Analyzing gully erosion and deposition patterns in loess tableland: Insights from small baseline subset interferometric synthetic aperture radar (SBAS InSAR).

The fragile Loess Plateau of China suffers substantial gully erosion. It is imperative to elucidate gully erosion patterns for implementing effective erosion control strategies. However, high spatiotemporal resolution quantification of gully dynamics remains limited across the Loess Plateau landscape. We utilized the small baseline subset interferometric synthetic aperture radar (SBAS InSAR) technique to investigate the phenomenon of gully erosion and deposition on the Dongzhiyuan tableland, which sits within the vast expanse of the Loess Plateau in China, over the period spanning 2020-2022. The tableland edges subsided while gully bottoms uplifted due to sedimentation. Low elevations underwent active deformation. Slope, aspect, and curvature modulated uplift and subsidence patterns by affecting runoff and sediment transport. Gentle downstream slopes displayed enhanced sedimentation. Southern gullies showed pronounced uplift compared to northern gullies. Heavy rainfall triggered extensive erosion followed by rapid uplift, reflecting an adaptive oscillation between erosion and deposition. Basin hydrology correlated with spatial patterns of deformation. Vegetation cover above 60 % of the maximum substantially increased InSAR error. Our study reveals intricate spatiotemporal behaviors of erosion and deposition in loess gullies using time-series InSAR. The findings provide new insights into gully geomorphology and evolution, and our study quantifies gully erosion and deposition patterns at high spatiotemporal resolution, enabling identification of the most vulnerable areas and prioritization of conservation efforts.

Retraction Note: Weather indicators and improving air quality in association with COVID-19 pandemic in India.

[This retracts the article DOI: 10.1007/s00500-021-06012-9.].

Turbidity dynamics in Indian peninsular river mouths derived from Kd490 reveals key anthropogenic drivers.

Large rivers, which act as natural integrators of surface processes, contribute massive volume of terrestrial materials to the coastal oceans. However, the accelerated climate warming and increasing anthropogenic activities recorded in recent years have been severely affecting the hydrologic and physical regimes of river systems. These changes have a direct impact on river discharge and runoff, some of which are occurred rapidly in the past two decades. Here, we present a quantitative analysis on the effects of changes in surface turbidity at coastal river mouths using diffuse attenuation coefficient at 490 nm (Kd490) as a proxy of turbidity for six major Indian peninsular rivers. The time series (2000-2022) trends of Kd490 obtained from Moderate Resolution Imaging Spectrometer (MODIS) images shows a significant decreasing trend in Kd values (p < 0.001) at the mouths of the Narmada, Tapti, Cauvery, Krishna, Godavari, and Mahanadi rivers. This is despite an increased rainfall trend observed for the six studied river basins which can likely intensifies the surface runoff and deliver more sediments, suggesting that other factors such as land use changes and increased number of dam constructions are primarily responsible for the decreased sediment load from rivers to coastal mouths.

Earthquake-induced soil landslides: volume estimates and uncertainties with the existing scaling exponents.

Quantifying landslide volumes in earthquake affected areas is critical to understand the orogenic processes and their surface effects at different spatio-temporal scales. Here, we build an accurate scaling relationship to estimate the volume of shallow soil landslides based on 1 m pre- and post-event LiDAR elevation models. On compiling an inventory of 1719 landslides for 2018 Mw 6.6 Hokkaido-Iburi earthquake epicentral region, we find that the volume of soil landslides can be estimated by γ = 1.15. The total volume of eroded debris from Hokkaido-Iburi catchments based on this new scaling relationship is estimated as 64-72 million m3. Based on the GNSS data approximation, we noticed that the co-seismic uplift volume is smaller than the eroded volume, suggesting that frequent large earthquakes (and rainfall extremes) may be counterbalancing the topographic uplift through erosion by landslides, especially in humid landscapes such as Japan, where soil properties are rather weak.

Weather indicators and improving air quality in association with COVID-19 pandemic in India.

The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad. Supplementary Information: The online version contains supplementary material available at 10.1007/s00500-021-06012-9.

Quantifying spatio-temporal variation in aquaculture production areas in Satkhira, Bangladesh using geospatial and social survey.

Despite Bangladesh being one of the leading countries in aquaculture food production worldwide, there is a considerable lack of updated scientific information about aquaculture activities in remote sites, making it difficult to manage sustainably. This study explored the use of geospatial and field data to monitor spatio-temporal changes in aquaculture production sites in the Satkhira district from 2017-2019. We used Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) to locate aquaculture ponds based on the terrain elevation and slope. Radar backscatter information from the Sentinel-1 satellite, and different water indices derived from Sentinel-2 were used to assess the spatio-temporal extents of aquaculture areas. An image segmentation algorithm was applied to detect aquaculture ponds based on backscattering intensity, size and shape characteristics. Our results show that the highest number of aquaculture ponds were observed in January, with a size of more than 30,000 ha. Object-based image classification of Sentinel-1 data showed an overall accuracy above 80%. The key factors responsible for the variation in aquaculture were investigated using field surveys. We noticed that despite a significant number of aquaculture ponds in the study area, shrimp production and export are decreasing because of a lack of infrastructure, poor governance, and lack of awareness in the local communities. The result of this study can provide in-depth information about aquaculture areas, which is vital for policymakers and environmental administrators for successful aquaculture management in Satkhira, Bangladesh and other countries with similar issues.

Natural and anthropogenic forces on suspended sediment dynamics in Asian estuaries.

Climate change and anthropogenic activities are affecting the hydrological conditions of rivers and may have altered nutrient and suspended sediments released into coastal seas. However, testing this hypothesis is difficult, confounded by the lack of observational data and the unavailability of globally accepted suspended sediment concentration (SSC) algorithms. Here, we analyzed the trends in SSC (2000-2020) at the mouths of 10 major Asian rivers using 10 available satellite-SSC algorithms. We identified spatially distinct trends, with SSC decreasing at the mouths of the Yellow, Pearl, and Indus rivers, and increasing trends at the mouths of the Narmada and Ganges-Brahmaputra rivers, while there were no significant trends at the mouths of the remaining rivers. River discharge, dams, and land use changes in basins individually did not suffice, but reproduced the observed SSC trends when used together. Our results imply that anthropogenic activities threaten the marine ecosystem more than climate forcing on Asian coasts.

Imminent threat of rock-ice avalanches in High Mountain Asia.

Upsurge of glacier-related hazards in High Mountain Asia (HMA) has been evident in recent years due to global warming. While many glacial-related hazards are instantaneous, some large landslides were preceded by slow gravitational deformation, which can be predicted to evade catastrophes. Here, we present robust evidence of historical deformation in 2021 Chamoli rock-ice avalanche of Himalaya using space imaging techniques. Multi-temporal satellite data provide evidence of a precursor event in 2016 and expansion of a linear fracture along joint planes, indicating 2021 rock-ice avalanche is a retrogressive wedge failure. The deformation history shows that the fracture propagated at a velocity of ~0.07 m day-1 until September 2020, and with an accelerated velocity (~0.14 m day-1 on average) lately. Analysis of recent similar cases in HMA supported our inference on global warming-induced glacier retreat and thermomechanical effects in enhancing the weakening of fractured rock masses in tectonically active mountain belts. Recent advances in Earth observation and seismic monitoring system can offer clues to the location and timing of impending catastrophic failures in high mountain regions.

Surface temperature controls the pattern of post-earthquake landslide activity.

The patterns and controls of the transient enhanced landsliding that follows strong earthquakes remain elusive. Geostatistical models can provide clues on the underlying processes by identifying relationships with a number of physical variables. These models do not typically consider thermal information, even though temperature is known to affect the hydro-mechanical behavior of geomaterials, which, in turn, controls slope stability. Here, we develop a slope unit-based multitemporal susceptibility model for the epicentral region of the 2008 Wenchuan earthquake to explore how land surface temperature (LST) relates to landslide patterns over time. We find that LST can explain post-earthquake landsliding while it has no visible effect on the coseismic scene, which is dominated by the strong shaking. Specifically, as the landscape progressively recovers and landslide rates decay to pre-earthquake levels, a positive relationship between LST and landslide persistence emerges. This seems consistent with the action of healing processes, capable of restoring the thermal sensitivity of the slope material after the seismic disturbance. Although analyses in other contexts (not necessarily seismic) are warranted, we advocate for the inclusion of thermal information in geostatistical modeling as it can help form a more physically consistent picture of slope stability controls.

Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis.

Peatlands in Indonesia are subject to subsidence in recent years, resulting in significant soil organic carbon loss. Their degradation is responsible for several environmental issues; however, understanding the causes of peatland subsidence is of prime concern for implementing mitigation measures. Here, we employed time-series Small BAseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) using ALOS PALSAR-2 images to assess the relationship between subsidence rates and land use/land cover (LULC) change (including drainage periods) derived from decadal Landsat data (1972-2019). Overall, the study area subsided with a mean rate of -2.646 ± 1.839 cm/year in 2018-2019. The subsidence rates slowed over time, with significant subsidence decreases in peatlands after being drained for 9 years. We found that the long-time persistence of vegetated areas leads to subsidence deceleration. The relatively lower subsidence rates are in areas that changed to rubber/mixed plantations. Further, the potential of subsidence prediction was assessed using Random Forest (RF) regression based on LULC change, distance from peat edge, and elevation. With an R2 of 0.532 (RMSE = 0.594 cm/year), this machine learning method potentially enlarges the spatial coverage of InSAR method for the higher frequency SAR data (such as Sentinel-1) that mainly have limited coverage due to decorrelation in vegetated areas. According to feature importance in the RF model, the contribution of LULC change (including drainage period) to the subsidence model is comparable with distance from peat edge and elevation. Other uncertainties are from unexplained factors related to drainage and peat condition, which need to be accounted for as well. This work shows the significance of decadal LULC change analysis to supplement InSAR measurement in tropical peatland subsidence monitoring programs.

Migrating rivers, consequent paleochannels: The unlikely partners and hotspots of flooding.

Furious floods have become an omnipresent reality with the dawn of climate change and its transition to adulthood. Since climate change has now become an accepted reality, analysing the factors that favour or disfavour floods are an urgent requirement. Here we showcase the role of paleochannels, a product of migrating rivers, in a catastrophic flood in the south-western part of the Indian Peninsula. This study exposes whether these geomorphic features facilitate or impede floods. For the purpose of extracting paleochannels and floodwater mapping, we utilized multiple satellite datasets and took advantage of diversified feature selection algorithms. Paleochannels were demarcated viz., initial identification of a few paleochannels from literature and confirmation through high-resolution Google Earth (GE) images, followed by Principal Component Analysis (PCA) of Sentinel-2 images using Google Earth Engine (GEE), and a supervised classification of the principal bands 1, 2, and 3. False-positives were eliminated using Object-Oriented Analysis (OOA), which reduced the 964,254 polygons to 23,254. These polygons were visually affirmed using GE images that resulted in 115 paleochannels as the final collection. A few locations were verified through Vertical Electrical Sounding (VES) using the Schlumberger method. The features were analysed with the floodwaters of the 2018 catastrophic flood, extracted from Synthetic Aperture Radar (SAR) data, which was delineated for different temporal limits including the day of peak flood of August 17, 2018. During the peak flood, the inundation of the study area extended to 534.86 km2 with all the paleochannels getting immersed in floodwater. After 44 days of peak flood, the post-flood analysis revealed that when the floodwater receded 50%, the paleochannels emptied 87.39%, with the midland paleochannels discharging more than those of lowlands. Thus, such geomorphic features can be flood hotspots, but can be considered for discharging floodwater to mitigate flood risk in case of unprecedented rain.

Analysis of long-term (2002-2020) trends and peak events in total suspended solids concentrations in the Chesapeake Bay using MODIS imagery.

Water quality monitoring programs have been widely implemented worldwide to monitor and assess water quality and to understand its trends. However, water quality analysis based on point-source field observations is difficult to perform at large spatial and temporal scales. In this paper, a fully automated Google Earth Engine (GEE) application algorithm was developed to estimate the total suspended solids (TSS) concentration in the Chesapeake Bay based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra imagery. Combining long-term archived satellite data (2002-2020) with field observations, the concentrations and spatiotemporal patterns of TSS in the bay water were evaluated. Time series analysis showed a statistically significant decreasing trend in TSS concentration between 2002 and 2020, suggesting that the sediment concentration in the bay has gradually been decreasing over the last two decades. The decreasing trend was observed in 49 out of 60 segments of the bay, implying that substantial progress has been made toward attaining the Chesapeake Bay water quality standards. Based on the monthly TSS analysis, 12 major peak events of TSS were identified in the Chesapeake Bay, which coincided with extreme winter blizzards and summer hurricane events. The GEE application and the results presented herein complement the existing monitoring program in attaining the water quality standards of the bay.

Basin-wide flood depth and exposure mapping from SAR images and machine learning models.

Recent years recorded an increasing number of short duration - high-intensity rainfall events in the Indian subcontinent consequent with urban and riverine flash floods. Rapid assessments of flooded areas are key for effective mitigation strategies and disaster risk plans, as well as to prepare operative policies for future events. Herein, we present an integrated methodology for rapidly mapping the flood extent, and depths based on Synthetic Aperture Radar (SAR) images and a digital elevation model (DEM). Incessant rain during August 2019 brought heavy riverine flooding in southern India, killed at least 280 people, and displaced about one million inhabitants from low-lying areas. We used SAR images by Sentinel-1 before, and during the flooding, and the MERIT DEM which enabled us to map the flood extent and flood depth of the inundation zones. Because the coverage of Sentinel-1 scene was limited to the Kabini river section during the flood period, flood extent and depth maps for the adjacent basin was generated by mapping the susceptibility for flooding using the training set obtained from the flood time Sentinel-1 images, and a set of predictive variables derived from DEM using random forest model. Qualitative analysis and cross-comparison with a numerical flood model proved the proposed approach is highly reliable with an accuracy value of 90% and 86% respectively for training and validation data, thus allowing a precise, simple, and fast flood mapping. The methodology presented here could be applied to other flooded areas having incomplete inventory in the context of flood risk assessment.

Unraveling the drivers of intensified landslide regimes in Western Ghats, India.

The Western Ghats (WG) mountain range in the Indian sub-continent is a biodiversity hotspot, now faces a severe threat to the valley population and ecosystem because of changing rainfall patterns and land-use changes. Here, we use the 2018-2019 landslide inventory data together with various geo-environmental factors and show that the landslide activity in the WG region is amplified by anthropogenic disturbances. We applied a generalized feature selection algorithm and a random forest susceptibility model to demonstrate the major topographic controls of landslides and the risk associated with them in the WG region. Our results show that road cutting and slopes modified to plantations are the strongest environmental variable (50% - 73% within 300 m buffer distance) related to the landslide patterns, whereas short-duration intense precipitation in the high elevated terrain, profile concavity, and stream power contributed to the initiation of landslides. The susceptibility models made for the present, and Global Climate Models (GCM) under the representative concentration pathway (RCP) 8.5 scenario predicts the vulnerable nature of WG for future climate extremes. Our results highlight the impacts of Anthropocene hazards and sensitivity of the WG ecosystem, and a greater focus therefore should be placed to reduce the vulnerability and increase preparedness for future events.

Complex anthropogenic interaction on vegetation greening in the Chinese Loess Plateau.

Vegetation greening steered by land use management in the Chinese Loess Plateau has been widely reported, however studies that quantitatively assessing and explicitly linking the anthropogenic forcing on vegetation greening and browning are scarce. Here in this study, we calculate the increment and rate of change of fractional vegetation cover (FVC) from 1998 to 2018 in the Loess Plateau, and compare the results with changing rainfall, soil types, and Gross Domestic Product (GDP), to detail a systematic assessment of the role of the climate-vegetation-human nexus. We have observed that nearly 80% of the study area has undergone greening, and noticed that rainfall was not the main driver of rapid vegetation change, instead of human land use management such as, irrigation along the Yellow River, snowmelt-runoff irrigation, and irrigation from reservoirs formed by check dams contributed the most for the increased FVC in the Chinese Loess Plateau. Concurrently, rapid vegetation browning is almost fully driven by urban expansion. Our findings show that GDP growth promotes both browning and greening, indicative of sustainable development in the Loess plateau region. These contrasting trends reveal that the relationship between human activities and greening is very complex.

Micro-topographic assessment of rill morphology highlights the shortcomings of current protective measures in loess landscapes.

Due to urban expansion and the rapid development of infrastructure in the loess area of China, artificial earth-fill embankments and excavated slopes are increasingly widespread in recent years. Erosion is typical in such loess slopes; however, quantitative statistical analyses of various counter-measures that affects rill erosion are still lacking. Here, we quantified rill morphology and rill erosion development in two newly constructed slopes with different engineering protection measures. We used high-resolution digital surface models (DSMs) acquired using an Unmanned Aerial Vehicle (UAV) to analyze the case areas during two-time periods. Our results from centimeter accuracy differential DSMs demonstrated that rapid rill erosion is prevalent in the study area, expressed as rill density varying between 2.03 km-2 and 8.81 km-2 at different slope surfaces (viz., erosion protected slopes [EPS], landslide protected slopes [LPS], and unprotected slopes [US]). The slope gradient responsible for rill erosion of the EPS, LPS and US are obviously different, and such information is essential for planning preventive measures in each slope type. At the EPS, the severity of erosion is maximum at the top of the ridges, whereas the gap between reinforced concrete lattice and loess deposits are of serious concern at the LPS. The current engineering measures employed in the study area are thus found ineffective for protection against rill erosion. We therefore propose an improved design by implementing an intercepting drain to the existing design for preventing further erosion.

COVID-19 and surface water quality: Improved lake water quality during the lockdown.

Human life comes to a standstill as many countries shut themselves off from the work due to the novel coronavirus disease pandemic (COVID-19) that hit the world severely in the first quarter of 2020. All types of industries, vehicle movement, and people's activity suddenly halted, perhaps for the first time in modern history. For a long time, it has been stated in various literature that the increased industrialization and anthropogenic activities in the last two decades polluted the atmosphere, hydrosphere, and biosphere. Since the industries and people's activities have been shut off for a month or more in many parts of the world, it is expected to show some improvement in the prevailing conditions in the aforementioned spheres of environment. Here, with the help of remote sensing images, this work quantitatively demonstrated the improvement in surface water quality in terms of suspended particulate matter (SPM) in the Vembanad Lake, the longest freshwater lake in India. The SPM estimated based on established turbidity algorithm from Landsat-8 OLI images showed that the SPM concentration during the lockdown period decreased by 15.9% on average (range: -10.3% to 36.4%, up to 8 mg/l decrease) compared with the pre-lockdown period. Time series analysis of satellite image collections (April 2013 - April 2020) showed that the SPM quantified for April 2020 is the lowest for 11 out of 20 zones of the Vembanad lake. When compared with preceding years, the percentage decrease in SPM for April 2020 is up to 34% from the previous minima.

Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning.

Predictive capability of landslide susceptibilities is assumed to be varied with different sampling techniques, such as (a) the landslide scarp centroid, (b) centroid of landslide body, (c) samples of the scrap region representing the scarp polygon, and (d) samples of the landslide body representing the entire landslide body. However, new advancements in statistical and machine learning algorithms continuously being updated the landslide susceptibility paradigm. This paper explores the predictive performance power of different sampling techniques in landslide susceptibility mapping in the wake of increased usage of artificial intelligence. We used logistic regression (LR), neural network (NNET), and deep learning neural network (DNN) model for testing and validation of the models. The tests were applied to the 2018 Hokkaido Earthquake affected areas using a set of 11 predictor variables (seismic, topographic, and hydrological). We found that the prediction rates are inconsequential with the DNN model irrespective of the sampling technique (AUC: 0.904 - 0.919). Whereas, testing with LR (AUC: 0.825 - 0.785) and NNET (AUC: 0.882 - 0.858) produces larger differences in the accuracies between the four datasets. Nonetheless, the highest success rates were obtained for samples within the landslide scarp area. The analogy was then validated with a published landslide inventory from the 2015 Gorkha earthquake. We, therefore, suggest that DNN models as an appropriate technique to increase the predictive performance of landslide susceptibilities if the landslide scarp and body are not characterized properly in an inventory.

COVID-19 and its impact on environment: Improved pollution levels during the lockdown period - A case from Ahmedabad, India.

The novel Coronavirus pandemic (COVID-19) hit the world severely in the first half of 2020 which forced several nations to impose severe restrictions on all sorts of activities involving human population. People were mainly advised to remain home quarantined to curb the virus spread. Industrial and vehicular movements were ceased as a result of lockdown, and therefore the rate of pollutants entering the ecosystem was also reduced in many places. Water and air pollution remained a major concern in the last few decades as these were gradually deteriorating in many spheres including the hydrosphere and atmosphere. As the nation-wide lockdown period in India completed more than two months, this study attempted to analyze the impact of lockdown on water and air quality to understand the short-term environmental changes. Using remote sensing data, this study demonstrated the improvements in ambient water quality in terms of decreased turbidity levels for a section of the Sabarmati River in the Ahmedabad region of India. The Suspended Particulate Matter (SPM) concentrations are evaluated to underline the turbidity levels in the study area before and during the lockdown period using the Landsat 8 OLI images. We noticed that the average SPM has significantly decreased by about 36.48% when compared with the pre-lockdown period; and a drop of 16.79% was observed from the previous year's average SPM. Overall, the average SPM concentration during the lockdown period (8.08 mg/l), was the lowest when compared with pre-lockdown average and long-term (2015-2019) April month average. The atmospheric pollution level (NO2, PM2.5, and PM10) data obtained from the Central Pollution Control Board for Ahmedabad city also shows a significant improvement during the study period, implying a positive response of COVID-19 imposed lockdown on the environmental fronts.

Assessing sustainable development prospects through remote sensing: A review.

The Earth's ecosystems face severe environmental stress from unsustainable socioeconomic development linked to population growth, urbanization, and industrialization. Governments worldwide are interested in sustainability measures to address these issues. Remote sensing allows for the measurement, integration, and presentation of useful information for effective decision-making at various temporal and spatial scales. Scientists and decision-makers have endorsed extensive use of remote sensing to bridge gaps among disciplines and achieve sustainable development. This paper presents an extensive review of remote sensing technology used to support sustainable development efforts, with a focus on natural resource management and assessment of natural hazards. We further explore how remote sensing can be used in a cross-cutting, interdisciplinary manner to support decision-making aimed at addressing sustainable development challenges. Remote sensing technology has improved significantly in terms of sensor resolution, data acquisition time, and accessibility over the past several years. This technology has also been widely applied to address key issues and challenges in sustainability. Furthermore, an evaluation of the suitability and limitations of various satellite-derived indices proposed in the literature for assessing sustainable development goals showed that these older indices still perform reasonably well. Nevertheless, with advancements in sensor radiometry and resolution, they were less exploited and new indices are less explored.