Egyptian Journal of Remote Sensing and Space Sciences最新文献

Paddy cultivation in Malaysia plays a crucial role in food production, with a focus on improving crop quality and quantity. With current national self-sufficiency levels ranging between 67 and 70%, the Malaysian government intends to produce higher-quality crops and boost agricultural production. However, the prominent paddy-producing state of Kedah has witnessed a decline in yields over the years. To address this, the study explores the effectiveness of unmanned aerial vehicles (UAVs) equipped with vegetation indices (VIs) for monitoring paddy plant health at various growth stages. Researchers acquired aerial imagery during two seasons in 2019, capturing three distinct growth stages: tillering (40 days after sowing), flowering (60 days after sowing), and ripening (100 days after sowing). These stages represent critical points in the paddy plant's life cycle. Agisoft Metashape software processed the images to extract VIs data. The study found that the Normalized Difference Vegetation Index (NDVI) and Blue Normalized Difference Vegetation Index (BNDVI) exhibited over 90% similarity. In contrast, the Normalized Difference Red Edge Index (NDRE), utilizing near-infrared and red-edge light reflections, demonstrated a unique relationship. NDRE outperformed NDVI and BNDVI with an R-squared value of 0.842, showcasing its superior accuracy, especially for dense crops like paddy plants sensitive to subtle changes in vegetation. In conclusion, this research highlights the potential of UAV-based VIs for effectively monitoring paddy plant health during different growth stages. The NDRE index, in particular, proves valuable for assessing dense crops, offering insights for precision agriculture and crop management in Malaysia.

Rapid urbanization and unplanned development have posed a threat to the thermal environment in a country like China. The urban heat island (UHI) phenomenon is one of the most serious issues because of its strong relation to thermal comfort, air pollution, and public health. The water bodies, as an important component of the urban ecosystem, are generally considered a vital resource to mitigate the UHI. This study provides direct evidence with the help of satellite observation and field measurement data using the mono-window algorithm, spatial buffer analysis, and linear regression methods to explore how the stream water affects the local temperature variation. The results showed that Land use/cover change (LUCC) and Land surface temperature (LST) on both banks of the Bahe River changed significantly. The LST of the river was significantly massively reduced within the distance range of 300 to 500 m, and 400 to 600 m, with an average temperature dip from 3.7 to 2.8 °C, and from 3.2 to 1.7 °C respectively were during the summer on east and west river banks. In addition, the surrounding LUCC composition and configuration could strongly affect the maximum cooling scale. The results provide insights for urban planners and managers to arrange the LUCC composition between the river design in urban areas and the cooling effect demands.

Thermal control of small satellites in low earth orbit (LEO) is not easy due to the intermittent heating conditions. The satellites in LEO are sometimes present in the illumination zone and other times in the eclipse zone, which imposes difficulties keeping their temperatures within the safe range. The present study investigates a thermal energy storage panel (TESP) integrated with phase change materials (PCM) to control the temperatures of satellite subsystems. The TESP was made of aluminium with outer dimensions of 100 mm long, 71 mm wide, and 25 mm high. The PCMs used were organic-based materials, which were RT 12, RT 22, and RT 31. The TESP was tested under two thermal powers of 11 W and 14 W. These powers are typical of satellite subsystems. The finite volume method was adopted for thermal analysis of the TESP. The significance of this study is that it provides a detailed computational analysis of the TESP for microsatellites' temperature management under typical LEO conditions. The research outcomes show a significant advancement in the thermal managing performance of PCM-based TESP. RT 22 could reduce the highest temperature by 4.7 % and raise the lowest by 9.5 %. It was observed from the analysis that the PCM with intermediary melting temperature provided better thermal control efficiency. RT 12 reported a lower extreme temperature difference (ETD) and could decrease it by 63.9 % relative to the case with no PCM. At the same time, RT 22 reported an ETD of 23 min and could reduce it by 63 % relative to the case with no PCM at 14 W. The present study concluded that PCMs show great potential as a viable approach for effectively thermally managing devices that experience cyclic thermal fluctuations, such as the subsystems of satellites operating in LEO.

This paper proposes a novel multimodal deep weakly-supervised learning framework, SinkholeNet, to classify and localize sinkhole(s) in high-resolution RGB-slope aerial images. The SinkholeNet first employs a multimodal Convolutional Neural Network (CNN) architecture that simultaneously extracts features from the input RGB image and ground slope map and then fuses the extracted features. It then uses an improved ShuffleNet architecture on the fused features to classify patches as sinkholes or non-sinkholes. Finally, the last extracted feature maps, belonging to the sinkhole class, are used as input of gradient-weighted class activation mapping (Grad-CAM) to localize sinkhole(s) in a weakly-supervised setting. The proposed weakly-supervised framework intends to increase the available labeled data for training and decrease the cost of human annotation. We also introduce a novel publicly available weakly labeled sinkhole dataset comprising RGB-slope paired image patches to support reproducible research. The experimental results on the newly introduced dataset show that the SinkholeNet outperforms the other methods considered in this paper both for sinkhole classification and localization.

The urbanization rate of India is 35.9 % by 2022 reports. In this 45.23 % of urbanization is happening in Maharashtra and it is the third most urbanized state of India after Tamil Nadu and Kerala. In metropolitan areas, the classification of land cover from satellite images has been the focus of remote sensing over the years. Due to complex architecture and a lack of labeled data, classifying buildings in metropolitan areas from very high resolution (VHR) satellite imagery is challenging. Traditional approaches for building classification include hand-crafted features and transfer learning methods. These methods often struggle with the variability in building shapes, orientation, and viewpoint, leading to low accuracy in densely populated urban areas and limited performance when dealing with high- resolution satellite images. A deep-learning based approach for semantic segmentation using U-Net with a backbone of ResNet-34 is proposed for building classification. Urban area Dataset with Images of 0.5 m resolution is prepared from SASPlanet. One hot Encoding is applied for classifying buildings. U-Net is trained with encoded data. The proposed model is evaluated on the Indian dataset, specifically, the urban areas of Nashik, Maharashtra state and the accuracy obtained for the classification dataset is 60 % and the accuracy of the building detection is about 85 %. Change detection is calculated from bi-temporal images. The GIS maps are updated to detect changes in buildings, represented by different colors to distinguish newly constructed buildings, existing structures and demolished ones.

Though soil nutrients play important roles in maintaining soil fertility and crop growth, their estimation requires direct soil sampling followed by laboratory analysis incurring huge cost and time. In this research work, soil nutrients were predicted using VIs-NIR reflectance spectroscopy (range 350–2500 nm) with Partial Least Squares Regression (PLSR) and Support Vector Machine Regression Model (SVMR) model through principal component analysis. Two hundred soil samples were collected from Tarekswar, Hooghly, West Bengal, India to predict eight selected soil nutrients, such as soil organic carbon (OC), pH, available nitrogen (N), available phosphorus (P), available potassium(K), electric conductivity (EC), zinc (Zn) and soil texture (sand, silt, and clay) levels. The OC content was predicted with sound accuracy (R²: 0.82, RPD: 2.28, RMSE: 0.13, RPIQ: 4.15 FD-SG), followed by P (R²: 0.71, RPD: 1.83, RMSE: 4575, RPIQ: 3.44 1st derivative). The soil parameters sensitive to the particular band of visible spectrum were also identified viz. wavelengths of 409, 444, 591 and 592 nm for OC, 430 and 505 nm for P, 464 nm for K; 580 nm for Zn, 492,511,596 and 698 nm for N; 493, 569 and 665 nm for EC; 492,567 and 652 nm for pH; 457 nm for sand and 515 nm for clay.

The soil nutrient levels were predicted by PLSR and SVMR models through PCA and Sentinel 2 imagery and soil suitability map were also generated for seven soil parameters such as OC, pH, EC, N, P, K and clay content. Through map query tool in ArcGIS software environment the PLSR and SVMR model successfully identify the suitability class with level of accuracy of 87.2% and 88.9%, respectively, against the direct soil analysis based suitability mapping.

The machine learning technique based soil nutrient and soil suitability prediction can be easily adopted in different regions. This will reduce the cost of laboratory soil analysis and optimize the total time requirement.

The study conducted aimed to examine the accuracy of Digital Elevation Models (DEMs) in Egypt, specifically the TanDEM-X mission's 30 m and 12 m resolution DEMs and the SRTM DEM with a 30 m resolution. The accuracy of DEMs is essential for various civil engineering and surveying applications, especially in geoscience applications. To ensure the comparison's accuracy, the study used ellipsoidal heights instead of orthometric heights, preventing errors caused by global geopotential models in the conversion process. The evaluation of the three DEMs was carried out using 352 GNSS points. The findings indicate that both TanDEM-X DEMs with 30 m and 12 m resolutions outperform the SRTM 30 m in terms of vertical accuracy, making them ideal for geomatic applications that require higher-resolution DEMs. The TanDEM-X 30 m generates a standard deviation (STD) and a Root Mean Square Error (RMSE) of approximately 3.03 m and 3.45 m, respectively. On the other hand, the TanDEM-X 12 m generates an STD and RMSE of approximately 2.86 m and 3.18 m, respectively. Comparatively, the SRTM 30 m produces an STD and RMSE of approximately 4.67 m and 5.35 m, respectively.

Climate changes have exposed many countries to the risks of heavy rains and floods that may lead to the loss of lives and damage to properties. The trend in prioritizing the establishment of stormwater drainage systems in urban areas in Egypt came after the provision and completion of drinking water. This necessitated the implementation of permanent solutions to absorb the unprecedented amounts of rainwater and torrential rain, which were not taken into account when designing the existing sewage networks. Furthermore, there is a lack of water resources and the existing water resources are less than the demand. Therefore, this research study aimed to take advantage of the stormwater that falls on residential neighborhoods by making underground reservoirs or retention ponds to protect these areas from the damage caused by the rains and reusing it in irrigating local green area inside these regions, to achieve sustainable water resources solutions for these areas. Satellite imagery, DEM & ArcGIS are used. A hydrological calculation for 24 different storms, with 4 different return periods for New Cairo City. However, it is the basin of interest that includes public green spaces for rain harvesting and storage for irrigation. For avoiding the risks of heavy rains in a city that hasn’t stormwater networks, and get a sustainable solution by using the water from rainfall for irrigation through using sub-areas of some public green spaces such as retention ponds or ground reservoirs, using the runoff volume shall save approximately 6 days of irrigation per storm.

The agricultural sector in Egypt is adversely affected by factors such as inadequate soil fertility and environmental hazards such as pestilence and diseases. The implementation of early pest prediction techniques has the potential to enhance agricultural yield. Bactrocera zonata and Ceratitis capitata, known as peach fruit fly and Mediterranean fruit fly, respectively, are the predominant pests that cause significant damage to fruits on a global scale. The present study proposes a deep learning-based approach for the detection and quantification of pests. The proposed approach entails the retrieval of data pertaining to the adhesive trap condition, followed by its examination and presentation through a mobile application. The YOLOV5 model has been implemented for the purpose of pest classification, localization, and quantification. In order to address the issue of a restricted dataset, a hybrid technique of transfer learning and data augmentation (copy and paste) was employed. The proposed approach offers an intelligent real time pest detection, thereby facilitating the prediction of treatment options. An application for smartphones has been developed to aid farmers and agricultural professionals in the management and treatment of pests. The proposed approach has the potential to aid farmers in identifying the existence of pests, thereby diminishing the duration and resources needed for farm inspection. As per the results of the conducted experiments, the proposed approach demonstrates a noteworthy increase in performance. The weighted average accuracy reaches 84%, while precision (P), mean average precision (mAP), and F1-score show enhancements of up to 15%, 18%, and 7% respectively.