The 2021 unrest at Vulcano: insights from ground-based and satellites observations

Simulation of the land surface temperature from moon-based Earth observations

Coal fire identification and state assessment by integrating multitemporal thermal infrared and InSAR remote sensing data: A case study of Midong District, Urumqi, China

Reconstruction of land surface temperature under cloudy conditions from Landsat 8 data using annual temperature cycle model

The aim of this research effort is to develop a method that will allow to map and evaluate thermal anomalies in SW USA from the MYD11A2 night land surface temperature (LST) imagery being available for the year 2014, that present higher spatial (1 km) and temporal (46 images per year) resolution than the MYD11C3 LST data (12 images per year at 5.6 km spatial resolution). The fact that is MYD11A2 LST imagery is projected to a rectangular grid did not affect the X, Y and elevation (H) spatial decorrelation stretch. Principal component analysis and linear regression models isolated and removed the X, Y, H (spatial) dependent variance included in the data while metrics devised verified the selective spatial variance reduction. The reconstructed 46 LST images represent the amount the LST deviates from the X, Y and H predicted for the year 2014. The thematic information content of the reconstructed LST images is verified by cluster analysis and mapped the spatial extend and the temporal variability of thermal anomalies within the study area. The positive thermal anomaly clusters are spatially arranged mainly west of Sierra Nevada in Great Basin Section where extensional tectonics create a series of titled elongated mountain blocks along the N to S direction in between basins bounded by normal faults, while the negative thermal anomaly clusters are spatially arranged along the coastal region, further north and in the western region far from the tilted mountain tectonic blocks of the Great Basin Section. The spatial maps that define regions with (positive or negative) thermal anomalies and distinct mean land response could assist landcover studies and support urban and rural planning in the context of emerging climatic change.

Preventive control of desert locusts is based on monitoring recession areas to detect outbreaks. Remote sensing has been increasingly used in the preventive control strategy. Soil moisture is a major ecological driver of desert locust populations but is still missing in the current imagery toolkit for preventive management. By means of statistical analyses, combining field observations of locust presence/absence and soil moisture estimates at 1 km resolution from a disaggregation algorithm, we assess the potential of soil moisture to help preventive management of desert locust. We observe that a soil moisture dynamics increase of above 0.09 cm3/cm3 for 20 days followed by a decrease of soil moisture may increase the chance to observe locusts 70 days later. We estimate the gains in early warning timing compared to using imagery from vegetation to be 3 weeks. We demonstrate that forecasting errors may be reduced by the combination of several types of indicators such as soil moisture and vegetation index in a common statistical model forecasting locust presence. Policy implications. Soil moisture estimates at 1 km resolution should be used to plan desert locust surveys in preventive management. When soil moisture increases in a dry area of potential habitat for the desert locust, field surveys should be conducted two months later to evaluate the need of further preventive actions. Remote sensing estimates of soil moisture could also be used for other applications of integrated pest management.

Artificial Neural Network based thermal anomalies associated with earthquakes in Pakistan from MODIS LST

Analyzing correlations between GNSS retrieved precipitable water vapor and land surface temperature after earthquakes occurrence

Under the background of global warming, it is of great significance to study the temporal and spatial evolution of land surface temperature (LST) on long-time scale and the impacts of land use in the fields of urban thermal environment and regional climate change. Based on MODIS LST long time series remote sensing data, the temporal and spatial evolution characteristics of pixel-wise LST in Jiangxi Province, the middle inland province of China from 2000 to 2020 were analyzed by using Theil-Sen + Mann-Kendall, coefficient of variation and Hurst index, and the response of LST to land use was identified by combining the contribution and diversity index. The results showed as follows: (1) LST was generally distributed as "high in Middle-East-West-South and low in North-northwest-southeast direction". LST showed an overall downward trend, indicating a weakening of the warming trend. The dynamic trend of LST was characterized by more descending than ascending tendency. The dynamic stability showed a coexistence of high and low fluctuation tendency, with a higher proportion of medium and low fluctuation areas having obvious spatial differences. The overall dynamic sustainability was characterized by uncertainty of future change trend. (2) The LST were strongly affected by land use in the past 20 years. Firstly, the areas of high LST were mostly located in construction land and unused land, while the areas of low LST were mostly in water area and forest land. However, forest land and water area of high temperature were gradually turned to construction land later on. Secondly, the land use structure and pattern had an strong effects on LST. With the increase of the area proportion of different land use, the LST showed significant differences. The more complex the spatial pattern of land use, the more obvious its impact on LST. The research results will provide some reference for the regions with the same characteristics as Jiangxi Province to deal with LST under the background of global climate change.

This study focuses on assessing the physical growth of cities and the land-cover changes resulting from it, which play a crucial role in understanding the environmental impacts and managing phenomena such as the Daytime Urban Surface Heat Island Intensity (DSUHII). Predicting the trends of these changes for the future provides valuable insights for urban planning and mitigating thermal effects in arid environments. This research aims to evaluate the spatial and temporal changes in the intensity of urban surface heat islands in cities under different climatic conditions, resulting from land-cover changes in the past, and to predict future trends. For this purpose, Landsat satellite data products, including Surface Reflectance with a 30-m resolution and Land Surface Temperature (LST) originally at a 100 (120)-meter resolution for Landsat 8 (Landsat 5) (resampled to 30 m for compatibility), along with a database of underlying criteria affecting urban growth, were used to analyze land-cover and LST changes. The land-cover classification was carried out using the Support Vector Machine (SVM) algorithm, and its accuracy was assessed. Spatial and temporal changes in LST and land-cover classes were quantified using cross-tabulation models and subtraction operators. Subsequently, the impact of land-cover changes on LST in different climates was analyzed, and the trends of land-cover and DUSHII changes were simulated for the future using the CA–Markov model. The results showed that in the humid climate (Babol and Rasht), built-up areas increased by over 100% from 1990 to 2023 and are projected to grow further by 2055, while green spaces significantly decreased. In the cold–dry climate (Mashhad), urban development increased dramatically, and green spaces nearly halved. In the hot–dry climate (Yazd and Kerman), built-up areas tripled, and the reduction of green spaces will continue. Additionally, in cities with hot and dry climates, a significant area of barren land was converted into built-up areas, and this trend is predicted to continue in the future. DSUHII in Babol increased from 2.5 °C in 1990 to 5.4 °C in 2023 and is projected to rise to 7.8 °C by 2055. In Rasht, this value increased from 2.9 °C to 5.5 °C, and is expected to reach 7.6 °C. In Mashhad, the DSUHII was negative, decreasing from −1.1 °C in 1990 to −1.5 °C in 2023, and is projected to decline to −1.9 °C by 2055. In Yazd, DSUHII also remained negative, decreasing from −2.5 °C in 1990 to −3.3 °C in 2023, with an expected drop to −6.4 °C by 2055. Similarly, in Kerman, the intensity of DSUHII decreased from −2.8 °C to −5.1 °C, and it is expected to reach −7.1 °C by 2055. Overall, the conclusions highlight that in humid climates, DSUHII has significantly increased, while green spaces have decreased. In moderate, cold, and dry climates, a gradual reduction in DSUHII is observed. In the hot–dry climate, the most substantial decrease in DSUHII is evident, indicating the varying impacts of land-cover changes on DSUHII across these regions.

A spatio-temporal temperature-based thresholding algorithm for underground coal fire detection with satellite thermal infrared and radar remote sensing

Rapid and uncontrolled urbanization is one of the drivers responsible for land cover Change dynamics in Ethiopia. In most of the cities and towns of Ethiopia, the proportion of different types of urban land surface cover and trends of change in each are unknown and thus hindering environmentally sustainable urban planning. This study describes different land surface cover types and their dynamics of change, and subsequent influence on the Land Surface Temperature of Hawassa city between 2011 and 2021. The i-Tree canopy and Landsat 5 (TM) and Landsat 8 (OLI) images were used for 2011 and 2021 to analyze the land surface cover and surface temperature change, respectively. The results show that bare soil is the dominant land surface cover type (23.4%), followed by tree canopy cover (21.4%), while impervious roads occupied the smallest land surface area cover (3.4%) along with water bodies (1%). In 10 years most of the land surface cover types increased positively, including tree covers by + 9.8%. The only exceptions were bare soil and herbaceous cover, which decreased drastically by − 34.6% and − 2.8%, respectively. As a result of tree cover increment, the average and maximum Land Surface Temperature showed a declining trend between the two periods. This study shows that increasing tree cover in line with the expansion of urbanization is inversely correlated with the land surface temperature which implies that integrating green coverage along with the built-up area can reduce heatwave in the fast growing urban areas. Therefore, the Hawassa city administration should set tree cover targets to achieve the desired balance between green and grey infrastructure and enhance the climate resilience level of the study area.

Monitoring thermal anomaly and radiative heat flux using thermal infrared satellite imagery – A case study at Tuzla geothermal region

<p>Starting from July 2021, a gradual unrest of Vulcano volcano was recorded by monitoring system managed by INGV, marked by a progressive change of many parameters from the multi-disciplinary networks.</p><p>The fumaroles located on the crater rim and along the flank of the cone shown temperature increase ( up to 350 degree Celsius) and  an increase of the flux of carbon dioxide and sulfur dioxide in gas emissions. Furthermore, the increase of the occurrence of with very-long-period (VLP) events was recorded by seismic network, and a rapid uplift of about 1 cm/month was recorded at VCRA GNSS permanent station located on the North slope of the “La Fossa” cone.</p><p>In order to image the ground deformation accompanying the unrest phase, we analyzed the 2020-2021 ascending and descending ESA-Copernicus Sentinel-1A and 1B C-band SAR (Synthetic Aperture Radar) acquired in TopSAR (Terrain Observation with Progressive Scans SAR) Interferometric Wide mode with A-DINSAR techniques. On October 2021 a new GNSS survey was performed on the ”Lipari-Vulcano” network. We integrated the SAR data and the GNSS data applying the SISTEM method, and the preliminary results are consistent with the Vulcano hydrothermal system dynamics, with a deformation pattern limited to the cone area.</p><p>In order to monitoring continuously and more in detail the change in ground deformation, on December 2021 we installed 4 additional GNSS mobile stations and a permanent GB-RAR (ground-based real aperture radar) on the island. The GB-RAR system was installed at the Lipari Observatory, at a distance of about 5 km from Vulcano, and it is able to image the whole Vulcano north area, with a rectangular pixel resolution of 3x30 m and a precision of the displacement along the line of sight of about 1 mm.</p><p>At time of this abstract no ground deformation have been recorded in the last month, the microseismic activity reduced but the fumarole temperatures at the crater and gas emissions of carbon and sulphur dioxide remained at high level.</p>

Land surface temperature (LST) derived from Meteosat Second Generation/ Spinning-Enhanced Visible and Infrared Imager MSG/SEVIRI data is an operational product of the Land Surface Analysis Satellite Applications Facility (LSA SAF). The LST has a temporal resolution of 15 minutes, a sampling distance of 3 km at nadir, and a targeted accuracy of better than 2 K. Gobabeb (Namibia) is one of Karlsruhe Institute of Technology's (KIT's) four dedicated stations for LST validation. In March 2010, a field survey was performed to characterize the Gobabeb site more closely. SAF LST and in situ LST obtained over a period of 3 days from additional measurements with a telescopic mast on the Namib gravel plains were in good agreement with each other (bias 1.0 K). For the same period, the bias between SAF LST and Gobabeb main station LST was even smaller (0.4 K). A mobile measurement system was set up by fixing the telescopic mast to a four-wheel drive. Around solar noon, LST from in situ measurements along a 40 km track and LST from Gobabeb main station had a bias of 0.4 K and a standard deviation of 1.2 K, which means that in situ LSTs at Gobabeb main station are representative for large parts of the gravel plains. Exploiting this relationship, 2 years of LST from MSG/SEVIRI were compared with in situ LST from Gobabeb main station. The magnitude of the monthly biases between the two data sets was generally less than 1.0 K and root mean square errors were below 1.5 K. Furthermore, the bias appears to exhibit a seasonality, which could be accounted for in future validation work.

BackgroundTemperature, as one of the effective environmental stimuli in many aspects of species life and ecosystems, can affect amphibians in many ways. Knowing and predicting temperature change and its possible effects on the habitat suitability and movements of amphibians have led many researchers to use climate change scenarios and species distribution models (SDMs). One of the important remote-sensing products that received less attention of conservation biologists is the land surface temperature (LST). Due to the small difference between LST and air temperature, this component can be used to investigate and monitor the daily and seasonal changes of habitats. This study aims to investigate the seasonal trend of LST in the habitat suitability and connectivity of the critically endangered newt (Neurergus derjugini) in its small distribution range, using the MODIS LST time series (2003 to 2021) and with the help of SDMs, Mann–Kendall (MK) and Pettitt non-parametric tests.ResultsIn the last decade, the increasing trend of LST versus its decreasing trends is obvious. Based on MK and Pettitt tests, in the winter and spring, with the decrease in latitude of 35.45° and increase in longitude of 46.14°, the core populations which are located in the southeast have experienced an increase in temperature. Considering the period time of breeding and overwintering, the continuity of winter and spring can be effective on the survival of adult newts as well as larvae in the microclimate. Linkages with the highest current flow between core populations in the winter and summer are the most likely to be vulnerable. At the level of habitat, the increase in LST is proportional to the trend of thermal landscape changes, and all seasons have had an increase in LST, but in winter and summer, the largest area of the habitat has been involved. By continuing the current trend, many high-altitude southern habitats in Iran will be endangered, and the species will be at risk of local extinction.ConclusionThe increasing trend of temperature in all seasons such as winter will affect many adaptations of the species and these effects are mostly evident in the southern parts of its distribution range therefore, captive breeding and reintroduction are recommended for the populations of these areas.