Study on Dynamic Change of Land Usage in Hongta District Based on Sentinel-2

This work utilizes Sentinel-2A L1C remote sensing photographs from the years 2018, 2020, and 2022 to identify the different land use categories in the study area using the support vector machine (SVM) technique. The accuracy of categorization is greater than 90%. This research explores four factors of the dynamic change in land use in Hongta District from 2018 to 2022: the proportion of various types of land;the extent of something like the changing land usage;land use transfer;and the dynamic degree of the change in land use. According to the study's results, the proportion of cultivated and grassland land grew, while the quantity of barren and construction land fell by 1.90 percent, 0.03 percent, and 0.69 percent, respectively. The water system land portion of total area increased by 2.58 percent and 0.13 percent, respectively. After comparing the two research periods, the entire dynamic degree of the second stage is determined to be 3.5 percent lower than that of the first stage, and the pace of land use change is quite sluggish, which may be associated with the worldwide COVID-19 outbreak in 2020. The outcomes of the research may give the natural resources department the knowledge it needs to manage land resources properly. © 2023 SPIE.

Comparative retrieval approaches for concentrated Suspended Particulate Matter (CSPM) detection in the Ganges using Sentinel-2 and Cloud Computing

In this paper, there are four distinct models utilized for the retrieval of CSPM from the Sentinel 2A/2B satellite imageries by using cloud computing techniques. In this study, a comparative analysis of different CSPM models was carried out at three different sites (Haridwar, Varanasi, and Hooghly). The study reveals that there are significant changes in CSPM in the Ganges in three different periods such as pre, during, and post-COVID. Noteworthy, fewer anthropogenic activities have generated important transformations in aquatic environments during the COVID. © 2023 IEEE.

Capturing Secchi disk depth by using Sentinel-2 MSI imagery in Jiaozhou Bay, China from 2017 to 2021

Water clarity is a key parameter for assessing changes of aquatic environment. Coastal waters are complex and variable, remote sensing of water clarity for it is often limited by low spatial resolution. The Sentinel-2 Multi-Spectral Instrument (MSI) imagery with a resolution of up to 10 m are employed to solve the problem from 2017 to 2021. Distribution and characteristics of Secchi disk depth (SDD) in Jiaozhou Bay (JZB) are analyzed. Subtle changes in localized small areas are discovered, and main factors affecting the changes are explored. Among natural factors, precipitation and wind play dominant roles in variation in SDD. Human activities have a significant influence on transparency, among which fishery farming has the greatest impact. This is clearly evidenced by the significant improvement of SDD in JZB due to the sharp decrease in human activities caused by coronavirus disease 2019 (COVID-19).Copyright © 2022 The Authors

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery.

Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.

Mapping Chini Lake (Pahang, Malaysia) using Sentinel-2 images to determine the effect of acid mine drainage in the pre- to post-COVID-19 restriction period.

Mining activities in the Chini Lake catchment area have been extensive for several years, contributing to acid mine drainage (AMD) events with high concentrations of iron (Fe) and other heavy metals impacting the surface water. However, during the restriction period due to the COVID-19 outbreak, anthropogenic activities have been suspended, which clearly shows a good opportunity for a better environment. Therefore, we aimed to analyze the variation of AMD-associated water pollution in three main zones of the Chini Lake catchment area using Sentinel-2 data for the periods pre-movement control order (MCO), during MCO, and post-MCO from 2019 to 2021. These three zones were chosen due to their proximity to mining areas: zone 1 in the northeastern part, zone 2 in the southeastern part, and zone 3 in the southern part of the Chini Lake area. The acid mine water index (AMWI) was a specific index used to estimate acid mine water. The AMWI values from Sentinel-2 images exhibited that the mean AMWI values in all zones during the MCO period decreased by 14% compared with the pre-MCO period. The spatiotemporal analysis found that the highest polluted zones were recorded in zone 1, followed by zone 3 and zone 2. As compared with during the MCO period, the maximum percentage of increment during post-MCO in all zones was up to 25%. The loosened restriction policy has resulted in more AMD flowing into surface water and increased pollution in Chini Lake. As a whole, our outputs revealed that Sentinel-2 data had a major potential for assessing the AMD-associated pollution of water.

Image processing of Sentinel-2MSI data for Lake Water Quality Analysis

In recent decades lake water resources are get deteriorating and declining due to an increase in urbanization and the high effects of anthropogenic activities. Lake is an important ecological asset to the earth system. It is necessary to monitor water resources. Due to the spread of the covid-19 pandemic virus, the global range shutdown was implemented so that all the activities come to hold resulting in recovering nature and its environment from pollution. The on-site monitoring and evaluation of the quality of water resources in the pandemic period are impossible. The satellite remote sensing techniques have been used for the water quality assessment for pre-pandemic and during pandemic periods. The result suggested that there is an up-gradation in the quality of lake water in the lockdown period than the pre pandemic period i.e. 30.60% increase in lake water clarity. The satellite image processing techniques had the potential for the estimation of the lake water quality during these difficult times. © 2022 IEEE.

Investigation of pre and post environmental impact of the lockdown (COVID-19) on the water quality of the Capibaribe and Tejipió rivers, Recife metropolitan region, Brazil.

The coronavirus pandemic has seriously affected human health, although some improvements on environmental indexes have temporarily occurred, due to changes on socio-cultural and economic standards. The objective of this study was to evaluate the impacts of the coronavirus and the influence of the lockdown associated with rainfall on the water quality of the Capibaribe and Tejipió rivers, Recife, Northeast Brazil, using cloud remote sensing on the Google Earth Engine (GEE) platform. The study was carried out based on eight representative images from Sentinel-2. Among the selected images, two refer to the year 2019 (before the pandemic), three refer to 2020 (during a pandemic), two from the lockdown period (2020), and one for the year 2021. The land use and land cover (LULC) and slope of the study region were determined and classified. Water turbidity data were subjected to descriptive and multivariate statistics. When analyzing the data on LULC for the riparian margin of the Capibaribe and Tejipió rivers, a low permanent preservation area was found, with a predominance of almost 100% of the urban area to which the deposition of soil particles in rivers are minimal. The results indicated that turbidity values in the water bodies varied from 6 mg. L-1 up to 40 mg. L-1. Overall, the reduction in human-based activities generated by the lockdown enabled improvements in water quality of these urban rivers.

Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment

Cover crops are planted to reduce soil erosion, increase soil fertility, and improve watershed management. In the Delmarva Peninsula of the eastern United States, winter cover crops are essential for reducing nutrient and sediment losses from farmland. Cost-share programs have been created to incentivize cover crops to achieve conservation objectives. This program required that cover crops be planted and terminated within a specified time window. Usually, farmers report cover crop termination dates for each enrolled field (∼28,000 per year), and conservation district staff confirm the report with field visits within two weeks of termination. This verification process is labor-intensive and time-consuming and became restricted in 2020–2021 due to the COVID-19 pandemic. This study used Harmonized Landsat and Sentinel-2 (HLS, version 2.0) time-series data and the within-season termination (WIST) algorithm to detect cover crop termination dates over Maryland and the Delmarva Peninsula. The estimated remote sensing termination dates were compared to roadside surveys and to farmer-reported termination dates from the Maryland Department of Agriculture database for the 2020–2021 cover crop season. The results show that the WIST algorithm using HLS detected 94% of terminations (statuses) for the enrolled fields (n = 28,190). Among the detected terminations, about 49%, 72%, 84%, and 90% of remote sensing detected termination dates were within one, two, three, and four weeks of agreement to farmer-reported dates, respectively. A real-time simulation showed that the termination dates could be detected one week after termination operation using routinely available HLS data, and termination dates detected after mid-May are more reliable than those from early spring when the Normalized Difference Vegetation Index (NDVI) was low. We conclude that HLS imagery and the WIST algorithm provide a fast and consistent approach for generating near-real-time cover crop termination maps over large areas, which can be used to support cost-share program verification.

Investigating the effects of drought and lockdowns on smallholder and commercial agricultural production in KwaZulu-Natal using remotely sensed data.

Not many efforts have been made so far to understand the effects of both the 2015-2016 drought and the 2020 lockdown measures on the agricultural production of smallholder vis-a-vis commercial farmers in Kwazulu-Natal. Google Earth Engine, and random forest algorithm, are used to generate a dataset that help to investigate this question. A regression is performed on double differenced data to investigate the effects of interest. A k-mean cluster analysis, is also used to determine whether the distribution patterns of crop production changed with drought and disruption of agricultural production input. Results show that: (1) droughts affected the agricultural production of both areas similarly. Crop cover declined in both areas for one season after droughts were broken. Then recovery was driven by greener, more productive crops rather than the expansion of crop area. (2) The response of both areas to the COVID-19 lockdown was also similar. Both smallholder and commercial areas' Normalised Difference Vegetation Index - a proxy for crop vitality - improved in response to regulations favourable to the sector and improved rainfall. No significant adjustments in crop cover were observed. Production therefore changed primarily at the intensive margin (improved productivity of existing croplands) rather than the extensive (changing the extent of land under cultivation). (3) Cluster analysis allows for a more granular view, showing that the positive impact of lockdowns on agriculture were concentrated in areas with high rainfall and close proximity to metropolitan markets. Both smallholder and commercial farmers therefore are reliant on market access together with favourable environmental conditions for improved production.

Monitoring gas flaring in Texas using time-series sentinel-2 MSI and landsat-8 OLI images

Since the shale Oil/Gas revolution, gas flaring and venting in the United States has garnered increasing attention. There is a pressing need to understand the spatial–temporal characteristics of gas flaring and track the associated greenhouse gas emissions. In this context, we use a thermal anomaly index (TAI) incorporating the Google Earth Engine (GEE) cloud computation and local batch processing for monitoring gas flaring and characterizing its spatial–temporal dynamics. We then apply a quantitative analysis of satellite-based carbon dioxide (CO2) and methane (CH4) in the gas flaring region. Here, we generate a gas flaring sites inventory in Texas from 2013 to 2022 based on > 83,500 multi-source moderate-resolution images (including 74,627 Sentinel-2 Multispectral Instrument [MSI] images and 8,969 Landsat-8 Operational Land Imager [OLI] images). Validations and comparisons demonstrate that our method is reliable for MSI and OLI images, with an overall accuracy of > 95 % and a low commission rate and omission rate. We detected 217,034 gas flares from 9,296 flaring sites in Texas, and the majority (>92 %) were found in the central and western regions of the Permian Basin and the Eagle Ford Shale. The number of detected gas flaring sites vastly outnumbered the existing Visible Infrared Imaging Radiometer Suite (VIIRS) fire products, with an upward trend from 2013 to 2019 and a downward trend from 2020 to 2022. Notably, the gas flaring sites dropped significantly at the beginning of the COVID-19 pandemic (from December 2019 to May 2020), with the lowest average monthly growth rate of −14.38 %, and fell to the level of mid-2017. Application of gas flaring data identifies the localized greenhouse gas (GHG) emission hotspots in Texas and demonstrates that the increased effect of CH4 released from gas flaring regions was significantly stronger than that of CO2. These findings can provide references for monitoring similar small industrial sources in the future, can be used as an essential supplement to low-resolution fire products, and improve our understanding of CO2 and CH4 emissions from gas flaring at fine spatial scales.

Exploring Transferable Techniques to Retrieve Crop Biophysical and Biochemical Variables Using Sentinel-2 Data

The current study aimed to determine the spatial transferability of eXtreme Gradient Boosting (XGBoost) models for estimating biophysical and biochemical variables (BVs), using Sentinel-2 data. The specific objectives were to: (1) assess the effect of different proportions of training samples (i.e., 25%, 50%, and 75%) available at the Target site (DT) on the spatial transferability of the XGBoost models and (2) evaluate the effect of the Source site (DS) (i.e., trained) model accuracy on the Target site (i.e., unseen) retrieval uncertainty. The results showed that the Bothaville (DS) → Harrismith (DT) Leaf Area Index (LAI) models required only fewer proportions, i.e., 25% or 50%, of the training samples to make optimal retrievals in the DT (i.e., RMSE: 0.61 m2 m−2;R2: 59%), while Harrismith (DS) →Bothaville (DT) LAI models required up to 75% of training samples in the DT to obtain optimal LAI retrievals (i.e., RMSE = 0.63 m2 m−2;R2 = 67%). In contrast, the chlorophyll content models for Bothaville (DS) → Harrismith (DT) required significant proportions of samples (i.e., 75%) from the DT to make optimal retrievals of Leaf Chlorophyll Content (LCab) (i.e., RMSE: 7.09 µg cm−2;R2: 58%) and Canopy Chlorophyll Content (CCC) (i.e., RMSE: 36.3 µg cm−2;R2: 61%), while Harrismith (DS) →Bothaville (DT) models required only 25% of the samples to achieve RMSEs of 8.16 µg cm−2 (R2: 83%) and 40.25 µg cm−2 (R2: 77%), for LCab and CCC, respectively. The results also showed that the source site model accuracy led to better transferability for LAI retrievals. In contrast, the accuracy of LCab and CCC source site models did not necessarily improve their transferability. Overall, the results elucidate the potential of transferable Machine Learning Regression Algorithms and are significant for the rapid retrieval of important crop BVs in data-scarce areas, thus facilitating spatially-explicit information for site-specific farm management.

Analysing the change in water quality parameters along river Ganga at Varanasi, Mirzapur and Ghazipur using Sentinel-2 and Landsat-8 satellite data during pre-lockdown, lockdown and post-lockdown associated with COVID-19

The proper functioning of the river ecosystem has been symbolised by healthy aquatic life. The river Ganga has shown signs of rejuvenation due to lockdown. In this study, an attempt has been made to analyse the change in river water quality using Sentinel-2 and Landsat-8 imageries. The quantitative analysis has been performed for temperature and normalised difference turbidity index (NDTI). The qualitative analysis has been performed for pH, dissolved oxygen (DO) and total suspended solids (TSSs). Ghazipur, Varanasi and Mirzapur stretches have been selected for this study. In the Ghazipur stretch, the river temperature decreased by 7.14% in May 2020 (lockdown period) as compared to May 2019 (1 year before lockdown). Similarly, in the Varanasi stretch, this decrease has been by 8.62%, and in the Mirzapur stretch, this decrease has been by 12.06% in May 2020 compared to May 2019. For the same period, NDTI in the Ghazipur, Varanasi and Mirzapur stretch has been decreased by 0.22, 0.26 and 0.24, respectively. The pH and DO of the river increased, and TSS decreased for the considered time period. The lockdown during the second wave of the coronavirus disease 2019 was not helpful for river rejuvenation. This study elicited how the behaviour of the parameters changed during the lockdown. Research highlights: River Ganga becomes much cleaner in the lockdown period (May 2020) compared to the pre-lockdown time. In the Mirzapur stretch, the temperature decreased most in May 2020 as compared to May 2019. In the Varanasi stretch, there is a maximum variation in the NDTI value in May 2020 in comparison with that of May 2019. The most significant task will be to maintain river conditions during post-lockdown similar to that prevailed during lockdown. In the second wave COVID-19 lockdown the river again became polluted like the pre-COVID times. [ FROM AUTHOR] Copyright of Journal of Earth System Science is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Flood Risk and Exposure: The Case of the Tajuña Valley (Madrid, Spain)

This paper aims to update the exposure to flood risk in a catchment area of the Community of Madrid (Spain) linked to primary sector activities, albeit affected by the urban expansion of the capital. This research starts with the updating of the flood inventory, encompassing episodes documented between 1629 and 2020. The inadequate occupation of the territory means that floods continue to cause significant damage nowadays. It is worth highlighting the two recent floods (2019) that occurred just 15 days apart and caused serious damage to several towns in the basin. The areas at risk of flooding are obtained from the National Floodplain Mapping System, and the maximum and minimum floodable volume in the sector of the Tajuña River basin with the highest exposure to flooding has been calculated. The Sentinel 2 image in false colour (RGB bands 11-2-3, 11-8-3 and 12-11-8) and its transformation to colour properties (Intensity, Hue and Saturation) has made it possible to determine the extension of the riparian vegetation and the irrigated crops located in the alluvial plain. The SPOT 6 image with higher spatial resolution has allowed us to update the mapping of buildings located in areas at risk of flooding. Finally, based on cadastral data, a detailed cartography of built-up areas in areas at risk of flooding is provided. They affect buildings built mainly between the 1960s and 1990s, although the most recent buildings are built on agricultural land in the alluvial plain, even though current regulations prevent the occupation of these lands.

A Daytime Multisensor Satellite System for Global Gas Flaring Monitoring

Natural gas flaring (GF) is a longstanding problem for the oil industry. Recent estimates indicate that this phenomenon has increased to levels recorded a full decade earlier. While in 2020 there was a decline in global GF due to COVID-19 pandemic, data suggest that GF continues to be a persistent issue, with solutions remaining difficult or uneconomical in certain countries. Nighttime satellite products are widely used to map and monitor GF affected areas, partially filling the general lack of information from oil companies and/or national reporting. In this work, we assess the potential of daytime infrared satellite observations at high spatial resolution from operational land imager (OLI) and multispectral instrument (MSI) sensors, respectively, onboard Landsat-8 (L8) and Sentinel-2 (S2) satellites, in monitoring GF activity. The normalized hotspot indices (NHI) algorithm is used for this purpose, testing its performance over six different GF sites. Results show the NHI capability in providing accurate information about GF-related thermal (e.g., 100 & x0025;of detections offshore;up to 92 & x0025;onshore), despite some limitations due to clouds and smoke. They open challenging scenarios regarding the possibility of quantifying the volume of emitted gas from daytime S2-MSI and L8-OLI data, integrating information from well-established nighttime operational systems.

Detecting Moving Trucks on Roads Using Sentinel-2 Data

In most countries, freight is predominantly transported by road cargo trucks. We present a new satellite remote sensing method for detecting moving trucks on roads using Sentinel-2 data. The method exploits a temporal sensing offset of the Sentinel-2 multispectral instrument, causing spatially and spectrally distorted signatures of moving objects. A random forest classifier was trained (overall accuracy: 84%) on visual-near-infrared-spectra of 2500 globally labelled targets. Based on the classification, the target objects were extracted using a developed recursive neighbourhood search. The speed and the heading of the objects were approximated. Detections were validated by employing 350 globally labelled target boxes (mean F1 score: 0.74). The lowest F1 score was achieved in Kenya (0.36), the highest in Poland (0.88). Furthermore, validated at 26 traffic count stations in Germany on in sum 390 dates, the truck detections correlate spatio-temporally with station figures (Pearson r-value: 0.82, RMSE: 43.7). Absolute counts were underestimated on 81% of the dates. The detection performance may differ by season and road condition. Hence, the method is only suitable for approximating the relative truck traffic abundance rather than providing accurate absolute counts. However, existing road cargo monitoring methods that rely on traffic count stations or very high resolution remote sensing data have limited global availability. The proposed moving truck detection method could fill this gap, particularly where other information on road cargo traffic are sparse by employing globally and freely available Sentinel-2 data. It is inferior to the accuracy and the temporal detail of station counts, but superior in terms of spatial coverage.

DETECTING AIRPORT ACTIVITY FROM SENTINEL-2 IMAGERY DURING COVID-19 PANDEMIC BY USING DEEP LEARNING

Since the global spread of COVID-19 in 2020, in order to reduce infections, the movement of people has been severely restricted. As a result, the economic environment in commercial aviation suffered an unprecedented impact. Therefore, it becomes important to study the impact of COVID-19 on commercial aviation. In this study, in order to understand the changing trend of the number of airplanes as an index of the airport activity, we applied a method that utilizes convolutional neural networks to effectively detect airplane in Sentinel-2 images. From the detection, we successfully obtained the changing trend of the number of airplanes in important airports around the world since the outbreak of COVID-19, and found different changing trends in different areas that may reflect different reactions to COIVD-19 situation in each country. © 2021 IEEE

Mapping the Trophic State Index of Eastern Lakes in China using an Empirical Model and Sentinel-2 Imagery Data

Lake eutrophication has become a critical environmental issue due to the global effects of anthropogenic activities and climate change, and has been comprehensively studied for many years. A series of models and indicators have been proposed to assess the trophic state of lakes. The trophic state index (TSI) is a synthetic index that integrates chlorophyll-a, water clarity, and total phosphorus and is widely used to evaluate the trophic state of aquatic environments. In this study, we collected in situ lake samples (N=431) from typical lakes to match Sentinel-2 MultiSpectral Instrument (MSI) imagery data using the Case 2 Regional Coast Color processor. Then we developed a new empirical model, TSI = –34.04 × (band 4/band 5) – 1.114 × (band 1/band 4) + 97.376). This model is valid for all of China, with good performance and few errors (RMSE=7.36;MAE=6.25) for the validation dataset. Recognizing that over 94% of the Chinese population located along eastern watersheds and large lakes have competing water uses, and given the TSI model on the seasonal scales, we further estimated the mean TSI and trophic state in eastern Chinese lakes (> 100 km2) from 2019 to 2020. The results revealed that more lakes were eutrophic in autumn (94.28%) than in spring (> 77.14%), indicating a serious eutrophication of eastern lakes. Although the eastern lakes have been studied in more detail, this study found that eutrophication still has markedly negative impacts on lake ecosystems. In addition, no significant improvement was observed in spring, most likely due to the months of curfew/lockdown from January 2020 onwards due to COVID-19. This may be due to the enrichment of nutrients deposited in sediment or watershed soil, which can be characterized as “autochthonous sources” of lake eutrophication, over decades with high rates of economic development. This study demonstrates the applicability of Sentinel-2 MSI data to monitor lake eutrophication as well as the feasibility of blue/red and red/red edge combinations. The framework and TSI model used bands available on MSI sensors to develop a novel approach for generating historical eutrophication data for large-scale evaluation of and decision-making related aquatic environmental changes, even in poorly studied areas.

Changes in turbidity and human activities along Haihe River Basin during lockdown of COVID-19 using satellite data.

During the outbreak of the COVID-19, China implemented an urban lockdown in the first period. These measures not only effectively curbed the spread of the virus but also brought a positive impact on the ecological environment. The water quality of urban inland river has a significant impact on urban ecology and public health. This study uses Sentinel-2 visible and near-infrared band reflectance and the Normalized Difference Turbidity Index (NDTI) to analyze the water quality of the Haihe River Basin during the control period of COVID-19. It is found that during the lockdown period, the river water quality was significantly improved compared to the same period in 2019. The average NDTI of the Haihe River Basin in March decreased by 0.27, a decrease of 219.06%; in April, it increased by 0.07, that is 38.38%. Further exploration using VIIRS lights found that the brightness of the lights in the main urban area was significantly lower in February, the beginning of the lockdown. However, as the city was unblocked, the lights rose sharply in March and then recovered to normal. There is obvious asynchrony in changes between river turbidity and light. The results can help understand the impact of human activities on the natural environment.

The immediate impact of the associated COVID-19's lockdown campaign on the native vegetation recovery of Wadi Al Batin Tri-state desert.

Law enforcement and massive media awareness, limiting the anthropogenic disturbance, is the way to go for implementing successful desert native vegetation recovery plans. A lesson learned on the resiliency of desert ecosystems throughout studying the native vegetation coverage in the Wadi Al-Batin desert ecosystem during the COVID-19 pandemic. Wadi Al-Batin tri-state desert (89,315 km2) covers the South-western part of Iraq, State of Kuwait, and the North-eastern part of Saudi Arabia. In this study, the spatiotemporal changes in vegetation coverage was detected, by using Sentinel-2A imageries, during the period from 2017 to 2020. For better understanding the impact of associated law enforcement and media practices during COVID-19 pandemic, native vegetation coverage of years with relevant rainfall records were compared. The results revealed that despite receiving the least amount of rain of the three years (≤93 mm), the COVID-19 year (2020) had the highest native vegetation coverage at 28.5% compared with 6% in 2017, and 2% in 2018. These results prove that the main drivers of desert vegetation deterioration are anthropogenic activities, such as quarrying, overgrazing, distractive camping, and off-road vehicle movements. Moreover, the estimated 63% vegetation coverage in Wadi Al-Batin desert in 2019 assures the significant role of precipitation in desert vegetation recovery. This bulk increase in vegetation coverage detected during COVID-19 pandemic shows that the desert vegetation adapts to harsh environments (low rainfall) and rapidly recovers once the source of the disturbance was removed by enforcing the environmental rules. Thus, the protection of natural resources and ecosystems can be achieved through the synergy between governments and civil communities, including intensive awareness of environmental impacts via media, enforcing environmental regulations, and promoting regional collaboration.

Water quality assessment of the Ganges River during COVID-19 lockdown.

Ganges River water quality was assessed to record the changes due to the nation-wide pandemic lockdown. Satellite-based (Sentinel-2) water quality analysis before and during lockdown was performed for seven selected locations spread across the entire stretch of the Ganges (Rishikesh-Dimond Harbour). Results revealed that due to the lockdown, the water quality of the Ganges improved with reference to specific water quality parameters, but the improvements were region specific. Along the entire stretch of Ganges, only the Haridwar site showed improvement to an extent of being potable as per the threshold set by the Central Pollution Control Board, New Delhi, India. A 55% decline in turbidity at that site during the lockdown was attributed to the abrupt halt in pilgrimage activities. Absorption by chromophoric dissolved organic matter which is an indicator of organic pollution declined all along the Ganges stretch with a maximum decline at the downstream location of Diamond Harbour. Restricted discharge of industrial effluent, urban pollution, sewage from hotels, lodges, and spiritual dwellings along the Ganges are some of the reasons behind such declines. No significant change in the geographic trend of chlorophyll-a was observed. The findings of this study highlight the importance of regular monitoring of the changes in the Ganges water quality using Sentinel-2 data to further isolate the anthropogenic impact, as India continues the phase-wise opening amidst the pandemic.