Assessment of On-going tectonic deformation in the Goriganga River Basin, Eastern Kumaon Himalaya Using Geospatial Technology

The Goriganga river basin lies in the Northeast Kumaon Himalaya and is found suitable for assessing active tectonics at different scales. In addition, this study focuses on the assessment of ongoing tectonic activity through morphotectonic measurement of the Goriganga river basin, which is an ideal location for such analysis and Goriganga river basin transects with three major domains of Himalaya’s lithotectonic structures viz., Tethys, Vaikrita, and Lesser Himalayan Domain. To realize this task, the ASTER Digital Elevation Model was used and found suitable to extract different morphotectonic indices such as Stream Length Gradient (SL), Hypsometric Integral (HI), Length of Overland Flow (Lg), Drainage Density (Dd) and Channel Sinuosity (Cs).  Results of these important indices, including SL (18- 4737) HI (0.26- 0.57), and Lg (0.08- 0.19) depict greater variability in the tectonics activity while these values are correspondingly high in the close proximity of lithotectonic units, showing strong tectonic activity. In the extreme south, the Rauntis Gad basin strongly influences tectonism due to transecting syncline and anticline as well as unknown active faults.

Remote sensing of inland Sabkha and a study of the salinity and temporal stability for sustainable development: A case study from the West coast of Qatar

The inland sabkha of the Arabian Gulf is important to study for the occurrence of minerals, rocks, soil salinity, and stability of the sabkha due to the high demand for infrastructure and agriculture development region. This study describes the spectral absorptions of evaporite minerals, discriminates rocks, maps salt crusts, gypsiferous soil flats, and soil salinity, and studies the temporal stability of an inland sabkha of the Dukhan area, west coast of the State of Qatar. This was performed using satellite data of the Hyperion of EO1, ASTER of Terra, and multispectral instrument (MSI) of Sentinel-2. The occurrence of minerals in the area is detected using Hyperion data by the linear spectral unmixing (LSU) method and studied for their spatial distribution. The different geological formations of the sabkha were discriminated by using the VNIR (visible and near-infrared) and SWIR (shortwave infrared) spectral bands from ASTER by principal component analysis (PCA). The image developed by using the principal components (R:PC2, G:PC3, B:PC5) showed the formations in different tones. Salinity of the area was mapped using monthly data of MSI from 2018 to 2020 by normalized difference salinity index (NDSI) (band11-band12)/(band11 + band12). The results of the index displayed the distribution of salinity in the area. Besides, moisture of the area was studied by using the normalized difference moisture index (NDMI) (b8-b11)/(b8 + b11) and described the temporal stability of the sabkha. All the results of image analyses were validated through field and laboratory studies. The study of laboratory spectra of evaporite minerals namely gypsum, anhydrite, and halite present in the salt crusts and gypsiferous soil flats showed their unique spectral absorptions in between 1.4–1.5 μm and 1.9–2.0 μm whereas, the calcite and dolomite minerals of the carbonate formations exhibited deep absorptions near 2.345 and 2.495 μm respectively.This study was supported by the Qatar National Research Fund under the National Priorities Research Program (grant no NPRP10-0214-170462)

Monitoring oil spill in Norilsk, Russia using satellite data.

This paper studies the oil spill, which occurred in the Norilsk and Taimyr region of Russia due to the collapse of the fuel tank at the power station on May 29, 2020. We monitored the snow, ice, water, vegetation and wetland of the region using data from the Multi-Spectral Instruments (MSI) of Sentinel-2 satellite. We analyzed the spectral band absorptions of Sentinel-2 data acquired before, during and after the incident, developed true and false-color composites (FCC), decorrelated spectral bands and used the indices, i.e. Snow Water Index (SWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The results of decorrelated spectral bands 3, 8, and 11 of Sentinel-2 well confirmed the results of SWI, NDWI, NDVI, and FCC images showing the intensive snow and ice melt between May 21 and 31, 2020. We used Sentinel-2 results, field photographs, analysis of the 1980-2020 daily air temperature and precipitation data, permafrost observations and modeling to explore the hypothesis that either the long-term dynamics of the frozen ground, changing climate and environmental factors, or abnormal weather conditions may have caused or contributed to the collapse of the oil tank.Open access funding provided by the Qatar National Library

Sentinel-2 image transformation methods for mapping oil spill – A case study with Wakashio oil spill in the Indian Ocean, off Mauritius

Although several indices have been constructed and available at the Index database (IDB) for Sentinel-2 satellite to map and study several earth resources, no indices have been developed to map oil spill. We constructed band ratios (5 + 6)/7, (3 + 4)/2, (11+12)/8 and 3/2, (3 + 4)/2, (6 + 7)/5 using the high-resolution MSI (multi-spectral instrument) visible-near infrared-shortwave infrared spectral bands of Sentinel-2 by summing-up the bands representing the shoulders of absorption features as numerator and the band located nearest to the absorption feature as denominator to discriminate oil spill, and demonstrate the potential of this method to map the Wakashio oil spill which occurred in the Indian Ocean, off Mauritius. The resulted images discriminated the oil spill well. We also decorrelated the spectral bands 4, 3 and 2 by studying the spectral band absorptions and discriminated the spill as very thick, thick and thin. The results of decorrelation stretch method exhibited the distribution of types of oil spill in a different tone, distinctly. Both the image transformation methods (band ratios and decorrelation stretch methods) showed their capability to map oil spills, and these methods are recommended to use for similar spectral bands of other sensors to map oil spills. • This study demonstrated the application of band ratios and decorrelation stretch methods to map oil spill. • The methods discriminated the oil spill off Mauritius, and showed spill thicknesses from the Sentinel-2 data. • The new methods are recommended to use for the spectral bands of other sensors to map oil spill.This work was supported by the Qatar University’s International Research Collaboration Co-Funds project (IRCC-2019-002). The authors are thankful to the Copernicus, European Space Agency for sharing the Sentinel-2 data through the Sentinel open access hub. The authors are thankful to Dr. Damià Barceló, the Editor in Chief and anonymous reviewers of the journal for their valuable reviews and constructive comments that have helped to present the work lucidly. The authors are thankful to Dr. Damià Barceló, Editor in Chief and anonymous reviewers of the journal for their valuable reviews, providing comments and suggestions that have helped to present the work lucidly. Open access funding provided by the Qatar National Library

Detection of Wakashio oil spill off Mauritius using Sentinel-1 and 2 data: Capability of sensors, image transformation methods and mapping

Oil spill incidents contaminate water bodies, and damage the coastal and marine environment including coral reefs and mangroves, and therefore, monitoring the oil spills is highly important. This study discriminates the Wakashio oil spill, which occurred off Mauritius, located in the Indian Ocean on August 06, 2020 using the Sentinel-1 and 2 data acquired before, during and after the spill to understand the spreading of the spill and assess its impact on the coastal environment. The interpretation of VV polarization images of Synthetic-Aperture Radar (SAR) C-band (5.404 GHz) of Sentinel-1 acquired between July 5 and September 3, 2020 showed the occurrence and distribution of oil spill as dark warped patches. The images of band ratios (5 + 6)/7, (3 + 4)/2, (11 + 12)/8 and 3/2, (3 + 4)/2, (6 + 7)/5 of the Sentinel-2 data detected the oil spill. The images of decorrelated spectral bands 4, 3 and 2 distinguished the very thick, thick and thin oil spills in a different tone and showed clearly their distribution over the lagoon and offshore, and the accumulation of spilled oil on the coral reefs and along the coast. The distribution of post-oil spill along the coast was interpreted using the images acquired after 21 August 2020. The accuracy of oil spill mapping was assessed by classifying the SAR-C data and decorrelated images of the MultiSpectral Instrument (MSI) data using the Parallelepiped supervised algorithm and confusion matrix. The results showed that the overall accuracy is on an average 91.72 and 98.77%, and Kappa coefficient 0.84 and 0.96, respectively. The satellite-derived results were validated with field studies. The MSI results showed the occurrence and spread of oil spill having different thicknesses, and supported the results of SAR. This study demonstrated the capability of Sentinel sensors and the potential of image processing methods to detect, monitor and assess oil spill impact on environment.This work was supported by the Qatar University’s International Research Collaboration Co-Funds project (IRCC-2019-002

Science of malaria elimination: using knowledge of bottlenecks and enablers from the Malaria Elimination Demonstration Project in Central India for eliminating malaria in the Asia Pacific region

Malaria poses a major public health challenge in the Asia Pacific. Malaria Elimination Demonstration Project was conducted as a public-private partnership initiative in Mandla between State government, ICMR, and FDEC India. The project employed controls for efficient operational and management decisions. IEC campaigns found crucial in schools and communities. Capacity building of local workers emphasized for better diagnosis and treatment. SOCH mobile app launched for complete digitalization. Better supervision for Indoor Residual Sprays and optimized Long Lasting Insecticidal Nets distribution. Significant malaria cases reduction in Mandla. Insights from MEDP crucial for malaria elimination strategies in other endemic regions of the Asia Pacific

Evaluating the stability of the relationship between land surface temperature and land use/land cover indices: a case study in Hyderabad city, India

ABSTRACTThe study focuses on evaluating the relationship between land surface temperature (LST) with four land use/land cover (LULC) indices (MNDWI, NDBaI, NDBI, and NDVI) in Hyderabad City of India using four Landsat 8 data from the winter season of 2020–21. Pearson’s linear correlation coefficient method is applied in determining the correlation analysis. The results represent a stable status of the indices in the winter season as the range of the mean is significantly low (0.04 for MNDWI, 0.04 for NDBaI, 0.02 for NDBI, and 0.05 for NDVI). All the LULC indices are very stable with each other. Moreover, these indices also build a stable relationship with LST. The indices respond differently to the change of LST. LST builds a neutral relationship with NDVI (average r = −0.07), a moderate negative relationship with MNDWI (average r = −0.57), and a moderate positive relationship with NDBaI (average r = 0.48) and NDBI (average r = 0.55). The dry winter season affects the vegetation life and generates a neutral relationship between LST and NDVI. Built-up and bare land surfaces enhance the LST while water surface reduces the LST. The study is suitable for a stable land use planning system

Seasonal impact on the relationship between land surface temperature and normalized difference vegetation index in an urban landscape

The present study assesses the seasonal variation of land surface temperature (LST) and the relationship between LST and normalized difference vegetation index (NDVI) on different types of land use/land cover (LULC) in Raipur City of India using 119 Landsat images of pre-monsoon, monsoon, post-monsoon and winter seasons from 1988 to 2019. The results show that the highest LST is found in the bare lands and built-up areas, whereas the lowest LST is observed in the green vegetation. The LST-NDVI correlation is strong negative in the monsoon (−0.51) and post-monsoon (−0.50) seasons, moderate negative (−0.46) in the pre-monsoon season and weak negative (−0.24) in the winter season. The different LULC types largely influence the nature and strength of the LST-NDVI correlation. The correlation is strong negative (−0.60) on green vegetation, moderate negative (−0.35) on the built-up area and bare land and nonlinear on the water bodies

Annual assessment on the relationship between land surface temperature and six remote sensing indices using landsat data from 1988 to 2019

The study focused on deriving the LST of the Raipur City of India and generating the relationships of LST with six selected remote sensing indices, like MNDWI, NDBaI, NDBI, NDVI, NDWI, and NMDI. The entire study was performed by using 210 cloud-free Landsat data of different months from 1988 to 2019. The LST retrieval mono-window algorithm was applied in the study. Based on Pearson's linear correlation coefficient (r), the study finds that LST builds a strong positive correlation (r = 0.65) with NDBI, a moderate positive correlation (r = 0.30) with NDBaI, a weak positive correlation with NDWI (r = 0.19), a strong negative relation with NMDI (r = −0.54), and a moderate negative correlation (r = −0.38) with MNDWI and NDVI. These relationships were consistent and stronger in earlier years. The LST-NDBI correlation is the most consistent (CV = 9.09), while the LST-NDBaI correlation is the most variable (CV = 60.21)

Monitoring LST-NDVI Relationship Using Premonsoon Landsat Datasets

The present study monitors the interrelationship of land surface temperature (LST) with normalized difference vegetation index (NDVI) in Raipur City of India using premonsoon Landsat satellite sensor for the season of 2002, 2006, 2010, 2014, and 2018. The results describe that the mean LST of Raipur City is gradually increased with time. The value of mean NDVI is higher in the area below mean LST compared to the area above mean LST. The value of mean NDVI is also higher in Landsat 8 data than Landsat 5 and Landsat 7 data. A strong negative LST-NDVI correlation is observed throughout the period. The correlation coefficient is higher in the area above mean LST and lower in the area below mean LST. The value of the correlation coefficient is decreased with time. The mixed urban landscape of the city is closely related to the changes of LST-NDVI relationship. These results provide systematic planning of the urban environment