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Research on the Geographic Information System (GIS) application in disaster response, prevention, mitigation, and management has been rife since the mid-1990s. Advanced data collection technologies for GIS analysis, such as LiDAR, is becoming increasingly popular. A combined link, time series, and trend analysis is used in this research to generate a web of keywords rooted in the main three: 'LiDAR' 'GIS' and 'Disaster' and the categorization of themes and trends of research articles published during the past decade from reputable publishers. The Scopus, Copernicus and Google Scholar search engine displays 222 links to a combination of the keywords LiDAR, GIS, and Disaster that explain the utilization of LiDAR together with GIS/remote sensing applications in disaster-related articles. The most relevant 80 documents covering journal articles and working papers were considered for in-depth analysis after selection processes using PRISMA method. The analysis result is then presented clearly in a comprehensive diagram of how from time to time the utilization of LiDAR technology for the four stages of disaster resilience. The results of the analysis also show how the use of LiDAR technology with GIS/Remote sensing applications and their limitations. The result is expected to benefit researchers in identifying research gaps in the topic for further pursuit, also policymakers, administrators, and other relevant stakeholders who oversee implementing required measures both during and after disasters should pay more attention to big data. [ABSTRACT FROM AUTHOR]

Directly or indirectly, soil moisture is important in determining the vegetation cover. Several hydrological events, such as drought and climate change, are associated with it. This work provides a procedure for determining the soil moisture index and using NDVI and LST. Data together, soil moisture may be determined. Based solely on biomass supplied by soil components. This work attempts a straightforward, uncomplicated, and quick approach for monitoring and assessing soil moisture using remote sensing. The NDVI values for October 2002 were calculated in this research by analyzing Landsat-5 TM images. The SMI raster map had values varying from -0.002 to 1 in the study area (Babylon Governorate). According to the findings, this approach makes full use of remote sensing data, accurately portraying desertification's land cover, water, and thermal environment and its variations, and the index is simple to calculate and very accurate in its readings. This technique improved the study and monitoring of desertification due to its simplicity (in the form of an index), accessibility, and accuracy. It also has the added benefit of automatically detecting instances of land desertification. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to identify and analyze the transformation of urban districts in Baghdad over the course of three decades, spanning from 1993 to 2023. The images of Baghdad captured by the Landsat sensors Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) were sourced from the official website of the United States Geological Survey (USGS). After done preprocessing, a supervised classification by the Support Vector Machine (SVM) algorithm was used to classify the images into (LULC) groups. The study area is classified to five (LULC) categories, namely urban areas, soil, vegetation, water bodies, and wetlands. The accuracy assessment calculated for the classification and the results were 89%, 91%, 92%, and 90% for 1993, 2003, 2013, and 2023, respectively. the change detection results show that urban areas grow over the specified time intervals. there was an increase of 1.34%, 1.62%, 1.98%, and 4.54% in 1993-2003, 2003-2013, 2013-2023, and 1993-2023, respectively. Then used change detection layers to produce a fourth-dimension map. [ABSTRACT FROM AUTHOR]

This study examines the application of Geographic Information Systems (GIS) and remote sensing techniques in the cartographic representation of the water bodies within Darbandikhan Dam Lake from 2015 to 2023, with the aim of identifying and analyzing alterations in the lake's surface area over this specified timeframe. These years are chosen to test the factors that occurred during these years, the first of which is that Iraq entered the seismic line in 2017 to determine the extent to which the storage capacity of the reservoir was affected after the earthquake struck Sulaymaniyah Governorate, within which the dam is located, and secondly, the climate factors, including rainfall, since these periods included a large variation in rainfall, As well as the rise in temperatures throughout Iraq(two years before the earthquake, the year of the earthquake, and two years after the earthquake) and the year 2023 was chosen to accurately determine the reasons affecting the difference in the surface area of the lake. The detection of this alteration was facilitated through the use of satellite images obtained from the Sentinel2 satellite, as well as the application of remote sensing techniques and Geographic Information Systems (GIS). The findings of this study reveal variations in the surface area of the lake from 2015 to 2019&2023, influenced by multiple factors. These factors encompass natural climatic conditions, such as precipitation levels and elevated temperatures, as well as political circumstances, specifically the cessation of water supply from the primary source, the Sirwan River, originating from Iran. Additionally, it was observed that the alteration is scheduled to take place in the year 2023. The surface area of the object in question was notably less compared to prior years, measuring 34.19 square kilometers. Consequently, recommendations were proposed at the conclusion of the study to effectively utilise the available information. [ABSTRACT FROM AUTHOR]

Up-to-date information about land use from crops can improve the quality of agricultural statistical data in Indonesia. However, updating activities through ground checks generally requires considerable resources so that various alternative methods continue to be developed to identify land use for agriculture. One alternative to identify land use is to utilize remote sensing data using the time-weighted Dynamic Time Wrapping (TWDTW) method. Several studies have proven that the TWDTW method can produce fairly accurate predictions. However, their studies are limited to small areas or only focused on a single food crop. When applied to a larger area with highly varied cropping patterns, the TWDTW method tends to produce inaccurate predictions. Therefore, this study combines methods to predict land use in large areas with various cropping patterns. This combination method involves creating subclasses using Multi-Layer DBSCAN to capture variations in cropping patterns, followed by predicting land use using the TWDTW method. In general, prediction results using the TWDTW method have better accuracy than those using DTW or Euclidean. In addition, combining them with the Multi-Layer DBSCAN method will increase the accuracy of the predictions. [ABSTRACT FROM AUTHOR]

Floods are one of the most frequent events caused by climate change and extreme weather with severe impact. Flood mapping and monitoring are very important so that authorities can take necessary measures to mitigate flood events. A timely detection and monitoring of floods can be made by utilizing remote sensing satellite data, such as from NOAA JPSS satellite series data. The Indonesian National Institute of Aeronautics and Space (LAPAN) has built the data processing system to produce flood detection products. However, the volume of each product was too large for some users, especially those who live in remote areas with no stable Internet connection. The data format was in HDF5, but most users demand popular data formats such as Portable Network Graphics and request quick look visualization in the dissemination system. An automated conversion module is proposed. The module includes three functions, viz. a function to perform pixel selection, one to trim the product to define AoI, and another to convert to PNG format. Measurements were conducted to compute time required in converting each product. An analysis was conducted by comparing the data size between the PNG data format as the output and the HDF5 and GeoTIFF as the inputs. Another analysis of the web-based dissemination speed was carried out and it was found that it takes less than three seconds to load. The module has been integrated into the remote sensing satellite data processing system in Remote Sensing Technology and Data Center, LAPAN and the converted products are ready to be downloaded through the web-based dissemination system. [ABSTRACT FROM AUTHOR]

Himawari-8 is a geostationary meteorological satellite launched by Japan Aerospace Exploration Agency (JAXA) which carries the Advanced Himawari Imager (AHI) instrument, providing spectral information ranging from visible to infrared wavelengths every 10 minutes. The data are disseminated by Japan Meteorological Agency (JMA) via two methods, direct receiving ground station (HimawariCast) and internet connection (HimawariCloud). Himawari-8 data are necessary for Indonesia to support in monitoring of environment, water resources, and climate change. Currently, LAPAN gets access to the HimawariCloud data via cloud service provided by National Oceanic and Atmospheric Administration (NOAA), as part of joint cooperation in building and developing a Hazard Monitoring System (HMS) for the Indonesian region. Challenge comes in how new data are available every 10 minutes, making it humanly impractical to download all the required data in a timely manner. In this paper, we designed and built an automated module to download the Himawari-8 data from the cloud service every time new data are available and store the data into the National Remote Sensing Data Center (BDPJN) system to be managed and disseminated further to users. The module has been functionally tested and proved to be able to adhere to its intended functions successfully. [ABSTRACT FROM AUTHOR]

This survey comprehensively explores the incorporation of Reconfigurable Intelligent Surfaces (RIS) and Deep Learning (DL) in wireless networks, carefully analyzing their combined ability to enhance network performance and significantly shift efficiency and effectiveness in wireless communication systems. Remote Intelligent Sensing (RIS) can alter the propagation of electromagnetic waves in real-time, resulting in enhanced signal receipt and transmission efficiency. Simultaneously, deep learning (DL) can enhance these modifications by utilizing predictive analytics and intelligent decision-making. The collaboration between RIS (Radio Interface System) and DL (Deep Learning) has significantly improved important metrics such as signal strength, network capacity, and energy efficiency. Although there have been positive results, there are still obstacles to overcome, such as the intricate nature of RIS settings and the requirement for real-time DL models that can adjust. The survey also delineates prospective avenues for research, with a particular emphasis on developing sophisticated algorithms and streamlined hardware designs, as well as assessing security ramifications in networks strengthened by RIS technology. [ABSTRACT FROM AUTHOR]

Image processing techniques are widely used in all domains of application, including digital imaging, precision agriculture, computer vision, remote sensing, medical imaging, and many more. The aforementioned applications utilize various types of images, such as RGB, Infrared, Multispectral, and so forth. The image generated by a single source, sensor, or modality is insufficient for precisely realizing the item in applications such as medical imaging and remote sensing. Image fusion provides a more effective and efficient way to produce highly useful data for human perception when used with individual input source data. Numerous image fusion techniques exist, including Laplacian pyramids, Gradient Pyramids, SF, IHS, PCA, DCT, and DWT. This study examines several spatial domain Image Fusion techniques to assess the efficacy of distinct techniques based on noise content, spectral degradation, and color distortion. Comparing the outcomes of various spatial domain methods, it is found that PCA is a good choice as its PSNR value is the largest of all spatial domain methods. [ABSTRACT FROM AUTHOR]

Within the broad field of remote sensing, object detection utilising SAR images is an important application such as deforestation, marine monitoring, security for the defence and civilian sectors, disaster management, etc. Object Detection in SAR images could be a difficult task, as these images are intrinsically affected with the speckle noise & strong clutter interference due to backscattering. Two environmental challenges for SAR-based object detection are camouflage and image quality. Sensor-based device problems include limited resolution, image processing indicator, small or glistening item indications, low signal-to-noise ratios, and so on. Here proposed the elaborated literature review on the object detection in SAR representational process on associated problems in SAR images. Major three issues in SAR images, Range Cell migration, Speckle noise, complex clutters are elaborated within the discussion. [ABSTRACT FROM AUTHOR]

Hyperspectral Imagery (HSI) often suffers from degradation caused by different types of noise, including Gaussian noise, impulse noise, and stripe noise. Restoring the quality of HSI is a complex task, particularly due to the high dimensionality of HSI datasets. In recent years, there has been a growing utilization of low-rank tensor-based and deep learning-based approaches for denoising HSI data, especially in scenarios involving mixed noise. In this research paper, we assess the performance of two low-rank-based methods and one deep learning-based method when applied to benchmark HSI datasets with a focus on the removal of mixed noise. We quantitatively evaluate the restored datasets using a range of quality metrics and subject them to qualitative visual assessment. Our experimental findings reveal that the low-rank-based methods exhibit promising results in effectively mitigating mixed noise in HSI data. These methods offer a viable solution for addressing the challenge of noise removal in hyperspectral imagery, contributing to improved data quality and usability. [ABSTRACT FROM AUTHOR]

Landuse Landcover pixel-by-pixel classification and change analysis play a vital role in sustainable development applications. A single study area is generally focused on classifying the landcover features using remote sensing images. This study aims to train the segmentation models based on multiple areas to prepare classified models, so it could be utilized to assign a unique label to the landcover features. Considered images were taken for five cities including Jaipur, Alwar, Ajmer, Sikar, and Tonk of Rajasthan, India, which are in the north, south, east, and west direction of Jaipur city. Landcover classes considered in this research are Vegetation, Water, Built-up, and Freeland. Landcover images with masks are created for training the deep segmentation models FPN, LinkNet, and UNet. UNet achieves 91.04% accuracy that outperforms the traditional FPN, and LinkNet models. The outcomes demonstrate that encoder-decoder-based UNet provides better results with an average accuracy rate of 88.06% across all four classes. The resolution of training images also affects the performance and computation speed. The resultant model can be applied to label or classify the land cover features for any large area. The purpose of this research is to evaluate the performance of three segmentation techniques for obtaining the four main landcover classifications. [ABSTRACT FROM AUTHOR]

Forest fires harm people and surrounding areas. Spatial analysis and remote sensing were used to track damaged areas in the forests of Mosul, located in northern Iraq. Geographic Information Systems (GIS) are crucial to achieving this. It showed the areas affected by fires for three years: the first (12-05-2017), the second (06-07-2021), and the third (2022-6-29). Using Sentinel-2 data, this study calculated the normalized burn rate (NBR) of affected areas before and after the fire. The Different Natural Burning Ratio (DNBR), which assesses fire intensity, was also utilized to estimate the damage caused by forest fires in Mosul. Furthermore, a map displaying the properties of forest vegetation was extracted using the Normalized Difference Vegetation Index (NDVI). To compare the vegetation before and after fire, the Difference Normalized Vegetation Index (DNDVI) was computed. To determine the relationship between DNDVI and DNBR also Enhancement vegetation Index 2 (EVI2) was calculated and the Difference Enhancement Vegetation Index (DEVI2). The methods employed were simple linear regression (R2) and Pearson correlation (r). Burn locations were identified using GIS. Using Pearson correlation and simple linear regression, fire severity was determined between three periods: 2022 (r=0.92, R2=0.8519), 2021 (r=0.49, R2=0.246), and 2017 (r=0.74, R2=0.5525). Also, the EVI2 (Enhanced Vegetation Index2), Different Normalized Difference Vegetation (DNDVI), Pearson correlation (r), and simple linear regression were used, as they were in 2017 (r=0.73, R²=0.5374) and in 2021 (r=0.47, R²=0.5374). 0.2237), and in 2022 (r=0.92, R²=0.8521). The study found that the maps resulting from these techniques can be useful for risk management and identifying areas with a high potential for fire danger. [ABSTRACT FROM AUTHOR]

The study's goal is to use Decision Tree (DT) and Making the Most of Structural Vector Machines on Accuracy haze removal under diverse weather circumstances (SVM). Materials and procedures: This experiment's data set consists of 30 photos for various techniques of haze reduction in remote sensing photographs. The sample size determined by Gpower is 20 per group. The classifier's accuracy and precision are tested and reported for both Decision Tree and SVM. Results: When the independent sample the T-test's two-tailed p-value of 0.023 (p0.005) demonstrated statistical significance. When compared to the Support Vector Machine method, the Decision Tree method fared better. technique in terms of accuracy (80% vs. 80%). 85.5%. haze removal analysis under different weather situations. [ABSTRACT FROM AUTHOR]

The issue of global warming is a threat to the survival of various ecosystems on earth. The purpose of the study was to determine changes in land cover of green open spaces in the urban forest of Mesjid Raya. Research data collected was primary data and secondary data. Primary data were obtained from surveys and field observations, namely by validating land use maps to obtain an overview of land conditions. The method used in this study was a remote sensing approach and geographic information system to extract information and analyze land cover changes that occur. The Landsat image analysis process was carried out to determine land cover changes. The results showed that the forest cover of Mesjid Raya in Banda Aceh did not decrease between 2004 and 2005, which was 0.1 ha. From 2005 to 2013 the area covered by 0.1 ha increased, and between 2013 and 2016 the forest cover area of Mesjid Raya increased by 0.1 ha. [ABSTRACT FROM AUTHOR]

CubeSats, nanosatellites, including microsatellites with a moisture content of up to 60 kg have all contributed to the fast expansion of the Earth Observation sector. This development has also been aided by the reduction in cost associated with reaching space. Image data that has been acquired serves as a vital source of information in a variety of fields. As more remote sensing data is collected, the available bandwidth capabilities for the data transfer, known as the downlink, will eventually be used up. Under this article, we explain six different methodologies, including Pruning, Quantization, Information Distillation, Present Sample, Tensor Decomposing, and Sub-quadratic Converter based approaches, for compaction of such modeling techniques to enable their implementations in real industry NLP projects. These methods include information extraction, present sample, tensor decomposition, and parametric sharing. We believe that this survey organises the vast amount of work that has been done in the field of "deep learning for natural language processing" over the past couple of years and introduces it as a coherent story. This is especially important in light of the important need to build implementations with effectual and small designs, as well as the huge portion of newly published work in this area. Examples are shown using three-channel remote sensing and pictures obtained using RS that are included in multispectral data. It has been proved that the quality of pictures compressed using Discrete Atomic Transform may be adjusted and controlled by adjusting the greatest absolute deviation. This parameter also has a direct and tight relationship with more conventional metrics such as root mean square error (RMSE) and peak transmission ratio (PSNR), all of which are within the control of the user. Nevertheless, the majority of attention is being paid to several antenna applications, including millimetre wave, body-centric, radiofrequency, satellite, unmanned aircraft systems, gps devices, and textiles. The objective of this study is to investigate the recent trends in research within this sphere. We look at a variety of optimization strategies that are presently used to cram resource-constrained embedded and mobile systems with computation- and memory-intensive algorithms and examine how these strategies may be improved. [ABSTRACT FROM AUTHOR]

Recently, the problem of drought has emerged in most countries in the world and in Iraq in particular. As a result of this problem, there is an urgent need to use modern technologies, including remote sensing. The research focused on monitoring and evaluating drought in Lake Sawa, which is located in the Iraqi governorate of Muthanna near the Euphrates River, 23 km west of the city of Samawah. With the presence of a number of studies that attempted to monitor drought factors, but they were not as accurate as the research is trying to reach. The research used a number of spectral indicators to determine drought and changes in its distribution in the basin during this period (2015-2021) in order to obtain the largest possible degree of accuracy in the interpretation. and analysis. Including the Normal Difference Water Index (NDWI), and the Water Ratio Index (WRI). The research also attempted to calculate the area of water receding in the lake and for the years from 2015 to 2021. The results of the research found a spatial and temporal variation in drought levels, where the area of water according to the (NDWI) index reached 4.2165 and 0.5382 for 2015 and 2021, respectively. It was also found that the water area according to the (WRI) index amounted to 4.1328 and 0.4338 for 2015 and 2021, respectively. In addition, monitoring wells were identified around the study area to monitor the water level and a solution was proposed to feed the lake from artesian wells, and a sample was taken at a distance of 2 km. [ABSTRACT FROM AUTHOR]

With satellite remote sensing techniques, it has become relatively easier to make and analyze accurate land cover and land uses maps and to monitor changes at a regular period. The objectives of this study is to demonstrate the role of integrated management in agricultural areas, and effective community participation for the protection of the vegetation cover and the prevention of unwanted changes in Aldorah area, south of Baghdad Governorate utilizing remote sensing technique for the period from 2013 to 2021. Changes in land cover for the study area as well as the land uses were determined through a set of Landsat 8 satellite images for the specific time period and Supervised Classification were implemented to determine the land cover using (Arc Map 10.8) software. The results shows that the area covered with Buildings, and Barren lands have increased from 25.47% and 12.35%, to 44.29% and 15.76% of the total study area respectively, while the Agricultural area cover declined from 62.17% to 39.96%. Therefore, appropriate land management practices, integrated agricultural area management and stakeholder involvement management should be promoted to protect vegetation cover and eliminate the undesirable change in the study area. The primary drivers behind these changes were considered to be the legislation of land ownership, political change, social upheaval, drought, air pollution from refineries and population expansion. [ABSTRACT FROM AUTHOR]

Remote sensing techniques have shown that soil moisture can be estimated using several satellite images, each with its own set of advantages and limitations. This study aims to apply the integration approach of Sentinel-2 and the Global Land Data Assimilation System (GLDAS) to estimate the surface soil moisture and its influencing factors in Baghdad City, central Iraq. The GLDAS/Noah model with 0.25° resolution and monthly temporal resolution was used to generate a soil moisture map for the first 10 cm of the land surface for the study area. The results showed that the estimated soil moisture values ranged between 11% and 29%, where the high percentage was in the center of the study area and gradually decreased in all directions around the study area. Six important factors, namely depth of groundwater, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), topography, soil texture and land use/land cover were compared with the estimated soil moisture to show which is the more influential. The result show that there is a good relationship between the depth of groundwater and calculated soil moisture where the depth of the groundwater was low, ranging between 11–13 m, in areas where the soil moisture is high, ranging from 27–29%. [ABSTRACT FROM AUTHOR]

Agricultural segment is one of the most significant segments that support the economy of a country and its food security. Therefore, high attention must be paid to the development of agriculture. Recently, noticeable degradation has been noted in agriculture spatially in Iraq due to many reasons. Optimization agricultural investment location is required to support the decision-makers and planners to improve the agricultural sector. This research proposes a model for identifying optimal location for agricultural investment in Abu Ghraib / Iraq based on several factors using Geographic Information System (GIS) and remote sensing data. The analytical hierarchy process (AHP) was also applied in this research to assess the effect (weight) of each factor in the in agricultural investment. This work adopted a maximum likelihood classification approach to derive land-use/land-cover (LU/LC) of the study area. The results show that Landsat data combined with maximum likelihood are efficient tolls for deriving (LU/LC). The modeling results preset that 38% of the study area is suitable to very suitable for agricultural investment. On the other hand, 36% of Abu Ghraib is not appropriate for agricultural investment. The outcomes of this research could support planners and decision-makers in developing agricultural investment. [ABSTRACT FROM AUTHOR]

This research paper aims to use UAV photogrammetry in volumetric measurements of different sort of materials that stack in an irregular shape. The research test and evaluate the results delivered from UAV photogrammetry with ground measurements obtained from conventional GNSS and total station techniques in order to show potential of automatic remote sensing techniques in Engineering works. Therefore, we choose an area that contains a stacked bulk materials and remnants of buildings. We used available low-cost hardware for data acquisition, SFM photogrammetric software for processing and analyses to calculate the volume of the stacked materials. Topcon-GR5 multi frequency GNSS receiver used for surveying the position of the base ground truth network precisely. However, mini-UAV drone from DJI Mavic family (DJI mini-2) was used for image acquisition, in addition to total station device to densifying ground points for the reference volume method calculations. The autopilot software used was (drone link) which is a mobile application utilized for flight planning and controlling of the drone. Whereas (Agi-soft) Meta shape photo scan software used for image processing and (AutoCAD) civil 3d for volume calculations. On one side GNSS and total station technologies could perform high accuracy measurements with high level of confidence of the work, but it could really be time consuming and needs high human efforts and remarkable cost for field work data collection. Therefore, this research aims to show the potential of UAV photogrammetry technology to offer a fast, accurate and lower-budget mapping method of large areas with one person managing the process. It considers a powerful volume calculation tool in the field of earthwork quantity surveying in excavation sites, stockpiles, etc. The results show the significant effectiveness of UAV photogrammetry over traditional techniques for estimation, management, and precise calculation of stock and dump materials in engineering surveying works. The resulted volume from UAV technique was 2692.39 m3 with spatial error of ±0.0826 m, while it was 3048.10 m3 from conventional technique with spatial error of ±1.51m. [ABSTRACT FROM AUTHOR]

Noise properties of polarimetric SAR (PolSAR) imagery have received more and more attentions in recent years, especially in marine oil spill surveillance. The property of noise is an important indicator to assessment the quality of the backscattered signals. In this paper, three Radarsat-2 PolSAR imageries of six selected areas including oil and ocean surface are investigated. Analysis results of dominant noise type over those target areas suggest that the behaviors of phase difference distributions are closely related to the noise properties. The co-polarized complex correlation coefficient ρco is more like an indicator of the dominant noise type instead of various sea surfaces. Moreover, A linear correlation between the μ, DoP, H and the corresponding ρco, indicating the performances of those parameters are closely related to the noise property. Besides, the cross-polarized complex correlation coefficient has the potential to distinguish oil-covered sea surface from its surroundings under high wind condition. In addition, the noise properties probably contribute to non-Bragg scattering over oil slick for low quality PolSAR imagery, yet the features of the oil slick for high quality PolSAR imagery are still dominated by Bragg scattering. [ABSTRACT FROM AUTHOR]

The demand for crop production has increased as well as human population. Consequently, agricultural production needs to be enhanced to meet these food requirements. Rice plays a crucial role as a commodity, producing rice, a staple food for humans. Therefore, to support this, a faster, easier, and more accurate modern method is needed to estimate harvest yields and determine food supply quantities. The use of remote sensing technology is becoming an alternative method for estimating harvest yields. This study was conducted in the organic rice fields of Candipuro District, Lumajang Regency. The objective of this study was to analyze and test the accuracy of SAVI (Soil Adjusted Vegetation Index) and EVI (Enhanced Vegetation Index) in estimating the harvest yield of organic rice. The images used were from Sentinel 2 acquired on November 6, 2020 (period I) and June 4, 2021 (period II). There were 30 land plots with a total of 311 sample points determined randomly. The best correlation results for periods I and II were found with EVI transformation, with correlation coefficient values (r) of 0.849 and 0.865, respectively. The highest accuracy level was obtained from the EVI calculation in period II with the equation Y = 35.262X - 4.062, resulting in an RMSE (Root Mean Square Error) value of 0.071 ton/ha. [ABSTRACT FROM AUTHOR]

With the advent of satellites, we have vastly simplified mineral mapping. Satellites are fitted with special cameras or payloads that capture pictures in extremely specific wavelengths of electromagnetic radiation. These are called spectral bands. Minerals have absorption and reflectance in specific wavelengths of energy and using the data from a combination of bands this technology can be used to identify and map the minerals of the study area. The OLI-1 sensor on Landsat-8 captures images in 12 bands from coastal aerosol to thermal infrared on the spectral scale. Landsat-8 was launched in 2013 and is still considered a workhorse for data for remote sensing images. Landsat-9 is the successor to Landsat-8 and was launched in 2021. The OLI-2 sensor is identical to its predecessor. After acquiring images captured by the Landsat-8 and Landsat-9 satellites over the Singh hum district in Jharkhand, a suite of classification algorithms were applied to the images and the results were compared with a georeferenced mineral map of the region. [ABSTRACT FROM AUTHOR]

UAVs (Unmanned Aerial Vehicles) gain wider acceptance for cadaster mapping related activities in Indonesia. This flying platform is usually equipped with consumer grades digital cameras. Such off-the shelf cameras have long been known to suffer unstable lens elements and electronics. Regardless of camera types being mounted on the UAV board, these instabilities are major impediment to attain a high accuracy of orthophoto generated from aerial images obtained by the UAV camera. Geometrical defects of those images would afflict a geometrical quality of the orthophoto which is produced by stitching and rectifying the photographs. This research seeks the quality of the generated orthophoto assembled by those defected images. The method of this study is to conduct geometric and radiometric comparison of the UAV's orthophoto against the well-established standards such as to the ASPRS (American Society of Photogrammetry and Remote Sensing) as well as regulation of State Minister of Agrarian standards. A provision of a 2.7cm ground sampling distance (GSD) of images enables comparative study be conducted through a provision of the independent check points (ICPs). The RMSE figures of areas discrepancies between the ones measured using geodetic type of GPS instruments reveal that the orthophoto map obtained by the UAV is suitable only up to a 1:500 of a map scale. [ABSTRACT FROM AUTHOR]

A more informative picture is produced by integrating complimentary data from several image modalities. Infrared photos may show the scene's temperature distribution, while visible photographs can provide textual information. While infrared pictures are unaffected by light availability, visible images are affected by the quantity of lighting. When it comes to identifying tiny targets, infrared imagers are useful since they can gather more information from an object's thermal characteristics. By merging infrared and visible images, a more detailed and realistic composite is created, which is in line with how the human visual system works. There are a number of fusion techniques that may be used to create fused images, each with its own unique level of fusion performance. At the same time, the computational benefits of deep learning approaches allow them to generate more attention. Many fields may benefit from fused pictures, including object detection, remote sensing, surveillance, and many more. In this research, we take a close look at the deep learning techniques that have been developed to fuse infrared and visible data. We used objective assessment measures such as entropy, mutual information, structural similarity index measure, etc., to evaluate the approaches' performance both statistically and qualitatively. After weighing the benefits and drawbacks of the current approach, the article wraps up by discussing the current state of deep learning for IR and visible picture fusion and offering some thoughts on potential future research. Researchers in the field of infrared and visible image fusion may find this review article useful. [ABSTRACT FROM AUTHOR]

Among renewable energy sources, solar energy is quickly becoming popular because it is inexhaustible, clean and reliable. It has also become more efficient as the energy conversion efficiency of photovoltaic solar cells has increased. Following these trends, solar energy will become more affordable in the coming years and significant investments should be expected. Management and rational use of land resources is currently a pressing problem in the world, in particular in Uzbekistan. A number of land plots are in some cases used irrationally and not for their intended purpose. Land survey methods implement technologies to determine optimal locations for the construction of solar photovoltaic power plants. They implement an integrated framework for land suitability assessment, which is based on a combination of GIS (geographic information system) and remote sensing (satellite imagery) techniques. The objective of this section is to develop a technology that will implement an integrated framework for assessing land suitability for optimal solar PV power plant locations and is based on a combination of GIS and remote sensing techniques and multi-criteria decision analysis. [ABSTRACT FROM AUTHOR]

The study used remote sensing-based techniques to analyze and characterize a terrestrial area. For this, satellite images (Landsat 8) were used, taken at five different times, between January and October 2021. MNDWI, MSAVI2, NDVI and NBR indices were calculated based on spectral information. Very strong correlations (r = 968 to r = 0.994) were identified at the indices, and between indices and temperature, except for NBR which had strong correlations with precipitation. The regression analysis facilitated the obtaining of models in the form of polynomial equations of degree 2, which described the variation of the indices MSAVI2 (R2 = 0.998, p = 0.0012) and NDVI (R2 = 0.966, p = 0.0334) in relation to the time over the period of study. Equations were obtained, as well as 3D and isoquants graphical models, which described the variation of the NDVI index in relation to climatic factors (temperature, precipitation) and the NBR index in relation to MNDWI and MSAVI2 in statistical safety conditions (R2 = 0.999, p < 0.001). [ABSTRACT FROM AUTHOR]

Remote sensing technology by analysis of multispectral images represents allows you to monitor climate, soil, and vegetation dynamics of a territory. By means spectral indices NDVI (Normalized Difference Vegetation Index), MSAVI (Modified Soil-Adjusted Vegetation Index), NDMI (Normalized Difference Moisture Index), NBR (Normalized Burn Ratio), Spectral Albedo values (BRDF) of Liang, BSI (Bare Soil Index) LST (Earth's surface temperature) and the GIS technology, is possible accelerating the acquisition of spatial-environmental data. The authors started out a research about the use of the aforementioned spectral data, considering as study area Mussomeli Municipality (Caltanissetta Province, Sicily), to evaluate the sensitivity of the area to desertification, in a short period (year, season). In this study, the satellite data are considered in MEDALUS (Mediterranean Desertification and land use) protocol, which adopts data attributable to four Macrofactors: protection, soil, climate, and land management. [ABSTRACT FROM AUTHOR]

Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings, roads, vegetation, water, agriculture land, crop types, plants, bare ground, cities, atmosphere conditions. Machine Learning (ML) approaches have been utilized effectively to develop a model for classification, detection, and segmentation tasks. Therefore, Satellite image processing and analysis purpose, ML techniques plays vital role and remotely sensed data become essential while training the model. The aim of this study is to investigate the various of ML techniques in satellite image analysis. However, to predict the various events in advance across the globe, it is necessary to focus more on remote sensed data and data processing techniques for accurate classification. Even though remote sensing quality has been increased and artificial intelligence solutions are equally increased. This paper addressed various types of advanced ML techniques utilized in the classification and assessment of satellite images and used to track the earthquakes, faulting, landslides, floodings, wildfire, and hazards associated with the stated activities. Still there is a gap and interference in the approaches and it is important to fill the gap by thorough review of recent classification approaches. In this connection it is necessary to look in depth to the state-of-the-art ML techniques of satellite image processing. [ABSTRACT FROM AUTHOR]

Soil is the most important parameter in planning regional development. Soil characteristics are strongly influenced by texture, mineral content, organic matter content, water content, and others. The remote sensing approach can be used to determine the distribution of soil over a large area. In this study, remote sensing methods will be used to interpret soil types using the Normalized Difference Soil Index (NDSI) and Tasseled Caps Wetness (TCW) methods. The results showed that based on the NDSI method, most of the Tanjung Api-Api area consisted of densely vegetated areas while the open area was still very few. Based on the level of moisture with the TCW method, the results show that the soil type in the Tanjung Api-Api area consists of fine-textured soil with a high level of moisture and water content, this is because the soil has a low capacity to drain water. [ABSTRACT FROM AUTHOR]

Leaves can be used to assess the health of a plant. The chlorophyll content and nitrogen status of plants might be used to assess plant health. Chlorophyll content of plant leaves is measurable in the laboratory using dimethyl sulfoxide or the Kjeldahl technique. These destructive investigations of plant tissues accurately evaluates chlorophyll concentration and nitrogen status. However, this procedure is costly and time-consuming. Using a SPAD instrument, non-destructive chlorophyll measurements were performed. However, the price of the instrument is high. This research intends to develop a technique for detecting chlorophyll content using a smartphone's built-in camera. The approach used is an advancement of ground-based remote sensing technology. The object of this research is maize. An RGB camera on a mobile phone was used to gather data on the greenness level of the leaf. Concurrently, the SPAD value was measured to determine the chlorophyll content. In this study, there were three primary phases, capturing leaf images with geotags, leaf image processing, and vegetation index (VIs) analysis. This research demonstrated that a mobile camera has potential as a tool for measuring chlorophyll content depending on the level of leaf greenness. Additionally, the GPS function on each data provides the chlorophyll distribution in a field. [ABSTRACT FROM AUTHOR]

A technique known as "image fusion" is used to combine important or useful information from a number of original or related input photos into a single output image that is more accurate and comprehensive than any of the input images. Image enhancement is a technique used in pre-processing to improve the quality of many different input pictures, especially in scientific fields including microscopy, robotics, computer vision, medical imaging, and remote sensing. In this work, we show how picture merging improves image quality. This is accomplished by utilizing DCT technology. Following DCT, many methods—including the zero matrices and positive coefficient are used to extract the distinctive properties. The inverse discrete cosine transform (IDCT) is applied in the next step. The combined picture is then created by putting the average fusion rules by using a simple average between blocks. The end result is an image with more information than any of the input images. To evaluate the fused picture, a scale structure similarity index metric is applied. [ABSTRACT FROM AUTHOR]

"Groundwater is invisible, but its impact is visible everywhere." Around two third of fresh water is constituted by ground water. In India most of the rural and parts urban population mainly depends upon groundwater for human and animal consumption. Groundwater plays a vital role in human consumption, agriculture, industries and for other purposes and any changes in its quality have adverse consequences. The major groundwater affected zones are concentrated as study area it includes parts of Tamil Nadu such as, North - western zone of Tamil Nadu (Salem, Dharmapuri, Nammakal, Krishnagiri and Perambalur). The main objective is to find the groundwater potentialzones of the study area. The factors like like Slope, Lineament density, Drainage density, Soil and Lithology were created using Sentinal-2 satellite data with remote sensing tools and softwares. The groundwater potential zones were mapped by combining the above mentioned factors with Normalized Pairwise Comparison Matrix method. Based on this study groundwater potential status was given to detect the changes in quality and give early warnings of groundwater potential to the farmers, industries, water department and rural population. [ABSTRACT FROM AUTHOR]

One of the areas of computer vision is superpixel segmentation, and the widespread use of superpixels in a wide range of practical applications shows that it has unique properties. A superpixel is created by combining pixels with similar content, which significant reduce the computational complexity of the algorithm at the next stages of image processing. After the superpixel is built, it is possible to solve problems such as classification, reduction of image dimensions, construction of spectral combination, selection of a certain channel, noise reduction, and change detection based on the application of superpixel to spectral images. This paper presents an analysis of SLIC, SEEDS, LSC, DFIC, AHP, DRW, WSS, BACA, and superpixel segmentation approaches on remote sensing images. The analysis of approaches was considered according to the characteristics of the color model, scale, regularity, connection, smoothness, texture, and boundary strength. Approaches were evaluated using the criteria of Undersegmentation error, Achievable segmentation accuracy, and Boundary Recall. The study revealed that superpixel segmentation techniques that employ all of the spectral image channels have not received enough attention; instead, they are typically applied using PCA to shrink the size or convert them to color models. Additionally, it is important to investigate the issue of applying deep learning techniques to the implementation of superpixel segmentation in remotely sensing spectral images. [ABSTRACT FROM AUTHOR]

In remote sensing images, numerous image fusion methods have been developed to produce high-resolution multispectral images from multispectral (MS) and panchromatic (PAN) image. This work suggests suitable algorithm that maintains the spectral and spatial information content and to overcome the spectral degradation of satellite images. Three statistical methods are used to implement the fusion process based on Discrete wavelet transform (DWT). Absolute Max, Mean and Standard Deviation and Simple Linear Regression (SLR). The DWT are applied on both MS bands and PAN images to produce high and low frequency components, then the statistical methods are applied on high components as a fusion method. Lastly the inverse DWT is implemented to produce the final fused image. Three metrics (Standard Deviation SD, Peak signal-to-noise ratio PSNR and Correlation Coefficient CC) are implemented to evaluate the obtain results quantitatively in addition to visual assessment. Good qualitative and quantitative results were obtained with a Absolute Max and SLR over Mean and Standard Deviation. [ABSTRACT FROM AUTHOR]

To manage the populations of hunting animals, to make managerial decisions in the hunting economy, it is necessary to know not only the actual, but also the effective (economically expedient) number. The purpose of the study is to determine the effective (economically feasible) number of hunting animals based on geographic information systems (GIS) and Earth remote sensing data. GIS "Kuzbass", specialized cartographic software, analysis of the normalized vegetation index were used. It is shown that the current documents incorrectly determine the effective number due to inaccurate boundaries and incorrect definition of classes and categories of hunting grounds. For all hunting grounds of the Kemerovo municipal district (Kemerovo region – Kuzbass, Russia), classes and categories of grounds have been specified. The effective number of hunting animals was re-evaluated. It is shown that some of the lands are erroneously classified as highly productive lands for bears and ungulates, so the estimate of their abundance has been adjusted downward. There are more swamps and clump forests in the study area than it is assumed by the current documents. Therefore, the effective abundance of sable and birds is higher than currently established. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic forced all industries to close their doors, and other human activities came to a stop. But the lockdown's effects on the hydrosphere, atmosphere and all life forms were profound. Government officials and environmentalists have been working to clean up the entire artificial facade for several years, but the lockdown has completely altered the situation. As before and during the lockdown water quality measures revealed considerable variations, the COVID-19 epidemic proved to be beneficial for the Yamuna River. The Yamuna is the second-largest Ganges tributary in India and is the country's longest river. Haryana, Uttar Pradesh, Himachal Pradesh, and Uttar Pradesh are the four states and a union territory through which the Yamuna flows (Delhi). For the study, a stretch of river was defined as the distance from Palla, upstream on the Yamuna, to Okhla Barrage, downstream in Delhi. When comparing water quality parameters between before the lockdown (January and February 2020) and during the lockdown (March andApril 2020), visual interpretation using remote sensing and geographic information systems was performed, demonstratingthat there was a considerable drop in turbidity value. In a conclusion, the application of remote sensing and GIS technologies is highly anticipated and beneficial for data processing. [ABSTRACT FROM AUTHOR]

Water, the most significant element of natural resource available on earth supports liquidity to the life cycle. Their changing status enables the hydrophilic and hydrophobic polarity among the elementary ecosystem which further explains the peculiarity by components, pollutants concentration, and increasing anthropogenic load. In terrestrial/ or inland water bodies, the cyclic affection of rapid urbanization and rising cumulate demands pose a continuous and direct threat to fresh water and its nutritional status. Continuous affection of these resources starts degradation in terms of water quality and it leads to the eutrophication processes. The eutrophication process starts with the enrichment of nutrients as plants and algae bloom to grow in the hydro-ecosystem and then turn the water unfit for aquatic organisms and consumption by humans and other living organisms. The present study concerns the degrading state of High-altitude lakes (HAL)'s and their compromising conditions in the region of Nainital, Uttarakhand. It is critically reviewed that, the regional hydro- ecosystem is under stress. The continuous over-stress affects the aesthetic importance and is primarily controlled by the surrounding 'Architecture with nature'. Observation depicts, the amalgamation does not fit and support its sensitive topography and impact in terms of abnormal responses including enrichment of algal (cyanobacterial) bloom, microbial biomass, and changing geochemical characteristics. The bibliography indicates the practices along the lake environment in the last many decades but is inconsistent in terms of time and position-based analysis. Recent advancement in remote sensing and GIS and directional development in such areas gives space for their continuous monitoring and mapping efficiently and cost-effectively. With the current issue, an urgent need is to monitor the reservoirs and lakes of the territory to check the processes whether rapidly changing or any influence affecting them. Landuse-Landcover (LULC) and their impact on water bodies, Spectral indices like Normalized difference water index (NDWI), Maximum chlorophyll index (M-Chl), Green normalized difference vegetation index (G-NDVI), Normalized Difference turbidity index (NDTI), Topographic State Index (TSI), etc. are some extensively used indices for the water quality estimation. However, recent time developments in merging techniques with drone and unmanned aerial vehicles can attribute the research to a more precise and finer scale. The present review article describes the use of remote sensing techniques to retrieve information on water quality and using land and space-based derived quality indicators and their co-relationships for model development. The retrieval strategies for various specific water quality indicators, such as chlorophyll-a, total suspended matter (TSM), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus, are summarized in this article. (TP). Considerable difficulties associated with the resolution and retrieval models in the contexts or focusing on specific geographic bound, temporal, and water complexity have also been discussed. Finally, potential answers to these problems have been suggested. The review can offer thorough references for upcoming work in retrieving water quality development and research. [ABSTRACT FROM AUTHOR]

Wildfires in Indonesia reached their peak during the drought of 2015, with estimated burnt forest and land of up to 2.6 million hectares. During the dry season of 2020, high temperatures and high winds were responsible for losing 659.02 hectares from forest fires in Lombok Island. The fires on the Island caused economic hardship, disrupted commerce, and led to short and long-term health problems for most of the population. This study aimed to use Geographic Information Systems (GIS) and Remote Sensing (RS) to map forest fire hotspots, establish fire brigades, and suppress forest recovery programs. Wildfire annual data were acquired from Satu Data NTB website, DEM from ASTER Global Elevation Model, and NDVI from Sentinel2B. DEM and NDVI were calculated and projected into 3D objects. The Wildfire geolocations were then added to 3D objects and the scale according to wildfire level. The performance of forest recovery is measured using the development of NDVI (Normalized Difference Vegetation Index) in affected fire areas. Results show the overall vegetation index increases in the area where fire forest has occurred. Combined RS and GIS efforts show the prospective application to evaluate forest fire control and recovery programs' performance. [ABSTRACT FROM AUTHOR]

The integration of remote sensing and GIS applications has provided researchers with advanced tools to manage the environment through the aid of data. And it shows their quantitative and qualitative characteristics using remote sensing techniques and geographic information systems. The research problem is producing thematic maps from high-resolution images and knowing their accuracy after production. The experimental approach was chosen since it was the ideal method for investigating throughout the research phase. This, of course, is contingent on the kind of research being conducted, the main objective of the study, the quantity and quality of the readily available data, and the area of research currently being looked at. The purpose of this study is to investigate the viability of employing geographic information systems in the direct production of thematic maps directly from satellite images, as well as making use of aerial survey data and field data collected using measuring tapes and Global Positioning System (GPS), and analyzing the quantitative and qualitative differences between the two types of maps. (Positional data file) Putting up a database of location information using topographic maps. The final product was generated using two different ways of cartographic processing. It uses the practical application of making a map via geographic information systems and then contrasting that map with the one found in the ArcGIS software that ESRI developed. The horizontal resolution of high-resolution satellite images is shown in this article. The images have a scale of 1:1000 and have a solution of (1*1) meters. The findings showed that the maps were accurate, with an average error of (-0.718 E,-0.714 N) meters for points, (-0.622) meters for lines, and (-320.033) meters for areas. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) techniques are intensively used for monitoring and mapping the land use and land cover. The integration between spatial and temporal capabilities of remote sensing applications with the spatial capacity of geographical information system (GIS) can be exploited as a powerful tool to manage and assess the land use and land cover of earth's surface. These techniques can be used for mapping land use land cover (LULC) and to detect, plan, and manage environmental variations on Al-Najaf city southern of Iraq as depending on an attempt to track changes in satellite images over the years 2007-2011 and 2011-2018 based on Landsat-5 Thematic Mapper (TM) and Landsat 8 (OLI) data. Supervised classification with the Maximum Likelihood Classifier (MLC) used to classify environmental variations in the study area. There are six types of ground cover were identified as the wet land, water bodies, vegetation, urban areas, bare land and agriculture. The urban areas, water bodies, vegetation and wet land results have increased by 21.16% (191.82km2), 7.75% (70.26km2), 6.95% (62.98km2) and 0.05% (0.49 km2) respectively. While bare land and agriculture have decreased by-26.95% (-244.35km2) and -8.95% (-81.17km2) respectively. The overall accuracy on satellite images during years 2007, 2011 and 2018 are as 77.78%, 94.4% and 92.2% with a Kappa coefficient as 0.73, 0.93 and 0.90 respectively. There are fundamental changes, which were observed in LULC between satellite images in 2007 and 2018.The accuracy results on the satellite images during years 2011 and 2018 were significantly better and higher than the values in 2007 images. [ABSTRACT FROM AUTHOR]

Flood issues have always been major disaster in many countries especially the tropical regions where extreme precipitations occurred during the monsoon seasons. Hence, well-established hydrological modelling framework is required to estimate the peak discharge in the river network as part of flood mitigation measures. Unfortunately, in poorly gauged river basin, observations data required for performing hydrological modelling such as landuse changes, climatology data, hydrological data, and soil information are often insufficient and difficult to obtained. The objectives of this study are to utilize readily accessible remote sensing data to generate the topography, land use characteristics, and climate data, and to establish the hydrological model framework for determining the peak streamflow discharge in poorly gauged basin. For this study, hydrological modelling was performed using ArcSWAT, an integrated extension in ArcGIS. Two types of data are used for the simulation, remote sensing climate data (RSCD) and observe climate data (OCD). There were 20 subbasins delineated for Kuantan River Basin with a total number of 87 Hydrological Response Unit (HRU) defined through ArcGIS. The periods used for calibration and validation of the hydrological model selected were from January 2010 to December 2011 and January 2017 to December 2018 based on the availability of the climate data to run the simulation. Both data will be the compared using observed streamflow data (OSD) at Station Bukit Kenau. Overall result obtained from the simulation, the observed climate data give better prediction than remote sensing climate data by comparing the performance rating using Ration of Standard deviation to RSME (RSR) and Percentage bias (PBIAS). [ABSTRACT FROM AUTHOR]

A comparison was made between the different phenomena of the land cover using satellite images imaged at multiple wavelengths from electromagnetic energy sources, where the spectral reflectivity curves of the terrestrial phenomena in the research area were obtained by analyzing the data using the (ERDAS) program to obtain spectral reflectivity charts. It was found that there is a relationship between the binary coordinates and the three-dimensional coordinates with the spectral reflection coefficient of the phenomenon. Satellite images (T.M & M.S) were used in this research. The main spectral groups of natural phenomena that represent (soil, water and plants) and the overlap between them, which affects the spectral reflectivity values of those phenomena, were studied. They differ in their basic components, in addition to the units of vegetation). The study also showed that many of the Earth's surface phenomena under study can be distinguished, located and studied based on their spectral characteristics. In addition, some land covers cannot be separated using a single wavelength, so the concept of multiple wavelengths using more than one wavelength was used to determine the types of land features prevailing in the region, as shown in the attached figures at the end of the research. [ABSTRACT FROM AUTHOR]

This study intends to evaluate the sustainability of water storage in the dam within Wadi Horan Region using remote sensing data (RS) and geographic information (GIS) systems. The sustainability of water storage in small dams improving the biophysical and socioeconomic situations of the study area. Remote sensing data (satellite images) and dam technical reports were used to establish a mathematical relationship that illustrates changes in water storage over the drought season (May-September). The quantity of water used, and water lost during the drought period of the dam was estimated using mathematical equations. The quantity of water used, water lost, and water stored in Horan 2 dam during the years 2019 and 2020 were 0.5, 2.8, and 0.8 MCM and 0.4, 1.2, and 0.278 MCM respectively. The research found that by increasing the numbers of animals, it is feasible to use the largest amount of water and therefore attain sustainability. [ABSTRACT FROM AUTHOR]

Corrosion of reinforced concrete structures has become a major problem worldwide, particularly in coastal areas due to the high concentration levels of salt. Structural failure is the last stage of a slow process happening internally with rare external symptoms. Early corrosion detection is a challenge especially if one requested a non-destructive technique to identify the initiation of corrosion. In this study, we aimed to evaluate the application of hyperspectral imaging (HSI) in the detection of the initiation of corrosion in steel-reinforced concrete. The use of hyperspectral imaging in the short-wave infrared band will be investigated to inspect from a distance the spectra of corrosion by-products that emerge to the surface. Since natural corrosion is a slow process, an accelerated corrosion setup is used to induce artificial corrosion in reinforced concrete specimens using the impressed voltage approach. Embedded Fiber Bragg Grating (FBG) strain sensors, categorized as passive sensors, and active ultrasonic guided waves (UGW) excited using surface-mounted transducers are also used as benchmarks to better understand the effectiveness of the proposed technique. Integrating the three types of sensors (strain, ultrasound, HSI) was employed on specimens manufactured in our lab. A 40-day test is used to induce artificial corrosion in the specimen. Clear correlations were found between the FBG data and the UGWs as indicators of different corrosion phases starting with the initiation, progression, and finally the diameter reduction phase. For the HSI remote sensing technique, the challenging task was to disentangle the effect of corrosion byproduct salts, on the surface of the specimen, from water absorption peaks and sodium chloride salts that migrate to the surface. Hyperspectral images were collected daily so a database was constructed. The spectral and temporal information from the obtained images has shown a rich set of information that can correlated to corrosion. Then, unsupervised material classification was performed using the Spectral Angle Mapper (SAM) where the migration of salt on the surface was observed over time. [ABSTRACT FROM AUTHOR]

Image fusion can be described as the process of combining multiple input images for obtaining a single enhanced image. It is not only constructs the output image that is more understandable to the human and machine perception, helps in decision also reduces the complexity and storage of redundant data. The growing demand for clear and more informative images has proven the necessity of image fusion very effective. The fused images speed up the image processing tasks in the respective fields. Recent applications which require image fusion are remote sensing applications, medical applications, surveillance applications, etc. In the recent years the importance of optimization is explored in various image process techniques which involve many non-linear functions. Optimization is the technique which is used for optimizing the nonlinear functions. In this article, a short view on the image fusion and the techniques that uses various optimization algorithms such as particle swarm, thermal exchange, norm, grass hopper, weighted least square, brain storm, gray wolf, ant colony is explored. [ABSTRACT FROM AUTHOR]

In most developing countries, political instability, along with fast urbanization and expansion, has resulted in an increase in spontaneous and informal settlements. These settlements have had a negative impact on society's social and environmental elements, but they have also sown seeds of animosity and created antagonism between people, particularly in multiethnic countries. This research applies intelligent tools to investigate spatial and temporal anomalies in urban development and land-use changes by merging GIS and remote sensing. Between 1984 and 2013, this research also examines the impact of political instability, particularly in a multiethnic metropolis such as Kirkuk in Iraq. As a result, two distinct patterns of urban growth were observed. The first alteration occurred in the 1980s, while the second occurred during the 2003 governmental transition. As a result, two distinct patterns of urban growth were seen in Kirkuk City. Numerous informal settlements spread as a result of a failure to adhere to the master plan without updating it, which resulted in significant modifications to the master plan's layout. The generated map depicts four classes, and highlights the area's losses and gains for each class across the research period. City planners and others who influence housing decisions may find these findings fascinating since they demonstrate that socio-political variables matter more than physical aspects in multiethnic cities, particularly when revising Kirkuk's Master Plan. [ABSTRACT FROM AUTHOR]

Urbanization plays an important role in the field of environmental science and for the growth of green society. Urbanization scaled up with the economical benefits in developing countries and in countries making sustainable development process on ecological paths with higher efficiency on the growth of the ecosystem. In civilization due to urbanization there are problems such as air excellence corrosion, limitations on the space for survival, issues in the health of urban. Towards the perspective advancement of urban ecology it is distorted from a speculative study in the field of interdisciplinary. This article provides a new idea using Machine Learning and Quantum Computing to produce an output where it gives an efficient building idea of integrating plants and trees with or around the building. The dataset are emission and absorption of carbon dioxide. In order to create the X_features, we either use approximation factors or real-time remote sensing. The main goal of the system is to train them with different machine learning models and show that if the data set time complexity increases exponentially, Quantum Computers can be used to run the models. [ABSTRACT FROM AUTHOR]

Twardowski and Tonizzo [1], TT18 herein, present a radiative transfer approximation called ZTT (Zaneveld-Twardowski-Tonizzo) based on the theoretical work of Zaneveld [2] that relates inherent optical properties (IOPs) of the ocean to remote sensing reflectance (rrs [rs−1]) and accounts for bidirectional reflectance distribution function (BRDF) effects. The relationships for ZTT do not have spectral resolution limitations and are generic to any natural waters, so the model is thus directly applicable to future hyperspectral satellite ocean color missions such as NASA PACE and Sentinel-10 CHIME. As a BRDF correction algorithm, IOPs are first derived through ZTT inversion at the prevailing solar-viewing geometry; the forward ZTT model is then carried out at the conceptual normalized geometry of zero solar zenith, nadir viewing. One of the few sensors available measuring the full hemispherical upwelling radiance distribution is NuRADS [3]. Herein an assessment of the ZTT BRDF model is carried out with NuRADS data for stations covering 3 field sites: the Ligurian Sea, the S. Pacific gyre, Monterey Bay. Comparisons are made with current state-of-the-art BRDF models and full radiative transfer simulations. [ABSTRACT FROM AUTHOR]