Remote sensing of desertification and study of temporal variability of aeolian deposits in parts of the Arabian Desert for sustainable development in an arid environment.

Aeolian deposit in part of the Arabian Desert is mapped using ASTER data to understand desertification, land encroachment, and degradation, and to assess agricultural development in arid regions. In this study, the interpretation of emissive spectra of sand deposits showed the presence of triplet absorptions in emissivity between 8 and 9.50 µm and studied with ASTER spectral bands to map the deposits. The ASTER quartz index (QI) images used to study the Abu Samra region, Qatar from 2000 to 2021 showed significant changes in desertification and land degradation. Analysis of temporal variability of deposits between 2000 and 2021 using ASTER band 12 by Parallelepiped image classification showed a decreasing trend from 9.70% to 2.94% in their distributions due to erosion and transportation. The changes are studied using FCC images (R:1; G:2; B:3) and hill-shaded images of 2000 and 2021. The results are confirmed from FCC (R:14; G:12; B:11) and Google Earth satellite images which showed the occurrence of sabkhas in 1985 and their disappearance from 2015, and the presence of agriculture in 2000 and their absence from 2005. The changes in desertification, land degradation, and agricultural development are verified in the field and evidenced. The grain size analysis of samples by ASTM method showed aeolian deposits have very fine to very coarse (63-2000 µm) sand types with silts of <3%. The samples analyzed by XRD and SEM-EDX methods showed the occurrence of dolomite, calcite, quartz, feldspar, and gypsum minerals with high sphericity and sub-angular to well-rounded characters and suggested transportations of grains from long distances. The geochemical elements analyses of samples reflected the chemistry of carbonates, aluminosilicates, and evaporites minerals which could have been derived from the carbonate, shale and sandstone formations, and sabkhas that occurred in Qatar and the Arabian Peninsula.

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.

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.

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.

The hazardous 2017-2019 surge and river damming by Shispare Glacier, Karakoram.

In 2017-2019 a surge of Shispare Glacier, a former tributary of the once larger Hasanabad Glacier (Hunza region), dammed the proglacial river of Muchuhar Glacier, which formed an ice-dammed lake and generated a small Glacial Lake Outburst Flood (GLOF). Surge movement produced the highest recorded Karakoram glacier surface flow rate using feature tracking (~18 ± 0.5 m d-1) and resulted in a glacier frontal advance of 1495 ± 47 m. The surge speed was less than reports of earlier Hasanabad advances during 1892/93 (9.3 km) and 1903 (9.7 km). Surges also occurred in 1973 and 2000-2001. Recent surges and lake evolution are examined using feature tracking in satellite images (1990-2019), DEM differencing (1973-2019), and thermal satellite data (2000-2019). The recent active phase of Shispare surge began in April 2018, showed two surface flow maxima in June 2018 and May 2019, and terminated following a GLOF on 22-23 June 2019. The surge likely had hydrological controls influenced in winter by compromised subglacial flow and low meltwater production. It terminated during summer probably because increased meltwater restored efficient channelized flow. We also identify considerable heterogeneity of movement, including spring/summer accelerations.

Seasonal evaluation of downscaled land surface temperature: A case study in a humid tropical city.

The present study evaluates the seasonal variation of estimated error in downscaled land surface temperatures (LST) over a heterogeneous urban land. Thermal sharpening (TsHARP) downscaling algorithm has been used with a separate combination of four selected remote sensing indices. This study assesses the capability of TsHARP technique over mixed land use/land covers (LULC) by analyzing the correlation between LST and remote sensing indices, namely, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized multi-band drought index (NMDI) and by determining the root mean square error (RMSE) and mean error (ME) produced by downscaled LST. Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images have been used for pre-monsoon, monsoon, post-monsoon, and winter seasons in 2014 covering the whole Raipur City, India. The RMSE of the downscaled LST decreases from 120 to 480 m spatial resolution in all the four seasons. It is concluded that NDBI is the most effective LULC index having the least error produced in TsHARP downscaling technique, irrespective of any season. Post-monsoon season reflects the most successful result followed by monsoon season. Even in the monsoon season of high vegetation coverage, NDBI presents a lower range of downscaled error compared to NDVI. This indicates better performance of NDBI in detecting the spatial and temporal distribution of mixed urban land.