Remote Sensing Research Articles

Target Detection and Characterization of Multi-Platform Remote Sensing Data

Detecting targets in remote sensing imagery, particularly when identifying sparsely distributed materials, is crucial for applications such as defense, mineral exploration, agriculture, and environmental monitoring. The effectiveness of detection and the precision of the results are influenced by several factors, including sensor configurations, platform properties, interactions between targets and their background, and the spectral contrast of the targets. Environmental factors, such as atmospheric conditions, also play a significant role. Conventionally, target detection in remote sensing has relied on statistical methods that typically assume a linear process for image formation. However, to enhance detection performance, it is critical to account for the geometric and spectral variabilities across multiple imaging platforms. In this research, we conducted a comprehensive target detection experiment using a unique benchmark multi-platform hyperspectral dataset, where man-made targets were deployed on various surface backgrounds. Data were collected using a hand-held spectroradiometer, UAV-mounted hyperspectral sensors, and airborne platforms, all within a half-hour time window. Multi-spectral space-based sensors (i.e., Worldview and Landsat) also flew over the scene and collected data. The experiment took place on 23 July 2021, at the Rochester Institute of Technology’s Tait Preserve in Penfield, NY, USA. We validated the detection outcomes through receiver operating characteristic (ROC) curves and spectral similarity metrics across various detection algorithms and imaging platforms. This multi-platform analysis provides critical insights into the challenges of hyperspectral target detection in complex, real-world landscapes, demonstrating the influence of platform variability on detection performance and the necessity for robust algorithmic approaches in multi-source data integration.

Research on Key Influencing Factors of Ecological Environment Quality in Barcelona Metropolitan Region Based on Remote Sensing

With the rapid development of urbanization, the ecological environment is being degraded. Taking the Barcelona Metropolitan Region as an example, this paper developed an ecological environment quality-assessment system suitable for different times and regions, based on remote sensing, to evaluate the quality of the ecological environment from 2006 to 2018. We also built various ordinary least squares models to analyze multiple variables affecting the ecological environment. Finally, the characteristic triangular spatial structure was used to explain the interaction between the two key variables. The results showed that the ecological quality was unevenly distributed. The largest green space contributed the most benefits but was decreasing and becoming fragmented. NDVI (normalized difference vegetation index) was the most significant natural variable related to the distribution of green space. Precipitation was the most closely related climate factor to NDVI. There was a complex two-way interaction mechanism between the two, and its boundary value was getting higher and higher. In conclusion, the environmental quality of the BMR needs improvement. The characteristic triangle can effectively explain the interaction mechanism between precipitation and NDVI. This study deeply analyzes how various factors affect environmental quality from both the global and internal perspectives and provides a scientific basis for urban ecological management and sustainable development.

Landslide Zonation Mapping and Vulnerability Assessment of Satara and Raigad District

Geographic information (GIS) systems are the influential tools which facilitate the retrieval, collection, analysis, storage and the exhibition of spatial evidence. Remote sensing (RS) is generally defined as the procedure for acquiring evidence on the surface of the earth deprived of corporeal interaction to analyse the vulnerability of the landlside. Analytical Hierarchy (AHP) Process is executed for the analysis. GIS and RS has modernized the regulating measures of the regular hazards. In this contemporary research, Landslide Zonation Mapping and Vulnerability Assessment of the Satara and Raigad District is briefed. The research has investigated Latitude, longitude, soil, texture and the categories of landslides on the study region of Patan and Mahad. Also, the site visit was completed, sample of the soil was collected, DEM were reclassified, digital elevation (DEM) model formation executed, vulnerable susceptibility zones are pin pointed and finally the model is prepared. Rainfall analysis is executed since most of the landslides are caused because of the rainfall. Slope, aspect, flow direction of the study area, and finally the land use and land cover (LULC) mapping are conducted. Based on the research it is concluded that vulnerability of the locations is medium or high. Region with intermediate slopes, decreased rainfall, forest cover on the opposite side it is named as the least vulnerable regions of Patan. In Mahad, 059 % of the region is in higher risk, whereas the 49.336% is in the intermediate risk, then 41.462% is in minimum risk and 9% comes under the category of no risk.

The impact of dam management and rainfall patterns on flooding in the Niger Delta: using Sentinel-1 SAR data

This study explored how dam operation and rainfall patterns interact to influence flooding in the Niger Delta, Nigeria. This study utilizes Sentinel-1 Synthetic Aperture Radar (SAR) data to analyze the flood events from 2018 to 2022, focusing on key locations such as Obiafu (downstream), Kainji/Jebba, and Lagdo dams (upstream). Analyzed Sentinel-1 satellite data [European Space Agency (ESA), 2018–2022], shuttle Radar Topography Mission, alongside rainfall data [Center for Hydrometeorology and Remote Sensing (CHRS), 2018–2022] and surface water level to assess flood variations between 2018 and 2022. The flood extent was differentiated from that of permanent water bodies using Sentinel-1 images with an RGB band (Snap 7.0), and the rainfall patterns were examined via inverse distance weighting (IDW) interpolation (ArcGIS 10.5). The findings reveal that the intensity and timing of flooding vary significantly across these regions. Obiafu experienced high flooding in October 2020 and 2022, while Kainji/Jebba and Lagdo dams had contrasting flood severities during these periods. Additionally, a comparative analysis of hydrograph data from these locations shows a delayed downstream response, highlighting the need for coordinated water release strategies to mitigate flood risks. The results also indicate that upstream dam operations, particularly water releases, contribute to downstream flooding, although local rainfall patterns remain a crucial factor. By aligning SAR data with real-time water levels, this study emphasizes the importance of integrating satellite-based monitoring with hydrological models to improve flood prediction accuracy and minimize socio-economic impacts in the Niger Delta. The study concludes that enhanced flood management strategies, incorporating both rainfall forecasting and dam operation schedules, are critical for reducing flood-related vulnerabilities in the region.

Edge and Texture Enhanced Reference based Super-Resolution Network for Remote Sensing Images

Edge and Texture Enhanced Reference based Super-Resolution Network for Remote Sensing Images

Multi-Source Remote Sensing Images Semantic Segmentation Based on Differential Feature Attention Fusion

Multi-source remote sensing image semantic segmentation can provide more detailed feature attribute information, making it an important research field for remote sensing intelligent interpretation. However, due to the complexity of remote sensing scenes and the feature redundancy caused by multi-source fusion, multi-source remote sensing semantic segmentation still faces some challenges. In this paper, we propose a multi-source remote sensing semantic segmentation method based on differential feature attention fusion (DFAFNet) to alleviate the problems of difficult multi-source discriminant feature extraction and the poor quality of decoder feature reconstruction. Specifically, we achieve effective fusion of multi-source remote sensing features through a differential feature fusion module and unsupervised adversarial loss. Additionally, we improve decoded feature reconstruction without introducing additional parameters by employing an attention-guided upsampling strategy. Experimental results show that our method achieved 2.8% and 2.0% mean intersection over union (mIoU) score improvements compared with the competitive baseline algorithm on the available US3D and ISPRS Potsdam datasets, respectively.

The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics

Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (r up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m2 were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty.

Hybrid Spatial Modelling on Urban Flood Hazard Using Remote Sensing, GIS, and Analytic Hierarchy Process: A Study of Delhi NCR, India

Hybrid Spatial Modelling on Urban Flood Hazard Using Remote Sensing, GIS, and Analytic Hierarchy Process: A Study of Delhi NCR, India

RA-YOLO: the detection of small vehicles in remote sensing images based on network reconstruction

ABSTRACT Accurately detecting small vehicle targets consisting of only a few pixels in remote sensing images (RSIs) remains highly challenging. General vehicle detection networks primarily design complex deep neural networks to learn the features of small targets in remote sensing images. However, while enhancing accuracy, this approach results in an increase in the model’s size. To tackle these issues, this paper presents the Restructuring Architecture – You Only Look Once (RA-YOLO) network based on network reconfiguration. By integrating the CA mechanism and adding the small target detection layer (STDL), enhances the extraction of shallow features of small targets. In addition, the feature fusion part in the middle of the original PAFPN layer is rewritten as a Lite-Asymptotic Feature Pyramid Network (L-AFPN) module, which not only enhances the extraction of shallow features of small targets, but also improves the multi-scale feature fusion capability. The results indicate that the RA-YOLO enhances the mean Average Precision (mAP) by 10.1%, 2% and 7.5%, respectively, on the remote sensing datasets DIOR, NWPU VHR-10 and DOTAv1.5-small vehicle. Moreover, the model parameters are reduced from 7.02 M to 5.22 M. Our proposed model shows a good trade-off between accuracy and model size compared with the state-of-the-art models.

Assessing Drone-Based Remote Sensing Indices for Monitoring Rice Nitrogen Plant Status Under Different Irrigation Techniques

Assessing Drone-Based Remote Sensing Indices for Monitoring Rice Nitrogen Plant Status Under Different Irrigation Techniques

Characterization and Classification of Soils of Jalna District of Marathwada Region by Using Remote Sensing and GIS Techniques

Characterization and Classification of Soils of Jalna District of Marathwada Region by Using Remote Sensing and GIS Techniques

The Decisive Influence of the Improved Remote Sensing Ecological Index on the Terrestrial Ecosystem in Typical Arid Areas of China

The Decisive Influence of the Improved Remote Sensing Ecological Index on the Terrestrial Ecosystem in Typical Arid Areas of China

Integrating axiomatic design and design structure matrix into model‐based systems engineering: A case study for emergency response space mission design

AbstractWith the increasing complexity of space missions due to technological advancements, model‐based systems engineering (MBSE) has become a new paradigm for systems engineering (SE), providing a more formal and accurate system engineering process while reducing costs. However, MBSE does not offer a comprehensive approach for effective system function design. This study introduces a conceptual design approach that integrates axiomatic design (AD) and design structure matrix (DSM) into the MBSE paradigm and applies it to functional and logical design. The research focuses on utilizing the design knowledge encapsulated by system models to facilitate the development of new systems. System models have the potential to bridge different design domains in AD, reveal implicit associations, and offer functional and structural analysis capabilities. To validate our approach, we conducted a case study on an Emergency Response Remote Sensing mission system. This case study demonstrates that our proposed methodology lends formality to traditional AD by deriving AD matrices and DSMs from a system model, thereby enhancing the system's structure while curtailing elemental alterations during iterative design processes. The novelty of this research lies in realizing the integration of traditional design theory with the model paradigm of SE, leading to an effective design solution during the functional and logical design stages.

Mapping Lithology with Hybrid Attention Mechanism–Long Short-Term Memory: A Hybrid Neural Network Approach Using Remote Sensing and Geophysical Data

Remote sensing provides an efficient roadmap in geological analysis and interpretation. However, some challenges arise when remote sensing techniques are integrated with machine learning in geological surveys. Factors including irregular spatial distribution, sample imbalance, interclass resemblances, regolith, and geochemical similarities impede geological feature diagnosis, interpretation, and identification across varied remote sensing datasets. To address these limitations, a hybrid-attention-integrated long short-term memory (LSTM) network is employed to diagnose, interpret, and identify lithological feature representations in a remote sensing-based geological analysis using multisource data fusion. The experimental design integrates varied datasets including Sentinel-2A, Landsat-9, ASTER, ALOS PALSAR DEM, and Bouguer anomaly gravity data. The proposed model incorporates a hybrid attention mechanism (HAM) comprising channel and spatial attention submodules. HAM utilizes an adaptive technique that merges global-average-pooled features with max-pooled features, enhancing the model’s accuracy in identifying lithological units. Additionally, a channel separation operation is employed to allot refined channel features into clusters based on channel attention maps along the channel dimension. The comprehensive analysis of results from comparative extensive experiments demonstrates HAM-LSTM’s state-of-the-art performance, outperforming existing attention modules and attention-based models (ViT, SE-LSTM, and CBAM-LSTM). Comparing HAM-LSTM to baseline LSTM, the HAM module’s integrated configurations equip the proposed model to better diagnose and identify lithological units, thereby increasing the accuracy by 3.69%.

Remote Sensing Technologies Quantify the Contribution of Ambient Air Pollution to Asthma Severity and Risk Factors in Greenness, Air Pollution, and Wildfire Ecological Settings: A Literature Review

Numerous epidemiologic studies have used remote sensing to quantify the contribution of greenness, air pollution, and wildfire smoke to asthma and other respiration outcomes. This is the first review paper to evaluate the influence of remote sensing exposures on specific outcome severity and risk factors in different ecological settings. Literature searches utilizing PubMed and Google Scholar identified 61 unique studies published between 2009 and 2023, with 198 specific outcomes. Respiration-specific outcomes were lower in greenness and higher in air pollution and wildfire ecological settings. Aerosol optical depth (AOD)-PM2.5 readings and specific outcomes were higher in economically developing than in economically developed countries. Prospective studies found prenatal and infant exposure to higher ambient AOD-PM2.5 concentration level readings contributed to higher childhood asthma incidence. Lung function was higher in greenness and lower in the other two ecological settings. Age, environment, gender, other, and total risk factors showed significant differences between health outcomes and ecological settings. Published studies utilized physiologic mechanisms of immune, inflammation, and oxidative stress to describe obtained results. Individual and total physiologic mechanisms differed between ecological settings. Study results were used to develop a descriptive physiologic asthma model and propose updated population-based asthma intervention program guidelines.

A New Sampling Strategy for Enhancing Forest Monitoring Leveraging Remote Sensing Data

A New Sampling Strategy for Enhancing Forest Monitoring Leveraging Remote Sensing Data

Spatiotemporal Assessment of Water Quality and Quantity of the Kaptai Lake at Rangamati, Bangladesh - An Approach of Remote Sensing, Field Investigation and Laboratory Analysis

Monitoring water quality and quantity is essential for sustainable water resource management. This study investigates changes in the extent and water quality of Kaptai Lake using remote sensing, field measurements, and laboratory analysis. Analysis of Landsat TM/OLI images from 1989, 2000, 2010, and 2021 shows that the lake's water extent ranges from 380 km² (in 2010) to 435 km² (in 2021), with an average of 407.5 km². The extent of water decreased by approximately 3% from 1989 to 2010 and increased by about 5% from 2010 to 2021 due to changing rainfall patterns, dam operations, sedimentation, and land use changes. Seasonal variations in chlorophyll-a concentration and turbidity, analyzed using Sentinel 2B images from 2022 and 2023, indicate mesotrophic to eutrophic conditions and higher turbidity in post-monsoon. The study found that nutrient enrichment from untreated sewage, agricultural runoff, and algal growth affect chlorophyll-a levels, while development activities, excessive rainfall, and sedimentation influence turbidity. Linear regression analyses show strong positive correlation between field obtained and image derived values both for chlorophyll-a (R2 0.621) and turbidity (R2 0.698). Physico-chemical parameters such as pH, EC and temperature have been measured in the field and hydrochemical parameters including, cations and anions have been measured by laboratory analysis to assess the contemporary water quality status. The average values for pH, EC and temperature are 7.55, 119.85 µS/cm and 22.17 °C respectively. The cationic dominance in the studied segment of the Lake is in the order of Ca2+ > Na2+ > Mg2+>K+ > Fe2+ >Mn2+. On the other hand, the anions are in the order of HCO3- > SO42- > Cl- > NO3-. All the physico-chemical parameters of the lake water fall within the Bangladesh Drinking Water Limit (BDWL) and are not linked to chlorophyll-a and turbidity variations. The findings of this study can help mitigate the deterioration of Kaptai Lake by addressing water quality and extent issues, promoting effective and sustainable lake management strategies. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 13(1), 2024, P 121-139

Long-Term Monitoring of Trends in Xerothermality and Vegetation Condition of a Northeast Mediterranean Island Using Meteorological and Remote Sensing Data

There is no doubt that global climate change is happening and affecting life on Earth in a variety of ways. It can be seen on the extreme events of natural disasters, prolonged periods of drought, and increased summer and annual temperatures. While climate change affects every place on Earth, the Mediterranean region is considered a hot spot of climate change. Temperature is expected to increase further, precipitation, especially during summer months, is expected to decrease, and extreme rainfall events are projected to increase. These projected changes will affect both continental and insular environments, with small islands being particularly vulnerable due to the lack of space for species to move into more favorable conditions. As a result, these environments need to be studied, the changes quantified, and the consequences monitored. The current study focuses on the island of Fournoi in the central eastern part of the Aegean Sea. We employed data from a local meteorological station, which operates for a limited period, the Climate Research Unit TS data, and remote sensing thermal data to monitor the trends in aridity over a period of almost 40 years. The results show that summer temperature has increased significantly over the last 40 years, and this is confirmed by both meteorological and remote sensing data. At the same time, precipitation seems to remain stable. Despite the increased aridity imposed by the increased temperature and stable precipitation, vegetation seems not to be experiencing extreme stress. On the contrary, it seems to be following a positive trend over the study period. This observation is explained by the extreme resilience of the plant species of the study area and the fact that vegetation has been recovering over the last 50 years after a period of human overexploitation, and this recovery overcomes the stress imposed by increased aridity.

Coupling Coordination and Influencing Mechanism of Ecosystem Services Using Remote Sensing: A Case Study of Food Provision and Soil Conservation

Understanding the trade-offs and synergies between ecosystem services is essential for effective ecological management. We selected food provisioning and soil conservation services to explore their intrinsic link and trade-offs. We evaluated these services in Minnesota from 1998 to 2018 using multi-source remote sensing data. The coupling coordination degree model (CCDM) was employed to quantify the relationship between these services. The CCDM evaluates the degree of coordination between systems by measuring their interactions. In addition, we used the geographically weighted regression (GWR) model to identify factors influencing this relationship. Our findings reveal that, while Minnesota’s food provision services have shown a significant overall upward trajectory, distinct declines occurred in 2008 and 2018. In contrast, soil conservation services showed considerable variability from year to year, without a clear trend. Over time, the relationship between food provision and soil conservation services evolved from uncoordinated and transitional to more coordinated development. Our analysis indicates that climate–soil indicators (Z1) exert the most significant influence on the coupling coordination degree (CCD), followed by topography (Z3), vegetation quality (Z4), and socio-economic indicators (Z2). This suggests that natural environmental factors have a greater impact than socio-economic factors. Spatial analysis highlights that topography exhibits significant spatial heterogeneity and serves as the primary spatial driving factor. This study explores the trade-offs between food provision and soil conservation ecosystem services in Minnesota, enhancing the understanding of trade-offs among different ecosystem services and providing insights for global sustainable agricultural development.

A self-supervised deep-driven model for automatic weather classification from remote sensing images

ABSTRACT The increasing use of Remote Sensing (RS) technology has resulted in the availability of large volumes of satellite imaging data, necessitating efficient, and scalable solutions for rapid analysis and classification in various interdisciplinary fields. This research focuses on addressing these challenges by examining different training and optimization techniques to achieve the highest classification accuracy on both labelled and unlabelled data from the Large-Scale Cloud Photos Dataset for Meteorology Research (LSCIDMR), which contains 10 high-resolution classes. A key aspect of this study is the implementation of a unique deep learning model based on the ResNet101 architecture, designed to handle the inter-class similarity problem through architectural improvements and imbalanced class distributions using data augmentation techniques. Furthermore, this model integrates a novel approach that combines self-learning with semi-supervised learning to process large amounts of unlabelled data. An iterative self-learning process is employed for continuous refinement, using pseudo-labels generated from the unlabelled data. To ensure higher quality and relevance, confidence-based selection of pseudo-labels is applied. Our model demonstrates superior performance in classification compared to recently reported deep learning algorithms, offering a robust solution for the automated categorization of satellite images. In this study, our ResNet101 model achieved a mean accuracy of 0.917 (±0.02), a mean precision of 0.917 (±0.01), with a mean F1 score of 0.9300 (±0.015) over five folds.