Journal of Radar and Optical Remote Sensing
Volume:6 Issue: 4, Autumn 2023

Water and wastewater companies are facing a substantial challenge in covering the costs of infrastructure development and maintenance while maintaining fair pricing. This paper aims to propose a new pricing model for urban water that takes into account the costs of development, distribution, and maintenance of the urban water network, as well as factors such as pressure, water quality, and location factors. To determine the impact of location factors, the GIS analysis was used, taking the length of the pipeline route to reach each customer as a distance factor. Experts were consulted and a multi-criteria decision-making (MCDM) approach was used to determine the weight of each factor. The proposed pricing formula was compared to the current pricing system by comparing water bills from subscribers in Yazd province. The results showed that with the proposed pricing, water revenues align with its costs. Additionally, only 20% of subscribers experience an increase in their bills, while 30% see a decrease, resulting in relative satisfaction among subscribers.

Weather unipolar ground-based radar measurements can experience momentous changes by using other effective parameters that directly compromise the accuracy of the hydrometeorology applications. These radar measurements however, need to be calibrated for more accurate rainfall estimation. In addition to the radar-rainfall relationship (Z-R), this is a pragmatic approach based on careful analyses of other parameters include distance from radar, altitudes and rainfall time duration. This article introduces a new calibration approach using altitude parameters and time-stepwise processing of reflectivity-rainfall rate (Z-R) relationship. Based on previous work utilizing the radar-rainfall relationship; this article hypothesizes that the rainfall measurement from ground based radar are affected by other parameters. This Research leads to introduce a new effective parameter and generate two new empirical coefficients (a/b and c) in radar-rainfall relationship. Two consecutive years unipolar ground-based radar data sets with 190 occurrences of rainfall from 43 stations in calibration window of three hours; and the corresponding rainfall measured from registered rain gauges were used in this study. The results indicated that radar-rainfall relationship Z=AR^(b )is better improvised with altitudes effect (H) and empirical coefficient (c), such that Z=AR^(b ) H^(c ). The radar-rainfall relationship is denoted by R2min= 58, R2max=98; when altitude effect (H) is considered, the relationship is better improvised (R2min=71, R2max =98). It is, therefore, concluded that the use of other effective parameters (distance from radar, altitudes and rainfall time duration) leads to optimum accuracy of Z-R relationship.

Most smart and unmanned aerial vehicles use optical imagers for imaging and distance measurement. But in the foggy environment, the quality of the images taken by these systems is not good enough and there is even a possibility of destroying the images. Because the light gets scattered in contact with water vapors and fog and destroys the image recorded in the imager. Therefore, image processing is very important in these systems, but in heavy fog, distance measurement faces a serious problem. Other alternative methods are generally not economical or efficient. In this article, a new method is introduced for distance measurement and imaging on the sea surface. This method scans the environment by using two stereo imagers whose optical axis is parallel and a linear laser located on one of the cameras, and using trigonometric relations, the difference of light lines recorded in the imagers is calculated and the image and sample 3D is created from the environment. The analysis of the obtained results shows that the system is able to measure the distance of the environment with an error of less than one centimeter, and due to the type of arrangement of imagers and laser, it overcomes the effects of fog in the images with a much lower cost than other hardware.

A land cover map stands as a cornerstone of urban planning endeavors, furnishing indispensable insights into the landscape's composition and distribution. However, traditional methodologies for map creation and maintenance often entail significant temporal and financial investments. Embracing deep-learning-based approaches presents a promising avenue for revolutionizing aerial map generation, offering efficiencies hitherto unattainable. This research endeavors to harness the power of neural networks rooted in deep learning to craft a comprehensive land cover map. Focusing on Shiraz city, this study endeavors to delineate urban land uses into four distinct categories: Almond, Pistachio, Bare soil, and Shadow of trees. Leveraging imagery captured by a Phantom DJI 4 drone, the research scrutinizes ground features to facilitate accurate classification. The adoption of convolutional neural networks (CNN) emerges as a pivotal component of the methodology, serving as the bedrock for the automated classification process. Preliminary findings underscore the efficacy of the CNN approach, yielding an impressive overall accuracy rate of approximately 86.56%. Such results not only underscore the viability of deep-learning-based methodologies in land cover mapping but also underscore the potential for scalability and applicability across diverse urban landscapes. By mitigating the resource-intensive nature of traditional mapping techniques, this study paves the way for more agile and cost-effective urban planning endeavors, poised to accommodate the dynamic nature of modern cities.

The attractiveness of key assets for the enemy does not have the same value, therefore, according to their leveling, the possibility of threats is considered. The purpose of this research will be focusing on the case study of Qazviny. The type of research is applied and the research method is descriptive-analytical and the tools of information gathering are library studies, interviews and questionnaires, which were selected as a statistical sample by using a targeted sampling of 50 experts. The method of information analysis for the leveling of assets was through the guidelines for the leveling of centers of gravity approved by the inactive defense organization and the assessment of the value of assets was also through the FEMA technique. The research results in the stratification section show that Qazvin Karaj Freeway, Shahid Rajaei Power Plant, Qazvin 16th Armored Division, Northwest Radio Communications Directorate, Chicken Ajdad Barkat Company are among assets with sensitive surface and Qazvin Governorate, Qazvin Governorate , Imam Khomeini International University, Shahid Rajaei Hospital and Qazvin Railway Station are among assets with an important level, and in the asset evaluation section, Shahid Rajaei Power Plant with a score of 9/33, Qazvin Governorate with a score of 7/96 respectively. Chicken Ajdad Barkat Company with 7/33, Qazvin Governorship with 6/84, Qazvin 16th Armored Division with 6/8, Shahid Rajaei Hospital with 6/17, Imam Khomeini International University with 5/88, Qazvin Railway Station with 5/75, General Directorate of Northwest Radio Communications with 5/5 and Qazvin Karaj Freeway with 5/48 are of high value.

Forests in the Zagros region have undergone many changes in recent years due to the uncontrolled interference of humans and the dependence of people on these resources. The use of remote sensing and geographic information systems is widely used in many scientific and research fields and it is important to use this science to obtain vegetation cover. In this study, we tried to use optical images including Landsat 8 OLI satellite imagery, sentinel 2 and Power Sentinel 1 radar images (GRDH) on dates 30/05/2017 to 23/10/2017 to obtain vegetation cover before and after from the fires of the region on the specified dates, then with the comparison between the backscatter values, the power and power values and the values of the normalized vegetation index (NDVI), from the optical images, the precision of the images used for Obtaining coverage was discussed.Further, the specified fire areas were determined by creating a buffer of 200metre away, and the average NDVI and Backscatter were calculated from the desired optics and radar images. The results showed that Landsat 8 and Sentinel 2 images are complementary, and both images can be used to calculate vegetation and can be used for studying low-lying forests such as Yasouj forests, NDVI indexes for obtaining The amount of vegetation is appropriate. Also, the use of Powerline images of Sentinel 1 due to C bandwidth can show us the amount of coating variation.