A method for continuous sub-annual mapping of forest disturbances using optical time series

Forest disturbances have a major impact on ecosystem dynamics both at local and global scales. Accordingly, it is important to acquire objective information about the location, nature and timing of such events to improve the understanding of their impact, update forest management policies and disturbance mitigation strategies. To this date, remotely sensed data have been widely used for the detection of stand replacing disturbances (SRD) such as windthrows and wildfires. In contrast, less effort has been devoted to the detection of non-stand replacing disturbances (NSRD), typically characterized by slower and gradual temporal dynamics. To address this gap, we propose a method for the automated detection of both SRD and NSRD. The proposed method can detect both past and recent disturbances, with a monthly temporal resolution, in a near real-time fashion by processing new images as they are acquired. Differently from existing approaches that handle the time series as a one-dimensional (1D) temporal trajectory, the method analyzes the sequence of images by organizing them in a two-dimensional (2D) grid-like structure. This representation allows us to model both the intra- and inter-annual variations of the time series taking advantage of the annual cyclical nature of the plant phenology. The method has been tested on study areas attacked by bark beetles achieving a user’s accuracy and producer’s accuracy of 0.91±0.08 and 0.81±0.07 (with 95% confidence intervals) for the disturbed areas, respectively

Could nearby star-forming galaxies light up the point-like neutrino sky?

Star-forming and starburst galaxies, which are well-known cosmic-rays reservoirs, are expected to emit gamma-rays and neutrinos predominantly via hadronic collisions. In this Letter, we analyze the 10-year Fermi-LAT spectral energy distributions of 13 nearby galaxies by means of a physical model which accounts for high-energy proton transport in starburst nuclei and includes the contribution of primary and secondary electrons. In particular, we test the hypothesis that the observed gamma-ray fluxes are mostly due to star-forming activity, in agreement with the available star formation rates coming from IR and UV observations. Through this observation-based approach, we determine the most-likely neutrino counterpart from star-forming and starburst galaxies and quantitatively assess the ability of current and upcoming neutrino telescopes to detect them as point-like sources. Remarkably, we find that the cores of the Small Magellanic Cloud and the Circinus galaxy are potentially observable by KM3NeT/ARCA with 6 years of observation. Moreover, most of the nearby galaxies are likely to be just a factor of a few below the KM3NeT and IceCube-Gen2 point-like sensitivities. After investigating the prospects for detection of gamma-rays above TeV energies from these sources, we conclude that the joint observations of high-energy neutrinos and gamma-rays with upcoming telescopes will be an objective test for our emission model and may provide compelling evidence of star-forming activity as a tracer of neutrino production.Comment: 7 pages, 2 figure

Starburst galaxies strike back: a multi-messenger analysis with Fermi-LAT and IceCube data

Starburst galaxies, which are known as "reservoirs" of high-energy cosmic-rays, can represent an important high-energy neutrino "factory" contributing to the diffuse neutrino flux observed by IceCube. In this paper, we revisit the constraints affecting the neutrino and gamma-ray hadronuclear emissions from this class of astrophysical objects. In particular, we go beyond the standard prototype-based approach leading to a simple power-law neutrino flux, and investigate a more realistic model based on a data-driven blending of spectral indexes, thereby capturing the observed changes in the properties of individual emitters. We then perform a multi-messenger analysis considering the extragalactic gamma-ray background (EGB) measured by Fermi-LAT and different IceCube data samples: the 7.5-year High-Energy Starting Events (HESE) and the 6-year high-energy cascade data. Along with starburst galaxies, we take into account the contributions from blazars and radio galaxies as well as the secondary gamma-rays from electromagnetic cascades. Remarkably, we find that, differently from the highly-constrained prototype scenario, the spectral index blending allows starburst galaxies to account for up to $40\%$ of the HESE events at $95.4\%$ CL, while satisfying the limit on the non-blazar EGB component. Moreover, values of $\mathcal{O}(100~\mathrm{PeV})$ for the maximal energy of accelerated cosmic-rays by supernovae remnants inside the starburst are disfavoured in our scenario. In broad terms, our analysis points out that a better modeling of astrophysical sources could alleviate the tension between neutrino and gamma-ray data interpretation.Comment: 20 pages, 15 figures. v2: updated to published versio

Detection of forest windthrows with bitemporal COSMO-SkyMed and Sentinel-1 SAR data

Wind represents a primary source of disturbances in forests, necessitating an assessment of the resulting damage to ensure appropriate forest management. Remote sensing, encompassing both active and passive techniques, offers a valuable and efficient approach for this purpose, enabling coverage of large areas while being costeffective. Passive remote sensing data could be affected by the presence of clouds, unlike active systems such as Synthetic Aperture Radar (SAR) which are relatively less affected. Therefore, this study aims to explore the utilization of bitemporal SAR data for windthrow detection in mountainous regions. Specifically, we investigated how the detection outcomes vary based on three factors: i) the SAR wavelength (X-band or C-band), ii) the acquisition period of the pre- and post-event images (summer, autumn, or winter), and iii) the forest type (evergreen vs. deciduous). Our analysis considers two SAR satellite constellations: COSMO-SkyMed (band-X, with a pixel spacing of 2.5 m and 10 m) and Sentinel-1 (band-C, with a pixel spacing of 10 m). We focused on three study sites located in the Trentino-South Tyrol region of Italy, which experienced significant forest damage during the Vaia storm from 27th to 30th October 2018. To accomplish our objectives, we employed a detailpreserving, scale-driven approach for change detection in bitemporal SAR data. The results demonstrate that: i) the algorithm exhibits notably better performance when utilizing X-band data, achieving a highest kappa accuracy of 0.473 and a balanced accuracy of 76.1%; ii) the pixel spacing has an influence on the accuracy, with COSMO-SkyMed data achieving kappa values of 0.473 and 0.394 at pixel spacings of 2.5 m and 10 m, respectively; iii) the post-event image acquisition season significantly affects the algorithm’s performance, with summer imagery yielding superior results compared to winter imagery; and iv) the forest type (evergreen vs. deciduous) has a noticeable impact on the results, particularly when considering autumn/winter dat

Gamma-Ray and Neutrino Emissions from Starforming and Starburst Galaxies

Experimental observations have demonstrated a strong correlation between the star formation rate and the gamma-ray lumosities of starforming and starburst galaxies (SFGs and SBGs). However, the real origin of these emissions is still under debate. In this contribution, we present several updates on their non-thermal radiations, revisiting both their point-like and cumulative (diffuse) emission properties. From the point-like side, we discuss the potential- ities of future neutrino (KM3NeT/ARCA, IceCube-gen2) telescopes to quanti- tively scrutinize their expected properties from different cosmic-ray transport models. From the diffuse perspective, we investigate a model based on a data- driven blending of spectral indexes, hence taking into account the changes in the properties of individual emitters. Strikingly, SFGs and SBGs can explain 25% (up to 40%) of the diffuse High-Energy Starting Events (HESE) data, without overshooting the gamma-ray limits regarding non-blazar sources

Disease-Modifying Therapies and Coronavirus Disease 2019 Severity in Multiple Sclerosis

Objective: This study was undertaken to assess the impact of immunosuppressive and immunomodulatory therapies on the severity of coronavirus disease 2019 (COVID-19) in people with multiple sclerosis (PwMS). Methods: We retrospectively collected data of PwMS with suspected or confirmed COVID-19. All the patients had complete follow-up to death or recovery. Severe COVID-19 was defined by a 3-level variable: mild disease not requiring hospitalization versus pneumonia or hospitalization versus intensive care unit (ICU) admission or death. We evaluated baseline characteristics and MS therapies associated with severe COVID-19 by multivariate and propensity score (PS)-weighted ordinal logistic models. Sensitivity analyses were run to confirm the results. Results: Of 844 PwMS with suspected (n = 565) or confirmed (n = 279) COVID-19, 13 (1.54%) died; 11 of them were in a progressive MS phase, and 8 were without any therapy. Thirty-eight (4.5%) were admitted to an ICU; 99 (11.7%) had radiologically documented pneumonia; 96 (11.4%) were hospitalized. After adjusting for region, age, sex, progressive MS course, Expanded Disability Status Scale, disease duration, body mass index, comorbidities, and recent methylprednisolone use, therapy with an anti-CD20 agent (ocrelizumab or rituximab) was significantly associated (odds ratio [OR] = 2.37, 95% confidence interval [CI] = 1.18-4.74, p = 0.015) with increased risk of severe COVID-19. Recent use (<1 month) of methylprednisolone was also associated with a worse outcome (OR = 5.24, 95% CI = 2.20-12.53, p = 0.001). Results were confirmed by the PS-weighted analysis and by all the sensitivity analyses. Interpretation: This study showed an acceptable level of safety of therapies with a broad array of mechanisms of action. However, some specific elements of risk emerged. These will need to be considered while the COVID-19 pandemic persists

COVID-19 Severity in Multiple Sclerosis: Putting Data Into Context

Background and objectives: It is unclear how multiple sclerosis (MS) affects the severity of COVID-19. The aim of this study is to compare COVID-19-related outcomes collected in an Italian cohort of patients with MS with the outcomes expected in the age- and sex-matched Italian population. Methods: Hospitalization, intensive care unit (ICU) admission, and death after COVID-19 diagnosis of 1,362 patients with MS were compared with the age- and sex-matched Italian population in a retrospective observational case-cohort study with population-based control. The observed vs the expected events were compared in the whole MS cohort and in different subgroups (higher risk: Expanded Disability Status Scale [EDSS] score > 3 or at least 1 comorbidity, lower risk: EDSS score ≤ 3 and no comorbidities) by the χ2 test, and the risk excess was quantified by risk ratios (RRs). Results: The risk of severe events was about twice the risk in the age- and sex-matched Italian population: RR = 2.12 for hospitalization (p < 0.001), RR = 2.19 for ICU admission (p < 0.001), and RR = 2.43 for death (p < 0.001). The excess of risk was confined to the higher-risk group (n = 553). In lower-risk patients (n = 809), the rate of events was close to that of the Italian age- and sex-matched population (RR = 1.12 for hospitalization, RR = 1.52 for ICU admission, and RR = 1.19 for death). In the lower-risk group, an increased hospitalization risk was detected in patients on anti-CD20 (RR = 3.03, p = 0.005), whereas a decrease was detected in patients on interferon (0 observed vs 4 expected events, p = 0.04). Discussion: Overall, the MS cohort had a risk of severe events that is twice the risk than the age- and sex-matched Italian population. This excess of risk is mainly explained by the EDSS score and comorbidities, whereas a residual increase of hospitalization risk was observed in patients on anti-CD20 therapies and a decrease in people on interferon

DMTs and Covid-19 severity in MS: a pooled analysis from Italy and France

We evaluated the effect of DMTs on Covid-19 severity in patients with MS, with a pooled-analysis of two large cohorts from Italy and France. The association of baseline characteristics and DMTs with Covid-19 severity was assessed by multivariate ordinal-logistic models and pooled by a fixed-effect meta-analysis. 1066 patients with MS from Italy and 721 from France were included. In the multivariate model, anti-CD20 therapies were significantly associated (OR = 2.05, 95%CI = 1.39–3.02, p < 0.001) with Covid-19 severity, whereas interferon indicated a decreased risk (OR = 0.42, 95%CI = 0.18–0.99, p = 0.047). This pooled-analysis confirms an increased risk of severe Covid-19 in patients on anti-CD20 therapies and supports the protective role of interferon