Fine Resolution Research Articles

Reconstruction of 30 m Land Cover in the Qilian Mountains from 1980 to 1990 Based on Super-Resolution Generative Adversarial Networks

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved.

Airborne lidar intensity correction for mapping snow cover extent and effective grain size in mountainous terrain

ABSTRACT Differentially mapping snow depth in mountain watersheds from airborne laser altimetry is a valuable hydrologic technique that has seen an expanded use in recent years. Additionally, lidar systems also record the strength of the returned light pulse (i.e. intensity), which can be used to characterize snow surface properties. For near-infrared lidar systems, return intensity is relatively high over snow and inversely related to the effective grain size, a primary control on snow albedo. Raw intensity is also sensitive to laser range and incidence angle, however, and requires a correction for snow property retrieval that is especially pertinent in mountainous terrain. Here, we describe a workflow to correct the intensity using the plane trajectory, lidar scan angle, and lidar-derived topography. As a proof of concept for snow retrievals, we apply the workflow to an airborne 1064 nm lidar flight over a snow-covered mountain basin in the Colorado Rockies. Corrected intensity was empirically related to reflectance before delineating snow extent and retrieving grain size. Relative to the traditional snow classification derived from optical imagery, the lidar-derived snow extent covered 5.4% more area due to the fine resolution point cloud and absence of shadows common in optical imagery. The lidar-derived grain size retrievals had a MAE of 32 µm compared to those from field spectroscopy, which translated to a 1% error in snow albedo. We found high incidence angles yielded an overcorrection in intensity that introduced a high bias in the grain size distribution and, therefore, suggest using an incidence angle threshold (40°). Developing methods specifically for quantitative snow surface property retrievals from lidar intensity is timely and relevant as aerial lidar is increasingly being used to map snow depth for hydrologic and cryospheric studies.

DRONES4BLUECARBONSEAGRASS: A Capacity-Building Program for Mapping Seagrasses using Unmanned Aerial System (UAS) Technology in the Philippines

Abstract. Coastal blue carbon ecosystems, such as mangrove forests and seagrass meadows, are important for various environmental functions, serving as habitats, food sources, and carbon sinks. Despite their importance, seagrass ecosystems are experiencing global decline due to numerous stressors, including anthropogenic activities. To address this, effective monitoring and management strategies are crucial. Traditional in situ surveys for seagrass monitoring are labor-intensive and time-consuming. Alternatively, remote sensing technologies, particularly satellite imagery, offer a more efficient means of mapping seagrass distribution over large areas. However, accurately assessing seagrass cover at a local scale remains challenging with satellite data alone. Recently, unmanned aerial system (UAS) technology has emerged as a promising approach for seagrass mapping. With its fine resolution and flexibility for image acquisition, UAS can provide more precise estimates of seagrass cover, complementing satellite data. This integration enhances accuracy assessments and expands data availability for training and validation purposes. Under the BlueCARES Project, a drone training program was developed to equip stakeholders with the skills needed for cost-effective UAS-based seagrass mapping. The drone training program encompasses lectures on UAS basics, flight planning, and image processing. Beyond training, collected data are used to create seagrass maps and assess seagrass density. This initiative not only advances seagrass conservation but also fosters collaboration between government agencies, academia, and local communities to achieve sustainable management of blue carbon ecosystems.

Downscaling MODIS evapotranspiration into finer resolution using machine learning approach on a small scale, Ribb watershed, Ethiopia.

By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agricultural practices, such as the Ribb watershed in Ethiopia. However, MODIS sensors have recently given high temporal resolution ET products across large areas, but their low spatial resolution limits its application on a local scale. The primary goal of the study was to downscale the MODIS ET (1km) product to a finer spatial resolution at the watershed level. The model's 12 predictor variables (NDVI, EVI, LAI, FVC, SAVI, NDMI, NDWI, Albedo, emissivity, LST, and DEM: slope and elevation) were produced using the random forest (RF) algorithm using Sentinel-2 (S-2) 20m and Landsat-8 (L-8) 30m. The RF regression model was used to assess the relationship between predicted variables and downscaled MODIS ET. The FAO-PM ET model, developed from meteorological stations, was validated by and RMSE for three seasons (rainy, post-rainy, and dry) in 2022. The results were in good agreement with MODIS ET, with an RMSE of 0.22 for S-2 and 0.28 for L-8. In the FAO-PM ET model, the downscaled result showed greater spatial details and better agreement with gage station readings ( and ). Thus, considering the effectiveness and simplicity of machine learning techniques, our study demonstrated the potential for ET downscaling. Furthermore, the study suggests integrating spatiotemporal time series data to reach higher resolution.

Global expansion of wildland-urban interface intensifies human exposure to wildfire risk in the 21st century.

Rapidly increasing human-nature interactions exacerbate the risk of exposure to wildfires for human society. The wildland-urban interface (WUI) represents the nexus of human-nature interactions, where the risk of exposure to natural hazards such as wildfire is most pronounced. However, quantifying long-term global WUI change and the corresponding driving factors at fine resolution remain challenging. Here, we mapped and analyzed the global WUI at 30-meter resolution in 2000, 2010, and 2020. Our analysis revealed that the global WUI expanded by 35.6% since 2000, reaching 1.93 million square kilometer in 2020. Notably, 85% of this growth occurred between 2010 and 2020. The increase in WUI was primarily driven by the unprecedented expansion of global urbanization, contributing an additional 589,914 square kilometer of WUI. In addition, the number of small fires occurring in WUI areas has increased substantially since 2010. These findings underscore the rising wildfire risk to human society and highlight the urgency of implementing tailored fire management strategies in WUI areas.

Global evaluation of sentinel 2 level 2A Sen2Cor aerosol optical thickness retrievals

ABSTRACT The present study aims to conduct the first known comprehensive global evaluation of Aerosol Optical Thickness (AOT) retrievals derived from the Sentinel 2 Sen2Cor algorithm between 2018 and 2022, using data from over 400 AERONET stations. The results indicate that Sentinel 2 tends to underestimate AOT, especially at higher aerosol loadings. Although there appears to be a good overall correlation (r = 0.57) with low RMSE (0.16) and relative mean bias (RMB = -2.46%) between AERONET and Sentinel 2 AOT datasets, regional analysis reveals significant variation in performance across regions, with areas like Europe and Americas exhibiting stronger correlations and lower RMSE than others like Indian subcontinent and Southern Africa. Temporal analysis also suggests an improvement in Sentinel 2 performance, particularly in capturing extreme AOT values in 2022. While elevation does not appear to have any noticeable impact on AOT estimation, slight variations could be observed over certain land cover types like sparse vegetation and permanent water bodies. Though improvements are certainly required over certain geographical regions like Indian subcontinent, Sentinel 2 AOT can, nevertheless, be reliably used for aerosol studies in urban areas or at a finer spatial resolution, especially in Europe, Mainland U.S.A. and East Asia.

Examining select sociodemographic characteristics of sub-county geographies for public health surveillance

BackgroundMapping health outcomes related to environmental health hazards at the county level can lead to a simplification of risks experienced by populations in that county. The Centers for Disease Control and Prevention’s National Environmental Public Health Tracking Program has developed sub-county geographies that aggregate census tracts to allow for stable, minimally suppressed data to be displayed. This helps to highlight more local variation in environmental health outcomes and risk data. However, we wanted to understand whether the aggregation method used was aggregating sociodemographically similar or dissimilar areas with one another. This analysis attempts to explore whether the distributions of select people who may be at increased risk for exposure to environmental health hazards as identified by the Tracking Program are preserved in these sub-county geographies with the census tracts used as the foundation to create them.MethodsMean values of three sociodemographic characteristics (persons aged 65 years and older, people from racial and ethnic minority groups, and population below the poverty level) for each sub-county geography in five states were calculated and placed into five break groups. Differences in break groups were determined and compared for each sub-county geography and census tract.ResultsThe sociodemographic characteristics among the census tracts and two aggregated sub-county geographies were similar. In some instances, census tracts with a low population or a highly skewed population (e.g., very high percentage of population aged 65 years and older) were aggregated with dissimilar census tracts out of necessity to meet the requirements set by the Tracking Program’s aggregation methodology. This pattern was detected in 2.41-6.59% of census tracts within the study area, depending on the sociodemographic variable and aggregation level.ConclusionsThe Tracking Program’s sub-county aggregation methodology aggregates census tracts with similar characteristics. The two new sub-county geographies can serve as a potential option for health officials and policymakers to develop targeted interventions using finer resolution health outcome and environmental hazard data compared to coarser resolution county-level data.

Congruent Long-Term Declines in Carbon and Biodiversity Are a Signature of Forest Degradation.

Recent global policy initiatives aimed at reducing forest degradation require practical definitions of degradation that are readily monitored. However, consistent approaches for monitoring forest degradation over the long term and at broad scales are lacking. We quantified the long-term effects of intensive wood harvest on above-ground carbon and biodiversity at fine resolutions (30 m2) and broad scales (New Brunswick, Canada; 72,908 km2). Model predictions for above-ground biomass were highly correlated with independent data (r = 0.77). After accounting for carbon stored in wood products, net CO2 emissions from forests for the region from 1985 to 2020 were 141 CO2e Tg (4.02 TgCO2e year-1; 32% of all reported emissions). We found strong positive correlations between locations with declines in above-ground carbon and habitats for old-forest bird species, which have lost > 20% habitat over 35 years. High congruence between biodiversity and forest carbon offers potential for policy incentives to conserve both objectives simultaneously and slow rates of forest degradation. These methods could be used to track forest degradation for managed forest regions worldwide.

Opinion: How will advances in aerosol science inform our understanding of the health impacts of outdoor particulate pollution?

Abstract. Air pollution, characterized by high levels of particulate matter (PM), poses the greatest environmental threat to human health, causing an estimated 7 million deaths annually and accounting for 5 % of the global gross domestic product (GDP). While the health impacts of PM are influenced by the toxicity of its individual chemical constituents, the mortality burden of PM is solely based on its total mass concentration. This is because of a lack of large-scale, high-resolution data on PM chemical composition, needed for epidemiological assessments. Identifying which PM constituents are harmful to health has been the “holy grail” of atmospheric science since the landmark 1993 study on six US cities established a definitive link between PM and mortality. Ever since, atmospheric scientists have focused on understanding aerosol composition, emission sources, and formation pathways, while longitudinal epidemiological studies have required individual-level exposure data, employing land use regression models for the prediction of exposures at fine resolutions. In this opinion article, we argue that the time has come to shift the focus towards incorporating PM chemical composition into epidemiological health assessments, laying the foundation for the development of new regulatory metrics. This shift will enable the creation of targeted guidelines and subsequent regulations, prioritizing mitigation efforts against the most harmful anthropogenic emissions. Central to this shift is the availability of global, long-term, high-resolution data on PM chemical composition that are obtained through field observations and modelling outputs. In the article, we underscore key milestones within aerosol science that have been integral for advancing this foundational shift. Specifically, we examine emerging modelling tools for estimating exposure to individual PM components, present the type of ambient observations needed for model developments, identify key gaps in our fundamental understanding of emissions and their atmospheric transformation, and propose advancing cross-disciplinary collaboration between aerosol scientists and epidemiologists to understand the health impacts of individual PM components. We contend that aerosol science has now reached a pivotal moment in elucidating the differential health impacts of PM components, representing a first step towards their incorporation into air quality guidelines.

Dosimetric optimization for dynamic mixed beam arc therapy (DYMBARC).

Non-coplanarity and mixed beam modality could be combined to further enhance dosimetric treatment plan quality. We introduce dynamic mixed beam arc therapy (DYMBARC) as an innovative technique that combines non-coplanar photon and electron arcs, dynamic gantry and collimator rotations, and intensity modulation with photon multileaf collimator (MLC). However, finding favorable beam directions for DYMBARC is challenging due to the large solution space, machine component constraints, and optimization parameters, posing a highly non-convex optimization problem. To establish DYMBARC and solve the pathfinding challenge by employing direct aperture optimization (DAO) to determine the table angles and gantry angle ranges of photon and electron arcs for different clinically motivated cases. The method starts by generating a grid of beam directions based on user-defined resolutions along the gantry and table angle axes for each beam quality considered. Beam directions causing collisions or entering through the end of CT are excluded. For electrons, a fixed source-to-surface distance of 80cm is used to reduce in-air scatter. Electron beam energies with insufficient range to reach the target or beam directions impinging on the table before reaching the patient are excluded. The remaining beam directions form the pathfinding solution space. Promising photon and electron MLC-defined apertures, with associated monitor unit (MU) weights, are iteratively added using a hybrid-DAO algorithm. This algorithm combines column generation to add apertures and simulated annealing to further refine aperture shapes and weights. Apertures are added until the requested number of paths are formed and the user-defined maximum total gantry angle range is reached. Paths are resampled to a finer gantry angle resolution and subject to DAO for simultaneous optimization of beam intensities along the photon/electron arcs. Subsequent final dose calculation and MU weight reoptimization result in a deliverable DYMBARC plan. DYMBARC plans are created for three clinically motivated cases (brain, breast, and pelvis) and compared to DYMBARC variants: colli-DTRT (dynamic collimator trajectory radiotherapy) using non-coplanar photon arcs; and Arc-MBRT (mixed beam radiotherapy) using photons and electrons but restricted to coplanar setup. Additionally, a manually defined volumetric modulated arc therapy (VMAT) setup serves as a reference clinical technique. Dose distributions, dose-volume histograms, and dosimetric endpoints are evaluated. Dosimetric validation with radiochromic film measurements (gamma evaluation, 3% / 2mm (global), 10% dose threshold) is performed on a TrueBeam system in developer mode for one case. While maintaining similar target coverage and homogeneity, DYMBARC reduced mean doses to organs-at-risk compared to VMAT by an average of 3.2, 0.5, and 2.9Gy for the brain, breast, and pelvis cases, respectively. Similar or smaller mean dose reductions were observed for Arc-MBRT or colli-DTRT, compared to VMAT. Electron contributions to the mean planning target volume dose ranged from 2% to 34% for DYMBARC and from 11% to 40% for Arc-MBRT. Measurement validation showed>99.7% gamma passing rate. DYMBARC was successfully established using a dosimetrically optimized pathfinding approach, combining non-coplanarity with mixed beam modality. DYMBARC facilitated the determination of photon and electron contributions on a case-by-case basis, enhancing more personalized treatment modalities.

Evaluating Burn Severity and Post-Fire Woody Vegetation Regrowth in the Kalahari Using UAV Imagery and Random Forest Algorithms

Accurate burn severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where the vegetation experiences above-ground combustion but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush burn severity and woody vegetation post-burn coppicing/regeneration events in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.71% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a >50% probability of woody vegetation regrowth in high-severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine-scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas.

A Modified Method for Reducing the Scale Effect in Land Surface Temperature Downscaling at 10 m Resolution

Satellite-derived Land Surface Temperature (LST) plays an important role in research on natural energy balance and water cycle. Considering the tradeoff between spatial and temporal resolutions, accurate fine-resolution LST must be obtained through the use of LST downscaling (DLST) technology. Various methods have been proposed for DLST at fine resolutions (e.g., 10 m) and small scales. However, the scale effect of these methods, which is inherent to DLST processes at different extents, has rarely been addressed, thus limiting their application. In this study, a modified daily 10 m resolution DLST method based on Google Earth Engine, called mDTSG, is proposed in order to reduce the scale effect at fine spatial resolutions. The proposed method introduces a convolution-based moving window into the DLST process for the fusion of different remote sensing data. The performance of the modified method is compared with the original method in six regions characterized by various extents and landscape heterogeneity. The results show that the scale effect is significant in the DLST process at fine resolutions across extents ranging from 100 km2 to 22,500 km2. Compared with the original method, mDTSG can effectively reduce the LST value differences between tile edges, especially when considering large extents (>22,500 km2) with an average R2 improvement of 33.75%. The average MAE is 1.63 °C, and the average RMSE is 2.3 °C in the mDTSG results, when compared with independent remote sensing products across the six regions. A comparison with in situ observations also shows promising results, with an MAE of 2.03 °C and an RMSE of 2.63 °C. These findings highlight the robustness and scalability of the mDTSG method, making it a valuable tool for fine-resolution LST applications in diverse and extensive landscapes.

An Entropy-based Method to Evaluate the Appropriate Spatial Resolution

Abstract. Geospatial data is available at increasingly finer spatial resolutions. However, such data can also be problematic because it may result in increased financial, resource and time cost. It might also be unnecessary if the phenomenon of interest does not vary at this scale or if the scientific application does not require it. This paper explores the scale effects associated with increased spatial resolution using the entropy-based local indicator of spatial association (LISA-ELSA). Results are illustrated for two datasets a digital elevation model (30 m ASTER GDEM) and for a raster PM2.5 air pollution map (1000 m). It is shown that increasing the size of the local window for the LISA can be used to explore the effect of reducing the spatial resolution. It is possible to identify areas where the spatial association is invariant with increasing window size and which could be represented with coarser pixels.

From urban heat islands to intra-urban heat islands: Role of urban fabric in redefining microclimates of tomorrow’s compact cities

A compact city is a urban area characterized by high density, mixed land use, and limited sprawl, designed to promote sustainable development, reduce urban sprawl, and enhance quality of life. This investigation focuses on the Intra Urban Heat Island (IUHI) effect, an intensified progression of the Urban Heat Island (UHI) phenomenon, specifically within a compact city transitioning from urban sprawl. The study employed advanced spatial analytics, which employed the concept of “space-time cube”, incorporating Getis Ord Gi*, and per pixel, Mann Kendall tests for the space–time pattern mining on a time series of high-resolution Remote Sensing data spanning from 1999 to 2023. The findings reveal distinctive spatial and temporal patterns in IUHI, identifying a total of 693,900 m2 of intensifying hot-spot areas in the selected compact city characterized by industrial, warehousing, and commercial developments. The detailed examination of urban fabric at a finer resolution (7.5 cm × 7.5 cm) identified rooftops with specific spectral characteristics (red/copper hue) as significant contributors to the IUHI phenomenon, inducing surface temperature increases above 5 °C compared to neighboring cells. Furthermore, high-rise developments emerge as land use forms that create cold spots in the urban fabric, improving the city’s thermal environment. The implications underscore the necessity for future urban planning to consider IUHI as a concentrated development of the UHI effect, urging a holistic understanding of the complex interconnections among various factors influencing microclimates in urban environments.

The Climate Data for Adaptation and Vulnerability Assessments and the Spatial Interactions Downscaling Method

This study presents the spatial interactions downscaling (SPID) method and introduces the climate data for adaptation and vulnerability assessments (ClimAVA) dataset. SPID employs random forest models to capture the relationship between spatial patterns at global circulation model (GCM) resolution and fine-resolution pixel values. In summary, a random forest model is trained for each fine spatial resolution pixel of the reference data as the predictand, and nine pixels from the spatially resampled (coarser) version of the reference data at the GCM’s resolutions as predictors. Models are then utilized to downscale the bias-corrected GCM data. The ClimAVA-SW dataset offers a high-resolution (4 km), bias-corrected, downscaled future climate projection derived from seventeen CMIP6 GCMs. It includes three variables (daily precipitation, minimum and maximum temperature) for three shared socioeconomic pathways (SSP245, SSP370, SSP585) across the U.S. Southwest region. The ClimAVA dataset sets itself apart with the SPID method’s capacity to provide remarkable climate realism, high physical plausibility of change, and excellent representation of extreme events while maintaining user-friendliness and requiring relatively low computational resources.

Monitoring Coastal Evolution and Geomorphological Processes Using Time-Series Remote Sensing and Geospatial Analysis: Application Between Cape Serrat and Kef Abbed, Northern Tunisia

The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, is promising in this context. The coastline of Northern Tunisia is currently showing geomorphic process, such as increasing erosion associated with lateral sedimentation. This study aims to investigate the potential of time-series optical data, namely Landsat (from 1985–2019) and Google Earth® satellite imagery (from 2007 to 2023), to analyze shoreline changes and morphosedimentary and geomorphological processes between Cape Serrat and Kef Abbed, Northern Tunisia. The Digital Shoreline Analysis System (DSAS) was used to quantify the multitemporal rates of shoreline using two metrics: the net shoreline movement (NSM) and the end-point rate (EPR). Erosion was observed around the tombolo and near river mouths, exacerbated by the presence of surrounding dams, where the NSM is up to −8.31 m/year. Despite a total NSM of −15 m, seasonal dynamics revealed a maximum erosion in winter (71% negative NSM) and accretion in spring (57% positive NSM). The effects of currents, winds, and dams on dune dynamics were studied using historical images of Google Earth®. In the period from 1994 to 2023, the area is marked by dune face retreat and removal in more than 40% of the site, showing the increasing erosion. At finer spatial resolution and according to the synergy of field observations and photointerpretation, four key geomorphic processes shaping the coastline were identified: wave/tide action, wind transport, pedogenesis, and deposition. Given the frequent changes in coastal areas, this method facilitates the maintenance and updating of coastline databases, which are essential for analyzing the impacts of the sea level rise in the southern Mediterranean region. Furthermore, the developed approach could be implemented with a range of forecast scenarios to simulate the impacts of a higher future sea-level enhanced climate change.

Drought dynamics in California and Mississippi: A wavelet analysis of meteorological to agricultural drought transition

Understanding the transition from meteorological to agricultural drought is crucial for developing effective drought management strategies and early warning systems. This study provides a unique perspective by utilizing hybrid drought indices to explore the temporal and spatial complexities of drought propagation across two large watersheds—California and Mississippi—that feature distinct agro-climatic conditions and irrigation practices. We assess the links between meteorological drought, measured by the Standardized Precipitation Index (SPI), and agricultural drought using three indicators: Vegetation Drought Response Index (VegDRI), GRACE Root Zone Soil Moisture Percentile (SMI), and the Evaporative Demand Drought Index (EDDI). By comparing watersheds with different irrigation systems, we also consider the role of irrigation in modifying drought dynamics. Our analysis identified the 14-day SPI as particularly effective in capturing drought dynamics. Soil moisture showed the earliest response to rainfall anomalies, with a lag of 41 days in California and 59 days in Mississippi. Vegetation stress followed, with a lag of 137 days in California and 75 days in Mississippi, while atmospheric conditions lagged by 143 and 139 days, respectively. Irrigation was found to delay drought impacts by up to two months. This framework offers a finer temporal resolution and a more nuanced understanding of drought propagation, potentially informing more targeted drought mitigation strategies than traditional methods.

Functional excitation-inhibition ratio indicates near-critical oscillations across frequencies

Abstract The concept of excitation/inhibition (E/I) balance plays an important role in understanding brain function in health and disease. We recently introduced an algorithm to determine a functional E/I ratio based on the critical brain dynamics that emerge in neuronal networks balancing between order and disorder. Little, however, is known about the frequency specificity of E/I regulation and how to measure it. Here, we optimized the algorithm for measuring functional excitation-inhibition ratio (fE/I) in narrow frequency ranges and validated it on a computational model of critical oscillations and EEG data. In the computational model, we confirmed that fE/I discriminated E/I connectivity differences across a wide range of frequencies (1–150 Hz). Twin EEG data revealed significant genetic influences on fE/I across frequencies, whereas contrasting eyes-open and -closed EEG indicated functional changes of fE/I restricted to a subset of alpha and beta oscillations and brain regions. We propose that assessing fE/I with finer frequency resolution will prove useful for understanding the functional role of E/I regulation in a spectrally refined fashion in health and disease.

Modelling the spatial variability and uncertainty for under-vaccination and zero-dose children in fragile settings

Universal access to childhood vaccination is important to child health and sustainable development. Here we identify, at a fine spatial scale, under-immunized children and zero-dose children. Using Chad, as an example, the most recent nationally representative household survey that included recommended vaccine antigens was assembled. Age-disaggregated population (12–23 months) and vaccination coverage were modelled at a fine spatial resolution scale (1km × 1 km) using a Bayesian geostatistical framework adjusting for a set of parsimonious covariates. There was a variation at fine spatial scale in the population 12–23 months a national mean of 18.6% (CrI 15.8%–22.6%) with the highest proportion in the South-East district of Laremanaye 20.0% (14.8–25.0). Modelled coverage at birth was 49.0% (31.2%–75.3%) for BCG, 44.8% (27.1–74.3) for DTP1, 24.7% (12.5–46.3) for DTP3 and 47.0% (30.6–71.0) for measles (MCV1). Combining coverage estimates with the modelled population at a fine spatial scale yielded 312,723 (Lower estimate 156055–409266) zero-dose children based on DTP1. Improving routine immunization will require investment in the health system as part of enhancing primary health care. The uncertainties in our estimates highlight areas that require further investigation and higher quality data to gain a better understanding of vaccination coverage.

Biogeography of the Iranian snakes.

The events of the Cenozoic era such as mountain formation caused Iran to become one of the most amazing biodiversity hotspots in the world today. This pioneering study on Iranian snake biogeography integrates historical and ecological analyses. A phylogeographic review traces speciation and dispersal, while cluster analysis with a new snake checklist assesses faunistic similarities within Iran and its surroundings. Jaccard and Sorenson indices generate similarity dendrograms, Indicator Species Analysis pinpoints regional key species, and Endemism index calculates regional endemism rates, enriching our knowledge of Iran's species diversity. Phylogeographic analyses identify four biogeographical corridors for snake ingress into Iran: the Arabian region through southwestern Iran, the Western Asian mountainous transition zone via northwestern Iran, the Turanian region into northeastern Iran, and the Indus River Valley into southeastern and eastern Iran. Dendrogram analysis divides snake fauna into three groups. The first group associates western Zagros and Khuzestan fauna with the Sahara and Arabian regions. The second group links Kopet Dagh and Turkmen Steppe fauna with the Turanian region, and Central Plateau and Baluchistan fauna with the Iranian region. The third group connects northwest highlands, Alborz and Zagros mountains, and Caspian Sea coasts with the Western Asian Mountain transition zone. The study validates broad biogeographic patterns via ecoregional associations and indicator species analysis, providing finer resolution. Species like Platyceps najadum in Caspian Hyrcanian mixed forests exemplify ecoregional alignment, while Zagros and Alborz mountains exhibit unique faunal indicators, indicating species-level divergence. Shared indicators among widespread ecoregions reflect habitat continuity; exclusive indicators emphasize regional distinctiveness. Despite endemic species prevalence, they seldom act as significant indicators due to various factors. Our research confirms the Zagros Mountains, Khuzestan Plain, Alborz Mountains, and Persian Gulf coasts as snake diversity hotspots, marked by higher species richness compared to other Iranian regions.