55 citations as recorded by crossref.

1. Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat A. Chan et al. https://doi.org/10.3390/rs15061489
2. Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas B. Li et al. https://doi.org/10.1117/1.JRS.18.014531
3. Burned-Area Mapping Using Post-Fire PlanetScope Images and a Convolutional Neural Network B. Kim et al. https://doi.org/10.3390/rs16142629
4. Mapping forest fire severity using bi-temporal unmixing of Sentinel-2 data - Towards a quantitative understanding of fire impacts K. Pfoch et al. https://doi.org/10.1016/j.srs.2023.100097
5. Global mapping of oil palm planting year from 1990 to 2021 A. Descals et al. https://doi.org/10.5194/essd-16-5111-2024
6. Agriculture, Development and Sustainability in the Covid-19 Era A. Halimatussadiah et al. https://doi.org/10.1080/00074918.2022.2056935
7. Monitoring active fires in Borneo from Sentinel-2 MSI images X. Guo et al. https://doi.org/10.1080/15481603.2025.2539551
8. Assessing Burned Area Detection in Indonesia Using the Stacking Ensemble Neural Network (SENN): A Comparative Analysis of C- and L-Band Performance D. Sudiana et al. https://doi.org/10.3390/computers14080337
9. Incremental Data Cube Architecture for Sentinel-2 Time Series: Multi-Cube Approaches to Dynamic Baseline Construction R. Trujillo & M. Solar https://doi.org/10.3390/rs18020260
10. Mono-temporal and multi-temporal approaches for burnt area detection using Sentinel-2 satellite imagery (a case study of Rokan Hilir Regency, Indonesia) N. Afira & A. Wijayanto https://doi.org/10.1016/j.ecoinf.2022.101677
11. Accurate forest burned area detection by integrating active fire data with enhanced Sentinel-2 multi-temporal imagery H. Ren et al. https://doi.org/10.1016/j.jag.2026.105163
12. Reimagine fire science for the anthropocene J. Shuman et al. https://doi.org/10.1093/pnasnexus/pgac115
13. Gastrointestinal parasites of wild Bornean orang-utans (Pongo pygmaeus) in a habitat affected by wildfire smoke A. Gwynn et al. https://doi.org/10.1016/j.gecco.2024.e03214
14. Unprecedented fire activity above the Arctic Circle linked to rising temperatures A. Descals et al. https://doi.org/10.1126/science.abn9768
15. Catastrophic impact of extreme 2019 Indonesian peatland fires on urban air quality and health M. Grosvenor et al. https://doi.org/10.1038/s43247-024-01813-w
16. In Search of Fire Villains V. Schreer https://doi.org/10.1215/22011919-11327404
17. Enhanced CH4 emissions from global wildfires likely due to undetected small fires J. Zhao et al. https://doi.org/10.1038/s41467-025-56218-w
18. Optimizing Spectral Inputs for Deep Learning-Based Vegetation Destruction Detection Using Sentinel-2 Imagery C. Zhao et al. https://doi.org/10.3390/rs18142333
19. A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data D. Sudiana et al. https://doi.org/10.3390/rs15030728
20. Fire frequency, intensity, and burn severity in Kalimantan’s threatened Peatland areas over two Decades A. Schmidt et al. https://doi.org/10.3389/ffgc.2024.1221797
21. A global behavioural model of human fire use and management: WHAM! v1.0 O. Perkins et al. https://doi.org/10.5194/gmd-17-3993-2024
22. OtsuSeg: an R package implementing Otsu’s thresholding technique for mapping forest fire scars with integrated accuracy assessment H. Achour et al. https://doi.org/10.1007/s41324-025-00644-x
23. Identifying Major Wildfires Using Long-Term Multi-Source Data and Anomaly Detection Algorithms K. Mathaba et al. https://doi.org/10.3390/earth7050153
24. Burned area detection using Sentinel-1 and Sentinel-2 features and analysis of ensemble models with explainable AI M. Günen & S. Türk https://doi.org/10.1007/s10651-025-00693-3
25. Wetscapes: Restoring and maintaining peatland landscapes for sustainable futures R. Temmink et al. https://doi.org/10.1007/s13280-023-01875-8
26. Monthly mapping of Indonesia’s burned areas: implementation, history, techniques, and future directions Y. Vetrita et al. https://doi.org/10.1080/01431161.2024.2421942
27. A Multi-Temporal Sentinel-2 and Machine Learning Approach for Precision Burned Area Mapping: The Sardinia Case Study C. Collu et al. https://doi.org/10.3390/rs18020267
28. Burned area semantic segmentation: A novel dataset and evaluation using convolutional networks T. Ribeiro et al. https://doi.org/10.1016/j.isprsjprs.2023.07.002
29. Mechanistic Analysis and Numerical Simulation of the 2021 Post‐Fire Debris Flow in Xiangjiao Catchment, China C. Ouyang et al. https://doi.org/10.1029/2022JF006846
30. Sentinel-2 sampling design and reference fire perimeters to assess accuracy of Burned Area products over Sub-Saharan Africa for the year 2019 D. Stroppiana et al. https://doi.org/10.1016/j.isprsjprs.2022.07.015
31. A comprehensive assessment of the FireCCILT11 global burned area product D. Chen & L. Giglio https://doi.org/10.1016/j.jag.2025.104893
32. Fire Impacts, vegetation Recovery, and environmental drivers in West African savannas (2014–2023): A High-Resolution remote sensing assessment B. Ouattara et al. https://doi.org/10.1016/j.jag.2025.104783
33. Enhancing Fire Monitoring Method over Peatlands and Non-Peatlands in Indonesia Using Visible Infrared Imaging Radiometer Suite Data A. Indradjad et al. https://doi.org/10.3390/fire7010009
34. Influence of wildfires on the conflict (2006–2022) in eastern Ukraine using remote sensing techniques (MODIS and Sentinel-2 images) F. Rodriguez-Jimenez et al. https://doi.org/10.1016/j.rsase.2024.101240
35. Meteorological drivers of air pollution impacts from straw burning in Henan Province, China H. Liu et al. https://doi.org/10.1016/j.agrformet.2025.110932
36. Beyond summer fires: shifting forest fire across European biogeographical regions D. D’Alò et al. https://doi.org/10.1088/1748-9326/ae4817
37. Nationwide monthly burned area monitoring in Indonesia using Sentinel-2 D. Gaveau et al. https://doi.org/10.1371/journal.pone.0331831
38. Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5) Y. Chen et al. https://doi.org/10.5194/essd-15-5227-2023
39. Assessing space-based smoldering peatland in the tropics with atmospheric products from multi-sensor satellites P. Sofan et al. https://doi.org/10.1007/s40808-023-01793-4
40. FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques J. Fernández-Guisuraga et al. https://doi.org/10.1016/j.ecoinf.2024.102591
41. Mapping wetland fire dynamics with Sentinel-2 and UAV data in the papyrus-dominated Mara Wetland, Tanzania F. Bregoli et al. https://doi.org/10.1016/j.rsase.2026.102142
42. Forty-Year Fire History Reconstruction from Landsat Data in Mediterranean Ecosystems of Algeria following International Standards M. Kouachi et al. https://doi.org/10.3390/rs16132500
43. Slowing deforestation in Indonesia follows declining oil palm expansion and lower oil prices D. Gaveau et al. https://doi.org/10.1371/journal.pone.0266178
44. An automatic procedure for mapping burned areas globally using Sentinel-2 and VIIRS/MODIS active fires in Google Earth Engine A. Bastarrika et al. https://doi.org/10.1016/j.isprsjprs.2024.08.019
45. The Global Forest Fire Emissions Prediction System version 1.0 K. Anderson et al. https://doi.org/10.5194/gmd-17-7713-2024
46. Building a small fire database for Sub-Saharan Africa from Sentinel-2 high-resolution images E. Chuvieco et al. https://doi.org/10.1016/j.scitotenv.2022.157139
47. Burned area detection using convolutional neural network based on spatial information of synthetic aperture radar data in Indonesia A. Lestari et al. https://doi.org/10.24057/2071-9388-2024-3109
48. Mapping sugarcane residue burnt areas in smallholder farming systems using machine learning approaches K. PNVR & V. Bandaru https://doi.org/10.1016/j.atech.2023.100347
49. Identifying long-term burned forests in the rugged terrain of Southwest China:A novel method based on remote sensing and ecological mechanisms E. Yu et al. https://doi.org/10.1016/j.jag.2024.104134
50. Single-Temporal Sentinel-2 for Analyzing Burned Area Detection Methods: A Study of 14 Cases in Republic of Korea Considering Land Cover D. Lee et al. https://doi.org/10.3390/rs16050884
51. Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED) D. van Wees et al. https://doi.org/10.5194/gmd-15-8411-2022
52. A Novel Spectral Index for Vegetation Destruction Event Detection Based on Multispectral Remote Sensing Imagery C. Zhao et al. https://doi.org/10.1109/JSTARS.2024.3412737
53. Assessing Burned Areas in Sikkim, India through Satellite Mapping K. SHARMA et al. https://doi.org/10.17475/kastorman.1394888
54. Madagascar's burned area from Sentinel-2 imagery (2016–2022): Four times higher than from lower resolution sensors V. Fernández-García et al. https://doi.org/10.1016/j.scitotenv.2024.169929
55. Road fragment edges enhance wildfire incidence and intensity, while suppressing global burned area S. Bowring et al. https://doi.org/10.1038/s41467-024-53460-6