Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Producing accurate Land-Use and Land-Cover (LU/LC) maps using low-spatial-resolution images is a difficult task. Pan-sharpening is crucial for estimating LU/LC patterns. This study aimed to identify the most precise procedure for estimating LU/LC by adopting two fusion approaches, namely Color Normalized Brovey (BM) and Gram-Schmidt Spectral Sharpening (GS), on high-spatial-resolution Multi-sensor and Multi-spectral images, such as (1) the Unmanned Aerial Vehicle (UAV) system, (2) the WorldView-2 satellite system, and (3) low-spatial-resolution images like the Sentinel-2 satellite, to generate six levels of fused images with the three original multi-spectral images. The Maximum Likelihood method (ML) was used for classifying all nine images. A confusion matrix was used to evaluate the accuracy of each single classified image. The obtained results were statistically compared to determine the most reliable, accurate, and appropriate LU/LC map and procedure. It was found that applying GS to the fused image, which integrated WorldView-2 and Sentinel-2 satellite images and was classified by the ML method, produced the most accurate results. This procedure has an overall accuracy of 88.47% and a kappa coefficient of 0.85. However, the overall accuracies of the three classified multispectral images range between 86.84% to 76.49%. Furthermore, the accuracy assessment of the fused images by the Brovey method and the rest of the GS method and classified by the ML method ranges between 85.75% to 76.68%. This proposed procedure shows a lot of promise in the academic sphere for mapping LU/LC. Previous researchers have mostly used satellite images or datasets with similar spatial and spectral resolution, at least for tropical areas like the study area of this research, to detect land surface patterns. However, no one has previously investigated and examined the use and application of different datasets that have different spectral and spatial resolutions and their accuracy for mapping LU/LC. This study has successfully adopted different datasets provided by different sensors with varying spectral and spatial levels to investigate this. Doi: 10.28991/ESJ-2023-07-04-013 Full Text: PD

Finish Roll Forming Gears by the Rack Die System : Improvement of Tooth Accuracy (Ist Report)

ラック形工具により自由駆動方式で高圧力角・低歯歯車を仕上げ転造する。素材歯部の転造代・歯ミゾ部とホブの関係を検討し,前加工はこのホブによった。転造代を変更してこれが歯底部の逃げミゾの有無による結果の製品精度への影響を調べた。主要な精度項目の測定結果から,ミゾ付の有利さがわかり,一定の歯車要目の場合の転造代の量がわかった。また,転造後の内径拡大量とも合せて検討し,今後の問題点(方向)を示した

Climate change model as a decision support tool for water resources management in northern Iraq: a case study of Greater Zab River

The northern region of Iraq heavily depends on rivers, such as the Greater Zab, for water supply and irrigation. Thus, river water management in light of future climate change is of paramount importance in the region. In this study, daily rainfall and temperature obtained from the Greater Zab catchment, for 1961–2008, were used in building rainfall and evapotranspiration models using LARS-WG and multiple linear regressions, respectively. A rainfall–runoff model, in the form of autoregressive model with exogenous factors, has been developed using observed flow, rainfall and evapotranspiration data. The calibrated rainfall–runoff model was subsequently used to investigate the impacts of climate change on the Greater Zab flows for the near (2011–2030), medium (2046–2065), and far (2080–2099) futures. Results from the impacts model showed that the catchment is projected to suffer a significant reduction in total annual flow in the far future; with more severe drop during the winter and spring seasons in the range of 25 to 65%. This would have serious ramifications for the current agricultural activities in the catchment. The results could be of significant benefits for water management planners in the catchment as they can be used in allocating water for different users in the catchment

Flood Modeling on Koya Catchment Area Using Hyfran, Web Map Service, and HEC-RAS Software

In this research, The boundaries of the Koya catchment area have been delineated, and valley paths in the region were drawn by using the Water Modeling System (WMS) software, Analysis of the morphometric information indicated that the morphometric characteristics of watersheds contribute to the floods. The average surface runoff depth depends on the curve number values that are determined based on the types of soil cover and soil class according to Harmonized World Soil Database HWSD software that indicates the soil class in the study area are Group B silt loam,  The results obtained also show that the potential for surface runoff varies with land use and soil characteristics. Also, the value of the curve number (CN) was determined to be 71. The hydrological modeling was performed by the HEC-HMS program that simulates the process of rainfall to runoff using the SCS curve number model. A flood hydrograph was constructed at the catchment area outlet and the floodplain delineation was verified by the HEC-RAS software. The results indicated that the 100-year return period flood could Reach critical areas such as the urban area, agricultural area, residential areas. the results of this study indicate that there are suitable sites in the catchment areas for constructing small dams and ponds for water harvesting.&nbsp

Study of Biomass Bottom Ash Efficiency as Phosphate Sorbent Material

Excessive richness of nutrients in water bodies such as rivers, lakes and ponds lead into deterioration of aquatic life as a results of dense growth of algae. Phosphate is one of the main nutrients that should be controlled to prevent this serious issue. Utilizing low cost material as a phosphate sorbent is offering a treatment method characterized as a sustainable solution. In this study the efficiency of biomass bottom ash BBA as phosphate sorbent material from aqueous solution is investigated. Batch experiments were undertaken, in which a particular mass of BBA was brought into contact with the phosphate solution. The experiments studied the influence of pH (different phosphate solutions were prepared with pH range 4 to 8), temperature (adsorption capacity measured at the temperature range of 10 to 30 °C), and contact time. In addition, the adsorption isotherm models were also applied to better understand the mechanism of phosphate sorption by BBA. The results revealed that the bonding between the cations (BBA surface) and anions (phosphate solution) is significantly affected by the pH of the solution. BBA presents an excellent phosphate sorption, especially, at low pH value and temperature around 20 oC. The method of this research can be adopted as a followed strategy for examination the capability of selected material for phosphorus removal from wastewater

Von den Früchten und Denkmählern der Reformations-Jubiläums-Feier 1817 in den Ostsee-Provinzen. Seinem Freunde Aug. Albanus zu dessen 25-sten Jahres-Tage der Predigt-Amts-Führung gewidmet von K. G. Sonntag. Riga, gedruckt bei Wilhelm Ferdinand Häcker

http://tartu.ester.ee/record=b2421128~S1*es

Fusion Landsat-8 Thermal TIRS and OLI Datasets for Superior Monitoring and Change Detection using Remote Sensing

Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing with the different classification methods, datasets, satellite images, and ancillary dataset types available. However, using just the low spatial resolution visible bands of the remotely sensed images will not provide good information with high accuracy. Remotely sensed thermal data contains very valuable information to monitor and investigate the CD of the LU/LC. So, it needs to involve the thermal datasets for better outcomes. Fusion plays a big role to map the CD. Therefore, this study aims to find out a refining method for estimating the accurate CD method of the LU/LC patterns by investigating the integration of the effectiveness of the thermal satellite data with visible datasets by (a) adopting a noise removal model, (b) satellite images resampling, (c) image fusion, combining and integrating between the visible and thermal images using the Grim Schmidt spectral (GS) method, (d) applying image classification using Mahalanobis distances (MH), Maximum likelihood (ML) and artificial neural network (ANN) classifiers on datasets captured from the Landsat-8 TIRS and OLI satellite system, these images were captured from operational land imager (OLI) and the thermal infrared (TIRS) sensors of 2015 and 2020 to generate about of twelve LC maps. (e) The comparison was made among all the twelve classifiers' results. The results reveal that adopting the ANN technique on the integrated images of the combined TIRS and OLI datasets has the highest accuracy compared to the rest of the applied image classification approaches. The obtained overall accuracy was 96.31% and 98.40%, and the kappa coefficients were (0.94) and (0.97) for the years 2015 and 2020, respectively. However, the ML classifier obtains better results compared to the MH approach. The image fusion and integration of the thermal images improve the accuracy results by 5%–6% from the proposed method better than using low spatial-resolution visible datasets alone. Doi: 10.28991/ESJ-2023-07-02-09 Full Text: PD

A comparison between reconstruction methods for generation of synthetic time series applied to wind speed simulation

Wind energy is an attractive renewable sources and its prediction is highly essential for multiple applications. Over the literature, there are several studies have been focused on the related researches of synthetic wind speed data generation. In this research, two reconstruction methods are developed for synthetic wind speed time series generation. The modeling is constructed based on different processes including independent values generation from the known probability distribution function, rearrangement of random values and segmentation. They have been named as Rank-wise and Step-wise reconstruction methods. The proposed methods are explained with the help of a standard time series and the examination on wind speed time series collected from Galicia, the autonomous region in the northwest of Spain. Results evidenced the potential of the developed models over the state-of-the-art synthetic time series generation methods and demonstrated a successful validation using the means of mean and median wind speed values, autocorrelations, probability distribution parameters with their corresponding histograms and confusion matrix. Pros and cons of both methods are discussed comprehensively

Short-, Medium-, and Long-Term Prediction of Carbon Dioxide Emissions using Wavelet-Enhanced Extreme Learning Machine

Carbon dioxide (CO2) is the main greenhouse gas responsible for global warming. Early prediction of CO2 is critical for developing strategies to mitigate the effects of climate change. A sophisticated version of the extreme learning machine (ELM), the wavelet enhanced extreme learning machine (W-EELM), is used to predict CO2 on different time scales (weekly, monthly, and yearly). Data were collected from the Mauna Loa Observatory station in Hawaii, which is ideal for global air sampling. Instead of the traditional method (singular value decomposition), a complete orthogonal decomposition (COD) was used to accurately calculate the weights of the ELM output layers. Another contribution of this study is the removal of noise from the input signal using the wavelet transform technique. The results of the W-EELM model are compared with the results of the classical ELM. Various statistical metrics are used to evaluate the models, and the comparative figures confirm the superiority of the applied models over the ELM model. The proposed W-EELM model proves to be a robust and applicable computer-based technology for modeling CO2concentrations, which contributes to the fundamental knowledge of the environmental engineering perspective. Doi: 10.28991/CEJ-2023-09-04-04 Full Text: PD