Derived Land Surface Temperature Research Articles

Mapping heat-related risks in Swiss cities under different urban tree scenarios

About three quarter of Swiss residents live in urban areas, and this proportion is expected to grow in future decades. An increasing number of people will therefore be exposed to urban heat, which can have adverse effects on human wellbeing, productivity and physical health.We explore the possibility to detect high-risk areas in five Swiss cities with the development of an urban heat-based risk-mapping approach. The included cities are Basel, Bern, Geneva, Lausanne and Zurich. The analysis is based on a combination of biophysical, including Landsat 8 derived Land Surface Temperature (LST), and socioeconomic data. Additionally, we assess the impact of urban trees on urban heat within the districts of these cities, helping to estimate how risk levels would change under two scenarios: one with increased tree cover (MaxTree) and another with no (NoTree) urban trees.The assessment on the impact of urban trees on heat showed that the areas with urban trees generally experience cooler temperatures compared to those without, both at the city and district levels. This underscores the positive role of urban trees in mitigating the urban heat effect.The risk mapping approach revealed a distinct spatial pattern for each city and high risk areas were identified.Generally, the high-risk areas in the analyzed cities cover the city centers and areas with high vulnerability.The ‘NoTree’ scenario showed higher risks compared to the baseline situation, illustrating that urban trees currently mitigate heat related risks in Swiss cities. The ‘MaxTree’ scenario results in lower risks, especially in the cities of Lausanne and Bern.The presented risk mapping approach, including the two idealized scenarios, can be used by policy- and decision-makers (e.g. city planners) can be a tool to determine where urban planning actions are the most urgent and where trees could be most beneficial in terms of adaptation to heat. The approach is easily adaptable and transferable to other cities, since it relies on a clear and simple methodological framework, openly available LST data, and basic socioeconomic variables at district scale that are available for many cities.

Land Surface Temperature and Thermal Comfort in the Cities of Punjab, India: Assessment Based on Remote Sensing Data

Objective: The present study aims to investigate thermal comfort in two cities in Punjab, India based on land surface temperature (LST), urban hot spots (UHS) and urban thermal field variance index (UTFVI). Method: Landsat 8 OLI/TIRS data are used to derive land surface temperature (LST), normalized difference vegetation index (NDVI) and enhanced built-up and bareness index (EBBI) for the year 2019. UTFVI reflects urban thermal conditions and demarcates comfort and discomfort zones in the cities. Findings: The results revealed that the mean LST (μ) of Ludhiana and Amritsar cities is 32.80 °C and 30.70 °C, respectively. LST shows a strong negative correlation with NDVI (-0.710 for Amritsar and -0.754 for Ludhiana) and a positive correlation with EBBI (0.531 for Amritsar and 0.541 for Ludhiana). About 57 and 52 per cent of geographical areas in Ludhiana and Amritsar city respectively are experiencing bad to worst ecological conditions. Novelty: (i) The study derived LST-based thermal comfort for the summer month in Amritsar and Ludhiana cities of Punjab which provide important information to urban planners and policymakers to design sustainable urban development policies to mitigate heat-related issues. (ii) Such information can be used to take steps to improve the situation in smart cities like Amritsar and Ludhiana. Keywords: Land surface temperature (LST), urban thermal field variance index (UTFVI), NDVI, Landsat 8, Punjab

Modelling Europe-wide fine resolution daily ambient temperature for 2003–2020 using machine learning

To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003–2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale.

SPATIO-TEMPORAL DYNAMICS OF URBAN THERMAL ENVIRONMENT IN UDAIPUR CITY, RAJASTHAN, INDIA

Abstract. The Udaipur urban agglomeration was selected to analyse the urban heat island (UHI) effect in the area from 2005 to 2017. Landsat 7 ETM+ sensor data was used to derive land surface temperatures (LST) and map landscape characteristics. The agglomeration was classified into seven land use land cover classes including agriculture cropped, agriculture fallow, barren, built up, scrub, vegetation and water using unsupervised classification method. The LST results were obtained using NDVI method. Urban heat island intensities were mapped using z-score method. Regions with UHI values of above 2 standard deviations were considered to reflect UHI effect. Results show an overall decrease of 10.2 percent in agricultural cropped and fallow lands. The scrub class also shows a moderate decline of 2.3 percent. Increase in area were observed for built up, barren and vegetation classes by 8.8, 2.1 and 1.9 percent respectively. The dominant LULC change was the transformation of agricultural lands to built-up class. The class-wise mean LST observations increase in order of water, vegetation, cropped, fallow, built up, scrub and barren land. The mean LST records an increase of 1.7° C from 30.1° C to 31.8° C over the study period. Results suggest that UHI effects are more prominent in urban fringe area corresponding with the built up and barren land cover classes contrary to its core area. Built up areas surrounded by barren lands left vacant for city’s sprawl in the future exhibit highest UHI intensities.

Thermal pattern of Tatun volcanic system by satellite-observed temperatures and its correlation with earthquake magnitudes

The land surface temperature (LST) of volcanoes detected from satellite sensors reflects the thermal status of heat sources in the subsurface. Volcanic earthquakes occur as magma and volcanic fluids transport to the surface from depth. Thus, both LST and earthquake magnitude are key parameters to the study of active volcanoes. Here we investigate the volcanic status of Tatun Volcanic Group (TVG) based on LST and seismic observations. The Earth-observing satellites onboard thermal sensor derived land surface temperature, and the seismic records retrieved volcanic earthquake magnitude are used to delineate the past and current pattern of volcanic activity plus the future trend of the TVG. The spatiotemporal distribution of LST and volcanic earthquake magnitude in TVG are analyzed. The high-similarity trends of the 4-decade LST time series and 3-decade earthquake magnitude time series are inspected. The retrieved surface thermal pattern shows the non-steady-state nature of the subsurface thermal sources at this volcanic complex. The LST trend exhibits a rather positive correlation with the energy released from volcanic earthquakes and consequently, the presumption on the connection between LSTs and earthquakes is validated.

Study of geothermal and volcanic activity at Mount Awu, the deadliest active volcano in North Sulawesi Province, Indonesia using Optical Satellite Imagery

The history of the north part of Sangihe Island is the motivation for using the strengths of remote sensing methods to monitor the volcano activities and geothermal potential of Awu Volcano. We used the last 50 years of satellite image data composed by Landsat 1, Landsat 4, Landsat 5, Landsat 7, ASTER, and Landsat 8 to get the multi-temporal analysis and results. This study used the single-channel algorithm to derive land surface temperature (LST). We also used the band combination and ratio to understand the relationship between the Awu Volcano's geological condition and geothermal potential. The NDVI result as a component of the LST calculation shows that the vegetation is increasing when there is no eruption event. However, the amount of vegetation decreased after the 1994 and 2004 eruptions. The LST results show the 2004 eruption gave the highest surface temperature in the last 50 years, with a number of 318.69 °K as the mean value in the caldera. The multi-temporal nature of LST explained the activity of Awu Volcano, including the increased status announced by the Center for Volcanology and Geological Hazard Mitigation (CVGHM) in 2016 and 2021. The accuracy of LST results is seen by the same pattern in the LST provided from the Landsat and ASTER data and the R2 of time and LST with the values of 0.1529 and 0.2436 in the volcano slope and caldera, respectively. The long gap between the phreatic eruption in 1994 and the previous eruption in 1964 revealed that there was a caldera lake formed in Awu Volcano, which was detected by the natural color band combination from 1972 to 1996. The geothermal potential is centralized in the volcano slope around the caldera, with a value of almost 10 °K and some areas in the slope with a value of >5 °K. The first area that contains high geothermal potential is located in the basaltic andesite, with the alteration detected by the combination of bands 7/4, 6/3, and 5/7. Mount Awu's multiple eruptions have shown that multi-source optical satellites could be used to keep an eye on volcanic activity and could also be used as a first step in figuring out geothermal potential.

Preliminary Assimilation of Satellite Derived Land Surface Temperature from SEVIRI in the Surface Scheme of the AROME-France Model

The goal of this study is to examine the impact of assimilating satellite derived surface temperature over land (LST) in the surface scheme of AROME-France model. The LST is retrieved from SEVIRI radiances during the assimilation process in the atmospheric model. The assimilation of LST is performed using an optimal interpolation technique, similarly to the assimilation of other near-surface parameters (temperature and relative humidity at 2 meters). Observation and background errors were diagnosed before to prescribe them in the surface assimilation scheme. First, this LST assimilation has been evaluated in terms of analysis and forecast quality over a two-month summer period. A positive impact has been found on the assimilation of 2 m temperature and relative humidity with a slight decrease in bias of the background departure. An improvement has also been found for the assimilation of microwave humidity sensitive channels. The assimilation of microwave sensors benefits from an updated land surface temperature through the retrieval of the emissivity. Moreover, the assimilation of SEVIRI LST has improved the nighttime forecasts of temperature and relative humidity near the surface and up to 700 hPa. Several open issues for improving these preliminary results are finally proposed.

A Hybrid Approach for Extracting Large-Scale and Accurate Built-Up Areas Using SAR and Multispectral Data

This article examines the use of multisensor data fusion for land classification in three Moroccan cities. The method employs a Random Forest classification algorithm based on multispectral, synthetic aperture radar (SAR), and derived land surface temperature (LST) data. The study compares the proposed approach to existing datasets on impervious surfaces (Global Artificial Impervious Area—GAIA, Global Human Settlement Layer—GHSL, and Global 30 m Impervious Surfaces Dynamic Dataset—GIS30D) using traditional evaluation metrics and a common training and validation dataset. The results indicate that the proposed approach has a higher precision (as measured by the F-score) than the existing datasets. The results of this study could be used to improve current databases and establish an urban data hub for impervious surfaces in Africa. The dynamic information of impervious surfaces is useful in urban planning as an indication of the intensity of human activities and economic development.

Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India

The Indian Himalayan region is experiencing frequent hazards and disasters related to permafrost. However, research on permafrost in this region has received very little or no attention. Therefore, it is important to have knowledge about the spatial distribution and state of permafrost in the Indian Himalayas. Modern remote sensing techniques, with the help of a geographic information system (GIS), can assess permafrost at high altitudes, largely over inaccessible mountainous terrains in the Himalayas. To assess the spatial distribution of permafrost in the Alaknanda Valley of the Chamoli district of Uttarakhand state, 198 rock glaciers were mapped (183 active and 15 relict) using high-resolution satellite data available in the Google Earth database. A logistic regression model (LRM) was used to identify a relationship between the presence of permafrost at the rock glacier sites and the predictor variables, i.e., the mean annual air temperature (MAAT), the potential incoming solar radiation (PISR) during the snow-free months, and the aspect near the margins of rock glaciers. Two other LRMs were also developed using moderate-resolution imaging spectroradiometer (MODIS)-derived land surface temperature (LST) and snow cover products. The MAAT-based model produced the best results, with a classification accuracy of 92.4%, followed by the snow-cover-based model (91.9%), with the LST-based model being the least accurate (82.4%). All three models were developed to compare their accuracy in predicting permafrost distribution. The results from the MAAT-based model were validated with the global permafrost zonation index (PZI) map, which showed no significant differences. However, the predicted model exhibited an underestimation of the area underlain by permafrost in the region compared to the PZI. Identifying the spatial distribution of permafrost will help us to better understand the impact of climate change on permafrost and its related hazards and provide necessary information to decision makers to mitigate permafrost-related disasters in the high mountain regions.

IHVI: AN OPEN-SOURCE TOOLKIT FOR CONSTRUCTING INTEGRATED HEAT VULNERABILITY INDEX IN AUSTRALIA

Abstract. To tackle the increasing issue of heat risk in Australia and pressure of population growth, this project aimed to establish a first nationwide dynamic and interactive heat vulnerability assessment toolkit. The toolkit integrated multiple data sources, cloud computing, and Web GIS technologies to deliver cool intelligence for more heat resilient Australian cities and suburbs. A cloud-based open-source tool, iGEE, was first developed to derive land surface temperature (LST), NDBI (normalized difference built-up index) and NDVI (normalized difference vegetation index) from multiple satellite imagery on Google Earth Engine (GEE), a no-code web application allowing users to retrieve satellite data for a large area at fine scales. Following that, a python-based desktop app was then developed to calculate an integrated Heat Vulnerability Index (iHVI) for any cities and area at a fine scale of Statistical Area 1 (SA1). The iHVI toolkit allows users to construct heat sensitivity, heat adaptive capability indicators, and composite heat vulnerability index, which enables modelling of the relationships between heat, environmental and socioeconomic factors.

Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment

Thermal infrared (TIR) satellite imagery collected by multispectral scanners is important to map land surface temperature on a global scale. However, the TIR spectral bands are typically available in coarser spatial resolution than other multispectral bands of shorter wavelengths. Therefore, the spatial resolution of the derived land surface temperature (LST) is limited to around 100 m. This constrains the applications of such thermal satellite sensors in which finer detail of LST spatial pattern is relevant, especially in an urban environment where the land cover structure is complex. Among the missions deployed on the Earth’s orbit, NASA’s TIRS sensor onboard Landsat 8 and Landsat 9, and ASTER onboard Terra provide the highest spatial resolution of the thermal band. On the other hand, ESA’s Sentinel-2 multispectral imagery is collected at a higher spatial resolution of 10 m with a 5-day temporal resolution, but scanning in the TIR band is not available. This study makes use of the known relationship between LST and land cover metrics, such as the normalized difference vegetation index (NDVI), built-up index (NDBI), and water index (NDWI). We define a multiple linear regression model based on the spectral indices and LST derived from Landsat 8 data to inform the same model in which the equivalent spectral indices derived from Sentinel-2 are used to predict LST at 10 m resolution. Results of this approach are demonstrated in a case study for Košice city, Slovakia, where the multiple linear model based on Landsat 8 data achieved an R2 of 0.642. The correlation between the observed Landsat 8 LST and predicted LST from Sentinel-2 aggregated to the same resolution as the observed LST was high (r = 0.91). Despite the imperfections of the downscaling model, the derived LST at 10 m resolution provides a better perception of the LST field that can be easily associated with land cover features present in urban environment. The LST downscaling approach was implemented into Google Earth Engine. It provides a user-friendly online application that can be used for any city or urban region for generating a more realistic spatial pattern of LST than can be directly observed by contemporary Earth observation satellites. The tool aids in urban decision making and planning on how to mitigate overheating of cities to improve the life quality of their citizens.

Toward a Reliable Correction of NOAA AVHRR Orbital Drift

The NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High-Resolution Radiometer) orbital drift prevents the use of its derived land surface temperature (LST) data for global studies of temperature trends, especially for the 80s and 90s over land. In a previous study, we showed how orbital drift correction methods could be validated by simulating a reference and drifted time series from alternative MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data, thanks to their high (15 min) temporal resolution. In this study, we show how these alternative data allow identifying orbital drift effects on different land covers, and how these effects could be mitigated with novel approaches. We also identify two key statistical parameters to assess orbital drift correction performance: the bias between corrected and drifted time series and the trend of their difference. We present two methods and compare their results with an alternative orbital drift correction, validated against in situ data by their authors. Considering an ideal case where the whole influence of the orbital drift is known, our novel approach allows for an almost complete removal of the orbital drift effect (zero bias and 0.05 K/yr difference trend). However, in real cases, when we have only access to the drifted time series, our approach’s performance decreases slightly, mainly through a larger spread of the retrieved statistics. As for the alternative correction method, its performance is poorer, even if it actually succeeds in removing part of the observed orbital drift. These results, as well as the new insights we provide on the orbital drift effect on LST, pave the way toward a reliable correction of NOAA AVHRR orbital drift. We therefore recommend the use of simulated LST time series such as the ones used in this study for the validation of orbital drift correction methods.

A simple and easy method to quantify the cool island intensity of urban greenspace

Accurately and easily quantifying the greenspace cool island intensity (GCII) is important to understand the cooling effect of urban greenspace for better urban greenspace planning and management. This study proposed two new methods (i.e., equal area method and equal radius method) to estimate GCII and compared them with two old methods (i.e., turning point method and fixed radius method) in terms of the GCII magnitudes, spatial variations, relationships with greenspace size, and the estimation of optimal greenspace size in cooling the environment (i.e., threshold value of efficiency (TVoE)). We performed the analysis based on Landsat derived land surface temperature (LST) in seven Chinese cities with varied climate background and geographic variations. The results showed that: (1) Different methods significantly impacted the estimated GCIIs with higher values by the turning point method and lower values by the equal area method. (2) GCIIs by different methods were positively and significantly correlated with each other and characterized similar spatial heterogeneities. (3) GCIIs by all methods showed significantly positive logarithmic relationships with greenspace area with higher R2 observed by the equal area method. (4) The four methods estimated close TVoEs with no significant difference among them. We recommend the equal radius method to quantify GCII for its equal effectiveness but easier calculation compared to other methods.

Estimating and Monitoring the Land Surface Temperature (LST) Using Landsat OLI 8 TIRS

Land Surface Temperature (LST) is average temperature of an element of the exact surface of the Earth calculated from measured radiance which depends on the albedo, the vegetation cover, and the soil moisture. Land Surface Temperature can affect human discomfort, health problem, higher energy bill and further reduce the habitability of urban and sub urban area as Makassar city has been recently undergoing massive urban development. This study tries to monitor and estimate Land Surface Temperature by using Landsat 8 TIRS and the data analyzed by vegetation index, and temperature index in order to derive Land Surface Temperature value. The result shows that the vegetation area declined around 3470 hectares in the last four years while the urban area increased approximately 1509 hectare. In addition, 2015, Makassar, South Sulawesi, Indonesia are experienced the highest temperature at 32 degree Celsius while 2019 shown that the maximum heat reached 29 degree celsius. However, the moderate and high temperature (26 – 29 degree Celsius) in 2019 expand and cover wider area than in 2015 as the area of vegetation declined and built-up area increased significantly

Remotely Sensed Derived Land Surface Temperature (LST) as a Proxy for Air Temperature and Thermal Comfort at a Small Geographical Scale

Urban Heat Islands (UHIs) and Urban Cool Islands (UCIs) can be measured by means of in situ measurements and interpolation methods, which often require densely distributed networks of sensors and can be time-consuming, expensive and in many cases infeasible. The use of satellite data to estimate Land Surface Temperature (LST) and spectral indices such as the Normalized Difference Vegetation Index (NDVI) has emerged in the last decade as a promising technique to map Surface Urban Heat Islands (SUHIs), primarily at large geographical scales. Furthermore, thermal comfort, the subjective perception and experience of humans of micro-climates, is also an important component of UHIs. It remains unanswered whether LST can be used to predict thermal comfort. The objective of this study is to evaluate the accuracy of remotely sensed data, including a derived LST, at a small geographical scale, in the case study of King Abdulaziz University (KAU) campus (Jeddah, Saudi Arabia) and four surrounding neighborhoods. We evaluate the potential use of LST estimates as proxy for air temperature (Tair) and thermal comfort. We estimate LST based on Landsat-8 measurements, Tair and other climatological parameters by means of in situ measurements and subjective thermal comfort by means of a Physiological Equivalent Temperature (PET) model. We find a significant correlation (r = 0.45, p < 0.001) between LST and mean Tair and the compatibility of LST and Tair as equivalent measures using Bland-Altman analysis. We evaluate several models with LST, NDVI, and Normalized Difference Built-up Index (NDBI) as data inputs to proxy Tair and find that they achieve error rates across metrics that are two orders of magnitude below that of a comparison with LST and Tair alone. We also find that, using only remotely sensed data, including LST, NDVI, and NDBI, random forest classifiers can detect sites with “very hot” classification of thermal comfort nearly as effectively as estimates using in situ data, with one such model attaining an F1 score of 0.65. This study demonstrates the potential use of remotely sensed measurements to infer the Physiological Equivalent Temperature (PET) and subjective thermal comfort at small geographical scales as well as the impacts of land cover and land use characteristics on UHI and UCI. Such insights are fundamental for sustainable urban planning and would contribute enormously to urban planning that considers people’s well-being and comfort.

Mapping spatiotemporal variability of the urban heat island across an urban gradient in Worcester, Massachusetts using in-situ Thermochrons and Landsat-8 Thermal Infrared Sensor (TIRS) data

ABSTRACT Landsat-8 derived Land Surface Temperature (LST) is used to measure Surface Urban Heat Island (SUHI) patterns and intensity in Worcester, MA, USA. Additionally, near-surface air temperature variability is measured using in-situ sensors to further contextualize the urban-to-rural land-cover driven thermal patterns in the study area. Despite the widespread applicability of thermal data, many SUHI studies do not compare LST with in-situ information. Comparisons between satellite-based and in-situ measurements of land surface temperature (LST) are important for establishing confidence in the utility of remotely sensed information to monitor and help improve the lived experience in cities. The objective of this study is to determine the capability of Landsat-8 Thermal Infrared Scanner (TIRS) to measure fine-scale temperature variation in a moderately sized urban area with a mixture of land-cover types. Ground-Level Temperature (GLT) was measured at 13 sites using iButton® Thermochron® temperature sensors from 13 June 2013 to 28 October 2014. Landsat-derived LST was compared to in-situ GLT using 30 Landsat-8 TIRS images for the commensurate time period. Sites with 1) eastern solar exposure; 2) low tree canopy coverage; and 3) proximity to impervious surfaces have higher annual temperature and greater offset from Landsat-derived LST. Sites with more than 47% tree canopy coverage have a more consistent LST and GLT relationship, (e.g. MAE < 3.74°C), show lower annual variability (e.g. r2 > 0.85), and also experienced low LST variability over the time series (e.g. coefficient of variation 0.007). Results indicate that site characteristics and land-cover type affect the offset between in-situ air temperature measurements and satellite-derived LST.

An Approach for the Retrieval of Land Surface Temperature from the Industrial Area Using Landsat-8 Thermal Infrared Sensors

The thermal imagine provides data with synoptic coverage for investigating thermal information from hot sources for detecting, mapping energy loss from the industrial area. This study attempts to retrieved heat loss from the industrial area using Landsat-8 TIRS experimented at an industrial area of Pasir Gudang, Peninsular Malaysia, the main objective is to investigate the sensitivity of Landsat-8 TIR for detecting industrial thermal energy within the various range of targets of different temperatures. An estimated heat map with absolute surface temperature values is the final output. Apart of the pre-processing of Landsat-8 TIRS data, data are processed for the retrieval of land surface temperature, then subjected to a downscaling process to final 30 x 30 m pixels, hence enable to merge with all Landsat-8 bands for visualization and validation of results. The split window algorithm (SWA) is used for the temperature retrieval from band 10 and 11, with other driven parameters. The Multiple Adaptive Regression Splines (MARS) model for spatial downscaling was adopted in this study. The generated thermal energy map was then validated over selected targets in the field and compared to corresponding downscaled MODIS LST product (MODIS11A2). TIR bands applied with SWA generated 13.7°C temperature dynamic range from 22.35˜51.36° C in comparison with MODIS LST product values range from 27.17 ˜ 37.65°C). Results indicated good agreement between the generated thermal energy map with the in-situ validations (RMSE=0.43 °C). It is therefore concluded that derived Land surface temperature map derived is suitable for study industrial thermal environment at 1:50,000 ˜ 100,000 scales, adequately to be used for environmental impact assessment.

Source characterizations of the New Cairo earthquake, Egypt

The New Cairo region was hit by a moderate size earthquake (Mw = 4.18) on midday Monday, 31st December 2018 that was felt throughout the greater Cairo region without any reported damage. The source mechanism solution and moment tensor inversion exhibit a normal fault with a minor strike-slip component. The NW-fault plane is the preferred rupture plane consistent with the geological and tectonic settings of the region. The dynamic source parameters have calculated, assuming a simplified circular source model, by using displacement spectra. The obtained source parameters are included average seismic moment is 2.41E+15 N-m, rupture radius of 0.46 Km, stress drop is 10.9 MPa, relative displacement is 0.012 cm, and energy releases are 1.2E+18 Erg. According to “Scholz” (1990), the studied earthquake might be considered as an intraplate type-(II) earthquake that related to plate boundaries. In the present study, the thermal infrared data is used to derive Land Surface Temperature (LST) as a precursory analysis for this earthquake. The results indicated that there was a correlation between LST with precursor seismic activity before the time of the New Cairo earthquake occurrence.

Processing of Near Real Time Land Surface Temperature and Its Application in Forecasting Forest Fire Danger Conditions.

Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users by LANCE (Land, Atmosphere Near real-time Capability) for Earth Observing System (EOS) of NASA (National Aeronautics and Space Administration) free of cost. Such Ts products are swath data with 5 min temporal increments of satellite acquisition, and the average latency is 60-125 min to be available in public domain. The swath data of Ts requires a specialized tool, i.e., HEG (HDF-EOS to GeoTIFF conversion tool) to process and make the data useful for further analysis. However, the file naming convention of the available swath data files in LANCE is not appropriate to download for an area of interest (AOI) to be processed by HEG. In this study, we developed a method/algorithm to overcome such issues in identifying the appropriate swath data files for an AOI that would be able to further processes supported by the HEG. In this case, we used Terra MODIS acquired NRT swath data of Ts, and further applied it to an existing framework of forecasting forest fires (as a case study) for the performance evaluation of our processed Ts. We were successful in selecting appropriate swath data files of Ts for our study area that was further processed by HEG, and finally were able to generate fire danger map in the existing forecasting model. Our proposed method/algorithm could be applied on any swath data product available in LANCE for any location in the world.

Monitoring drought using composite drought indices based on remote sensing

Drought is one of the most frequent disasters occurring in North China and has a great influence on agriculture, ecology and economy. To monitor drought of typical dry areas in North China, Shandong Province, this paper proposed composite drought indices using multivariable linear regression (MCDIs) to integrate Tropical Rainfall Measuring Mission (TRMM) derived precipitation, Global Land Data Assimilation System Version 2.1 (GLDAS-2.1) derived soil moisture, Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST) and normalized difference vegetation index (NDVI) from 2013 to 2017 (March to September). Pearson correlation analyses were performed between single remote sensing drought indices and in-situ drought indices, standardized precipitation evapotranspiration index (SPEI), in different time scales to assess the capability of single indices over Shandong Province. The multivariable linear regression method was used to established MCDIs, and mediator and moderator variables were introduced to optimize the model. The correlation coefficients (r) between MCDIs and SPEIs was higher than that between each single index and SPEIs. Additionally, when we investigate the correlations of different MCDIs with both standardized precipitation index (SPI) and moisture index (MI), the highest r values with both 1-month SPI and MI were acquired by the MCDI based on 1-month SPEI (MCDI-1). This suggested MCDI-1 was suitable to monitor meteorological drought. Also, the comparison between MCDI based on 9-month SPEI (MCDI-9) and soil moisture showed MCDI-9 was a good indicator for agricultural drought. Therefore, multivariable linear regression and MCDIs were recommended to be an effective method and indices for monitoring drought across Shandong Province and similar areas.