Egyptian Journal of Remote Sensing and Space Sciences最新文献

Hyperspectral anomaly detection using collaborative representation (CR) has attracted high interest in recent years. Ignoring global information and the use of fixed dual window, which is inappropriate for targets with different sizes, are some disadvantages of the existing methods. In this paper, the adaptive window based CR, called as AWCR, is proposed, which utilizes the results of two segmentation maps with different numbers of superpixels to find appropriate size of inner and outer windows for each test pixel. In addition to local information contained in adaptive dual windows, two individual dictionaries are obtained for background and anomaly subspaces from the whole image to provide the global information. Both local and global residual terms are fused to result in the final residual term in AWCR. The experiments show high detection performance with a reasonable computation time for AWCR compared to several serious competitors.

The UV Index is a useful tool to alert people with possible risks of exposure to solar UV radiation in Egypt. Ground UV-Index observation is a primary source to monitor solar UV levels, however the spatial coverage of the ground station is quite limited. The validation of available measurements were used frequently to define the possibility of using satellite data when measurements are not available, this was carried out for (leave area index and temperatures) for example (Ganguly et al., 2012) and (Laraby and Schott, 2018). In order to test the validity of the UV-index satellite products against ground observations, three satellite instruments (OMI, Terra + Aqua, and Terra + Npp) was performed at noontime in all sky conditions in the period 2012–2017 at three sites; Aswan, Cairo, and Matruh. The aforementioned sites were selected to represent different climates in Egypt. Annual intercomparison highlighted higher relative bias (rbias) at OMI (6.4 %) than both Terra + Aqua (2.3%) and Terra + Npp (2.8%). Also, Mean Absolute Percentage Error (MAPE), shows that OMI (10.6%) is relatively higher than both Terra + Aqua and Terra + Npp. (8.5 %). Based on these results, both Terra + Aqua and Terra + Npp have a better performance with respect to ground observations than OMI. This was due to OMI being more sensitive to dust and cloud than, Terra + Aqua and Terra + Npp.

The land use and land cover study (LULC) play an essential role in regional socio-economic development and natural resource management to develop sustainable development in vegetation changes, water quantity and quality, land resources, and coastal management. This study uses remote sensing data to investigate LULC in the Northern Border Region (NBR) in the Kingdom of Saudi Arabia. The purpose of this study is to obtain a better understanding of the patterns and drivers of changes in LULC in the NBR over the past three decades. Remote sensing data from Landsat imagery between 1990 and 2022 were used to classify LULC types, and a time series analysis was performed using Landsat imagery to detect changes over time. The classification finds four main classes: bare land, built-up area, rocks, and vegetation. The results indicate a significant increase in urban development. The outcomes revealed that most urbanization occurred in the outskirts of the cities, where previously there were bare soil lands. The main drivers of urbanization were population growth and economic development. These findings have important implications for city planning, the management of green spaces, and the sustainable development of cities. Maximum Likelihood classifier was used to perform the classification. The accuracy assessment demonstrated satisfactory results, with an overall accuracy of 92.6%. The study paves the way for further monitoring LULC changes in the NBR geographic location. The technique used was adequate to address the objectives of this study.

The Urban Heat Island (UHI) issue is a result of the undesirable effects of urban growth on the environment, such as temperature rises and landscape changes that cause environmental dangers. Thus, the purpose of this research is to investigate the effect of Land Use Land Cover (LULC) change on Land Surface Temperature (LST) and then study UHI in Sharqiyah from 2001 to 2022 using remote sensing data. This data was collected from the Landsat satellite and Moderate Resolution Imaging Spectroradiometer (MODIS) 11A Thermal sensors. A Mono-Window Algorithm was used on Landsat 8 and 9 data to estimate the LST. To determine the LST and UHI, the thermal band was utilized. LULC maps were created using the Support Vector Machine (SVM) classification technique. To evaluate various LULC indices in the Sharqiyah and find their correlation with LST, the spectral indices Normalized Difference Vegetation Index (NDVI), Normalized Difference Bare Land Index (NDBaI), and Normalized Difference Built-up Index (NDBI) were obtained from the processing of multispectral Landsat data. To check data sources, air temperature measurements for Sharqiyah were also acquired. The results show that urban expansion has increased in a noticeable trend. The built-up area increased by 18.9% during the research phase, and the region's mean LST increased within 3.98℃. The UHI threshold temperature increased by 4.27℃. This research is critical for Planning Engineers and environmental scientists to realize LULC variations effects on LST and to suggest suitable political steps to regulate urbanization in Sharqiyah Governorate.

Even though there are expert monitoring and assessment stations in large cities, air quality monitoring and measurement have a high cost and face significant issues. Data on air pollution can be acquired from remote sensing satellites for large areas and at a reasonable expense to compensate for monitoring stations on the ground. This research presented a method for retrieving PM₁₀ from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Aerosol Optical Thickness (AOT) records. The study depends on a previously established equation to retrieve PM₁₀ over Malaysia in September 2014. In Contrast Reduction Technique, we used Aerosol Robotic Network AERONET AOT to define the reference. The conversion factors, representing the relationship between AOT and PM₁₀ satellite columns, were determined using a mathematical approach. The size and type of aerosol, relative humidity, and boundary layer height vary globally and seasonally, thus the AOT–PM₁₀ relationship fluctuates spatially and temporally. The conversion factor was then applied to the MODIS image to predict the surface level of PM₁₀ concentrations in micrograms via cubic meter (μg/m³). Moreover, the achieved correlation coefficient R² of calculated PM₁₀ based on ground truth data was equal to 0.92. Based on the results obtained from the thematic maps, PM₁₀ levels are significantly higher in specific cities such as Kuala Lumpur and Johor. Where PM₁₀ ranged from (26.5 to 72) µg/m³, while AOT values were between (0.12 and 0.83). It raises concerns about the environmental health threats and their relationship to air quality in these regions as a research topic.

To solve problems for false detection, inadequate regression performance of anchor frames, and the inability to detect small targets in traditional multiscale target detection methods based on YOLOv4, we propose a novel target detection framework named as Enhanced YOLOv4. Firstly, our improved BiFPN replaced the original PANet as the feature fusion module, which can achieve multi-scale feature fusion by way of shared weights. Secondly, the channel attention mechanism (CAM) was embedded before the detection head to highlight the correlation between channels so that small targets can be get more attention. At last, to improve the anchor box regression effect and accelerate the training speed of YOLOv4, we improved the net training loss function, in which the original CIoU was replaced by CDIoU. The experimental results on the DOTA dataset validate our improvement. The mAP of our method is 90.88%, and the frame rate reached 58.76 FPS, at the same time, the speed of detection is not affected significantly.

The tropopause layer is a crucial stratum of the earth's atmosphere that attracts more interest from climate and atmospheric researchers. The observables of the global navigation satellite system (GNSS) allow for continuous and long-term research of the atmosphere. The Meteorological Operational Satellite Program (MetOp) mission has a large number of radio occultation (RO) events globally with a high vertical resolution. For investigating the atmosphere, GNSS RO is regarded as a great active remote sensing approach. The present paper investigates the tropopause height (TPH) globally using 5,738,483 GNSS RO measurements of MetOp from 2007 to 2021 to analyze the monthly and yearly variability patterns of TPHs. The spatiotemporal variation of TPH confirms a bell shape. According to the analysis, the TPH varies with latitude, with the highest level reaching up to 17 km in the equatorial region and decreasing gradually to get its lowest value of 8 km at the poles. The global TPH estimated from GNSS RO is very well matched with the TPH estimated from the ECMWF Reanalysis v5 (ERA5) model with a correlation of 0.9997 in 2021. The findings of this study will contribute to a better understanding of TPH variations. As a result, our findings may be helpful in advancing atmospheric modeling and estimating wet delay for GNSS observations.

With the rapid expansion of cities, monitoring urban sprawl is recognized as a vital tool by many researchers who use this information in several applications like urban planning, microclimate modelling, policy development, etc. However, accurate land cover (LC) prediction is still challenging, even with technological advancements. Machine learning (ML) and artificial intelligence (AI) have gained a reputation amongst diverse science applications, including their popularity in monitoring land cover. Therefore, the present study investigates the performance of the ML-based classification algorithm random forest (RF) in monitoring LC classes for 2016 and 2021 for the metropolitan region of Kuwait City, Kuwait. The accuracy assessment for the derived land use maps achieved an overall accuracy of 93.6% and 95.3% and kappa coefficient values of 0.86 and 0.93 for 2016 and 2021, respectively. The results show an increase in built-up cover by ∼11 %. The land use maps for 2016 and 2021 were further used to predict the urban built-up for 2026 using an artificial neural network (ANN) based on multi-layer perceptron neural networks (MLPNNs). It was predicted with an overall accuracy of 83.6%. The built-up was predicted to increase by 15% in 2021–2026, and mostly expansion was observed on the western and southern sides. The outcomes exhibit that MLPNN techniques combined with Remote sensing and Geographic Information Systems (RS and GIS) can be adopted to derive the land cover and predict the urban sprawl with fair accuracy and precision. Such studies would prove valuable to city governments and urban planners to improve future sustainable development strategies.

Orthometric heights are important for various applications such as GIS, geomatics, engineering, and geoscience. The ellipsoidal heights can be computed by Global Navigation Satellite System (GNSS) as an accurate, rapid, and efficient method for height determination. The accurate geoid is essential to convert the ellipsoidal heights from GNSS to orthometric heights. The research developed a new geoid called “KW-FWGM2022″ specifically for Kuwait. We used a composite global geopotential model from SPW R5 with EGM2008 and the digital elevation model from SRTM1. The Wong and Gore modified with Airy-Heiskanen topographic-isotactic reduction were used to compute the geoid model. To assess the accuracy of the KW-FWGM2022 geoid, GNSS/leveling stations were used and the assessment showed that the model's accuracy was better than 1.8 cm as a standard deviation. This demonstrates that the KW-FWGM2022 geoid model is highly accurate and suitable for use in various GIS and Geomatics applications in Kuwait.

This research investigates the effect of the ‘future’ gravity mission (FGM) architectures i.e. Bender, Helix, Pendulum, and Cartwheel up to spherical harmonics (SH) degree/order (d/o) 120/120 to improve the estimation of the gravitational field in the Saudi Arabia. For this purpose, we evaluate ground-truth gravity anomalies and GNSS/Leveling data with the satellite-based gravity models of each aforementioned FGMs. The comparison with gravity anomalies given by the FGM provides refinements of about 57 – 61 μGal with respect to (w.r.t.) those of GRACE- and GOCE-based GGMs. The comparison with GNSS/Leveling indicates that the least differences in terms of standard deviations (STD) of geoid heights are provided by the Bender-type FGM that provides the overall least STD differences of about 62.58 cm w.r.t. the GRACE- and GOCE-based GGMs, that provide STD differences of about 62.88 cm and 62.62 cm, respectively. The outcome of this study shows that implementing additional gravity information in different flight directions of the proposed FGMs (i.e along-track, cross-track, and radial) showed slight improvement (sub of a millimeter).