Search Results (9)

Forest fires are a global environmental threat to human life and ecosystems. This study compiles smoke alarm images from five high-definition surveillance cameras in Foshan City, Guangdong, China, collected over one year, to create a smoke-based early warning dataset. The dataset presents two key challenges: (1) high false positive rates caused by pseudo-smoke interference, including non-fire conditions like cooking smoke and industrial emissions, and (2) significant regional data imbalances, influenced by varying human activity intensities and terrain features, which impair the generalizability of traditional pre-train–fine-tune strategies. To address these challenges, we explore the use of visual language models to differentiate between true alarms and false alarms. Additionally, our method incorporates a prompt tuning strategy which helps to improve performance by at least 12.45% in zero-shot learning tasks and also enhances performance in few-shot learning tasks, demonstrating enhanced regional generalization compared to baselines. Full article

Chyulu Hills, Kenya, serves as one of the region’s water towers by supplying groundwater to surrounding streams and springs in southern Kenya. In a semiarid region, this water is crucial to the survival of local people, farms, and wildlife. The Chyulu Hills is also very prone to fires, and large areas of the range burn each year during the dry season. Currently, there are no detailed fire records or burn scar maps to track the burn history. Mapping burn scars using remote sensing is a cost-effective approach to monitor fire activity over time. However, it is not clear whether spectral burn indices developed elsewhere can be directly applied here when Chyulu Hills contains mostly grassland and bushland vegetation. Additionally, burn scars are usually no longer detectable after an intervening rainy season. In this study, we calculated the Differenced Normalized Burn Ratio (dNBR) and two versions of the Relative Differenced Normalized Burn Ratio (RdNBR) using Landsat Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data to determine which index, threshold values, instrument, and Sentinel near-infrared (NIR) band work best to map burn scars in Chyulu Hills, Kenya. The results indicate that the Relative Differenced Normalized Burn Ratio from Landsat OLI had the highest accuracy for mapping burn scars while also minimizing false positives (commission error). While mapping burn scars, it became clear that adjusting the threshold value for an index resulted in tradeoffs between false positives and false negatives. While none were perfect, this is an important consideration going forward. Given the length of the Landsat archive, there is potential to expand this work to additional years. Full article

More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, more than half of these types of fires occurred during the spring season. Although human activities are the primary cause of forest fires, the fact that they are concentrated in the spring underscores the strong association between forest fires and meteorological factors. When meteorological conditions favor the occurrence of forest fires, certain triggering factors can lead to their ignition more easily. The purpose of this study is to analyze the meteorological factors influencing forest fires and to develop a machine learning-based prediction model for forest fire occurrence, focusing on meteorological data. The study focuses on four regions within Gangwon province in South Korea, which have experienced substantial damage from forest fires. To construct the model, historical meteorological data were collected, surrogate variables were calculated, and a variable selection process was applied to identify relevant meteorological factors. Five machine learning models were then used to predict forest fire occurrence and ensemble techniques were employed to enhance the model’s performance. The performance of the developed forest fire prediction model was evaluated using evaluation metrics. The results indicate that the ensemble model outperformed the individual models, with a higher F1-score and a notable reduction in false positives compared to the individual models. This suggests that the model developed in this study, when combined with meteorological forecast data, can potentially predict forest fire occurrence and provide insights into the expected severity of fires. This information could support decision-making for forest fire management, aiding in the development of more effective fire response plans. Full article

This study collects the results obtained using the Getis-Ord local spatial autocorrelation index (Gi) with the aim of improving the classification of burned area detection maps generated from spectral indices (i.e., dNBR index) derived from Sentinel-2 satellite data. Therefore, the work proposes an adaptive thresholding approach that also includes the application of a similarity index (Sorensen–Dice Similarity Index) with the aim of adaptively correcting classification errors (false-positive burned pixels) related to the spectral response of burned/unburned areas. In this way, two new indices derived from the application of the Getis-Ord local autocorrelation analysis were created to test their effectiveness. Three wildfire events were considered, two of which occurred in Southern Italy in the summer of 2017 and one in Sardinia in the summer of 2019. The accuracy assessment analysis was carried out using the CEMS (Copernicus Emergency Management Service) on-demand maps. The results show the remarkable performance of the two new indices in terms of their ability to reduce the false positives generated by dNBR. In the three sites considered, the false-positive reduction percentage was around 95–96%. The proposed approach seems to be adaptable to different vegetation contexts, and above all, it could be a useful tool for mapping burned areas to support post-fire management activities. Full article

Photoelectric smoke detectors (SDs) often emit false alarms in studio-type apartments, where fire prevention is crucial. This study investigates the response characteristics of conventional and analog smoke detection factors to reduce false positives in studio-type apartments. A mock-up was tested based on relevant domestic laws, standards, statistical data, and experimental cases. A simulation of a cooking scenario involving burned food items was conducted, and optical density, particulate matter (PM), and carbon monoxide levels were measured and compared with actual smoke detection at six different locations. The measured values of conventional smoke detectors (CSDs) and analog smoke detectors (ASDs) at these locations were used to derive the activation time of CSDs and ASDs for the entire mock-up space. The results showed that the CSD activated at 7.42 min, while the ASD activated at 11.57 min. PM10.0, CO, and CO₂ showed similar activation time trends. The PM10.0, CO, and CO₂ concentrations at the time of SD activation were estimated. The findings suggest that a sensor with a consistent coefficient of variation, such as PM10.0 and CO, should be recommended. Full article

In this study, we explored the characteristics of thermal anomalies other than biomass burning to establish a zone map of false-positive active fires to support efficient ground validation for firefighters. We used the ASCII file of VIIRS active fire data (VNP14IMGML), which provides attributes of thermal anomalies every month from 2012 to 2020 in Indonesia. The characteristics of thermal anomalies other than biomass burning were explored using fire radiative power (FRP) values, confidence levels of active fire, fire pixel areas, and their allocations to permanent geographical features (i.e., volcano, river, lake, coastal line, road, and industrial/settlement areas). The Tukey test showed that there was a significant difference between the mean FRP values of the other thermal anomalies, type-1 (active volcano), type-2 (other static land sources), and type-3 (detection over water/offshore), at a confidence level of 95%. Most thermal anomalies other than biomass burning were in the nominal confidence level with a fire pixel area of 0.21 km². High spatial images validated these thermal anomaly types as false positives of biomass burning. A zone map of potential false-positive active fire for biomass burning was established in this study by referring to the allocation of thermal anomalies from permanent geographical features. Implementing the zone map removed approximately 13% of the VIIRS active fires as the false positive of biomass burning. Insights gleaned through this study will support efficient ground validation of actual forest/land fires. Full article

The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It exploits active fire points to train the fusion function of factors deemed influential in determining the evidence of burned conditions from reflectance values of multispectral Sentinel-2 (S2) data. The fusion function is used to compute a map of seeds (burned pixels) that are adaptively expanded by applying a Region Growing (RG) algorithm to generate the final burned area map. The fusion function is an Ordered Weighted Averaging (OWA) operator, learnt through the application of a machine learning (ML) algorithm from a set of highly reliable fire points. Its semantics are characterized by two measures, the degrees of pessimism/optimism and democracy/monarchy. The former allows the prediction of the results of the fusion as affected by more false positives (commission errors) than false negatives (omission errors) in the case of pessimism, or vice versa; the latter foresees if there are only a few highly influential factors or many low influential ones that determine the result. The prediction on the degree of pessimism/optimism allows the expansion of the seeds to be appropriately tuned by selecting the most suited growing layer for the RG algorithm thus adapting the algorithm to the context. The paper illustrates the application of the automatic method in four study areas in southern Europe to map burned areas for the 2017 fire season. Thematic accuracy at each site was assessed by comparison to reference perimeters to prove the adaptability of the approach to the context; estimated average accuracy metrics are omission error = 0.057, commission error = 0.068, Dice coefficient = 0.94 and relative bias = 0.0046. Full article

Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters during 24-h shifts using sensor belts equipped with a dry-lead electrocardiograph (ECG) and a three-axial accelerometer. Levels of stress experienced during fire incidents were evaluated via a brief self-assessment questionnaire. Types of physical activity were distinguished basing on accelerometer readings, and heart rate variability (HRV) time series were segmented accordingly into corresponding fragments. Those segments were classified as stress/no-stress conditions. Receiver Operating Characteristic (ROC) analysis showed true positive classification as stress condition for 15% of incidents (while maintaining almost zero False Positive Rate), which parallels the amount of truly stressful incidents reported in the questionnaires. These results show a firm correspondence between the perceived stress level and physiological data. Psychophysiological measurements are reliable indicators of stress even in ecological settings and appear promising for chronic stress monitoring in high-risk jobs, such as firefighting. Full article

The Global Fire Emissions Database (GFED)—currently by far the most widely used global fire emissions inventory—is primarily driven by the 500 m MODIS MCD64A1 burned area (BA) product. This product is unable to detect many smaller fires, and the new v4.1s of GFED addresses this deficiency by using a ‘small fire boost’ (SFB) methodology that estimates the ‘small fire’ burned area from MODIS active fire (AF) detections. We evaluate the performance of this approach in two globally significant agricultural burning regions dominated by small fires, eastern China and north-western India. We find the GFED4.1s SFB can affect the burned area and fire emissions data reported by GFED very significantly, and the approach shows some potential for reducing low biases in GFED’s fire emissions estimates of agricultural burning regions. However, it also introduces several significant errors. In north-western India, the SFB slightly improves the temporal distribution of agricultural burning, but the magnitude of the additional burned area added by the SFB is far too low. In eastern China, the SFB appears to have some positive effects on the magnitude of agricultural burning reported in June and October, but significant errors are introduced in the summer months via false alarms in the MODIS AF product. This results in a completely inaccurate ‘August’ burning period in GFED4.1s, where false fires are erroneously stated to be responsible for roughly the same amount of dry matter fuel consumption as fires in June and October. Even without the SFB, we also find problems with some of the burns detected by the MCD64A1 burned area product in these agricultural regions. Overall, we conclude that the SFB methodology requires further optimisation and that the efficacy of GFED4.1s’ ‘boosted’ BA and resulting fire emissions estimates require careful consideration by users focusing in areas where small fires dominate. Full article