scholarly journals Implementation of the NHI (Normalized Hot Spot Indices) Algorithm on Infrared ASTER Data: Results and Future Perspectives

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1538
Author(s):  
Giuseppe Mazzeo ◽  
Micheal S. Ramsey ◽  
Francesco Marchese ◽  
Nicola Genzano ◽  
Nicola Pergola
Keyword(s):  
Near Infrared ◽  
Thermal Emission ◽  
Hot Spot ◽  
Thermal Anomaly ◽  
Google Earth ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Aster Data ◽  
Shortwave Infrared

The Normalized Hotspot Indices (NHI) tool is a Google Earth Engine (GEE)-App developed to investigate and map worldwide volcanic thermal anomalies in daylight conditions, using shortwave infrared (SWIR) and near infrared (NIR) data from the Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel 2 and Landsat 8 satellites. The NHI tool offers the possibility of ingesting data from other sensors. In this direction, we tested the NHI algorithm for the first time on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. In this study, we show the results of this preliminary implementation, achieved investigating the Kilauea (Hawaii, USA), Klyuchevskoy (Kamchatka; Russia), Shishaldin (Alaska; USA), and Telica (Nicaragua) thermal activities of March 2000–2008. We assessed the NHI detections through comparison with the ASTER Volcano Archive (AVA), the manual inspection of satellite imagery, and the information from volcanological reports. Results show that NHI integrated the AVA observations, with a percentage of unique thermal anomaly detections ranging between 8.8% (at Kilauea) and 100% (at Shishaldin). These results demonstrate the successful NHI exportability to ASTER data acquired before the failure of SWIR subsystem. The full ingestion of the ASTER data collection, available in GEE, within the NHI tool allows us to develop a suite of multi-platform satellite observations, including thermal anomaly products from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), which could support the investigation of active volcanoes from space, complementing information from other systems.

A Google Earth Engine application for mapping volcanic thermal anomalies at a global scale by means of Sentinel 2 MSI and Landsat 8 OLI data

2020 ◽  
Author(s):  
Nicola Genzano ◽  
Francesco Marchese ◽  
Alfredo Falconieri ◽  
Giuseppe Mazzeo ◽  
Nicola Pergola
Keyword(s):  
Near Infrared ◽  
Global Scale ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Thermal Anomalies ◽  
Google Earth Engine ◽  
Infrared Radiances ◽  
Shortwave Infrared ◽  
Sentinel 2

<p>NHI (Normalized Hotspot Indices) is an original multichannel algorithm recently developed for mapping volcanic thermal anomalies in daylight conditions by means of infrared Sentinel 2 MSI and Landsat 8 OLI data. The algorithm, which uses two normalized indices analyzing SWIR (Shortwave Infrared) and NIR (Near Infrared) radiances, was tested with success in different volcanic areas, assessing results by means of independent ground and satellite-based observations.</p><p>Here we present and describe the NHI-based tool, which exploits the high computation capabilities of Google Earth Engine to perform the rapid mapping of hot volcanic features at a global scale. The tool allows the users to retrieve information also about changes of thermal volcanic activity, giving the opportunity of performing time series analysis of hotspot pixel number and total SWIR radiance. Advantages of using the NHI tool as a complement to current satellite-based volcanoes monitoring systems are then analysed and discussed, such as its future upgrades.</p>

scholarly journals A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3232
Author(s):  
Nicola Genzano ◽  
Nicola Pergola ◽  
Francesco Marchese
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Hot Spot ◽  
Global Scale ◽  
Google Earth ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Google Earth Engine ◽  
Sentinel 2

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.

scholarly journals Neural Network and Multiple Linear Regression for Estimating Surface Albedo from ASTER Visible and Near-Infrared Spectral Bands

2013 ◽  
Vol 17 (3) ◽  
pp. 1-20 ◽  
Author(s):  
Mohammad H. Mokhtari ◽  
Ibrahim Busu ◽  
Hossein Mokhtari ◽  
Gholamreza Zahedi ◽  
Leila Sheikhattar ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Surface Albedo ◽  
Near Infrared ◽  
Thermal Emission ◽  
Current Model ◽  
Primary Analysis ◽  
Aster Data ◽  
Spectral Bands ◽  
Shortwave Infrared

Abstract The current Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-based broadband albedo model requires shortwave infrared bands 5 (2.145–2.185 nm), 6 (2.185–2.225 nm), 8 (2.295–2.365 nm), and 9 (2.360–2.430 nm) and visible/near-infrared bands 1 (0.52–0.60 nm) and 3 (0.78–0.86 nm). However, because of sensor irregularities at high temperatures, shortwave infrared wavelengths are not recorded in the ASTER data acquired after April 2008. Therefore, this study seeks to evaluate the performance of artificial neural networks (ANN) in estimating surface albedo using visible/near-infrared bands available in the data obtained after April 2008. It also compares the outcomes with the results of multiple linear regression (MLR) modeling. First, the most influential spectral bands used in the current model as well as band 2 (0.63–0.69 nm) (which is also available after April 2008 in the visible/near-infrared part) were determined by a primary analysis of the data acquired before April 2008. Then, multiple linear regression and ANN models were developed by using bands with a relatively high level of contribution. The results showed that bands 1 and 3 were the most important spectral ones for estimating albedo where land cover consisted of soil and vegetation. These two bands were used as the study input, and the albedo (estimated through a model that utilized bands 1, 3, 5, 6, 8, and 9) served as a target to remodel albedo. Because of its high collinearity with band 1, band 2 was identified less effectively by MLR as well as ANN. The study confirmed that a combination of bands 1 and 3, which are available in the current ASTER data, could be modeled through ANN and MLR to estimate surface albedo. However, because of its higher accuracy, ANN method was superior to MLR in developing objective functions.

scholarly journals Intercomparison of MODIS and VIIRS Fire Products in Khanty-Mansiysk Russia: Implication for Characterizing Gas Flaring from Space

2017 ◽  
Author(s):  
Ambrish Sharma ◽  
Jun Wang
Keyword(s):  
Infrared Imaging ◽  
Thermal Anomaly ◽  
Google Earth ◽  
Anthropogenic Source ◽  
Thermal Anomalies ◽  
Gas Flaring ◽  
Cold Environments ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Shortwave Infrared ◽  
Level 2

Gas flaring is commonly used by industrial plants for processing oil and natural gases in the atmosphere, and hence is an important anthropogenic source for various pollutants including CO2, CO, and aerosols. This study evaluates the feasibility of using satellite data to characterize gas flaring form space by focusing on the Khanty Mansiysk Autonomous Okrug in Russia, a region that is well known for its dominatingly gas flaring activities. Multiple satellite-based thermal anomaly data products at night are inter-compared and analyzed, including MODIS (Moderate Resolution Imaging Spectroradiometer) Terra level-2 Thermal Anomalies product (MOD14), MODIS Aqua level-2 Thermal Anomalies product (MYD14), VIIRS (Visible Infrared Imaging Radiometer Suite) Active Fires Applications Related Product (VAFP), and VIIRS level-2 data based Nightfire product (VNF). The analysis compares and contrasts the efficacy of these sensor products in detecting small, hot sources like flares on the ground in extremely cold environments such as Russia. We found that the VNF algorithm recently launched by NOAA has the unprecedented accuracy and efficiency in characterizing gas flares in the region owing primarily to the use of Shortwave Infrared (SWIR) bands. Reconciliation of VNF’s differences and similarities with other nighttime fire products is also conducted, indicating that MOD14/MYD14 and VAFP data are only effective in detecting those gas flaring pixels that are among the hottest in the region. Validation of VNF product of gas flaring location with Google Earth images are made. It is shown that that VNF’s estimates of gas flaring area (the area of gas flaming) agree well the counterparts from Google images with a linear correlation of 0.91, highlighting its potential use for routinely monitoring emissions of gas flaring from space.

scholarly journals Mapping the Lithological Features and Ore-Controlling Structures Related to Ni–Cu Mineralization in the Eastern Tian Shan, NW China from ASTER Data

2021 ◽  
Vol 13 (2) ◽  
pp. 206
Author(s):  
Shuo Zheng ◽  
Yanfei An ◽  
Pilong Shi ◽  
Tian Zhao
Keyword(s):  
Near Infrared ◽  
Thermal Emission ◽  
Strike Slip ◽  
Fault System ◽  
Multispectral Data ◽  
Aster Data ◽  
Ore Bodies ◽  
Dextral Strike Slip Fault ◽  
Shortwave Infrared ◽  
Tian Shan

The study of lithological features and tectonic evolution related to mineralization in the eastern Tian Shan is crucial for understanding the ore-controlling mechanism. In this paper, the lithological features and ore-controlling structure of the Huangshan Ni–Cu ore belt in the eastern Tian Shan are documented using advanced spaceborne thermal emission and reflection radiometer (ASTER) multispectral data based on spectral image processing algorithms, mineral indices and directional filter technology. Our results show that the algorithms of b2/b1, b6/b7 and b4/b8 from ASTER visible and near-infrared (VNIR)- shortwave infrared (SWIR) bands and of mafic index (MI), carbonate index (CI) and silica index (SI) from thermal infrared (TIR) bands are helpful to extract regional pyroxenite, external foliated gabbro bearing Ni–Cu ore bodies as well as the country rocks in the study area. The detailed interpretations and analyses of the geometrical feature of fault system and intrusive facies suggest that the Ni–Cu metallogenic belts are related to Carboniferous arc intrusive rocks and Permian wrench tectonics locating at the intersection of EW- and NEE-striking dextral strike-slip fault system, and the emplacement at the releasing bends in the southern margin of Kanggur Fault obviously controlled by secondary faults orthogonal or oblique to the Kanggur Fault in the post-collision extensional environment. Therefore, the ASTER data-based approach to map lithological features and ore-controlling structures related to the Ni–Cu mineralization are well performed. Moreover, a 3D geodynamic sketch map proposes that the strike-slip movement of Kanggur Fault in Huangshan-Kanggur Shear Zone (HKSZ) during early Permian controlled the migration and emplacement of three mafic/ultramafic intrusions bearing Ni–Cu derived from partial mantle melting and also favored CO2-rich fluids leaking to the participation of metallogenic processes.

scholarly journals Monitoring of the 2015 Villarrica Volcano Eruption by Means of DLR’s Experimental TET-1 Satellite

2018 ◽  
Vol 10 (9) ◽  
pp. 1379 ◽  
Author(s):  
Simon Plank ◽  
Michael Nolde ◽  
Rudolf Richter ◽  
Christian Fischer ◽  
Sandro Martinis ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Thermal Emission ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Villarrica Volcano ◽  
Radiant Power ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
German Aerospace ◽  
Active Volcanoes

Villarrica Volcano is one of the most active volcanoes in the South Andes Volcanic Zone. This article presents the results of a monitoring of the time before and after the 3 March 2015 eruption by analyzing nine satellite images acquired by the Technology Experiment Carrier-1 (TET-1), a small experimental German Aerospace Center (DLR) satellite. An atmospheric correction of the TET-1 data is presented, based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Database (GDEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor data with the shortest temporal baseline to the TET-1 acquisitions. Next, the temperature, area coverage, and radiant power of the detected thermal hotspots were derived at subpixel level and compared with observations derived from MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) data. Thermal anomalies were detected nine days before the eruption. After the decrease of the radiant power following the 3 March 2015 eruption, a stronger increase of the radiant power was observed on 25 April 2015. In addition, we show that the eruption-related ash coverage of the glacier at Villarrica Volcano could clearly be detected in TET-1 imagery. Landsat-8 imagery was analyzed for comparison. The information extracted from the TET-1 thermal data is thought be used in future to support and complement ground-based observations of active volcanoes.

scholarly journals A Novel Index for Impervious Surface Area Mapping: Development and Validation

2018 ◽  
Vol 10 (10) ◽  
pp. 1521 ◽  
Author(s):  
Yugang Tian ◽  
Hui Chen ◽  
Qingju Song ◽  
Kun Zheng
Keyword(s):  
Near Infrared ◽  
Thermal Emission ◽  
Impervious Surface ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Capital Cities ◽  
Heat Islands ◽  
Surface Areas ◽  
Hydrological Cycles

The distribution and dynamic changes in impervious surface areas (ISAs) are crucial to understanding urbanization and its impact on urban heat islands, earth surface energy balance, hydrological cycles, and biodiversity. Remotely sensed data play an essential role in ISA mapping, and numerous methods have been developed and successfully applied for ISA extraction. However, the heterogeneity of ISA spectra and the high similarity of the spectra between ISA and soil have not been effectively addressed. In this study, we selected data from the US Geological Survey (USGS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral libraries as samples and used blue and near-infrared bands as characteristic bands based on spectral analysis to propose a novel index, the perpendicular impervious surface index (PISI). Landsat 8 operational land imager data in four provincial capital cities of China (Wuhan, Shenyang, Guangzhou, and Xining) were selected as test data to examine the performance of the proposed PISI in four different environments. Threshold analysis results show that there is a significant positive correlation between PISI and the proportion of ISA, and threshold can be adjusted according to different needs with different accuracy. Furthermore, comparative analyses, which involved separability analysis and extraction precision analysis, were conducted among PISI, biophysical composition index (BCI), and normalized difference built-up index (NDBI). Results indicate that PISI is more accurate and has better separability for ISA and soil as well as ISA and vegetation in the ISA extraction than the BCI and NDBI under different conditions. The accuracy of PISI in the four cities is 94.13%, 96.50%, 89.51%, and 93.46% respectively, while BCI and NDBI showed accuracy of 77.53%, 93.49%, 78.02%, and 84.03% and 58.25%, 57.53%, 77.77%, and 64.83%, respectively. In general, the proposed PISI is a convenient index to extract ISA with higher accuracy and better separability for ISA and soil as well as ISA and vegetation. Meanwhile, as PISI only uses blue and near-infrared bands, it can be used in a wider variety of remote sensing images.

scholarly journals Identification of Continental Wetlands Using Different Orbital Remote Sensors

Terr Plural ◽  
2021 ◽  
Vol 15 ◽  
pp. 1-25
Author(s):  
Isadora Taborda Silva ◽  
Jéssica Rabito Chaves ◽  
Helen Rezende Figueiredo ◽  
Bruno Silva Ferreira ◽  
César Claudio Cáceres Encina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Band ◽  
Landsat 8 ◽  
Remote Sensors ◽  
False Color ◽  
False Color Composite ◽  
International Importance ◽  
Color Composite ◽  
Shortwave Infrared ◽  
Sentinel 2

This paper evaluates the potential of false-color composite images, from 3 different remote sensing satellites, for the identification of continental wetlands. Landsat 8, Sentinel-2 and CBERS-4 scenes from three different Ramsar sites (i.e., sites designated to be of international importance) two sites located within the Mato-Grossense Pantanal and one within the Sul-mato-grossense were used for analyses. For each site, images from both the dry and rainy seasons were analyzed using Near-Infrared (NIR), Shortwave Infrared (SWIR), and visible (VIS) bands. The results show that false-color composite images from both the Landsat 8 and the Sentinel-2 satellites, with both SWIR 2-NIR-BLUE and NIR-SWIR-RED spectral band combinations, allow the identification of wetlands.

scholarly journals BURNT AREA DETECTION USING MEDIUM RESOLUTION SENTINEL 2 AND LANDSAT 8 SATELLITES

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 131-137
Author(s):  
D. Ongeri ◽  
B. K. Kenduiywo
Keyword(s):  
Forest Disturbance ◽  
Hot Spot ◽  
Google Earth ◽  
Landsat 8 ◽  
Burnt Area ◽  
Resource Management System ◽  
Medium Resolution ◽  
The Difference ◽  
Fire Information ◽  
Sentinel 2

Abstract. Forest fire is one of the most serious environmental problems in Kenya that influences human activities, climate change and biodiversity. The main goal of this study is to apply medium resolution sensors (Landsat 8 OLI and Sentinel 2 MSI) to produce burnt area severity maps that will include small fires (< 100 ha) in order to improve burnt area detection and mapping in Kenya. Normalized burnt area indices were generated for specified pre- and post-fire periods. The difference between pre- and post-fire Normalized Burnt Ration (NBR) was used to compute δNBR index depicting forest disturbance by fire events. Thresholded classes were derived from the computed δNBR indices to obtain burnt severity maps. The spatial and temporal agreements of the Burnt area detection dates were validated by comparing against the MODIS MCD641 500 m products and MODIS Fire Information for Resource Management System (FIRMS) 1 km daily product hot-spot acquisition dates. This approach was implemented on Google Earth Engine (GEE) platform with a simple user interface that allows users to auto-generate burnt area maps and statistics. The operational GEE application developed can be used to obtain burnt area severity maps and statistics that allow for initial accurate approximation of fire damage.

Sign in / Sign up

Export Citation Format

Share Document