{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:04:55Z","timestamp":1790244295512,"version":"4.1.0"},"reference-count":226,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wildfires are becoming more frequent, severe, and long-lasting, driving rapid growth in sensor-based and artificial intelligence (AI)-enabled systems for early detection and risk assessment. This article presents a systematic literature review, based on 169 studies screened from 7511 records, of wildfire monitoring approaches using satellite and aerial remote sensing, fixed cameras, wireless sensor networks, and Internet of Things (IoT) platforms combined with machine learning (ML) and deep learning (DL) models for ignition detection, fire-weather indices, spread prediction, and burned-area mapping. The review organizes existing work by sensing modality, spatial and temporal scale, learning task, model type, and deployment architecture, and identifies the environmental drivers most commonly used across systems, including temperature, humidity, vegetation state, drought indices, and smoke or air quality. Based on this analysis, the review summarizes key technical challenges, including data sparsity in remote regions, high false-alarm rates, limited edge resources, and difficulty fusing heterogeneous data streams in real time. As an exploratory future direction, the review discusses bioindicator signals from wildlife and managed species, using honeybee colonies as a case example. Current bee bioacoustic studies support the detection of colony states and environmental stress proxies, but they do not yet validate wildfire or smoke detection. Therefore, this review proposes bee bioacoustics only as a potential complementary contextual signal for future hybrid wildfire monitoring systems.<\/jats:p>","DOI":"10.3390\/s26185851","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T18:23:53Z","timestamp":1789496633000},"page":"5851","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Acoustic Bioindicator Integration in AI-Based Wildfire Monitoring: A Systematic Review"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8459-3204","authenticated-orcid":false,"given":"Saba","family":"Mustafa","sequence":"first","affiliation":[{"name":"School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology (AUT), Auckland 1010, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2228-8300","authenticated-orcid":false,"given":"Mahsa","family":"Mohaghegh","sequence":"additional","affiliation":[{"name":"School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology (AUT), Auckland 1010, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9476-9539","authenticated-orcid":false,"given":"Iman","family":"Ardekani","sequence":"additional","affiliation":[{"name":"School of Arts & Sciences, The University of Notre Dame Australia, Sydney, NSW 2008, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0816-589X","authenticated-orcid":false,"given":"Abdolhossein","family":"Sarrafzadeh","sequence":"additional","affiliation":[{"name":"Batten College of Engineering and Technology, Old Dominion University, Norfolk, VA 23529, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"ref_1","unstructured":"Intergovernmental Panel on Climate Change (2025, October 12). Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems. Available online: https:\/\/www.ipcc.ch\/srccl\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.1038\/s41467-022-30030-2","article-title":"Human-ignited fires result in more extreme fire behavior and ecosystem impacts","volume":"13","author":"Hantson","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_3","first-page":"e2119915119","article-title":"Global and Regional Trends and Drivers of Fire Under Climate Change","volume":"119","author":"Jones","year":"2022","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, X., Ma, W., Shen, J., Long, D., Feng, Y., Su, W., Xu, K., Du, Y., and Jiang, Y. (2019). Tolerance and response of two honeybee species Apis cerana and Apis mellifera to high temperature and relative humidity. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0217921"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s12061-018-9269-3","article-title":"Geospatial patterns and drivers of forest fire occurrence in Romania","volume":"12","author":"Mallinis","year":"2019","journal-title":"Appl. Spat. Anal. Policy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"157214","DOI":"10.1016\/j.scitotenv.2022.157214","article-title":"The impacts of wildfires of different burn severities on vegetation structure across the western United States rangelands","volume":"845","author":"Li","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"33","DOI":"10.5194\/isprs-archives-XLVI-4-W5-2021-33-2021","article-title":"Developing validation of forest fire risk maps based on historical fire incidences","volume":"Volume 46","author":"Akay","year":"2021","journal-title":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: XXIV ISPRS Congress Imaging Today, Foreseeing Tomorrow, Commission IV"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"27","DOI":"10.5194\/isprs-archives-XLVI-4-W5-2021-27-2021","article-title":"Assessment of the visibility capabilities of forest fire lookout towers: The case of Gemlik, Bursa, Turkey","volume":"Volume 46","author":"Akay","year":"2021","journal-title":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: XXIV ISPRS Congress Imaging Today, Foreseeing Tomorrow, Commission IV"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"\u00c7oban, H., and Bereket, H. (2020). Visibility analysis of fire lookout towers protecting the Mediterranean forest ecosystems in Turkey. \u0160umar. List, 144.","DOI":"10.31298\/sl.144.7-8.5"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9819","DOI":"10.1109\/ACCESS.2026.3654863","article-title":"Survey of AI-enabled IoT for wildfire prediction, detection, and post-operations","volume":"14","author":"Badary","year":"2026","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mohapatra, A., and Trinh, T. (2022). Early wildfire detection technologies in practice: A review. Sustainability, 14.","DOI":"10.3390\/su141912270"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cattau, M.E., Mahood, A.L., Balch, J.K., and Wessman, C.A. (2022). Modern pyromes: Biogeographical patterns of fire characteristics across the contiguous United States. Fire, 5.","DOI":"10.3390\/fire5040095"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., and Grammalidis, N. (2020). A review on early forest fire detection systems using optical remote sensing. Sensors, 20.","DOI":"10.3390\/s20226442"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1080\/17538947.2018.1433727","article-title":"Spatial and temporal intercomparison of four global burned area products","volume":"12","author":"Humber","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Afghah, F., Razi, A., Chakareski, J., and Ashdown, J. (May, January 29). Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles. Proceedings of the IEEE INFOCOM 2019\u2014IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, France.","DOI":"10.1109\/INFCOMW.2019.8845309"},{"key":"ref_16","first-page":"496","article-title":"Early forest fire detection system using wireless sensor network and deep learning","volume":"11","author":"Benzekri","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fitriany, A.A., Flatau, P.J., Khoirunurrofik, K., and Riama, N.F. (2021). Assessment on the use of meteorological and social media information for forest fire detection and prediction in Riau, Indonesia. Sustainability, 13.","DOI":"10.20944\/preprints202108.0438.v1"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Huang, H.T., Downey, A.R., and Bakos, J.D. (2022). Audio-based wildfire detection on embedded systems. Electronics, 11.","DOI":"10.3390\/electronics11091417"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102963","DOI":"10.1016\/j.ecoinf.2024.102963","article-title":"Enhancing prediction of wildfire occurrence and behavior in Alaska using spatio-temporal clustering and ensemble machine learning","volume":"85","author":"Ahajjam","year":"2025","journal-title":"Ecol. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hu, X., Ban, Y., and Nascetti, A. (2021). Uni-temporal multispectral imagery for burned area mapping with deep learning. Remote Sens., 13.","DOI":"10.3390\/rs13081509"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ali, A.W., and Kurnaz, S. (2025). Optimizing deep learning models for fire detection, classification, and segmentation using satellite images. Fire, 8.","DOI":"10.3390\/fire8020036"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, J., Zhang, Q., and Zheng, Y. (2024, January 7\u201312). Forest Early Wildfire Detection via Multi-Source and Multi-Type Information Fusion. Proceedings of the IGARSS 2024\u20142024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece.","DOI":"10.1109\/IGARSS53475.2024.10641537"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s11760-023-02728-3","article-title":"Wildfire detection via transfer learning: A survey","volume":"18","author":"Hong","year":"2024","journal-title":"Signal Image Video Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e23127","DOI":"10.1016\/j.heliyon.2023.e23127","article-title":"Forest fire surveillance systems: A review of deep learning methods","volume":"10","author":"Saleh","year":"2024","journal-title":"Heliyon"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zaman, M., Upadhyay, D., Purcell, R., Mutakabbir, A., Sampalli, S., Lung, C.H., and Naik, K. (2025). A systematic machine learning methodology for enhancing accuracy and reducing computational complexity in forest fire detection. Fire, 8.","DOI":"10.2139\/ssrn.5219813"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Di Martino, T., Le Saux, B., Guinvarc\u2019h, R., Thirion-Lefevre, L., and Colin, E. (2023). Detection of forest fires through deep unsupervised learning modeling of Sentinel-1 time series. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12080332"},{"key":"ref_27","first-page":"1062","article-title":"Early wildfire detection using machine learning model deployed in the fog\/edge layers of IoT","volume":"27","author":"Grari","year":"2022","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"\u00dcremek, \u0130., Leahy, P., and Popovici, E. (2024). A system for efficient detection of forest fires through low power environmental data monitoring and AI. Eng. Proc., 68.","DOI":"10.3390\/engproc2024068038"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"48185","DOI":"10.1109\/ACCESS.2021.3060549","article-title":"Intelligent framework using IoT-based WSNs for wildfire detection","volume":"9","author":"Verma","year":"2021","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"147953","DOI":"10.1109\/ACCESS.2025.3599330","article-title":"Leveraging machine learning approaches to decode hive sounds for stress prediction","volume":"13","author":"Mustafa","year":"2025","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"115","DOI":"10.4996\/fireecology.0601115","article-title":"First-order fire effects on animals: Review and recommendations","volume":"6","author":"Engstrom","year":"2010","journal-title":"Fire Ecol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10682-018-9927-6","article-title":"Towards an understanding of the evolutionary role of fire in animals","volume":"32","author":"Pausas","year":"2018","journal-title":"Evol. Ecol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"123003","DOI":"10.1088\/1748-9326\/ac30f6","article-title":"A review of the effects of wildfire smoke on the health and behavior of wildlife","volume":"16","author":"Sanderfoot","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s42408-021-00113-4","article-title":"From flames to inflammation: How wildfires affect patterns of wildlife disease","volume":"17","author":"Albery","year":"2021","journal-title":"Fire Ecol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0269-7491(00)00038-5","article-title":"What do sentinels stand for?","volume":"112","author":"Beeby","year":"2001","journal-title":"Environ. Pollut."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chowdhury, S., Dubey, V.K., Choudhury, S., Das, A., Jeengar, D., Sujatha, B., Kumar, A., Kumar, N., Semwal, A., and Kumar, V. (2023). Insects as bioindicator: A hidden gem for environmental monitoring. Front. Environ. Sci., 11.","DOI":"10.3389\/fenvs.2023.1146052"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"20230111","DOI":"10.1098\/rstb.2023.0111","article-title":"Towards acoustic monitoring of bees: Wingbeat sounds are related to species and individual traits","volume":"379","author":"Hevia","year":"2024","journal-title":"Philos. Trans. R. Soc. B"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s11829-026-10253-6","article-title":"How do bees respond to fire? An assessment of functional traits and behavioral strategies","volume":"20","year":"2026","journal-title":"Arthropod-Plant Interact."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Allison, R.S., Johnston, J.M., Craig, G., and Jennings, S. (2016). Airborne optical and thermal remote sensing for wildfire detection and monitoring. Sensors, 16.","DOI":"10.3390\/s16081310"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s11676-016-0361-8","article-title":"The progress of operational forest fire monitoring with infrared remote sensing","volume":"28","author":"Hua","year":"2017","journal-title":"J. For. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9777","DOI":"10.1007\/s11069-025-07163-w","article-title":"Active wildfire detection via satellite imagery and machine learning: An empirical investigation of Australian wildfires","volume":"121","author":"Singh","year":"2025","journal-title":"Nat. Hazards"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Thangavel, K., Spiller, D., Sabatini, R., Amici, S., Sasidharan, S.T., Fayek, H., and Marzocca, P. (2023). Autonomous satellite wildfire detection using hyperspectral imagery and neural networks: A case study on Australian wildfire. Remote Sens., 15.","DOI":"10.3390\/rs15030720"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wang, H., Bi, Y., Nie, G., and Wang, X. (2025). MCDet: Target-aware fusion for RGB-T fire detection. Forests, 16.","DOI":"10.3390\/f16071088"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5408213","DOI":"10.1109\/TGRS.2024.3434434","article-title":"Wildfire detection based on the spatiotemporal and spectral features of Himawari-8 data","volume":"62","author":"Zheng","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"172785","DOI":"10.1109\/ACCESS.2024.3501336","article-title":"A survey on IoT ground sensing systems for early wildfire detection: Technologies, challenges, and opportunities","volume":"12","author":"Chan","year":"2024","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kaur, P., Kaur, K., Singh, K., and Kim, S. (2023). Early forest fire detection using a protocol for energy-efficient clustering with weighted-based optimization in wireless sensor networks. Appl. Sci., 13.","DOI":"10.3390\/app13053048"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zope, V., Dadlani, T., Matai, A., Tembhurnikar, P., and Kalani, R. (2020, January 13\u201315). IoT Sensor and Deep Neural Network based Wildfire Prediction System. Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS48265.2020.9120949"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Peruzzi, G., Pozzebon, A., and Van Der Meer, M. (2023). Fight fire with fire: Detecting forest fires with embedded machine learning models dealing with audio and images on low power IoT devices. Sensors, 23.","DOI":"10.3390\/s23020783"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Taktak, H., Boukadi, K., Gu\u00e9gan, C.G., Mrissa, M., and Gargouri, F. (2021). Towards knowledge-driven automatic service composition for wildfire prediction. Service-Oriented Computing\u2014ICSOC 2020 Workshops, Springer.","DOI":"10.1007\/978-3-030-76352-7_38"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Akbari Asanjan, A., Memarzadeh, M., Lott, P.A., Rieffel, E., and Grabbe, S. (2023). Probabilistic wildfire segmentation using supervised deep generative model from satellite imagery. Remote Sens., 15.","DOI":"10.3390\/rs15112718"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"46824","DOI":"10.1109\/ACCESS.2025.3528983","article-title":"Study on improving detection performance of wildfire and non-fire events early using Swin Transformer","volume":"13","author":"Choi","year":"2025","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ghali, R., Akhloufi, M.A., and Mseddi, W.S. (2022). Deep learning and transformer approaches for UAV-based wildfire detection and segmentation. Sensors, 22.","DOI":"10.3390\/s22051977"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hu, Z., Shi, S., Ye, Y., He, W., Yang, Z., Li, X., and Feng, S. (2024, January 1\u20133). Edge computing-based wildfire detection and optimization algorithm. Proceedings of the 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE), Shanghai, China.","DOI":"10.1109\/ICAACE61206.2024.10548259"},{"key":"ref_54","first-page":"102799","article-title":"Classifying surface fuel types based on forest stand photographs and satellite time series using deep learning","volume":"109","author":"Labenski","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mukhiddinov, M., Abdusalomov, A.B., and Cho, J. (2022). A wildfire smoke detection system using unmanned aerial vehicle images based on the optimized YOLOv5. Sensors, 22.","DOI":"10.3390\/s22239384"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1007\/s10694-019-00872-2","article-title":"Dual deep learning model for image based smoke detection","volume":"55","author":"Pundir","year":"2019","journal-title":"Fire Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"7001","DOI":"10.1109\/JSTARS.2021.3093625","article-title":"Wildfire detection from multisensor satellite imagery using deep semantic segmentation","volume":"14","author":"Rashkovetsky","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sykas, D., Zografakis, D., and Demestichas, K. (2024). Deep learning approaches for wildfire severity prediction: A comparative study of image segmentation networks and visual transformers on the EO4WildFires dataset. Fire, 7.","DOI":"10.20944\/preprints202409.0702.v1"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"10858","DOI":"10.1109\/ACCESS.2021.3050628","article-title":"ATT Squeeze U-Net: A lightweight network for forest fire detection and recognition","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"117908","DOI":"10.1016\/j.jenvman.2023.117908","article-title":"A multi-modal wildfire prediction and early-warning system based on a novel machine learning framework","volume":"341","author":"Bhowmik","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"102369","DOI":"10.1016\/j.inffus.2024.102369","article-title":"A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management","volume":"108","author":"Boroujeni","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_62","first-page":"1","article-title":"Coupling physical model and deep learning for near real-time wildfire detection","volume":"20","author":"Ji","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lever, J., Cheng, S., and Arcucci, R. (2023, January 3\u20135). Human-sensors and physics-aware machine learning for wildfire detection and nowcasting. Proceedings of the Computational Science\u2014ICCS 2023, Prague, Czech Republic.","DOI":"10.1007\/978-3-031-36027-5_33"},{"key":"ref_64","first-page":"569","article-title":"Optimized IoT-based multimodal fusion for early forest fire detection and prediction","volume":"19","author":"Muhaimin","year":"2025","journal-title":"ECTI Trans. Comput. Inf. Technol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4409516","DOI":"10.1109\/TGRS.2024.3418475","article-title":"Enhancing satellite-based wildfire monitoring: Advanced contextual model using environmental and structural information","volume":"62","author":"Sung","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/JSAIT.2023.3276054","article-title":"Spatio-temporal wildfire prediction using multi-modal data","volume":"4","author":"Xu","year":"2023","journal-title":"IEEE J. Sel. Areas Inf. Theory"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"9705","DOI":"10.1007\/s00371-025-03994-7","article-title":"Lightweight segmentation network for real-time wildfire detection: LSNet\u2019s parallel feature multiplication and attentional fusion","volume":"41","author":"Zhang","year":"2025","journal-title":"Vis. Comput."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"7889","DOI":"10.1109\/TII.2021.3138752","article-title":"EdgeFireSmoke: A novel lightweight CNN model for real-time video fire\u2013smoke detection","volume":"18","author":"Almeida","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1109\/JSYST.2019.2923635","article-title":"Energy-efficient IoT-fog-cloud architectural paradigm for real-time wildfire prediction and forecasting","volume":"14","author":"Kaur","year":"2020","journal-title":"IEEE Syst. J."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"26733","DOI":"10.1007\/s11042-023-16647-5","article-title":"Wireless sensor network assisted automated forest fire detection using deep learning and computer vision model","volume":"83","author":"Paidipati","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Supriya, Y., and Gadekallu, T.R. (2023). Particle swarm-based federated learning approach for early detection of forest fires. Sustainability, 15.","DOI":"10.3390\/su15020964"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1080\/10095020.2024.2347922","article-title":"An open flame and smoke detection dataset for deep learning in remote sensing based fire detection","volume":"28","author":"Wang","year":"2024","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"R996","DOI":"10.1016\/j.cub.2023.08.096","article-title":"Acorn Woodpecker movements and social networks change with wildfire smoke","volume":"33","author":"Johnson","year":"2023","journal-title":"Curr. Biol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s00040-017-0573-8","article-title":"A review of impacts of temperature and relative humidity on various activities of honey bees","volume":"64","author":"Owayss","year":"2017","journal-title":"Insectes Soc."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1002\/ece3.7145","article-title":"Foraging trip duration of honeybee increases during a poor air quality episode and the increase persists thereafter","volume":"11","author":"Cho","year":"2021","journal-title":"Ecol. Evol."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Bencsik, M., McVeigh, A., Tsakonas, C., Kumar, T., Chamberlain, L., and Newton, M.I. (2023). A monitoring system for carbon dioxide in honeybee hives: An indicator of colony health. Sensors, 23.","DOI":"10.3390\/s23073588"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"245","DOI":"10.11118\/actaun201462010245","article-title":"The acoustic emission in the nest of the honey bee depending on the extreme weather conditions","volume":"62","year":"2014","journal-title":"Acta Univ. Agric. Silvic. Mendel. Brun."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Kanelis, D., Liolios, V., Papadopoulou, F., Rodopoulou, M.A., Kampelopoulos, D., Siozios, K., and Tananaki, C. (2023). Decoding the behavior of a queenless colony using sound signals. Biology, 12.","DOI":"10.3390\/biology12111392"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"6882","DOI":"10.1080\/01431161.2023.2277160","article-title":"The unusual stubble burning season of 2020 in northern India: A satellite perspective","volume":"44","author":"Gupta","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1134\/S0010952520040085","article-title":"Choosing the structure of satellite systems for meteorology and forest fire detection on the basis of a vector model of surveying the earth","volume":"58","year":"2020","journal-title":"Cosm. Res."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1080\/2150704X.2017.1350303","article-title":"Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8","volume":"8","author":"Xu","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1080\/15481603.2022.2143872","article-title":"A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency","volume":"59","author":"Kang","year":"2022","journal-title":"GISci. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Zhang, P., Hu, X., Ban, Y., Nascetti, A., and Gong, M. (2024). Assessing Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data for large-scale wildfire-burned area mapping: Insights from the 2017\u20132019 Canada wildfires. Remote Sens., 16.","DOI":"10.3390\/rs16030556"},{"key":"ref_85","first-page":"101513","article-title":"DLSR-FireCNet: A deep learning framework for burned area mapping based on decision level super-resolution","volume":"37","author":"Seydi","year":"2025","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"10827","DOI":"10.1109\/JSTARS.2024.3403146","article-title":"A deep learning framework: Predicting fire radiative power from the combination of polar-orbiting and geostationary satellite data during wildfire spread","volume":"17","author":"Dong","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Thornberry, T.D., Gao, R.S., Ciciora, S.J., Watts, L.A., McLaughlin, R.J., Leonardi, A., and Zucker, M. (2023). A lightweight remote sensing payload for wildfire detection and fire radiative power measurements. Sensors, 23.","DOI":"10.3390\/s23073514"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"5606121","DOI":"10.1109\/TGRS.2025.3528641","article-title":"Wildfire detection and mapping by satellite with an enhanced configuration of the normalized hotspot indices: Results from Sentinel-2 and Landsat 8\/9 data integration","volume":"63","author":"Mazzeo","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Maeda, N., and Tonooka, H. (2022). Early stage forest fire detection from Himawari-8 AHI images using a modified MOD14 algorithm combined with machine learning. Sensors, 23.","DOI":"10.3390\/s23010210"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"James, G.L., Ansaf, R.B., Al Samahi, S.S., Parker, R.D., Cutler, J.M., Gachette, R.V., and Ansaf, B.I. (2023). An efficient wildfire detection system for AI-embedded applications using satellite imagery. Fire, 6.","DOI":"10.3390\/fire6040169"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Hossain, F.A., Zhang, Y., and Yuan, C. (2019, January 29\u201331). A Survey on Forest Fire Monitoring Using Unmanned Aerial Vehicles. Proceedings of the 2019 3rd International Symposium on Autonomous Systems (ISAS), Shanghai, China.","DOI":"10.1109\/ISASS.2019.8757707"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Partheepan, S., Sanati, F., and Hassan, J. (2023). Autonomous unmanned aerial vehicles in bushfire management: Challenges and opportunities. Drones, 7.","DOI":"10.3390\/drones7010047"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Saydirasulovich, S.N., Mukhiddinov, M., Djuraev, O., Abdusalomov, A., and Cho, Y.I. (2023). An improved wildfire smoke detection based on YOLOv8 and UAV images. Sensors, 23.","DOI":"10.3390\/s23208374"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"108977","DOI":"10.1016\/j.engappai.2024.108977","article-title":"Wildfire and smoke early detection for drone applications: A light-weight deep learning approach","volume":"136","author":"Kumar","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"14516","DOI":"10.1109\/ACCESS.2025.3526974","article-title":"FCDNet: A lightweight network for real-time wildfire core detection in drone thermal imaging","volume":"13","author":"Wang","year":"2025","journal-title":"IEEE Access"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Yun, B., Xu, X., Zeng, J., Lin, Z., He, J., and Dai, Q. (2025). An improved unmanned aerial vehicle forest fire detection model based on YOLOv8. Fire, 8.","DOI":"10.3390\/fire8040138"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"6617","DOI":"10.1109\/JSTARS.2025.3541205","article-title":"Early forest fire detection with UAV image fusion: A novel deep learning method using visible and infrared sensors","volume":"18","author":"Niu","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"16695","DOI":"10.1109\/TIE.2024.3387089","article-title":"Early wildfire detection and distance estimation using aerial visible-infrared images","volume":"71","author":"Qiao","year":"2024","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.11591\/eei.v13i4.7663","article-title":"Semantic segmentation and thermal imaging for forest fires detection and monitoring by drones","volume":"13","author":"Yandouzi","year":"2024","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"16984","DOI":"10.1109\/JIOT.2021.3077593","article-title":"The role of UAV-IoT networks in future wildfire detection","volume":"8","author":"Bushnaq","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/s42979-022-01160-7","article-title":"IDrone: IoT-enabled unmanned aerial vehicles for detecting wildfires using convolutional neural networks","volume":"3","author":"Sethuraman","year":"2022","journal-title":"SN Comput. Sci."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"e70528","DOI":"10.1002\/cpe.70528","article-title":"Enhancing wildfire preparedness and response: A drone network-based early warning system for bushfires","volume":"38","author":"Reshan","year":"2026","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Demir, K., Tumen, V., Kosunalp, S., and Iliev, T. (2024). A deep reinforcement learning algorithm for trajectory planning of swarm UAV fulfilling wildfire reconnaissance. Electronics, 13.","DOI":"10.3390\/electronics13132568"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Yan, X., and Chen, R. (2024). Application strategy of unmanned aerial vehicle swarms in forest fire detection based on the fusion of particle swarm optimization and artificial bee colony algorithm. Appl. Sci., 14.","DOI":"10.3390\/app14114937"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Kritikou, G., Xofis, P., Souflas, K., and Moulianitis, V. (2024). Path planning of multiple UAVs for the early detection of wildfires in the National Park of Kotychi and Strofylia Wetlands. Fire, 7.","DOI":"10.3390\/fire7120444"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Al-Kaff, A., Madridano, \u00c1., Campos, S., Garc\u00eda, F., Mart\u00edn, D., and de la Escalera, A. (2020). Emergency support unmanned aerial vehicle for forest fire surveillance. Electronics, 9.","DOI":"10.3390\/electronics9020260"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"10006","DOI":"10.35534\/dav.2024.10006","article-title":"An architecture for early wildfire detection and spread estimation using unmanned aerial vehicles, base stations, and space assets","volume":"1","author":"Meimetis","year":"2024","journal-title":"Drones Auton. Veh."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Sherstjuk, V., Zharikova, M., and Sokol, I. (2018, January 21\u201325). Forest Fire Monitoring System Based on UAV Team, Remote Sensing, and Image Processing. Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine.","DOI":"10.1109\/DSMP.2018.8478590"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"139865","DOI":"10.1109\/ACCESS.2024.3462436","article-title":"Drone-based AI system for wildfire monitoring and risk prediction","volume":"12","author":"Lelis","year":"2024","journal-title":"IEEE Access"},{"key":"ref_110","first-page":"146","article-title":"Safeguarding forest ecosystems: Harnessing IoT for fire detection","volume":"3","author":"Atighi","year":"2023","journal-title":"Big Data Comput. Vis."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"33579","DOI":"10.1109\/ACCESS.2025.3543570","article-title":"A comprehensive survey of Fire Weather Index (FWI) systems and IoT applications in peatland fire management","volume":"13","author":"Li","year":"2025","journal-title":"IEEE Access"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Babu, N.J., Praveena, M., Nagaraju, B., Begum, D.R., Surekha, P., and Vivek, K. (2024, January 12\u201313). Advancements in forest fire prediction: Techniques and technologies. Proceedings of the 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), Tamil Nadu, India.","DOI":"10.1109\/ICUIS64676.2024.10867242"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1","DOI":"10.35940\/ijeat.E1001.0886S19","article-title":"Novel protocol for detection and optimization of overlapping coverage in wireless sensor networks","volume":"8","author":"Narayan","year":"2019","journal-title":"Int. J. Eng. Adv. Technol."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Pedditi, R.B., and Debasis, K. (2023). Energy efficient routing protocol for an IoT-based WSN system to detect forest fires. Appl. Sci., 13.","DOI":"10.3390\/app13053026"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"392","DOI":"10.11591\/ijres.v12.i3.pp392-402","article-title":"An effective gossip routing based wireless sensor network framework for forest fire detection","volume":"12","author":"Alagarsamy","year":"2023","journal-title":"Int. J. Reconfigurable Embed. Syst."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"253","DOI":"10.17762\/ijritcc.v11i7.7919","article-title":"Energy optimization efficiency in wireless sensor networks for forest fire detection: An innovative sleep technique","volume":"11","author":"Ibraheem","year":"2023","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Blalack, T., Ellis, D., Long, M., Brown, C., Kemp, R., and Khan, M. (2019, January 11\u201314). Low-power distributed sensor network for wildfire detection. Proceedings of the 2019 SoutheastCon, Huntsville, AL, USA.","DOI":"10.1109\/SoutheastCon42311.2019.9020478"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10559-025-00753-3","article-title":"Assessment of the reliability of wireless sensor networks for forest fire monitoring systems considering fatal combinations of multiple sensor failures","volume":"61","author":"Leichenko","year":"2025","journal-title":"Cybern. Syst. Anal."},{"key":"ref_119","first-page":"4001","article-title":"Forest fire detection using optimized solar powered Zigbee wireless sensor networks","volume":"11","author":"Raikar","year":"2024","journal-title":"Int. J. Sci. Eng. Res."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Tehseen, A., Zafar, N.A., Ali, T., Jameel, F., and Alkhammash, E.H. (2021). Formal modeling of IoT and drone-based forest fire detection and counteraction system. Electronics, 11.","DOI":"10.3390\/electronics11010128"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s41976-024-00139-7","article-title":"A logical remote sensing based disaster management and alert system using AI-assisted Internet of Things technology","volume":"7","author":"Nagaiah","year":"2024","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Putzu, M., Loru, D., Carta, F., Ledda, A., Chirigu, A., Sole, M., and Giusto, D. (2024). Enhancing wildfire risk management through sensor-based AI integration in social IoT frameworks. Eng. Proc., 78.","DOI":"10.3390\/engproc2024078004"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"5381","DOI":"10.1007\/s00477-025-03070-5","article-title":"Early prediction of forest fire events integrating multi-sensor datasets and machine learning techniques","volume":"39","author":"Bera","year":"2025","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Bowman, D.M. (2023). Detecting, monitoring and foreseeing wildland fire requires similar multiscale viewpoints as meteorology and climatology. Fire, 6.","DOI":"10.3390\/fire6040160"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.11591\/eei.v14i2.8613","article-title":"A guide for selection of wireless communication technology for effective and robust early forest fire detection system","volume":"14","author":"Salaria","year":"2025","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s43247-024-01228-7","article-title":"Enhanced future vegetation growth with elevated carbon dioxide concentrations could increase fire activity","volume":"5","author":"Allen","year":"2024","journal-title":"Commun. Earth Environ."},{"key":"ref_127","first-page":"56","article-title":"Climate-vegetation intersection in determining the burn rate in an area of the Brazilian Cerrado","volume":"18","author":"Juvanhol","year":"2024","journal-title":"Earth Sci. Inform."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"012121","DOI":"10.1088\/1755-1315\/979\/1\/012121","article-title":"A relationship between weather conditions and a number of forest fires","volume":"979","author":"Ivchenko","year":"2022","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"5211","DOI":"10.1111\/gcb.16283","article-title":"The fuel-climate-fire conundrum: How will fire regimes change in temperate eucalypt forests under climate change?","volume":"28","author":"Bennett","year":"2022","journal-title":"Glob. Change Biol."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Muthukumar, P., Nagrecha, K., Comer, D., Calvert, C.F., Amini, N., Holm, J., and Pourhomayoun, M. (2022). PM2.5 air pollution prediction through deep learning using multisource meteorological, wildfire, and heat data. Atmosphere, 13.","DOI":"10.3390\/atmos13050822"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"105005","DOI":"10.1088\/2515-7620\/ac2e6f","article-title":"Highly anomalous fire emissions from the 2019\u20132020 Australian bushfires","volume":"3","author":"Li","year":"2021","journal-title":"Environ. Res. Commun."},{"key":"ref_132","first-page":"1","article-title":"Simulating long-term wildfire impacts on boreal forest structure in Central Yakutia, Siberia, since the Last Glacial Maximum","volume":"20","author":"Gloy","year":"2024","journal-title":"Fire Ecol."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Stanek, J.E., Gadek, C.R., Milligan, S.E., Thompson, B.E., Tranquillo, K.A., Trader, L.L., and Fettig, S.M. (2025). Fire and drought affect multiple aspects of diversity in a migratory bird stopover community. Biology, 14.","DOI":"10.3390\/biology14060597"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1080\/01490451.2025.2460516","article-title":"Metagenome analysis of forest fire soil to understand the impact of fire on the diversity of microorganisms","volume":"42","author":"Bhatt","year":"2025","journal-title":"Geomicrobiol. J."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, J., Zhang, L., Liu, H., and Zhang, F. (2024). CNTCB-YOLOv7: An effective forest fire detection model based on ConvNeXtV2 and CBAM. Fire, 7.","DOI":"10.3390\/fire7020054"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Mutanu, L., Gohil, J., Gupta, K., Wagio, P., and Kotonya, G. (2022). A review of automated bioacoustics and general acoustics classification research. Sensors, 22.","DOI":"10.3390\/s22218361"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1038\/s41598-021-03882-9","article-title":"Forest fire detection system using wireless sensor networks and machine learning","volume":"12","author":"Dampage","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Bhamra, J.K., Anantha Ramaprasad, S., Baldota, S., Luna, S., Zen, E., Ramachandra, R., Kim, H., Schmidt, C., Arends, C., and Block, J. (2023). Multimodal wildland fire smoke detection. Remote Sens., 15.","DOI":"10.3390\/rs15112790"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1139\/er-2020-0019","article-title":"A review of machine learning applications in wildfire science and management","volume":"28","author":"Jain","year":"2020","journal-title":"Environ. Rev."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Khan, S., and Khan, A. (2022). FFireNet: Deep learning based forest fire classification and detection in smart cities. Symmetry, 14.","DOI":"10.3390\/sym14102155"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Fernandes, A., Utkin, A., and Chaves, P. (2024). Enhanced automatic wildfire detection system using big data and EfficientNets. Fire, 7.","DOI":"10.3390\/fire7080286"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Zhan, J., Hu, Y., Cai, W., Zhou, G., and Li, L. (2021). PDAM\u2013STPNNet: A small target detection approach for wildland fire smoke through remote sensing images. Symmetry, 13.","DOI":"10.3390\/sym13122260"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zhi, L., Zhao, C., and Zheng, W. (2022). Fire-YOLO: A small target object detection method for fire inspection. Sustainability, 14.","DOI":"10.3390\/su14094930"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Guan, Z., Min, F., He, W., Fang, W., and Lu, T. (2022). Forest fire detection via feature entropy guided neural network. Entropy, 24.","DOI":"10.3390\/e24010128"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Shi, Z., Wu, F., Han, C., Song, D., and Wu, Y. (2025). Detection model for cotton picker fire recognition based on lightweight improved YOLOv11. Agriculture, 15.","DOI":"10.3390\/agriculture15151608"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s12145-025-01863-4","article-title":"Enhanced wildfire detection and semantic segmentation via fine-tuned deep learning models","volume":"18","author":"Quang","year":"2025","journal-title":"Earth Sci. Inform."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Farguell, A., Mandel, J., Haley, J., Mallia, D.V., Kochanski, A., and Hilburn, K. (2021). Machine learning estimation of fire arrival time from level-2 active fires satellite data. Remote Sens., 13.","DOI":"10.3390\/rs13112203"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"012092","DOI":"10.1088\/1757-899X\/1047\/1\/012092","article-title":"Forecasting steppe fires using remote sensing data of time series","volume":"1047","author":"Goryaev","year":"2021","journal-title":"Iop Conf. Ser. Mater. Sci. Eng."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/TETCI.2024.3445450","article-title":"Deep learning surrogate models of JULES-INFERNO for wildfire prediction on a global scale","volume":"9","author":"Cheng","year":"2025","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1007\/s12145-024-01652-5","article-title":"Daily wildfire risk prediction by mining global and local spatio-temporal dependency","volume":"18","author":"Deng","year":"2025","journal-title":"Earth Sci. Inform."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Liu, X., Wang, L., Li, J., Mahmud, K.R., and Pang, S. (2025). Enhancing wildfire detection via trend estimation under auto-regression errors. Mathematics, 13.","DOI":"10.3390\/math13071046"},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Ide, R., and Yang, L. (2025). Adversarial robustness for deep learning-based wildfire prediction models. Fire, 8.","DOI":"10.20944\/preprints202501.0279.v1"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Davis, M., and Shekaramiz, M. (2023). Desert\/forest fire detection using machine\/deep learning techniques. Fire, 6.","DOI":"10.3390\/fire6110418"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Mahajan, R., and Priyadarshini, R. (2023, January 22\u201323). Integrated wireless sensor network and machine learning algorithm based forest fire detection system. Proceedings of the 2023 12th International Conference on System Modeling & Advancement in Research Trends, Moradabad, India.","DOI":"10.1109\/SMART59791.2023.10428403"},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Almarri, S., Al Safwan, H., Al Qisoom, S., Gdaim, S., and Zitouni, A. (2025). Optimized wireless sensor network architecture for AI-based wildfire detection in remote areas. Fire, 8.","DOI":"10.20944\/preprints202505.1661.v1"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Wei, C., Xu, J., Li, Q., and Jiang, S. (2022). An intelligent wildfire detection approach through cameras based on deep learning. Sustainability, 14.","DOI":"10.3390\/su142315690"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Harjoko, A., Dharmawan, A., Adhinata, F.D., Kosala, G., and Jo, K.H. (2022). Real-time forest fire detection framework based on artificial intelligence using color probability model and motion feature analysis. Fire, 5.","DOI":"10.3390\/fire5010023"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"83427","DOI":"10.1007\/s11042-024-18685-z","article-title":"Computer vision for wildfire detection: A critical brief review","volume":"83","author":"Ramos","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s40808-025-02569-8","article-title":"Enhancing forest fires classification using a hybrid convolutional and BiLSTM deep learning model","volume":"11","author":"Elshewey","year":"2025","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s44267-024-00053-y","article-title":"Efficient forest fire detection based on an improved YOLO model","volume":"2","author":"Cao","year":"2024","journal-title":"Vis. Intell."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Yuan, W., Qiao, L., and Tang, L. (2024). Forest wildfire detection from images captured by drones using window transformer without shift. Forests, 15.","DOI":"10.3390\/f15081337"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Toan, N.T., Thanh Cong, P., Viet Hung, N.Q., and Jo, J. (2019, January 1\u20133). A deep learning approach for early wildfire detection from hyperspectral satellite images. Proceedings of the 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA), Daejeon, Republic of Korea.","DOI":"10.1109\/RITAPP.2019.8932740"},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Pan, H., Badawi, D., and Cetin, A.E. (2020). Computationally efficient wildfire detection method using a deep convolutional network pruned via Fourier analysis. Sensors, 20.","DOI":"10.3390\/s20102891"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Yun, B., Zheng, Y., Lin, Z., and Li, T. (2024). FFYOLO: A lightweight forest fire detection model based on YOLOv8. Fire, 7.","DOI":"10.3390\/fire7030093"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"96554","DOI":"10.1109\/ACCESS.2023.3312217","article-title":"Assessing the effectiveness of YOLO architectures for smoke and wildfire detection","volume":"11","author":"Casas","year":"2023","journal-title":"IEEE Access"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Chopde, A., Magon, A., and Bhatkar, S. (2022, January 10\u201312). Forest fire detection and prediction from image processing using RCNN. Proceedings of the World Congress on Civil, Structural, and Environmental Engineering, Lisbon, Portugal.","DOI":"10.11159\/iceptp22.204"},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Muksimova, S., Umirzakova, S., Mardieva, S., Abdullaev, M., and Cho, Y.I. (2024). Revolutionizing wildfire detection through UAV-driven fire monitoring with a transformer-based approach. Fire, 7.","DOI":"10.3390\/fire7120443"},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Guo, B., Liu, D., Shen, Z., and Wang, T. (2026). FF-Mamba-YOLO: An SSM-based benchmark for forest fire detection in UAV remote sensing images. J. Imaging, 12.","DOI":"10.3390\/jimaging12010043"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.combustflame.2020.02.007","article-title":"Learning-based prediction of wildfire spread with real-time rate of spread measurement","volume":"215","author":"Zhai","year":"2020","journal-title":"Combust. Flame"},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Gholami, S., Kodandapani, N., Wang, J., and Ferres, J.L. (2021, January 2\u20139). Where there\u2019s smoke, there\u2019s fire: Wildfire risk predictive modeling via historical climate data. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual Event.","DOI":"10.1609\/aaai.v35i17.17797"},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Venkateswaran, S., Keng, V., Vaidya, H., Kondar, D., Sawant, S., and Joshi, A. (2024, January 6\u20137). Wildfire area prediction using federated learning. Proceedings of the 2024 Asian Conference on Intelligent Technologies (ACOIT), Kolar, India.","DOI":"10.1109\/ACOIT62457.2024.10939554"},{"key":"ref_172","first-page":"103506","article-title":"Beyond being wise after the event: Combining spatial, temporal and spectral information for Himawari-8 early-stage wildfire detection","volume":"124","author":"Zhang","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1002\/eap.1367","article-title":"Do temporal changes in vegetation structure additional to time since fire predict changes in bird occurrence?","volume":"26","author":"Lindenmayer","year":"2016","journal-title":"Ecol. Appl."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10164-019-00628-z","article-title":"A wake-up call for sleepy lizards: The olfactory-driven response of Tiliqua rugosa (Reptilia: Squamata: Sauria) to smoke and its implications for fire avoidance behavior","volume":"38","author":"Mendyk","year":"2020","journal-title":"J. Ethol."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.1016\/j.jksuci.2021.04.013","article-title":"Classification of bird sounds as an early warning method of forest fires using Convolutional Neural Network (CNN) algorithm","volume":"34","author":"Permana","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"K, A., Chatterjee, P., Jha, A.K., Herle, A., Gupta, S., and Das, S. (2024, January 5\u20137). Ecological Surveillance and Crisis Management: Early Forest Fire Prediction and Endangered Species Identification. Proceedings of the 2024 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India.","DOI":"10.1109\/ESCI59607.2024.10497383"},{"key":"ref_177","first-page":"133","article-title":"Tolerance of two honey bee races to various temperature and relative humidity gradients","volume":"10","author":"Mohamed","year":"2012","journal-title":"Environ. Exp. Biol."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1080\/00218839.2023.2179728","article-title":"Behavioural impairments, foraging behaviour and brood development of Apis mellifera L. (Hymenoptera: Apidae) driven by air pollutants particulate matter in agro-industrial ecosystem","volume":"63","author":"Khan","year":"2023","journal-title":"J. Apic. Res."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"20230290","DOI":"10.1098\/rsif.2023.0290","article-title":"How honeybees respond to heat stress from the individual to colony level","volume":"20","author":"Jhawar","year":"2023","journal-title":"J. R. Soc. Interface"},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/s41893-020-0493-x","article-title":"Vulnerability of honey bee queens to heat-induced loss of fertility","volume":"3","author":"McAfee","year":"2020","journal-title":"Nat. Sustain."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Gounari, S., Proutsos, N., and Goras, G. (2022). How does weather impact on beehive productivity in a Mediterranean island?. Ital. J. Agrometeorol., 65\u201381.","DOI":"10.36253\/ijam-1195"},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Burkle, L.A., Simanonok, M.P., Durney, J.S., Myers, J.A., and Belote, R.T. (2019). Wildfires influence abundance, diversity, and intraspecific and interspecific trait variation of native bees and flowering plants across burned and unburned landscapes. Front. Ecol. Evol., 7.","DOI":"10.3389\/fevo.2019.00252"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"e02668","DOI":"10.1002\/ecs2.2668","article-title":"Wild bee diversity increases with local fire severity in a fire-prone landscape","volume":"10","author":"Galbraith","year":"2019","journal-title":"Ecosphere"},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"3827","DOI":"10.1038\/s41598-024-54107-8","article-title":"Quantitative assessments of honeybee colony\u2019s response to an artificial vibrational pulse resulting in non-invasive measurements of colony\u2019s overall mobility and restfulness","volume":"14","author":"Bencsik","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_185","first-page":"450","article-title":"Effect of environmental factors on the foraging activities of major bee pollinators","volume":"8","author":"Usha","year":"2020","journal-title":"J. Entomol. Zool. Stud."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"405","DOI":"10.4025\/actascianimsci.v37i4.27463","article-title":"Effects of abiotic factors on the foraging activity of Apis mellifera Linnaeus, 1758 in inflorescences of Vernonia polyanthes Less (Asteraceae)","volume":"37","author":"Alves","year":"2015","journal-title":"Acta Sci. Anim. Sci."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1007\/s13592-020-00830-w","article-title":"Response mechanisms to heat stress in bees","volume":"52","author":"Zhao","year":"2021","journal-title":"Apidologie"},{"key":"ref_188","doi-asserted-by":"crossref","unstructured":"Bar-Shai, N., Motro, U., Shmida, A., and Bloch, G. (2022). Earlier morning arrival to pollen-rewarding flowers may enable feral bumble bees to successfully compete with local bee species and expand their distribution range in a Mediterranean habitat. Insects, 13.","DOI":"10.3390\/insects13090816"},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"20240352","DOI":"10.1098\/rspb.2024.0352","article-title":"The heat is on: Reduced detection of floral scents after heatwaves in bumblebees","volume":"291","author":"Nooten","year":"2024","journal-title":"Proc. R. Soc. B Biol. Sci."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"140647","DOI":"10.1016\/j.chemosphere.2023.140647","article-title":"Ozone exposure induces metabolic stress and olfactory memory disturbance in honey bees","volume":"346","author":"Demares","year":"2024","journal-title":"Chemosphere"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1007\/s10886-016-0717-8","article-title":"Ozone differentially affects perception of plant volatiles in western honey bees","volume":"42","author":"Dotterl","year":"2016","journal-title":"J. Chem. Ecol."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"3760","DOI":"10.1038\/s41598-017-03944-x","article-title":"Colony adaptive response to simulated heat waves and consequences at the individual level in honeybees (Apis mellifera)","volume":"7","author":"Bordier","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"1","DOI":"10.56557\/upjoz\/2024\/v45i124098","article-title":"Understanding the impact of heat stress on honeybee populations: Insights into thermoregulation, colony dynamics, and environmental interactions","volume":"45","author":"Kamboj","year":"2024","journal-title":"Uttar Pradesh J. Zool."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"104635","DOI":"10.1016\/j.jinsphys.2024.104635","article-title":"Increasing temperature and time in glasshouses increases honey bee activity and affects internal brood conditions","volume":"155","author":"Baker","year":"2024","journal-title":"J. Insect Physiol."},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Eouzan, I., Garnery, L., Pinto, M.A., Delalande, D., Neves, C.J., Fabre, F., Lesobre, J., Houte, S., Estonba, A., and Montes, I. (2019). Hygroregulation, a key ability for eusocial insects: Native western European honeybees as a case study. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0200048"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"286","DOI":"10.30574\/wjarr.2024.21.2.0412","article-title":"Enhancing honeybee breeding for sustainable agriculture through temperature and relative humidity monitoring","volume":"21","author":"Bakri","year":"2024","journal-title":"World J. Adv. Res. Rev."},{"key":"ref_197","doi-asserted-by":"crossref","unstructured":"Newton, M.I., Chamberlain, L., McVeigh, A., and Bencsik, M. (2024). Winter carbon dioxide measurement in honeybee hives. Appl. Sci., 14.","DOI":"10.3390\/app14041679"},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"103831","DOI":"10.1016\/j.ibmb.2022.103831","article-title":"CO2 narcosis induces a metabolic shift mediated via juvenile hormone in Bombus impatiens gynes","volume":"149","author":"Barie","year":"2022","journal-title":"Insect Biochem. Mol. Biol."},{"key":"ref_199","first-page":"67","article-title":"The effect of different concentrations of carbon dioxide (CO2) in a mixture with air or nitrogen upon the survival of the honey bee (Apis mellifera)","volume":"48","year":"2015","journal-title":"J. Apic. Res."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"jeb244746","DOI":"10.1242\/jeb.244746","article-title":"Impacts and mechanisms of CO2 narcosis in bumble bees: Narcosis depends on dose, caste and mating status and is not induced by anoxia","volume":"226","author":"Cressman","year":"2023","journal-title":"J. Exp. Biol."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"105161","DOI":"10.1016\/j.compag.2019.105161","article-title":"A method for mining combined data from in-hive sensors, weather and apiary inspections to forecast the health status of honey bee colonies","volume":"169","author":"Braga","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_202","doi-asserted-by":"crossref","unstructured":"Al Zein, H., Yaacoub, A., and Haidar, S. (2025, January 10\u201313). Beehive state and events recognition through sound analysis using TinyML. Proceedings of the Seventeenth International Conference on Machine Vision (ICMV 2024), Edinburgh, UK.","DOI":"10.1117\/12.3055282"},{"key":"ref_203","unstructured":"Cecchi, S., Terenzi, A., Orcioni, S., and Piazza, F. (2019, January 16\u201319). Analysis of the sound emitted by honey bees in a beehive. Proceedings of the 147th Audio Engineering Society Convention, New York, NY, USA."},{"key":"ref_204","doi-asserted-by":"crossref","unstructured":"Astuti, P.K., Hegedus, B., Oleksa, A., Bagi, Z., and Kusza, S. (2024). Buzzing with intelligence: Current issues in apiculture and the role of artificial intelligence (AI) to tackle it. Insects, 15.","DOI":"10.3390\/insects15060418"},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s42003-022-03092-7","article-title":"Drone honey bees are disproportionately sensitive to abiotic stressors despite expressing high levels of stress response proteins","volume":"5","author":"McAfee","year":"2022","journal-title":"Commun. Biol."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"107207","DOI":"10.1016\/j.compag.2022.107207","article-title":"Detection of anomalies in bee colony using transitioning state and contrastive autoencoders","volume":"200","author":"Cejrowski","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_207","doi-asserted-by":"crossref","unstructured":"\u0160abi\u0107, J., Perkovi\u0107, T., \u0160oli\u0107, P., and \u0160eri\u0107, L. (2025). Buzzing with intelligence: A systematic review of smart beehive technologies. Sensors, 25.","DOI":"10.20944\/preprints202507.2193.v1"},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Masram, B., Mendhe, A., Mishra, A., Pathade, A., Shindekar, N., and Kangale, Y. (2024, January 29\u201331). A Smart Forest Fire Detection and Notification System using IOT and Machine Learning. Proceedings of the 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), Greater Noida, India.","DOI":"10.1109\/ICEECT61758.2024.10739106"},{"key":"ref_209","doi-asserted-by":"crossref","unstructured":"Patil, G., Kadam, B., Gujalwar, V., Patil, V., and Wagh, K. (2024). IoT and ML-driven smart fire alarm and crowd tracking. Proceedings of the International Conference on Advances in Information Communication Technology & Computing, Springer.","DOI":"10.1007\/978-981-97-6103-6_15"},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/TCSVT.2018.2889193","article-title":"3D parallel fully convolutional networks for real-time video wildfire smoke detection","volume":"30","author":"Li","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1007\/s12145-022-00801-y","article-title":"A novel custom optimized convolutional neural network for a satellite image by using forest fire detection","volume":"15","author":"Kalaivani","year":"2022","journal-title":"Earth Sci. Inform."},{"key":"ref_212","doi-asserted-by":"crossref","unstructured":"Singh, A., Madan, P., Arya, P.K., Sheikh, V., Gupta, M., and Kumar, R. (2024, January 15\u201316). Detecting Wildfires Using Satellite Imagery. Proceedings of the 2024 International Conference on Cybernation and Computation (CYBERCOM), Dehradun, India.","DOI":"10.1109\/CYBERCOM63683.2024.10803151"},{"key":"ref_213","doi-asserted-by":"crossref","unstructured":"Hu, X., Jiang, F., Qin, X., Huang, S., Yang, X., and Meng, F. (2024). An optimized smoke segmentation method for forest and grassland fire based on the U-Net framework. Fire, 7.","DOI":"10.3390\/fire7030068"},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Tan, L., and Robert, T.L.K. (2024). An improved fire and smoke detection method based on YOLOv8n for smart factories. Sensors, 24.","DOI":"10.3390\/s24154786"},{"key":"ref_215","first-page":"35","article-title":"Forest fire detection based on earlier pre-fire conditions using analytic hierarchy process in a semi-arid climate: A case study of Belezma National Park, Algeria","volume":"114","author":"Lahmar","year":"2024","journal-title":"Acta Geogr. Lodz."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1007\/s41976-024-00140-0","article-title":"Prioritizing the right to environment: Enhancing forest fire detection and prevention through satellite data and machine learning algorithms for early warning systems","volume":"7","author":"Lakshmanaswamy","year":"2024","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_217","doi-asserted-by":"crossref","unstructured":"Gao, B., Jia, W., Wang, Q., and Yang, G. (2025). All-weather forest fire automatic monitoring and early warning application based on multi-source remote sensing data: Case study of Yunnan. Fire, 8.","DOI":"10.3390\/fire8090344"},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1007\/s10661-022-10318-y","article-title":"Modeling wildfire risk in western Iran based on the integration of AHP and GIS","volume":"194","author":"Nasiri","year":"2022","journal-title":"Environ. Monit. Assess."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"012068","DOI":"10.1088\/1757-899X\/1022\/1\/012068","article-title":"IoT-based forest fire detection system in cloud paradigm","volume":"1022","author":"Singh","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"124898","DOI":"10.1016\/j.eswa.2024.124898","article-title":"A machine learning-based multiclass classification model for bee colony anomaly identification using an IoT-based audio monitoring system with an edge computing framework","volume":"255","author":"Chen","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TAFE.2024.3406648","article-title":"A machine learning approach for queen bee detection through remote audio sensing to safeguard honeybee colonies","volume":"2","author":"Barbisan","year":"2024","journal-title":"IEEE Trans. Agrifood Electron."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"148405","DOI":"10.1109\/ACCESS.2024.3471895","article-title":"Empirical analysis of honeybees acoustics as biosensors signals for swarm prediction in beehives","volume":"12","author":"Iqbal","year":"2024","journal-title":"IEEE Access"},{"key":"ref_223","doi-asserted-by":"crossref","unstructured":"Camayo, A.I.C., Mu\u00f1oz, M.A.C., and Corrales, J.C. (2024). ApIsoT: An IoT function aggregation mechanism for detecting Varroa infestation in Apis mellifera species. Agriculture, 14.","DOI":"10.3390\/agriculture14060846"},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1038\/s41597-025-04877-1","article-title":"High-resolution acoustic and environmental data to monitor Bombus dahlbomii amid invasive species and habitat loss","volume":"12","author":"Chwalek","year":"2025","journal-title":"Sci. Data"},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1038\/s43247-025-02082-x","article-title":"Poor air quality raises mortality in honey bees, a concern for all pollinators","volume":"6","author":"Coallier","year":"2025","journal-title":"Commun. Earth Environ."},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1111\/brv.70082","article-title":"Bees feeling the burn","volume":"101","author":"Prendergast","year":"2025","journal-title":"Biol. Rev."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5851\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:16:33Z","timestamp":1790241393000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5851"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":226,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185851"],"URL":"https:\/\/doi.org\/10.3390\/s26185851","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]}}}