Index Map Research Articles

GIS-Based Analytical Hierarchy Process Modelling and Mapping of Erosion Vulnerability in the Coastal Areas of Rivers State, Nigeria

The problem of coastal erosion in rivers State Nigeria is a significant issue that has far-reaching consequences for the environment and local communities. Despite the efforts of previous research there remains a lack of comprehensive understanding of the factors contributing to erosion vulnerability and their relative importance, hindering effective decision-making and management practices aimed at mitigating the effects of coastal erosion in Rivers State. Therefore, this study aimed at a GIS-based analytical hierarchy process modeling and mapping of coastal erosion vulnerability in Rivers State, Nigeria. The objectives are to establish and classify the geophysical factors according to the levels of coastal erosion risk, calculate the reliability index of the classified geophysical factors, determine the coastal vulnerable areas across Rivers State using analytical hierarchical process and to produce a coastal vulnerability index map defining the extent of erosion vulnerability in Rivers State. The methodology comprises of the acquisition of primary and secondary data, image pre-processing, image classification, DEM processing, classification and standardization of factors, development of pairwise comparism, and weighted linear combination analysis. The study revealed three distinct coastal erosion vulnerability zones: high, moderate, and low vulnerability. The high vulnerability zone encompassed a total expanse of 545.29 square kilometers, constituting 6.38% of the study area. In contrast, the moderate and low vulnerability zones covered 1941.33 square kilometers and 6052.51 square kilometers, respectively, making up 22.73% and 70.89% of the total area. Bonny (139.28 sq km) was ranked as the most vulnerable due to its role as an oil and gas hub. Degema (111.28 sq km) ranked second and requires urgent erosion control. Okrika and Andoni (71.73 sq km and 62.20 sq km) were third and fourth respectively. It is recommended that an advocate for the systematic approach to coastal vulnerability zoning be introduced in the study. The categorization of areas into high, moderate, and low vulnerability zones provides a standardized framework for assessing coastal regions' susceptibility to erosion. This approach can be applied to other regions to facilitate consistent vulnerability assessments.

Early forecasting of corn yield and price variations using satellite vegetation products

Several approaches based on process-based, statistical or economic models have been developed and are currently used by market analysts and governmental agencies to forecast agricultural yields and commodity prices at the national and global scales. However, these models require input data that are often difficult to obtain before crop harvest and their predictions are not always reliable. Satellite data products provide easily accessible sources of information on vegetation characteristics, potentially relevant for predicting crop yield and commodity prices at a large scale, before harvest. This study presents and evaluates several methods for forecasting variations in US corn yield and global corn prices using regional vegetation satellite products. The spatial dimension of maps of vegetation indices derived from satellite products in the US Corn Belt is first reduced either by their spatial means or by principal components derived using the Empirical Orthogonal Function (EOF) method. Mean values and EOF principal components are then used as explanatory variables in generalized LASSO linear models to forecast corn price and yield changes at different time horizons, before harvest. A cross-validation method was implemented to assess the model performances using Area under the ROC curve (AUC), the Brier Skill Score (BSS) and the Matthew Correlation Coefficient (MCC). The results revealed that EOF principal components derived from satellite-based gross primary production (GPP) in July could be used to predict variations in US corn yields, with an AUC of 0.97 (95% CI 0.92-1), a BSS of 0.75 and an MCC of 0.83. The results also showed that the July spatial averages of satellite GPP data obtained in the US Corn Belt could be used to forecast the forthcoming increase or decrease of global corn prices as evidenced by an AUC reaching up to 0.91, a BSS up to 0.5, and an MCC up to 0.67. Finally, we compared our forecasts of yield and price variations to those derived from the WASDE forecasting method currently implemented in the US. This comparison showed that our approach was at least as accurate and often more accurate than WASDE. This study demonstrates how satellite data can be used to forecast variations in crop yields and corn prices several months before harvest.

A Geospatial Analysis of Environmental Impacts of Climate Change on Osogbo and Environs

Climate change is one of the alarming global environmental changes likely to have deleterious effects on natural, social, cultural, and human systems. The risks associated with it call for a broad spectrum of policy responses and strategies at local, regional, national, and global levels. This research aims to carry out a geospatial analysis of the environmental impact of climate change on Osogbo and its environs with a view to providing information towards mitigating the negative impact of climate change in the study area. This study uses Landsat 4, Landsat 7, and Landsat 8 satellite data from 1982, 2002, and 2022, respectively, to evaluate land cover and temperature variation as a climatic change in the study area over four decades. The land survey method was carried out to collect ground truth data for validation of the satellite imageries. The drought map was produced based on the Vegetation Condition Index (VCI) map computed from Normalized Difference Vegetation Index (NDVI) datasets and was further classified into three categories: moderate, mild, and no drought. Analysis of the Land Use Land Cover (LULC) of the study area shows that the vegetation decreased from 84% in 1982 to 59% in 2002 and 56% in 2022. The built-up/exposed surface increased from 15.28% in 1982 to 38% in 2002 and 41.28% in 2022. The water bodies also increased from 0.17% in 1982 to 15.31% in 2022. LST analysis reveals that the mean temperature of the study area increased by 10.73 °C during the past four decades (from 1982 to 2022), with the most accelerated warming (11.41 °C) occurring during the last two decades (from 2002 to 2022). The Vegetation Condition Index (VCI) based drought map shows that there was no extreme or severe drought in the study area. However, moderate and mild droughts were observed mostly in the developed part of the study area, which confirmed the presence of drought. It is hereby recommended to introduce sustainable water management strategies to mitigate water shortage in the study area.

Gradient Weakly Sensitive Multi-Source Sensor Image Registration Method

Aiming at the nonlinear radiometric differences between multi-source sensor images and coherent spot noise and other factors that lead to alignment difficulties, the registration method of gradient weakly sensitive multi-source sensor images is proposed, which does not need to extract the image gradient in the whole process and has rotational invariance. In the feature point detection stage, the maximum moment map is obtained by using the phase consistency transform to replace the gradient edge map for chunked Harris feature point detection, thus increasing the number of repeated feature points in the heterogeneous image. To have rotational invariance of the subsequent descriptors, a method to determine the main phase angle is proposed. The phase angle of the region near the feature point is counted, and the parabolic interpolation method is used to estimate the more accurate main phase angle under the determined interval. In the feature description stage, the Log-Gabor convolution sequence is used to construct the index map with the maximum phase amplitude, the heterogeneous image is converted to an isomorphic image, and the isomorphic image of the region around the feature point is rotated by using the main phase angle, which is in turn used to construct the feature vector with the feature point as the center by the quadratic interpolation method. In the feature matching stage, feature matching is performed by using the sum of squares of Euclidean distances as a similarity metric. Finally, after qualitative and quantitative experiments of six groups of five pairs of different multi-source sensor image alignment correct matching rates, root mean square errors, and the number of correctly matched points statistics, this algorithm is verified to have the advantage of robust accuracy compared with the current algorithms.

Tree-Related Microhabitats and Multi-Taxon Biodiversity Quantification Exploiting ALS Data

The quantification of tree-related microhabitats (TreMs) and multi-taxon biodiversity is pivotal to the implementation of forest conservation policies, which are crucial under the current climate change scenarios. We assessed the capacity of Airborne Laser Scanning (ALS) data to quantify biodiversity indices related to both forest beetle and bird communities and TreMs, calculating the species richness and types of saproxylic and epixylic TreMs using the Shannon index. As biodiversity predictors, 240 ALS-derived metrics were calculated: 214 were point-cloud based, 14 were pixel-level from the canopy height model, and 12 were RGB spectral statistics. We used the random forests algorithm to predict species richness and the Shannon diversity index, using the field plot measures as dependent variables and the ALS-derived metrics as predictors for each taxon and TreMs type. The final models were used to produce wall-to-wall maps of biodiversity indices. The Shannon index produced the best performance for each group considered, with a mean difference of −6.7%. Likewise, the highest R2 was for the Shannon index (0.17, against 0.14 for richness). Our results confirm the importance of ALS data in assessing forest biodiversity indicators that are relevant for monitoring forest habitats. The proposed method supports the quantification and monitoring of the measures needed to implement better forest stands and multi-taxon biodiversity conservation.

Drought stress and its characteristics in China from 2001 to 2020 considering vegetation response and drought creep effect

Study areaChina Study focusDrought is an extremely serious disaster, typically characterized by slow onset, long duration, and cumulative impacts on multifaceted aspects. Developing a combined index that integrates its various characteristics can help deepen our understanding of drought evolution and provide accurate references for farmers to take drought mitigation measures. In this study, we proposed a Modified version of the Type Response-Aided Drought Index (MTRADI), simultaneously considering the various responses of different vegetation and the slow-varying properties of drought development. New hydrological insights for the regionsThe suitability of MTRADI was evaluated through correlation analysis with Palmer Drought Severity Index (PDSI) and qualitative comparison with Meteorological Drought Composite Index (MCI) maps. The MTRADI achieved significantly higher correlation coefficients with the PDSI, outperforming other combined drought indices. Based on the run theory, Mann-Kendall (MK) test, and Sen’s slope, drought characteristics and trends in mainland China during 2001–2020 were analyzed and the results show that drought frequency and severity are relatively higher in eastern Inner Mongolia, North China Plain, and Northeast of China. Furthermore, areas with significant wetting trends are observed to be mainly distributed near the Hu line, indicating that the water conditions in China are gradually improving at the transition zone of arid and humid climate regions.

Redesain Penataan Pohon Pada Rest Area Jalibar Desa Oro Oro Ombo, Kota Batu

Title: Redesign of Tree Arrangement in Jalibar Rest Area, Oro Oro Ombo Village, Batu City The Jalibar Rest Area is one of the tourist support facilities in Oro Oro Ombo Village. However, this rest area does not yet have a good green planning plan, even though one of the supporting comfort areas in the rest area is the proper arrangement of trees. This research identifies existing tree species and analyzes their functions, evaluates tree arrangements, and recommends tree arrangements based on their function and planting location. The research was conducted in the Jalibar Rest Area in Oro Oro Ombo Village, Batu District, Batu City, East Java. The analytical methods used are Key Performance Index (KPI), Comparative Study, and Spatial Mapping. Based on field observations, it is known that the Jalibar Rest Area has 123 trees with 16 types of trees. Based on the evaluation results, it is known that the suitability of the tree's function to its location, namely that the outer zone of the rest area should be planted with shade trees, guides and trees of aesthetic value. The parking zone is planted with trees that function as shade trees, guides and absorb pollutants. As well as service zones planted with trees with the functions of shade, absorbing pollutants and aesthetics. Recommendations for redesigning tree arrangements in the Jalibar Rest Area include adjusting the function of trees in each zone by maintaining appropriate tree species, replacing inappropriate tree species, adding and subtracting trees based on their planting distance.

Two shell- and wing-shaped supernova remnants

Supernova remnants (SNRs) are profoundly affected by their ambient medium. In particular, SNRs with a mixed morphology (characterised by a shell-like radio morphology and centrally filled X-ray emission) are thought to be the result of the interaction of a supernova explosion with a dense environment. In this work, we present carbon monoxide (CO) observations around two mixed morphology SNRs, VRO 42.05.01 and G 350.0-2.0, that look remarkably similar in continuum radio emission, showing what we refer to as a shell and wing shape. It has been proposed that the shell and wing shape is the result of environmental effects, in the form of a sharp density gradient or discontinuity. Therefore, our motivation for studying these two sources jointly is that if the dense molecular environment causes the development of these sources’ shell and wing shape, then these two sources’ environments must be similar. This is contrary to what we observe. In the case of VRO 42.05.01, we have found direct evidence of an interaction with its molecular environment, in the form of broadened 12CO line profiles, high 12CO (J = 2−1) to 12CO (J = 1−0) line ratios, and arc features in position-velocity space. We interpret some of these features to be associated with the SNR shock, and some of them to be due to the presence of a pre-supernova stellar wind. We have found no such features in the abundant molecular gas surrounding G 350.02.0. In addition to the spectral line analysis, we have used radio continuum data to make a spectral index map of G 350.0-2.0, and we see that the radio spectrum of G 350.0-2.0 steepens significantly at frequencies <200 MHz, much like that of VRO 42.05.01. In spite of their spectral and morphological similarities, these two sources look substantially different in their observed optical and infrared emission. The lack of large-scale correspondence between the radio continuum and the molecular material, in either case, as well as the differences in the excitation and morphological properties of the molecular gas surrounding both sources, lead us to conclude that the shell and wing morphology of these two sources is not due to interactions with a similar ambient molecular interstellar medium.

State-of-the-art nondestructive high-speed raster scanning inspection for food safety and quality using terahertz refractive index mapping

Various methods are employed for food inspection; however, the persistent demand from both manufacturers and consumers remains due to the limitations of existing inspection technologies. This study introduces a novel technique for enhancing food safety and conducting large-area quality assessments using terahertz (THz) waves. This approach enables the classification of quality grades and the safe detection of soft matter, facilitated by high-speed mechanical raster scan imaging. Leveraging a two-dimensional complex refractive index map, we address the challenges posed by the uniformity in optical properties within food materials and foreign bodies, attributed to high water absorption and the presence of diverse compounds. Consequently, we anticipate improved foreign substance inspection and detection. These cutting-edge THz spectroscopic and imaging techniques, representing nondestructive inspection approaches, have the potential to elevate food safety and quality standards.

High-Density Polyethylene Custom Focusing Lenses for High-Resolution Transient Terahertz Biomedical Imaging Sensors.

Transient terahertz time-domain spectroscopy (THz-TDS) imaging has emerged as a novel non-ionizing and noninvasive biomedical imaging modality, designed for the detection and characterization of a variety of tissue malignancies due to their high signal-to-noise ratio and submillimeter resolution. We report our design of a pair of aspheric focusing lenses using a commercially available lens-design software that resulted in about 200 × 200-μm2 focal spot size corresponding to the 1-THz frequency. The lenses are made of high-density polyethylene (HDPE) obtained using a lathe fabrication and are integrated into a THz-TDS system that includes low-temperature GaAs photoconductive antennae as both a THz emitter and detector. The system is used to generate high-resolution, two-dimensional (2D) images of formalin-fixed, paraffin-embedded murine pancreas tissue blocks. The performance of these focusing lenses is compared to the older system based on a pair of short-focal-length, hemispherical polytetrafluoroethylene (TeflonTM) lenses and is characterized using THz-domain measurements, resulting in 2D maps of the tissue refractive index and absorption coefficient as imaging markers. For a quantitative evaluation of the lens effect on the image resolution, we formulated a lateral resolution parameter, R2080, defined as the distance required for a 20-80% transition of the imaging marker from the bare paraffin region to the tissue region in the same image frame. The R2080 parameter clearly demonstrates the advantage of the HDPE lenses over TeflonTM lenses. The lens-design approach presented here can be successfully implemented in other THz-TDS setups with known THz emitter and detector specifications.

Landslide susceptibility evaluation in the Beas River Basin of North-Western Himalaya: A geospatial analysis employing the Analytical Hierarchy Process (AHP) method

In the North-Western Himalayas, particularly within the Beas Basin of Himachal Pradesh, landslide incidents are frequent, primarily due to the unique interplay of adverse geological conditions, heavy rainfall, and human factors. These incidents result in substantial loss of life and property each year. To mitigate such issues, systematic landslide research is essential, encompassing aspects like inventory mapping and risk assessment. This study leverages the Analytical Hierarchy Process (AHP) for an in-depth Landslide Susceptibility Index (LSI) mapping in the Beas River basin, employing remote sensing data to analyze key factors contributing to the region's instability. The process involved a detailed selection and mapping of landslide conditioning variables, guided by validated landslide inventory data and high-resolution remote sensing images, ensuring an accurate representation of the basin's geographical variations. The creation of the landslide susceptibility map utilized the weighted overlay approach, categorizing the area into five levels of susceptibility: very low, low, moderate, high, and very high. This classification incorporated ten critical factors influencing landslide occurrence, including elevation, slope aspect, slope angle, distance from drainage, lithology, distance from lineament, geomorphology, rainfall, and land use/land cover (LULC). The LSI was calculated through the Weighted Linear Combination (WLC) technique, leveraging the weights and ratings derived via the AHP method. This analysis revealed that approximately 634.1 square kilometers, or 12.8% of the region, face very high landslide susceptibility, followed by 22.6% at high, 25.8% with moderate, 24.4% at low, and 14.5% at very low susceptibility. The LSI map's accuracy in predicting landslides was affirmed through Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) evaluations, showcasing an 86.3% precision rate. This classification facilitates focused interventions in high-risk areas, guiding planners in landslide-conscious development and infrastructure planning. It directs engineers toward engineering solutions like slope stabilization and drainage improvements to mitigate landslide effects. Moreover, this approach supports the creation of evacuation and emergency response plans, bolstering community resilience to landslide threats in the river basin.

Extension of the soil monitoring network via tea bag initiatives: A 3000 km latitudinal gradient in European Russia

The increasing popularity and recognition of citizen science approaches to monitor soil health have promoted the idea to assess soil microbial decomposition based on a standard litter sample - tea bags. Although tea bag initiatives are expanding across the world, the global datasets remain biased in regard to investigating regions and biomes. This study aimed to expand the tea bag initiative to European Russia, which remains a “white spot” on the tea bag index map. We also added urban soils into the analysis, which were underestimated previously. We compared the standard and local tea brands to explore possible adaptations of the standard approach to regions with limited access to standard tea brands. The established monitoring network included natural and urban sites in six vegetation zones along a 3000 km latitudinal gradient. There was a very close linear relationship (R2 = 0.94–0.98) in the mass loss of alternative and standard tea litter. The mass loss of green tea in soil along the latitudinal gradient showed an increasing trend from north to south. Variations in the microbial decomposition of green tea were mainly explained by the latitudinal gradient, with low soil temperature identified as key factors hampering decomposition. Mass loss of the more recalcitrant rooibos tea was mainly determined via land use, with decomposition rates on average 1.3 times higher in urban soils. This pattern was in line with higher soil temperatures and pH in urban sites compared to natural counterparts. The findings of our study could prove valuable in extending the tea bag network of soil decomposition assessment into broader territories, including urban areas. Additionally, they could facilitate the involvement of citizen science and complete the database for C cycle modeling depending on climatic conditions.

Implementing policies to mitigate urban heat islands: Analyzing urban development factors with an innovative machine learning approach

Taiwan is located in a hot and humid subtropical climate. Urban development, building heat emissions, and human activities lead to the urban heat island (UHI) effect in which urban air temperatures are significantly higher than those in the surrounding suburban areas. In this study, the factors contributing to the UHI effect were investigated in a case study of Taichung City, Taiwan by using high-resolution geographic climate data. Geographic Information System tools were used for grid-based spatial data analysis, and a physiological equivalent temperature index map was calculated and overlaid to identify heat zones. The results revealed that environmental factors have complex, nonlinear effects on temperatures in in urban areas; hence, the analysis was challenging. The decision tree algorithm was used to identified key environmental factors contributing to urban heat in areas with different levels of development; a weighting analysis was then performed to select effective urban cooling strategies. Finally, backpropagation neural network models tailored to different development levels were used for simulations to validate the proposed cooling policies. The results of this study were incorporated into the urban planning policies of Taichung City, Taiwan to mitigate the UHI effect and create a more suitable urban living environment.

Intelligent assessment of building damage of 2023 Turkey-Syria Earthquake by multiple remote sensing approaches

A catastrophic Mw7.8 earthquake hit southeast Turkey and northwest Syria on February 6th, 2023, leading to more than 44 k deaths and 160 k building collapses. The interpretation of earthquake-triggered building damage is usually subjective, labor intensive, and limited by accessibility to the sites and the availability of instant, high-resolution images. Here we propose a multi-class damage detection (MCDD) model enlightened by artificial intelligence to synergize four variables, i.e., amplitude dispersion index (ADI) and damage proxy (DP) map derived from Synthetic Aperture Radar (SAR) images, the change of the normalized difference built-up index (NDBI) derived from optical remote sensing images, as well as peak ground acceleration (PGA). This approach allows us to characterize damage on a large, tectonic scale and a small, individual-building scale. The integration of multiple variables in classifying damage levels into no damage, slight damage, and serious damage (including partial or complete collapses) excels the traditional practice of solely use of DP by 11.25% in performance. Our proposed approach can quantitatively and automatically sort out different building damage levels from publicly available satellite observations, which helps prioritize the rescue mission in response to emergent disasters.

Quantitative and qualitative assessment of groundwater resources for drinking water supply in the peri-urban area of Dhaka, Bangladesh

Groundwater is the primary source of freshwater for drinking, industrial, and agricultural water in many nations worldwide. This vital resource is deteriorating due to rapidly growing urbanization, pollution, overexploitation, and climate change. However, it is unclear how much this specific resource deteriorates over time. Therefore, this study aimed to investigate groundwater resources qualitatively and quantitatively in the peri-urban area of Dhaka, Bangladesh. The study used the modified Mann-Kendall trend test, hotspot analysis, groundwater recharge estimation, multivariate statistical analysis, and the water quality index (WQI) mapping. The trend analysis of groundwater levels indicates that in the most recent year, groundwater levels close to industrial areas have declined at a maximum rate of 1.18 m/year. The estimated groundwater recharge reveals that it is highest (480 mm/year) on rural agricultural land and lowest (220 mm/year) in urban areas. With a 99% confidence level, the groundwater water trend cluster analysis indicates the hotspots of groundwater level depletion near the industrial area. The results showed that Mn, Fe, and As exceeded the World Health Organization's (WHO) recommended limit for drinking water by 76.92%, 61.53%, and 23.07%, respectively. The principal component analysis and hierarchical clustering results indicate that groundwater quality is influenced by rock-water interaction and human interventions. The WQI results suggest that 47.05% of the samples were excellent, 7.69% were good, 9.96% were poor, 17.65% were very poor, and the remaining 17.65% were unfit for drinking purposes. The depletion of groundwater hotspots was identified in northeast region of the study area. Overall, an integrated framework was proposed that offers a holistic approach to sustainable groundwater resource management amid urbanization and industrialization. These findings will provide an essential guideline for groundwater resource management in the peri-urban area of Dhaka city.

Quantitative Evaluation of the Topographical Maps of Three-Dimensional Choroidal Vascularity Index in Children With Different Degrees of Myopia.

To investigate topographical maps of the three-dimensional choroidal vascularity index (3D-CVI) in children with different levels of myopia. We enrolled 274 eyes from 143 children with various severity of myopia, including emmetropia (EM), low myopia (LM), and moderate-high myopia (MHM). The choroidal vessel volume (CVV), choroidal stroma volume (CSV), and 3D-CVI in different eccentricities (fovea, parafovea, and perifovea) and quadrants (nasal, temporal, superior, and inferior) were obtained from swept-source optical coherence tomography angiography (SS-OCTA) volume scans. All choroidal parameters were compared among groups, and the associated factors contributing to different 3D-CVIs were analyzed. Compared to the less myopic group, the more myopic group showed a significant decrease in CVV and CSV (MHM < LM < EM) and a significant increase in the 3D-CVI (MHM > LM > EM) in most areas (all P < 0.05). The nasal quadrant had the greatest 3D-CVI and lowest CSV and CVV, and vice versa in the temporal quadrant. The 3D-CVIs of the EM and LM groups gradually increased from the fovea to the perifovea, whereas the 3D-CVI of the MHM group first decreased and then increased. Regression analysis showed that axial length was an independent risk factor affecting foveal and parafoveal 3D-CVIs. Restricted cubic spline analysis revealed that the 3D-CVI increased with spherical equivalent (SE) when the SE was less than threshold and decreased when the SE was greater than threshold (SE thresholds for foveal, parafoveal, and perifoveal 3D-CVIs were -5.25 D, -5.125 D, and -2.00 D, respectively; all P < 0.05). Children with myopia exhibited decreased CSV and CVV, increased 3D-CVIs, and altered 3D-CVI eccentricity characteristics (from the fovea to the perifovea). The quadratic relationship between the 3D-CVI and SE should be explored in longitudinal investigations.

An investigation of PPP degradations over Asia-Pacific region during Tonga volcanic eruption on 15 January 2022

On 15 January 2022, a significant volcanic eruption occurred at Hunga Tonga-Hunga Ha’apai (HTHH), causing global ionospheric disturbances. To investigate ionospheric anomalies and the kinematic GNSS PPP responses to this volcanic eruption over the Asia-Pacific region, the rate of the total electron content index (ROTI) map derived from about 1100 GNSS stations data and ionospheric scintillation indices S4 and σφ are used. We first selected data from six stations for kinematic PPP experiments. Multi-constellation and multi-frequency Precision Positioning (GAMP) software and GNSS analysis software are employed in the process. We then found that the positioning errors of three directions (east, north, up) during disturbance time are larger than those on an ionospheric quiet day (3 January 2022). To explore the reasons for PPP degradation, we conduct a quality analysis of GNSS data on these two days, including carrier-to-noise power-density ratio (C/N0) and the number of cycle slips. The number of satellites affected by cycle slips increased obviously in the disturbance time. The statistics of cycle slip occurrence rate at low latitudes is 12.35 % while that on ionospheric quiet day is only 6.05 %. The strategy of expanding the cycle slip detection thresholds from 0.15 m to 1.5 m for phase geometry free (PGF) combination and 5 cycles to 12 cycles for Hatch–Melbourne-Wübbena (HMW) combination failed to significantly improve the PPP solutions based on data from 357 stations. It was found that only increasing the detection thresholds could not mitigate the PPP degradations. Our investigations contribute to the study of space weather and kinematic GNSS PPP responses to volcanic eruptions.

Numerical study of the effects of hydrodynamic interactions among cells for microfluidic holographic cyto-tomography

When cells in a suspension flow through a microfluidic channel and rotate within the field of view (FOV) of a holographic microscope, they become accessible to a light beam from various angles. This allows the retrieval of a three-dimensional refractive index map for each flowing cell, essentially a 3D phase-contrast tomogram. Understanding the effects of hydrodynamic interactions among cells on their rotational behaviour during flow is crucial for designing microfluidic devices for holographic imaging. In this study, we employ direct numerical simulations to investigate the dynamics of cell clusters suspended in a Newtonian liquid under pressure-driven flow within a microfluidic channel, with the aim of clarifying the influence of hydrodynamic interactions on cell rotation.

Flood risk index mapping in data scarce region by considering GIS and MCDA (FRI mapping in data scarce region by considering GIS and MCDA)

Flood risk index mapping in data scarce region by considering GIS and MCDA (FRI mapping in data scarce region by considering GIS and MCDA)

Terrestrial environmental sensitivity index mapping (TESIM) of the coastal areas of the Niger Delta region of Nigeria

It can be argued that despite years of research, the true impacts of oil spill pollution on the environment and human sources of livelihoods are yet to be completely understood. Tools such as environmental sensitivity index (ESI) mapping can provide useful information that can significantly mitigate or help eliminate the known impacts of crude oil pollution, by proactively identifying environmentally sensitive areas. This study adapted the analytical hierarchy process (AHP) and geographic information systems (GIS) based multiple criteria analysis (MCA) approach in the objective determination of sensitive sections of the NDR coastal areas, by combining several competing and or related evaluation criteria. The AHP was used to determine and assign relative weights to the data layers through a pairwise comparison evaluation matrix of mutually exclusive evaluation criteria using the Delphi technique, while the MCA was used to aggregate the evaluation criteria into a single map. The results of the MCA analysis show that over 57 % of the study area is sensitive to oil spill pollution in one form or the other. 68 % of the identified sensitive areas are classified as extremely environmentally sensitive with 11,752.14km2, while 7.1 % are classified as less sensitive. This relative classification of environmental sensitivity in the study area is termed the Niger Delta Relative Environmental Sensitivity Index (NDRESI). The implication of these results is that more has to be done if the very sensitive environment of the NDR is to be conserved by tougher environmental legislations, environmental monitoring and environmental education.