Egyptian Journal of Remote Sensing and Space Sciences最新文献

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).

Critical watersheds that exceed their carrying capacity occur in many regions of the world; their formation is facilitated by a significant driving factor known as land use/land cover (LULC) changes. This study aims to identify the LULC changes in Cisadane Watershed, Indonesia, in 2010, 2015, 2021, and simulate future LULC for 2030 and 2050. Landsat 2010 and 2015 and Sentinel 2A images from 2020 were employed for deriving LULC maps using Random Forest. This study applied a Land change modeler (LCM) under the multi-layer perception Markov chain (MLP-MC) to predict the future LULC in three scenarios. The scenarios are business as usual (BAU), protecting paddy fields (PPF), and protecting forest areas (PFA). The results showed that all the LULC maps demonstrated excellent accuracy, indicated by >83% overall accuracy. Furthermore, BAU produces the worst effect of decreasing forest and paddy field areas. PPF tends to cause forest loss, while PFA is predicted to reduce the paddy fields. There is a trade-off between maintaining food security and conserving natural resources. The study reveals the importance of efficient land use planning in the future amidst increasing resource demand due to population growth while existing land resources are limited.

Synthetic Aperture Radar (SAR) data processing evolving from level-0 raw data is complicated, especially in data decoding, manifesting in obtaining a well-focused SAR image. This paper is intended to present a complete MATLAB-based SAR data processing tool, which helps the end-user to treat simply the steps of image generation. This paper would enrich the research community in the field of SAR processors, especially in the area of understanding, handling, and developing a SAR processor based on space packet protocol standard (STD 01) used in many SAR systems such as Sentinel-1, ERS-1, CubeL, JPSS-2, 3, and 4. Also, this work opens the door for researchers to decode other space packet protocol standards and even to create an algorithm based on fully understanding the image formation algorithm from its roots. Moreover, the work in this paper could be a stepping-stone for the beginner in the field of SAR signal processing to become familiarized with SAR image generation procedures. The level-0 raw data used in this paper is based on Sentinel 1 SAR satellite obtained from the European Space Agency Copernicus website, a free open-source for level-0 and level-1 data types. The MATLAB program allows users to compare their generated image with the level-1 single-look complex (S1-L1-SLC) image utilizing entropy, contrast, and sharpness image quality metrics.

The results showed that the images produced by the proposed algorithm are comparable to Sentinel-1 level-1 SAR images for the same scene and achieved satisfactory accuracy under the requirements for image quality.

Coasts are spaces that have natural resources and host frequent human activities such as settlement, transportation, tourism, and trade. This is why coasts continue to deteriorate and change. There is a lot of legislation in Turkey about the protection and use of coastlines. Designating the coastal edge line and coastal planning are important procedures. In the identification of changes on the coast, aerial photographs and satellite images obtained at different times are frequently utilized. This way, destruction on the coast is easily revealed. These examinations guide official institutions regarding the protection-use balance, as well as the inspections and procedures that need to be carried out. In this study, the coastal areas of the Dörtyol district of the province of Hatay in the Gulf of İskenderun in the Eastern Mediterranean Region were investigated. Aerial photographs dated 1975 and 2011 and an orthophoto map dated 2015 were used in the analyses. Ownership analyses were carried out by comparing these photographs and maps to the approval coastal edge line points. Encroachments into the approved coastal edge line and local (plot-based) plan revisions were investigated. As a result of these examinations, the effects of changes in land use planning on ownership and the coastal edge line were revealed. Contributions will be made to the conduct of necessary interventions by sharing the results of this study with institutions responsible for preservation, planning, and inspection.

Desertification leads to a decline in land production, resulting from biophysical interaction and human factors with temporal and spatial changes. This paper aims to evaluate the desertification sensitivity using Mediterranean Desertification and Land Use (MEDALUS) approach in the Northwestern El Minya Governorate in Egypt. The study area is one of the promising and important areas for land reclamation and developmental projects. Four indicators; climate, vegetation, management, and soil, were used in this study. Landsat 8 (OLI) satellite image 2021 was processed to calculate NDVI index to assess vegetation quality index. Then, it was classified into land cover classes to calculate the management quality index. ASTERDEM satellite data and the climate data were used in the climate quality index. Seventy soil samples were collected and analyzed for chemical and physical properties to be used for the retrieval of soil quality index. These indicators were integrated and used as input criteria in the model of desertification sensitivity. The physiographic unit extraction indicated that significant landforms were included in the study area namely; Old wadi Sediments, Peniplain, old river terraces, Shoulders, Sand sheets, Rock outcrops, and Sand dunes, Erosional Plains, Hill and Plain out wash. The obtained results showed that the severely sensitive area to desertification was represented by 86.7 % of the study area. The main factors affecting the sensitivity were climate conditions (arid, semi-arid), low soil quality and management in addition to the low vegetation cover. Furthermore, about 3% of the study area was located in the moderate sensitive class while the low sensitive area exhibits 1.06 %. Present study supports decision makers to reduce the constraining environmental conditions.

The raw orbital images captured by the new launched SDGSAT-1 Multispectral Image for Inshore (MII) are plagued by wide-irregular stripe noise, due to inconsistent unit response. This paper proposed a new method for destriping wide-irregular stripe noise in MII using the characteristics of side-slither data. Firstly, the raw side-slither data was standardized using line detection to guarantee that each row observed the same ground object. Then, the whole orbital side-slither image was segmented into blocks of equal length, and it was found that the response of wide-irregular stripe noise is consistent within a certain length. The Inverse Distance Weight was used to interpolate the DN values of striped pixels as referenced values, and the segmented length was determined by calculating Pearson correlation coefficient between the original and referenced DN values. Thirdly, the Random Sample Consensus (RANSAC) algorithm was used to find the inliers and calculate the correction parameters, after it was discovered that the original and referenced DN values had a linear correlation. The proposed method, SIR (consists of image segmentation, pixel interpolation and RANSAC fitting), can directly destripe the raw orbital image. One orbital side-slither data and six ordinary orbital data were selected for verification. Twelve state-of-the-art methods were chosen for comparison with SIR. The accuracy scores of SIR on three assessment indexes were higher than those of twelve other methods. The destriping outcomes for the images of city, cloud, forest, and river demonstrated the effectiveness of SIR in correcting wide-irregular stripe noise in MII images.

Accurate land surface reflectance plays an important role in the accurate inversion of surface parameters, and atmospheric correction plays a decisive role in obtaining accurate reflectance. For GF-1 WFV and GF-6 WFV images, there are two major issues to be addressed, including the spectral differences between nadir with far off-nadir pixels and the spatial variability of atmospheric components for wide imaging. Therefore, this study focuses on these two issues using the Sensor Invariant Atmospheric Correction (SIAC) method. Our results reveal that the SIAC approach improves the correlation accuracy from 0.8868 to 0.9173 for GF-1 WFV image compared with Sentinel-2 reflectance, from 0.9530 to 0.9620 for GF-6 WFV image compared with the results using FLAASH model. For alleviating wide-swathed anisotropy, the directional imaging angle is calculated with the result ranging from 5.6450° to 33.7497°. Furthermore, the atmospheric components have been inversed pixel by pixel with obvious spatial variation. And the correlation of inversed aerosol optical thickness (AOT) and total column water vapor (TCWV) with a spatial resolution of 500 m TCWV with measured results of AERONET (AErosol RObotic NETwork) observation stations are 0.9175 and 0.4442, respectively. These results reveal that the atmospheric correction method works well, which is effective for the wide swath of Chinese GF-1 WFV and GF-6 WFV images on land.