Search Results (23)

Pixel-based Active Fire Spot (AFS) statistics alone are insufficient for characterizing forest fire activity in fragmented mountainous agroforestry regions because cross-sensor differences, geometric distortion, and discontinuous satellite overpasses can fragment physically continuous fires into multiple detections. To address this problem, we developed a reconstruction framework that combines optical–thermal cross-validation with multi-level spatio-temporal clustering to identify physically independent fires in Yunnan Province, China. Starting from 497,834 raw AFSs detected during 2012–2024, the framework removed unusable detections, aggregated the retained AFSs, and identified 41,215 validated Forest Fire Events (FFEs). The reconstructed database revealed clear temporal, spatial, and topographic heterogeneity. Fire activity was strongly concentrated in the late dry season, with 32.8% of all FFEs occurring during the main spring fire window. Daytime FFEs accounted for 82.8% of all FFEs, but nocturnal activity increased substantially in some years, reaching 20.7% in 2023. Persistence showed a long-tailed structure under both observation frameworks, although the operational thresholds differed between 2012–2017 (105 min) and 2018–2024 (75 min). Regionally, Southeast and Southwest Yunnan concentrated most reconstructed FFEs, whereas Northwest and Central Yunnan showed much higher CFRP per event. Topographically, fire energy was concentrated mainly on gentle-to-moderate slopes, and nighttime fires were centered 215.03 m higher than daytime fires. The typology analysis further showed that event frequency and physical fire impact were not distributed proportionally across fire types. Random Forest validation indicated high reproducibility of the rule-based typology system (Macro-F1 = 0.9935; Weighted-F1 = 0.9964), whereas the first two principal components explained 42.65% of the total variance. These results show that event-level reconstruction provides a stronger basis than AFS counts alone for understanding fire heterogeneity and supporting zone-specific fire management in Yunnan. Full article

Thermal contextual algorithms for 375 m VIIRS active fire detection can produce substantial commission errors over persistent non-wildfire heat sources (e.g., refineries, gas flares, and volcanoes), and globally fixed thresholds may be suboptimal under heterogeneous thermal backgrounds. We present a lightweight spatiotemporal prior layer that augments by applying prior-guided, pixel-level parameter switching during the discrimination stage. The layer combines: (i) a persistent non-wildfire thermal anomaly mask (PFM) derived from multi-year VNP14IMG recurrence and seasonality statistics on a 0.004° grid, and (ii) a short-term heat-source mask (SHM) based on nighttime VIIRS I4/I5 brightness temperature stability to capture newly emerged or rapidly intensifying static sources. Pixels flagged by either prior are processed with a stricter parameter set, while other pixels follow the baseline setting. We evaluate the method using a stratified validation dataset (N = 3435) spanning industrial/urban clusters, volcanic regions, forest/grassland wildfires, and fragmented crop residue burning, with validation supported by independent high-resolution imagery (Sentinel-2/Landsat) and external POI datasets. The framework markedly reduces false positives in high-interference zones (industrial/urban false positive rate from 88.6% to 22.7%; volcanic from 100.0% to 57.3%) while preserving high performance for forest/grassland wildfires (F1 ≈ 0.999). For fragmented residue burning, omission error decreases from 11.2% to 1.3%, improving detection completeness without an apparent increase in commission errors. Overall, the results suggest that integrating long- and short-term spatiotemporal priors via threshold switching can improve the robustness and interpretability of contextual VIIRS fire detection under complex thermal backgrounds in the evaluated scenarios. Full article

Open biomass burning (OBB) adversely affects air quality, climate systems, and public health. Large-scale OBB, including forest fires and crop residue burning, is prevalent in Southeast Asia (SEA), a region with agrarian countries. The characteristics of OBB have been widely studied in SEA; however, the understanding of daytime and nighttime variations in fire activity and the effects of fire production remains limited. A significant amount of particulate matter (PM) is released into the atmosphere during OBB episodes. This study employs the Visible Infrared Imaging Radiometer Suite (VIIRS) to detect active fires during daytime and nighttime from OBB in Chiang Mai, Thailand, during March–April 2020, and investigates the mass concentration of size-specific PM down to PM_0.1. The results showed that hotspots occur more often at night than during the day. The VIIRS fire detection data provided a better response to small fires and improved mapping of extensive fire perimeters. PM_0.5–1.0 showed the highest mass concentration among particle sizes. Moreover, fire hotpots show the highest correlations with PM_0.1–0.5 during the daytime and PM_0.5–1.0 during the nighttime, and the large OBB in Chiang Mai significantly contributes to ambient PM. Overall, this study offers crucial insights into particulate pollution from biomass burning. Full article

Cultural practices such as Catalonia’s correfocs (fire parades) represent a vibrant expression of intangible heritage. Outdoor activities are conditioned by weather and threatened by climate change. This study analyses the long-term evolution of night-time thermal conditions during correfoc festivals performed in six Catalan towns located on the coast and in the pre-coastal region from 1950 to 2023, using reanalysis-based indicators of air temperature, humidity, and perceived heat as a first exploratory step prior to incorporating in situ meteorological records. Specifically, the Heat Index (HI) and the Universal Thermal Climate Index (UTCI) were computed for the typical event window (21:00–23:00 local time) to assess changes in human thermal comfort. Results reveal a clear and statistically significant warming trend in most pre-coastal locations—particularly Reus, El Vendrell, and Vilafranca—while coastal cities such as Barcelona exhibit weaker or non-significant changes, likely due to maritime moderation. The frequency and intensity of positive temperature anomalies have increased since the 1990s, with a growing proportion of events falling into “caution” or “moderate heat stress” categories under HI and UTCI classifications. These findings demonstrate that correfocs are now celebrated under markedly warmer night-time conditions than in the mid-twentieth century, implying a tangible rise in thermal discomfort and potential safety risks for participants. By integrating climatic and cultural perspectives, this research shows that rising night-time heat can constrain attendance, participation conditions, and event scheduling for correfocs, thereby directly exposing weather-sensitive form of intangible cultural heritage to climate risks. It therefore underscores the need for climate adaptation frameworks and to promote context-specific strategies to sustain these community-based traditions under ongoing Mediterranean warming. Full article

Fire in China, driven by both natural and anthropogenic factors, significantly influences ecological stability. This study provides a comprehensive spatiotemporal analysis of active fires across China from 2003 to 2024 using MODIS Collection 6.1 active fire and land cover products. Our results reveal a significant national decline in fire counts since 2016, accompanied by with a marked geographical shift in hotspots from East China to Northeast China. It clarifies that croplands and savannas are the main fire-prone land covers, yet they have also experienced the most substantial decline in fire counts. East China (46.8%) and Central China (27.1%) were the largest contributors to the reduction in cropland fire counts. Temporal displacement toward nighttime straw burning was observed in East China. The decline in average fire radiative power (FRP) of daytime agricultural fires indicates that straw burning bans effectively reduced both the frequency and intensity of fires. Persistent savanna and forest fires are highly clustered in Southern China, while new emerging grassland fires are concentrated in Western China. Persistent cropland fires overlap with emerging zones in Northeast and Central China. Our study can assist in optimizing targeted fire policies and supporting better fire risk management. Full article

Amid the growing frequency of forest fires in southwestern China, this study aims to quantify pollutant emissions and identify key meteorological drivers using multi-source satellite data. Active fire data from Himawari-8/9, MODIS, and VIIRS were integrated to construct a top-down emission inventory for 2016–2023, while the Geodetector method was applied to evaluate meteorological influences. Results indicate mean annual emissions (×10³ t·a⁻¹) of 5623.58 (±1554.33) for CO₂, 356.84 (±98.63) for CO, and substantial amounts of particulate and gaseous pollutants. Spatially, Yunnan and Sichuan were the dominant emitters; temporally, emissions peaked in January–April and November–December, with daytime levels surpassing nighttime levels. Relative humidity was identified as the dominant meteorological driver (Q = 0.1223), while the interaction between temperature and relative humidity (Q = 0.1486) further enhanced explanatory power. These findings improve the precision of emission inventories and provide essential support for regional fire management and air quality modeling in complex environments. Full article

In this study, typical haze pollution influenced by biomass burning (BB) activities in Changsha in the autumn of 2024 was investigated through the mixing state and evolution process of BB particles via the real-time measurement of single-particle aerosol mass spectrometry (SPAMS). From the clean period to the haze period, the PM_2.5 concentration increased from 25 μg·m⁻³ at 12:00 to 273 μg·m⁻³ at 21:00 on 12 October, and the proportion of total BB single particles in the total detected particles increased from 17.2% to 54%. This indicates that the rapid increase in PM_2.5 concentration was accompanied by a concurrent increase in the contribution of particles originating from BB sources. The detected BB particles were classified into two types based on their mixing states and temporal variations: BB1 and BB2, which accounted for 71.7% and 28.3% of the total BB particles, respectively. The analysis of backward trajectories and fire spots suggested that BB1 particles originated from straw burning emissions at northern Changsha, while BB2 particles were primarily related to local nighttime cooking emissions in Changsha. In addition, a special type of K-containing single particles without K cluster ions was found closely associated with BB1 type particles, which were designated as secondarily processed BB particles (BB-sec). The BB-sec particles contained abundant sulfate and ammonium signals and showed lagged appearance after the peak of BB1-type particles, which was possibly due to the aging and formation of ammonium sulfate on the freshly emitted particles. In all, this study provides insights into understanding the substantial impact of BB sources on regional air quality during the crop harvest season and the appropriate disposal of crop straw, including conversion into high-efficiency fuel through secondary processing or clean energy via biological fermentation, which is of great significance for the mitigation of local haze pollution. Full article

We present a low-cost prototype of a visible and near-infrared (VIS-NIR) remote sensing platform, optimized to detect and characterize natural flaming fire fronts from airborne nighttime light (NTL) observations, and its radiometric calibration. It uses commercially available CMOS sensor cameras and filters with roughly 100 nm bandwidths to effectively discriminate burning biomass from other sources of NTL, a critical ability for wildfire monitoring near populated areas. Our filter choice takes advantage of the strong potassium line emission near 770 nm present in natural flaming. The calibrated cameras operate at 20 ms of exposure time and boast radiance measurements with a sensitivity floor, depending on the filter, in the range 3–5 $\times {10}^{-6}$ W m⁻² sr⁻¹ nm⁻¹ with uncertainties lower than 5% and dynamic ranges near 3000–4000. An additional exposure time with a tenth of the duration is calibrated and extends the dynamic range by a factor of 10. We show images of a spatially resolved fire front from an airborne observation of flaming biomass within this radiance range. Full article

Every year about fifty volcanoes erupt on average, posing a serious threat for populations living in the neighboring areas. To mitigate the volcanic risk, many satellite monitoring systems have been developed. Information from the medium infrared (MIR) and thermal infrared (TIR) bands of sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) is commonly exploited for this purpose. However, the potential of daytime shortwave infrared (SWIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) aboard Sentinel-3 satellites in supporting the near-real-time monitoring of thermal volcanic activity has not been fully evaluated so far. In this work, we assess this potential by exploring the contribution of a normalized hotspot index (NHI) in the monitoring of the recent Home Reef (Tonga Islands) eruption. By analyzing the time series of the maximum NHI_SWIR value, computed over the Home Reef area, we inferred information about the waxing/waning phases of lava effusion during four distinct subaerial eruptions. The results indicate that the first eruption phase (September–October 2022) was more intense than the second one (September–November 2023) and comparable with the fourth eruptive phase (June–August 2024) in terms of intensity level; the third eruption phase (January 2024) was more difficult to investigate because of cloudy conditions. Moreover, by adapting the NHI algorithm to daytime SLSTR SWIR data, we found that the detected thermal anomalies complemented those in night-time conditions identified and quantified by the operational Level 2 SLSTR fire radiative power (FRP) product. This study demonstrates that NHI-based algorithms may contribute to investigating active volcanoes located even in remote areas through SWIR data at 500 m spatial resolution, encouraging the development of an automated processing chain for the near-real-time monitoring of thermal volcanic activity by means of night-time/daytime Sentinel-3 SLSTR data. Full article

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented. Full article

Underground coal fires in coal fields endanger the mine surface ecological environment, endanger coal resources, threaten mine safety and workers’ health, and cause geological disasters. The study of methods by which to monitor the laws that determine the way underground coal fires spread is helpful in the safe production of coal and the smooth execution of fire extinguishing projects. Based on night-time ASTER thermal infrared images of 2002, 2003, 2005 and 2007 in Huangbaici and Wuhushan mining areas in the Wuda coalfield, an adaptive-edge-threshold algorithm was used to extract time-series for underground coal fire areas. A method of time-series dynamic analysis for geometric centers of underground coal fire areas was proposed to study the propagation law and development trend of underground coal fires. The results indicate that, due to the effective prevention of the external influences of solar irradiance, topographic relief and land cover, the identification accuracy of coal fires via the use of a night-time ASTER thermal infrared image was higher by 7.70%, 13.19% and 14.51% than that of the daytime Landsat thermal infrared image in terms of producer accuracy, user accuracy and overall accuracy, respectively. The propagation direction of the geometric center of the time-series coal fire areas can be used to represent the propagation direction of underground coal fires. There exists a linear regression relationship between the migration distance of the geometric center of coal fire areas and the variable-area of coal fires in adjacent years, with the correlation coefficient reaching 0.705, which indicates that the migration distance of the geometric center of a coal fire area can be used to represent the intensity variation of underground coal fires. This method can be applied to the analysis of the trends of underground coal fires under both natural conditions and human intervention. The experimental results show that the Wuda underground coal fires spread to the southeast and that the area of the coal fires increased by 0.71 km² during the period of 2002–2003. From 2003 to 2005, Wuda’s underground coal fires spread to the northwest under natural conditions, and the area of coal fires decreased by 0.30 km² due to the closure of some small coal mines. From 2005 to 2007, due to increased mining activities, underground coal fires in Wuda spread to the east, south, west and north, and the area of coal fires increased dramatically by 1.76 km². Full article

The spatial distribution of night-time lights (NTL) provides a new perspective for studying the range and influence of human activities. However, most studies employing NTL time series are based on monthly or annual composite data, and time series studies incorporating sliding windows are currently lacking. Therefore, using National Polar-Orbiting Partnership’s visible infrared imaging radiometer suite (NPP-VIIRS) night-time light remote sensing (NTLRS) data, VNP46A2, toponym, and Yunnan census statistical data, this study proposes a sliding-window-based NTLRS time series detection and analysis method. We extracted ethnic minority areas on the PyCharm platform using ethnic minority population proportion data and toponym and excluding data representing interference from urban areas. We used a sliding window approach to analyze NTLRS time series data of each ethnic group and calculated the cosine similarity between the NTL brightness curve of original data and the sliding window analysis result. The cosine similarity was greater than 0.96 from 2018 to 2020; we also conducted a field trip to the 2019 Torch Festival to demonstrate the applicability of the employed method. Finally, the temporal and spatial pattern of the Torch Festival was analyzed using the festival in Yunnan Province as an example. Results showed that the Torch Festival, mostly celebrated by the Yi ethnic group, was usually held on the 24th (and ranged from the 22nd to 26th) day in the sixth month of the lunar calendar (LC) every year. We found that during the Torch Festival, the greater the increase in the percentage of NTL brightness reduction in the main urban area of Kunming, the greater the percentage of ethnic minorities’ NTL brightness. The width of the sliding window can be adjusted appropriately according to the research objective, with these results showing good continuity. Our study presents a new application of the sliding window approach in the field of remote sensing, suitable for research into festivals related to night lights and fire all over the world. Full article

The severity of wildfires is increasing and has driven increases in nighttime fire activity. Enhanced capability to detect the active burning regions of wildfires at night could significantly improve the effectiveness of wildfire management operations. Potassium line emission in the NIR near 770 nm is a signature of active burning. We test the use of multi-band imaging from an aircraft at night to distinguish a wood-burning fire from artificial light sources. We find that a simple ratio of the signals in two broad bands, one including 770 nm, effectively discriminates the fire from artificial light sources. This offers the possibility of nighttime fire detection with high spatial resolution using silicon sensors sensitive in the NIR. Full article

The development of industrial infrastructure in the Beijing–Tianjin–Hebei(BTH) region has been accompanied by a disorderly expansion of industrial zones and other inappropriate development. Accurate industrial heat source classification data become important to evaluate the policies of industrial restructuring and air quality improvement. In this study, a new classification of industrial heat source objects model based on active fire point density segmentation and spatial topological correlation analysis in the BTH Region was proposed. First, industrial heat source objects were detected with an active fire point density segmentation method using NPP-VIIRS active fire/hotspot data. Then, industrial heat source objects were classified into five categories based on a spatial topological correlation analysis method using POI data. Then, identification and classification results were manually validated based on Google Earth imagery. Finally, we evaluated the factors influencing the number of industrial heat sources based on an OLS regression model. A total of 493 industrial heat source objects were identified in this study with an identification accuracy of 96.14%(474/493). Compared with results for nighttime fires, the number of industrial heat source objects that were identified was higher, and the spatial coverage was greater; the minimum size of the detected objects was also smaller. Based on the function of the identified industrial heat source objects, the objects in the BTH region were then divided into five categories: cement plants (21.73%), steel plants (53.80%), coal and chemical industry (12.66%), oil and gas developments (7.81%), and other (4.01%). An analysis of their operations showed that the number of industrial heat source objects in operation in the BTH region tended to first rise and then decline during the 2012–2021 period, with the peak being reached in 2013. The results of this study will aid the rationalization of industrial infrastructure in the BTH region and, by extension, in China as a whole. Full article

It is well-documented that global nighttime temperatures have been increasing during the past few decades. For example, the average California nighttime temperature has increased at a rate of 0.7 °C per decade over the past 20 years. Temperature and atmospheric moisture (typically indicated by relative humidity in fire danger indices) are closely related, and dead fuel moisture (DFM) is a function of temperature and moisture via the equilibrium moisture content. Typically, as night temperature decreases, relative humidity increases, as does the DFM. Higher values of DFM is a factor in reducing fire behavior as the increased moisture reduces flammability. However, warmer nighttime temperatures and lower humidity allow fuel to stay drier, thus enabling fires to be more active throughout the night. Historically, fire management would often count on fires “laying down” at night as part of their tactical planning. However, an increasing number of incident reports across the western U.S have been highlighting active nocturnal fire behavior. This has consequences for firefighter safety and suppression success, impacting managed fire activities during the night, as well as the carryover into the next day. In this presentation, we examine the western U.S. trend in nighttime temperature in the context of nighttime fire behavior, discuss the potential fire management impact, and provide a global perspective. Full article