Geospatial analysis of wildfire patterns and temporal trends in Kohat Division, Pakistan, using Remote Sensing and GIS

Teachers’ proactive and reactive classroom management strategies are a significant component of teaching effectiveness. Teachers need to develop such strategies to structure a positive classroom environment. In addition, teachers’ self-efficacy beliefs concerning their classroom management strategies are equally significant. This research aimed to identify the teachers’ effective proactive and reactive classroom management strategies. It also sought to investigate the obstacles that inhibit proactive classroom management use and identify the association between teachers’ self-efficacy and classroom management practices. The research adopted a mixed-methods paradigm, consisting of two tools: a questionnaire and semi-structured interviews. The sampling included 80 Saudi teachers of English as a foreign language (EFL) participated in the survey and eight teachers participated in the interviews. The results showed that EFL teachers find proactive classroom management strategies more effective than reactive strategies. In addition, there was a difference between novice and experienced teachers’ effective classroom management strategies, in which experienced teachers found proactive strategies more effective. The findings also indicated that there are four types of obstacles that hinder proactive classroom management strategies. System-related obstacles (subject-centered curriculum and institutional rules), system/teacher related obstacles (institutional rules and teachers’ predispositions concerning e-tools), teacher-related obstacles (lack of understanding of the discipline plan), and student-related obstacles (unmotivated students). The final finding cited the positive association between teachers’ high self-efficacy and proactive classroom management application.

Wildfires are a dominant disturbance to boreal forests, and in North America, they typically cause widespread tree mortality. Forest fire burn severity is often measured at a plot scale using the Composite Burn Index (CBI), which was originally developed as a means of assigning severity levels to the Normalized Burn Ratio (NBR) computed from Landsat satellite imagery. Our study investigated the potential to map biophysical indicators of burn severity (residual green vegetation and charred organic surface) at very high (3 cm) resolution, using color orthomosaics and vegetation height models derived from UAV-based photographic surveys and Structure from Motion methods. These indicators were scaled to 30 m resolution Landsat pixel footprints and compared to the post-burn NBR (post-NBR) and differenced NBR (dNBR) ratios computed from pre- and post-fire Landsat imagery. The post-NBR showed the strongest relationship to both the fraction of charred surface (exponential R2 = 0.79) and the fraction of green crown vegetation above 5 m (exponential R2 = 0.81), while the dNBR was more closely related to the total green vegetation fraction (exponential R2 = 0.69). Additionally, the UAV green fraction and Landsat indices could individually explain more than 50% of the variance in the overall CBI measured in 39 plots. These results provide a proof-of-concept for using low-cost UAV photogrammetric mapping to quantify key measures of boreal burn severity at landscape scales, which could be used to calibrate and assign a biophysical meaning to Landsat spectral indices for mapping severity at regional scales.

We assessed an existing method of remote sensing of wildland fire burn severity for its applicability in south-eastern USA vegetation types. This method uses Landsat satellite imagery to calculate the Normalised Burn Ratio (NBR) of reflectance bands sensitive to fire effects, and the change in NBR from pre- to post fire (dNBR) to estimate burn severity. To ground-truth ranges of NBR and dNBR that correspond to levels of burn severity, we measured severity using the Composite Burn Index at 731 locations stratified by plant community type, season of measurement, and time since fire. Best-fit curves relating Composite Burn Index to NBR or dNBR were used to determine reflectance value breakpoints that delimit levels of burn severity. Remotely estimated levels of burn severity within 3 months following fire had an average of 78% agreement with ground measurements using NBR and 75% agreement using dNBR. However, percentage agreement varied among habitat types and season of measurement, with either NBR or dNBR being advantageous under specific combinations of conditions. The results suggest this method will be useful for monitoring burned area and burn severity in south-eastern USA vegetation types if the provided recommendations and limitations are considered.

Remote sensing using Landsat Thematic Mapper (TM) satellite imagery is increasingly used for mapping wildland fire burned area and burn severity, owing to its frequency of collection, relatively high resolution, and availability free of charge. However, rapid response of vegetation following fire and frequent cloud cover pose challenges to this approach in the southeastern US. We assessed these timing constraints by using a series of Landsat TM images to determine how rapidly the remotely sensed burn scar signature fades following prescribed burns in wet flatwoods and depression swamp community types in the Apalachicola National Forest, Florida, USA during 2006. We used both the Normalized Burn Ratio (NBR) of reflectance bands sensitive to vegetation and exposed soil cover, as well as the change in NBR from before to after fire (dNBR), to estimate burned area. We also determined the average and maximum amount of time following fire required to obtain a cloud-free image for burns in each month of the year, as well as the predicted effect of this time lag on percent accuracy of burn scar estimates. Using both NBR and dNBR, the detectable area decreased linearly 9% per month on average over the first four months following fire. Our findings suggest that the NBR and dNBR methods for monitoring burned area in common southeastern US vegetation community types are limited to an average of 78–90% accuracy among months of the year, with individual burns having values as low as 38%, if restricted to use of Landsat 5 TM imagery. However, the majority of burns can still be mapped at accuracies similar to those in other regions of the US, and access to additional sources of satellite imagery would improve overall accuracy.

The rapid construction of expressways in China has brought significant economic and social benefits, but it has also imposed substantial ecological pressures, particularly in sensitive regions. Landscape ecological risk assessment, as an important means to predict and measure the adverse effects of human activities on the ecological environment, is being paid more and more attention. However, most studies focus on the static landscape mosaic pattern and lack dynamic analysis. Moreover, they mainly focus on the ecological effect of the road operation stage, ignoring the monitoring and analysis of the whole construction process. Based on this, the current study examines the landscape ecological risk and land use changes along the Linghua Expressway in Gansu Province using high-resolution GF-1 remote sensing imagery. A landscape ecological risk assessment (LERA) model was employed to quantify the land use changes and assess the ecological risks before and after the expressway construction between 2018 and 2022. The results revealed a decrease in cropland and forest land, accompanied by an increase in the grassland and road areas. The landscape ecological risk index decreased from 0.318 in 2018 to 0.174 in 2022, indicating an improvement in ecological resilience. However, high-risk zones remain near the expressway, emphasizing the need for continuous monitoring and proactive ecological management strategies. These findings contribute to sustainable infrastructure planning, particularly in ecologically sensitive regions.

This study investigates long-term trends in Land Surface Temperature (LST) and vegetation cover in four rapidly urbanizing cities of the Indochina peninsula. These cities include Bangkok, Ho Chi Minh City, Vientiane, and Phnom Penh, and the analysis covers a 30-year period. The objective is to understand how urban expansion has influenced local climate and vegetation dynamics. Using Landsat satellite imagery and the cloud-based computing platform, Google Earth Engine, the Enhanced Vegetation Index (EVI) and LST were estimated and analyzed. The Mann–Kendall (MK) non-parametric statistical trend test was applied to identify significant temporal trends and it’s spatial variation. This study stands out by analyzing 30 years of satellite data to examine changes in LST and vegetation across multiple cities. Unlike earlier research, it also considers how these environmental changes affect population exposure, providing a more complete picture of urban growth impacts. Results reveal consistent increases in LST and declines in vegetation across all cities, with strong spatial correlation in urbanized zones. A notable inverse relationship was observed between EVI and LST, indicating that vegetation loss contributes significantly to local warming. The Mekong River plays a moderating role in Vientiane and Phnom Penh, showing a cooling effect in its immediate surroundings while stabilizing vegetation trends. Population exposure to LST changes was assessed by overlaying trend results with population data. Findings show that approximately 34.84% of the combined urban population, totaling over 9.3 million people, reside in areas with significant surface temperature increases. Among the four cities, Phnom Penh showed the highest affected percentage, while Vientiane had the lowest. These findings underscore the critical need for climate-resilient urban planning and the expansion of green infrastructure to mitigate the impacts of urban heat islands in Indochina’s fast-growing cities.

Forest fires represent a critical global threat to both humans and ecosystems. This study examines the intensity and impacts of Chilgoza (Pinus gerardiana) Pine Forest fires by using advanced remote sensing techniques comprising Normalized Burn Ratio (NBR) and Difference Normalized Burn Ratio (dNBR) analyses based on Landsat 9 datasets. The study highlights the severe effect of these fires, resulting in noteworthy losses of livestock and private properties and widespread damage to 10,156.53 acres of the Chilgoza Pine Forest. A comprehensive variable correlation analysis is conducted to gain deeper insights into the influencing factors causing forest fires. Spearman's Rank Correlation Coefficient was used to assess the association between burnt and unburnt areas and various independent factors. The analysis reveals compelling evidence of significant correlations with forest fire prevalence. This study found moderate negative (-0.532, p < 0.05) and positive (0.513, p < 0.05) correlations with elevation and Land Surface Temperature (LST), respectively, and a weak positive correlation (0.252, p < 0.05) with a Wind Speed (V). To predict forest fire susceptibility and better understand the contributing factors, three machine learning models, Random Forest (RF), XGBoost, and logistic regression, are applied to assess variable importance scores. Among the considered factors, LST is the most critical variable, with consistently high variable importance scores (100 %, 96 %, and 59 %) across all three models. Wind Speed (V) also proved influential in all models, with variable importance scores of 78 %, 83 %, and 61 % for RF, XGBoost, and logistic regression, respectively. Moreover, elevation significantly influences the frequency of forest fires, as evidenced by variable importance scores ranging from 26 % to 100 %. Comparatively, the Random Forest model outperforms XGBoost and Logistic Regression in predicting forest fire vulnerability. During the training stage, the Random Forest (RF) model achieves an impressive classification accuracy of 99.1 %, followed by XGBoost with 94.5 % and Logistic Regression with 85.6 %. On evaluation with the validation dataset, the accuracies remain promising, with RF at 96.4 %, XGBoost at 91.1 %, and Logistic Regression at 84.6 %. Based on the Random Forest model, the identified high-risk sites offer valuable insights for proactive fire management and prevention strategies. This study provides a robust predictive model and a comprehensive understanding of forest fire severity and impacts. Future research should consider climate change scenarios and account for human activities to enhance fire behavior predictions and risk assessment models.

Abstract. Forest fires in Türkiye, like in other regions, have detrimental effects on wildlife habitats, water quality, air pollution, climate change, and the economy. These fires become particularly concerning during the dry summer months. In 2021, forest fires affected over 150 thousand hectares of land across the country, with the Manavgat district in Antalya province alone witnessing the burning of approximately 60 thousand hectares of forest area. This study aims to assess the applicability and suitability of Fire Weather Index (FWI) data derived from meteorological station data in the Antalya region, as well as EFFIS FWI data generated using satellite-based meteorological information, for fire danger mapping during the Manavgat forest fire that occurred between 28 July and 6 August 2021. Additionally, correlation analyses were performed between the two FWI datasets and other relevant variables, including the difference in Normalized Burn Ratio (dNBR), the difference in Land Surface Temperature (dLST), and Fire Radiative Power (FRP) data detected from MODIS and VIIRS satellites. The results of the correlation analysis indicated that the FWI values obtained using in-situ meteorology station data showed much higher correlations than FWI values obtained from EFFIS, with the highest correlation (73%) observed with dLST data. Consequently, the fire danger map was created using the in-situ meteorological data, given its stronger correlation. The results prominently revealed a widespread high-risk level across the entire Antalya province, with the Manavgat district classified into the "Extreme" and "Very Extreme" FWI classes, emphasizing the critical importance of utilizing in-situ meteorological data for precise fire danger assessments and proactive fire management strategies.

BackgroundAdherence to positive airway pressure (PAP) therapy is essential for the benefits of therapy to be realised. Telemedicine-based strategies provide a new option for enhanced monitoring and intervention to promote adherence during PAP. This study investigated the impact of telemedicine-based proactive patient management on PAP therapy termination rates versus standard care.MethodsObservational data were obtained from ResMed Germany Healthcare, a German homecare provider. Patients were undergoing routine homecare using either a standard or proactive management strategy. The proactive strategy used data from AirView™, a cloud-based remote monitoring system, to prompt patient contact and information sharing/education. Patients receiving their first PAP therapy were included and analysed in matched pairs.ResultsIn all, 3401 patients were included in each group. In the first year of PAP therapy, overall therapy termination rate was significantly lower (5.4% vs 11.0%; p < 0.001) and time to therapy termination was significantly longer (348 ± 58 vs 337 ± 76 days; p < 0.05) in the proactive versus standard care group. Cox proportional hazard analysis revealed a significantly reduced risk of PAP termination in the proactive versus the standard care group (hazard ratio 0.48, 95% confidence interval 0.4–0.57). Findings were consistent in subanalyses according to gender, type of device and insurance status, and in patients aged ≥40 years. However, in the subgroup of patients aged younger than 40 years, the risk of PAP termination was similar in the proactive and standard groups.ConclusionA telemedicine-based proactive management strategy compared with standard care of PAP patients was associated with a lower long-term therapy termination rate.

This paper sought to extend the existing talent management literature through an exploratory investigation of talent loss resulting from the possible departure of talented employees from the procurement function of hotels. Through a multiple case study of five organizations in the hospitality industry, we found that the departure of talented procurement managers disrupts supply chain operations and leads to a loss of valuable explicit, tacit, and relational knowledge. Procurement managers were found to hold critical skills and knowledge that are essential for the case organizations. Hence, more proactive management strategies were adopted. Managers seeking to minimize the negative impact of talent loss in the procurement function would be well-advised to treat this procurement function as a strategic one and to adopt proactive and documented management strategies. Firms should be aware of the type of important knowledge and of the importance of aligning their strategies with such knowledge. To retain relational knowledge in case of talent loss, strategies should be designed to reduce the firm’s reliance on personal contacts and emotion-based trust in supplier relationships. We concluded the paper with implications for future research and managerial practice for managing talent loss, with an emphasis on sustainability in the hospitality industry.

Mapping fire extent and burn severity in Alaskan tussock tundra: An analysis of the spectral response of tundra vegetation to wildland fire

Fishing effects in northeast Atlantic shelf seas: patterns in fishing effort, diversity and community structure. III. International trawling effort in the North Sea: an analysis of spatial and temporal trends

Fires were once a natural phenomenon that helped to shape species distribution, contributed to the persistence of fire-dependent species, and assisted the natural evolution of ecosystems. However, nowadays, most of the forest fires worldwide are not of natural causes. Therefore, wildfires have received significant attention over the past few decades. Major ecological and policy changes were stimulated by historical frequency, extent, and severity of fires in the dry forests. These fires are important at both local to regional scales, as it might change the maintenance of landscape structure, composition, and function. Moreover, it affects pollutants, impacts air quality and raises human health risks. Many studies suggested using remote sensing data and techniques to assess fire characteristics and post-fire effects. Due to its ability to quantify patterns of variation in space and time, the remote sensing data are especially important to detect active fire extents at local and regional scales, mapping fuel loading and identify areas with long or problematic natural recovery. In the past few decades, the advantages of multi-temporal remote sensing techniques to monitor landscape change in a rapid and cost-effective manner, are reported in the scientific literature. Many studies focused on the development of techniques to evaluate and quantify fire behavior and fuel combustion. Yet the main contribution is recorded for spectral indices, e.g. the Normalized Burn Ratio (NBR), the difference in the Normalized Burn Ratio between pre- and post-fire images (dNBR), and the Normalized Difference Vegetation Index (NDVI), which are calculated by a simple combinations of different sensor bands, rely on spectral changes of the burning or burned surfaces. Numerous papers are focused on more advanced and very detailed spectral models of fuel and post-fire ash residues, mainly using laboratory spectrometers, e.g., Fourier Transform Infrared (FTIR). However, many of the developed models are not applicable in the real world. In the current talk, we will present the most recent studies and scientific activities in the field of (1) active fire detection and characterization, using mainly hyperspectral ground and airborne technologies; (2) future space-borne applications on board of nano- and micro-satellites; (3) discuss the contribution of detailed and precise spectral models for post-fire ecological effects studies; (4) describe field assessment; (5) discuss management applications and future directions of fire-related remote sensing research.

Several studies have used satellite data to map different levels of fire severity present within burned areas. Increasingly, fire severity has been estimated using a spectral index called the normalized burn ratio (NBR). This letter assesses the performance of the NBR against ideal requirements of a spectral index designed to measure fire severity. According to index theory, the NBR would be optimal for quantifying fire severity if the trajectory in spectral feature space caused by different levels of severity occurred perpendicular to the NBR isolines. We assess how well NBR meets this condition using reflectance data sensed before and shortly after fires in the South African savanna, Australian savanna, Russian Federation boreal forest, and South American tropical forest. Although previous studies report high correlation between fire severity measured in the field- and satellite-derived NBR, our results do not provide evidence that the performance of the NBR is optimal in describing fire severity shortly after fire occurrence. Spectral displacements due to burning occur in numerous directions relative to the NBR index isolines, suggesting that the NBR may not be primarily and consistently sensitive to fire severity. Findings suggest that the development of the next generation of methods to estimate fire severity remotely should incorporate knowledge of how fires of different severity displace the position of prefire vegetation in multispectral space.

Remote Sensing is a way to provide information efficiently both in terms of cost and time. In addition, the use of remote sensing in mapping the burned area can be carried out on a large scale but with a fast time for the prevention of land fires. Monitoring of forest fires is carried out to see locations that often become fire hotspots every year and to prevent frequent land and forest fires.The method used in this study is to map the burned area using multi-temporal data using the Normalized burning Ratio and Normalized burning Ratio 2 indices from Sentinel-2 images in May and September 2019. The trend data can be used to evaluate the moratorium on forest business permits or a peatland. Sentinel 2 has a higher spatial resolution of 20 meters compared to other multispectral images that can be accessed easily and free of charge, so it is suitable to be applied in areas that are not too large and minimizes the value of estimation errors, using Sentinel 2 imagery which has 13 channels will Selected several channels that can be used for index transformation, namely the NBR (Normalized Burn Ratio) and NBR2 (Normalized Burn Ratio2) indexes, both indices utilize Near Infrared, SWIR1, and SWIR2 channels which can provide information about the area after land fires,The research results obtained that burned areas are often identified as barren land, such as peat canals or vacant land. This is because the spectral reflection characteristics of objects in burnt areas are the same as those in non-vegetated land areas when the NBR index is transformed using the NIR, SWIR1 and SWIR2 channels.&#x0D; Keywords: Forest and land fires, NBR Indices, Sentinel-2.