Search

The Shuttle Radar Topography Mission (SRTM) is a digital representation of the terrain surface morphology that contains rich terrain information and is widely used in environmental analyses. However, SRTM is adversely affected by mixed noise, which typically include random and stripe noise. Mixed noise results in the significant loss of topographic information, which reduce the validity of related research. To eliminate mixed noise in SRTM data, we propose an adaptive low‐rank group sparse model based on edge preservation (ALGS_EP) to remove mixed noise from datasets. The method relies on a low‐rank group sparse model that considers the gradient features of the terrain. It calculates a terrain factor to adapt the noise elimination model to terrain changes. Additionally, it integrates with the edge structure of elevation data and applies a double‐gradient constraint to preserve the structural details of the elevation data. The proposed model, built upon the alternating direction multiplier method framework, enhances the traditional weighted kernel paradigm minimization algorithm by introducing variable weights that adjust according to the gradient of elevation data during iterations. Additionally, it incorporates the correlation between strip noise and residual data blocks when computing the iteration count, ensuring an iterative solution approach that converges to the optimal solution. We used ALGS_EP to process global SRTM 1 data and published a higher‐quality and higher‐precision elevation dataset. The elevation data noise before and after noise elimination were statistically analyzed. Simulated and empirical results show that the model is highly robust and more effective than existing methods in both visual and quantitative evaluations. The noise elimination rate was 97.6%, compared to the original data. Therefore, this research was valuable for applications that use digital elevation model as an important data layer. [ABSTRACT FROM AUTHOR]

In this study we examine the effect of land use land cover (LULC) types on MODIS land surface temperature (LST) in Ghana. The purpose of the research is to explore the relationship between air temperature ([Formula: see text]) and LST over LULC types and to identify their association with elevations. Datasets consist of MODIS land cover type-product (MCD12Q1), Shuttle Radar Topography Mission (SRTM) DEM, and composite dataset of 1 km terra MODIS and aqua MODIS 8-day LSTs (MOD11A2 and MYD11A2). The study time period ranged from February 2003 to January 2019. Two categorical steps were employed: (i) Assessment of LST over LULC types based on all pixels. (ii) Assessment of LST over LULC types based on meteorological station locations. Validation of LSTs was done using the air temperature of station records. Seventy station sites across Ghana were selected. The results confirm an estimation of the average [Formula: see text] consisting of [Formula: see text][Formula: see text] and [Formula: see text] RMSE, MBE, and MAE were also computed. There was a direct positive correlation between [Formula: see text] and LST over LULC types. ANOVA test method was applied to verify the accuracy of LSTs over LULC types. Croplands have the highest day LST of 39.6 °C whereas evergreen broadleaf forest has the lowest (27.0 °C). The lowest night LST was observed in savannas, grasslands, and croplands with each having 18.6 °C while the highest was water body (23.6 °C). The study revealed that an increase in elevations results in a decrease in LSTs. Also, climatic conditions and specific heat capacities of LULC types had an influence on LST estimates.

In this study we examine the effect of land use land cover (LULC) types on MODIS land surface temperature (LST) in Ghana. The purpose of the research is to explore the relationship between air temperature ( T air ) and LST over LULC types and to identify their association with elevations. Datasets consist of MODIS land cover type-product (MCD12Q1), Shuttle Radar Topography Mission (SRTM) DEM, and composite dataset of 1 km terra MODIS and aqua MODIS 8-day LSTs (MOD11A2 and MYD11A2). The study time period ranged from February 2003 to January 2019. Two categorical steps were employed: (i) Assessment of LST over LULC types based on all pixels. (ii) Assessment of LST over LULC types based on meteorological station locations. Validation of LSTs was done using the air temperature of station records. Seventy station sites across Ghana were selected. The results confirm an estimation of the average T air consisting of T max , T min , and T mean . RMSE, MBE, and MAE were also computed. There was a direct positive correlation between T air and LST over LULC types. ANOVA test method was applied to verify the accuracy of LSTs over LULC types. Croplands have the highest day LST of 39.6 °C whereas evergreen broadleaf forest has the lowest (27.0 °C). The lowest night LST was observed in savannas, grasslands, and croplands with each having 18.6 °C while the highest was water body (23.6 °C). The study revealed that an increase in elevations results in a decrease in LSTs. Also, climatic conditions and specific heat capacities of LULC types had an influence on LST estimates.This study is aims to analyze the effects of LULC types on land surface temperature in Ghana. It was revealed that LULC types had a noticeable influence on the LST estimates. This study provides practical information for urban planners, natural resources managers, environmental experts, extension officers, and farmers to manage natural landscapes to be sustainable and healthy. [ABSTRACT FROM AUTHOR]

Flooding is one of the most catastrophic natural disasters in terms of provoking socio-economic losses. The current study is to foster a flood susceptibility map of Krishna District in Andhra Pradesh (AP) through integrating remote sensing data, geographical information system (GIS), and the analytical hierarchy process (AHP). Eleven factors, including elevation, slope, aspect, land use/land cover (LULC), drainage density, topographic wetness index, stream power index, lithology, soil, precipitation, and distance from the streams, are considered for identifying and evaluating the spatial distribution of critical flood-susceptible regions. Thematic maps of different factors were derived in GIS using remote sensing data obtained from Sentinel-2A (satellite sensor), shuttle radar topography mission digital elevation model (SRTM DEM v3), and other scientific data products. An analytical hierarchy process is a mathematical approach for decision support, primarily based on the weight and rank of different causative factors. AHP technique is implemented for flood hazard modeling and ascertaining the Flood Hazard Index (FHI) to produce a flood susceptibility map. Different thematic maps weighed with the AHP framework are combined using overlay analysis to produce the flood susceptibility map of the study region. The outcomes of the study demonstrate the potential of GIS and AHP in providing a premise to recognize the vulnerable areas that are susceptible to flood. According to the findings, the Flood Hazard Index is 42% and the study region is classified into very high, high, moderate, low, and very low susceptible, respectively. Following that, historical flood data was used to validate the accuracy of the generated flood susceptibility map. This shows that a maximum of 90% of the data points are within floodplain. [ABSTRACT FROM AUTHOR]

Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m−2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m−2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m−2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m−2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m−2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m−2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Sciences, Life & Applied Sciences is the property of Arab Journal of Sciences & Research Publishing (AJSRP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The need for water security in different regions of the world has led to the deployment of remote sensing (RS) and geographic information systems (GIS) as decision support tools with geophysical methods. In this study, the remote sensing, geospatial and aeromagnetic data were integrated for mapping the groundwater potential at the University of Ilorin, Nigeria in West Africa. Several remote sensing and geospatial datasets (geomorphology, lineament density, slope, rainfall, land use/land cover, soil type and drainage density) were enhanced, weighted, prioritised and ranked using the analytical hierarchy process (AHP) technique. Subsequently, the thematic datasets were integrated into a GIS platform to produce a groundwater potentiality zonation (GWPZ) map. Visualisation of the corrected aeromagnetic data was improved by using some image enhancement techniques (filters) to produce the magnetic anomaly maps that revealed gross and subtle subsurface features. Multiple validation of the GWPZ map was achieved using the magnetic anomalies maps, borehole groundwater potential indices (GWPI) and the receiver operating characteristic (ROC) curves. The GWPZ map generated was classified into three groundwater potential classes with different spatial distributions. These include moderate (60.6%), high (5.9%), and low (33.5%). The magnetic anomalies maps agreed well with the remotely sensed models through surface-subsurface lineaments superposition. The area under curve (AUC) of the ROC showed that the predictive rate of the GWPZ model was 0.73. This value suggested that the model satisfactorily predicted the groundwater potential of the study area. Thus, this present study demonstrated the relevance of geospatial and geophysical techniques for regional groundwater potential mapping at the assessment phase of integrated water resources management towards providing a better understanding of the hydrogeology for easy decision-making and better groundwater management.

Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m−2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m−2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m−2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m−2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m−2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m−2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.

The Panama Bight eco-region along the Pacific coast of central and South America is considered to have one of the best-preserved mangrove ecosystems in the American continent. The regional climate, with rainfall easily reaching 5–8 m every year and weak wind conditions, contribute to the exceptionally tall mangroves along the southern Colombian and northern Ecuadorian Pacific coasts (Nariño Department and Esmeraldas Province areas). Here we evaluate the use of different methods (ground-based measurements, drone imagery and radar data [Shuttle Radar Topography mission-SRTM and TanDEM-X]) to characterize the structure of the tallest of these forests. In November 2019, three mangrove sites with canopy heights between 50 and 60 m, previously identified with SRTM data, were sampled close to the town of Guapi, Colombia. In addition to in situ field measurements of trees, we conducted airborne drone surveys in order to generate georeferenced orthomosaics and digital surface models (DSMs). We found that the extensive mangrove forests in this area of the Colombian Pacific are almost entirely composed of Rhizophora spp. trees. The tallest mangrove tree measured in the three plots was 57 m. With ca. 900 drone photographs, three orthomosaics (2 cm pixel–1 resolution) and digital surface models (3.5 cm pixel–1) with average area of 4,0 ha were generated. The field-measured canopy heights were used to validate the drone-derived and radar-derived data, confirming these mangrove forests as the tallest in the Americas. The drone-derived orthomosaics showed significant patches of the Golden Leather Fern, Acrostichum aureum, an opportunistic species that can be associated to mangrove degradation, indicating that the mangrove forests investigated here may be threatened from increased selective logging requiring improvements and effective implementation of the current mangrove management plans in Colombia. The techniques used here are highly complementary and may represent the three tiers for carbon reporting, whereby the drone-derived canopy height maps, calibrated with local in situ measurements, provides cheap but reliable Tier 3 estimates of carbon stocks at the project level.

The present study combined analysis of satellite, aeromagnetic and radiometric data for evaluation of structural features within the Ife-Ilesha schist belt. Shuttle Radar Topographic Mission digital elevation data have been enhanced using hill-shading technique for the delineation of morphological features. The superposition of total gradient amplitude lineaments on the 3-D Euler deconvolution map revealed the trends and depth of structural features within the study region. The major trends are NE–SW, NNE–SSW, E–W and minor trends in the N–S direction, including the Ifewara shear zone that trends in the NNE–SSW. The estimated depths to the top of the sources within the shear zone varies from 90 to 200 m. Complementary analysis of the airborne radiometric data revealed that the Ifewara shear zone and adjacent regions are characterized by radiometric anomalies, indicating regional mineralization alteration zone. Generally, there is a good correlation between the satellite, radiometric, aeromagnetic maps which provides new insights and re-evaluation of structural features. [ABSTRACT FROM AUTHOR]

The Digital Elevation Model (DEM) has been known as a quantitative description of the surface of the Earth, which provides essential information about the terrain. DEMs are significant information sources for a number of practical applications that need surface elevation data. The open-source DEM datasets, such as the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), and the Advanced Land Observing Satellite (ALOS) usually have approximately low accuracy and coarser resolution. The errors in many datasets of DEMs have already been generally examined for their importance, where their quality could be affected within different aspects, including the types of sensors, algorithms, terrain types, and other features. Ground control points (GCPs) used in this study were observed through the utilization of differential global positioning system (DGPS) with dual frequencies. Statistical indices were used to compare, evaluate, and validate the DEMs data against DGPS data. Statistical analysis of DEMs pointed out that SRTM accuracy was higher, with Root Mean Square Error (RMSE) of ±6.276m as compared to the other DEMs. ASTER showed the biggest residual error with an RMSE of ±10.241m. Nevertheless, ALOS was noticeably improved by having an RMSE of ±6.988m. [ABSTRACT FROM AUTHOR]

We present geodetically derived coseismic slip models for four Mw ~6 strike-slip earthquakes that struck the mainland island of Sumatra, Indonesia, between 2007 and 2016. Three of these earthquakes ruptured segments of the Sumatran Fault Zone: the 2007 Mw 6.3 and 6.4 Lake Singkarak doublet, and the 2009Mw 6.6 Dikit event. The fourth earthquake, the 2016Mw 6.6 Pidie Jaya event, ruptured an unmapped active fault in a region of historically low seismicity near the northern coast of Aceh. Our results suggest that (1) the estimated rupture extents of the 2007 Lake Singkarak doublet and 2009 Dikit event were limited by geological structures such as step overs that define segment boundaries, (2) the 2016 Pidie Jaya event did not produce surface rupture despite the fact that it produced large shallow slip of more than 2.5 m, and (3) the remaining seismic gaps on the Sumatran Fault Zone are primarily located between the equator and northern Sumatra. We strongly recommend that seismic hazard assessment for densely populated areas in Sumatra include threats not only from the Sumatran Fault Zone but also from additional potentially active faults outside of the main Sumatran Fault Zone. [ABSTRACT FROM AUTHOR]