Remote-sensing Methods Research Articles

Advances in Remote Sensing and Machine Learning Methods for Invasive Plants Study: A Comprehensive Review

This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems.

Integrated geophysical and GIS approaches for groundwater potential assessment: a case study of Aladja, Delta State, Nigeria

ABSTRACT This study explores the groundwater potential in Aladja, Udu, Local Government Area of Delta State, Nigeria, using an integrated approach combining electrical resistivity techniques with Remote Sensing (RS) and Geographic Information System (GIS) methods. The primary aim is to delineate aquifers and comprehensively assess groundwater resources. Methods include Vertical Electrical Sounding (VES) to gather resistivity data, RS and GIS for analyzing geological and hydrological parameters such as lineament density, drainage density, slope, soil type, and land use. The resistivity values ranged from 147 to 460.50 Ωm, with aquifer thicknesses varying between 6.50 and 36.30 m. The RS and GIS analysis indicated high groundwater potential in regions characterized by significant lineament density and moderate slopes. The study highlights the complementarity of VES and RS/GIS in groundwater exploration, revealing substantial groundwater resources in the northwestern regions of the study area. This approach underscores the importance of integrating these methods for a more accurate assessment of groundwater potential. The novelty of this study lies in validating RS and GIS findings with VES data, ensuring reliable groundwater potential mapping and effective resource management. This integrated methodology offers a robust framework for sustainable groundwater resource assessment and management in similar geological settings.

The archaeology of a Nazi synthetic-fuel plant and its legacy: the Hydrierwerke Pölitz AG

The Hydrierwerke Pölitz AG was a synthetic-fuel plant of strategic importance to the Nazi war machine. The surrounding area contained labour camps, factories and other military infrastructure. The area was a target for sustained Allied bombardment causing extensive damage to the plant and nearby towns and villages. After the war, the plant's troubled past faded before interest was revived in the 1990s. Here, with the aid of historical aerial photographs and modern remote-sensing methods, the authors document the physical remains of the site, reconstruct its ‘dark history’ and reflect on the significance of the Hydrierwerke for the discourse on neglected and appropriated Second World War heritage.

Remote sensing of artisanal and small-scale mining: A review of scalable mapping approaches

Artisanal and small-scale mining (ASM) significantly influences the socio-economic development of many low-to-middle-income countries, albeit sometimes at the expense of environmental and human health. Characterized by its labor-intensive extraction from confined (<5 ha) or peripheral mineral reserves, congregated ASM practices can rival the spatial footprint of industrial mines. The unregulated and informal nature of many ASM activities presents monitoring challenges that remote sensing (RS) methods aim to address. While local-scale ASM mapping has seen success, scaling these methods to regional or global levels remains unclear. We review literature on mapping ASM to determine: (1) if studies represent the global distribution and diversity of ASM activities, (2) how ASM's unique characteristics influence the choice of RS methods, and (3) which RS approaches are the most accurate and cost-effective. We found current studies disproportionately focused on ASM regions in Africa, which highlights the need to extend the research to other regions with unique ASM characteristics, such as coal and sand mining in India and China. The selection of RS approaches is heavily influenced by local ASM contexts, the scale of analysis, and resource constraints such as funding for high-resolution imagery and validation data availability. We argue that accurate regional-scale ASM mapping (>100,000 km2) requires innovative combinations of data and methods to overcome data management and storage challenges. Local community participation, including miners, is vital for on-ground mapping and monitoring capacity. We outline a research agenda needed to develop a range of approaches for mapping and monitoring ASM in under-studied regions. By synthesizing effective methods, we provide a foundation for generating accurate and comprehensive spatial data, addressing the issues of inaccurate and incomplete data that global ASM platforms aim to resolve. This spatial data can guide policymakers, NGOs, and businesses in making informed decisions and targeted interventions to improve ASM sector safety, sustainability, and efficiency. Leveraging cloud-based geoprocessing platforms, with regularly updated global satellite image archives, combined with crowd-sourced on-ground information offers a potential solution for sustained regional-scale monitoring.

Specularly reflected whistler: A low-latitude channel to couple lightning energy to the magnetosphere.

Lightning-generated whistlers profoundly affect the energetic particle population in Earth's radiation belts, influencing space weather and endangering astronauts and satellites. We report the discovery of specularly reflected (SR) whistler in which the lightning energy injected into the ionosphere at low latitudes reaches the magnetosphere after undergoing a specular reflection in the conjugate ionosphere, contradicting previous claims that lightning energy injected at low latitudes cannot escape the ionosphere. SR whistlers provide a low-latitude channel to transport lightning energy to the magnetosphere. We calculate the relative contributions of SR, magnetospherically reflected, subprotonospheric, and ducted whistlers to the lightning energy reaching the magnetosphere. When SR whistlers are considered, the global lightning energy contribution to the magnetosphere doubles, implying that the previous estimates of the impact of lightning energy on radiation belts may need substantial revisions. Whistler dispersion and intensity analyses quantitatively confirm our results and suggest new remote-sensing methods of the magnetosphere, ionosphere, Earth-ionosphere waveguide, and lightning flashes.

An Investigation of Urban Heat Island Effect and Environmental Livability based on Remote Sensing Methods

An Investigation of Urban Heat Island Effect and Environmental Livability based on Remote Sensing Methods

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Abstract. Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kg m−3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval.

Biomass and Carbon Stock Estimation through Remote Sensing and Field Methods of Subtropical Himalayan Forest under Threat Due to Developmental Activities

Biomass and Carbon Stock Estimation through Remote Sensing and Field Methods of Subtropical Himalayan Forest under Threat Due to Developmental Activities

Integrating Remote Sensing Methods for Monitoring Lake Water Quality: A Comprehensive Review

Remote sensing methods have the potential to improve lake water quality monitoring and decision-making in water management. This review discusses the use of remote sensing methods for monitoring and assessing water quality in lakes. It explains the principles of remote sensing and the different methods used for retrieving water quality parameters in complex waterbodies. The review highlights the importance of considering the variability of optically active parameters and the need for comprehensive studies that encompass different seasons and time frames. The paper addresses the specific physical and biological parameters that can be effectively estimated using remote sensing, such as chlorophyll-α, turbidity, water transparency (Secchi disk depth), electrical conductivity, surface salinity, and water temperature. It further provides a comprehensive summary of the bands, band combinations, and band equations commonly used for remote sensing of these parameters per satellite sensor. It also discusses the limitations of remote sensing methods and the challenges associated with satellite systems. The review recommends integrating remote sensing methods using in situ measurements and computer modelling to improve the understanding of water quality. It suggests future research directions, including the importance of optimizing grid selection and time frame for in situ measurements by combining hydrodynamic models with remote sensing retrieval methods, considering variability in water quality parameters when analysing satellite imagery, the development of advanced technologies, and the integration of machine learning algorithms for effective water quality problem-solving. The review concludes with a proposed workflow for monitoring and assessing water quality parameters in lakes using remote sensing methods.

Clarification of the Boundaries of Lands of Historical and Cultural Heritage and Determination of Their Protection Zones by Remote Sensing Methods

Clarification of the Boundaries of Lands of Historical and Cultural Heritage and Determination of Their Protection Zones by Remote Sensing Methods

Flood risk assessment in arid and semi-arid regions using Multi-criteria approaches and remote sensing in a data-scarce region

Flooding is a natural disaster that poses a threat to both people and the environment, necessitating proactive assessment and mitigation strategies to protect vulnerable communities and ecosystems. These measures are necessary to reduce the risk of flooding and moderate the impact of rainfall. In this study, an Analytical Hierarchy Process (AHP) was used to evaluate flood risk in a data-limited region by integrating Remote Sensing (RS) and Geographic Information System (GIS) methods. The study identified several key flood risk indicators, including topographic wetness index, elevation, slope, land cover, precipitation, distance to river, distance to road, and NDVI. The flood risk map had a score range of 8.71–30.99 %, with higher scores indicating a greater susceptibility to flooding. These scores were then used to classify the flood risk into five categories: very low, low, moderate, high, and very high. The percentages of regions falling into each category were 8.71 %, 23.52 %, 30.99 %, 22.68 %, and 14.09 % respectively. The area under the Curve (AUC) approach was used to validate the flood risk map, which showed a high degree of accuracy (0.86). The results of this study provide valuable insights for monitoring, and forecasting the probability of floods in the Dosso Region.

Agricultural Irrigation Water Requirement and Its Response to Climatic Factors Based on Remote Sensing and Single Crop Coefficient Method

Agricultural Irrigation Water Requirement and Its Response to Climatic Factors Based on Remote Sensing and Single Crop Coefficient Method

A combined Remote Sensing and GIS-based method for Local Climate Zone mapping using PRISMA and Sentinel-2 imagery

In the last decade, several methods have been developed for Local Climate Zone (LCZ) mapping, encompassing Remote Sensing and Geographic Information Systems (GIS) −based procedures. Combined approaches have also been proposed to compensate for intrinsic limitations that characterized their separate application. Recent work has disclosed the potential of hyperspectral satellite imagery for improving LCZ identification. However, the use of hyperspectral data for LCZ mapping is yet to be fully unfolded. A combined Remote Sensing and GIS-based method for LCZ mapping is proposed to exploit the integration of hyperspectral PRISMA and multispectral Sentinel-2 images with ancillary urban canopy parameter layers. Random Forest algorithm is applied to the feature sets to obtain the LCZ classification. The method is tested on the Metropolitan City of Milan (Italy), for the period from February to August 2023. A spectral separability analysis is carried out to investigate the improvement in LCZ identification using PRISMA in comparison to Sentinel-2 data, as well as improvements in LCZ spectral separability on PRISMA pan-sharpened images. The resulting maps’ quality is evaluated by extracting accuracy metrics and performing inter-comparisons with maps computed from the LCZ Generator benchmark tool. Inter-comparisons yield promising results with a mean Overall Accuracy increase of 16% using PRISMA for each LCZ class. Furthermore, we find that PRISMA improves the detection of LCZs compared to Sentinel-2, with a mean Overall Accuracy increase of 5%, in line with the higher spectral separability of PRISMA spectral signatures computed on the training samples.

Preparation of future development scenarios of urban landscapes in accordance with natural and socio-economic conditions (on the example of the cities of the Kura-Araz lowland)

Problems Statement and Purpose. More than 50% of the world's population lives in cities. In Azerbaijan, 53% of the population lives in cities. For this reason, almost all global problems originate from urban landscapes. Therefore, it is very important to study the development of cities and prepare the future scenario. Our goal is to study the cities located in the Kura-Araz plain, determine their annual growth rate and forecast future development trends. Data and Methods. In the article, the development of 17 cities located in the Kur-Araz plain in the central part of Azerbaijan during the historical period and its future forecast were analyzed. Modern methods were especially preferred during the research. Satellite images of cities were processed and the results were analyzed. Satellite images from 1975 and 2023 were used and deciphered in determining the boundaries of cities and studying their dynamics. The dynamics of changes in the areas of the cities were studied by determining the boundaries of the cities, and the dynamics of population growth was studied and analyzed. Statistical analyzes were used in the study of the population. Results and Discussion. The cities of Kura-Araz lowland cover 25% of the cities of the republic. These rivers are the main source of the formation of cities. However, as these coastal cities grew, the ecological problems of the Kura and Araz rivers increased and the water level decreased. Cities have grown in all directions and are still growing. For the first time in Azerbaijan, urban landscapes were studied based on GIS technologies and Remote Sensing methods. For the first time, the factors of natural conditions, the absolute height, inclination and exposure of the terrain were analyzed based on GIS technologies, and it was determined that 2/3 of the research area is located below sea level, and 1/3 is located in areas up to a maximum of 200 m absolute height. The inclination of the area continues up to a maximum of 5⁰. Cities have been classified in different directions. Conclusion. For the first time, issues of territorial management of urban landscapes were conducted and mapped on the basis of GIS technologies. At this time, it was determined that cities have grown more than 2 times during 1975-2023. However, this increase cannot be compared with the dynamics of population growth. Because during these years, the population growth has varied between 10-30%. Urban landscapes have grown mainly in riverside areas, along transport routes.

Geometrical Variation Analysis of Landslides in Different Geological Settings Using Satellite Images: Case Studies in Japan and Sri Lanka

Over the past three decades, Sri Lanka has observed a substantial rise in landslide occurrences linked to intensified rainfall. However, the lack of comprehensive landslide inventories has hampered the development of effective risk analysis and simulation systems, requiring Sri Lanka to rely heavily on foreign-developed models, despite the difficulty of fully examining the similarities between the characteristics of landslides in Sri Lanka and the areas where the model has been developed. Satellite images have become readily available in recent years and have provided information about the Earth’s surface conditions over the past few decades. Thus, this study verifies the utility of satellite images as a cost-effective remote-sensing method to clarify the commonalities and differences in the characteristics of landslides in two regions Ikawa, Japan, and Sabaragamuwa, Sri Lanka, which exhibit different geological formations despite similar annual rainfall. Using Google Earth satellite images from 2013 to 2023, we evaluated land-slide density, types, and geometry. The findings reveal that Ikawa exhibits a higher landslide density and experiences multiple-type landslides. In contrast, both areas have similar initiation areas; however, Sabaragamuwa predominantly experiences single landslides that are widespread and mobile. The findings also reveal that various characteristics of landslides are mainly influenced by varied topography. Here, we confirmed that even in areas where comprehensive information on landslides is conventionally lacking, we can understand the characteristics of landslides by comparing landslide geometry between sites using satellite imagery.

Cylinder flow and noise control by active base blowing

An extensive experimental investigation was undertaken to control the flow and noise characteristics influenced by vortex shedding from a circular cylinder by implementing air blowing at the base of the cylinder. The study synchronised near-field pressure and far-field noise measurements with the wake velocity field to understand the noise reduction mechanism of base blowing. Surface pressure fluctuations were measured using pressure taps distributed around the cylinder's circumference through a remote-sensing method, while velocity measurements were obtained using planar particle image velocimetry at the midspan to examine the flow dynamics. The study unveiled the crucial role of near-field pressure, particularly induced at the shoulders of the cylinder, in generating far-field noise. The rapid vertical flow movement, arising from the interaction between shear layers, was identified as a mechanism responsible for inducing surface pressure fluctuations. This phenomenon occurred as high-momentum fluid moved from the free stream into the interior of the vortex-formation region. By applying base blowing, a remarkable reduction in both near-field pressure and far-field noise was achieved at the fundamental vortex-shedding frequency, with reductions of approximately 20 and 25 dB, respectively, compared with the baseline. Additionally, base blowing caused the shear layers to roll up farther downstream than in the baseline by decreasing the entrainment of fluid-bearing opposite vorticity by the shear layer upstream of the growing vortex. Consequently, there was a substantial decrease in turbulent kinetic energy and Reynolds stress near the cylinder, resulting in slower vertical flow movement and weaker near-field pressure.

Estimation of Environmental Effects and Response Time in Gas-Phase Explosives Detection Using Photoluminescence Quenching Method.

Detecting the presence of explosives is important to protect human lives during military conflicts and peacetime. Gas-phase detection of explosives can make use of the change of material properties, which can be sensitive to environmental conditions such as temperature and humidity. This paper describes a remote-controlled automatic shutter method for the environmental impact assessment of photoluminescence (PL) sensors under near-open conditions. Utilizing the remote-sensing method, we obtained environmental effects without being exposed to sensing vapor molecules and explained how PL intensity was influenced by the temperature, humidity, and exposure time. We also developed a theoretical model including the effect of exciton diffusion for PL quenching, which worked well under limited molecular diffusions. Incomplete recovery of PL intensity or the degradation effect was considered as an additional factor in the model.

Identifying the Climatic and Anthropogenic Impact on Vegetation Surrounding the Natural Springs of the Arava Valley Using Remote Sensing Methods

Natural springs, recognized as biodiversity hotspots and keystone ecosystems, exert positive ecological influences beyond their immediate extent, particularly in dryland environments. The water feeding these springs, largely governed by natural climatic conditions, is susceptible to anthropogenic impacts. The objective of this study was to determine the factors that cause fluctuations in water availability to springs of the hyper-arid Arava Valley (Israel/Jordan). Using the Standard Precipitation Index, we statistically classified the historical record of yearly rainfall for the past four decades into clusters of dry and wet sub-periods. We assessed changes in vegetation cover around the springs using the Landsat-derived Normalized Difference Vegetation Index (NDVI) for each sub-period. To assess the anthropogenic effects, we examined the correlations between vegetation cover, water extraction from the aquifer, and the status of adjacent agricultural plots that share a hydrological connection with the springs. Our findings revealed fluctuations between wet and dry sub-periods over the last four decades. We observed high responsiveness of vegetation cover around the springs to these fluctuating sub-periods. Of the 25 studied springs, 12 were directly influenced by anthropogenic factors—7 experienced a decline in vegetation, which we attributed to water extraction from the aquifers, while vegetation increase in 5 springs was attributed to water seepage from agricultural areas upstream. In conclusion, addressing vital habitats such as natural springs in arid drylands requires a holistic approach that integrates long-term climatic, ecological, and anthropogenic observations.

"Integrated Geospatial Solutions for Enhanced Kukadi River Basin Delineation using Remote Sensing and GIS Techniques"

This study offers an innovative approach to enhance the precision and accuracy of the delineation of the Kukadi River Basin by integrating cutting-edge geospatial technologies, particularly Remote Sensing (RS) and Geographic Information System (GIS) methods. Due to its varied topography and land cover, the Kukadi River Basin play a crucial hydrological entity in the area which presents difficulties for standard delineation techniques. High-resolution satellite imagery is used in the study to provide exact information on land cover in the Kukadi River Basin. The work aims to extract comprehensive land cover data by utilizing spectral analysis and image classification techniques, which are part of RS. In order to improve the delineation of the Basin borders, this data is seamlessly integrated with GIS spatial analytic tools, which also incorporate digital elevation models, slope, and other terrain metrics. The research findings demonstrate the possibility of combining RS and GIS technology to overcome the challenges associated with varying terrains, hence furthering the improvement of Basin delineation approaches. The results provide a useful and dependable paradigm for defining river Basin in a variety of geographical contexts, which is important for water resource management, Environmental Conservation, Land use planning, decision-makers, researchers, and practitioners working on hydrological studies.

Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

Land-area classification (LAC) research offers a promising avenue to address the intricacies of urban planning, agricultural zoning, and environmental monitoring, with a specific focus on urban areas and their complex land usage patterns. The potential of LAC research is significantly propelled by advancements in high-resolution satellite imagery and machine learning strategies, particularly the use of convolutional neural networks (CNNs). Accurate LAC is paramount for informed urban development and effective land management. Traditional remote-sensing methods encounter limitations in precisely classifying dynamic and complex urban land areas. Therefore, in this study, we investigated the application of transfer learning with Inception-v3 and DenseNet121 architectures to establish a reliable LAC system for identifying urban land use classes. Leveraging transfer learning with these models provided distinct advantages, as it allows the LAC system to benefit from pre-trained features on large datasets, enhancing model generalization and performance compared to starting from scratch. Transfer learning also facilitates the effective utilization of limited labeled data for fine-tuning, making it a valuable strategy for optimizing model accuracy in complex urban land classification tasks. Moreover, we strategically employ fine-tuned versions of Inception-v3 and DenseNet121 networks, emphasizing the transformative impact of these architectures. The fine-tuning process enables the model to leverage pre-existing knowledge from extensive datasets, enhancing its adaptability to the intricacies of LC classification. By aligning with these advanced techniques, our research not only contributes to the evolution of remote-sensing methodologies but also underscores the paramount importance of incorporating cutting-edge methodologies, such as fine-tuning and the use of specific network architectures, in the continual enhancement of LC classification systems. Through experiments conducted on the UC-Merced_LandUse dataset, we demonstrate the effectiveness of our approach, achieving remarkable results, including 92% accuracy, 93% recall, 92% precision, and a 92% F1-score. Moreover, employing heatmap analysis further elucidates the decision-making process of the models, providing insights into the classification mechanism. The successful application of CNNs in LAC, coupled with heatmap analysis, opens promising avenues for enhanced urban planning, agricultural zoning, and environmental monitoring through more accurate and automated land-area classification.