{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:13:53Z","timestamp":1760228033999,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSCF Project","doi-asserted-by":"publisher","award":["41871285"],"award-info":[{"award-number":["41871285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lightning is an important cause of casualties, and of the interruption of power supply and distribution facilities. Monitoring lightning locations is essential in disaster prevention and mitigation. Although there are many ways to obtain lightning information, there are still substantial problems in intelligent lightning monitoring. Deep learning combined with weather radar data and land attribute data can lay the foundation for future monitoring of lightning locations. Therefore, based on the residual network, the Lightning Monitoring Residual Network (LM-ResNet) is proposed in this paper to monitor lightning location. Furthermore, comparisons with GoogLeNet and DenseNet were also conducted to evaluate the proposed model. The results show that the LM-ResNet model has significant potential in monitoring lightning locations. In this study, we converted the lightning monitoring problem into a binary classification problem and then obtained weather radar product data (including the plan position indicator (PPI), composite reflectance (CR), echo top (ET), vertical integral liquid water (VIL), and average radial velocity (V)) and land attribute data (including aspect, slope, land use, and NDVI) to establish a lightning feature dataset. During model training, the focal loss function was adopted as a loss function to address the constructed imbalanced lightning feature dataset. Moreover, we conducted stepwise sensitivity analysis and single factor sensitivity analysis. The results of stepwise sensitivity analysis show that the best performance can be achieved using all the data, followed by the combination of PPI, CR, ET, and VIL. The single factor sensitivity analysis results show that the ET radar product data are very important for the monitoring of lightning locations, and the NDVI land attribute data also make significant contributions.<\/jats:p>","DOI":"10.3390\/rs14092200","type":"journal-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T02:19:48Z","timestamp":1651717188000},"page":"2200","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Mingyue","family":"Lu","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China"},{"name":"Geographic Science College, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Yadong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China"},{"name":"Geographic Science College, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8922-8789","authenticated-orcid":false,"given":"Min","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6769-7517","authenticated-orcid":false,"given":"Manzhu","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA"}]},{"given":"Menglong","family":"Wang","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China"},{"name":"Geographic Science College, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119638","DOI":"10.1016\/j.foreco.2021.119638","article-title":"Important meteorological predictors for long-range wildfires in China","volume":"499","author":"Zhao","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101264","DOI":"10.1016\/j.ijdrr.2019.101264","article-title":"Fatalities related to lightning occurrence and agriculture in Bangladesh","volume":"41","author":"Holle","year":"2019","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.wem.2014.06.010","article-title":"A Lightning Multiple Casualty Incident in Sequoia and Kings Canyon National Parks","volume":"26","author":"Spano","year":"2015","journal-title":"Wilderness Environ. Med."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cooper, M.A., and Holle, R.L. (2019). Current Global Estimates of Lightning Fatalities and Injuries. Reducing Lightning Injuries Worldwide, Springer.","DOI":"10.1007\/978-3-319-77563-0"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"012001","DOI":"10.1088\/1742-6596\/1878\/1\/012001","article-title":"Analytical calculation of lightning strike probability for floating roof tanks","volume":"1878","author":"Adekitan","year":"2021","journal-title":"J. Physics Conf. Ser."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Johari, D., Amir, M.F.A.M., Hashim, N., Baharom, R., and Haris, F.A. (2021, January 8\u20139). Positive Cloud-To-Ground Lightning Observed in Shah Alam, Malaysia based on SAFIR 3000 Lightning Location System. Proceedings of the 2021 IEEE International Conference in Power Engineering Application (ICPEA), Virtual.","DOI":"10.1109\/ICPEA51500.2021.9417761"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/S1364-6826(02)00085-8","article-title":"VLF lightning location by time of group arrival (TOGA) at multiple sites","volume":"64","author":"Dowden","year":"2002","journal-title":"J. Atmos. Solar-Terrestrial Physics"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fata, A.L., Tosi, I., Brignone, M., Procopio, R., and Delfino, F. (2020, January 9\u201312). A Review of Lightning Location Systems: Part I-Methodologies and Techniques. Proceedings of the 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC\/I&CPS Europe), Madrid, Spain.","DOI":"10.1109\/EEEIC\/ICPSEurope49358.2020.9160534"},{"key":"ref_9","first-page":"356","article-title":"Striking Distance Determined From High-Speed Videos and Measured Currents in Negative Cloud-to-Ground Lightning","volume":"122","author":"Visacro","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.atmosres.2011.08.016","article-title":"Application of the time reversal of electromagnetic fields to locate lightning discharges","volume":"117","author":"Mora","year":"2012","journal-title":"Atmos. Res."},{"key":"ref_11","first-page":"21","article-title":"Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City","volume":"10","author":"Ghasemi","year":"2020","journal-title":"Geogr. Sustain. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2020GL091148","article-title":"A Machine-Learning Approach to Classify Cloud-to-Ground and Intracloud Lightning","volume":"48","author":"Zhu","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_13","first-page":"AE11B-2705","article-title":"Lightning Waveform Classification Based on Deep Convolutional Neural Network","volume":"2018","author":"Peng","year":"2018","journal-title":"AGUFM"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, J. (2020, January 4\u20137). Identification technology of substation lightning overvoltage based on wavelet transform research and application. Proceedings of the 2020 5th Asia Conference on Power and Electrical Engineering (ACPEE), Chengdu, China.","DOI":"10.1109\/ACPEE48638.2020.9136291"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, Y., Tan, Y., Chen, Z., Zheng, D., Zhang, Y., and Fan, Y. (2021). Fast and Fine Location of Total Lightning from Low Frequency Signals Based on Deep-Learning Encoding Features. Remote Sens., 13.","DOI":"10.3390\/rs13112212"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1002\/qj.49712152703","article-title":"A computational study of the relationships linking lightning frequency and other thundercloud parameters","volume":"121","author":"Baker","year":"1995","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5735","DOI":"10.1109\/JSTARS.2021.3083647","article-title":"Using Conditional Generative Adversarial 3-D Convolutional Neural Network for Precise Radar Extrapolation","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.5194\/gmd-15-1467-2022","article-title":"A Generative Adversarial Model for Radar Echo Extrapolation Based on Convolutional Recurrent Units","volume":"15","author":"Zheng","year":"2022","journal-title":"Geosci. Model Dev. Discuss."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cha, D., Wang, X., and Kim, J.W. (2017). Assessing Lightning and Wildfire Hazard by Land Properties and Cloud to Ground Lightning Data with Association Rule Mining in Alberta, Canada. Sensors, 17.","DOI":"10.3390\/s17102413"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105683","DOI":"10.1016\/j.atmosres.2021.105683","article-title":"Regional variation of electrical and lightning properties of thunderclouds during the pre-monsoon season over the north-eastern and eastern part of India","volume":"260","author":"Biswasharma","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wan, X., Liu, F., Tang, Y., Liu, Y., and Xue, M. (2021, January 28\u201330). Study on measurement error analysis and correction method of ground flash data in Hunan. Proceedings of the 2021 2nd International Conference on Artificial Intelligence and Information Systems, Chongqing, China.","DOI":"10.1145\/3469213.3470242"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1175\/JTECH-D-19-0146.1","article-title":"A Deep Learning Network for Cloud-to-Ground Lightning Nowcasting with Multisource Data","volume":"37","author":"Zhou","year":"2020","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_23","first-page":"70","article-title":"Aircraft Lightning Potential Area Detection Enhanced by Echo Top Height and its Evaluation with Winter Lightning Cases","volume":"39","author":"Yoshikawa","year":"2021","journal-title":"J. Atmos. Electr."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yin, W., Jin, W., Zhou, C., Liu, Y., Tang, Q., Liu, M., Chen, G., and Zhao, Z. (2021). Lightning Detection and Imaging Based on VHF Radar Interferometry. Remote Sens., 13.","DOI":"10.3390\/rs13112065"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105577","DOI":"10.1016\/j.atmosres.2021.105577","article-title":"Thunderstorm charge structures favouring cloud-to-ground lightning","volume":"257","author":"Salvador","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/JRPROC.1948.226265","article-title":"The Visibility of Small Echoes on Radar PPI Displays","volume":"36","year":"1948","journal-title":"Proc. IRE"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1127\/0941-2948\/2006\/0154","article-title":"Article Improvements in weather radar rain rate estimates using a method for identifying the vertical profile of reflectivity from volume radar scans","volume":"15","author":"Franco","year":"2006","journal-title":"Meteorol. Zeitschrift"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1007\/s12665-018-7985-2","article-title":"3D modelling strategy for weather radar data analysis","volume":"77","author":"Lu","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Raghavan, S. (2003). Weather Radar Signal Processing and Display. Radar Meteorology, Springer.","DOI":"10.1007\/978-94-017-0201-0"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1002\/met.1512","article-title":"Assessment of severe hailstorms and hail risk using weather radar data","volume":"22","author":"Burcea","year":"2015","journal-title":"Meteorol. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1175\/WAF-D-12-00084.1","article-title":"An Improved Method for Estimating Radar Echo-Top Height","volume":"28","author":"Lakshmanan","year":"2013","journal-title":"Weather Forecast."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1017\/S1350482706002374","article-title":"Uncertainties in radar echo top heights used for hail detection","volume":"13","author":"Delobbe","year":"2006","journal-title":"Meteorol. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1175\/1520-0426(2003)020<0807:AOVILV>2.0.CO;2","article-title":"Assessment of vertically integrated liquid (VIL) water content radar measurement","volume":"20","author":"Boudevillain","year":"2003","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_34","unstructured":"Yanovsky, F.J., Lekhovytskiy, D.I., and Atamanskiy, D.V. (November, January 31). Advanced algorithm of velocity measurement for modern meteorological radar. Proceedings of the 2012 9th European Radar Conference, Amsterdam, The Netherlands."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1109\/PROC.1979.11511","article-title":"Doppler Weather Radar","volume":"67","author":"Doviak","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Han, W., Zhang, D., Zhao, H., Wei, S., Pang, S., and Pan, W. (2021, January 28\u201330). Research on the Variation of Lightning Activity Parameters with Land Types Based on Lightning Location System. Proceedings of the 2021 IEEE 4th International Electrical and Energy Conference (CIEEC), Wuhan, China.","DOI":"10.1109\/CIEEC50170.2021.9511064"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/s12040-009-0040-7","article-title":"A study of lightning activity over land and oceanic regions of India","volume":"118","author":"Nath","year":"2009","journal-title":"J. Earth Syst. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5156416","DOI":"10.1155\/2019\/5156416","article-title":"Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss","volume":"2019","author":"Tran","year":"2019","journal-title":"J. Healthc. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103866","DOI":"10.1016\/j.compbiomed.2020.103866","article-title":"Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss","volume":"123","author":"Romdhane","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Reed, S., Sermanet, P., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017). Densely Connected Convolutional Networks. arXiv, 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_44","first-page":"102375","article-title":"A deep learning framework under attention mechanism for wheat yield estimation using remotely sensed indices in the Guanzhong Plain, PR China","volume":"102","author":"Tian","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lu, M., Zhang, Y., Ma, Z., Yu, M., Chen, M., Zheng, J., and Wang, M. (2021). Lightning Strike Location Identification Based on 3D Weather Radar Data. Front. Environ. Sci., 329.","DOI":"10.3389\/fenvs.2021.714067"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1002\/2017JD026536","article-title":"Lightning Evolution In Two North Central Florida Summer Multicell Storms and Three Winter\/Spring Frontal Storms","volume":"123","author":"Caicedo","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1175\/2010WAF2222387.1","article-title":"Investigating the potential of using radar Echo reflectivity to nowcast cloud-to-ground lightning initiation over southern Ontario","volume":"25","author":"Yang","year":"2010","journal-title":"Weather Forecast."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Smorgonskiy, A., Rachidi, F., Rubinstein, M., and Diendorfer, G. (2013, January 7\u201311). On the relation between lightning flash density and terrain elevation. Proceedings of the 2013 International Symposium on Lightning Protection (XII SIPDA), Belo Horizonte, Brazil.","DOI":"10.1109\/SIPDA.2013.6729216"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2200\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:06:08Z","timestamp":1760137568000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2200"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":48,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092200"],"URL":"https:\/\/doi.org\/10.3390\/rs14092200","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,5,4]]}}}