Egyptian Journal of Remote Sensing and Space Sciences最新文献

Natural crises manifested in floods and droughts are counted as the most severe impacts of climate change on the world. In this regard, flash floods are the most common cause of economic and human losses worldwide. However, the present study focuses on the flash flood-affected area between Zafaarana and Ras Ghareb coastal roads. Sentinel-2 satellite images of recent years before and after the flash flood have been utilized to detect flooded areas and investigate their environmental conditions.

Initially, the captured images were pre-processed to compare the environmental conditions before and after flooding. Consequently, the Normalized Difference Water Index (NDWI) was utilized to classify water bodies in different bands. Finally, an image difference feature (IDF) model with computation of per-pixel features, merging image disparities, and calculation of the characteristic value phases was constructed to extract various image differences after photo processing, that's to identify flooded pixels in the images and assess their performance in the proposed model. The proposed IDF model was compared by rating each model on the same test set, while changing the training set. In conclusion, the proposed algorithm shows an accuracy of 98.9%, which is a better flood image processing technique than other methods. The insights from this research will help decision makers in structuring their rescue strategies and evacuation maps during and before the environmental crisis.

Moving towards horizontal expansion in the vast barren lands to alleviate overpopulation along the Nile River is imperative to Egypt’s 2030 sustainable development strategy. Accordingly, a mega reclamation project was advocated with a key goal of achieving food self-sufficiency. Solar-powered groundwater pumping system was adopted as the main water supply. Planning efforts are therefore inevitable to help locate the most favourable sites for such extensive cultivation activities. Herein, a multicriteria decision analysis was conducted to facilitate the zoning of potential rural communities across the northern portions of the Western Desert of Egypt. For this purpose, data of groundwater exploration, soil characterization, terrestrial accessibility, insolation intensity, and terrain information were fused to produce a high-resolution suitability map. The analytical hierarchy process approach was adopted to set the weighted importance of adopted criteria. The study area was categorized into Best, Good, Moderate, Fair, Poor, and Restricted classes at 1.7%, 13%, 42.6%, 26%, 10%, and 3%, respectively, of the entire region, while the constrained plots were masked out. The implemented and proposed wells fields within the underway national rural development project extend over agriculturally suitable pixels affirming the validity of the developed geospatial model. About 1.5 million ha, representing 7.2% of the undeveloped area, were found to be highly suitable for future expansion of agribusiness activities. The generated priority map will assist the decision-makers in the planning procedures for ongoing reclamation activities throughout Egypt.

Cross-resolution person re-identification is a challenging problem in the field of person re-identification. In order to solve the problem of resolution mismatch, many studies introduce super-resolution into person re-identification tasks. In this work, we propose a cross-resolution person re-identification method based on double transformer residual super-resolution network (DTRSR), which mainly includes super-resolution module and person re-identification module. In the super-resolution module, we propose the double transformer network as our attention module. First of all, we divide the features extracted by the residual network. Then calculate the similarity between each local feature and the global feature after average pooling and maximum pooling respectively, which makes our module quickly capture the hidden weight information in the spatial domain. In the person re-identification module, we propose an effective fusion method based on key point features (KPFF). The key point extraction model can not only solve the problem that local features can not be accurately aligned, but also remove the interference of background noise. In order to fully mine the relationship between the features of each key point, we calculate the two-way correlation between each key point feature and other features, and then superimpose the two-way correlation with the feature itself to get the superposition feature which contains global and local information. The effectiveness of this method is proved by extensive experiments. Compared with the most advanced methods, the test results in the three datasets show that our method improves rank-1 by 1.1%, 3.5% and 1.7%; and rank-5 by 1.3%, 1.7% and 0.3%; and rank-10 by 0.1%, 0.4% and 0.1%, respectively.

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.