Remote Sensing Technology Research Articles

Non-invasive investigation of a submerged medieval harbour, a case study from Puck Lagoon

This study presents an innovative approach to underwater archaeological prospection using non-invasive methods of seabed exploration. The research focuses on the Puck medieval harbour, a cultural heritage site, and utilises acoustic and optical underwater remote-sensing technology. The primary objectives include optimising the use of Airborne Laser Bathymetry in underwater archaeology, enhancing the filtration process for mapping underwater sites, and utilising data from both multibeam echosounder and bathymetric LiDAR for detailed archaeological research. A systematic approach to data processing is advocated to maximise the value of collected data and inform subsequent investigations. The study demonstrates the high accuracy of bathymetric datasets from optical and acoustic sources, with an R-squared correlation coefficient of 0.9853. The application of these techniques is demonstrated, underscoring the compatibility and effective combination of these methods in underwater archaeological exploration. This research provides valuable insights and directions for future archaeological prospections based on active remote-sensing measurements.

The 2023 Mw 6.8 Morocco earthquake induced atmospheric and ionospheric anomalies

Earth observations through Global Navigation Satellite System (GNSS) and Remote Sensing (RS) technologies play a significant role in natural hazard surveillance, particularly in the context of earthquake prediction and detection. This study introduces a distinctive Deep Learning (DL) based approach to identify ionospheric and atmospheric precursors, utilizing data from multiple satellite sources and provides a comprehensive analysis of spatiotemporally varying precursors, contributing to the understanding and monitoring of seismic activity in earthquake-prone regions. In our investigation of the Morocco earthquake on September 08, 2023 (Mw 6.8), we analyzed various precursors including Total Electron Content (TEC), Air Pressure (AP), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Air Temperature (AT). Our study aims to identify a synchronized anomalous window of potential earthquake precursors using Standard Deviation (STDEV), Continuous Wavelet Transform (CWT), and Long Short-Term Memory Inputs (LSTM) network. Both statistical and deep learning methods revealed abnormal fluctuations as precursors occurring within 8–9 days before the earthquake near the epicenter. Additionally, we detected geomagnetic anomalies in the ionosphere 6 days prior to and 4 days after the earthquake, coinciding with active geomagnetic storm days. This research underlined the importance of combining multiple earthquake precursors using statistical and deep learning approaches to support the understanding of the Lithosphere-Atmosphere-Ionosphere-Coupling (LAIC) phenomena.

The Application of Remote Sensing Technology in Post-Disaster Emergency Investigations of Debris Flows: A Case Study of the Shuimo Catchment in the Bailong River, China

The Bailongjiang River Basin is a high-risk area for debris flow in China. On 17 August 2020, a debris flow occurred in the Shuimo catchment, Wen County, which blocked the Baishui River, forming a barrier lake and causing significant casualties and property damage. In this study, remote sensing, InSAR, field surveys, and unmanned aerial vehicle (UAV) techniques were used to analyze the causal characteristics, material source characteristics, dynamic processes, and disaster characteristics after the debris flow. The results showed that the Shuimo catchment belongs to low-frequency debris flows, with a recurrence cycle of more than 100 years and concealed features. High vegetation coverage (72%) and a long main channel (11.49 km) increase the rainfall-triggering conditions for debris flow occurrence, making it more hidden and less noticed. The Shuimo catchment has a large drainage area of 31.26 km2, 15 tributaries, significant elevation differences of 2017 m, and favorable hydraulic conditions for debris flow. The main sources of debris flow material supply are channel erosion and slope erosion, which account for 84.4% of the total material. The collapse of landslides blocking both sides of the main channel resulted in an amplification of the debris flow scale, leading to the blockage of the Baishui River. The scale of the accumulation fan is 28 × 104 m3, and the barrier lake area is 37.4 × 104 m2. The formation mechanism can be summarized as follows: rainfall triggering → shallow landslides → slope debris flow → channel erosion → landslide damming → dam failure and increased discharge → deposition and river blockage. The results of this study provide references for remote sensing emergency investigation and analysis of similar low-frequency and concealed debris flows, as well as a scientific basis for local disaster prevention and reduction.

Examining the Land Use and Land Cover Impacts of Highway Capacity Expansions in California Using Remote Sensing Technology

Highway capacity expansion has wide impacts on land use and land cover (LULC). Cost–benefit analyses and environmental review processes for roadway capacity expansion and maintenance decisions do not comprehensively consider the LULC impacts. This study examined land cover changes directly associated with highway expansion in California, and the relationship between land use and the vegetation impacts of highway projects using satellite remote sensing data. The methodology involves a geospatial analysis of normalized difference vegetation index (NDVI) data in 18 sites across California before and after highway expansion project completion. We accounted for seasonality and included a set of control sites. Findings indicate that the impacts of highway expansion on changes in NDVI are diverse, stressing the importance of the environmental context around each individual project site. Sites that are located near less-developed areas with more extensive natural vegetation (e.g., sprawled areas or exurbs), show a significant decline in NDVI values. Virtually all sites with insignificant changes in NDVI after highway expansion are located in areas that already exhibit heavy urban development (e.g., Los Angeles, San José) or are otherwise located near large expanses of bare, non-vegetated earth. Also, project sites that experienced multiple types of construction (i.e., adding more lanes, widening sections, bridge renovation, etc.) were more likely to exhibit decreasing NDVI values compared with project sites that only experienced one type of construction. Decisions about highway construction and capacity expansion should consider the context and the full environmental impacts, including land use and land cover changes over time.

Bibliometric Analysis of Remote Sensing over Marine Areas for Sustainable Development: Global Trends and Worldwide Collaboration

More than two-thirds of the Earth’s surface is covered by oceans and yet only a small portion of these oceans has been directly explored in detail, highlighting the need for powerful tools like remote sensing (RS) technology to bridge this gap. International frameworks, the 2030 Agenda for Sustainable Development, and Ocean Decade point out the significance of marine areas for achieving sustainable growth. This study conducts a bibliometric analysis of RS over marine areas for sustainable development to identify key contributors, collaboration networks, and evolving research themes from the beginning of the 21st century until last year. Using the Web of Science Core Collection database, 499 relevant articles published between 2000 and 2023 were identified. The bibliometric analysis showed a significant increase in scientific productivity related to the field. On an international level, China emerges as the most productive country, but international collaboration has played a crucial role, with 36.87% of articles resulting from international co-authorship, pointing to the global nature of research in this field. RS technology has continuously evolved from airborne sensors to the augmentation of Earth Observation missions. Our findings reveal a shift towards automated analysis and processing of RS data using machine learning techniques to integrate large datasets and develop robust scientific solutions.

Broad Scope of Site‐Specific Crop Management and Specific Role of Remote Sensing Technologies Within It—A Review

ABSTRACTPrecision agriculture (PA) has great potential to increase agricultural productivity and profitability while reducing input costs and environmental impacts. Within PA, site‐specific crop management (SSCM) is considered the main premise, in which tillage operations and precise crop inputs (such as seed, fertiliser, water, pesticide and agrochemical) are applied according to field variability. The main aim of this review was to highlight the methods and tools used for spatial crop monitoring, soil and weather data influencing crop productivity and to support the adoption of SSCM technology. To achieve this goal: we discussed the main five components of SSCM, methods for monitoring crop and soil data, delineating field management zones (FMZs) and variable rate technologies (VRT) such as precision planting and digital smart sensors used for SSCM application. The review summarised that recent advances in plant and soil sensing systems, artificial intelligence (AI) and machine learning should be used in retrieving and analysing GIS big data for optimised crop inputs supply. Within VRT, light‐bar systems, automatic controllers and sensors are user‐friendly technologies that should be employed in SSCM solution. The authors highlight that adoption of PA can be increased through proper training and education of the farmers, and developing simple, affordable and efficient PA technologies. The review suggests five criteria that should be strictly adopted to get maximum benefits from SSCM: (i) all factors influencing crop yields can be identified; (ii) their effects on crop yields can be determined by using appropriate digital tools and crop modelling; (iii) variable rate crop inputs (VRCIs) should be calculated based on accurate information obtained from plant, soil and environment; (iv) targeted crop inputs should be exercised through global positioning system (GPS) enabled automatic controllers or wireless sensors network (WSN); and (v) right doses of crop inputs (e.g., nitrogen and irrigation) must be applied at the right time and place.

Can Multi-Temporal Vegetation Indices and Machine Learning Algorithms Be Used for Estimation of Groundnut Canopy State Variables?

The objective of this research was to assess the feasibility of remote sensing (RS) technology, specifically an unmanned aerial system (UAS), to estimate Bambara groundnut canopy state variables including leaf area index (LAI), canopy chlorophyll content (CCC), aboveground biomass (AGB), and fractional vegetation cover (FVC). RS and ground data were acquired during Malaysia’s 2018/2019 Bambara groundnut growing season at six phenological stages; vegetative, flowering, podding, podfilling, maturity, and senescence. Five vegetation indices (VIs) were determined from the RS data, resulting in single-stage VIs and cumulative VIs (∑VIs). Pearson’s correlation was used to investigate the relationship between canopy state variables and single stage VIs and ∑VIs over several stages. Linear parametric and non-linear non-parametric machine learning (ML) regressions including CatBoost Regressor (CBR), Random Forest Regressor (RFR), AdaBoost Regressor (ABR), Huber Regressor (HR), Multiple Linear Regressor (MLR), Theil-Sen Regressor (TSR), Partial Least Squares Regressor (PLSR), and Ridge Regressor (RR) were used to estimate canopy state variables using VIs/∑VIs as input. The best single-stage correlations between canopy state variables and VIs were observed at flowering (r > 0.50). Moreover, ∑VIs acquired from vegetative to senescence stage had the strongest correlation with all measured canopy state variables (r > 0.70). In estimating AGB, MLR achieved the best testing performance (R2 = 0.77, RMSE = 0.30). For CCC, RFR excelled with R2 of 0.85 and RMSE of 2.88. Most models performed well in FVC estimation with testing R2 of 0.98–0.99 and low RMSE. For LAI, MLR stood out in testing with R2 of 0.74, and RMSE of 0.63. Results demonstrate the UAS-based RS technology potential for estimating Bambara groundnut canopy variables.

Active Remote Sensing Assessment of Biomass Productivity and Canopy Structure of Short-Rotation Coppice American Sycamore (Platanus occidentalis L.)

The short-rotation coppice (SRC) culture of trees provides a sustainable form of renewable biomass energy, while simultaneously sequestering carbon and contributing to the regional carbon feedstock balance. To understand the role of SRC in carbon feedstock balances, field inventories with selective destructive tree sampling are commonly used to estimate aboveground biomass (AGB) and canopy structure dynamics. However, these methods are resource intensive and spatially limited. To address these constraints, we examined the utility of publicly available airborne Light Detection and Ranging (LiDAR) data and easily accessible imagery from Unmanned Aerial Systems (UASs) to estimate the AGB and canopy structure of an American sycamore SRC in the piedmont region of North Carolina, USA. We compared LiDAR-derived AGB estimates to field estimates from 2015, and UAS-derived AGB estimates to field estimates from 2022 across four planting densities (10,000, 5000, 2500, and 1250 trees per hectare (tph)). The results showed significant effects of planting density treatments on LIDAR- and UAS-derived canopy metrics and significant relationships between these canopy metrics and AGB. In the 10,000 tph, the field-estimated AGB in 2015 (7.00 ± 1.56 Mg ha−1) and LiDAR-derived AGB (7.19 ± 0.13 Mg ha−1) were comparable. On the other hand, the UAS-derived AGB was overestimated in the 10,000 tph planting density and underestimated in the 1250 tph compared to the 2022 field-estimated AGB. This study demonstrates that the remote sensing-derived estimates are within an acceptable level of error for biomass estimation when compared to precise field estimates, thereby showing the potential for increasing the use of accessible remote-sensing technology to estimate AGB of SRC plantations.

Analysis of Land-Use/Cover-Type Extraction Results of Tamarix Shrub Forest of China Based on Remote Sensing Technology

The endmember spectrum method can improve image classification quality based on the spectral features of pure pixels in remote sensing images. The CART (Classification and Regression Tree) is a powerful machine learning algorithm that can also be used for remote sensing image classification. In this study, the Tamarix chinensis forest in the Changyi National Marine Ecological Special Reserve in Shandong Province was taken as the research object, and the endmember spectrum method and the CART decision tree method were used to compare and analyze the results of land-use/cover-type classification extraction. In the extraction process, the land use/cover types of the Tamarix forest in the study area were first divided into forested land types such as high-density forest land, medium-density forest land, and low-density forest land, as well as non-forested land types such as water bodies, roads, dams, buildings, and bare soil. Through analysis, the following conclusions could be drawn: while the overall forest cover of the Tamarix forest is high, there is still some room for further afforestation and ecological restoration in the protection area; from the results of land-use/cover extraction results based on the endmember spectrum method in the study area, it can be seen that this method has better results when extracting well-grown forested land, such as high-density Tamarix chinensis forests and medium-density Tamarix chinensis forests, and poorer results when extracting non-forested land, such as low-density tamarisk forests, roads, buildings, dams, and water bodies; from the results of land use/cover extraction based on a CART decision tree in the study area, it can be seen that this method is more effective when extracting non-forested land, such as roads, buildings, dams, and water bodies, but less effective when extracting forested land, such as high-density Tamarix chinensis forests, medium-density Tamarix chinensis forests, and low-density Tamarix chinensis forests. The relevant research results and conclusions of this study can provide some reference for the classification and extraction of large-scale shrub forest cover types based on remote sensing images.

Estimating Chemical Concentrations of Dust PM2.5 in Iraq: A Climatic Perspective Using Polynomial Model and Remote Sensing Technology

Air pollution and climate change are interrelated issues, with air pollution levels in Iraq currently exceeding World Health Organization standards. This study aimed to evaluate air quality in Iraq by utilizing climatic data, such as temperature, humidity, and gaseous pollutants for assessing the health effects based on processed and estimated data. The research was conducted between August and November 2020, using remotely sensed images and geographical information techniques. Two methods; Geographic Information Systems GIS-based multiple regression and a polynomial model, were employed to estimate PM2.5 levels in the study area. The results showed a significant influence of climatic variables on air pollution in Iraq, with varying effects on PM2.5 estimation. The health impact ranged from good to unhealthy, with most provinces experiencing poor air quality. Southern parts of Iraq exhibited PM2.5 levels surpassing the healthy threshold. The predictive linear and polynomial model's accuracy was assessed through regression, yielding high correlation coefficients (R2 ) of 0.89, 0.95, 0.98, and 0.96 for August to November, respectively. While model validation accuracy ranged between 85–94 %. The study emphasizes the vital role of climate data in understanding the dispersion of air pollutants and their significant impacts on the environment. Addressing air pollution and climate change, as per the SGS-13 "Climate Action", are interconnected and require comprehensive strategies for mitigation.

A user-friendly method for estimating discrete choice land-use model in a panel data setting

Land-use modeling stands as a pivotal tool in shaping sustainable development policies. With the rapid advancement of remote-sensing technology and the widespread adoption of satellite imagery-based land cover products, these datasets have emerged as primary sources for understanding land-use dynamics due to their high spatial and temporal resolutions. Yet, it remains challenging to effectively integrate such rich panel data into nonlinear econometric land-use models. This paper introduces a method to seamlessly incorporate land cover panel data into econometric models, enabling comprehensive utilization of temporal information within a single framework.-By capturing dynamic land-use patterns, the method enhances prediction accuracy while mitigating issues such as autocorrelated error terms commonly encountered in panel data analysis.-The method is straightforward to implement and applicable to many nonlinear models, making it particularly suitable for datasets with large sample sizes.

Assessing the Impact of Heatwaves on Wheat Crop Health in Ludhiana District Using Remote Sensing Technologies

Climate change is becoming one of the major constraints for agricultural production. Impact on the crop yield due to unseasonal rain, hailstorms, and sudden temperature rise is becoming more frequent. The study assesses the damage done by the heat wave in the Ludhiana district of Punjab in the year 2021-22. Satellite datasets have been used named Sentinel-2 and MODIS datasets. By using the Sentinel-2 Images crop discrimination was performed and wheat crop mask was generated for the year 2018-19 to 2021-22. A comparison of the Normalized Difference Vegetation Index (NDVI) was performed for the study period. MODIS dataset was used to calculate the Temperature Condition Index (TCI) and Temperature Stress Index, which reflects the effect of the heat wave that happened in March 2022. APY (Area, Production, Yield) and data on the procurement of wheat for the year 2021-22 and normal years were compared to visualize the impact of heat wave on the various market dynamics such as production, productivity, etc.

Monitoring Water Diversity and Water Quality with Remote Sensing and Traits

Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies.

Advancements in Remote Sensing Technologies for Assessing Abiotic Stress in Plants: a Comprehensive Review

One of the key elements influencing crop production across the globe is abiotic stress. Early detection and corrective action in this regard can help to lessen the effects of stresses on crop growth and output. Recent advances in remote sensing hold a vital role in the early identification of abiotic stress across a broader area with less involvement of money, time, and labour. Remote sensing technology has become an indispensable tool in the field of agriculture, particularly for detecting abiotic stress in plants. This paper reviews the application of remote sensing techniques for the assessment of abiotic stress factors such as drought, flood, salinity, and nutrient deficiency. Furthermore, the review explores the critical aspects of data acquisition, processing, and analysis, underscoring the importance of machine learning and artificial intelligence techniques in enhancing the accuracy of stress detection.

Groundwater Potential Mapping in Fractured Aquifers Using Remote Sensing and GIS Technology in the Moulouya Region, Morocco

Groundwater Potential Mapping in Fractured Aquifers Using Remote Sensing and GIS Technology in the Moulouya Region, Morocco

Geo-Spatial Approach for Estimating Carbon Stock in Context of Siwalik Forests of Arunachal Pradesh, India: A Review

Climate change has been identified as a global issue that must be controlled at every possible step. Arunachal Pradesh is considered a biodiversity hotspot that needs to be conserved. Carbon stock assessment of an area, especially forests, has emerged as an important aspect of understanding the carbon storage capacities of trees of that forest, helping to mitigate climate change along with promoting sustainable conservation of biodiversity. Siwalik refers to the foothill part of the Himalayas comprising of small hillocks. It is characterised by rugged and undulating topography. Due to increasing anthropogenic activities, this region is growing fragile and needs to be conserved. This review article presents various aspects of carbon stock assessment, highlighting the benefits and the challenges that hinder the process of carbon stock estimation. Various articles based upon assessing forest biomass and carbon stock of Arunachal Pradesh were reviewed. To provide insights about associated opportunities, futuristic tendencies, and challenges in carbon stock assessment using geospatial technology, the present article was planned. Reach to understand the preferred method of estimating forest biomass and related carbon stock. Also to get to know about the obstacles that was confronted during the assessment of the carbon potential of forests. The article will review and highlight the use of field-based data inventory in combination with Remote Sensing technology for carbon stock assessment of an area along with the challenges, benefits, and futuristic scope related to them.

Application of Remote Sensing Technologies for Nature Reserves Monitoring

The scientific article is devoted to the urgent problem of conservation of natural resources and ecosystems through the effective use of information technologies, especially methods of remote sensing of the Earth. Examples of the use of Landsat 8, Sentinel-2 and Resurs-P satellite systems for monitoring the state of nature, including the detection and analysis of wildfires, are given. Attention is also paid to the use of vegetation indices, such as the normalized vegetation index, to assess the consequences of fires in nature reserves.

Research on Urban Land Use based on Remote Sensing Technology

Research on Urban Land Use based on Remote Sensing Technology

Exploration of the Application of UAV Remote Sensing Technology in Engineering Surveying and Mapping

Exploration of the Application of UAV Remote Sensing Technology in Engineering Surveying and Mapping

Integrating Multi-source Remote Sensing Technology to Improve Water Resource Management Methods at Poyang Lake

Integrating Multi-source Remote Sensing Technology to Improve Water Resource Management Methods at Poyang Lake