scholarly journals Classifying Major Explosions and Paroxysms at Stromboli Volcano (Italy) from Space

2021 ◽  
Vol 13 (20) ◽  
pp. 4080
Author(s):  
Claudia Corradino ◽  
Eleonora Amato ◽  
Federica Torrisi ◽  
Sonia Calvari ◽  
Ciro Del Negro
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
High Energy ◽  
Temporal Variations ◽  
Stromboli Volcano ◽  
Plume Height ◽  
Volcanic Deposits ◽  
Ash Plume ◽  
Thermal Content ◽  
Explosive Events

Stromboli volcano has a persistent activity that is almost exclusively explosive. Predominated by low intensity events, this activity is occasionally interspersed with more powerful episodes, known as major explosions and paroxysms, which represent the main hazards for the inhabitants of the island. Here, we propose a machine learning approach to distinguish between paroxysms and major explosions by using satellite-derived measurements. We investigated the high energy explosive events occurring in the period January 2018–April 2021. Three distinguishing features are taken into account, namely (i) the temporal variations of surface temperature over the summit area, (ii) the magnitude of the explosive volcanic deposits emplaced during each explosion, and (iii) the height of the volcanic ash plume produced by the explosive events. We use optical satellite imagery to compute the land surface temperature (LST) and the ash plume height (PH). The magnitude of the explosive volcanic deposits (EVD) is estimated by using multi-temporal Synthetic Aperture Radar (SAR) intensity images. Once the input feature vectors were identified, we designed a k-means unsupervised classifier to group the explosive events at Stromboli volcano based on their similarities in two clusters: (1) paroxysms and (2) major explosions. The major explosions are identified by low/medium thermal content, i.e., LSTI around 1.4 °C, low plume height, i.e., PH around 420 m, and low production of explosive deposits, i.e., EVD around 2.5. The paroxysms are extreme events mainly characterized by medium/high thermal content, i.e., LSTI around 2.3 °C, medium/high plume height, i.e., PH around 3330 m, and high production of explosive deposits, i.e., EVD around 10.17. The centroids with coordinates (PH, EVD, LSTI) are: Cp (3330, 10.7, 2.3) for the paroxysms, and Cme (420, 2.5, 1.4) for the major explosions.

scholarly journals Variable Magnitude and Intensity of Strombolian Explosions: Focus on the Eruptive Processes for a First Classification Scheme for Stromboli Volcano (Italy)

2021 ◽  
Vol 13 (5) ◽  
pp. 944
Author(s):  
Sonia Calvari ◽  
Flora Giudicepietro ◽  
Federico Di Traglia ◽  
Alessandro Bonaccorso ◽  
Giovanni Macedonio ◽  
...  
Keyword(s):  
Ground Deformation ◽  
Local Population ◽  
High Energy ◽  
Explosive Activity ◽  
Stromboli Volcano ◽  
Strombolian Activity ◽  
Quantitative Classification ◽  
Strombolian Explosions ◽  
Explosive Events

Strombolian activity varies in magnitude and intensity and may evolve into a threat for the local populations living on volcanoes with persistent or semi-persistent activity. A key example comes from the activity of Stromboli volcano (Italy). The “ordinary” Strombolian activity, consisting in intermittent ejection of bombs and lapilli around the eruptive vents, is sometimes interrupted by high-energy explosive events (locally called major or paroxysmal explosions), which can affect very large areas. Recently, the 3 July 2019 explosive paroxysm at Stromboli volcano caused serious concerns in the local population and media, having killed one tourist while hiking on the volcano. Major explosions, albeit not endangering inhabited areas, often produce a fallout of bombs and lapilli in zones frequented by tourists. Despite this, the classification of Strombolian explosions on the basis of their intensity derives from measurements that are not always replicable (i.e., field surveys). Hence the need for a fast, objective and quantitative classification of explosive activity. Here, we use images of the monitoring camera network, seismicity and ground deformation data, to characterize and distinguish paroxysms, impacting the whole island, from major explosions, that affect the summit of the volcano above 500 m elevation, and from the persistent, mild explosive activity that normally has no impact on the local population. This analysis comprises 12 explosive events occurring at Stromboli after 25 June 2019 and is updated to 6 December 2020.

scholarly journals MODELLING OF LAND SURFACE TEMPERATURE USING GRAY LEVEL CO-OCCURRENCE MATRIX AND RANDOM FOREST REGRESSION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 23-28
Author(s):  
A. C. Blanco ◽  
J. B. Babaan ◽  
J. E. Escoto ◽  
C. K. Alcantara
Keyword(s):  
Random Forest ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Volume Ratio ◽  
Temporal Variations ◽  
Surface Model ◽  
Gray Level ◽  
Random Forest Regression

Abstract. Modelling of land surface temperature (LST) is conducted to be able to explain the spatial and temporal variations of LST using a set of explanatory variables. LST in a previous study was modelled as a linear function of vegetation cover and built up cover as quantified by the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI), respectively, and other variables, namely, albedo, solar radiation (SR), surface area-volume ratio (SVR), and skyview factor (SVF). SVF requires a digital surface model of sufficient resolution while SVR computation needs 3D volumetric features representing buildings as input. These inputs are typically not readily available. In addition, NDVI and NDBI do not fully describe the spatial variability of vegetation and built-up cover within an LST pixel. In this study, PlanetScope images (3m resolution) were processed to provide soil-adjusted vegetation index (SAVI) and VgNIR Built-up Index (VgNIR-BI) layers. The following gray level co-occurrence matrices (GLCM) were generated from SAVI and VgNIR-BI: Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, and Correlation. Random Forest regression was run for several cases with different combinations of GLCM features and non-GLCM variables. Using GLCM features alone yielded less satisfactory models. However, the use of additional GLCM features in combination with other variables resulted in lower MSE and a slight increase in R2. Considering NDBI, NDVI, SAVI_GLCM_contrast, VgNIR-BI_GLCM_contrast, VgNIR-BI_GLCM_dissimilarity, and SAVI_GLCM_contrast only, the RF model yielded an MSE=1.657 and validation R2=0.822. While this 6-variable model’s performance is slightly less, the need for DSM and 3D building models which are necessary for the generation of SVF and SVR layers is eliminated. Exploratory regression (ER) was also conducted. The best 6-variable ER model (Adj. R2=0.79) consists of SVR, NDBI, NDVI, SAVI_GLCM_second_moment, VgNIR-BI_GLCM_mean, and VgNIR-BI_GLCM_entropy. In comparison, OLS regression using the 6 non-GLCM variables yielded an Adj. R2=0.691. The results of RFR and ER both indicate the value of GLCM features in providing valuable information to the models of LST. LST is best described through a combination of GLCM features describing relatively homogenous areas (i.e., dominant land cover or low-frequency areas) and the more heterogenous areas (i.e., edges or high-frequency areas) and non-GLCM variables.

scholarly journals Remote Sensing Based Analysis of the Role of Land Use/Land Cover on Surface Temperature and Temporal Changes in Temperature; a Case Study of Ajmer District, Rajasthan

2014 ◽  
Vol XL-8 ◽  
pp. 1447-1454 ◽  
Author(s):  
A. Hussain ◽  
P. Bhalla ◽  
S. Palria
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Temporal Variations ◽  
Landsat Tm ◽  
Spectral Radiance ◽  
Ajmer District

An attempt has been made in this research to analyze temporal variations in surface temperature in Ajmer District Rajasthan. The research is carried out to assess the relationship between the land surface temperatures (LST) and land cover (LC) changes both in quantitative and qualitative ways in Ajmer District area using Landsat TM/ETM+ data over the period 1989 to 2013.in this period we used three temporal TM/ETM data 1989, 2001 and 2013. Remote sensing of Land surface temperature (LST) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)–vegetation relationship. Unsupervised classification methods have been taken to prepare the LC map. LST is derived from the thermal band of Landsat TM/ETM+ using the calibration of spectral radiance and emissivity correction of remote sensing. NDVI is derived from the NIR & RED Band using image enhancement technique (Indices). Arc-GIS have been utilized for data visualization. This procedure allowed analyzing whether LULC classes match LST classes. However, the results of such overlaying are hard to interpret. LST and LULC maps of these areas give the understanding on how the classes and corresponding LST have changed from one date to the other. Another option is to collect statistical data. it was impossible to calculate linear regression between LULC map and LST map. A solution to that matter is to use Normalized Vegetation Index (NDVI) instead of LULC classification result.

scholarly journals Effects of groundwater pumping on ground surface temperature: A regional modeling study in the North China Plain

2020 ◽  
Author(s):  
You-Kuan Zhang ◽  
Chen Yang ◽  
Xiaofan Yang
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
North China Plain ◽  
North China ◽  
Ground Surface ◽  
Temporal Variations ◽  
Heat Fluxes ◽  
Ground Surface Temperature ◽  
The North ◽  
The North China Plain

<p>It is recognized that groundwater (GW) may play an important role in the subsurface–land-surface–atmosphere system and that pumping of GW may affect soil moisture which in turn influences local weather and climate through land-atmosphere interactions. In this study effects of GW pumping on ground surface temperature (GST) in the North China Plain (NCP) were investigated with a coupled ParFlow.CLM model of subsurface and land-surface processes and their interactions. The model was validated using the water and energy fluxes reported in previous studies and from the JRA-55 reanalysis. Numerical experiments were designed to examine the impacts of GW pumping and irrigation on GST. Results show significant effects of GW pumping on GST in the NCP. Generally, the subsurface acts as a buffer to temporal variations in heat fluxes at the land-surface, but long-term pumping can gradually weaken this buffer, resulting in increases in the spatio-temporal variability of GST, as exemplified by hotter summers and colder winters. Considering that changes of water table depth (WTD) can significantly affect land surface heat fluxes when WTD ranges between 1–10 m, the 0.5 m/year increase of WTD simulated by the model due to pumping can continue to raise GST for about 20 years from the pre-pumping WTD in the NCP. The increase of GST is expected to be faster initially and gradually slow down. The findings from this study may implicate similar GST increases may occur in other regions with GW depletion.</p>

Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

2018 ◽  
Vol 18 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Georgiana Grigoraș ◽  
Bogdan Urițescu
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Hot Spots ◽  
Land Surface ◽  
Hot Spot ◽  
Monitoring Network ◽  
Residential Areas ◽  
Hotspot Analysis ◽  
The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

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