Search Results (951)

Traumatic optic neuropathy (TON) has been regarded a vision-threatening condition caused by either ocular or blunt/penetrating head trauma, which is characterized by direct or indirect TON. Injury happens during sports, vehicle accidents and mainly in military war and combat exposure. Earlier, we have demonstrated that remote ischemic post-conditioning (RIC) therapy is protective in TON, and here we report that AMPKα1 activation is crucial. AMPKα1 is the catalytic subunit of the heterotrimeric enzyme AMPK, the master regulator of cellular energetics and metabolism. The α1 isoform predominates in immune cells including macrophages (Mφs). Myeloid-specific AMPKα1 KO mice were generated by crossing AMPKα1^Flox/Flox and LysM^cre to carry out the study. We induced TON in mice by using a controlled impact system. Mice (mixed sex) were randomized in six experimental groups for Sham (mock); Sham (RIC); AMPKα1^F/F (TON); AMPKα1^F/F (TON+RIC); AMPKα1^F/F LysM^Cre (TON); AMPKα1^F/F LysM^Cre (TON+RIC). RIC therapy was given every day (5–7 days following TON). Data were generated by using Western blotting (pAMPKα1, ICAM1, Brn3 and GAP43), immunofluorescence (pAMPKα1, cd11b, TMEM119 and ICAM1), flow cytometry (CD11b, F4/80, CD68, CD206, IL-10 and LY6G), ELISA (TNF-α and IL-10) and transmission electron microscopy (TEM, for demyelination and axonal degeneration), and retinal oxygenation was measured by a Unisense sensor system. First, we observed retinal morphology with funduscopic images and found TON has vascular inflammation. H&E staining data suggested that TON increased retinal inflammation and RIC attenuates retinal ganglion cell death. Immunofluorescence and Western blot data showed increased microglial activation and decreased retinal ganglion cell (RGCs) marker Brn3 and axonal regeneration marker GAP43 expression in the TON [AMPKα1^F/F] vs. Sham group, but TON+RIC [AMPKα1^F/F] attenuated the expression level of these markers. Interestingly, higher microglia activation was observed in the myeloid AMPKα1^F/F KO group following TON, and RIC therapy did not attenuate microglial expression. Flow cytometry, ELISA and retinal tissue oxygen data revealed that RIC therapy significantly reduced the pro-inflammatory signaling markers, increased anti-inflammatory macrophage polarization and improved oxygen level in the TON+RIC [AMPKα1^F/F] group; however, RIC therapy did not reduce inflammatory signaling activation in the myeloid AMPKα1 KO mice. The transmission electron microscopy (TEM) data of the optic nerve showed increased demyelination and axonal degeneration in the TON [AMPKα1^F/F] group, and RIC improved the myelination process in TON [AMPKα1^F/F], but RIC had no significant effect in the AMPKα1 KO mice. The myeloid AMPKα1c deletion attenuated RIC induced anti-inflammatory macrophage polarization, and that suggests a molecular link between RIC and immune activation. Overall, these data suggest that RIC therapy provided protection against inflammation and neurodegeneration via myeloid AMPKα1 activation, but the deletion of myeloid AMPKα1 is not protective in TON. Further investigation of RIC and AMPKα1 signaling is warranted in TON. Full article

Effective agricultural management hinges upon timely decision-making. Here, we evaluated whether drone and satellite imagery could improve real-time and remote monitoring of pasture management. Using unmanned aerial systems (UAS), we quantified grassland biomass through changes in sward height pre- and post-grazing by sheep. As optical spectral data from Sentinel-2 satellite imagery is often hindered by cloud contamination, we assessed whether machine learning could help improve the accuracy of pasture biomass prognostics. The calibration of UAS biomass using field measurements from sward height change through 3D photogrammetry resulted in an improved regression (R² = 0.75, RMSE = 1240 kg DM/ha, and MAE = 980 kg DM/ha) compared with using the same field measurements with random forest-machine learning and Sentinel-2 imagery (R² = 0.56, RMSE = 2140 kg DM/ha, and MAE = 1585 kg DM/ha). The standard error of the mean (SEM) for the field biomass, derived from UAS-measured sward height changes, was 1240 kg DM/ha. When UAS data were integrated with the Sentinel-2-random forest model, SEM reduced from 1642 kg DM/ha to 1473 kg DM/ha, demonstrating that integration of UAS data improved model accuracy. We show that modelled biomass from 3D photogrammetry has significantly higher accuracy than that predicted from Sentinel-2 imagery with random forest modelling (S2-RF). Our study demonstrates that timely, accurate quantification of pasture biomass is conducive to improved decision-making agility, and that coupling of UAS with satellite imagery may improve the accuracy and timeliness of agricultural biomass prognostics. Full article

This study investigates the performance of published bio-optical remote sensing indices/algorithms for monitoring water quality changes in Lake Karla, Greece, caused by Storm Daniel after the September 2023 flooding event. Commonly applied indices were utilised to estimate chlorophyll-a (Chl-a) and total suspended solids (TSS) using Sentinel-2 high-resolution optical imagery. In situ measurements were undertaken and water samples were collected during the pre-flooding period, post-flooding, and one-year post-flood, providing a basis for validating the remote sensing models. Monitoring results showed that most physicochemical parameters changed considerably. Chl-a and TSS were estimated by testing five and seven indices, respectively. Regarding the Chl-a estimation, the NDCI and 2-BDA indices outperformed other models, having high correlations with in situ Chl-a measurements and effectively following the in situ Chl-a temporal trends. Among the TSS indices, NDWI and TUR-IND demonstrated better performances, effectively capturing the variations in suspended solids. Overall, this study highlights the potential of Sentinel-2 imagery in assessing water quality changes, particularly in response to flooding events. It is an exploratory approach to assess the feasibility of utilising optical satellite data for evaluating the environmental impacts of natural disasters on lake water quality and supports decision-making in environmental management. Additionally, it identifies potential challenges and considerations that must be addressed to ensure effective application. Full article

Monitoring water quality is crucial for understanding aquatic ecosystem health and changes in physical, chemical, and microbial water quality standards. Water quality critically influences industrial, agricultural, and domestic uses of water. Remote sensing techniques can monitor and measure water quality parameters accurately and quantitatively. Earth observation satellites equipped with optical and thermal sensors have proven effective in providing the temporal and spatial data required for monitoring the water quality of inland water bodies. However, using satellite-derived data are associated with coarse spatial resolution and thus are unsuitable for monitoring the water quality of small inland water bodies. With the development of unmanned aerial vehicles (UAVs) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval of small water bodies, which provides water for crop irrigation. This article presents the application of remotely sensed data from UAVs to retrieve key water quality parameters such as surface water temperature, total suspended solids (TSS), and Chromophoric dissolved organic matter (CDOM) in inland water bodies. In particular, the review comprehensively analyses the potential advancements in utilising drone technology along with machine learning algorithms, platform type, sensor characteristics, statistical metrics, and validation techniques for monitoring these water quality parameters. The study discusses the strengths, challenges, and limitations of using UAVs in estimating water temperature, TSS, and CDOM in small water bodies. Finally, possible solutions and remarks for retrieving water quality parameters using UAVs are provided. The review is important for future development and research in water quality for agricultural production in small water bodies. Full article

This study addresses the challenges posed by the complex topography and forest structure in karst mountainous areas, as well as the difficulties in estimating forest stock using traditional methods. We propose a method that integrates optical remote sensing data from Sentinel-2 into airborne LiDAR data to estimate forest stock in karst areas. First, an Allometric Growth Model correlating tree height and diameter at breast height (DBH) in karst areas was developed based on field measurements. Tree height information extracted from LiDAR data was then combined with the binary wood volume model specific to fir trees in Guizhou Province to calculate the individual tree biomass of fir trees. In addition, this study evaluated the robustness of three machine learning methods, the Random Forest Regression Model, K-Nearest Neighbors Regression Model, and Backpropagation Neural Network Model, in estimating forest stock in karst mountainous areas. The results indicate the following: (1) The Allometric Growth Model based on field data showed strong predictive power for DBH and can be used for large-scale estimation. (2) The distribution characteristics of individual tree biomass and plot biomass under different site conditions revealed the distribution pattern of fir trees in the study area, providing important information for understanding the growth status of forest stock in the region. (3) The Random Forest Regression Model demonstrated exceptional accuracy, generalization capability, and robustness in the estimation of forest stock within karst mountainous regions. This study provides an effective technical tool for estimating forest stock in karst areas and under complex terrain conditions and has significant scientific value and practical implications for the monitoring and management of forest ecosystem carbon sinks. Full article

Cyanobacterial blooms in lakes have increased in frequency and intensity over the past two decades, negatively affecting ecological and biogeochemical processes. This study focuses on the phytoplankton composition of Lake Maggiore, with a special emphasis on cyanobacteria detection through pigment composition. While microscopy is the standard method for phytoplankton identification, pigment-based methods provide broader spatiotemporal coverage. Between May and September 2023, five measurement campaigns were conducted in Lake Maggiore, collecting bio-geochemical and bio-optical data at 27 stations. The total Chlorophyll-a (TChl a) was measured, with concentrations ranging from 1.13 to 6.9 mg/m³. Phytoplankton pigment composition was analyzed using High-Performance Liquid Chromatography (HPLC) and the CHEMTAX approach was applied for phytoplankton classification. The results were cross-validated using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and microscopic counts. Cyanobacteria were identified based on unique pigment markers, such as carotenoids. The HPLC-derived pigment classification results aligned well with both PCA and HCA and microscopic counts verified the accuracy of the pigment-based chemotaxonomy. The study demonstrates that pigment-based classification methods, when combined with statistical analyses, offer a reliable alternative for identifying cyanobacteria and other phytoplankton groups, with potential applications in support of remote sensing algorithm development. Full article

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, a_cdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating a_cdom at the wavelength 412 nm (a_cdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, R_rs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite R_rs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate a_cdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m⁻¹ from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived a_cdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of a_cdom (412) is provided for a coastal transect in the south of Vietnam. Full article

This article reviews rotational seismology, considering different areas of interest, as well as measuring devices used for rotational events investigations. After a short theoretical description defining the fundamental parameters, the authors summarized data published in the literature in areas such as the indirect numerical investigation of rotational effects, rotation measured during earthquakes, teleseismic wave investigation, rotation induced by artificial explosions, and mining activity. The fundamental data on the measured rotation parameters and devices used for the recording are summarized and compared for the above areas. In the section on recording the rotational effects associated with artificial explosions and mining activities, the authors included results recorded by a rotational seismograph of their construction—FOSREM (fibre-optic system for rotational events and phenomena monitoring). FOSREM has a broad range of capabilities to measure rotation rates, from several dozen nrad/s to 10 rad/. It can be controlled remotely and operated autonomously for a long time. It is a useful tool for systematic seismological investigations in various places. The report concludes with a short discussion of the importance of rotational seismology and the great need to obtain experimental data in this field. Full article

The Greater Amanzule Peatlands (GAP) in Ghana is an important biodiversity hotspot facing increasing pressure from anthropogenic land-use activities driven by rapid agricultural plantation expansion, urbanisation, and the burgeoning oil and gas industry. Accurate measurement of how these pressures alter land cover over time, along with the projection of future changes, is crucial for sustainable management. This study aims to analyse these changes from 2010 to 2020 and predict future scenarios up to 2040 using multi-source remote sensing and machine learning techniques. Optical, radar, and topographical remote sensing data from Landsat-7, Landsat-8, ALOS/PALSAR, and Shuttle Radar Topography Mission derived digital elevation models (DEMs) were integrated to perform land cover change analysis using Random Forest (RF), while Cellular Automata Artificial Neural Networks (CA-ANNs) were employed for predictive modelling. The classification model achieved overall accuracies of 93% in 2010 and 94% in both 2015 and 2020, with weighted F1 scores of 80.0%, 75.8%, and 75.7%, respectively. Validation of the predictive model yielded a Kappa value of 0.70, with an overall accuracy rate of 80%, ensuring reliable spatial predictions of future land cover dynamics. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, they do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the understudied GAP region, offering critical insights for developing targeted conservation strategies. Additionally, the methodological framework, which combines optical, radar, and topographical data with machine learning, provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands that is applicable to other regions facing similar environmental challenges. Full article

In recent years, disaster chains caused by glacier movements have occurred frequently in the lower Yarlung Tsangpo River in southwest China. However, it is still unclear whether earthquakes significantly contribute to glacier movements and disaster chains. In addition, it is difficult to measure the high-frequency and large gradient displacement time series with optical remote sensing images due to cloud coverage. To this end, we take the Sedongpu disaster chain as an example, where the Milin earthquake, with an epicenter 11 km away, occurred on 18 November 2017. Firstly, to deal with the cloud coverage problem for single optical remote sensing analysis, we employed multiple platform optical images and conducted a cross-platform correlation technique to invert the two-dimensional displacement rate and the cumulative displacement time series of the Sedongpu glacier. To reveal the correlation between earthquakes and disaster chains, we divided the optical images into three classes according to the Milin earthquake event. Lastly, to increase the accuracy and reliability, we propose two strategies for displacement monitoring, that is, a four-quadrant block registration strategy and a multi-window fusion strategy. Results show that the RMSE reduction percentage of the proposed registration method reaches 80%, and the fusion method can retrieve the large magnitude displacements and complete displacement field. Secondly, the Milin earthquake accelerated the Sedongpu glacier movement, where the pre-seismic velocities were less than 0.5 m/day, the co-seismic velocities increased to 1 to 6 m/day, and the post-seismic velocities decreased to 0.5 to 3 m/day. Lastly, the earthquake had a triggering effect around 33 days on the Sedongpu disaster chain event on 21 December 2017. The failure pattern can be summarized as ice and rock collapse in the source area, large magnitude glacier displacement in the moraine area, and a large volume of sediment in the deposition area, causing a river blockage. Full article

Trophic state index (TSI) is a critical ecological and environmental issue in water resource management that has garnered significant attention. Given the complexity of optical characteristics in aquatic environments, this study employs fuzzy classification methods (FCM) and composite nutrient status indices to meticulously classify in-situ remote sensing reflectance data, aiming to develop evaluation models for different nutrient status categories to facilitate the assessment of the Daihai River in Inner Mongolia, China. Subsequently, we applied this model to MSI data to analyze the nutrient status of Daihai Lake from 2016 to 2021. Furthermore, a structural equation model (SEM) was utilized to explore the primary driving factors influencing nutrient status. The results indicated that the water bodies in Daihai Lake can be broadly classified into three categories, with the nutrient status models demonstrating robust performance for each category (R² = 0.80, R² = 0.83, and R² = 0.74). Comparisons were made between nutrient status accuracies obtained through the NCM and FCM based on measured data, yielding R² values of 0.74 and 0.85, respectively. Furthermore, the TSI results derived from MSI inversion were validated, with NCM achieving an R² of 0.49, RMSE of 6.88, and MAPE of 10.36%, while FCM exhibited an R² of 0.55, RMSE of 8.89, and MAPE of 13.18%. An SEM–based analysis revealed that over the long term, human activities exerted a more substantial impact on eutrophication in Daihai Lake, while climatic factors played an accelerating and reinforcing role. These results are consistent with prior research in the Daihai area, indicating a state of mild eutrophication and the potential of the fuzzy classification method and comprehensive trophic status index method in eutrophication assessment. Full article

The positional accuracy of satellite imagery is essential for remote sensing cameras. However, vibrations and temperature changes during launch and operation can alter the exterior orientation parameters of remote sensing cameras, significantly reducing image positional accuracy. To address this issue, this article proposes an exterior orientation parameter variation real-time monitoring system (EOPV-RTMS). This system employs lasers to establish a full-link active optical monitoring path, which is free from time and space constraints. By simultaneously receiving star and laser signals with the star tracker, the system monitors changes in the exterior orientation parameters of the remote sensing camera in real time. Based on the in-orbit calibration geometric model, a new theoretical model and process for the calibration of exterior orientation parameters are proposed, and the accuracy and effectiveness of the system design are verified by ground experiments. The results indicate that, under the condition of a centroid extraction error of 0.1 pixel for the star tracker, the EOPV-RTMS achieves a measurement accuracy of up to 0.6″(3σ) for a single image. Displacement variation experiments validate that the measurement error of the system deviates by at most 0.05″ from the theoretical calculation results. The proposed EOPV-RTMS provides a new design solution for improving in-orbit calibration technology and image positional accuracy. Full article

The bidirectional reflectance distribution function (BRDF) can effectively characterize the reflectance properties of a target, which can be used to correct infrared remote sensing data and improve the accuracy of remote sensing measurements. When the surface temperature changes, the reflectance characteristics of the target usually change, and it is necessary to carry out BRDF measurements under variable temperature conditions. In this paper, a variable-temperature infrared BRDF measurement system based on a robotic arm is developed to realize high-resolution wide-temperature region measurement of BRDF. To improve the measurement accuracy, the shaping optical path was used to expand the laser beam, combined with the laser level to accurately adjust the three-dimensional coordinates of the robotic arm, and the dichotomy method is used to calibrate the detector nonlinearly. A portable heater suitable for the mechanical arm corner mechanism is developed, and fast and high-precision temperature control is realized by proportional integral derivative (PID) control. The specular and diffuse BRDF distributions were measured at room temperature to verify the effectiveness of the system. The BRDF distribution of SUS314 stainless steel samples with different roughness is measured during two temperature increases from 20 °C to 1000 °C, and the changing rule of BRDF under variable temperature environment is summarized, which provides technical support for evaluating the optical properties of high-temperature materials and improving the measurement accuracy of remote sensing data. Full article

Transcutaneous carbon dioxide (TcPCO2) monitoring provides a non-invasive alternative to measuring arterial carbon dioxide (PaCO2), making it valuable for various applications, such as sleep diagnostics and neonatal care. However, traditional transcutaneous monitors are bulky, expensive, and pose risks such as skin burns. To address these limitations, we have introduced a compact, cost-effective CMOS imager-based sensor for TcPCO2 detection by utilizing colorimetric reactions with metal–organic framework (MOF)-based nano-hybrid materials. The sensor, with a colorimetric sensing array fabricated on an ultrathin PDMS membrane and then adhered to the CMOS imager surface, can record real-time sensing data through image processing without the need for additional optical components, which significantly reduces the sensor’s size. Our system shows impressive sensitivity and selectivity, with a low detection limit of 26 ppm, a broad detection range of 0–2% CO₂, and strong resistance to interference from common skin gases. Feasibility tests on human subjects demonstrate the potential of this MOF-CMOS imager-based colorimetric sensor for clinical applications. Additionally, its compact design and responsiveness make it suitable for sports and exercise settings, offering valuable insights into respiratory function and performance. The sensing system’s compact size, low cost, and reversible and highly sensitive TcPCO2 monitoring capability make it ideal for integration into wearable devices for remote health tracking. Full article

The Vilcanota is the second-largest snow-capped mountain range in Peru, featuring 380 individual glaciers, each with its own unique characteristics that must be studied independently. However, few studies have been conducted in the Vilcanota range to monitor and track the area and volume changes of the Suyuparina and Quisoquipina glaciers. Notably, there are only a few studies that have approached this issue using LIDAR technology. Our methodology is based on a combination of optical, radar and LIDAR data sources, which allowed for constructing coherent temporal series for the both the perimeter and volume changes of the Suyuparina and Quisoquipina glaciers while accounting for the uncertainty in the perimeter detection procedure. Our results indicated that, from 1990 to 2013, there was a reduction in snow cover of 12,694.35 m² per year for Quisoquipina and 16,599.2 m² per year for Suyuparina. This represents a loss of 12.18% for Quisoquipina and 22.45% for Suyuparina. From 2006 to 2013, the volume of the Quisoquipina glacier decreased from 11.73 km³ in 2006 to 11.04 km³ in 2010, while the Suyuparina glacier decreased from 6.26 km³ to 5.93 km³. Likewise, when analyzing the correlation between glacier area and precipitation, a moderate inverse correlation (R = −0.52, p < 0.05) was found for Quisoquipina. In contrast, the correlation for Suyuparina was low and nonsignificant, showing inconsistency in the effect of precipitation. Additionally, the correlation between the snow cover area and the annual mean air temperature (R = −0.34, p > 0.05) and annual minimum air temperature (R = −0.36, p > 0.05) was low, inverse, and not significant for Quisoquipina. Meanwhile, snow cover on Suyuparina had a low nonsignificant correlation (R = −0.31, p > 0.05) with the annual maximum air temperature, indicating a minimal influence of the measured climatic variables near this glacier on its retreat. In general, it was possible to establish a reduction in both the area and volume of the Suyuparina and Quisoquipina glaciers based on freely accessible remote sensing data. Full article