Digital Elevation Model Research Articles

A geospatial approach to delineate lineaments on the granite gneissic terrain of Mpwapwa district, Dodoma, central Tanzania

The study sought to utilise Geographic Information Systems (GIS), Remote Sensing, and a blend of manual and automatic techniques to delineate and analyse the spatial distribution and orientation of lineaments within the terrain of Mpwapwa District. The study employed various techniques, including the Optimum Index Factor (OIF), Band Combination, Principal Component Analysis (PCA), and Directional Filtering on the Landsat 8 imagery, and Hill Shading technique was used on Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). These techniques were employed in the processing steps for both manual and automatic lineament extraction, utilizing various software tools. Lineament control and validation were executed using digitized geological maps, topographical maps, and Google Earth images.The results revealed lineaments with a total length of 2060.6 km, non-uniformly distributed and predominantly present in hard rock lithologies, such as migmatitic granite, migmatitic biotite gneiss, biotite gneiss, granitoid gneiss, and porphyroblastic gneiss. The findings further indicate that NE-SW and NW-SE are the dominant lineament trends. Additionally, the study revealed that areas with medium, high, and very high lineament density and intersection are primarily located in the southwest and northeast parts of the Mpwapwa District. Meanwhile, regions with low lineament density are spread through the entire district.In this study, GIS and remote sensing emerged as valuable tools for identifying and mapping lineaments within a challenging hydrogeological setting. Findings from this study will serve as a foundational framework for subsequent geological and hydrogeological investigations in the area of study.

Rapidly Evaluating Species at Risk Using Endemic Plants of Kosrae, Federated States of Micronesia

The International Union for Conservation of Nature (IUCN)'s Red List of Threatened Species is regarded as the primary source of global extinction risk for species. Despite this importance, the IUCN has assessed less than 20% of the world′s estimated 400,000 flowering plants due to issues like insufficient data or a lack of experts. Thousands of conservation status assessments were generated recently in response to the call to action by the revised Target 2 of the Global Strategy for Plant Conservation (GSPC), “An assessment of the conservation status of all known plant species, as far as possible, to guide conservation action.” Accelerated extinction risk assessments are needed to identify species that are most at risk of extinction, which can then be thoroughly evaluated by the Red List assessment procedure. The land area in the Pacific Islands is significantly smaller than in other parts of the world. Plant species restricted to these islands may be identified as “At Risk” due to the limited land area of these islands, despite being widespread locally. Distribution and abundance data of single-island endemic plant taxa of Kosrae, Federated States of Micronesia, are analyzed and combined with digital elevation model files from this island, ArcGIS, and herbarium data, to calculate the percentage of land area that is available within a species′ elevational occurrence, a metric we have coined the Extent of Inhabitable Elevation of Island Occurrence (EIEIO). This metric can be used as a streamlined system to rapidly identify species that need plant conservation attention and allows for a swifter response to Target 2 of the GSPC.

Spatio-Temporal Monitoring and Risk Mapping of Glacial Lake Outburst Flood in Hunza Valley, Pakistan

Glacial lake outburst flood (GLOF) disasters are serious and potentially increase huge risks to livelihoods and infrastructure in the mountain regions of the world. The northern highland regions of Pakistan are home to some of the biggest alpine glaciers. In this investigation, the Hunza Valley of Pakistan has undergone remote sensing-based risk assessment for Glacial Lake Outburst Floods. Borith and Passu Lakes were chosen to identify flood risk in the downstream areas. For such, Landsat images were used from 1990-2020. Different spectral indices such as the Normalized Difference Snow Index (NDSI), Normalized Difference Glacier Index (NDGI) and Normalized Difference Water Index (NDWI) were applied to evaluate snow cover changes. Furthermore, these Lakes were digitized to evaluate and check the total increase in Lake Areas. Also, built-up areas close to lakes were digitized to identify total risk. The Land Surface Temperature (LST), NDSI, NDGI, NDWI, Digital Elevation Model (DEM) and Slope variables were given weights using the Analytic Hierarchy Process (AHP) method. In the analysis of flood risk mapping, maximum weight was assigned to Land Surface Temperature, and minimum weight was assigned to the slope. The result revealed that the settlements located in the Ghulkin, Gulmit, Husseini, Passu, Zarabad, and Khorramabad are at moderate risk while settlements located near Hunza River such as Karimabad, Khanna Abad, and Aliabad are at high risk. The outcome also showed that Borith Lake's area expanded, going from 0.059 km2 in 1985 to 0.074 km2 in 2020 and Passu Lake's area also grew, going from 0.074 km2 in 2005 to 0.077 km2 in 2020. In last, Buffer analysis was performed to identify areas that are likely to be affected by the flood. The result of the study can help carry out a downstream risk assessment and better preparedness for future flood hazards.

Using Neural Networks for Sustainable Land Use Prediction in Sumbawa Regency, Indonesia

Agriculture is vital to Sumbawa Regency's economy, with key activities such as rice cultivation, corn production, onion farming, and cattle rearing. This study applies artificial neural networks (ANN) to predict land cover changes, focusing on agricultural land expansion. Using land cover datasets from ESRI, digital elevation model, and topographical maps, we analyzed land cover changes from 2017 to 2023 and generated future projections for 2050 with the MOLUSCE plugin in qGIS. The predictive model achieved an 85% accuracy rate when comparing 2023 actual data with predictions. Results indicate a significant increase in agricultural land cover by 2050. The key finding is that over a long-term period, the simulation of land use and land cover (LULC) change in Sumbawa reveals an increase of crop areas in the Lunyuk and Labangka Districts. This study highlights the effectiveness of ANN in land cover prediction and emphasizes the need for sustainable practices to balance agricultural expansion. AI-driven insights can aid policymakers in opti-mizing resource allocation and ensuring long-term environmental and economic stability in Sumbawa Regency. Future research should refine models and incorporate additional factors for improved accuracy.

Herbage biomass predictions from UAV data using a derived digital terrain model and machine learning

AbstractMore than 70% of Switzerland's agricultural area is covered by grasslands that often exhibit highly diverse species compositions and heterogeneous growth patterns. An essential requirement for efficient and effective pasture management is the regular estimation of herbage biomass. While various methods exist for estimating herbage biomass, they are often time‐consuming and may not accurately capture the variability within pastures. This highlights the need for more efficient, accurate estimation techniques. To help improve herbage biomass estimation, we present estiGrass3D+, a Random Forest model. This model predicts pasture biomass using a digital terrain model (DTM) derived from a digital surface model (DSM) for sward height modelling, along with vegetation indices and agronomic variables from UAV images only. The model was successfully evaluated with independent test data from different sites on the Swiss central plateau, including both grazed and mown areas. Model performance on an independent validation dataset achieved a NRMSE of 20.3%, while the training dataset had an NRMSE of 21.5%. These consistent results confirm that estiGrass3D+ is both transferable and applicable to unseen data while maintaining accuracy and reliability across different datasets. The wide applicability of our method demonstrates its practicality for predicting herbage biomass under different pasture management scenarios. Additionally, our method of deriving a DTM directly from a DSM simplifies the measurement of grass sward height by UAVs, eliminating the need for prior ground control point (GCP) marking and subsequent aligning, enhancing the efficiency of herbage biomass estimation.

Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method

Abstract This study aims to develop a comprehensive method for automated rockfall mapping and susceptibility assessment using unmanned aerial vehicle (UAV) tilt photography and the support vector machine (SVM) algorithm. By employing Jinzhong Town in the mountainous forest landscapes of Southwest China as a case study, we leverage photogrammetry principles and computer vision algorithms to generate high-precision, high-resolution digital surface models (DSMs), and digital orthophoto maps through a UAV remote sensing system. The rockfall inventory is accurately and automatically mapped using the object-based classification method and SVM algorithm. The automated rockfall identification method achieves a 93% accuracy with a Kappa coefficient of 0.7967. Statistical analyses of spatial distribution characteristics reveal a significant correlation between rockfall volume and area with a correlation coefficient (R²) of 0.92316 under logarithmic coordinates. In addition, a power function relationship describes the link between rockfall volume and slope height (R² = 0.87), while the relationship with sliding distance is characterized by a weaker linear correlation (R² = 0.65). Rockfall runout distance also shows a significant linear correlation with slope height (R² = 0.79) but exhibits a less-pronounced association with rockfall volume (R² = 0.58). The SVM model employed to assess rockfall susceptibility indicates high accuracy (area under the curve = 0.896), affirming its efficacy in rockfall susceptibility assessment. Our findings underscore the utility of UAV remote sensing for rockfall information extraction and susceptibility evaluation, particularly in challenging mountainous forest environments characterized by intricate topography and geological complexities.

Картографирование почв на основе аэрофотосъемки с БПЛА

The article presents the results of research on the creation of a detailed soil map (scale 1:2500) using the example of a land plot of the Federal State Budgetary Educational Institution of Higher Education Perm State Agrarian University. The purpose of the research is to improve the methodology of detailed soil mapping using an unmanned aerial vehicle (UAV) and methods of geoinformation analysis of aerial photographs. The total area of the plot is 13.4 hectares, on which a UAV survey was carried out on September 12, 2023, and a soil survey was carried out on a detailed scale from June 1 to 24, 2024. Soil mapping was carried out on the basis of field data and a digital elevation model created by photogrammetry methods based on UAV survey materials. The map creation process is automated by creating geoprocessing models in QGIS model builder. Based on the results of creating a detailed soil map, a conclusion was made about the close relationship of soils with characteristic relief elements, which are reflected in classes according to the topographic position index TPI. Keywords: UAV, GEOINFORMATIONAL MAPPING, SOIL MAP

Characteristics of air temperature on the southern and southeastern slopes of the Greater Caucasus in the modern period

In the paper, the characteristics of air temperature in the south part of the Greater Caucasus region and the effects of regional climate changes on this area in the modern period were studied. In the research, the air temperature observation data of 8 hydrometeorological stations operating in the region for the years 1961-2022 were used, and the modern classification of the average monthly, seasonal, annual and altitude time-space distributions of temperature was given.Based on the data of hydrometeorological stations in the south and southeast piedmonts of the Greater Caucasus Mountains, an electronic map was prepared for the distribution of average annual air temperature indicators on the territory, and for determining the indicators of areas without data. DEM (Digital Elevation Model) files obtained fromthe decoding of satellite images (satellite data) were used in the prepared map. The research shows that July and August are the hottest months when the temperature is 22.80C, while January is the coldest by temperature about 0.50C in this region. In this part of the Greater Caucasus province, in the cold months of the year, the average monthly temperature is indicating below 00C in the area located above the moderately mountainous part of the region. The dynamics of the average indicators of temperature of the multi-year period (1961-2022) are expressed by a positive trend. The average annual temperature distribution map of the territory shows that the air temperature is 15.00C in the low mountainous part of the region, while it decreases upwards and, according to the adiabatic law, equals 00C at altitudes above 3500 m. According to the climate studies conducted in the region, compared to the years 1981-2010, it was found that there was a temperature increase of 1.20C in the mountainous region in 2011-2022. Also, a comparative analysis of 10-year averages of air temperature trends, using the Holt- Winters model, shows that temperatures in 2100 are expected to increase by 3.00C compared to 2030. In the region, climate changes that worsen every year lead to water scarcity, desertification, drought, etc. and can expand the area of recurrence of environmental crises and destructive natural events. This research can be used in future climate change mitigation , climate atlases, infrastructure, agricultural and industrial projects in the region.

A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data.

Building height is important information in disaster management and damage assessment. It is also a key parameter in studies such as population modeling and urbanization. Relatively few studies have been conducted on extracting building height in rural areas using imagery from China's Gaofen-7 satellite (GF-7). In this study, we developed a method combining photogrammetry and deep learning to extract building height using GF-7 data in the rural area of Pingquan in northern China. The deep learning model DELaMa was proposed for digital surface model (DSM) editing based on the Large Mask Inpainting (LaMa) architecture. It not only preserves topographic details but also reasonably predicts the topography inside the building mask. The percentile value of the normalized digital surface model (nDSM) in the building footprint was taken as the building height. The extracted building heights in the study area are highly consistent with the reference building heights measured from the ICESat-2 LiDAR point cloud, with an R2 of 0.83, an MAE of 1.81 m and an RMSE of 2.13 m for all validation buildings. Overall, the proposed method in this paper helps to promote the use of satellite data in large-scale building height surveys, especially in rural areas.

Strandline records of high frequency, low magnitude drops in water level, glacial Lake Agassiz basin, Central Polk County, MN, USA

Glacial Lake Agassiz strandlines expressed in LiDAR-based digital elevation models are detailed records of past water level change that reflect outlet incision and switching. Detailed 1:24,000 scale surficial geology mapping of a small section of coastline east of Crookston, MN, reveals strandlines of the Lockhart, Emerson, and Nipigon phases. Other than the Campbell and youngest Tintah Beach, the strandlines are not identified using the naming convention established near the southern outlet (Herman, Norcross, Upham, Tintah) because of the increasing number of strandlines northwards. The strandlines, for the most part, naturally exist in elevation clusters, and are assigned to 11 strandline groups (SG). SGs 1–9 are of Lockhart age at elevations higher than the Tintah Campbell Gap (TCGap). Elevation gaps between SGs are ~1–3 m, greater than the 0.5–2 m gap between successive strandlines within a SG. The many (n = 70) small, low-relief strandlines are interpreted to record nearly continuous incision of the southern outlet sill during Lockhart Phase. Beach ridges are estimated to have formed every ~20 years. The TCGap is ~4 km wide in the study area. The Campbell Beach of Emerson Phase age consists of several embayments, beach ridges and the large Melvin Spit. Based on morphological comparison with the modern Gull Point Spit in the Lake Erie basin, the Melvin Spit took ~500 years to form, during which water level dropped 8 m. The Nipigon Phase strandlines are small discontinuous beach ridges. North-south elevation plots of the most continuous strandline in each SG records southward slopes of 1.1 to 0.19 m/km explained by differential glacioisostatic adjustment active during strandline development that decreases through time at lower elevations. The three highest elevation OSL dates agree with published ages for the older strandlines (~14.4 ± 1.5 ka), but younger dated Tintah, Campbell, and sub-Campbell beaches are 2–5 ka older than published ages. Ground penetrating radar (GPR) datasets acquired at 11 sites at frequencies between 100 and 500 MHz, representing a cumulative distance of 11 km, form the basis for a simple beach ridge formation model augmented with hand auger holes and shallow hand-dug pits. Six radar facies (RF) were identified, dominated by RF2 which consists of lakeward-dipping reflections with a downlapping lower boundary recording a depositional regressive system, and RF4 which consists of landward-dipping reflections with a downlapping lower boundary recording overwash deposits from proposed storm events. Spit orientations and GPR data indicate littoral drift was southwards with offshore bars possibly nucleating successive beach ridges upon ~1-m drops in water level. Detailed analysis of strandlines from paleo lakes offer promise for high resolution reconstructions of past water level history.

Variation of Soil Erosion Estimates when Using Different Maps of Arable Land of the Belgorod Region

Current medium- and small-scale estimates of soil erosion in Russia are very few. At the same time, a favorable situation has now developed for assessing the rates and volumes of soil erosion losses. Erosion models have been developed that are adapted to available digital elevation models, various farmland masks and climate databases have been created. The paper studies the accuracy of erosion estimates using various maps of arable land. Two maps are public (ESA WC, GLCLU), the third is the official Ministry of Agriculture (MA) of the Russian Federation, the fourth map is an author’s reference map of Alekseevsky district. It has been established that the map of the MA gives the most average arable land areas among the first three maps. Public access maps showed maximum and minimum estimates of arable land area. Comparison with the standard showed that the accuracy of the map of the MA does not exceed 90%, the remaining maps – 84 and 83%. The area of arable land in the Belgorod region varies slightly (from 1,445 to 1,586 thousand hectares); so the region is favorable for erosion modelling. Deviations from the average rates of soil erosion calculated using different maps of arable land in the region as a whole amounted to 7%, and in some areas reached 27%. Thus, today assessments of soil erosion at the regional level can be carried out with an error of at least 10–15% only as a result of the uncertainty in mapping the boundaries of arable land. In the Russia as a whole, data on the area of arable land varies significantly, from 80 to 132 million hectares. Consequently, the use of existing maps of arable land can lead to significant uncertainties in soil erosion estimates averaged at the level of districts and above.

Accuracy assessment of the geomorphon approach to detect ecological sites in the Dry Chaco region of Argentina

Ecological sites are a classification of local-land types based on differences in important environmental factors including soil properties, slope and landscape position within a geomorphologic and climatic zone. The concept is pivotal to conduct adaptative management in arid rangelands because it defines homogeneous areas over which specific soil-vegetation-disturbance dynamic occurs. Geomorphon is a computationally efficient method that uses Digital Elevation Models (DEMs) for the classification of landforms at multiple scales. The objective of this study was to evaluate the accuracy of the geomorphon approach to map ecological sites in a 11,259-ha study area within the Dry Chaco region of Argentina, utilizing local topographic positions along soil catena as a predictor of broader ecological sites classes. We used two DEM pixel resolutions (12.5 m and 30 m) and optimized the flatness threshold (t) and search radius (L) geomorphon parameters. As “ground truth” we used legacy data of 62 soil profiles descriptions. The geomorphon elements detected were reclassified into the three main ecological sites of the study area: highlands, midlands and lowlands. We used the overall disagreement (D) as the main metric for evaluating the accuracy of ecological site classifications. We found that: i) the lowest t value (0.05°) captured subtle topographical changes and thus more effectively reflected the soil-landscape relationship, and ii) larger L values paired with the lower pixel resolution (30 m) diminished the impact of minor landforms, improving the accuracy of ecological site detections. We determined that the geomorphon model with the pixel resolution of 30 m, t-value of 0.05°, and L-value of 12 produced the ecological site classification map with the highest accuracy, achieving a moderate-high accuracy (D of 33.9 %). Our study suggests that the geomorphon approach shows potential for consistent, reproducible and easily updatable ecological site mapping over larger areas.

Evaluation of urban drainage capacity using different satellite image sources and SWMM

ABSTRACT This research aims to cost-effectively identify inundation due to combined rainfall with tidal surges by evaluating the existing urban drainage system. The Storm Water Management Model (SWMM) applied two scenarios based on different Digital Elevation Model (DEM) sources. These are scenario A, i.e. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and scenario B, i.e. Shuttle Radar Topography Mission (SRTM). Then, catchment delineation and SWMM parameterization in the low-lying urban region of Chattogram City were executed. In contrast to scenario A, i.e. ASTER DEM, identified inundated nodes in the northwest part of the city, scenario B, i.e. SRTM DEM, showed more inundated nodes. In both scenarios, drainage capacity was observed in the field at two outlet points of Ispahani Khal (R2 = 0.91 and 0.85) and Chaktai Khal (R2 = 0.90 and 0.92), which reasonably mimics the model outcome. With an intensive field dataset, this model could provide relevant information for betterment.

Mapping of Soil Erosion Vulnerability in Wadi Bin Abdullah, Saudi Arabia through RUSLE and Remote Sensing

This study investigates soil loss in the Wadi Bin Abdullah watershed using the Revised Universal Soil Loss Equation (RUSLE) combined with advanced tools, such as remote sensing and the Geographic Information System (GIS). By leveraging the ALOS PALSAR Digital Elevation Model (DEM), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall data, and the Digital Soil Map of the World (DSMW), the research accurately evaluates soil loss loads. The methodology identifies significant variations in soil loss rates across the entire watershed, with values ranging from 1 to 1189 tons per hectare per year. The classification of soil loss into four stages—very low (0–15 t/ha/yr), low (15–45 t/ha/yr), moderate (45–75 t/ha/yr), and high (>75 t/ha/yr)—provides a nuanced perspective on soil loss dynamics. Notably, 20% of the basin exhibited a soil loss rate of 36 tons per hectare per year. These high rates of soil erosion are attributed to certain factors, such as steep slopes, sparse vegetation cover, and intense rainfall events. These results align with regional and global studies and highlight the impact of topography, land use, and soil properties on soil loss. Moreover, the research emphasizes the importance of integrating empirical soil loss models with modern technological approaches to identify soil loss-prone locations and precisely quantify soil loss rates. These findings provide valuable insights for developing environmental management strategies aimed at mitigating the impacts of soil loss, promoting sustainable land use practices, and supporting resource conservation efforts in arid and semi-arid regions.

Quantifying the Pabu Normal Fault Scarp, Southern Tibetan Plateau: Insights into Regional Earthquake Risk

The location of the main boundary fault of the Yadong-Gulu Rift (YGR) shifts from the east side in the southern segment to the west side in the northern segment. The Nyemo Graben Group (NGG) connects the southern and northern segments of the YGR and provides clues for understanding the migration of boundary fault locations along the YGR. However, the NGG has received very little attention. In this study, we map the geometry of the Pabu normal fault, which is the boundary fault of the westernmost graben in the NGG, using high-resolution remote sensing images. We then utilized a digital elevation model (DEM) with a spatial resolution of 1 m. Morphometric parameters such as scarp height, width, and slope were obtained from elevation profiles in three typical deformation regions. Our results reveal a fault segment approximately 3 km long that links the southern and northern segments of the Pabu Fault. Each fault segment could be a major segment. Furthermore, based on regional tectonic activity, the Pabu Fault has the potential to produce an earthquake with a magnitude of around M 6.7.

Westward Migration of the Chenghai–Jinsha Drainage Divide and Its Implication for the Initiation of the Chenghai Fault

The Chenghai Fault in the Chuan–Dian block terminates at the northwestern segment of the Red River Fault, and is a significant seismogenic structure. The kinematic evolution of this fault should be closely related to the regional tectonic deformation. However, it is difficult to obtain information on structural deformation of the Chenghai Fault due to the large amount of precipitation and well-developed vegetation. The Chenghai normal faulting may drive drainage reorganization in this region, which provides a new perspective for reconstructing and evaluating the tectonic history. High-resolution digital elevation models (DEM) obtained by remote sensing greatly facilitate the study of drainage evolution and active tectonics. We use two methods (χ-plot and Gilbert metrics) to measure the drainage divide stability based on the ALOS DEM (12.5 m resolution) and further reproduce the drainage evolution process in response to the asymmetric uplift by numerical modeling. The results show that the Chenghai–Jinsha drainage divide, hosted by the footwall block of the Chenghai Fault, is migrating westward (away from the Chenghai Fault) and will continue moving ~2.2–3.5 km to reach a steady state. Its migration is controlled by the Chenghai normal faulting. The Chenghai–Jinsha drainage divide formed close to the Chenghai Fault’s surface trace and continues to migrate westward in response to the asymmetric uplift. It only took a few million years for the Chenghai–Jinsha drainage divide to migrate to its current location based on the numerical modeling. The restoration of the drainage reorganization implies that the Chenghai Fault initiated in the Pliocene, which probably results from kinematic reversal along the Red River Fault.

A synergic approach using the model and remote sensing data for flood monitoring in under-observed transboundary rivers

ABSTRACT The southern plain of Nepal recognized as the ‘granary of Nepal’, confronts recurrent monsoon-induced flooding, posing a substantial threat to its pivotal role as a major agricultural contributor to the national economy. As an analysis, this study employs advanced satellite imagery to delineate historical floods in nine flood-prone transboundary basins and compares the rainfall-induced model-based inundation in the West Rapti Basin (WRB) to validate the result. The extent of flooding was mapped between 2015 and 2022 using Sentinel-1 Synthetic Aperture Radar data processed on Google Earth Engine. Hydrodynamic modelling centred on the WRB, incorporated daily measured precipitation data with varying return periods over a 10 m resolution digital elevation model generated through an in situ survey. The model was calibrated for the August 2017 flood event with Nash–Sutcliffe efficiency greater than 70% and validation reasonably with satellite-derived flood maps with Cohen's Kappa value of 0.58 and an overall accuracy metric of 0.84. This synergic approach integrates climatology, remote sensing data, and hydraulics to monitor transboundary river floods in Nepal where precise hydro-meteorological data are limited, thus, offering continuous all-weather monitoring.

A Raster-oriented Method for Creating Eclipse Maps

Traditional total solar eclipse maps based on 19th-century calculation methods do not take full advantage of important refinements recently made possible by fast computers and high-resolution digital elevation models of both the Earth and the Moon. Ignoring the terrain of both bodies introduces errors on the order of kilometers in the ground track of the umbra and seconds in the duration and contact times of totality. While this has been understood to some degree for decades, it has not been visualized in maps until quite recently, revealing only in the last decade the surprising polygonal shape of the umbra. Beginning in December of 2016, we published maps and animations of the 2017 August 21 total solar eclipse created with a raster-oriented approach that relies on remote-sensing data sets and computer graphics techniques to produce more accurate maps and professional-quality animations. These were an important component of NASA’s public outreach for the 2017 eclipse.

An Automated Machine Learning Approach to the Retrieval of Daily Soil Moisture in South Korea Using Satellite Images, Meteorological Data, and Digital Elevation Model

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea.

Landslide Analysis with Incomplete Data: A Framework for Critical Parameter Estimation

Landslides are one of the most common geohazards, posing significant risks to infrastructure, recreation, and human life. Slope stability analyses rely on detailed data, accurate materials testing, and careful model parameter selection. These factors are not always readily available, and estimations must be made, introducing uncertainty and error to the final slope stability analysis results. The most critical slope stability parameters that are often missing or incompletely constrained include slope topography, depth to water table, depth to failure plane, and material property parameters. Though estimation of these values is common practice, there is limited guidance or best practice instruction for this important step in the analysis. Guidance is provided for the estimation of: original and/or post-failure slope topography via traditional methods as well as the use of open-source digital elevation models, water table depth across variable hydrologic settings, and the iterative estimation of depth to failure plane and slope material properties. Workflows are proposed for the systematic estimation of critical parameters based primarily on slide type and scale. The efficacy of the proposed estimation techniques, uncertainty quantification, and final parameter estimation protocol for data-sparse landslide analysis is demonstrated via application at a landslide in Colorado, USA.