Sensing Data Research Articles

Tourism and Environmental Change in Saint Martin Island, Bangladesh: Insights from Remote Sensing Data

Tourism and Environmental Change in Saint Martin Island, Bangladesh: Insights from Remote Sensing Data

Flood Monitoring Based on Multi-Source Remote Sensing Data Fusion Driven by HIS-NSCT Model

Flood Monitoring Based on Multi-Source Remote Sensing Data Fusion Driven by HIS-NSCT Model

Building Extraction for Urban Infrastructure Mapping Using Deep Neural Networks and High-Resolution Remote Sensing Data

Building Extraction for Urban Infrastructure Mapping Using Deep Neural Networks and High-Resolution Remote Sensing Data

Spatial Estimation of Trophic State for Reservoir Using Ground Monitoring and Remote Sensing Data

Spatial Estimation of Trophic State for Reservoir Using Ground Monitoring and Remote Sensing Data

Correction to “Remote Sensing Data Assimilation to Improve the Seasonal Snow Cover Simulations Over the Heihe River Basin, Northwest China”

Correction to “Remote Sensing Data Assimilation to Improve the Seasonal Snow Cover Simulations Over the Heihe River Basin, Northwest China”

Distinguishing Lithofacies of Flysch Formations Using Deep Learning Models: Integrating Remote Sensing Data with Morphological Indexes

The main objective of the present study was to develop an integrated approach combining remote sensing techniques and U-Net-based deep learning models for lithology mapping. The methodology incorporates Landsat 8 imagery, ALOS PALSAR data, and field surveys, complemented by derived products such as False Color Composites (FCCs), Minimum Noise Fraction (MNF), and Principal Component Analysis (PCA). The Dissection Index, a morphological index, was calculated to characterize the geomorphological variability of the region. Three variations of the deep learning U-Net architecture, Dense U-Net, Residual U-Net, and Attention U-Net, were implemented to evaluate the performance in lithological classification. Validation was conducted using metrics such as the accuracy, precision, recall, F1-score, and mean intersection over union (mIoU). The results highlight the effectiveness of the Attention U-Net model, which provided the highest mapping accuracy and superior feature extraction for delineating flysch formations and associated lithological units. This study demonstrates the potential of integrating remote sensing data with advanced machine learning models to enhance geological mapping in challenging terrains.

Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data

This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. This dataset contains 6334 aerial images from UAV (unmanned aerial vehicles) images and satellite images. The Climate Change Dataset was then used to train Deep Learning (DL) models to identify natural disasters. Four different Machine Learning (ML) models were used: convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50. These ML models were trained on our Climate Change Dataset so that their performance could be compared. DenseNet201 was chosen for optimization. All four ML models performed well. DenseNet201 and ResNet50 achieved the highest testing accuracies of 99.37% and 99.21%, respectively. This research project demonstrates the potential of AI to address environmental challenges, such as climate change-related natural disasters. This study’s approach is novel by creating a new dataset, optimizing an ML model, cross-validating, and presenting desertification as one of our natural disasters for DL detection. Three categories were used (Flooded, Desert, Neither). Our study relates to AI for Climate Change and Environmental Sustainability. Drone emergency response would be a practical application for our research project.

MSIMRS: Multi-Scale Superpixel Segmentation Integrating Multi-Source Remote Sensing Data for Lithology Identification in Semi-Arid Area

Lithology classification stands as a pivotal research domain within geological Remote Sensing (RS). In recent years, extracting lithology information from multi-source RS data has become an inevitable trend. Various classification image primitives yield distinct outcomes in lithology classification. The current research on lithology classification utilizing RS data has predominantly concentrated on pixel-level classification, which suffers from a long classification time and high sensitivity to noise. In order to explore the application potential of superpixel segmentation in lithology classification, this study proposed the Multi-scale superpixel Segmentation Integrating Multi-source RS data (MSIMRS), and conducted a lithology classification study in Duolun County, Inner Mongolia Autonomous Region, China combining MSIMRS and the Support Vector Machine (MSIMRS-SVM). In addition, pixel-level K-Nearest Neighbor (KNN), Random Forest (RF) and SVM classification algorithms, as well as deep-learning models including Resnet50 (Res50), Efficientnet_B8 (Effi_B8), and Vision Transformer (ViT) were chosen for a comparative analysis. Among these methods, our proposed MSIMRS-SVM achieved the highest accuracy in lithology classification in a typical semi-arid area, Duolun County, with an overall accuracy and Kappa coefficient of 92.9% and 0.92. Moreover, the findings indicate that incorporating superpixel segmentation into lithology classification resulted in notably fewer fragmented patches and significantly improved the visualization effect. The results showcase the application potential of superpixel primitives in lithology information extraction within semi-arid areas.

Japanese space policy: An international legal perspective

Japan’s advancements in technology and its strategic focus on space policy have driven swift advancements in the field, attributed to Japan’s ability to balance scientific exploration, industrial growth, and national defense. This article examines Japan’s national legal framework governing space activities. The key pieces of legislation analyzed are the Law Concerning Japan Aerospace Exploration Agency of 2002, the Basic Space Law of 2008, the Act on Launching of Spacecraft, etc. and Control of Spacecraft of 2016, the Act on Ensuring Appropriate Handling of Satellite Remote Sensing Data of 2016, and the Act on the Promotion of Business Activities for the Exploration and Development of Space Resources of 2021. The analysis of these activities is conducted in accordance with the relevant international space legal framework. The article delves into the shift in Japan’s space policy from “non-military” to “non-aggressive” in 2008 and its impact on national legislation. An examination is provided of the fourth law in the world (following similar actions by the USA, Luxembourg, and the UAE) concerning the exploitation of natural resources of celestial bodies, enabling private entities to conduct exploration and extraction activities. This is especially noteworthy given the lack of direct international legal oversight on space resource utilization. This article also examines Japan’s international legal space policy, including the Japan-U.S. Framework Agreement on Cooperation in the Exploration and Peaceful Uses of Outer Space, including the Moon and Other Celestial Bodies 2023, in light of the particularly close Japan-U.S. cooperation in space exploration and use. Japan’s enactment of a law regarding the exploration and exploitation of natural resources of celestial bodies, along with Japan’s participation in projects such as Artemis, Lunar Gateway, and Hakuto-R, demonstrates its growing role in international space projects and its pursuit of the closest possible cooperation with the United States in ensuring national security through the use of outer space. The conclusion offers summarizing statements.

Classification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data Fusion.

Salt marsh vegetation in the Yellow River Delta, including Phragmites australis (P. australis), Suaeda salsa (S. salsa), and Tamarix chinensis (T. chinensis), is essential for the stability of wetland ecosystems. In recent years, salt marsh vegetation has experienced severe degradation, which is primarily due to invasive species and human activities. Therefore, the accurate monitoring of the spatial distribution of these vegetation types is critical for the ecological protection and restoration of the Yellow River Delta. This study proposes a multi-source remote sensing data fusion method based on Sentinel-1 and Sentinel-2 imagery, integrating the temporal characteristics of optical and SAR (synthetic aperture radar) data for the classification mapping of salt marsh vegetation in the Yellow River Delta. Phenological and polarization features were extracted to capture vegetation characteristics. A random forest algorithm was then applied to evaluate the impact of different feature combinations on classification accuracy. Combining optical and SAR time-series data significantly enhanced classification accuracy, particularly in differentiating P. australis, S. salsa, and T. chinensis. The integration of phenological features, polarization ratio, and polarization difference achieved a classification accuracy of 93.51% with a Kappa coefficient of 0.917, outperforming the use of individual data sources.

Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing Data

The present study focuses on analysis of the soil cover structure (SCS, SCSs), which is the most detailed level of soil organization in space. The detail in which complex SCS can be studied is often insufficient, since until now it has not been possible to map it over large areas at scales larger than 1:10,000. To increase the detail in which SCS can be studied, the methods of identifying the bare soil surface (BSS) and averaging its multitemporal spectral characteristics were used, which opens up new possibilities for mapping complex SCS over large areas. New SCSs of leached chernozems (Luvic Chernic Phaeozem) were discovered, which can produce patterns on satellite images similar to sections of Timan agate—agate-like soil cover structures (ASCS, ASCSs). ASCSs are formed on Quaternary sediments of varying thickness from 0.3 to 6 m, underlain by carbonate and red sediments of the Permian period. The ASCS pattern is formed by ring-shaped stripes (rings) of different colors and brightness, which are determined by the carbonate and red-colored inclusions involved in the arable horizon. Eight soil varieties were identified to describe ASCSs during the study. According to the WRB, there are six main soil types, and according to the classification of Russian soils in 1977, there are four types. ASCSs were identified over large areas and soil maps of ASCSs were constructed using multitemporal spectral characteristics of the BSS in the form of multitemporal soil line coefficients. Neural networks were used to identify BSS on big remote sensing data. ASCSs have contrasting soil properties and contrasting fertility (productivity of agricultural crops). ASCS maps can serve as the basis for task maps of precision farming systems. Perhaps ASCSs are unique objects for the area of chernozem distribution, where in one soil profile there are rocks with an age from the first thousand years (Quaternary) to 250 million years (Permian). Chernozems are fertile, studied, mercilessly exploited, but sometimes they are simply beautiful—agate-like.

Crop Mapping Using Advanced Deep Learning Framework in Remote Sensing Data

Crop Mapping Using Advanced Deep Learning Framework in Remote Sensing Data

Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces.

This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot's terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings). The results demonstrate that the proposed framework provides the robot with an enhanced capability to perceive and interpret its environment and adapt to dynamic environment changes. This paper contributes to the advancement of robotic navigation and path-planning systems by providing a scalable and efficient framework for environment analysis. The integration of various point cloud processing techniques into a single architecture not only improves computational efficiency but also enhances the robot's interaction with its environment, making it more capable of operating in complex, hazardous, unstructured settings.

Analysing the Land Use Land cover by using Multi–Spectral Remote Sensing Data in Tehri Garhwal, Uttarakhand

Land use and land cover (LULC) changed drastically throughout the time frame, today there are various factors which is responsible for changes in agricultural land use, anthropogenic and natural factors both have reasons for it, anthropogenic factors like infrastructural development, agricultural activities, recreation activities play major role, natural factors like forest fires, climate change mainly responsible, The investigation studies in modifications in LULC, More than two decades, researcher prepared LULC maps with the assistance of multi-spectral remote sensing Landsat 5 series data used for preparing maps of 2001 and 2011, 2021 map created with Sentinel 2 series data, two decades maps of LULC gives picture about variations in land cover and use, 2001 taken as base year of study, LULC in mountainous regions of Tehri Garhwal different from plain regions, cropping pattern is different, agricultural productivity is different, Seven major LULC types (Crop land, Built Up Area, Forest cover, Water, Range land, Snow Cover and bare Grounds) built-up areas have steadily increased during the past 20 years, demonstrating substantial infrastructural development and urbanization, Crop land is consistently declining, snow cover continuing decline from 2001 to 2021 indicating signifies a move away from conventional agricultural uses, Planning for urbanization must integrate economic expansion with preservation of the environment., addressing the reduction in snow cover and its ripple consequences requires mitigation as well as adaptation measures, such as encouraging sustainable behaviours and renewable energy.

Fusion of Remotely Sensed Data with Monitoring Well Measurements for Groundwater Level Management

In the realm of hydrological engineering, integrating extensive geospatial raster data from remote sensing (Big Data) with sparse field measurements offers a promising approach to improve prediction accuracy in groundwater studies. In this study, we integrated multisource data by applying the LMC to model the spatial relationships of variables and then utilized block support regularization with collocated block cokriging (CBCK) to enhance our predictions. A critical engineering challenge addressed in this study is support homogenization, where we adjusted punctual variances to block variances and ensure consistency in spatial predictions. Our case study focused on mapping groundwater table depth to improve water management and planning in a mixed land use area in Southeast Brazil that is occupied by sugarcane crops, silviculture (Eucalyptus), regenerating fields, and natural vegetation. We utilized the 90 m resolution TanDEM-X digital surface model and STEEP (Seasonal Tropical Ecosystem Energy Partitioning) data with a 500 m resolution to support the spatial interpolation of groundwater table depth measurements collected from 56 locations during the hydrological year 2015–16. Ordinary block kriging (OBK) and CBCK methods were employed. The CBCK method provided more reliable and accurate spatial predictions of groundwater depth levels (RMSE = 0.49 m), outperforming the OBK method (RMSE = 2.89 m). An OBK-based map concentrated deeper measurements near their wells and gave shallow depths for most of the points during estimation. The CBCK-based map shows more deeper predicted points due to its relationship with the covariates. Using covariates improved the groundwater table depth mapping by detecting the interconnection of varied land uses, supporting the water management for agronomic planning connected with ecosystem sustainability.

Assessing the Impact of Climate Change on the Landscape Stability in the Mediterranean World Heritage Site Based on Multi-Sourced Remote Sensing Data: A Case Study of the Causses and Cévennes, France

Global climate fluctuations pose challenges not only to natural environments but also to the conservation and transmission of human cultural and historical heritage. World Heritage Sites are pivotal regions for studying climate change impacts and devising adaptation strategies, with remote sensing technology showcasing significant utility in monitoring these impacts, especially in the Mediterranean region’s diverse and sensitive climate context. Although existing work has begun to explore the role of remote sensing in monitoring the effects of climate change, detailed analysis of the spatial distribution and temporal trends of landscape stability remains limited. Leveraging remote sensing data and its derived products, this study assessed climate change impacts on the Causses and Cévennes Heritage Site, a typical Mediterranean heritage landscape. Specifically, this study utilized remote sensing data to analyze the trends in various climatic factors from 1985 to 2020. The landscape stability model was developed utilizing land cover information and landscape indicators to explore the landscape stability and its distribution features within the study area. Finally, we adopted the Geographical Detector to quantify the extent to which climatic factors influence the landscape stability’s spatial distribution across different periods. The results demonstrated that (1) the climate showed a warming and drying pattern during the study period, with distinct climate characteristics in different zones. (2) The dominance of woodland decreased (area proportion dropped from 76% to 66.5%); transitions primarily occurred among woodland, cropland, shrubland, and grasslands; landscape fragmentation intensified; and development towards diversification and uniformity was observed. (3) Significant spatiotemporal differences in landscape stability within the heritage site were noted, with an overall downward trend. (4) Precipitation had a high contribution rate in factor detection, with the interactive enhancement effects between temperature and precipitation being the most prominent. The present study delivers a thorough examination of how climate change affects the Causses and Cévennes Heritage Landscape, reveals its vulnerabilities, and offers crucial information for sustainable conservation efforts. Moreover, the results offer guidance for the preservation of similar Mediterranean heritage sites and contribute to the advancement and deepening of global heritage conservation initiatives.

Review on the Application of Remote Sensing Data and Machine Learning to the Estimation of Anthropogenic Heat Emissions

Anthropogenic heat is the heat generated by human activities such as industry, construction, transport, and metabolism. Accurate estimates of anthropogenic heat are essential for studying the impacts of human activities on the climate and atmospheric environment. Commonly applied methods for estimating anthropogenic heat include the inventory method, the energy balance equation method, and the building model simulation method. In recent years, the rapid development of computer technology and the availability of massive data have made machine learning a powerful tool for estimating anthropogenic heat fluxes and assessing its effects. Multi-source remote sensing data have also been widely used to obtain more details of the spatial and temporal distribution characteristics of anthropogenic heat. This paper reviews the main approaches for estimating anthropogenic heat emissions. The typical algorithms of the abovementioned three methods are introduced, and their advantages and limitations are also evaluated. Moreover, the recent progress in the application of remote sensing data and machine learning are discussed as well. Based on big data and machine learning techniques, the research on feature engineering and model fusion will bring about major changes in data analysis and modeling of anthropogenic heat. More in-depth research of this issue is recommended to provide important support for curbing global warming, mitigating air pollution, and achieving the national goals of carbon peak and a carbon neutrality strategy.

Integrated Structural Analysis for Geothermal Exploration: A New Protocol Combining Remote Sensing and Aeromagnetic Geophysical Data

Integrated Structural Analysis for Geothermal Exploration: A New Protocol Combining Remote Sensing and Aeromagnetic Geophysical Data

A Gaussian Process Regression Method to Nowcast Cloud-to-Ground Lightning From Remote Sensing and Numerical Weather Modeling Data

A Gaussian Process Regression Method to Nowcast Cloud-to-Ground Lightning From Remote Sensing and Numerical Weather Modeling Data

A Data Assimilation-Based Method for Real-Time Monitoring and Estimation of Gas Influx Size and Distribution During Gas Migration in a Marine Drilling Riser

Effective well control is essential in marine drilling operations to prevent catastrophic blowouts that endanger human lives and cause severe environmental damage, including long-lasting impacts on marine ecosystems. Accurate real-time monitoring and management of well conditions after taking gas influxes are critical for maintaining safety and environmental protection during offshore drilling operations.When a gas influx becomes trapped in a closed marine drilling riser, its upward migration can result in excessively high surface pressure, presenting significant risks to offshore drilling and well control. Therefore, the accurate estimation of downhole gas influx size and distribution during gas migration events is crucial for effectively managing these situations. The complexities of wellbore two-phase flow and gas migration make direct measurements challenging, leading to potential inaccuracies in traditional modeling approaches. This study introduces a Data Assimilation (DA) technique, employing the Ensemble Kalman Filter (EnKF), to improve the real-time estimation of gas influx rates and distribution, thereby enhancing the accuracy of two-phase flow models in marine drilling.This research utilizes full-scale gas influx migration experiments conducted at Louisiana State University, where gas was injected from the bottom of an experimental well to simulate a riser gas event in a Water-based Mud (WBM) system. Key real-time measurements, including downhole pressure gauges at multiple depths and Distributed Acoustic Sensing (DAS) data, were employed to validate the estimated gas influx size and distribution within the riser using the EnKF. The overall gas influx distribution was captured through the real-time estimation of a dimensionless distribution index (DI), offering additional insights into the spatial characteristics of the migrating gas. Additionally, a Drift Flux Model (DFM) calibrated online through DA was used to predict flow parameters such as gas void fraction profiles within the riser during migration. The validation of these predictions demonstrated a strong correlation between the estimated and measured values.The outcomes of this study underscore the effectiveness of the proposed method in estimating parameters that are otherwise difficult to measure directly, thereby improving the predictive accuracy of gas influx behavior in marine drilling risers. This method offers significant practical value by enhancing real-time decision-making and risk management in marine drilling operations that involve gas migration, contributing to the broader goals of offshore drilling safety and marine environment protection.