Search Results (80)

This study proposes an innovative method that integrates multi-source remote sensing technologies and artificial intelligence to meet the urgent needs of deformation monitoring and ecohydrological environment analysis in Great Wall heritage protection. By integrating synthetic aperture radar (InSAR) technology, low-altitude oblique photogrammetry models, and the three-dimensional Gaussian splatting model, an integrated air–space–ground system for monitoring and understanding the Great Wall is constructed. Low-altitude tilt photogrammetry combined with the Gaussian splatting model, through drone images and intelligent generation algorithms (e.g., generative adversarial networks), quickly constructs high-precision 3D models, significantly improving texture details and reconstruction efficiency. Based on the 3D Gaussian splatting model of the AHLLM-3D network, the integration of point cloud data and the large language model achieves multimodal semantic understanding and spatial analysis of the Great Wall’s architectural structure. The results show that the multi-source data fusion method can effectively identify high-risk deformation zones (with annual subsidence reaching −25 mm) and optimize modeling accuracy through intelligent algorithms (reducing detail error by 30%), providing accurate deformation warnings and repair bases for Great Wall protection. Future studies will further combine the concept of ecological water wisdom to explore heritage protection strategies under multi-hazard coupling, promoting the digital transformation of cultural heritage preservation. Full article

As key components of the climate system, clouds exert a significant influence on the Earth’s radiation budget and hydrological cycle. However, studies focusing on cloud properties over Central Asia are still limited, and the impacts of cloud variability on regional temperature and precipitation remain poorly understood. This study uses reanalysis and multi-source remote sensing datasets to investigate the spatiotemporal characteristics of clouds and their influence on regional climate. The cloud cover increases from the southwest to the northeast, with mid and low-level clouds predominating in high-altitude regions. All clouds have shown a declining trend during 1981–2020. According to satellite data, the sharpest decline in total cloud cover occurs in summer, while reanalysis data show a more significant reduction in spring. In addition, cloud cover changes influence the local climate through radiative forcing mechanisms. Specifically, the weakening of shortwave reflective cooling and the enhancement of longwave heating of clouds collectively exacerbate surface warming. Meanwhile, precipitation is positively correlated with cloud cover, and its spatial distribution aligns with the cloud water path. The cloud phase composition in Central Asia is dominated by liquid water, accounting for over 40%, a microphysical characteristic that further impacts the regional hydrological cycle. Full article

The increasing congestion of low Earth orbit (LEO) has raised the need for efficient collision avoidance strategies, especially for CubeSats without propulsion systems. This study proposes a methodology for evaluating passive collision avoidance maneuvers using aerodynamic control via a satellite’s Attitude Determination and Control System (ADCS). By adjusting orientation, the satellite modifies its exposed surface area, altering atmospheric drag and lift forces to shift its orbit. This new approach integrates atmospheric modeling (NRLMSISE-00), aerodynamic coefficient estimation using the ADBSat panel method, and orbital simulations in Systems Tool Kit (STK). The LUME-1 CubeSat mission is used as a reference case, with simulations at three altitudes (500, 460, and 420 km). Results show that attitude-induced drag modulation can generate significant orbital displacements—measured by Horizontal and Vertical Distance Differences (HDD and VDD)—sufficient to reduce collision risk. Compared to constant-drag models, the panel method offers more accurate, orientation-dependent predictions. While lift forces are minor, their inclusion enhances modeling fidelity. This methodology supports the development of low-resource, autonomous collision avoidance systems for future CubeSat missions, particularly in remote sensing applications where orbital precision is essential. Full article

Knowledge of ionospheric plasma altitudinal distribution is crucial for the effective operation of radio wave propagation, communication, and navigation systems. High-frequency sounding radars—ionosondes—provide unbiased benchmark measurements of ionospheric plasma density due to a direct relationship between the frequency of sound waves and ionospheric electron density. But ground-based ionosonde observations are limited by the F2 layer peak height and cannot probe the topside ionosphere. GNSS Radio Occultation (RO) onboard Low-Earth-Orbiting satellites can provide measurements of plasma distribution from the lower ionosphere up to satellite orbit altitudes (~500–600 km). The main goal of this study is to investigate opportunities to obtain full observation-based ionospheric electron density profiles (EDPs) by combining advantages of ground-based ionosondes and GNSS RO. We utilized the high-rate Ebre and El Arenosillo ionosonde observations and COSMIC-2 RO EDPs colocated over the ionosonde’s area of operation. Using two types of ionospheric remote sensing techniques, we demonstrated how to create the combined ionospheric EDPs based solely on real high-quality observations from both the bottomside and topside parts of the ionosphere. Such combined EDPs can serve as an analogy for incoherent scatter radar-derived “full profiles”, providing a reference for the altitudinal distribution of ionospheric plasma density. Using the combined reference EDPs, we analyzed the performance of the International Reference Ionosphere model to evaluate model–data discrepancies. Hence, these new profiles can play a significant role in validating empirical models of the ionosphere towards their further improvements. Full article

Vegetation photosynthesis is a key Earth system process that can fix carbon dioxide in the atmosphere. Mountainous areas usually have high productivity and extensive vegetation cover, but their study requires a higher spatiotemporal resolution due to the complex climate and vegetation variations with altitude. In this study, we analyzed the variations and climatic responses of vegetation gross primary productivity (GPP) in northwestern Hubei, China, at a 30 m spatial resolution from 2001 to 2020, based on the fusion of multi-source remote sensing data. A GPP estimation framework based on the CASA model was applied, and spatiotemporal fusion of Landsat and MODIS data was achieved using the STNLFFM algorithm. The results indicate that GPP exhibits higher values in the mountainous regions of west Shennongjia, compared to the eastern plain regions, with a generally increasing trend with increasing elevation. GPP has shown an overall increasing trend over the past 20 years, with almost 90% of the high-elevation regions showing an increasing trend, and the low-elevation regions showing an opposite trend. The relationship between GPP and climate factors is greatly impacted by the temporal scale, with the most pronounced correlation at a seasonal scale. The impact of temperature has been generally stable over the past 20 years across different altitudes, while the relationship with precipitation has exhibited an overall decreasing trend with the increase of altitude. Precipitation and temperature correlations show opposing variations in different months and elevations, which can be mainly attributed to the varied climatic conditions in the different elevations. Full article

Integrating remote-controlled (RC) helicopters and drones equipped with variable-rate sprayer systems represents a significant advancement in agricultural practices, particularly for the precise management of crop diseases. This study utilizes low-altitude remote sensing platforms to monitor crop growth and disease infestation, proposing advanced technology for managing and monitoring disease outbreaks in cassava fields. The performance of these systems was evaluated using statistical analysis and Geographic Information System (GIS) applications for mapping, with a particular emphasis on the relationship between vegetation indices (NDVI and GNDVI) and the growth stages of cassava. The results indicated that NDVI values obtained from both the RC helicopter and drone systems decreased with increasing altitude. The RC helicopter system exhibited NDVI values ranging from 0.709 to 0.352, while the drone system showed values from 0.726 to 0.361. Based on the relationship between NDVI and GNDVI of cassava plants at different growth stages, the study recommends a variable-rate spray system that utilizes standard instruments to measure chlorophyll levels. Furthermore, the study found that the RC helicopter system effectively measured chlorophyll levels, while the drone system demonstrated superior overall quality. Both systems showed strong correlations between NDVI/GNDVI values and cassava health, which has significant implications for disease management. The image processing algorithms and calibration methods used were deemed acceptable, with drones equipped with variable-rate sprayer systems outperforming RC helicopters in overall quality. These findings support the adoption of advanced remote sensing and spraying technologies in precision agriculture, particularly to enhance the management of cassava crops. Full article

In this study, we analysed remote sensing data collected during the Beirut port explosion on 4 August 2020 at 15.08 UT. For this purpose, we selected three Low-Earth-Orbit (LEO) satellite missions that passed near the Beirut port explosion site immediately after the event. The satellites involved were Swarm-B, the Defence Meteorological Satellite Program (DMSP-F17), and the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2). This study focused on identifying the possible ionospheric signatures of explosion in both hemispheres. The conjugate hemispheric points were traced using the International Geomagnetic Reference Field (IGRF) model. We found that the satellite data revealed disturbances not only over the explosion site in the Northern Hemisphere, but also in its corresponding conjugate region in the Southern Hemisphere. Ionospheric electron density disturbances were observed poleward in the conjugate hemispheres along the paths of the Swarm and DMSP satellites, whereas the magnetic field data from Swarm-B showed both equatorward and poleward disturbances. Additionally, the ionospheric disturbances detected by Swarm-B (18:52 UT) and DMSP-F17 (16:30 UT) at the same location suggested travelling ionospheric disturbance (TID) oscillations with identical spatial patterns for both satellites, whereas the disturbances observed by COSMIC-2 south of the explosion site (10°N) indicated the radial propagation of TIDs. COSMIC-2 not only recorded equatorward topside (>550 km) ionospheric electron density disturbances, but also in the conjugate hemispheres, which aligns with the time frame reported in previous studies. These ionospheric features observed by multiple LEO satellites indicate that the detected signatures originated from the event, highlighting the importance of integrating space missions for monitoring and gaining deeper insight into space hazards. The absence of equatorward ionospheric disturbances at the altitudes of DMSP-F17 and Swarm-B warrant further investigation. Full article

With advancements in cloud computing and machine learning algorithms, an increasing number of cropland datasets have been developed, including the China land-cover dataset (CLCD) and GlobeLand30 (GLC). The unique climatic conditions of the Tibetan Plateau (TP) introduce significant differences and uncertainties to these datasets. Here, we used a quantitative and visual integrated assessment approach to assess the accuracy and spatial consistency of five cropland datasets around 2020 in the TP, namely the CLCD, GLC30, land-use remote sensing monitoring dataset in China (CNLUCC), Global Land Analysis and Discovery (GLAD), and global land-cover product with a fine classification system (GLC_FCS). We analyzed the impact of terrain, climate, population, and vegetation indices on cropland spatial consistency using structural equation modeling (SEM). In this study, the GLAD cropland area had the highest fit with the national land survey (R² = 0.88). County-level analysis revealed that the CLCD and GLC_FCS underestimated cropland areas in high-elevation counties, whereas the GLC and CNLUCC tended to overestimate cropland areas on the TP. Considering overall accuracy, GLC and GLAD performed the best with scores of 0.76 and 0.75, respectively. In contrast, CLCD (0.640), GLC_FCS (0.640), and CNLUCC (0.620) exhibited poor overall accuracy. This study highlights the significantly low spatial consistency of croplands on the TP, with only 10.60% consistency in high and complete agreement. The results showed substantial differences in spatial accuracy among zones, with relatively higher consistency observed in low-altitude zones and notably poorer accuracy in zones with sparse or fragmented cropland. The SEM results indicated that elevation and slope directly influenced cropland consistency, whereas temperature and precipitation indirectly affected cropland consistency by influencing vegetation indices. This study provides a valuable reference for implementing cropland datasets and future cropland mapping studies on the TP region. Full article

Acquiring a large number of in situ water spectral measurements is fundamental for constructing water color remote-sensing retrieval models and validating the accuracy of water color remote-sensing products. However, traditional manual site-based water spectral measurements are time-consuming and labor-intensive, resulting in an insufficient number of in situ water spectral samples to date. To resolve this issue, this study develops an unmanned aerial vehicle-based hyperspectral remote-sensing reflectance measurement system (UAV-RRS) capable of continuous on-the-move water spectral measurements. This paper provides a detailed introduction to the system components and conducts precise experiments on the correction and calibration of the spectral sensors. Using this system, an in situ–UAV–satellite multi-source remote-sensing reflectance comparison experiment was conducted in the middle reaches of the Qiantang River, East China, to evaluate the accuracy and reliability of UAV-RRS and extend the analysis to satellite data across different spatial scales. The results demonstrate that, in small-scale water bodies, UAV-RRS achieves higher spatial precision and spectral accuracy, offering a valuable solution for high-precision, low-altitude continuous water body observations. Full article

Wheat canopy height is an important parameter for monitoring growth status. Accurately predicting the wheat canopy height can improve field management efficiency and optimize fertilization and irrigation. Changes in the growth characteristics of wheat at different growth stages affect the canopy structure, leading to changes in the quality of the LiDAR point cloud (e.g., lower density, more noise points). Multispectral data can capture these changes in the crop canopy and provide more information about the growth status of wheat. Therefore, a method is proposed that fuses LiDAR point cloud features and multispectral feature parameters to estimate the canopy height of winter wheat. Low-altitude unmanned aerial systems (UASs) equipped with LiDAR and multispectral cameras were used to collect point cloud and multispectral data from experimental winter wheat fields during three key growth stages: green-up (GUS), jointing (JS), and booting (BS). Analysis of variance, variance inflation factor, and Pearson correlation analysis were employed to extract point cloud features and multispectral feature parameters significantly correlated with the canopy height. Four wheat canopy height estimation models were constructed based on the Optuna-optimized RF (OP-RF), Elastic Net regression, Extreme Gradient Boosting, and Support Vector Regression models. The model training results showed that the OP-RF model provided the best performance across all three growth stages of wheat. The coefficient of determination values were 0.921, 0.936, and 0.842 at the GUS, JS, and BS, respectively. The root mean square error values were 0.009 m, 0.016 m, and 0.015 m. The mean absolute error values were 0.006 m, 0.011 m, and 0.011 m, respectively. At the same time, it was obtained that the estimation results of fusing point cloud features and multispectral feature parameters were better than the estimation results of a single type of feature parameters. The results meet the requirements for canopy height prediction. These results demonstrate that the fusion of point cloud features and multispectral parameters can improve the accuracy of crop canopy height monitoring. The method provides a valuable method for the remote sensing monitoring of phenotypic information of low and densely planted crops and also provides important data support for crop growth assessment and field management. Full article

Forest fires are an important disturbance that affects ecosystem stability and pose a serious threat to the ecosystem. However, the recovery process of forest ecological quality (EQ) after a fire in plateau mountain areas is not well understood. This study utilizes the Google Earth Engine (GEE) and Landsat data to generate difference indices, including NDVI, NBR, EVI, NDMI, NDWI, SAVI, and BSI. After segmentation using the Simple Non-Iterative Clustering (SNIC) method, the data were input into a random forest (RF) model to accurately extract the burned area. A 2005–2020 remote sensing ecological index (RSEI) time series was constructed, and the recovery of post-fire forest EQ was evaluated through Theil–Sen slope estimation, Mann–Kendall (MK) trend test, stability analysis, and integration with topographic information systems. The study shows that (1) from 2006 to 2020, the post-fire forest EQ improved year by year, with an average annual increase rate of 0.014/a. The recovery process exhibited an overall trend of “decline initially-fluctuating increase-stabilization”, indicating that RSEI can be used to evaluate the post-fire forest EQ in complex plateau mountainous regions. (2) Between 2006 and 2020, the EQ of forests exhibited a significant increasing trend spatially, with 84.32% of the areas showing notable growth in RSEI, while 1.80% of the regions experienced a declining trend. (3) The coefficient of variation (CV) of RSEI in the study area was 0.16 during the period 2006–2020, indicating good overall stability in the process of post-fire forest EQ recovery. (4) Fire has a significant impact on the EQ of forests in low-altitude areas, steep slopes, and sun-facing slopes, and recovery is slow. This study offers scientific evidence for monitoring and assessing the recovery of post-fire forest EQ in plateau mountainous regions and can also inform ecological restoration and management efforts in similar areas. Full article

Sporadic E (Es) layers are irregular structures that occur at the E-layer height of the ionosphere, significantly affecting the reliability and accuracy of wireless communications, navigation, and satellite remote sensing. This study utilized the S4max data collected from the Constellation Observing System for the Meteorology, Ionosphere, and Climate (COSMIC) occultation observations from 2007 to 2016 to identify the Es layer and investigate its climatological variations. The Horizontal Wind Field model (HWM14), in conjunction with the International Geomagnetic Reference Field model (IGRF13), is used to calculate vertical ion convergence (VIC) and analyze its correlation to the Es layers. The results of this study showed that the occurrence of Es has apparent hemispheric asymmetry. In the mid- and low latitudes, Es layer activity is more intense in the summer hemispheres, with center peak altitudes of around 105 km. The summer hemisphere exhibits a semi-diurnal periodic pattern, whereas the winter hemisphere shows a weakened diurnal variation. Simulation studies indicate that VIC induced by neutral wind shear contributes to the asymmetry in Es layer activities observed between the Northern and Southern hemispheres, and the zonal wind shear plays a more critical role than the meridional wind one. Full article

Reflectance anisotropy in remote sensing images can complicate the interpretation of spectral signature, and extracting precise structural information under these pixels is a promising approach. Low-altitude unmanned aerial vehicle (UAV) systems can capture high-resolution imagery even to centimeter-level detail, potentially simplifying the characterization of leaf anisotropic reflectance. We proposed a novel maize point cloud generation method that combines an advanced UAV cross-circling oblique (CCO) photography route with the Structure from the Motion-Multi-View Stereo (SfM-MVS) algorithm. A multi-spectral point cloud was then generated by fusing multi-spectral imagery with the point cloud using a DSM-based approach. The Rahman–Pinty–Verstraete (RPV) model was finally applied to establish maize leaf-level anisotropic reflectance models. Our results indicated a high degree of similarity between measured and estimated maize structural parameters (R² = 0.89 for leaf length and 0.96 for plant height) based on accurate point cloud data obtained from the CCO route. Most data points clustered around the principal plane due to a constant angle between the sun and view vectors, resulting in a limited range of view azimuths. Leaf reflectance anisotropy was characterized by the RPV model with R² ranging from 0.38 to 0.75 for five wavelength bands. These findings hold significant promise for promoting the decoupling of plant structural information and leaf optical characteristics within remote sensing data. Full article

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have been widely employed in positioning, navigation, and timing (PNT). Furthermore, GNSS refraction, reflection, and scattering signals can remotely sense the Earth’s surface and atmosphere with powerful implications for environmental remote sensing. In this paper, the recent developments and new application progress of multi-GNSS in Earth observation are presented and reviewed, including the methods of BDS/GNSS for Earth observations, GNSS navigation and positioning performance (e.g., GNSS-PPP and GNSS-NRTK), GNSS ionospheric modelling and space weather monitoring, GNSS meteorology, and GNSS-reflectometry and its applications. For instance, the static Precise Point Positioning (PPP) precision of most MGEX stations was improved by 35.1%, 18.7%, and 8.7% in the east, north, and upward directions, respectively, with PPP ambiguity resolution (AR) based on factor graph optimization. A two-layer ionospheric model was constructed using IGS station data through three-dimensional ionospheric model constraints and TEC accuracy was increased by about 20–27% with the GIM model. Ten-minute water level change with centimeter-level accuracy was estimated with ground-based multiple GNSS-R data based on a weighted iterative least-squares method. Furthermore, a cyclone and its positions were detected by utilizing the GNSS-reflectometry from the space-borne Cyclone GNSS (CYGNSS) mission. Over the years, GNSS has become a dominant technology among Earth observation with powerful applications, not only for conventional positioning, navigation and timing techniques, but also for integrated remote sensing solutions, such as monitoring typhoons, river water level changes, geological geohazard warnings, low-altitude UAV navigation, etc., due to its high performance, low cost, all time and all weather. Full article

Unmanned aerial systems (UAS), commonly known as drones, have gained widespread use due to their affordability and versatility across various domains, including military, commercial, and recreational sectors. Applications such as remote sensing, aerial imaging, agriculture, firefighting, search and rescue, infrastructure inspection, and public safety have extensively adopted this technology. However, environmental impacts, particularly noise, have raised concerns among the public and local communities. Unlike traditional crewed aircraft, drones typically operate in low-altitude airspace (below 400 feet or 122 m), making their noise impact more significant when they are closer to houses, people, and livestock. Numerous studies have explored methods for monitoring, assessing, and predicting the noise footprint of drones. This study employs a bibliometric analysis of relevant scholarly works in the Web of Science Core Collection, published from 2015 to 2024, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) data collection and screening procedures. The International Journal of Environmental Research and Public Health, Aerospace Science and Technology, and the Journal of the Acoustical Society of America are the top three preferred outlets for publications in this area. This review unveils trends, topics, key authors and institutions, and national contributions in the field through co-authorship analysis, co-citation analysis, and other statistical methods. By addressing the identified challenges, leveraging emerging technologies, and fostering collaborations, the field can move towards more effective noise abatement strategies, ultimately contributing to the broader acceptance and sustainable integration of UASs into various aspects of society. Full article