Search Results (365)

Flash floods are catastrophic phenomena that pose a serious risk to coastal infrastructures, towns, villages, and cities. This study assesses the risk of flash floods in the ungauged Mekkah province region based on specific and effective morphometric and topographic features characterizing the study region. Shuttle Radar Topography Mission (SRTM) data were employed to construct a digital elevation model (DEM) for a detailed analysis, and the geographical information systems software 10.4 (GIS) was utilized to assess the linear, area, and relief aspects of the morphometric parameters. The ArcHydro tool was used to prepare the primary parameters, including the watershed border, flow accumulation, flow direction, flow length, and stream ordering. The study region’s flash flood hazard degrees were assessed using several morphometric characteristics that were measured, computed, and connected. Two different and effective methods were used to independently develop two models of flood vulnerability behaviors. The integrated method analysis revealed that most of the eastern and western parts of the studied province provide high levels of flood vulnerability. Due to it being one of the most helpful topographic indices, the integrated flood vulnerability final map was overlayed with the topographic position index (TPI). The integrated results aided in understanding the link between the general basins’ morphometric characteristics and their topographical features for mapping the different flood susceptibility locations over the entire studied province. Thus, this can be applied to investigate a surface-specific reduction plan against the impacts of flood hazards in the studied landscape. Full article

This study assesses the relative performance of Sentinel-1 and -2 and their combination with topographic information for plow agricultural land soil salinity mapping. A learning database made of 255 soil samples’ electrical conductivity (EC) along with corresponding radar (R), optical (O), and topographic (T) information derived from Sentinel-2 (S2), Sentinel-1 (S1), and the SRTM digital elevation model, respectively, was used to train four machine learning models (Decision tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB). Each model was separately trained/validated for four scenarios based on four combinations of R, O, and T (R, O, R+O, R+O+T), with and without feature selection. The Recursive Feature Elimination with k-fold cross validation (RFEcv 10-fold) and the Variance Inflation Factor (VIF) were used for the feature selection process to minimize multicollinearity by selecting the most relevant features. The most reliable salinity estimates are obtained for the R+O+T scenario, considering the feature selection process, with R² of 0.73, 0.74, 0.75, and 0.76 for DT, GB, RF, and XGB, respectively. Conversely, models based on R information led to unreliable soil salinity estimates due to the saturation of the C-band signal in plowed lands. Full article

In recent decades, the creation and availability of Voluntary Geographic Information (VGI) have changed the paradigm associated with the production of Geospatial Information (GI), since, due to its free access, citizens can view, analyse, process, and validate this type of data. One of the most popular examples of VGI is the collaborative OpenStreetMap (OSM) project which covers a wide range of themes or characteristics associated with the real world. One of these themes is the feature “waterway” that represents watercourses. The quality of OSM data characteristics is a topic that has been published by many authors in recent years, particularly on the analysis of the completeness indicator. However, few references are found in the literature about studies that analyse the completeness of OSM watercourses or even watercourses obtained by other sources. All this motivated the authors to develop a study that aims to analyse the completeness of these specific lines that have so much relevance to hydrologists. The study presents an analysis of the variation over time in completeness/coverage of the OSM “waterway” feature in the period between 2014 and 2023 in a mountainous region included in the Mondego River basin, located in the Inland of Portugal. The methodology applied is supported by classical methods of measuring the completeness of lines that may be found in the literature. The total length of the watercourses was calculated and compared in percentage terms with the total length of the reference watercourses for dates under analysis. The watercourses of the military official hydrography of the 1/25,000 scale were used as a reference. The relation of the OSM completeness with some indicators related to terrain surface (altitude, slope, and location/proximity settlements) was also analysed. The choice of these indicators was motivated by the fact that the study area has strong mountain characteristics and is crossed by the main Portuguese river. The analysis was performed using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) data and satellite image of Geographic Information System software. The results show that the completeness of this OSM feature (waterway) has a slight increase, considering the amplitude of the studied period (nine years) and the fact that, nowadays, digital mobile devices enable easy access to satellite images, allowing the digitalization of geographic entities or objects of the real world remotely. Regarding the indicator altitude, slope, and location/proximity of the settlements, we believe that there is no influence of these indicators on the evolution of the completeness of the OSM waterways in the study area. Full article

This article presents a methodology for examining the impact of terrain on the level of groundwater in a well with an unconfined table aquifer. For this purpose, data from the groundwater observation and research network of the National Hydrogeological Service; airborne laser scanning technology; an SRTM height raster; orthophoto maps; and a WMTS raster were used and integrated for the specific parcels of Warmia and Mazury County. Groundwater is the largest and most important source of fresh drinking water. Apart from the influence of precipitation amount on groundwater level, the terrain is also important and is often omitted in comprehensive assessments. The research undertaken in this study provides new insights and a new methodology for the interpretation of hydrological data by taking into account the terrain, and it can be expanded with new data and increased research area or resolution. Research has shown that the attractiveness of the parcel in terms of construction development and excavation possibilities is greatly influenced by the groundwater level. Full article

Amidst the escalating threat of global warming, which manifests in more frequent forest fires, the prompt and accurate detection of forest fires has ascended to paramount importance. The current surveillance algorithms employed for forest fire monitoring—including, but not limited to, fixed threshold algorithms, multi-channel threshold algorithms, and contextual algorithms—rely primarily upon the degree of deviation between the pixel temperature and the background temperature to discern pyric events. Notwithstanding, these algorithms typically fail to account for the spatial heterogeneity of the background temperature, precipitating the consequential oversight of low-temperature fire point pixels, thus impeding the expedited detection of fires in their initial stages. For the amelioration of this deficiency, the present study introduces a spatial feature-based (STF) method for forest fire detection, leveraging Himawari-8/9 imagery as the main data source, complemented by the Shuttle Radar Topography Mission (SRTM) DEM data inputs. Our proposed modality reconstructs the surface temperature information via selecting the optimally designated machine learning model, subsequently identifying the fire point through utilizing the difference between the reconstructed surface temperatures and empirical observations, in tandem with the spatial contextual algorithm. The results confirm that the random forest model demonstrates superior efficacy in the reconstruction of the surface temperature. Benchmarking the STF method against both the fire point datasets disseminated by the China Forest and Grassland Fire Prevention and Suppression Network (CFGFPN) and the Wild Land Fire (WLF) fire point product validation datasets from Himawari-8/9 yielded a zero rate of omission errors and a comprehensive evaluative index, predominantly surpassing 0.74. These findings show that the STF method proposed herein significantly augments the identification of lower-temperature fire point pixels, thereby amplifying the sensitivity of forest surveillance. Full article

Located in the south of the Iberian Peninsula, the Doñana salt marshes occupy around half of Doñana National Park and are currently considered among the most important wetlands worldwide due to the importance of their ecosystem. In this research work, using a novel patented procedure, the effects of climate change on the study area between 2009 and 2020 were evaluated. For this reason, DEMs were downloaded from the 30-meter Shuttle Radar Topography Mission (SRTM). Furthermore, to check the depth of the flooded area, 792 satellite images (L5 TM, L7 ETM+, and L8 OLI) with a resolution of 30 m were analyzed. The results show how the combined use of geomatic techniques, such as radar, optical, and geographic information system (GIS) data, along with regression models and iterative processes, plays a key role in the prediction and analysis of the flooded area volume in the Doñana salt marshes. Another significant contribution of this work is the development of a new remote sensing index. In conclusion, given that the study area depends on its aquifers’ status, it would be advisable to implement policies aimed at eradicating illegal aquifer extraction, as well as recovery plans to avoid the complete clogging of this salt marsh. Full article

Schmitt’s map was one of the outstanding survey products of the late 18th century, produced through Habsburg military mapping in the shadow of the Napoleonic Wars in the area of today’s southern Germany and some neighboring regions. The main geodetic basis for the map work was the series of surveys in Germany conducted by C.-F. Cassini de Thury in the 1760s. However, this was only a horizontal control for part of Schmitt’s map. The Cassini survey chains were linked in the 1790s by a complementary survey in the northern part of the map work: the Staržinsky-Sarret survey, which is the subject of this study. The authors have searched through the archive summary drafts of this survey. The georeferencing of the photographed sketches in the Cassini projection was feasible with surprisingly low error. By using the global SRTM elevation database, it was possible to identify the points/summits of the Staržinsky-Sarret survey between which visibility is possible. Thus, despite the fact that only one of the seven map sketches examined explicitly presents a triangulation structure, we present a possible triangulation pattern that could have been used to provide geodetic control in the northern part of the Schmitt map. The authors consider this survey as the basis for the assumption that georeferencing the Schmitt map in its own projection is possible in this area with relatively small residual errors. Full article

The Relief Degree of Land Surface (RDLS) is an important index to evaluate regional environment. It has a significant effect on the local climate, geologic hazards, the path and speed of fire spreading, the migrations of wild animals, and the runoff path and speed of precipitation. The forest-steppe ecotone in northern China is one of ecological fragile zones. In-depth study of the RDLS of the forest-steppe ecotone in northern China will help to implement ecological projects scientifically and promote the construction of the national ecological security barrier. The Shuttle Radar Topography Mission (SRTM-GL1 30 m) data were used to determine the optimal analysis window for RDLS based on the mean change-point method, and the elevation difference was extracted based on the window analysis method. The RDLS model was used to extract RDLS of the forest-steppe ecotone and analyzed with the help of a spatial auto-correlation model. The correlation between mean elevation, relative elevation difference, and RDLS was also analyzed. The results show that the optimal analysis window size for RDLS was 29 × 29, corresponding to an area of 0.76 km². The RDLS under the optimal analysis window extracted from SRTM-GL1 (30 m) ranged from 0.084 to 3.516. The RDLS had significant spatial clustering, with high RDLS mainly distributed in the mountainous areas and low RDLS mainly distributed in mountain-to-plain transition zone; the RDLS between different administrative units and different watersheds had obvious variability. Overall, the RDLS was characterized as decreasing, increasing, and then decreasing from the south to north, while it was high in the west and low in the east. And the RDLS was linearly positively correlated with mean elevation and relative elevation difference. In the future, the implementation of major ecological projects in the forest-steppe ecotone in northern China, such as soil and water conservation, afforestation tree species selection, ecological corridor design, ecological management, geological disaster prevention, and forest fire prevention, should fully consider the local topographic conditions. These research results can provide topographic references for the implementation of ecological planning and engineering in this area and similar areas. It contributes to sustainable development and maximization of ecological benefits and promotes the establishment of a national ecological security barrier. Full article

Nanostructured lipid carriers (NLCs) are typically composed of liquid lipids, solid lipids, and surfactants, enabling the encapsulation of lipophilic drugs. Butamben is a Class II anesthetic drug, according to the Biopharmaceutical Classification System (BCS); it has a log P of 2.87 and is considered a ‘brick dust’ (poorly water-soluble and poorly lipid-soluble) drug. This characteristic poses a challenge for the development of NLCs, as they are not soluble in the liquid lipid present in the NLC core. In a previous study, we developed an NLC core consisting of a solid lipid (Crodamol^TM CP), a lipophilic liquid with medium polarity (SR^TM Lauryl lactate), and a hydrophilic excipient (SR^TM DMI) that allowed the solubilization of ‘brick dust’ types of drugs, including butamben. In this study, starting from the NLC core formulation previously developed we carried out an optimization of the surfactant system and evaluated their performance in aqueous medium. Three different surfactants (Crodasol^TM HS HP, Synperonic^TM PE/F68, and Croduret^TM 40) were studied and, for each of them, a 2³ factorial design was stablished, with total lipids, % surfactant, and sonication time (min) as the input variables and particle size (nm), polydispersity index (PDI), and zeta potential (mV) as the response variables. Stable NLCs were obtained using Crodasol^TM HS HP and Synperonic^TM PE/F68 as surfactants. Through a comparison between NLCs developed with and without SR^TM DMI, it was observed that besides helping the solubilization of butamben in the NLC core, this excipient helped in stabilizing the system and decreasing particle size. NLCs containing Crodasol^TM HS HP and Synperonic^TM PE/F68 presented particle size values in the nanometric scale, PDI values lower than 0.3, and zeta potentials above |10|mV. Concerning NLCs’ stability, SBTB-NLC with Synperonic^TM PE/F68 and butamben demonstrated stability over a 3-month period in aqueous medium. The remaining NLCs showed phase separation or precipitation during the 3-month analysis. Nevertheless, these formulations could be freeze-dried after preparation, which would avoid precipitation in an aqueous medium. Full article

(1) Monitoring salinized soil in saline–alkali land is essential, requiring regional-scale soil salinity inversion. This study aims to identify sensitive variables for predicting electrical conductivity (EC) in soil, focusing on effective feature selection methods. (2) The study systematically selects a feature subset from Sentinel-1 C SAR, Sentinel-2 MSI, and SRTM DEM data. Various feature selection methods (correlation analysis, LASSO, RFE, and GRA) are employed on 79 variables. Regression models using random forest regression (RF) and partial least squares regression (PLSR) algorithms are constructed and compared. (3) The results highlight the effectiveness of the RFE algorithm in reducing model complexity. The model incorporates significant environmental factors like soil moisture, topography, and soil texture, which play an important role in modeling. Combining the method with RF improved soil salinity prediction (R² = 0.71, RMSE = 1.47, RPD = 1.84). Overall, salinization in Minqin oasis soils was evident, especially in the unutilized land at the edge of the oasis. (4) Integrating data from different sources to construct characterization variables overcomes the limitations of a single data source. Variable selection is an effective means to address the redundancy of variable information, providing insights into feature engineering and variable selection for soil salinity estimation in arid and semi-arid regions. Full article

Focusing on the trend of continuously seeking high-precision tree species classification results in small areas from the perspectives of sensors and classification algorithms. This study aimed to explore the effects of data sources, classifiers, and seasons on classification accuracy in regions with significant environmental variation, examining patterns of tree species classification to enhance the transferability of classification. Considering two typical forest distribution regions in the north and south of China, this study utilized the revisitation cycle and open-source advantages of Sentinel-2 and Landsat-8. Leveraging the Google Earth Engine (GEE) platform, this study captured spectral features, vegetation indices, and texture features for single seasonal and seasonal combination images. With the assistance of Sentinel-1A and SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model), backscattering coefficient features and topographical features were extracted and input with features captured from Sentinel-2 and Landsat-8 into three types of classifiers: random forest (RF), support vector machine (SVM), and gradient tree boosting (GTB) for major tree species classification. In this research, we discovered that the best classification for single season in the northern study area was spring, whereas, for the southern study area, it was winter. Seasonal combination images effectively improved the classification accuracy of single seasonal images, with Sentinel-2 imagery displaying better classification performance compared to Landsat-8, and the optimal classifier differing between the north and the south. The inclusion of topographical or backscattering coefficient features in the four-season combination imagery contributed to improvements in classification accuracy, with topographical features significantly enhancing the classification performance in the topographically varied southern study area. The evaluation of feature importance indicated that elevation was the most critical feature for classification, while spectral features and vegetation indices were also significant. In the southern study area with large topographical discrepancies, subdividing into different terrain units led to improved tree species classification accuracy in medium-altitude, gentle slope areas. These findings provide insights into the regularity of enhancing tree species classification accuracy in environmentally diverse areas through the use of multi-source remote sensing data and multi-seasonal imagery. Consequently, the results offer a reference for the identification of tree species across large areas and the creation of spatial distribution maps. Full article

Digital elevation models (DEMs) are widely used in digital terrain analysis, global change research, digital Earth applications, and studies concerning natural disasters. In this investigation, a thorough examination and comparison of five open-source DEMs (ALOS PALSAR, SRTM1 DEM, SRTM3 DEM, NASADEM, and ASTER GDEM V3) was carried out, with a focus on the Chongqing region as a specific case study. By utilizing ICESat-2 ATL08 data for validation and employing a random forest model to refine terrain variables such as slope, aspect, land cover, and landform type, a study was undertaken to assess the precision of DEM data. Research indicates that spatial resolution significantly impacts the accuracy of DEMs. ALOS PALSAR demonstrated satisfactory performance, reducing the corrected root mean square error (RMSE) from 13.29 m to 9.15 m. The implementation of the random forest model resulted in a significant improvement in the accuracy of the 30 m resolution NASADEM product. This improvement was supported by a decrease in the RMSE from 38.24 m to 9.77 m, demonstrating a significant 74.45% enhancement in accuracy. Consequently, the ALOS PALSAR and NASADEM datasets are considered the preferred data sources for mountainous urban areas. Furthermore, the study established a clear relationship between the precision of DEMs and slope, demonstrating a consistent decline in precision as slope steepness increases. The influence of aspect on accuracy was considered to be relatively minor, while vegetated areas and medium-to-high-relief mountainous terrains were identified as the main challenges in attaining accuracy in the DEMs. This study offers valuable insights into selecting DEM datasets for complex terrains in mountainous urban areas, highlighting the critical importance of choosing the appropriate DEM data for scientific research. Full article

The Vanj River Basin contains a dynamic glacier, the Medvezhiy glacier, which occasionally poses a danger to local residents due to its surging, flooding, and frequent blockages of the Abdukahor River, leading to intense glacial lake outburst floods (GLOF). This study offers a new perspective on the quantitative assessment of glacier surface velocities and associated lake changes during six surges from 1968 to 2023 by using time-series imagery (Corona, Hexagon, Landsat), SRTM elevation maps, ITS_LIVE, unmanned aerial vehicles, local climate, and glacier surface elevation changes. Six turbulent periods (1968, 1973, 1977, 1989–1990, 2001, and 2011) were investigated, each lasting three years within a 10–11-year cycle. During inactive phases, a reduction in the thickness of the glacier tongue in the ablation zone occurred. During a surge in 2011, the flow accelerated, creating an ice dam and conditions for GLOF. Using these datasets, we reconstructed the process of the Medvezhiy glacier surge with high detail and identified a clear signal of uplift in the surface above the lower glacier tongue as well as a uniform increase in velocities associated with the onset of the surge. The increased activity of the Medvezhiy glacier and seasonal fluctuations in surface runoff are closely linked to climatic factors throughout the surge phase, and recent UAV observations indicate the absence of GLOFs in the glacier’s channel. Comprehending the processes of glacier movements and related changes at a regional level is crucial for implementing more proactive measures and identifying appropriate strategies for mitigation. Full article

The World Heritage Committee has been meeting to discuss the arrangements of existing World Heritage Sites, and, on 22–26 October, the area from Giza to the Dahshur was included in the list of World Heritage Sites. According to the Egyptian Antiquities Authority (EAA), the groundwater levels at the Pyramids Plateau are too shallow, which threatens the ancient Sphinx and Pyramids in Giza, Egypt. In addition, many geophysical studies have been carried out in the archaeological area of Giza, which prove that the area is facing the risk of a high level of groundwater, specifically threatening the Sphinx. Recent developments in Earth observation have helped in the field of land monitoring such as land use changes, risk observation, and the creation of models for protecting cultural heritage sites. This study aimed to examine the impact of land use changes on on the archaeological sites of the Giza Necropolis area by integrating various data sources including optical satellite imagery and SRTM data during the period of 1965–2019. A historical database of Corona 1965 and Landsat 2009 data was investigated along with the new acquisitions of Sentinel-2 2016 and Sentinel-1 2016 and 2019. In addition, the radar Sentinel-1 SLC data were collected and analyzed for calculating the land subsidence value in the area of interest through two periods between 6–30 July 2016 and 30 July–15 December 2016. Various methods were implemented, including cluster outliers, the Moran index, and spatial autocorrelation to examine the changes in urban masses. Additionally, the relationship between groundwater leakage and land subsidence in the region was investigated. The analysis was carried out using Envi5.3, ArcMap10.6.1, and SNAP6.0 software to extract spatial data from the raw data. The results from our investigation highlighted rapid changes in urban areas between 1965 and 2019. The data obtained and analyzed from optical and radar satellite imagery showed that changes in land use can cause changes in the topographic situation by decreasing the level of groundwater, which adversely affects Egyptian monumental pyramids and the Sphinx. Land use analysis showed that the urban area represented 7.63% of the total area of the study area in 1965, however it reached 32.72% in 2009, approximately half of the total area in 2016, and in 2019, the urban mass area increased to nearly two-thirds of the total area. The annual growth rate between 1965 and 2019 was estimated by nearly 0.642 km²/year. These land-use changes possibly affected the land subsidence value (−0.0138 m), causing the rising groundwater level close to the Sphinx. Using the information obtained from our RS- and GIS-based analysis, mitigation strategies have also been identified to support archaeological area preservation. Full article

Lake volume variation is closely related to climate change and human activities, which can be monitored by multi-source remote-sensing data from space. Although there are usually two routine ways to construct the lake volume by the digital elevation model (DEM) or satellite altimetric data combined with the lake area, rarely has a comparison been made between the two methods. Therefore, we conducted a comparison between the two methods in Texas for 14 lakes with abundant validation data. First, we constructed the lake hypsometric curve by five commonly applied DEMs (SRTM, ASTER, ALOS, GMTED2010, and NED) or satellite altimetric products combined with the gauge lake area. Second, the lake volume was estimated by combining the hypsometric curve with the gauge lake area time series. Finally, the estimation error has been quantitatively calculated. The results show that the relative lake volume estimation error (rVSD) of the altimetric data (4%) is only 10–18% of that of the DEMs (22–41%), and the DEM with the highest resolution (NED) has the least rVSD with an average of 22%. Therefore, for large-scale lake monitoring, we suggest the application of satellite altimetric data with the lake area to estimate the lake volume of large lakes, and the application of high-resolution DEM with the lake area to calculate the lake volume of small lakes that are gapped by satellite altimetric data. Full article