High-severity Areas Research Articles

Comparison between artificial restoration and natural recovery in vegetation regrowth following high-frequency fire disturbances in the Hengduan Mountains, Southwest China

The Hengduan Mountains in Southwest China are currently facing a recurring and escalating threat of forest fires, posing a significant challenge to vegetation restoration efforts given the ecological value of this region. However, the effects of artificial restoration compared to natural regeneration on vegetation recovery dynamics have been less explored. This study focused on two sites in the Hengduan Mountains, primarily composed of conifer forests. One site employed a mix of artificial restoration measures, including tree planting, aerial grass seeding, and fertilizer application. The other site, which is relatively drier and colder, relied on natural regeneration. Four fire events in these two sites were compared to examine the effects of artificial restoration on vegetation recovery using remotely sensed data from Landsat and MODIS. Fire severity was classified using the differenced Normalized Burn Ratio (dNBR) and validated with field samples. Vegetation greenness loss and recovery trajectory were quantified using the near-infrared reflectance of vegetation (NIRv). The effects of artificial restoration were further analyzed after employing unburnt controls to minimize the impact of climate fluctuations. Results showed that fire severity mapping achieved an overall accuracy of 82%. The average NIRv loss across the four fires was 30%, 49%, and 69% in areas of low, moderate, and high severity, respectively. In low severity areas, natural and artificial recovery rates were similar, at 21% and 26% respectively, three years post-fire. In moderate severity areas, artificial restoration achieved a 46% recovery rate compared to 31% for natural recovery. In high severity areas, artificial restoration resulted in a 63% recovery rate versus 45% for natural recovery. Although the differences in recovery patterns may also be attributed to inherent variations in the vegetation composition, physical environment, and their interactions, this study suggests that high severity areas benefit significantly from artificial intervention for rapid recovery. Future studies should include more controlled experiments at the plot scale to further elucidate the processes of vegetation response to different artificial restoration measures.

Assessing the Capacity of High-resolution Commercial Satellite Imagery for Grapevine Downy Mildew Detection and Surveillance in New York state.

Grapevine downy mildew (GDM), caused by the oomycete Plasmopara viticola, can cause 100% yield loss and vine death under conducive conditions. High resolution multispectral satellite platforms offer the opportunity to track rapidly spreading diseases like GDM over large, heterogeneous fields. Here, we investigate the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance. A team of trained scouts rated GDM severity and incidence at a research vineyard in Geneva, NY, USA from June to August of 2020, 2021, and 2022. Satellite imagery acquired within 72 hours of scouting was processed to extract single-band reflectance and vegetation indices (VIs). Random forest models trained on spectral bands and VIs from both image datasets could classify areas of high and low GDM incidence and severity with maximum accuracies of 0.85 (SkySat) and 0.92 (PlanetScope). However, we did not observe significant differences between VIs of high and low damage classes until late July-early August. We identified cloud cover, image co-registration, and low spectral resolution as key challenges to operationalizing satellite-based GDM surveillance. This work establishes the capacity of spaceborne multispectral sensors to detect late-stage GDM and outlines steps towards incorporating satellite remote sensing in grapevine disease surveillance systems.

Microenvironment heterogeneity affected by anthropogenic wildfire-perturbed soil mediates bacterial community in Pinus tabulaeformis forests.

In recent years, the frequency and intensity of anthropogenic wildfires have drastically increased, significantly altering terrestrial ecosystems worldwide. These fires not only devastate vegetative cover but also impact soil environments and microbial communities, affecting ecosystem structure and function. The extent to which fire severity, soil depth, and their interaction influence these effects remains unclear, particularly in Pinus tabulaeformis forests. This study investigated the impact of wildfire intensity and soil stratification on soil physicochemical properties and microbial diversity within P. tabulaeformis forests in North China. Soil samples were collected from different fire severity zones (Control, Light, Moderate, High) and depths (topsoil: 0-10 cm; subsoil: 10-20 cm). Analyses included measurements of soil pH, organic carbon (SOC), total nitrogen (TN), and other nutrients. Microbial diversity was assessed using 16S rRNA gene sequencing. Our findings revealed significant variations in soil pH, SOC, TN, and other nutrients with fire severity and soil depth, profoundly affecting microbial community composition and diversity. Soil pH emerged as a critical determinant, closely linked to microbial α-diversity and community structure. We found that fire severity significantly altered soil pH (p = 0.001), pointing to noteworthy changes in acidity linked to varying severity levels. Topsoil microbial communities primarily differentiated between burned and unburned conditions, whereas subsoil layers showed more pronounced effects of fire severity on microbial structures. Analysis of bacterial phyla across different fire severity levels and soil depths revealed significant shifts in microbial communities. Proteobacteria consistently dominated across all conditions, indicating strong resilience, while Acidobacteriota and Actinobacteriota showed increased abundances in high-severity and light/moderate-severity areas, respectively. Verrucomicrobiota were more prevalent in control samples and decreased significantly in fire-impacted soils. Chloroflexi and Bacteroidota displayed increased abundance in moderate and high-severity areas, respectively. Correlation analyses illustrated significant relationships between soil environmental factors and dominant bacterial phyla. Soil organic carbon (SOC) showed positive correlations with total nitrogen (TN) and alkaline hydrolysable nitrogen (AN). Soil pH exhibited a negative correlation with multiple soil environmental factors. Soil pH and available phosphorus (AP) significantly influenced the abundance of the phylum Myxococcota. Soil water content (WC) significantly affected the abundances of Acidobacteriota and Actinobacteriota. Additionally, ammonium nitrogen (NH4 +-N) and nitrate nitrogen (NO3 --N) jointly and significantly impacted the abundance of the phylum Chloroflexi. This study highlights the significant long-term effects of anthropogenic wildfires on soil microenvironment heterogeneity and bacterial community structure in P. tabulaeformis forests in North China, 6 years post-fire. Our findings demonstrate that fire severity significantly influences soil pH, which in turn affects soil nutrient dynamics and enhances microbial diversity. We observed notable shifts in the abundance of dominant bacterial phyla, emphasizing the critical role of soil pH and nutrient availability in shaping microbial communities. The results underscore the importance of soil stratification, as different soil layers showed varying responses to fire severity, highlighting the need for tailored management strategies. Future research should focus on long-term monitoring to further elucidate the temporal dynamics of soil microbial recovery and nutrient cycling following wildfires. Studies investigating the roles of specific microbial taxa in ecosystem resilience and their functional contributions under varying fire regimes will provide deeper insights. Additionally, exploring soil amendments and management practices aimed at optimizing pH and nutrient availability could enhance post-fire recovery processes, supporting sustainable ecosystem recovery and resilience.

Assessment of Pre‐ and Post‐Fire Fuel Availability for Wildfire Management Based on L‐Band Polarimetric SAR

AbstractMany communities coexist with wildfires that lead to loss of lives, property, and ecosystem services. Remote sensing tools can aid disaster response and post‐event assessment, offering fire agencies opportunities for additional surveillance with radar, an all‐weather instrument that can image day or night. The Station (2009) and Bobcat (2020) Fires are the two largest fires in Los Angeles County history, each burning over 100,000 acres. These areas are imaged with NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar L‐band instrument. We test whether polarimetric radar can detect fire scars, burn severity, and different fuel types through its sensitivity to different scattering mechanisms. Polarimetric SAR products are moved into geographic information system‐friendly formats, and in lieu of available field measurements are analyzed alongside agency data showing fire perimeters, burn progression outlines, and soil burn severity. We find that the HV polarization returns and the primary scattering mechanism, quantified through the Cloude‐Pottier decomposition, are the most sensitive parameters. Higher HV values pre‐fire correspond well to areas of moderate and high soil burn severity, and the pattern of fire progression follows higher HV to some extent. Using an HV difference threshold of 1.5 dB, the Bobcat burn scar is identified at 0.70 accuracy when compared with the published fire perimeter. Alpha 1 Angle can also demonstrate sensitivity to soil burn severity pre‐ and post‐fire, showing vegetation types with increased surface scattering post‐fire, which can be used to map burn scars and track recovery by vegetation type.

Dynamics of major environmental disasters involving fire in the Brazilian Pantanal

The 2020 environmental catastrophe in Pantanal has highlighted the fragility of environmental policies and practices for managing and fighting fires in this biome. Therefore, it is essential to know the causes and circumstances that potentiate these fires. This study aimed to: (I) assess the relationship between fire foci and carbon absorption (GPP), precipitation, and carbon dioxide (CO2) flux; (ii) analyze vegetation recovery using the differenced normalized burn ratio (ΔNBR) in Brazilian Pantanal between 2001 and 2022; and (iii) identify priority areas, where the highest intensities of fire foci have occurred, in order to guide public policies in Brazil to maintain local conservation. To this purpose, fire foci were detected using data from the MODIS MOD14/MYD14 algorithm, annual precipitation with CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), and CO2 flux using the MODIS/MODO9A1 product, and Gross Primary Production (GPP) with the MODIS/MOD17A2 product. The severity of the burned area was also assessed using the ΔNBR index and the risk areas were determined using the averages of these images. During the time series studied, a total of 300,127 fire foci were detected throughout the Pantanal, where 2020 had the highest number of foci and the lowest accumulated precipitation. The years with the highest precipitation were 2014 and 2018. The year 2018 was also the second year with the highest GPP value. The Pettit test showed a trend for 2008 and 2011 as the points of change in the CO2 flux and GPP variables. Principal component analysis clustered fire foci and precipitation on opposite sides, as well as GPP and CO2 flux, while ΔNBR clustered HS, MHS and MLS classes with the years 2020, 2019, 2002 and 2021. There was a high negative correlation between fire foci × rainfall and GPP × CO2 flux. The years with the largest areas of High severity (HS), Moderate-high severity (MHS) and Moderate-low severity (MLS) classes were 2020 and 2019, respectively. The most vulnerable areas for severe fires were the municipalities of Cáceres, Poconé, and Corumbá. The major fire catastrophe in 2020 is correlated with the low precipitation in 2019, the high precipitation in 2018, and the increased GPP, as well government policies unfavorable to the environment.

MAPPING FIRE SEVERITY FROM RECENT CALIFORNIA WILDFIRES USING SATELLITE IMAGERY

Abstract. Urban sprawl has become a huge concern for cities like Los Angeles, New York, and Chicago in recent years. As urban sprawl pushes urbanization into city suburbs and outskirts, forest fragmentation becomes evidently prevalent and exposes forests to high temperatures, pollution, pests, and fires that threaten forest health. A 2021 report titled Rebuilding for a Resilient Recovery affirmed that the frequency and damage potential of wildfires have been exacerbated by climate change and urban sprawl especially in California. Globally, these fires can be attributed to both natural and anthropogenic drivers such as deforestation, agriculture, mining, and industrialization. Future projections predict that these incidences of fires will only worsen as the planet continues to warm further, with emphasis on the spread and intensities of the annual California wildfires over the decade. Quantifying the consequences of these fires on global climate change has become crucial and with the emergence of advanced GIS mapping tools, focus, visualization, and interpretation of fire and burn severity has become easier. However, knowledge and understanding of wildfire dynamics is limited especially in terms of fuel load, impacts on vegetation health, aerosol release and associated movement in the atmosphere. It is therefore important to address these gaps to make better and informed actions towards forest use, protection, management, and policies and broadly towards ambitious climate goals such as the UN’s Carbon Neutral goal by 2050. This study uses Sentinel 2A data from the Copernicus fleet between 2018 and 2022 to identify and assess the burn severity of affected areas in Sonoma County, California. The aim of the study is to understand the impacts of fires of fire on vegetation health and the post-fire recovery process. The Normalized Burn Ration Index (NBRI) was used to identify and measure the extent of the burnt areas within the county and their severity and Normalized Difference Vegetation Index (NDVI) was used as a measure of forest heath. The results show that Sonoma County has become a high burn severity area with a major decrease in unburned areas between 2018 and 2022. NDVI values recorded all decrease from January to December for all the years because of pre-fire season drought. The wildfire season begins in May and before then there are seasonal droughts that occur hence accounting for the initial decline in NDVI. The least values recorded were between 0.5 and 0.57 for September, indicating sparse and unhealthy vegetation because of sharp declines during the fire season.

Modelling and mapping of soil erosion risk based on GIS and PAP/RAC guidelines in the watershed of Tassaoute (Central High-Atlas, Morocco)

Morocco watersheds, which provided many ecosystem services necessary for the socio-economic life of rural communities, are experiencing significant change and environmental problems. Therefore, examining potential soil erosion considered a major problem in the Moroccan highlands is very important to prioritize high erosion severity areas. Keeping in view of the above aspects, the present study aimed to evaluate and map areas at risk of water erosion in the upstream Tassaoute watershed (central High Atlas, Morocco), using the Priority Action Program/Regional Activity Center (PAP/RAC) method associated with Geographic Information Systems (GIS) and remote sensing. The PAP/RAC approach consisted of integrating the natural factors that influence water erosion, namely slope, lithology, vegetation cover and land use. This method provided an accurate cartographic product that reflects the reality of the state of soil degradation and the qualitative assessment of erosion. The generated erosion risk map of the study area showed that the phenomenon of erosion threatens this basin, especially in the middle and downstream, such that 40% of the basin surface has significant erosion and the high and very high degree of erosion represented 27% of the total surface of the study area. These results therefore demonstrated the PAP/CAR model reliability in assessing and mapping of water erosion risks in the upstream Tassaoute basin.

Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index

ABSTRACT Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics by using free and open-access remote-sensing images. However, mapping consecutive multiple disturbance agents is challenging using existing automated change-detection algorithms because the reduced canopy reflectance and the smoothing of consecutive disturbance signals mean that the initial disturbance cannot be spectrally separated from the second disturbance. Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). This contributes to biases in long-term forest-recovery monitoring, which are not conducive to the guidance of post-fire vegetation recovery. Here, we propose a modified disturbance index to separate the spectral characteristics of fire and forest logging using normalized tasseled-cap components (brightness and wetness) and detect the spatiotemporal distribution of post-fire logging by means of an index threshold and image differencing. On this basis, the recovery patterns of the post-fire forest are differentiated by considering the cumulative effect of fire, post-fire logging, and recovery approaches. The method is tested in the burn areas of the 5.6 Fire in the Greater Hinggan Mountain area (the largest forest fire in recorded history in China), giving an overall accuracy of 85% in post-fire forest logging mapping. Our results confirm that biological legacies (i.e. trees, logs, and snags) were removed across many areas in the fire, with activities peaking in the second year after the fire and located chiefly in areas of moderate and high burn severity. By identifying post-fire logging, the fluctuation and high disturbance index of the conventional temporal trajectory in the early stage of forest recovery are explained. The large-scale salvage logging slowed the recovery of the post-fire forest ecosystem and influenced the recovery process through the interaction of burn severity and active forest restoration. In areas of high burn severity, assisted natural regeneration (i.e. natural regeneration with artificial aids such as clearing the snags, weeding, digging pits, and supplemental planting) preserved biological legacies and achieved a higher proportion of forest recovery, second only to non-logged areas where natural regeneration (i.e. forestry recovering naturally without any artificial intervention) occurred. By contrast, salvage logging followed by artificial regeneration (i.e. clear-cutting all dead or damaged trees followed by tree planting or artificial seeding) hindered vegetation recovery in the early stage, but it improved the recovery rate in years 10–20 and approached the recovery proportion of non-logged areas where natural regeneration occurred as the recovery progressed and habitat conditions improved. The proposed method is shown to offer important advantages in detecting post-fire salvage logging, and it provides improved guidance for forest managers in developing strategies for forest recovery.

Mapping burn severity and monitoring CO content in Türkiye's 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform.

This study investigated forest fires in the Mediterranean of Türkiye between July 28, 2021, and August 11, 2021. Burn severity maps were produced with the difference normalised burned ratio index (dNBR) and difference normalised difference vegetation index (dNDVI) using Sentinel-2 images on the Google Earth Engine (GEE) cloud platform. The burned areas were estimated based on the determined burning severity degrees. Vegetation density losses in burned areas were analysed using the normalised difference vegetation index (NDVI) time series. At the same time, the post-fire Carbon Monoxide (CO) column number densities were determined using the Sentinel-5P satellite data. According to the burn severity maps obtained with dNBR, the sum of high and moderate severity areas constitutes 34.64%, 20.57%, 46.43%, 51.50% and 18.88% of the entire area in Manavgat, Gündoğmuş, Marmaris, Bodrum and Köyceğiz districts, respectively. Likewise, according to the burn severity maps obtained with dNDVI, the sum of the areas of very high severity and high severity constitutes 41.17%, 30.16%, 30.50%, 42.35%, and 10.40% of the entire region, respectively. In post-fire NDVI time series analyses, sharp decreases were observed in NDVI values from 0.8 to 0.1 in all burned areas. While the Tropospheric CO column number density was 0.03mol/m2 in all regions burned before the fire, it was observed that this value increased to 0.14mol/m2 after the fire. Moreover, when the area was examined more broadly with Sentinel 5P data, it was observed that the amount of CO increased up to a maximum value of 0.333mol/m2. The results of this study present significant information in terms of determining the severity of forest fires in the Mediterranean region in 2021 and the determination of the CO column number density after the fire. In addition, monitoring polluting gases with RS techniques after forest fires is essential in understanding the extent of the damage they can cause to the environment.

Wildfire Burn Severity Affects Postfire Shifts in Evapotranspiration in Subalpine Forests

Highlights After wildfire, actual evapotranspiration (ETa) in four subalpine areas decreased proportionally to burn severity. High-severity burn areas maintained the highest ETa before fire and the lowest ETa after fire. After fire, ETa decreased more in areas that had higher prefire ETa. ETa was a useful system-level measure of susceptibility to fire and impact after fire. Abstract. The ecohydrologic response to fire is complex, with devastating consequences for source-water hydrology. Postfire canopy structure, leaf area, infiltration, and soil-water storage all may act to reduce evapotranspiration (ET), a key hydrologic variable and indicator of ecohydrologic function. This study explored the use of 30-m resolution Landsat-based SSEBop-model estimates of actual ET (ETa) to assess the effects of fire in the Upper Rio Grande Basin (2002 Million Fire, CO; 1996 Hondo Fire, NM; 2002 Montoya Fire, NM; 2000 Cerro Grande Fire, NM). SSEBop ETa data from 1985-2019 were analyzed to better understand postfire response and recovery in several sub-alpine burned areas. Results show step reductions in ETa after fire across all burn severities in all four fire areas. Prior to the fire, ETa was generally higher in high-severity burn areas, indicative of higher fuel loads, although high variability indicated the influence of additional factors. All areas had a greater response (decreased ETa) to fire in high-severity areas and a lesser response as burn severity decreased. The decrease in ETa after high-severity fire ranged from 42% to 63%, compared to the decrease after low-severity fire, which ranged from 24% to 44%. None of the four burn areas demonstrated postfire ETa recovery after 17 to 23 years. This study demonstrates the clear utility for remote-sensing ETa estimates as a system-level measure of susceptibility to fire, impact of fire, and, potentially, recovery after fire. However, additional research is needed to account for the covariate effects of other fuel, topographic, weather, and climate factors. Keywords: Ecohydrology, Postfire recovery, Remote sensing, Upper Rio Grande Basin.

Simulating burn severity maps at 30 meters in two forested regions in California

Climate change is altering wildfire and vegetation regimes in California’s forested ecosystems. Present day fires are seeing an increase in high burn severity area and high severity patch size. The ability to predict future burn severity patterns could better support policy and land management decisions. Here we demonstrate a methodology to first, statistically estimate individual burn severity classes at 30 meters and second, cluster and smooth high severity patches onto a known landscape. Our goal here was not to exactly replicate observed burn severity maps, but rather to utilize observed maps as one realization of a random process dependent on climate, topography, fire weather, and fuels, to inform creation of additional realizations through our simulation technique. We developed two sets of empirical models with two different vegetation datasets to test if coarse vegetation could accurately model for burn severity. While visual acuity can be used to assess the performance of our simulation process, we also employ the Ripley’s K function to compare spatial point processes at different scales to test if the simulation is capturing an appropriate amount of clustering. We utilize FRAGSTATS to obtain high severity patch metrics to test the contiguity of our high severity simulation. Ripley’s K function helped identify the number of clustering iterations and FRAGSTATS showed how different focal window sizes affected our ability to cluster high severity patches. Improving our ability to simulate burn severity may help advance our understanding of the potential influence of land and fuels management on ecosystem-level response variables that are important for decision-makers. Simulated burn severity maps could support managing habitat and estimating risks of habitat loss, protecting infrastructure and homes, improving future wildfire emissions projections, and better mapping and planning for fuels treatment scenarios.

Forest Fire Effects on Landscape Snow Albedo Recovery and Decay

Surface snow albedo (SSA) darkens immediately following a forest fire, while landscape snow albedo (LSA) brightens as more of the snow-covered surface becomes visible under the charred canopy. The duration and variability of the post-fire snow albedo recovery process remain unknown beyond a few years following the fire. We evaluated the temporal variability of post-fire snow albedo recovery relative to burn severity across a chronosequence of eight burned forests burned from 2000 to 2019, using pre- and post-fire daily, seasonal, and annual landscape snow albedo data derived from the Moderate Resolution Imaging Spectroradiometer (MOD10A1). Post-fire annual LSA increased by 21% the first year following the fire and increased continually by 33% on average across all eight forest fires and burn severity classifications over the period of record (18 years following a fire). Post-fire LSA measurements increased by 63% and 53% in high and moderate burn severity areas over ten years following fire. While minimum and maximum snow albedo values increased relative to annual post-fire LSA recovery, daily snow albedo decay following fresh snowfall accelerated following forest fire during the snowmelt period. Snow albedo recovery over 10 years following fire did not resemble the antecedent pre-fire unburned forest but more resembled open meadows. The degradation of forest canopy structure is the key driver underlying the paradox of the post-fire snow albedo change (SSA vs. LSA).

Evaluation of Historical Wildfires in Tohoku Region Using Satellite-Based High-Fire-Severity Index

Crown fires represent an extreme fire behavior that leads to high fire severity, and dryness plays a vital role in this behavior. Due to the lack of fire severity data in Tohoku, high fire severity was estimated using a satellite-based high-fire-severity index (HFSI). HFSI is the ratio of the identified area of high fire severity sensed using the Landsat-differenced normalized difference vegetation index (dNDVI) to the reported total burnt area. Using the HFSI, only six wildfires could be identified as having high fire severity areas from an evaluation of 55 wildfires with burnt areas greater than 0.1 km2 reported in Tohoku from 1995 to 2017. The low HFSI values computed for these wildfires implied that fireline intensity was not high for crown fires to occur in Tohoku. Additionally, the soil moisture (SM) content for three layers, the surface, root, and recharge zones simulated using a land surface model (the Simple Biosphere Model including Urban Canopy (SiBUC) model), were used to assess the dryness. The highest HFSI value calculated among all wildfires was that of the largest wildfire that ever occurred in Japan in the period between 1995 and 2017, the 2017 Kamaishi wildfire. The conditions before this fire were among the driest of the six wildfires with HFSI values.

Spatio-Temporal Variation in Soil Nutrients and Plant Recovery across a Fire-Severity Gradient in Old-Growth Araucaria-Nothofagus Forests of South-Central Chile

Forest fires can alter essential ecosystem processes, including soil nutrient cycling, potentially cascading into permanent vegetation changes. This is key to elucidate in ecosystems where fires are infrequent. We assessed the effects of fire severity on the spatio-temporal response of soil nutrients and plant diversity in old-growth forests of south-central Chile, 1, 2, and 3 years after a fire that occurred in 2015. Within ancient, old-growth Araucaria araucana (Mol.) K. Koch and Nothofagus pumilio (Poepp. and Endl.) Krasser forests, ranging from areas burned with fire of high severity to unburned forests, we evaluated nitrogen (N), phosphorus (P), potassium (K), and soil organic matter (SOM) content using spatial interpolation to predict their spatial distribution and assessed their availability over time. We also assessed plant species richness and abundance following the fire. The availability of N noticeably increased during the first year after fire but rapidly decreased in the following years, especially in areas of high fire severity. P, K, and SOM were less affected by the fire, remaining more constant over time. In the short term, plant species richness and diversity significantly decreased in severely burned areas, but over time, they became more similar to those of the unburned forests. The time since a fire and its severity determine a heterogeneous distribution of soil nutrients, with N shifting to a significantly lower availability after fire, which was more notable in areas of high fire severity. Here, vegetation exhibited a decrease in plant diversity and the establishment of exotic species, likely producing cascading effects at the community level.

Monitoring burn severity and air pollutants in wildfire events using remote sensing data: the case of Mersin wildfires in summer 2021

Remotely sensed data have been used to investigate air pollutants and matter toxicity, land cover changes and burn severity. The goal of this study is to analyze land and atmospheric data for wildfire events that occurred in Mersin Province between July 28 and August 3, 2021. We used a variety of open-access remotely sensed data sets (MODIS, Sentinel 2A and Sentinel 5P TROPOMI) from the pre-fire (20-27 July), fire (28 July – 3 August), and post-fire period (4-10 August). This comprehensive study's findings can be divided into two categories. The first group is the land cover output, which includes maps of the affected region's land surface temperature and burn severity, as well as comparisons of the two. The atmospheric output, which consists of trace gas column density maps (for carbon monoxide, formaldehyde, sulphur dioxide and ozone), is the second group. The quantitative results of these analyses indicate that high severity areas correspond to 16.536 hectares, and the maximum column number density reached to 0.071 mol/m2 for carbon monoxide, 0.0043 mol/m2 for formaldehyde, 0.00049 mol/m2 for sulphure dioxide and 0.137 mol/m2 for ozone. The burn severity is found to be highly correlated with land surface temperatures. Pollutant levels in the atmosphere were found to be rising during and after the wildfire. There has not been any evidence of a significant increase in air pollutants near urban areas. However, ozone concentrations rose significantly after the wildfire because the province's nitrogen oxide levels were high enough to produce ozone.

Detection of forest fire burn-area using landsat-8 and sentinel-2: a case study of Nivale (Kolhapur) beat of Chandoli National Park, Maharashtra, India

Forests play an important role in maintaining environmental equilibrium in the ecosystem. Forest fires, which can occur for a variety of reasons, are the greatest threat to forests. In order to control forest fires, it is critical to assess the formation and behavioral characteristics of forest fires. Detecting fire areas and the severity is much easier with satellite images obtained with advancing technologies. The main objective of this study is to extract burn area from Landsat-8 and Sentinel-2 satellite images of 2017 using six vegetation indices. Remote sensing data was used to study a forest fire in the Nivale (Kolhapur) beat of Chandoli National Park of Maharashtra. The Department of Divisional Forest Officer (Wildlife)- Chandoli National Park at Karad (DDFO-CNP) provided reference data indicating that the fire had damaged 194 hectares of Scrub forest. In addition, forest fire areas were determined using an objectbased image classification technique. When the findings of the study are compared to the values obtained by DDFO- CNP, it is found that Sentinel-2's object-based analysis provided the highest accuracy with an overall accuracy of 84 % and 0.795 Kappa statistics. Landsat-8 image has 82 % overall accuracy and 0.765 Kappa value. The findings of Sentinel-2 and Landsat-8 spectral indices showed the Sentinel-2 had better results in all indices. Differenced Normalized Difference Vegetation Index (dNDVI) and Relative Difference Normalized Burn Ratio (RdNBR) performed better than other indexes with a difference of only 18.27 and 30.88 hectares respectively. According to the fire severity analysis, a burn area with high intensity in Sentinel-2 was identified as moderate-high in Landsat-8. As per the research findings, sentinel-2 had a high severity area of 28.72 ha and a low severity area of 37.04 ha. It shows that satellite images of Sentinel-2 are highly suitable than Landsat-8 for estimating scrub forest fire areas. Finally, the findings of this study could be useful to forest managers to monitor burn regions quickly after the fire and in reducing severity and frequency of forest fires.

Temporal evolution of the hydromechanical properties of soil-root systems in a forest fire in China

Plant roots generally enhance soil strength and stabilize slopes through hydro-mechanical effects, especially in forested areas prone to shallow slope failure. Forest fires can severely weaken the hydro-mechanical contribution of roots to slopes, however, the hydro-mechanical characteristics of soil–root systems (SRS) affected by wildfire remain poorly understood. To obtain insight into the post-fire hydro-mechanical characteristics of SRS, a subalpine conifer forested area in Sichuan Province, China that suffered a wildfire on March 30, 2019 was continuously monitored over two consecutive years. Samples from zones with different degrees of burn severity were collected and tests both for roots and SRS were performed. The results revealed a substantial decline in root number, which decreased by 46%–58% two years after the wildfire in the medium- and high-severity areas. The tensile strength tests indicated a reduction of root tensile strength by 36%–47% for roots with diameters less than 2 mm. The shear strength of the SRS determined from saturated direct shear tests strongly and had degraded by 55%–82% two years after the wildfire because of root death and reduced root reinforcement. The results of hydraulic conductivity tests over the same time period indicated an abrupt reduction of SRS hydraulic conductivity within several months after the fire owing to ash clogging and the formation of a hydrophobic layer. After more time had elapsed, however, hydraulic conductivity had increased unexpectedly by a factor of 2.2–3.2 greater than that of unburned soil. We attribute this observation to the formation of macropore flow pathways from decayed roots, which was observed by scanning electron microscopy. The findings presented here provide important insight into the temporal changes of the hydro-mechanical characteristics of SRS in burned areas and their associated mechanisms and could be a useful reference to better evaluate post-wildfire stability of subalpine conifer forest in similar environmental conditions.

Mental Health of Chinese People During the COVID-19 Pandemic: Associations With Infection Severity of Region of Residence and Filial Piety.

This study aims to investigate mental health among Chinese people living in areas with differing levels of infection severity during the COVID-19 outbreak. It also assesses the association between reciprocal and authoritarian filial piety and mental health in times of crises. A sample of 1,201 Chinese participants was surveyed between April and June 2020. Wuhan city (where 23.4% of participants resided), Hubei province outside Wuhan (13.4% of participants), and elsewhere in China (63.1% of participants) were categorized into high, moderate, and low infection severity areas, respectively. The Depression, Anxiety, and Stress Scale’s severity cut-points were used to categorize participants. In the overall sample, 20.9, 34.2, and 29.0% of the participants showed elevated (mild to extremely severe) levels of stress, anxiety, and depression. Those in the highest infection severity group were significantly more likely to be categorized as having elevated levels of stress, anxiety, and depression. General linear modeling was performed on a composite mental distress variable (taking into account stress, anxiety, and depression scores). This model indicated that, even after adjusting for group differences in age, gender, education, and filial piety, the high infection severity group displayed more mental distress than the low infection severity groups. The model also found reciprocal filial piety to have a negative association with mental distress. Conversely, authoritarian filial piety was found to be unrelated to mental distress when controlling for the other variables in the model. No evidence was found for an interaction between either authoritarian or reciprocal filial piety and infection severity, which suggests that the negative association observed between reciprocal filial piety and mental distress was relatively consistent across the three infection severity groups. The findings suggest that future public health programs may integrate the promotion of filial piety as a strategy to help Chinese people maintain good mental health in the face of pandemic crises.

Effectiveness of post‐fire salvage logging stream buffer management for hillslope erosion in the U.S. Inland Northwest Mountains

AbstractActive wildfire seasons in the western U.S. warrant the evaluation of post‐fire forest management strategies. Ground‐based salvage logging is often used to recover economic loss of burned timber. In unburned forests, ground‐based logging often follows best management practices by leaving undisturbed areas near streams called stream buffers. However, the effectiveness of these buffers has not been tested in a post‐wildfire setting. This experiment tested buffer width effectiveness with a novel field‐simulated rill experiment using sediment‐laden runoff (25 g/L) released over 40 min at evenly timed flow rates (50, 100 and 150 L/min) to measure surface runoff travel length and sediment concentration under unburned and high and low soil burn severity conditions at 2‐, 10‐ and 22‐month post‐fire. High severity areas 2‐month post‐fire had rill lengths of up to 100 m. Rill length significantly decreased over time as vegetation regrowth provided ground cover. Sediment concentration and sediment dropout rate also varied significantly by soil burn severity. Sediment concentrations were 19 g/L for the highest flow 2‐month post‐fire and reduced to 6.9–14 g/L 10‐month post‐fire due to abundant vegetation recovery. The amount of sediment dropping out of the flow consistently increased over the study period with the low burn severity rate of 1.15 g L−1 m−1 approaching the unburned rate of 1.29 g L−1 m−1 by 2‐year post‐fire. These results suggest that an often‐used standard, 15 m buffer, was sufficient to contain surface runoff and reduce sediment concentration on unburned sites, however buffers on high burn severity sites need to be eight times greater (120 m) immediately after wildfire and four times greater (60 m) 1‐year post‐fire. Low burn severity areas 1‐year post‐fire may need to be only twice the width of an unburned buffer (30 m), and 2‐year post‐fire these could return to unburned widths.

Strategic Radiology Outreach Planning for Underserved Populations Using Geographic Information Systems.

Geographic information systems (GIS) are widely used in public health research but rarely used in radiology research. GIS can be an impactful tool in radiology global health to locate medically underserved populations and poor transportation infrastructure, characterize medical needs, and design outreach programs. Using the example of aircraft-based outreach in Alaska, we demonstrate the utility of GIS in radiological program planning for global health. Multicriteria GIS evaluations were performed to create a health severity index, using life expectancy and percentage uninsured data, and an accessibility severity index, using distance from roads and health centers or hospitals. These indices were combined with population density to create a final health access severity index (HASI). A map presenting suitable hybrid airship operating areas was produced using land cover data. Alaskan health care facilities were georeferenced to create a coordinate data set. Infrastructure was obtained from OpenStreetMap. Health data were accessed from the 2017 American Community Survey and CDC US Small-area Life Expectancy Estimates Project. GIS analyzed 738,050 Alaskans. The health severity index identified decreased health outcomes (high or very high severity) in 285,446 (39%) Alaskans, and the accessibility severity index determined decreased access to care in 218,201 (30%). Combined, the HASI established 165,108 (22%) Alaskans as underserved with high or very high overall severity. Thirty-nine percent of Alaska land area is suitable for hybrid airship operations, including 27% of HASI high and very high severity areas. GIS identified underserved populations for mobile radiology outreach in Alaska and may be useful for global health outreach planning and resource allocation.