10.1016/j.atmosenv.2015.11.049. DOI
Rahmani R., Hedenström E., Schroeder M. SPME Collection and GC-MS Analysis of Volatiles Emitted during the Attack of Male Polygraphus Poligraphus (Coleoptera, Curcolionidae) on Norway Spruce. Z. Nat. C. 2015;70:265–273. doi: 10.1515/znc-2015-5035. PubMed DOI
Zhou B., Wang J. Use of Electronic Nose Technology for Identifying Rice Infestation by Nilaparvata Lugens. Sens. Actuators B Chem. 2011;160:15–21. doi: 10.1016/j.snb.2011.07.002. DOI
Cellini A., Blasioli S., Biondi E., Bertaccini A., Braschi I., Spinelli F. Potential Applications and Limitations of Electronic Nose Devices for Plant Disease Diagnosis. Sensors. 2017;17:2596. doi: 10.3390/s17112596. PubMed DOI PMC
ÚHÚL: Informace o Lesním Hospodářství. [(accessed on 18 November 2022)]. Available online: https://geoportal.uhul.cz/mapy/mapylhpovyst.html.
Půdní Mapa 1:50,000. [(accessed on 18 November 2022)]. Available online: https://mapy.geology.cz/pudy/#.
Portál ČHMÚ: Historická Data: Počasí: Mapy Charakteristik Klimatu. [(accessed on 18 November 2022)]. Available online: https://www.chmi.cz/historicka-data/pocasi/mapy-charakteristik-klimatu.
Sniffer4D–Mobile Air Poluttant Mapping System–Drone-Based Air Pollutant Mapping System. [(accessed on 18 November 2022)]. Available online: http://sniffer4d.eu/
Ogris N., Ferlan M., Hauptman T., Pavlin R., Kavčič A., Jurc M., de Groot M. RITY–A Phenology Model of Ips Typographus as a Tool for Optimization of Its Monitoring. Ecol. Model. 2019;410:108775. doi: 10.1016/j.ecolmodel.2019.108775. DOI
Wermelinger B. Ecology and Management of the Spruce Bark Beetle Ips Typographus—A Review of Recent Research. For. Ecol. Manag. 2004;202:67–82. doi: 10.1016/j.foreco.2004.07.018. DOI
Abdullah A.H., Sudin S., Mat Ajit M.I., Ahmad Saad F.S., Kamaruddin K., Ghazali F., Ahmad Z.A., Abu Bakar M.A. Development of ESP32-Based Wi-Fi Electronic Nose System for Monitoring LPG Leakage at Gas Cylinder Refurbish Plant; Proceedings of the 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA); Kuching, Malaysia. 15–17 August 2018; pp. 1–5. DOI
Sudama K.A., Rivai M., Aulia D., Mujiono T. Electronic Nose Based on Gas Sensor Array and Neural Network for Indoor Hydrogen Gas Control System; Proceedings of the 2022 1st International Conference on Information System & Information Technology (ICISIT); Yogyakarta, Indonesia. 26–27 July 2022; pp. 187–192. DOI
Arroyo P., Meléndez F., Suárez J.I., Herrero J.L., Rodríguez S., Lozano J. Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements. Sensors. 2020;20:786. doi: 10.3390/s20030786. PubMed DOI PMC
Rahman S., Alwadie A.S., Irfan M., Nawaz R., Raza M., Javed E., Awais M. Wireless E-Nose Sensors to Detect Volatile Organic Gases through Multivariate Analysis. Micromachines. 2020;11:597. doi: 10.3390/mi11060597. PubMed DOI PMC
Hedworth H., Page J., Sohl J., Saad T. Investigating Errors Observed during UAV-Based Vertical Measurements Using Computational Fluid Dynamics. Drones. 2022;6:253. doi: 10.3390/drones6090253. DOI
Wang T., Han W., Zhang M., Yao X., Zhang L., Peng X., Li C., Dan X. Unmanned Aerial Vehicle-Borne Sensor System for Atmosphere-Particulate-Matter Measurements: Design and Experiments. Sensors. 2019;20:57. doi: 10.3390/s20010057. PubMed DOI PMC
Valente J., Almeida R., Kooistra L. A Comprehensive Study of the Potential Application of Flying Ethylene-Sensitive Sensors for Ripeness Detection in Apple Orchards. Sensors. 2019;19:372. doi: 10.3390/s19020372. PubMed DOI PMC