Integrated Satellite System for Fire Detection and Prioritization

Several studies have shown the relevance of satellite systems in detecting, monitoring, and characterizing fire events as support to fire management activities. On the other hand, up to now, only a few satellite-based platforms provide immediately and easily usable information about events in progress, in terms of both hotspots, which identify and localize active fires, and the danger conditions of the affected area. However, this kind of information is usually provided through separated layers, without any synthetic indicator which, indeed, could be helpful, if timely provided, for planning the priority of the intervention of firefighting resources in case of concurrent fires. In this study, we try to fill these gaps by presenting an Integrated Satellite System (ISS) for fire detection and prioritization, mainly based on the Robust Satellite Techniques (RST), and the Fire Danger Dynamic Index (FDDI), an original re-structuration of the Índice Combinado de Risco de Incêndio Florestal (ICRIF), for the first time presented here. The system, using Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data, provides near real-time integrated information about both the fire presence and danger over the affected area. These satellite-based products are generated in common formats, ready to be ingested in Geographic Information System (GIS) technologies. Results shown and discussed here, on the occasion of concurrent winter and summer fires in Italy, in agreement with information from independent sources, demonstrate that the ISS system, operating at a regional/national scale, may provide an important contribution to fire prioritization. This may result in the mitigation of fire impact in populated areas, infrastructures, and the environment.

Conservation Status of Forest Types Vary Greatly in Myanmar - the Most Forested Country in the Indo-burma Biodiversity Hotspot

Diverse forests with distinct forest types, harbor exceptional biodiversity and provide many ecosystem goods and services, making some forest types more economically valuable and prone to exploitation than others. The high rates of deforestation in Southeast Asia endanger the existence of such vulnerable forest types. Myanmar, the region’s largest forest frontier provides a last opportunity to conserve these vulnerable forest types. However, the exact distribution and spatial extent of Myanmar’s forest types has not been well characterized. To address this research gap, we developed a national scale Forest Type map of Myanmar at 20m resolution, using moderate resolution, multi-sensor satellite images (Sentinel-1, Sentinel-2 and ALOS-PALSAR), extensive field data, and a machine learning model (RandomForest). We mapped nine major forest types and developed a Conservation Status Score to evaluate the conservation status of the mapped forest types. Swamp, Mangrove, Dry Deciduous, Lowland Evergreen and Thorn forests were ranked as the five least conserved forest types. We also identified the largest remaining patch for each of the five least conserved forest types and determined their protection status to inform future forest conservation policy. In most cases, these patches lay outside protected areas indicating areas that may be prioritized for future conservation.

Clouds and the Earth’s Radiant Energy System (CERES) FluxByCldTyp Edition 4 Data Product

The Clouds and the Earth’s Radiant Energy System (CERES) project has provided the climate community 20 years of globally observed top of the atmosphere (TOA) fluxes critical for climate and cloud feedback studies. The CERES Flux By Cloud Type (FBCT) product contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and also reveal more insight into the interactions between clouds and climate. The FBCT product provides 1° regional daily and monthly shortwave (SW) and longwave (LW) cloud-type fluxes and cloud properties sorted by 7 pressure layers and 6 optical depth bins. Historically, cloud-type fluxes have been computed using radiative transfer models based on observed cloud properties. Instead of relying on radiative transfer models, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. These biases are mitigated by constraining the cloud-type fluxes within each footprint with the CERES Single Scanner Footprint (SSF) observed flux. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. Several examples of FBCT data are presented to highlight its utility for scientific applications.

Estimating Hourly Full-Coverage PM2.5 Concentrations Based On MODIS Data Over The Northeast of Thailand.

Particulate matter (PM2.5) pollutants are a significant health issue with impacts on human health; however, monitoring of PM2.5 is very limited in developing countries. Satellite remote sensing can expand spatial coverage, potentially enhancing our ability in a specific area for estimating PM2.5; however, some have reported poor predictive performance. An innovative combination of MODIS AOD was developed to fulfill all missing aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Therefore, hourly PM2.5 concentrations were obtained in Northeastern Thailand. A Linear mixed-effects (LME) model was used to predict location-specific hourly PM2.5 levels. Hourly PM2.5 concentrations measured at 20 PM2.5 monitoring sites and 10- fold cross-validation were addressed for model validation. The observed and predicted concentrations suggested that LME obtained from MODIS AOD data and other factors are a potentially useful predictor of hourly PM2.5 concentrations (R2 >0.70), providing more detailed spatial information for local scales studies. Interestingly, PM2.5 along the Mekong River area was observed higher than in the plain area. The finding can infer that the monsoon wind brings polluted air into the province from sources outside the region. The results will be helpful to analyze air pollution-related health studies.

A Multi-Year Study of GOES-13 Droplet Effective Radius Retrievals for Warm Clouds over South America and Southeast Pacific

Geostationary satellites can retrieve the cloud droplet effective radius (re) but suffer biases from cloud inhomogeneities, internal retrieval nonlinearities, and 3-D scattering/shadowing from neighboring clouds, among others. A 1-D retrieval method was applied to Geostationary Operational Environmental Satellite 13 (GOES-13) imagery, over large areas in South America (+5∘ to −30∘ N5∘ N–30∘ S; −20∘ to −70∘E20∘–70∘ W), the Southeast Pacific (+5∘ to −30∘ N5∘ N–30∘ S; −70∘ to −120∘E70∘–120∘ W), and the Amazon (+2∘ to −7∘ N2∘ N–7∘ S; −54∘ to −73∘E54∘–73∘ W), for four months in each year from 2014–2017. Results were regressedcompared against in situ aircraft measurements and the Moderate Resolution Imaging Spectroradiometer cloud product for Terra and Aqua satellites. Monthly regression parameters approximately followed a seasonal pattern. With up to 108,009 of matchups, slope, intercept, and correlation for Terra (Aqua) ranged from about 0.71 to 1.17, −2.8 to 2.5 μm, and 0.61 to 0.91 (0.54 to 0.78, −1.5 to 1.8 μm, 0.63 to 0.89), respectively. We identified evidence for re overestimation (underestimation) correlated with shadowing (enhanced reflectance) in the forward (backscattering) hemisphere, and limitations to illumination/ and viewing configurations accessible by GOES-13, depending on the time of day and season. A proposition is hypothesized to ameliorate 3-D biases by studying relative illumination and cloud spatial inhomogeneity.

Structural insights into the ion selectivity of the MgtE channel for Mg2+ over Ca2+

MgtE is a Mg2+-selective ion channel whose orthologs are widely distributed from prokaryotes to eukaryotes, including humans, and play an important role in the maintenance of cellular Mg2+ homeostasis. Previous functional analyses showed that MgtE transports divalent cations with high selectivity for Mg2+ over Ca2+. Whereas the high-resolution structure determination of the MgtE transmembrane (TM) domain in complex with Mg2+ ions revealed a Mg2+ recognition mechanism of MgtE, the previous Ca2+-bound structure of the MgtE TM domain was determined only at moderate resolution (3.2 angstrom resolution), which was insufficient to visualize the water molecules coordinated to Ca2+ ions. Thus, the structural basis of the ion selectivity of MgtE for Mg2+ over Ca2+ has remained unclear. Here, we showed that the metal-binding site of the MgtE TM domain binds to Mg2+ ~500-fold more strongly than Ca2+. We then determined the crystal structure of the MgtE TM domain in complex with Ca2+ ions at a higher resolution (2.5 angstrom resolution), allowing us to reveal hexahydrated Ca2+, which is similarly observed in the previously determined Mg2+-bound structure but with extended metal-oxygen bond lengths. Our structural, biochemical, and computational analyses provide mechanistic insights into the ion selectivity of MgtE for Mg2+ over Ca2+.

Grounding Event of Iceberg D28 and Its Interactions with Seabed Topography

Iceberg D28, a giant tabular iceberg that calved from Amery Ice Shelf in September 2019, grounded off Kemp Coast, East Antarctica, from August to September of 2020. The motion of the iceberg is characterized herein by time-series images captured by synthetic aperture radar (SAR) on Sentinel-1 and the moderate resolution imaging spectroradiometer (MODIS) boarded on Terra from 6 August to 15 September 2020. The thickness of iceberg D28 was estimated by utilizing data from altimeters on Cryosat-2, Sentinel-3, and ICESat-2. By using the iceberg draft and grounding point locations inferred from its motion, the maximum water depths at grounding points were determined, varying from 221.72 ± 21.77 m to 269.42 ± 25.66 m. The largest disagreements in seabed elevation inferred from the grounded iceberg and terrain models from the Bedmap2 and BedMachine datasets were over 570 m and 350 m, respectively, indicating a more complicated submarine topography in the study area than that presented by the existing seabed terrain models. Wind and sea water velocities from reanalysis products imply that the driving force from sea water is a more dominant factor than the wind in propelling iceberg D28 during its grounding, which is consistent with previous findings on iceberg dynamics.

Aerosol Characteristics and Their Impact on the Himalayan Energy Budget

The extensive work on the increasing burden of aerosols and resultant climate implications shows a matter of great concern. In this study, we investigate the aerosol optical depth (AOD) variations in the Indian Himalayan Region (IHR) between its plains and alpine regions and the corresponding consequences on the energy balance on the Himalayan glaciers. For this purpose, AOD data from Moderate Resolution Imaging Spectroradiometer (MODIS, MOD-L3), Aerosol Robotic Network (AERONET), India, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) were analyzed. Aerosol radiative forcing (ARF) was assessed using the atmospheric radiation transfer model (RTM) integrated into AERONET inversion code based on the Discrete Ordinate Radiative Transfer (DISORT) module. Further, air mass trajectory over the entire IHR was analyzed using a hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We estimated that between 2001 and 2015, the monthly average ARF at the surface (ARFSFC), top of the atmosphere (ARFTOA), and atmosphere (ARFATM) were −89.6 ± 18.6 Wm−2, −25.2 ± 6.8 Wm−2, and +64.4 ± 16.5 Wm−2, respectively. We observed that during dust aerosol transport days, the ARFSFC and TOA changed by −112.2 and −40.7 Wm−2, respectively, compared with low aerosol loading days, thereby accounting for the decrease in the solar radiation by 207% reaching the surface. This substantial decrease in the solar radiation reaching the Earth’s surface increases the heating rate in the atmosphere by 3.1-fold, thereby acting as an additional forcing factor for accelerated melting of the snow and glacier resources of the IHR.

Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation

Accurate estimation of gross primary productivity (GPP) is necessary to better understand the interaction of global terrestrial ecosystems with climate change and human activities. Light use efficiency (LUE)-based GPP models are widely used for retrieving several GPP products with various temporal and spatial resolutions. However, most LUE-based models assume a clear-sky condition, and the influence of diffuse radiation on GPP estimations has not been well considered. In this paper, a diffuse and direct (DDA) absorbed photosynthetically active radiation (APAR)-based method is proposed for better estimation of half-hourly GPP, which partitions APAR under diffuse and direct radiation conditions. Firstly, energy balance residual (EBR) FAPAR, moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) (MCD15A2H) and clumping index (CI) products, as well as solar radiation records supplied by FLUXNET2015 were used to calculate diffuse and direct APAR at a half-hourly scale. Then, an eddy covariance-LUE (EC-LUE) model and meteorological observations from FLUXNET2015 data sets were used for obtaining corresponding LUE values. A co-variation relationship between LUE and diffuse fraction was observed, and the LUE was higher under more diffuse radiation conditions. Finally, the DDA-based method was tested using the half-hourly FLUXNET GPP and compared with half-hourly GPP calculated using total APAR (GPP_TA). The results indicated that the half-hourly GPP estimated using the DDA-based method (GPP_DDA) was more accurate, giving higher R2 values, lower RMSE and RMSE* values (R2 varied from 0.565 to 0.682, RMSE ranged from 3.219 to 12.405 and RMSE* were within the range of 2.785 to 8.395) than the GPP_TA (R2 varied from 0.558 to 0.653, RMSE ranged from 3.407 to 13.081 and RMSE* were within the range of 3.321 to 9.625) across FLUXNET sites within different vegetation types. This study explored the effects of partitioning the diffuse and direct APAR on half-hourly GPP estimations, which demonstrates a higher agreement with FLUXNET GPP than total APAR-based GPP.