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The paper describes utilizing remotely sensed RGB data to support monitoring horses in a natural environment on demand. Data are sensed using an unmanned aerial vehicle (UAV). UAVs provide very high spatial resolution data sensed at a low altitude on demand. Sensing is limited by weather conditions and legal rules only. Terrain does not need to be accessible. The paper provides a comparison of several pixel-based and object-based classification methods, namely Maximum Likelihood, Random Trees, SVM, and K-NN. Manual classification is used as a reference method. [ABSTRACT FROM AUTHOR]

The integration of the National Institute of Aeronautics and Space (LAPAN) with other major research and development government institutes into the National Research and Innovation Agency (BRIN) causes some impacts, one of them affecting the management of space operations, especially in remote sensing governance and operation. As of 2022, the operation of remote sensing services for regular data is actively managed by the Information and Data Center (PUSDATIN) and supported by the Research Center for Remote Sensing and Deputy of Infrastructure BRIN. This study aims to examine the impact of an intervention on governance change in remote sensing operations on service level performance and customer satisfaction by employing quantitative and qualitative methods. The data was gathered from primary and secondary sources. The result of this study shows the integration of LAPAN into BRIN causes decreased performance on remote sensing operational services. [ABSTRACT FROM AUTHOR]

This paper presents a meticulous exploration of advanced machine learning techniques for precise forest type classification using multi-temporal remote sensing data within a woodland environment. The study comprehensively evaluates a diverse range of models, ranging from advanced (ensemble) machine learning (ML) methods to several finely tuned support vector machine (SVM) variants, with a specific focus on Bayesian-optimized SVM with a radial basis function (RBF) kernel. Our findings highlight the robust performance of the Bayesian-optimized SVM, achieving a high accuracy of up to 94.27% and average precision and recall of 94.46% and 94.27%, respectively. Notably, this accuracy aligns with the levels attained by acclaimed ensemble techniques such as random forest and CatBoost while also surpassing those of XGBoost and LightGBM. These results highlight the potential of these methodologies to significantly enhance forest type mapping accuracy compared to traditional (linear) SVM and black-box neural networks. This, in turn, can enable the reliable identification and quantification of key services, including carbon storage and erosion protection, intrinsic to the forest ecosystem. The findings of our comparative study emphasize the profound impact of employing and fine-tuning ML approaches in the realm of remote sensing-based environmental analysis. [ABSTRACT FROM AUTHOR]

Catchment analysis is an efficient method for understanding of how climate variability and catchment characteristics interact to define a hydrological response. This paper analyses the geomorphological and morphometric parameters of the Tarnava Mica river basin using GIS. Târnava Mică River has a length of 196 km and a surface of the river basin of 2071 km2. The remote sensing technique is a tool to assess the morphometric parameter of basin hydrology, and it helps to understand basin hydrology and water resources management. [ABSTRACT FROM AUTHOR]

In the task of remote sensing, the Hyperspectral image (HSI) classification to analyze land cover is an established research topic. However, the nature of remote sensing data still poses several challenges including, the curse of dimensionality, the negligible number of samples during training or the presence of unbalanced data which makes learning difficult. Having a training set of pixels with the label of the assigned class, the operation that is performed in the classification of hyperspectral images is to assign a class label to each pixel in the test set based on the knowledge acquired with the training set. This paper discusses a new approach in the supervised classification of HS images considering the statistical tool of Copulas. Comparison with well-established techniques shows the good behaviour of this technique. [ABSTRACT FROM AUTHOR]

Advancements in the field of physics gave birth to remote sensing for analyzing climate problems effectively and smoothly in form of providing vulnerable and significant data. Mathematics and statistics play a pivot role in the succeeding domains to cater to data analysis in a structured and lucid manner. This paper incubates a climate-related study with the help of enrichments in mathematics and physics. The paper states clearly all about the temperature profile of an Indian city, Ajmer. The incorporation of data from 2011 to 2020 depicts a good picture of land skin temperature as a result. Duo-Satellite imageries of ten years had been examined carefully and software-derived data was acted upon by mathematical analysis to find a pattern meticulously. Significant findings were that the summer season was found out the hottest among all the seasons during both day and night time. The winter season had the least anomalies and the monsoon had the most. The paper and its findings were showed alarming information about the gradual rise in temperature which will create a difficult situation in near future for human beings. [ABSTRACT FROM AUTHOR]

Our paper describes a case study, on a 27 km-long section of the Moldova River in Romania, on which floods often occur. Floods are some of the most disastrous phenomena caused by combined factors: atmospheric and hydrological with significant repercussions on the environment, with great material damage and loss of life. According to this application, GIS technology is a remarkable solution that can solve rationally, intelligently and efficiently the increasingly difficult problems related to these phenomena, to the use of terrestrial resources. GIS facilitates the processing and analysis of both conventional and remote sensing spatial data, integrated in complex and heterogeneous databases. Our paper shows real-time flood modelling by applying established mathematical-hydrological models. The identification and mapping of flooded areas are represented by field data collection and the use of radar images taken at the time of the floods, in combination with satellite images, taken before or after the events. Field data collection is often done using mobile equipment, which allows direct real-time connection to a resource center and fast information exchange. The GIS application described in this paper shows that both field equipment use and subsequent data processing allow fast mapping of floodplains and identification of affected areas and enable one to make fast calculations to determine, for instance, the optimal path between points of interest, depending on the actual situation on the ground (inaccessible roads, damaged bridges, etc.). This study suggests a solution for managing flood emergencies, both by directly collecting updated field information and by calibrating flood patterns by quickly mapping floodplains. [ABSTRACT FROM AUTHOR]

One of the purposes of river management is the disaster protection of the nearby population. The effect of riparian vegetation on hydraulic resistance and conveyance capacity makes it a vital parameter for this purpose. With remote sensing techniques, vegetation information can be estimated. This paper's objective is to combine UAV and satellite imagery to obtain vegetation parameters with moderate resolution for hydraulic modeling, and to assess the seasonal effect of the vegetation on the Manning coefficient. Typhoon Hagibis was simulated with a 2D hydraulic model with a dynamic vegetative roughness estimation routine. Results demonstrate that this method achieved less error than the traditional static roughness value method of hydraulic modeling. The seasonal effect of the vegetation on the roughness was shown by a relationship between the percentage of vegetation cover and the average Manning in the stretch. [ABSTRACT FROM AUTHOR]

Over the past decade unmanned aircraft systems (UAS) have become increasingly popular, affordable, and technically capable. No longer are UAS considered to be simply for recreation and hobby, today's relatively inexpensive UAS offer significant capabilities for flight and recording of imagery. Combined with the easing of previously onerous Federal Aviation Administration (FAA) rules governing the use of UAS for commercial/professional purposes, these platforms now provide a viable option for scientists and engineers to apply these as tools to conduct remote sensing missions around the world. While basic operation of UAS may not be considered a major challenge for those willing to learn and practice their skills, this level of training is not always sufficient. For universities or businesses vying for lucrative grants, oftentimes these teams may not be considered competitive without having some level of formal education and experience with UAS/sensor suites. Engineers and scientists may desire to serve as Program Investigators (PI) for grants or as Mission Directors for team flight operations, but often lack practical experience. Likewise, newer UAS pilots may lack requisite technical expertise in UAS/sensor capabilities. In the academic world, students are also looking for ways to gain practical aerospace engineering, remote sensing, and UAS missions experience. University of Alaska Fairbanks (UAF) course, AERO 658, UAS Operations, provides students with the opportunity to gain this expertise while solving a real-world mission requirement in arctic research, public service missions, or supporting critical infrastructure. The course provides students with the knowledge and tools needed to serve in the role of Mission Director for UAS flight operations for remote sensing missions, and to successfully compete for technical grants involving UAS operations. In the inaugural offering of AERO 658 during the spring of 2021, students tackled several real-world mission sets, including watershed drainage, support for mining exploration, and the environmental and safety impacts of glacial melt in nearby Juneau and Valdez glaciers. Students came away from the course with a grounded understanding of the capabilities and limitations of UAS, how UAS/sensors could best be applied to mission campaigns, how tradeoffs in capability/cost can affect mission planning, experience with data analysis and rendering tools, experience liaising with professional UAS flight operations teams, hands on experience with consumer-grade UAS, and in creation of technical documents and multimedia capturing their results. This paper details the course organization, how it has been structured to satisfy the diverse interests of our student population in tackling important contemporary issues with modern technology (while doing so with limited university resources), how this body of experience is expected to help them in their own careers and endeavors, and how that experience ultimately strengthens the university program for future students. The paper is authored by the course instructor and coauthored by students who took the course (participating in the Juneau/Valdez glacier studies), providing perspectives from both a personal and institutional point of view. [ABSTRACT FROM AUTHOR]

Remote sensing images Landsat 8 are widely used in fields such as exploration of land cover changes, urban islands, environmental monitoring, and retrieval of the Earth's surface temperature, due to the high spatial resolution of up to 30 meters and continuous coverage of the Earth, and the data are freely available. Landsat 8 contains two thermal image channels, 10 and 11. In particular, the sensor's thermal emission in band ten (10.60–11.19 µm) shows the ability to monitor temperature changes. Thermal images help to determine the thermal properties of a given area and to discover anomalies in temperature. The locations with high LST are the anomalies showing a high possibility of geothermal resources. In this paper, band 10 was used to retrieve the Earth's surface temperature using three different algorithms, LST is performed with Artis and Carnahan, (1982) models, Modify the Stefan-Boltzmann and Kirchoff's Law, mono-window algorithm, respectively. Accordingly, this paper proposed a comparative strategy for the Landsat 8 image in Temperature retrieval methods based on the correlation coefficient. The objective of this study is to compare the results of the three algorithms to obtain the best accuracy in recovering the Earth's surface temperature. The results indicated that the first and second method gives better matching results than the third method. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the frequent climatic cataclysms that have been occurring lately, and the ecological and humanitarian problems that have arisen as a result. Analyzed are the achievements and the role of contemporary photogrammetric and remote sensing methods in studying these cataclysms from a distance, in real time, and in an objective, cost-effective, and timely manner. Remotely sensed information about the quality and quantitative characteristics of various cataclysms has a decisive role to play in making timely and optimal managerial decisions to protect the population, study their parametres, and understand their real impact on the environment. Remote sensing data are invaluable for scientific research, studying, and forecasting different climatic cataclysms, tracking their changes in time, evaluating consequences, and more. They serve as the primary source of information for the corresponding geographical informational systems related to natural disasters and the creation of different models. [ABSTRACT FROM AUTHOR]

Reciprocity is an acoustic property that describes the symmetry of sound transmission between two points. However, this property is undesirable in certain applications, and this has led to significant interest in the development of nonreciprocal acoustic devices that achieve one-way sound transmission. These devices typically achieve nonreciprocal sound transmission by introducing nonlinearities or directional biasing. Previously proposed nonreciprocal acoustic devices generally have limitations; for example, they may not be fully tuneable, they can introduce signal distortions such as additional harmonics, or they can only exhibit nonreciprocal behaviour over a narrow bandwidth. To overcome these challenges, previous work has demonstrated how a wave-based active control system can be used to drive an array of acoustic sources to achieve reversible and broadband non-reciprocal behaviour. However these wave-based active control systems use external far-field pressure sensors to achieve broadband nonreciprocal behaviour and, thus, these active control systems are not self-sufficient. This paper therefore presents an experimental investigation into how remote sensing techniques can be incorporated into the previously proposed wave-based active control systems to create more self-contained nonreciprocal acoustic devices that still achieve broadband nonreciprocal behaviour in a one-dimensional acoustic system. [ABSTRACT FROM AUTHOR]

In this paper, the Global Surface Water Explorer (GSWE) was combined with bathymetric and historical meteorological data to quantify water balance during the period 1984–2020 in the Primera de Palos freshwater lagoon (Southwest Spain). This allowed us, through a water balance approach, to estimate all water inputs and outputs to analyse the hydrological changes in the lagoon. The results showed high fluctuations with seasonal changes marked by the climatic regime during the first two decades of the study period. After this initial period, water extension remained stable above 70 % of the maximum lagoon extent. Thus, the natural hydrological regime of the lagoon was modified by water inputs from irrigation return, which are difficult to quantify. Thanks to a water balance approach, these irrigation returns were quantified as the closure term of the water balance. Three scenarios of irrigation return inputs can be defined, 4500, 1700, and 500 m 3 d -1 , depending on the cropping season. The use of remote sensing combined with bathymetric and meteorological data can provide the knowledge to support better informed water-management decision-making, although it may have some limitations in dry periods related to image processing in border data. [ABSTRACT FROM AUTHOR]

Collaborative Online International Learning (coil) activities have become a widespread approach to promote internationalization because it is resilient, as proven during the pandemic COVID-19. Also, coil activities have other advantages such as inexpensive, easy to implement and coordinate. However, the design of these activities depends highly on coil partners and subjects. The objective of this paper is to reflect on these activities from the technical perspective of an engineering course. To reflect on these activities, we performed a SWOT (Strength, Weaknesses, Opportunities and Threats) analysis. To build this analysis we mixed our own observation with a literature review, which provides completeness. The final matrix contains 4 points on average per category. While the least points are defined in Strength, these points can outweigh the other categories in terms of importance, but this remains to be consulted to students and instructors. [ABSTRACT FROM AUTHOR]

MAUP theory is applied to a LiDAR dataset acquired over a forested scene. The Weibull Probability Density Function (PDF) has been fit to LiDAR derived canopy height profiles for plots covering the complete 1 × 1 km scene. Ten plot sizes are tested from 10 – 300 m. Parameters describing the location and scale of the PDF are used as analogous of canopy height and canopy length respectively. Results suggest that, for a structurally homogenous forested scene, localised variance decreases for canopy height with increasing plot dimensions. The opposite is apparent for canopy length, it is suggested this is a result of a spatially heterogeneous understorey layer negatively skewing the distribution. [ABSTRACT FROM PUBLISHER]

This article is devoted to the questions of the reliability and the accuracy of the invented algorithm. Background: The implementation of autonomous navigation system for unmanned airborne vehicle is required for the reliable navigation in case of the instability of the satellite navigation system. Traditionally this problem is solved by the terrain landform navigation system, also the synthetic aperture radar navigation systems could be used as the correction system for the inertial navigation system. Nevertheless, these correction systems do not allow designers to provide necessary stability and reliability of whole system. Therefore, we use lengthened objects as the reference objects, which are stable enough either in different weather seasons. Also these objects (such as roads, rivers, railways) are very often on flat landforms, where the terrain landform navigation system does not work. Methods: We process the amplitude distribution of the reflected signal in order to get the parameter of the algorithm. The obtained distributions could be compared with etalon distributions, which can be computed for various terrain types. Therefore, we can use the intersection area as the indicator of the underlying terrain. This algorithm could be found in previous papers. This paper describes in detail problem of choice necessary counts number for reliable detection of lengthened objects. The suggested solutions allow user to implement this algorithm in the real systems. Also the comparison of the developed algorithm with algorithm based on the KolmogorovSmirnov criterion briefly described there. Conclusion: The implementation of the designed algorithm contains resolving of many small but important questions such as choice of necessary counts number of the accuracy of the algorithm. These questions are described in this paper. [ABSTRACT FROM AUTHOR]

Background: This article is devoted to the question of correction system implementation. The correction system is necessary to provide the ability of autonomous navigation the unmanned airborne vehicle. We can see a lot of examples of terrain landform correction systems and “synthetic aperture radar” – based systems. But in the case of using information about lengthened objects, we have a great opportunity to provide robustness objects, which we can reliably cross during our flight. Nevertheless, these objects should be placed in some on-board system, which will process this information to make a suggestion about unmanned airborne vehicle position. Methods: Recently it was suggested such an algorithm, which is based on the histogram comparison of the reflected signal of the pulse radar altimeter. It is well known that the distribution of the reflected signal of the pulse radar is depend on the terrain from which this signal was reflected. Therefore, the idea of using this information as the parameter, which helps to define the terrain type, is described in this paper. The comparison of the histograms (one is etalon, other is current histogram) in this algorithm is based on the evaluation of the intersection area of these histograms. Results: The main part of this paper reveals how this idea could be implemented in the real navigation system structure. The question of performance is also discussed there. It is shown a useful case of implementation of this algorithm. Conclusion: The suggested algorithm could be implemented in the real on-board system and it allows us to increase the accuracy of autonomous navigation without implementing any additional hardware decisions. [ABSTRACT FROM AUTHOR]

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]

As technology develops, the use of artificial intelligence is increasingly widespread, including in remote sensing. Artificial intelligence can also be used to obtain various information from satellite imagery such as image processing, recognition, or identification. The existence of this technology is in-line with the increasing availability of various existing satellite images. Artificial intelligence can assist users in detecting objects in the image more faster than the traditional ones. In the identification process through image, training data is needed in the form of a dataset with a certain label. In this paper, we will discuss the method creation of remote sensing dataset for estimating burned area by using Landsat-8 imagery with Python and Geographic Information System (GIS) software. The final purpose of this study is to provide the dataset of burned area considering that almost every year Indonesia experiences land fires. With the dataset for the burnt area, it is hoped that it can help respond to and handle fire disasters more quickly and efficiently. [ABSTRACT FROM AUTHOR]

A small format aerial image is an image of the Earth's surface taken from an aerial vehicle using an ordinary camera. The resulting image is a typical image without geospatial information that can be used for mapping purposes. Rigorous sensor model (RSM) is a method that can be used to convert small format aerial images into georeferenced images by connecting the relationship between camera coordinates and earth coordinates. As the advantage of this method, it does not require any ground control point (GCP) in its computation. Therefore, the method is very suitable to be used for mapping in inaccessible or remote areas where it is difficult to get GCP data. This paper presents a transformation process to link image coordinates and earth coordinates by using RSM for a small format aerial image. The image was taken with Indraku-2a, a remote sensing experimental camera, in Kertajati on July 13, 2018. For processing purpose, navigation data accompanying the image data are used. The data consist of position (longitude, latitude, altitude) and attitude (yaw, pitch, roll) data. For the results, we successfully obtained earth coordinate values for each pixel of the images by using the RSM method without have to use field measurement data although there is a shift in the values due to out-of-sync timestamp between the images acquisition time and the recording time of navigation time. This differences have to be corrected by checking the configuration settings of the navigation data and adjust them based on on-site measurement data. [ABSTRACT FROM AUTHOR]

Super-resolution (SR) is a well-established technique used to enhance the resolution of low-resolution images. In this paper, we introduce a novel approach for the super-resolution of Sentinel-2 10 m RGB images using higher-resolution Venus 5 m RGB images. The proposed method takes advantage of a modified SRResNet network, integrates perceptual loss based on the VGG network, and incorporates a learning rate decay strategy for improved performance. By leveraging higher-resolution VENµS 5 m RGB images as reference images, this approach aims to generate high-quality super-resolved images of Sentinel-2 10 m RGB images. The modified SRResNet network was designed to capture and learn underlying patterns and details present in Venus images, enabling it to effectively enhance the resolution of Sentinel-2 images. In addition, the inclusion of perceptual loss based on the VGG network helps preserve important visual features and maintain the overall image quality. The learning rate decay strategy ensures the network converges to an optimal solution by gradually reducing the learning rate during the training process. Our research contributes to the field of super-resolution by offering a novel approach specifically tailored for enhancing the resolution of Sentinel-2 10 m RGB images using Venus 5 m RGB images. The proposed methodology has the potential to benefit various applications, such as remote sensing, land cover analysis, and environmental monitoring, where high-resolution imagery is crucial for accurate and detailed analysis. In summary, our approach presents a promising solution for the super-resolution of Sentinel-2 10 m RGB images, providing an effective means to obtain higher-resolution imagery by leveraging the complementary information from Venus 5 m RGB images. We used the SEN2VENµS dataset for this research. The SEN2VENµS dataset comprises cloud-free surface reflectance patches obtained from Sentinel-2 imagery. Notably, these patches are accompanied by corresponding reference surface reflectance patches captured at a remarkable 5 m resolution by the VENµS Micro-Satellite on the same acquisition day. To assess the effectiveness of the proposed approach, we evaluated it using widely used metrics such as the mean squared error (MSE), the peak signal-to-noise ratio (PSNR), and the structural similarity index (SSIM). These metrics provided quantitative measurements of the quality and fidelity of the super-resolved images. Experimental results demonstrate the effectiveness of our proposed approach in achieving improved super-resolution performance compared to existing methods. As an example, our method achieved a PSNR of 35.70 and a SSIM of 0.94 on the training dataset, outperforming the bicubic interpolation method, which yielded a PSNR of 29.53 and a SSIM of 0.92. On the validation dataset, our approach achieved a PSNR of 40.3809 and a SSIM of 0.98, while the bicubic interpolation method achieved a PSNR of 34.26 and a SSIM of 0.94. Finally, on the test dataset, our approach achieved a PSNR of 29.8231 and a SSIM of 0.90, whereas the bicubic interpolation method yielded a PSNR of 26.99 and a SSIM of 0.85. The evaluation based on MSE, PSNR, and SSIM metrics showcases the enhanced visual quality, increased image resolution, and improved similarity to the reference Venus images. [ABSTRACT FROM AUTHOR]

eXplainable Artificial Intelligence (XAI) has emerged as an essential requirement when dealing with mission-critical applications, ensuring transparency and interpretability of the employed black box AI models. The significance of XAI spans various domains, from healthcare to finance, where understanding the decision-making process of deep learning algorithms is essential. Most AI-based computer vision models are often black boxes; hence, providing the explainability of deep neural networks in image processing is crucial for their wide adoption and deployment in medical image analysis, autonomous driving, and remote sensing applications. Existing XAI methods aim to provide insights about the methodology used by the black-box model in making decisions by highlighting the most relevant regions within the input image that contribute to the model's prediction. Recently, several XAI methods for image classification tasks have been introduced. In contrast, image segmentation has received comparatively less attention in the context of explainability, although it is a fundamental task in computer vision applications, especially in remote sensing. Only some research proposes gradient-based XAI algorithms for image segmentation. This paper adapts the recent gradient-free Sobol XAI method for semantic segmentation. To measure the performance of the Sobol method for segmentation, we propose a quantitative XAI evaluation method based on a learnable noise model. The main objective of this model is to induce noise on the explanation maps, where a higher induced noise signifies low accuracy and vice versa. A benchmark analysis is conducted to evaluate and compare the performances of three XAI methods, Seg-Grad-CAM, Seg-Grad-CAM++ and Seg-Sobol, using the proposed noise-based evaluation technique. This constitutes the first attempt to run and evaluate XAI methods using high-resolution satellite images. Our code is publicly available at GitHub. [ABSTRACT FROM AUTHOR]

This paper presents a technique for mapping the vegetation cover of mountainous areas based on seasonal satellite data from Landsat-OLI 8, using information about vegetation growth conditions. This mapping is based on the creation of a layer of relatively homogeneous areas in terms of relief and climate. Training samples for the classification of images were formed within these areas. Satellite images were classified using the maximum likelihood method. The created map reflects the spatial distribution of 9 classes of forest vegetation and 10 classes of non-forest vegetation. [ABSTRACT FROM AUTHOR]

Terrestrial laser scans (TLS) are routinely used to document as-built surfaces for underground cavities. Although TLS are recorded from a single point and prone to "occlusions" by protruding edges, the surfaces developed from TLS point clouds record detail as fine as slickensides. Statistical analysis of discontinuities picked from TLS scans, including by virtual scanlines, allowed rapid characterization of excavated volumes at the Kidston PHES from vertical, lateral and forward projection of access tunnel and construction adit cuts. Photogrammetry can overcome the issue with occlusions in the data when used in conjunction with TLS. Near-equivalent geometric surfaces can be produced using a handheld camera at a variety of locations. We explored strategies for photogrammetry in the Kidston and Sydney Metro excavations. They uniformly illustrate the value embedded in photo-textured surfaces, particularly recognition and characterization of poorly exposed planes and improved understanding of lithology. However, lighting, camera positioning and reference controls are critical for developing a high-quality product. Overall, digital surfaces facilitate agile response to developing situations, add value to geotechnical deliverables and ultimately derisk projects. They also can provide owners with a full as-built geological model for asset management and future trouble shooting. [ABSTRACT FROM AUTHOR]

Autonomous vehicles have become indispensable in precision agriculture for a target-specific rather than uniform application of resources over large area. Guidance for each autonomous vehicle to efficiently traverse complex field vegetation and terrain are challenges for which the research community still needs to find a robust solution. Moreover, generating a reference global waypoint path is essential for autonomous vehicles' safe and efficient navigation in row-based crop fields. In this paper, a deep learning-based approach is proposed to generate a usable waypoint path in the world geodetic system from remote sensing imagery of a potato crop yield. The proposed network estimates the keypoints representing crop-rows and their sizes from the given aerial image using a deep convolutional keypoint detection network. Using a simple path planning algorithm, the global path is generated from the predicted keypoints and sizes. Then the global waypoint path is transformed into geodetic coordinates. Experimentation was done on a wide variety of real-field images captured using a high-resolution multispectral and RGB camera system mounted on a low-altitude (25 m) flying Unmanned Aerial Vehicle (UAV). The best average precision score for the crop-row keypoint estimation reached up to 98% and whole crop-row detection reached up to 86%. A deep learning-based network is trained to detect crop-row with multispectral images containing near infra-red spectrum information has performed better than the network trained on RGB images. The quick global path generation by the developed lightweight model offers a possibility for real-time applications in the future. [ABSTRACT FROM AUTHOR]

Assessment of human-ecosystem interactions in coastal environments is carried out by monitoring the change in the coastal line. In this study, we give an analysis of shoreline alterations along the Iraqi coast over the last five decades, during the period from (1973 to 2021), data series of Landsat (MSS, TM, ETM+, and OLI) were used and incorporated into GIS (Geographical Information System) for executing a temporal-spatial analysis for alterations in the coastline by applying (DSAS 5.1) Digital Shoreline Analysis System approach. Linear regression rate and endpoint rate quantified the high accretion at rates of more than 50 m /year also the net shoreline movement analysis identified about 2500 m toward the sea. The findings of this paper demonstrate an understanding of shoreline evolution and the ability to forecast future variations to support decision-makers in developing long-term management to safeguard our marine environment. [ABSTRACT FROM AUTHOR]

Remote sensing multi-band hyperspectral image classification is a challenging task and is of current interest too. Proper extraction of spectral features of the data results in higher classification accuracy. This paper proposes a hybrid approach to obtain maximum classification accuracy of hyperspectral data. First and foremost, the Fast Fuzzy Cluster Means (FastFCM) algorithm is presented for overcoming the limitation of high computational time of conventional FCM for high-dimensional data. The segmented outcome as spectral information is then combined with the spatial information obtained by the Extended Multiattribute Profiles (EMAPs) to form a composite kernel (filter). In this experiment, the Support Vector Machine (SVM) is used as a classifier to train the data. The classification outcome in terms of three parameters Overall Accuracy (OA), Average Accuracy (AA), and Kappa Index (KI) of the proposed approach (named as (FastFCM-SVM)) has been compared with some current state-of-art methods to investigate its better stability and classification performance. [ABSTRACT FROM AUTHOR]

This research focus on the urban land uses and covers techniques that have used for classification and mapping which is considered to be one of the main aspects of the decision making in the developing countries that urbanized widespread. Land use and land cover LULC classification and mapping are two aspects of decision making in developing countries with widespread urbanization, and can be used in climate change, agriculture, ecology, water quality, improving living conditions, and identifying and developing regional development plans. This classification and mapping used in climate change, agriculture, ecology, water quality, improving living standards and identifying and developing plans for regional development. Remote sensing imagery have been used for land cover and land uses classification and urban land mapping. However, there is limitation due to hardly identifying urban land use and land function issues due to the rare of high-resolution urban land use maps availability along with having a low accuracy. In this paper several different methods have been discussed adopted for this purpose including the use data from multisource remote sensing with WeChat mobile application data, Landsat 8 Imagery with open social data, and area of interest AOI method. All of these methods have proved to be successful in their area in urban land use mapping and LULC classification by giving an improved accuracy in mapping and classification. [ABSTRACT FROM AUTHOR]

All stages of mining activities, starting with the exploration phase up to the end of the extraction works lead to significant natural landscape alteration and formation of new, anthropogenic surface features. In order to mitigate the effects caused by mining, both in Poland and in the world, a number of legal regulations have been developed, which oblige mainly the mining entrepreneurs to restore use values of degraded or devastated lands. This process called reclamation is a long-term, complex operation and covers large areas. Therefore, the academic society publishes more and more papers concerning monitoring reclamation status, as well as supporting landscape planning processes using different technologies and measurement methods. It should be emphasized that scientific publications are not only limited to the mentioned subject fields. The main purpose of this paper is to exemplify issues connected with reclamation process, which have been investigated by means of remote sensing and GIS technologies. As a part of this publication 25 scientific papers written by Polish and foreign academics have been studied. The results of a conducted analysis provide a comprehensive summary in terms of quantity and quality concerning scientific materials published so far on the subject of reclamation process, remote sensing and GIS technologies. [ABSTRACT FROM AUTHOR]

Super-resolution image fusion aims to produce an image with finer spectral and spatial details than the input images. However, the super-resolution fusion is mainly applied to enhance a visual representation of the images and its potential benefits to the final thematic classification is an open question. In this paper, we present an experimental investigation of the remote sensing image classification performance in the case of the multi-sensor super-resolution image fusion. The research aims to compare classification performance obtained for the fused image and the low resolution input ones using different standard-of-the-art classifiers and feature extraction methods. Input data are supposed to be multispectral data obtained in visible and near infrared spectral ranges by the different remote sensing systems. To perform a multi-sensor super-resolution image fusion, we used a gradient-descent optimization approach with a B-TV regularization successfully adapted for remote sensing images with different spatial and spectral sampling characteristics by the authors of the paper. As for features, we applied brightness in spectral channels, attribute profiles and local feature attribute profiles. The classification was performed using support vector machines and random forest classifiers that have been proved to be very effective for remote sensing data classification. The experimental research included the multi-sensor input data simulation for four remote sensing systems, the super-resolution image fusion of all simulated images and the thematic classification of the fused image and the images obtained as an average input for each of the simulated imaging systems. The spatial resolution of the fused image was in 2, 3, 4 and 5 times better than the spatial resolution of the modeled input images. The average bandwidth of the fused image was 29 nm whereas for the input low resolution images it was in the range from 37 to 83 nm. Experimental results have shown that random forest classification is better to use with fusion, whereas support vector machines demonstrated better results without fusion. The feature extraction test showed that extended attribute profiles enhance the random forest classification accuracy of the fused image. Thus, the classification results have shown that super-resolution image fusion leads to the classification accuracy increase in the case of random forest classifier and there is no need to apply fusion in the case of support vector machines. [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]

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]

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]

This paper will discuss the automation of Terra-MODIS 8-day data processing to support the availability of Analysis Ready Data (ARD). Terra-MODIS (MOD09A1) data covering the territory of Indonesia used in this processing will change the projection and tile size. The data conversion, mosaic, and tilling automation software used in this processing were created using GDAL and Strawberry Perl. The automation of the data conversion process takes 2 minutes, while mosaic and tilling take 1 minute. The result of this processing is data with a geographic projection (latitude, longitude) WGS 84, 5x5 degrees tile size, and 11Kb file size. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) technology and Geographic Information Systems (GIS) have changed the traditional way of working that takes costs, time, and labor consumes to become modern and efficient. This paper review aimed at the role of remote sensing and GIS in grassland management. The review used a descriptive methodology to analyze literature from a number of journals concerning applied spatial research studies to look for the best method to be applied in grassland research in East Sumba Regency, Indonesia. In conclusion, the use of satellite imagery for both supervised and unsupervised classification is very helpful in determining the location, area, and limiting factors around it. A Maximum Likelihood Classification is the most widely used form of supervised classification and is used in a variety of applications. However, the ISODATA is the most effective method for unsupervised classification. The effort to determine a grassland or other purposes with land-use suitability analysis can be done with Multi-Criteria Evaluation (MCE) through an Analytical Hierarchy Process (AHP) approach. This method is very helpful for decision-makers, especially in the farm industry. Both an expert#x2019;s judgment and literature review can be as source indicator factors use in suitability analysis. Considering the natural condition of land cover and land use in the East Sumba Regency, the identification and management of grassland will face a lot of resources and is time-consuming. Through remote sensing and GIS approaches, researchers are very helpful in increasing the level of accuracy and saving time. [ABSTRACT FROM AUTHOR]

Geotechnical is a branch of Geological science that studies soil and rock and their relation to the surrounding buildings. Geotechnical assessment on a slope that is indicated to be prone to landslides is very important to do, especially if the slope is in a location that is often passed by humans. In this paper, a geotechnical assessment is carried out on the landslide-prone road cut slope on the Jabungan-Mluweh Road, Semarang, Indonesia with the aim of knowing the level of vulnerability to landslides on the slopes around the road. The method used is remote sensing and field observations. The remote sensing methodology for determining the distribution of landslide-prone by 5 parameters, that is rock resistance, land use, slope, fault fracture density, and normalized difference vegetation index. After that, detailed observations were made in the field and it was found that there were 10 locations prone to landslides, at those 10 locations a more detailed analysis was carried out to determine the geological strength index (GSI), slope geometry, and joint conditions. Slope modeling is done using phase 2 software by finite element method. The results of the analysis show that Geotechnical assessment on road cut slopes can be done using laboratory data, but an empirical approach using Schmidt hammer rebound value (SHRV) can also be applied in the assessment if the observed terrain is quite difficult and dangerous. The slope stability analysis show that there are 7 slopes in unstable conditions, 1 slope is marginally stable, and 2 slopes are stable. Therefore, several suggestions are proposed to forstrengthen the stability of the cut slope. [ABSTRACT FROM AUTHOR]

Flood disasters cause material and human damage every year. This is due to high and uneven rainfall in an area. One of the areas in Indonesia that are affected by the flood is Klaten Regency. Therefore, it is necessary to detect the potential for flooding that can be used as a base for developing suitable management. This paper aims to map the potential for flooding by utilizing remote sensing data and GIS. Aspects used as criteria are rainfall, slope, soil type, geological conditions, rivers, normalized difference vegetation index, normalized difference water index, and land use. Based on the results, Klaten Regency has two sub-districts with a high flood hazard category, 21 sub-districts with a moderate category, and three sub-districts with a low category. Bayat and Cawas are sub-districts that have a high flood hazard category. Meanwhile, Kemalang, Karangnongko, and Polanharjo are sub-districts with a low flood hazard category. The main factors for flooding in Klaten Regency are slope, land use, and is crossed by the Dengkeng river. [ABSTRACT FROM AUTHOR]

In this contribution, we present the results of two recent papers [1,2] published in Optics Express, dedicated to the development of two new lidar methodologies. In [1], while the carbon aerosol (for example, soot particles) is recognized as a major uncertainty on climate and public health, we couple lidar remote sensing with Laser- Induced-Incandescence (LII) to allow retrieving the vertical profile of very low thermal radiation emitted by the carbon aerosol, in agreement with Planck's law, in an urban atmosphere over several hundred meters altitude. In paper [2], awarded as June 2014 OSA Spotlight, we identify the optical requirements ensuring an elastic lidar to be sensitive to new particles formation events (NPFevents) in the atmosphere, while, in the literature, all the ingredients initiating nucleation are still being unrevealed [3]. Both papers proceed with the same methodology by identifying the optical requirements from numerical simulation (Planck and Kirchhoff's laws in [1], Mie and T-matrix numerical codes in [2]), then presenting lidar field application case studies. We believe these new lidar methodologies may be useful for climate, geophysical, as well as fundamental purposes. [ABSTRACT FROM AUTHOR]

Developments in satellite-based scanning systems over the past few decades have led to a remarkable increase in the amount of remote sensing data. With data volumes increasing all the time, it is important to always be mindful of ways to decrease satellite image processing time. One of the most complex problems of image processing that requires an effective solution is the segmentation of original image into homogenous regions. In this paper we present a modified Kruskal's algorithm as an effective graph partitioning method for image segmentation. The proposed algorithm creates a tree structure for image description by mapping the tree to the array-based implementation of disjoint sets which are explicitly calculated with Kruskal's algorithm instead of successive tree forming for different weights of original graph. [ABSTRACT FROM AUTHOR]

Remote sensing information services have characteristics of multi-dimension, heterogeneity, and high dynamic performance, so it is very difficult to realize online search by use of UDDI registry of business search engines. It has become a bottle-neck restricting the farther development of remote sensing information services. Considering the characteristics of remote sensing information services, the paper introduces remote sensing information ontologies into the online search of the remote sensing information services and proposes a semantics clustering based and multi-QoS (Quality of Service) constraints method to improve the performance of remote sensing information services discovery. Experimental results show that the method proposed in the paper can attain well search performance. [ABSTRACT FROM PUBLISHER]

This paper depicts the early phase of development for an integrated system tailored for emergency response actions and remote sensing. This paper focuses on the support system envisioned as an integrated Unmanned Aerial System (UAS) system that consists of one or more ultralight multifunctional aerial units with a configuration that can be adapted to the nature of the intervention: monitoring, mapping, observation and logistics etc. Starting from wing airfoil and material selection and ending with the experimental model manufacture, the paper will present the development of a single sail paraglider wing that can meet the operational demands for emergency response situations. The wing was designed mainly to have an easy handling and to have a predictable deployment at all times. The entire system and the aerial units were designed with increased modularity in order to be tailored for specific operational requirements of the intervention. The mapping module relies on photogrammetry and a low power LiDAR sensor. [ABSTRACT FROM AUTHOR]

In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection. [ABSTRACT FROM AUTHOR]

Tonal noise suppression of a cylinder placed in uniform flow has been achieved, to some extent, by coating it with a structured porous material as a form of passive flow and noise control. A structured porous-coated cylinder (SPCC) was investigated in an anechoic wind tunnel to determine the relationship between the far-field vortex shedding noise and the pressure recorded on the outer porous surface. To date, no experimental studies have been conducted on the surface pressure of any type of porous-coated cylinder. Acoustic measurements were carried out using a far-field microphone and simultaneously unsteady surface pressure fluctuations were obtained around the cylinder mid-span circumference using remote-sensing techniques. By obtaining simultaneous timedependent signals, more light is shed on the underlying noise-reduction mechanism of the structured porous-coated cylinder. The pressure distribution results demonstrated a delay in the boundary layer separation in the case of the SPCC compared to the bare cylinder. The far-field noise measurement results showed that significant noise reduction can be achieved by the use of an SPCC. The surface pressure results and directivity pattern of the tonal noise level have also shown that substantial noise reduction can be achieved with the applications of the SPCC. In this paper, strong relationships between surface pressures and acoustic signals were revealed at the vortex shedding frequency in the case of the bare cylinder, while it was insignificant for the SPCC, signifying the strong role of the structured porous media in suppression of the surface pressure energy content. [ABSTRACT FROM AUTHOR]