Search Results (93)

Concentrations of oxides of nitrogen (NO_x), as the sum total of nitric oxide (NO) and nitrogen dioxide (NO₂), the individual parts, i.e., NO and NO₂, (NO_x = NO + NO₂), and wind speed and direction measurements were gathered over a thirteen-year period (2011–2023) at the Giordan Lighthouse Geosciences Observatory, located on the Island of Gozo, forming part of the Maltese Archipelago (Central Mediterranean). The atmospheric concentration measurements were recorded with a Thermo Scientific Model 42i NO_x analyser, which employs the chemiluminescence technique to detect atmospheric traces of NO_x concentrations. In this case study, an investigation was conducted to understand the wind and seasonal variabilities of the measured concentrations. The highest NO_x concentrations occurred when the prevailing wind originated from the SE, while a broad minimum was observed when the wind blew from the S–W sector. The maxima were primarily associated with land-based sources, predominantly vehicular emissions on the main island, i.e., Malta. The amplitudes for NO, NO₂, and NO_x in relation to wind direction were 63%, 125%, and 121%, respectively. Significant variabilities were observed during the autumn season. Regarding wind speed, the NO_x concentrations reached their peak during high-wind-speed events, which are associated with transboundary pollution. A secondary broad maximum was observed for wind forces between 2 and 4, while the lowest concentrations were recorded at wind force 9. The NO_x concentrations exhibited a seasonal maximum in spring and a minimum in winter, which contrasts with the findings from the Monte Cimone station in Italy. The seasonal amplitudes for NO, NO₂, and NO_x were 46%, 15%, and 17%, respectively. It is evident that NO concentrations exhibited a greater seasonal variability, whereas NO₂ concentrations demonstrated significant variability in relation to wind direction. Full article

Butterflies are important biological indicators for assessing the environment and habitat quality. Dulongjiang in Yunnan, China, a global biodiversity hotspot, has undergone recent socioeconomic development, yet the impact of resultant land-use changes on its butterfly fauna remains poorly understood. This study conducted a systematic survey across three land-use types (forest, cropland, and construction land) over four months in 2024, employing area-time counts at 12 observatory sites. A total of 4805 individual specimens from 142 species, 88 genera, and 6 families were recorded. Nymphalidae dominated in species richness, while Pieridae was most abundant. Species rarefication curves indicated well-represented sampling. Diversity was significantly different between the four months, with a peak in June, when environment conditions are favourable. The forest harboured the least butterfly richness but higher evenness, while construction land showed the highest richness and lower evenness. Butterfly communities in three land-use types showed no significant differences, attributed to the fragmented topography in the area, which facilitates butterfly dispersal. Our findings reveal that butterfly diversity in Dulongjiang is influenced by a combination of seasonal climatic variations and land use. Full article

Recovery from disasters is the complex process requiring coordinated measures to restore infrastructure, services and quality of life. While remote sensing is a well-established means for damage assessment, so far very few studies have shown how satellite imagery can be used by technical officers of affected countries to provide crucial, up-to-date information to monitor the reconstruction progress and natural restoration. To address this gap, the present study proposes a multi-temporal observatory method relying on GIS, change detection techniques and open and free multi-sensor satellite imagery to generate thematic maps documenting, over time, the impact and recovery from hydrological disasters such as hurricanes, tropical storms and induced flooding. The demonstration is carried out with regard to Hurricane Matthew, which struck Haiti in October 2016 and triggered a humanitarian crisis in the Sud and Grand’Anse regions. Synthetic Aperture Radar (SAR) amplitude change detection techniques were applied to pre-, cross- and post-disaster Sentinel-1 image pairs from August 2016 to September 2020, while optical Sentinel-2 images were used for verification and land cover classification. With regard to inundated areas, the analysis allowed us to determine the needed time for water recession and rural plain areas to be reclaimed for agricultural exploitation. With regard to buildings, the cities of Jérémie and Les Cayes were not only the most impacted areas, but also were those where most reconstruction efforts were made. However, some instances of new settlements located in at-risk zones, and thus being susceptible to future hurricanes, were found. This result suggests that the thematic maps can support policy-makers and regulators in reducing risk and making the reconstruction more resilient. Finally, to evaluate the replicability of the proposed method, an example at a country-scale is discussed with regard to the June 2023 flooding event. Full article

Woody plant encroachment (WPE) is transforming grasslands globally, yet accurately mapping this process remains challenging. State-funded, publicly available high-resolution aerial imagery offers a potential solution, including the USDA’s National Agriculture Imagery Program (NAIP) and NSF’s National Ecological Observatory Network (NEON) Aerial Observation Platform (AOP). We evaluated the accuracy of land cover classification using NAIP, NEON, and both sources combined. We compared two machine learning models—support vector machines and random forests—implemented in R using large training and evaluation data sets. Our study site, Konza Prairie Biological Station, is a long-term experiment in which variable fire and grazing have created mosaics of herbaceous plants, shrubs, deciduous trees, and evergreen trees (Juniperus virginiana). All models achieved high overall accuracy (>90%), with NEON slightly outperforming NAIP. NAIP underperformed in detecting evergreen trees (52–78% vs. 83–86% accuracy with NEON). NEON models relied on LiDAR-based canopy height data, whereas NAIP relied on multispectral bands. Combining data from both platforms yielded the best results, with 97.7% overall accuracy. Vegetation indices contributed little to model accuracy, including NDVI (normalized digital vegetation index) and EVI (enhanced vegetation index). Both machine learning methods achieved similar accuracy. Our results demonstrate that free, high-resolution imagery and open-source tools can enable accurate, high-resolution, landscape-scale WPE monitoring. Broader adoption of such approaches could substantially improve the monitoring and management of grassland biodiversity, ecosystem function, ecosystem services, and environmental resilience. Full article

The COllaborative Carbon Column Observing Network has become a reliable source of high-quality ground-based remote sensing network data that provide column-averaged dry-air mole fractions of carbon dioxide (XCO₂), methane (XCH₄), and carbon monoxide (XCO). The fiducial reference measurements of these gases from the COCCON complement the TCCON and NDACC-IRWG data. This study shows the application of COCCON data for the validation of existing greenhouse gas satellite products. This study includes the validation of XCH₄ and XCO products from the European Copernicus Sentinel-5 Precursor (S5P) mission, XCO₂ products from the American Orbiting Carbon Observatory-2 (OCO-2) mission, and XCO₂ and XCH₄ products from the Japanese Greenhouse gases Observing SATellite (GOSAT). A total of 27 datasets contributed to this study; some of these were collected in the framework of campaign activities and covered only a short time period. In addition, several permanent stations provided long-term observations. The random uncertainties in the validation results, specifically for S5P with a lot of coincidences pairs, are found to be similar to the comparison with the TCCON. The comparison results of OCO-2 land nadir and land glint observation modes to the COCCON on a global scale, despite limited coincidences, are very promising. The stations can, therefore, expand on the coverage of the already existing ground-based reference remote sensing sites from the TCCON and the NDACC network. The COCCON data can be used for future satellite and model validation studies and carbon cycle studies. Full article

The coast is the most dynamic part of the Earth’s surface due to its strategic position at the interface of the land and the sea. It is, therefore, exposed to hazards and specific risks because of the geography as well as the geological and environmental characteristics of different countries. The coastal environment is essentially dynamic and evolving in time and space, marked by waves, tides, and seasons; moreover, it is subjected to many marine and continental processes (forcing). This succession of events significantly influences the frequency and severity of coastal hazards. The present paper aims at describing and characterizing the hazards and vulnerabilities on the Cameroonian coast. Cameroon possesses 400 km of coastline, which is exposed to various hazards. It is important to determine the probabilities of these hazards, the associated effects, and the related vulnerabilities. In this study, in this stable intraplate setting, the methodology used was diverse and combined techniques for the study of the shore and methods for the treatment of climatic data. Also, historical data were collected during field observations and from the CRED website for all the natural hazards recorded in Cameroon. In addition, documents on climate change were consulted. Remotely sensed data, combined with GIS tools, helped to determine and assess the associated risks. A critical grid combining a severity and frequency analysis was used to better understand these hazards and the coastal vulnerabilities of Cameroon. The results show that Cameroon’s coastal margins are subject to natural processes that cause shoreline changes, including inundation, erosion, and accretion. This study identified seven primary hazard types (earthquakes, volcanism, landslides, floods, erosion, sea level rise, and black tides) affecting the Cameroonian coastline, with the erosion rate exceeding 1.15 m/year at Cape Cameroon. Coastal populations are continuously threatened by these natural or man-induced hazards, and they are periodically subjected to catastrophic disasters such as floods and landslides, as experienced in Cameroon. In addition, despite the existence of the National Contingency Plan devised by the Directorate of Civil Protection, National Risk, and Climate Change Observatories, the implementation of disaster risk reduction and mitigation strategies is suboptimal. Full article

Household food waste accounts for a significant share of total food waste. In 2022, around 1.05 billion tons of food waste were generated—60% of which came from households. In the EU, households generate 54% of the total food waste. In Italy, according to a former diary study, avoidable household food waste accounts for 529.9 g per capita per week. Building on this data, this study assesses the monetary value of food waste at the household level in 6 provinces across the country, considering the prices of food items recorded by the Italian Observatory of market prices. Moreover, the environmental impacts of household food waste (greenhouse gas emissions, water consumed, and land used) were investigated based on existing data from well-grounded scientific literature. The results show that the monetary value of food waste ranges from EUR 357.43 to EUR 404.62 per household per year, corresponding to 5–7% of the average household expenditure for food. The environmental impacts per household per year account for 149 kgCO₂eq, which contributes to climate change. In addition, household food waste is responsible for 303,498 L of water consumed and 1426 m² of land used. The results of this study can be integrated into National Energy and Climate Plans (NECPs), to integrate food waste reduction into energy savings and greenhouse gas mitigation strategies. Full article

The ICESat-2 Land and Vegetation Height (ATL08) product is a new control point dataset for large-scale topographic mapping and geodetic surveying. However, its elevation accuracy is typically affected by multiple factors. The study aims to propose a new approach to classify ATL08 terrain estimates into different accuracy levels and extract reliable ground control points (GCPs) from ICESat-2 ATL08. Specifically, the methodology is divided into three stages. First, the ATL08 terrain estimates are matched with the raw ATL03 photon cloud data, and the ATL08 terrain estimates are used to fit a continuous terrain curve. Then, using the fitted continuous terrain curve and raw ATL03 photon cloud data, a pseudo-waveform is generated for grading the ATL08 terrain estimates. Finally, all the ATL08 terrain estimates are graded based on the peak characteristics of the generated pseudo-waveform. To validate the feasibility of the proposed method, four study areas from the National Ecological Observatory Network (NEON), characterized by various terrain features and forest types were selected. High-accuracy airborne lidar data were used to evaluate the accuracy of graded ICESat-2 terrain estimates. The results demonstrate that the method effectively classified all ATL08 terrain estimates into different accuracy levels and successfully extracted high-accuracy GCPs. The root mean square errors (RMSEs) of the first accuracy level in the four selected study areas were 0.99 m, 0.51 m, 1.88 m, and 0.65 m, representing accuracy improvement of 51.7%, 58.2%, 83.1%, and 68.8%, respectively, compared to the original ATL08 terrain estimates before classifying. Additionally, a comparison with the conventional threshold-based GCP extraction method demonstrated the superior performance of our proposed approach. This study introduces a new approach to extract high-quality elevation control points from ICESat-2 ATL08 data, particularly in forested areas. Full article

Previous research has found higher temperature trends at urban observatories. This study examines in depth the features of the urban environment, the thermal behaviour of land use and land cover, and the changes that have taken place in five urban areas of the Spanish Mediterranean. The CORINE Land Cover database was used to delimit the primary land use land cover (LULC) and its changes between 1990 and 2018. Once this had been established, land surface temperatures (LSTs) between 1985 and 2023 were retrieved from the Landsat database available on the Climate Engine website. There has been a significant advance in artificial land uses, which have become the main uses in the urban areas in Valencia and Alicante. An analysis of the primary land cover showed the greatest thermal increase in artificial surfaces, especially in the industrial, commercial, and transport units that are common on their outskirts, without exception in any urban area. The results are less clear for urban fabrics and agricultural areas due to their diversity and complexity. The density of vegetation is a key factor in the magnitude of the UHI, which is higher in the urban areas with more vegetated agriculture areas, therefore showing lower LST than both industrial units and urban fabrics. Another important conclusion is the role of breezes in limiting or eliminating the strength of the UHI. Sea breezes help to explain the monthly variation of UHIs. Both bodies of water and areas of dense tree vegetation provided the lowest LST, a fact of special interest for mitigating the effects of heat waves in increasingly large urban areas. This study also concludes the different effect of each LULC on the temperatures recorded by urban observatories and enables better decision-making when setting up weather stations for a more detailed time study of the urban heat island (UHI). Full article

The Lithospheric–Atmospheric–Ionospheric Coupling (LAIC) mechanism stands as the leading model for the prediction of seismic activities. It consists of a cascade of physical processes that are initiated days before a major earthquake. The onset is marked by the discharge of ionized gases, such as radon, through subterranean fissures that develop in the lead-up to the quake. This discharge augments the ionization at the lower atmospheric layers, instigating disturbances that extend from the Earth’s surface to the lower ionosphere. A critical component of the LAIC sequence involves the distinctive perturbations of Extremely Low Electromagnetic Frequencies (ELF) within the Schumann Resonances (SR) spectrum of 2 to 50 Hz, detectable days ahead of the seismic event. Our study examines 10 earthquakes that transpired over a span of 3.5 months—averaging nearly three quakes monthly—which concurrently generated 45 discernible potential precursor seismic signals. Notably, each earthquake originated in Southern Greece, within a radius of 30 to 250 km from the observatory on Mount Parnon. Our research seeks to resolve two important issues. The first concerns the association between specific ELF signals and individual earthquakes—a question of significant importance in seismogenic regions like Greece, where earthquakes occur frequently. The second inquiry concerns the parameters that determine the detectability of an earthquake by a given station, including the requisite proximity and magnitude. Initial findings suggest that SR signals can be reliably linked to a particular earthquake if the observatory is situated within the earthquake’s preparatory zone. Conversely, outside this zone, the correlation becomes indeterminate. Additionally, we observe a differentiation in SR signals based on whether the earthquake took place over land or offshore. The latter category exhibits unique signal behaviors, potentially attributable to the water layers above the epicenter acting as a barrier to the ascending gases, thereby affecting the atmospheric–ionospheric ionization process. Full article

The leaf area index (LAI) is a critical variable for forest ecosystem processes. Passive optical and active LiDAR remote sensing have been used to retrieve LAI. LiDAR data have good penetration to provide vertical structure distribution and deliver the ability to estimate forest LAI, such as the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Segment size and beam type are important for ICESat-2 LAI estimation, as they affect the amount of signal photons returned. However, the current ICESat-2 LAI estimation only covered a limited number of sites, and the performance of LAI estimation with different segment sizes has not been clearly compared. Moreover, ICESat-2 LAIs derived from strong and weak beams lack a comparative analysis. This study derived and evaluated LAI from ICESat-2 data over the National Ecological Observatory Network (NEON) sites in North America. The LAI estimated from ICESat-2 for different segment sizes (20, 100, and 200 m) and beam types (strong beam and weak beam) were compared with those from the airborne laser scanning (ALS) and the Copernicus Global Land Service (CGLS). The results show that the LAI derived from strong beams performs better than that of weak beams because more photon signals are received. The LAI estimated from the strong beam at the 200 m segment size shows the highest consistency with those from the ALS data (R = 0.67). Weak beams also present the potential to estimate LAI and have moderate agreement with ALS (R = 0.52). The ICESat-2 LAI shows moderate consistency with ALS for most forest types, except for the evergreen forest. The ICESat-2 LAI shows satisfactory agreement with the CGLS 300 m LAI product (R = 0.67, RMSE = 1.94) and presents a higher upper boundary. Overall, the ICESat-2 can characterize canopy structural parameters and provides the ability to estimate LAI, which may promote the LAI product generated from the photon-counting LiDAR. Full article

The regional differences in carbon dioxide (CO₂) variations from the Orbiting Carbon Observatory-2 (OCO-2) over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 2015 to 2021 are analyzed in this study. This study shows an annual increase and a seasonal cycle; the CO₂ annual growth rate was about 2.63 ppm year⁻¹, with the highest value being in spring and the lowest in summer. The spatial distribution is unbalanced, regional differences are prominent, and the CO₂ concentration is lower in the north of the Jing-Jin-Ji region (like Zhangjiakou, Chengde, and Qinhuangdao). Land-type structures and population economy distributions are the key factors affecting CO₂ concentration. By analyzing the land-type structures over Jing-Jin-Ji in 2020, we find that cropland, woodland, and grassland (CWG) are the main land cover types in Jing-Jin-Ji; the proportion of these three types is about 83.3%. The woodland areas in Zhangjiakou, Chengde, and Qinhuangdao account for about 65% of the total woodland areas in Jing-Jin-Ji; meanwhile, the grassland areas in these three regions account for 62% of the total grassland areas in Jing-Jin-Ji. CO₂ concentration variation shows a high negative correlation with CWG land areas (coefficient of determination (R²) > 0.76). The regions with lower population and GDP secondary industry (SI) density also have lower CO₂ concentration (like Zhangjiakou, Chengde, and Qinhuangdao), and the regions with higher population and GDP SI density also have higher CO₂ concentration (like the southeast of Jing-Jin-Jin). Full article

In Southern Mediterranean regions, the issue of summer fires related to agriculture practices is a periodic recurrence. It implies a significant increase in carbon dioxide (CO₂) emissions and other combustion-related gaseous and particles compounds emitted into the atmosphere with potential impacts on air quality and global climate. In this work, we performed an analysis of summer fire events that occurred on August 2021. Measurements were carried out at the permanent World Meteorological Organization (WMO)/Global Atmosphere Watch (GAW) station of Lamezia Terme (Code: LMT) in Calabria, Southern Italy. The observatory is equipped with greenhouse gases and black carbon analyzers, an atmospheric particulate impactor system, and a meteo-station for atmospheric parameters to characterize atmospheric mechanisms and transport for land and sea breezes occurrences. High mole fractions of carbon monoxide (CO) and carbon dioxide (CO₂) coming from quadrants of inland areas were correlated with fire counts detected via the MODIS satellite (GFED-Global Fire Emissions Database) at 1 km of spatial resolution. In comparison with the typical summer values, higher CO and CO₂ were observed in August 2021. Furthermore, the growth in CO concentration values in the tropospheric column was also highlighted by the analyses of the L2 products of the Copernicus SP5 satellite. Wind fields were reconstructed via a Weather Research and Forecasting (WRF) output, the latter suggesting a possible contribution from open fire events observed at the inland region near the observatory. So far, there have been no documented estimates of the effect of prescribed burning on carbon emissions in this region. This study suggested that data collected at the LMT station can be useful in recognizing and consequently quantifying emission sources related to open fires. Full article

Understanding the dynamics of volcanic activity is crucial for volcano observatories in their efforts to forecast volcanic hazards. Satellite imager data hold promise in offering crucial insights into the thermal behavior of active volcanoes worldwide, facilitating the assessment of volcanic activity levels and identifying significant changes during periods of volcano unrest. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, aboard NASA’s Terra and Aqua satellites, provides invaluable data with high temporal and spectral resolution, enabling comprehensive thermal monitoring of eruptive activity. The accuracy of volcanic activity characterization depends on the quality of models used to relate the relationship between volcanic phenomena and target variables such as temperature. Under these circumstances, machine learning (ML) techniques such as decision trees can be employed to develop reliable models without necessarily offering any particular or explicit insights. Here, we present a ML approach for quantifying volcanic thermal activity levels in near real time using thermal infrared satellite data. We develop an unsupervised Isolation Forest machine learning algorithm, fully implemented in Google Colab using Google Earth Engine (GEE) which utilizes MODIS Land Surface Temperature (LST) data to automatically retrieve information on the thermal state of volcanoes. We evaluate the algorithm on various volcanoes worldwide characterized by different levels of volcanic activity. Full article

A series of typhoons and tropical storms have produced extreme precipitation events in Vietnam during the first part of the 2020 monsoon season: events of this magnitude pose significant challenges to remote sensing Quantitative Precipitation Estimation (QPE) techniques. The weather-monitoring needs of modern human activities require that these challenges be overcome. In order to address this issue, in this work, seven precipitation products were validated with high spatial and temporal detail against over 1200 rain gauges in Vietnam during six case studies tailored around the most intense events of 2020. The data sources included the Vietnamese weather radar network, IMERG Early run and Final run, the South Korean GEO-KOMPSAT-2A and Chinese FengYun-4A geostationary satellites, DPR on board the GPM-Core Observatory, and European ERA5-Land reanalysis. All products were resampled to a standardized 0.02° grid and compared at hourly scale with ground stations measurements. The results indicated that the radars product was the most capable of reproducing the information collected by the rain gauges during the selected extreme events, with a correlation coefficient of 0.70 and a coefficient of variation of 1.38. However, it exhibited some underestimation, approximately 30%, in both occurrence and intensity. Conversely, geostationary products tended to overestimate moderate rain rates (FY-4A) and areas with low precipitation (GK-2A). More complex products such as ERA5-Land and IMERG failed to capture the highest intensities typical of extreme events, while GPM-DPR showed promising results in detecting the highest rain rates, but its capability to observe isolated events was limited by its intermittent coverage. Full article