United States Geological Survey Research Articles

Geospatial assessment of built environment on land surface temperature in district Sheikhupura, Punjab Pakistan

The built environment is associated with higher air and surface temperatures in urban areas compared to rural counterparts. This study focuses on the Sheikhupura district in Punjab, Pakistan, where rapid urbanization and deforestation have significantly impacted land surface temperature (LST) from 2000 to 2020. Utilizing Geographic Information Systems (GIS) and Remote Sensing (RS) techniques, four Landsat images from the United States Geological Survey (USGS) were analyzed to assess spatiotemporal variations in LULC and LST. The single infrared channel approach was employed to calculate LST, and LULC maps were created using supervised classification for the years 2000, 2010, and 2020. The results indicate a substantial increase in built-up areas from 66.9708 square kilometers in 2000 to 259.8147 square kilometers in 2020, accompanied by a significant reduction in vegetation and agricultural land. Correspondingly, the maximum LST rose from 28.9354 °C in 2000 to 31.5173 °C in 2020, highlighting the urban heat island effect. Zonal statistics revealed a decrease in mean high temperatures from 24.45 to 20.3 °C over the same period, suggesting variability in temperature distribution due to changes in land cover types. These findings underscore the need for sustainable urban planning to mitigate the adverse effects of urbanization on local climates. Future work should focus on developing strategies to increase green spaces and reforestation efforts to counteract rising temperatures. Additionally, further research is recommended to explore the long-term impacts of these changes on local biodiversity and ecosystem services, providing a holistic approach to urban sustainability.

Exploring the utility of the Enhanced Vegetation Index as rainfall and agricultural proxy in a Caribbean case study event

Highly fragile small island states experience disproportionate climate impacts given their limited capacity to implement cost‐effective tools for detecting emerging signals of drying conditions and monitoring systems for sensitive sectors such as agriculture, especially for uncertain, ‘creeping’ events such as droughts. Despite the existence of open‐source Google Earth Engine datasets, untapped potential remains for their full deployment in disaster management infrastructure. Given this gap, this paper explores the utility of the Enhanced Vegetation Index (EVI) for detecting spatio‐temporal variations of Mid‐Summer Drought (MSD) impacts on vegetation in the small island of Jamaica, with emphasis on major historical drought events. Geospatial analyses of EVI datasets from the Terra Moderate‐Resolution Imaging Spectroradiometer (MODIS) between 2000−2015 archived by the United States Geological Survey (USGS), were computed, and validated by station‐based precipitation and production data for selected parishes for historical case study MSD events. Results revealed highly asymmetrical drought impacts, with Jamaica's agriculturally intense Southern coastline displaying the most stressed vegetation (EVI < 0.5). North‐Western and North‐Eastern regions had the healthiest vegetation during the MSD (EVI > 0.6). A ‘fair’ to ‘moderate’ concurrent correlation was found between EVI and precipitation (R > 0.6), with lower correlations vis‐a‐vis agricultural production (R = 0.2–0.4). The results provide evidence of EVI's utility as a drought monitoring tool in a small island context.

Anomalous geomagnetic activities before the Karangasem - Bali, Indonesia earthquakes on December 13, 2022

Abstract A series of earthquakes occurred on Bali Island, Indonesia, on December 13, 2022. The United States Geological Survey (USGS) recorded four shallow earthquakes around Karangasem - Bali with magnitude (M) > 4 at that time. The largest was the M 5.2 earthquake, which occurred at 10:38:21.67 UTC with 10 km of depth. We analyzed the anomalous geomagnetic activities during these earthquakes by utilizing the geomagnetic data from the Bayan geomagnetic station located on Lombok Island, less than 100 km from the earthquake’s epicenters. We conducted the polarization ratio analysis by applying the Fast Fourier Transform (FFT) on the five hours of night geomagnetic data (16:00 - 21:00 UTC). The spectral power values of X, Y, and Z geomagnetic data at frequencies 0.04 - 0.06 Hz were calculated and compared with the disturbance storm time (Dst) to find their correlation. The Pearson correlation analysis indicates that they are significantly uncorrelated. Finally, we calculated the Sz/Sg to analyze the geomagnetic anomalies and found them 6 - 11 days before the earthquakes at frequencies 0.04 - 0.06 Hz. We consider that these anomalies are possibly caused by the M 5.2 earthquake since it had the highest magnitude, Es, and Kls values.

Probabilistic Seismic Hazard Assessment of Sitamarhi near the Central Himalayan Region

ABSTRACT Sitamarhi, situated in the North-Bihar region of the Himalayan foothills is one of the seismically vulnerable regions of India. This region faced several devastating earthquakes, such as the 1934 and 1988 Bihar Nepal earthquake and the 2015 Nepal earthquake due to the movement of tectonic plates. The purpose of the present study is to exhibit the uniform hazard spectrum (UHS) and peak ground acceleration (PGA) values based on the probabilistic seismic hazard method using different seismotectonic parameters. The Gutenberg Richter (G-R) seismicity parameters ‘a’ and ‘b’ were estimated on the basis of regional earthquake magnitude data for the studied region. Regional earthquakes data were taken from the United States Geological Survey (USGS), International Seismological Centre (ISC), Indian Meteorological Department (IMD), and Seismotectonic Atlas of India. The region of interest for the probabilistic seismic hazard analysis (PSHA) is within a 500 km radius considering past seismicity and 62 seismotectonic sources which were identified in the study area. Large-scale geological features were utilised to describe the regional seismic source zones, which were used to derive the relationship between return period and ground motion parameters. Estimated PGA values are 0.92g for 2% and 0.68g for 10% probability of exceedance in 50 years time period, respectively. This research infer that the West Patna fault (WPF) and Sitamarhi fault (SMF) are the two maximum contributors in PGA for the Sitamarhi region.

Land use and land cover changes, and woody vegetation status of the Tsimur Gebriel Monastery in Northern Ethiopia

Ethiopian Orthodox churches are significant habitats for endemic and threatened plant species, yet their vegetation status and the land use systems impacting them, are little known. Therefore, this study assessed the land use and land cover changes (LULCC) within a 3 km buffer area and the woody vegetation status of the Tsimur Gebriel Monastery in the Tigray region, Northern Ethiopia. The United States Geological Survey's multi-dated (1986, 1999, and 2018) Landsat imagery was used for LULCC analysis. A supervised classification technique was employed for image classification using a maximum likelihood algorithm. Systematic sampling techniques were used to collect vegetation data (tree species, height, and DBH), using 20 sample plots (20 × 20 m) distanced 100 m apart. The results highlighted that among the five identified LULCC types in the buffer zone of the monastery, the farmland area has expanded from 56 to 78 % at the expense of shrublands between 1986 and 2018. At the monastery, 19 woody tree species from 13 families were identified, with an evenness of 0.5 and a Shannon diversity index of 2.4. The stem density was 336 stems per hectare, and the forest cover was approximately 65 %. Olea europaea was the dominant tree species, while Juniperus procera showed a lack of regeneration at the monastery. Despite the fair natural regeneration, the monastery exhibited lower species diversity, richness, and evenness. However, the monastery remains an important habitat for rare and threatened tree species and may supply seeds for the restoration of degraded lands. Therefore, establishing exclosures in the buffer zone, strengthening stone walls and enrichment planting of degraded tree species should be implemented to ensure the sustainable conservation of valuable tree species.

GIS Tips & Tricks ‐ USGS Adds 100K Topo Scale to OnDemand Map Products

For this months GIS Tips & Tricks, we are revisiting the U.S. Geological Survey (USGS) National Map topoBuilder tool. topoBuilder was featured in this column shortly after its initial release in October 2022. Since then, topoBuilder has become the “goto” application for USGS topographic maps and recently updated to accommodate even more features. This month, our guest columnist, Ariel Doumbouya, USGS, provides an update and tutorial to some new features in topoBuilder.

Where Is That Crater? Best Practices for Obtaining Accurate Coordinates from LROC NAC Data

The Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images and their derived products make up one of the highest-resolution and most spatially accurate data sets available for the Moon, making them a crucial resource for planners of landed lunar missions. However, it is essential to understand the best uses for each data type and the limits on the accuracy and resolution of the data sets available. With this understanding, users can better interpret and communicate their results. In this paper, we describe what assumptions may be made about the accuracy of LROC NAC images and the various derived products created by the LROC team. NAC digital terrain models and their corresponding orthophotos have the best accuracy of all NAC products, usually better than 10 m horizontally, and should be used where available. Other controlled NAC products usually have accuracies better than 30 m. For areas without controlled products, we describe how to process NAC images to obtain coordinates with the highest possible accuracy. We also recommend best practices for data users interacting with LROC data through online map servers, such as QuickMap or Lunaserv, and for processing LROC data locally using the US Geological Survey Integrated Software for Imagers and Spectrometers.

A machine learning model for estimating the temperature of small rivers using satellite-based spatial data

The influence of anthropogenic activity and land cover alteration on stream temperatures has major ecological implications, such as limiting fish survival. While these ecological impacts have been extensively studied at varying spatial scales for major rivers, our understanding of the range and complexity of this relationship across climates and geographies for smaller rivers (less than ∼60 m wide) remains limited. This is in part because although such rivers comprise a vast majority (∼97%) of total stream length, most smaller rivers lack routine temperature observations despite extensive gage networks. While existing gage networks in many regions are sufficient to support satellite remote sensing-based modeling of small stream temperatures, most large regions including the contiguous United States do not have modeled data products at high spatial and temporal resolution for small rivers.In this study, we developed a statistical model, TempEst (“temperature estimation”), to estimate monthly average water temperatures for smaller rivers and evaluated the model with rivers ranging from 3 m to 1000 m (1 km) in width, using land cover, topography, and satellite-based land surface temperature. The rivers chosen for development of the model are representative of a range of urban and rural land uses and cover eight US Environmental Protection Agency Level I ecoregions. The model was calibrated and validated using observed stream temperatures from the United States Geological Survey. TempEst is able to predict monthly average temperature for streams of any size with an overall median RMSE of about 1.5 °C and bias of 0%. Model accuracy has a consistent trend with training gauge network density, with median RMSE increasing to about 2.0 °C in more sparsely-gauged regions. As a simple demonstration, we used TempEst to estimate thermal suitability conditions for cutthroat trout in Rocky Mountain streams in the United States. The developed model, available as open-source R (model) and Google Earth Engine (data retrieval) scripts, will facilitate the study of stream temperature behaviors at higher resolutions than previously available across the contiguous United States and can be easily adapted to support global river systems.

Southern breeding populations drive declining migration distances in Arctic and subarctic geese

Migration is a prevalent strategy among birds used to track seasonal resources throughout the year. Individual and population‐level migratory movements provide insight to life‐history variation, carry‐over effects, and impacts of climate change. Our understanding of how geographic variation in a species' breeding or wintering grounds can impact migration distances is limited. However, changes in migration distances can have important fitness consequences for individuals and conservation implications for populations, particularly if migratory connectivity is altered during the annual cycle. In this study, we use three decades of data from the United States Geological Survey Bird Banding Laboratory for six migratory species of Arctic and subarctic breeding geese. We employ a Bayesian hierarchical framework to test if the distance between breeding and wintering locations has changed over time, while accounting for the latitude of the breeding grounds. A model that included only a temporal trend estimated the average rate of change in migration distance, across all six species, at −3.0 km/year over the period 1990–2019. Five of the six species showed a significant decrease in migration distances. Including an interaction effect with breeding latitude revealed that the reduction in migration distance was strongest in the southernmost populations for four of the six species. For those species, migration distance in northern populations were all either relatively unchanged or increasing. This indicates that southern breeding populations of geese had a stronger association with the observed spatiotemporal changes in wintering ranges, potentially influenced by a combination of climatic and biotic factors (e.g. resource availability or competitive interactions) that uniquely impact these populations. Abundant, long‐term banding data shows promise for use in illuminating changes in migratory patterns under climate change, leading to improved management and conservation outcomes, from regional to continental scales.

The Ojai California Earthquake of 20 August 2023: Earthquake Early Warning Performance and Alert Recipient Response in the Mw 5.1 Event

Abstract A magnitude 5.1 earthquake in California rarely generates more than momentary notice—a headline in local newspapers and a mention with footage on the evening news—then fades into obscurity for most people. But this earthquake, which occurred near the city of Ojai, is important for seismologists, social scientists, emergency managers, policymakers, and others who are engaged in implementing and improving earthquake early warning (EEW) technology and in assessing its value in public warnings. In this earthquake, ShakeAlert, the EEW system for the West Coast of the United States operated by the U.S. Geological Survey (USGS), was publicly activated and, for the first time, a substantial number of those who received alerts provided feedback on various aspects of the alerts they received. To capture data related to public attitudes and assessments regarding this and future alerts, a supplemental questionnaire was developed and associated with the “Did You Feel It?” (DYFI) earthquake reporting system, also operated by the USGS. The DYFI system received over 14,000 felt reports; 2490 of these were by people who received or expected to receive an alert before the onset of earthquake motion at their locations. This article analyzes the aggregate results of these EEW user reports, touching on the respondent’s situation upon receiving the alert, characteristics of the alert received, and, perhaps, most importantly, how the alert recipient responded if received before feeling earthquake motion. The new DYFI EEW supplemental questionnaire also inquired about respondent views of alert usefulness and preferences in future alerts. Our report provides a first glimpse of a range of behaviors, attitudes, and assessments by users of the recently implemented EEW system for the U.S. West Coast.

A note on the seismicity of Sumatra, western Sunda Arc, Indonesia, in relation to the potential for back-arc thrusting

The existence of back-arc thrust faults along the eastern part of the Sunda Arc, ranging westwards from Flores to the western tip of Java, has been recognised for decades. In contrast, it is still unknown whether such back-arc thrust faults exist in Sumatra, which is located in the western part of the Sunda Arc. To investigate the possible existence of back-arc thrusts in Sumatra, we examine regional earthquake data reported by the Agency for Meteorology, Climatology and Geophysics of Indonesia, as well as global earthquake data reported by the International Seismological Centre and the United States Geological Survey. It appears that back-arc thrusts in the study area are not extensively developed, unlike in the eastern Sunda Arc, which may be caused by oblique subduction beneath the Sumatran forearc. The stress associated with the trench-parallel component of subduction is largely accommodated by the ~ 1650-km-long dextral strike-slip fault zone of the Great Sumatran Fault. The seismicity data from various sources do, however, show that there is a dipping seismogenic zone in several parts of the back-arc region of Sumatra, in the opposite direction to the NNE subduction of the Indo-Australian plate. This new observation may be related to the presence of spatially intermittent back-arc thrust faults in the study area, which may need to be taken into account when improving Indonesia's national earthquake hazard maps.

Water quality fluctuations prediction and Debi estimation based on stochastic optimized weighted ensemble learning machine

Today, humanity faces the complex phenomenon of global development. Problems such as limited resources, financial constraints, time constraints, and the involved nature of issues surrounding water resources management pose challenges to effectively monitor water resources issue parameter management (WRIPM). Nevertheless, the importance of this issue cannot be ignored. The focus of this study is on WRIPM issues, and new machine learning models are utilized to investigate and estimate fluctuations in WRIPM parameters. The initial dataset utilized in this study was sourced from the US Geological Survey, specifically sampling stations information from the South Platte River in the United States. Two new models were developed: the Ensemble Bagged Machine (EBM) and the Stochastic Weighted Ensemble Bagged Machine (SWEBM), which further optimized the characteristics of the EBM model using Bayesian Optimization (BN). These models were employed to simulate Dissolved Oxygen (DO), Electrical Conductivity (EC), Power of hydrogen (pH), and river flow rate (Debi) parameters. Additionally, the research employed various scenarios for evaluation and validation. Uncertainties were calculated using the Wilson analysis. The results demonstrated the superiority of the SWEBM in all modeling aspects, yielding the best indices, namely, R2, MAE, and RMSE, at 0.984, 0.0259, and 0.0394, respectively. Performing an receiver operating characteristic analysis revealed that the area over the RROC curve value for the superior model was 36.16, demonstrating excellent performance in modeling the pH parameter. The WM analysis method confirmed that the SWEBM model was the most trustworthy, delivering the greatest precision and best modeling for the pH parameter (WOUB=0.00683, MOPE=0.00138, and STD=0.1665) even though it was slightly overestimated. The probability density function analysis revealed that the SWEBM was the best model. While other models had results that were relatively close. This research represents valuable findings that can be shared with stakeholders, including experts, scholars, and organizations responsible for overseeing water quality administration.

Mapping Lava Flows on Venus Using SAR and InSAR: Hawaiʻi Case Study

AbstractWe explore the potential for repeat‐pass SAR Interferometry (InSAR) correlation to track volcanic activity on Venus' surface motivated by future SAR missions to Earth's sister planet. We use Hawai'i as a natural laboratory to test whether InSAR can detect lava flows assuming orbital and instrument parameters similar to that of a Venus mission. Hawai'i was chosen because lava flows are frequent, and well documented by the United States Geological Survey, and because Hawai'i is a SAR supersite, where space agencies have offered open radar data sets for analysis. These data sets have different wavelengths (L, C, and X bands), bandwidths, polarizations, look angles, and a variety of orbital baselines, giving opportunity to assess the suitability of parameters for detecting lava flows. We analyze data from ALOS‐2 (L‐band), Sentinel‐1 (C‐band), and COSMO‐SkyMed (X‐band) spanning 2018 and 2022. We perform SAR amplitude and InSAR correlation analysis over temporal baselines and perpendicular baselines similar to those of a Venus mission. Fresh lava flows create a sharp, noticeable decrease in InSAR correlation that persists indefinitely for images spanning the event. The same lava flows are not always visible in the corresponding amplitude images. Moreover, noticeable decorrelation persists in image pairs acquired months after the events due to post‐emplacement contraction of flows. Post‐emplacement effects are hypothesized to last longer on the Venusian surface, increasing the likelihood of detecting Venus lava flows using InSAR. We argue for further focus on repeat‐pass InSAR capabilities in upcoming Venus missions, to detect and quantify volcanic activity on Earth's hotter twin.

A Comparative Study of Land Use Land Cover (LULC) Change Detection: A Case Study of Tehsil Babuzai, Swat (Pakistan)

Tehsil Babuzai, District Swat, Khyber Pakhtunkhwa, Pakistan, has experienced a fast population increase, substantially influencing land use and land cover (LULC), raising ecological and environmental issues. This study examines changes in LULC between 2001 and 2020 using US Geological Survey (USGS) satellite images. The paper analyses six primary land cover classes: farmland, built-up area, woodland, water bodies, rangeland, and barren Land using maximum likelihood supervised classification. The findings show a significant rise in rangeland (from 49.1% to 53.7%), built-up areas (from 8.5% to 13.7%), and barren Land (from 5.1% to 11.1%). On the other hand, there is a noticeable decline in the number of water bodies (from 2.1% to 1.6%), agriculture (from 21.2% to 12.2%), and forest cover (from 13.7% to 7.5%). These alterations underscore the detrimental consequences of urban growth on the area's natural resources, demanding prompt and deliberate planning to protect the ecosystem and guarantee sustainable development. This research is crucial for understanding land use changes' dynamics and impacts, providing valuable insights for policymakers and urban planners. It highlights the need for sustainable land management practices to mitigate adverse environmental effects and promote balanced regional development.

Implications of Gold Trading for the Global Economy

One of the precious metals found in the earth’s crust, whose economic value dates back several centuries and remains relevant in our contemporary socio-economic settings, is gold. Society’s veneration for the precious metal surges from one generation to the other. However, most developing economies endowed with large quantities of gold are plagued with unbridled activities of warlords, foreign-backed militias, rebels, and illegal artisanal miners, some of whom operate on behalf of political stalwarts who mask their true identity. The purpose of this research was to examine how trading activities related to gold tend to affect gross domestic product (GDP) values at the national and global levels. The quantitative approach to scientific inquiry was adapted and used in the study. Specifically, the cross-sectional design formed the basis of the research. Data required for the conduct of the research were obtained mainly from secondary sources. These included textbooks, peer-reviewed articles published in journals and grey literature. Other sources were the Google Search Engine, including databases of Macrotrends and World Bank, databases and archives of the United States Geological Survey, among other significant sources. Respective data on Ghana’s annual gold production volumes, gold revenues and GDP values from 1980 through 2020 and respective data on annual average closing prices per ounce of gold, global gold production volumes, global gold revenues and global GDP values from 1969 through 2020 were used in the research. Descriptive statistics and regression models were used to describe the research variables and to evaluate their behaviour over the stated time frame on national and global GDP values. The study revealed gold as a viable source of alternative investment and an effective portfolio diversifier in periods of volatility in the global equity markets. The average closing price per ounce of gold in 2020 was the highest since 1969. More than a third of the world’s total gold reserves (34.26%) were held by Australia, Russia and South Africa, and China remained the world’s largest producer of gold in 2019. For most developing economies with large production volumes, the percentage and monetary contribution of gold revenue to GDP was determined to be significant. However, for most advanced economies, percentage contribution was insignificant, albeit monetary contribution was found to be quite colossal and capable of impacting positively on national expenditures. Findings from the research revealed a positive and significant relationship between Ghana’s gold revenue and GDP (coefficient value = 9.574151593; p = 0.000, p < 0.05); and a positive but non-significant relationship between global gold revenue and global GDP (coefficient value = 527.3364872; p = 8.111, p > 0.05). Gold revenue accounted for nearly 97.64% of the variation in Ghana’s GDP from 1980 through 2020, while global gold revenue accounted for about 88.16% of the variation in global GDP from 1969 through 2020. During 2019, Ghana was the leading producer and highest-earner from gold sales in Africa; and the economy with the fifth-highest percentage contribution of gold revenue to GDP (9.52%); after Burkina Faso (17.37%), Mali (15.87%), Sudan (11.25%) and Guinea (10.02%) respectively. The statistical analysis confirmed and validated, to a large extent, the valuable contribution of gold revenue to national GDP, a clarion call for regulation and control of the activities of illegal artisanal miners to improve and actualise annual national estimates for total gold production volumes and revenues; the need for minerals and mining policy reforms and expeditious implementation of same to control environmental pollution and hazards. Dramatic transformation and an increase in recycling technologies are required to curb the accelerated depletion rate of gold reserves. It is imperative for the major gold-producing economies to take proactive steps towards the creation of economic parsimony in supply to positively affect the price per ounce of gold in the global market.

Advancing freshwater science with sensor data collected by community scientists

Autonomous sensor networks providing real‐time data are growing in popularity with community scientists due to instant availability of high‐frequency data. What role does this monitoring play in watershed assessment alongside agency‐run monitoring programs? How accessible, interoperable, and reusable are the data for other researchers? We compared a community science‐led stream monitoring network—EnviroDIY—in the Delaware River Basin, in which more than 50 watershed organizations have deployed more than 100 stations monitoring temperature, electric conductivity, depth, and sometimes turbidity, with the Basin's US Geological Survey (USGS) stream gauge network. The EnviroDIY network (n = 124) complemented the USGS network (n = 102) by monitoring sites with different watershed sizes and land‐use distributions. Although data were accessible and interoperable using a web data portal, community scientists had difficulty sharing metadata that would enable data reuse outside this project and they required support analyzing these large datasets to understand threats to watershed conditions. We address those needs here with a conceptual framework for interpreting data and communicating results.

Printing Ochre

The discipline of geology was relatively new at the start of the United States Geological Survey (USGS) in the late 1800s, but through the organisation of an efficient government bureau and the voice of its printed page, it became a dominant perspective in the way generations of citizens relate to and view the natural world, human and nonhuman, life and nonlife. I choose to position my press in direct opposition to the maintenance of these dualisms. I will argue the critical role of print in facilitating USGS domination of terrestrial beings in a practical sense, yet beyond the methods and reach of publication, I believe the products of the USGS mediate our understanding of and relation to these beings. Instead, design practices can be directed towards changing attitudes and understanding between humans and nonhumans. Monika Bakke argues for the importance of artistic endeavours in addressing our collective futurity: Drawing on both life’s mineral origins and its key role in shaping mineral species, artists are turning to technoscience in order to develop, outside expert circles, better understanding of physical, chemical, and biological environments, not just of the geological past but also those to come in the future. ... Yet, their methodologies are specific to art which offers creative ontological and ethical contributions to public debate. (2017, p. 43) I am developing a design research paradigm relating to ochre as an epistemic tool for human and nonhuman intersubjectivity and ontological reconciliation or reunification between life and nonlife. I model my practice after a simplification of the USGS: to survey (observe, describe, collect) and to report (archive, document, record), with some critical variance in method and outcomes.

Particulate matter (pm10) monitoring in the United Arab Emirates using a satellite remote sensing based model

Particulate matter (PM) is one of the major factors causing air pollution, which is considered a concern for human health. Hence, measuring and monitoring the concentrations of these particles is essential. In this study, the main objective is to develop a remote sensing based PM10 monitoring model for the United Arab Emirates (UAE) using Landsat 8 imagery. Landsat 8 images acquired during the four-year period from 2016 to 2022 were obtained and used along with PM10 data collected at 41 ground monitoring stations corresponding to the acquisition of the satellite data (data from 30 stations used for model development 11 stations were used for model testing). The Landsat 8 data was obtained from the United States Geological Survey (USGS) Core Science Systems in the form of Digital Numbers (DNs). The DNs of the four optical bands of Landsat 8 were then converted to top of the atmosphere reflectance (TOA) through radiometric processing, and then used to estimate the Aerosol Optical Thickness. A spectral PM10 model was then developed through regression analysis, correlating AOT to PM10 values obtained at the ground stations. The model provided an R-squared value of 65% and a Root Mean Square Error (RMSE) of 12.55 µg/m3. The results suggest that the developed model is robust in estimating PM10 values and can therefore be used for satellite-based monitoring at any location in the UAE.

Earthquake-triggered ground-failure inventory associated with the M7.1 2018 Southcentral Alaska earthquake

The 30 November 2018, magnitude (Mw) 7.1 earthquake in Southcentral Alaska triggered substantial landslides, liquefaction, and ground cracking throughout the region, resulting in widespread geotechnical damage to buildings and infrastructure. Despite a challenging reconnaissance and remote-sensing environment, we constructed a detailed digital inventory of ground failure associated with the event from several sources. Sources included information derived from remotely sensed data, and data compiled from literature, social media postings, and earthquake damage information compiled by local, state, and federal agencies. Each instance of ground failure within the inventory contains information on the location and type of observed ground failure, and the methods and data used to document the occurrence. Where high-quality data, such as LIDAR or satellite imagery, were available and showed the ground-failure instance clearly, the extent is mapped as a polygon or polyline. All other locations are mapped as points. There are a total of 886 ground-failure instances documented within the inventory (400 landslides, 286 liquefaction features, and 200 features unattributed to specific processes). A semi-quantitative confidence scheme is used to describe mapping certainty associated with each ground-failure feature. This inventory represents a relatively moderate ground-failure-triggering event that occurred in a subarctic environment. This data paper describes the content within the inventory, the inventory data collection procedures, and limitations of the data. Events of this type are not often documented in detail; thus, adding the inventory data to the US Geological Survey Open Repository of Earthquake-Triggered Ground-Failure Inventories further diversifies the datasets available to the scientific community to be used to better understand and model earthquake-triggered ground failure.

Wetland Changes and Ecosystem Services Valuation of Kapla Beel in Assam

Background: Wetlands are productive ecosystem with immense potentiality of providing various goods and services. But they are fast depleting due to various natural and anthropogenic causes. Kapla beel is a freshwater wetland located in Barpeta district in the lower Brahmaputra valley of Assam. Area of the Kapla beel has been decreasing over the years. This study shall make an attempt to analyse the causes of land use and land cover changes of Kapla beel of Assam and assess its ecosystem services value changes during the period 1990 to 2022. Methods: To assess the changes, two Landsat images for the year 1990 and 2022 of Kapla beel were taken from the United State Geological Survey (USGS) Earth Explorer. Land-use change analysis was assessed by comparing the areas occupied by each land use in each period using the ArcGIS software. The dynamicity index is used to calculate the rate of change in land cover. In order to assess the changes in the wetland ecosystem service value due to wetland changes, Benefit Transfer Method is used which involves transferring monetary values of ecosystem services from previous studies. Result: The valuation of ecosystem services underlines the fact that wetlands are highly productive and economically viable ecosystems. Accounting of ecosystem services provides information about the need for optimal decision -making towards judicious use of wetland resources for their sustenance.