{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:22:21Z","timestamp":1780766541093,"version":"3.54.1"},"reference-count":50,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:00:00Z","timestamp":1643587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fire in Brazilian Pantanal represents a serious threat to biodiversity. The Brazilian National Institute of Spatial Research (INPE) has a program named Queimadas, which estimated from January 2020 to October 2020, a burned area in Pantanal of approximately 40,606 km2. This program also provides daily data of active fire (fires spots) from a methodology that uses MODIS (Aqua and Terra) sensor data as reference satellites, which presents limitations mainly when dealing with small active fires. Remote sensing researches on active fire dynamics have contributed to wildfire comprehension, despite generally applying low spatial resolution data. Convolutional Neural Networks (CNN) associated with high- and medium-resolution remote sensing data may provide a complementary strategy to small active fire detection. We propose an approach based on object detection methods to map active fire in the Pantanal. In this approach, a post-processing strategy is adopted based on Non-Max Suppression (NMS) to reduce the number of highly overlapped detections. Extensive experiments were conducted, generating 150 models, as five-folds were considered. We generate a public dataset with 775-RGB image patches from the Wide Field Imager (WFI) sensor onboard the China Brazil Earth Resources Satellite (CBERS) 4A. The patches resulted from 49 images acquired from May to August 2020 and present a spatial and temporal resolutions of 55 m and five days, respectively. The proposed approach uses a point (active fire) to generate squared bounding boxes. Our findings indicate that accurate results were achieved, even considering recent images from 2021, showing the generalization capability of our models to complement other researches and wildfire databases such as the current program Queimadas in detecting active fire in this complex environment. The approach may be extended and evaluated in other environmental conditions worldwide where active fire detection is still a required information in fire fighting and rescue initiatives.<\/jats:p>","DOI":"10.3390\/rs14030688","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T22:16:18Z","timestamp":1643753778000},"page":"688","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Active Fire Mapping on Brazilian Pantanal Based on Deep Learning and CBERS 04A Imagery"],"prefix":"10.3390","volume":"14","author":[{"given":"Leandro","family":"Higa","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9096-6866","authenticated-orcid":false,"given":"Jos\u00e9","family":"Marcato Junior","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0902-6824","authenticated-orcid":false,"given":"Thiago","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Laboratory of Atmospheric Sciences, Institute of Physics, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro","family":"Zamboni","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0197-3392","authenticated-orcid":false,"given":"Rodrigo","family":"Silva","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laisa","family":"Almeida","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0564-7818","authenticated-orcid":false,"given":"Veraldo","family":"Liesenberg","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, Santa Catarina State University, Lages 88520-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F\u00e1bio","family":"Roque","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7570-1993","authenticated-orcid":false,"given":"Renata","family":"Libonati","sequence":"additional","affiliation":[{"name":"Departamento de Meteorologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-916, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8815-6653","authenticated-orcid":false,"given":"Wesley Nunes","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"},{"name":"Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Silva","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"},{"name":"Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"key":"ref_1","unstructured":"Calheiros, D.F., Oliveira, M., and Padovani, C.R. (2012). Hydro-ecological processes and anthropogenic impacts on the ecosystem services of the Pantanal wetland. Tropical Wetland Management: The South-American Pantanal and the International Experience, Routledge."},{"key":"ref_2","first-page":"108559","article-title":"Temporal variability in evapotranspiration and energy partitioning over a seasonally flooded scrub forest of the Brazilian Pantanal","volume":"308","author":"Junior","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","unstructured":"do Brasil, S.F. (1988). Constitui\u00e7\u00e3o da Rep\u00fablica Federativa do Brasil, Senado Federal, Centro Gr\u00e1fico."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"958","DOI":"10.4257\/oeco.2012.1604.17","article-title":"Seasonal Pantanal flood pulse: Implications for biodiversity","volume":"16","author":"Alho","year":"2012","journal-title":"Oecologia Aust."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1007\/s00027-006-0851-4","article-title":"Biodiversity and its conservation in the Pantanal of Mato Grosso, Brazil","volume":"68","author":"Junk","year":"2006","journal-title":"Aquat. Sci."},{"key":"ref_6","first-page":"157","article-title":"A estrutura fundi\u00e1ria do pantanal brasileiro","volume":"55","author":"Braz","year":"2020","journal-title":"Finisterra"},{"key":"ref_7","unstructured":"INPE (2019, September 01). Portal do Monitoramento de Queimadas e Inc\u00eandios Florestais. Available online: http:\/\/www.inpe.br\/queimadas."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e2021GL093789","DOI":"10.1029\/2021GL093789","article-title":"Active Fire Dynamics in the Amazon: New Perspectives From High-Resolution Satellite Observations","volume":"48","author":"Xu","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2018.01.004","article-title":"PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval","volume":"145","author":"Zhou","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","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_15","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_16","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Xu, J., Xu, L., and Guo, H. (2016, January 30\u201331). Deep convolutional neural networks for forest fire detection. Proceedings of the 2016 International Forum on Management, Education and Information Technology Application, Guangzhou, China.","DOI":"10.2991\/ifmeita-16.2016.105"},{"key":"ref_17","first-page":"455","article-title":"Smoke and Fire Detection Base on Convolutional Neural Network","volume":"7","author":"Wahyuni","year":"2019","journal-title":"ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1748302619887689","DOI":"10.1177\/1748302619887689","article-title":"Forest fire image recognition based on convolutional neural network","volume":"13","author":"Wang","year":"2019","journal-title":"J. Algorithms Comput. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s11760-019-01600-7","article-title":"Additive neural network for forest fire detection","volume":"14","author":"Pan","year":"2019","journal-title":"Signal Image Video Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, Y., Xin, J., Yi, Y., Liu, D., and Liu, H. (2018, January 25\u201327). A UAV-based forest fire detection algorithm using convolutional neural network. Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China.","DOI":"10.23919\/ChiCC.2018.8484035"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jiao, Z., Zhang, Y., Xin, J., Mu, L., Yi, Y., Liu, H., and Liu, D. (2019, January 22\u201326). A deep learning based forest fire detection approach using UAV and YOLOv3. Proceedings of the 2019 1st International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China.","DOI":"10.1109\/ICIAI.2019.8850815"},{"key":"ref_22","unstructured":"Lee, W., Kim, S., Lee, Y.T., Lee, H.W., and Choi, M. (2017, January 12\u201314). Deep neural networks for wild fire detection with unmanned aerial vehicle. Proceedings of the 2017 IEEE international conference on consumer electronics (ICCE), Taipei, Taiwan."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Govil, K., Welch, M.L., Ball, J.T., and Pennypacker, C.R. (2020). Preliminary results from a wildfire detection system using deep learning on remote camera images. Remote Sens., 12.","DOI":"10.3390\/rs12010166"},{"key":"ref_24","unstructured":"Vani, K. (2019, January 8\u201320). Deep learning based forest fire classification and detection in satellite images. Proceedings of the 2019 11th International Conference on Advanced Computing (ICoAC), Chennai, India."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ba, R., Chen, C., Yuan, J., Song, W., and Lo, S. (2019). Smokenet: Satellite smoke scene detection using convolutional neural network with spatial and channel-wise attention. Remote Sens., 11.","DOI":"10.3390\/rs11141702"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2019.12.014","article-title":"A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images","volume":"160","author":"Pinto","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, W., Cao, Y., Chen, K., Pang, J., Gong, T., Shi, J., Loy, C.C., and Lin, D. (2020, January 23\u201328). Side-aware boundary localization for more precise object detection. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58548-8_24"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 14\u201319). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, Y., Dayoub, F., and Sunderhauf, N. (2021, January 19\u201324). Varifocalnet: An iou-aware dense object detector. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kim, K., and Lee, H.S. (2020, January 23\u201328). Probabilistic anchor assignment with iou prediction for object detection. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part XXV 16.","DOI":"10.1007\/978-3-030-58595-2_22"},{"key":"ref_31","unstructured":"Gef (Global Environment Facility) Pantanal\/Upper Paraguay Project (2004). Implementation of Integrated River Basin Management Practices in the Pantanal and Upper Paraguay River Basin. Strategic Action Program for the Integrated Management of the Pantanal and Upper Paraguay River Basin. ANA\/GEF\/UNEP\/OAS, TDA Desenho & Arte Ltda."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1127\/0941-2948\/2013\/0507","article-title":"K\u00f6ppen\u2019s climate classification map for Brazil","volume":"22","author":"Alvares","year":"2013","journal-title":"Meteorol. Z."},{"key":"ref_33","unstructured":"IBGE (2019, September 01). Biomas, Available online: https:\/\/www.ibge.gov.br\/geociencias\/informacoes-ambientais\/vegetacao\/15842-biomas.html?=&t=downloads."},{"key":"ref_34","unstructured":"INPE (2019, September 01). CBERS 4A. Available online: http:\/\/www.cbers.inpe.br\/sobre\/cbers04a.php."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1109\/IGARSS.2002.1026158","article-title":"Day and night-time active fire detection over North America using NOAA-16 AVHRR data","volume":"Volume 3","author":"Abuelgasim","year":"2002","journal-title":"Proceedings of the IEEE International Geoscience and Remote Sensing Symposium"},{"key":"ref_36","unstructured":"Christopher, S.A., Wang, M., Barbieri, K., Welch, R.M., and Yang, S.K. (1997, January 3\u20138). Satellite remote sensing of fires, smoke and regional radiative energy budgets. Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings, IGARSS\u201997, Remote Sensing\u2014A Scientific Vision for Sustainable Development, Singapore."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(03)00184-6","article-title":"An enhanced contextual fire detection algorithm for MODIS","volume":"87","author":"Giglio","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_38","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019, January 27\u201328). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_43","unstructured":"Zhang, X., Wan, F., Liu, C., Ji, R., and Ye, Q. (2019). Freeanchor: Learning to match anchors for visual object detection. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ke, W., Zhang, T., Huang, Z., Ye, Q., Liu, J., and Huang, D. (2020, January 14\u201319). Multiple anchor learning for visual object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01022"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, H., Wu, Z., Zhu, C., Xiong, C., Socher, R., and Davis, L.S. (2020, January 14\u201319). Learning from noisy anchors for one-stage object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01060"},{"key":"ref_46","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/nav.3800020109","article-title":"The Hungarian Method for the Assignment Problem","volume":"2","author":"Kuhn","year":"1955","journal-title":"Nav. Res. Logist. Q."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gomes, M., Silva, J., Gon\u00e7alves, D., Zamboni, P., Perez, J., Batista, E., Ramos, A., Osco, L., Matsubara, E., and Li, J. (2020). Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method. Sensors, 20.","DOI":"10.3390\/s20216070"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Biffi, L.J., Mitishita, E., Liesenberg, V., Santos, A.A.d., Gon\u00e7alves, D.N., Estrabis, N.V., Silva, J.d.A., Osco, L.P., Ramos, A.P.M., and Centeno, J.A.S. (2021). ATSS Deep Learning-Based Approach to Detect Apple Fruits. Remote Sens., 13.","DOI":"10.3390\/rs13010054"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/688\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:12:12Z","timestamp":1760134332000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/688"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,31]]},"references-count":50,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030688"],"URL":"https:\/\/doi.org\/10.3390\/rs14030688","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,31]]}}}