Search Results (14)

Water is vital for producing summer maize (SM) and winter wheat (WW); therefore, its proper management is crucial for sustainable farming. This study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW. We conducted irrigation treatments, including W0, W1, W2, W3, and W4, in SM–WW rotations to address this issue. Canopy reflectance was measured with a field spectroradiometer. Tri-band hyperspectral vegetation indices were constructed: Normalised Water Stress Index (NWSI), Normalised Difference Index (NDI), and Exponential Water Stress Index (EWSI), for assessing the PMC of SM and WW. Results indicate that NWSI outperformed other indices. In the maize trials, the correlation reached R = −0.8369, while in wheat, it reached R = −0.9313, surpassing traditional indices. Four mainstream machine learning models (Random Forest, Partial Least Squares Regression, Support Vector Machine, and Artificial Neural Network) were employed for modelling. NWSI-PLSR exhibited the best index-type performance with an R² of 0.7878. When the new indices were combined with traditional indices as input data, the NWSI-Published indices-SVM model achieved superior performance with an R² of 0.8203, outperforming other models. The RF model produced the most consistent performance and achieved the highest average R² across all input types. The NDI-Published indices models also outperformed those of the published indices alone. This indicates that these new indices improve the accuracy of moisture content monitoring in SM and WW fields. It provides a technical basis and support for precision irrigation, holding significant potential for application. Full article

Global climate change has led to frequent extreme weather events such as high temperatures and droughts, severely threatening the heat and water balance during the growing season of summer maize. To adapt to these changes, adjusting planting dates to optimize crop development has become a key agronomic measure for mitigating climate stress and ensuring yield. Against this backdrop, precise monitoring of leaf area index (LAI) is crucial for evaluating the effectiveness of planting date regulation and achieving precision management. To reveal the impact of planting date variations on summer maize LAI inversion and address the limitations of single data sources in comprehensively reflecting complex environmental conditions affecting crop growth, this study examined summer maize at different planting dates across the North China Plain. Through stepwise regression analysis (SRA), multiple vegetation indices (VIs) and 0–2nd order fractional order derivatives (FODs), spectral parameters were dynamically screened. These were then integrated with effective accumulated temperature (EAT) to optimize model inputs. Partial Least Squares Regression (PLSR), Random Forest (RF), Support Vector Regression (SVR), and Adaptive Boosting Regression (AdaBoot) algorithms were employed to construct LAI inversion models for summer maize across different planting dates and mixed planting dates. Results indicate that, compared to empirical VIs and “tri-band” parameters, randomly selected dual-band combination VIs exhibit the strongest correlation with summer maize LAI. Key bands identified through SRA screening concentrated in the 0.7–1.2 order range, primarily distributed across the red edge and near-infrared bands. Multi-feature models incorporating EAT significantly improved retrieval accuracy compared to single-feature models. Optimal models and feature combinations varied across planting dates. Overall, the VIs + EAT combination exhibited the highest stability across all models. Ensemble learning algorithms RF and AdaBoost performed exceptionally well, achieving average R² values of 0.93 and 0.92, respectively. The model accuracy for the 20-day delayed planting (S4) decreased significantly, with an average R² of 0.62, while the average R² for other planting dates exceeded 0.90. This indicates that the altered environmental conditions during the later growth stages of LAI due to delayed planting hindered LAI estimation. This study provides an effective method for estimating summer maize LAI across different planting dates under climate change, offering scientific basis for optimizing adaptive cultivation strategies for maize in the North China Plain. Full article

Graphitic carbon nitride (g-C₃N₄) is an intriguing nanomaterial that exhibits photoconductive fluorescence properties under UV–visible light. Dopamine (DA) coating of g-C₃N₄ prepared from melamine was accomplished via self-polymerization of DA as polydopamine (PDA). The g-C₃N₄ was coated with PDA 1, 3, and 5 times repeatedly as (PDA@g-C₃N₄) in tris buffer at pH 8.5. As the number of PDA coatings was increased on g-C₃N₄, the peak intensity at 1512 cm⁻¹ for N–H bending increased. In addition, the increased weight loss values of PDA@g-C₃N₄ structures at 600 °C from TGA thermograms confirmed that the coating was accomplished. The band gap of g-C₃N₄, 2.72 eV, was reduced to 0.87 eV after five coatings with PDA. A pristine g-C₃N₄ was found to have an isoelectric point (IEP) of 4.0, whereas the isoelectric points of 1PDA@g-C₃N₄ and 3PDA@g-C₃N₄ are close to each other at 3.94 and 3.91, respectively. On the other hand, the IEP of 5PDA@g-C₃N₄ was determined at pH 5.75 assuming complete coating with g-C₃N₄. The biocompatibility of g-C₃N₄ and PDA@g-C₃N₄ against L929 fibroblast cell lines revealed that all PDA@g-C₃N₄ coatings were found to be biocompatible up to a 1000 mg/mL concentration, establishing that PDA coatings did not adversely affect the biocompatibility of the composite materials. In addition, PDA@g-C₃N₄ was screened for antioxidant potential via total phenol content (TPC) and total flavonoid content assays and it was found that PDA@g-C₃N₄ has recognizable TPC values and increased linearly with an increased number of PDA coatings. Furthermore, blood compatibility of pristine g-C₃N₄ is enhanced considerably upon PDA coating, affirmed by hemolysis and the blood clotting index%. Additionally, α-glucosidase inhibitory properties of PDA@g-C₃N₄ structures revealed that 67.6 + 9.8% of this enzyme was evenly inhibited by 3PDA@g-C₃N₄ structure. Full article

The disease status, progression, and treatment effect of essential tremor (ET) patients are currently assessed with clinical scores, such as the Fahn–Tolosa–Marin Clinical Rating Scale for Tremor (FTM). The use of objective and rater-independent monitoring of tremors may improve clinical care for patients with ET. Therefore, the focus of this study is to develop an objective accelerometry-based method to quantify ET, based on FTM criteria. Thirteen patients with ET and thirteen matched healthy participants underwent FTM tests to rate tremor severity, paired with tri-axial accelerometric measurements at the index fingers. Analogue FTM assessments were performed by four independent raters based on video recordings. Quantitative measures were derived from the accelerometric data, e.g., the area under the curve of power in the 4–8 Hz frequency band (AUCP) and maximal tremor amplitude. As such, accelerometric tremor scores were computed, using thresholds based on healthy measurements and FTM criteria. Agreement between accelerometric and clinical FTM scores was analyzed with Cohen’s kappa coefficient. It was assessed whether there was a relationship between mean FTM scores and the natural logarithm (ln) of the accelerometric outcome measures using linear regression. The agreement between accelerometric and FTM scores was substantial for resting and intention tremor tests (≥72.7%). However, the agreement between accelerometric postural tremor data and clinical FTM ratings (κ = 0.459) was low, although their logarithmic (ln) relationship was substantial (R² ≥ 0.724). Accelerometric test–retest reliability was good to excellent (ICC ≥ 0.753). This pilot study shows that tremors can be quantified with accelerometry, using healthy thresholds and FTM criteria. The test–retest reliability of the accelerometric tremor scoring algorithm indicates that our low-cost accelerometry-based approach is a promising one. The proposed easy-to-use technology could diminish the rater dependency of FTM scores and enable physicians to monitor ET patients more objectively in clinical, intraoperative, and home settings. Full article

Beans are legumes that play extremely important roles in human nutrition, serving as good sources of protein, vitamins, minerals, and antioxidants. In this study, we tried to elucidate the genetic diversity and population structure of 40 Turkish bean (Phaseolus vulgaris L.) local varieties and 5 commercial cultivars collected from 8 different locations in Erzurum-Ispir by using inter-primary binding site (iPBS) retrotransposon markers. For molecular characterization, the 26 most polymorphic iPBS primers were used; 52 bands per primer and 1350 bands in total were recorded. The mean polymorphism information content was 0.331. Various diversity indices, such as the mean effective allele number (0.706), mean Shannon’s information index (0.546), and gene diversity (0.361) revealed the presence of sufficient genetic diversity in the germplasm examined. Molecular analysis of variance (AMOVA) revealed that 67% of variation in bean germplasm was due to differences within populations. In addition, population structure analysis exposed all local and commercial bean varieties from five sub-populations. Expected heterozygosity values ranged between 0.1567 (the fourth sub-population) and 0.3210 (first sub-population), with an average value of 0.2103. In contrary, population differentiation measurement (Fst) was identified as 0.0062 for the first sub-population, 0.6372 for the fourth subpopulations. This is the first study to investigate the genetic diversity and population structure of bean germplasm in Erzurum-Ispir region using the iPBS-retrotransposon marker system. Overall, the current results showed that iPBS markers could be used consistently to elucidate the genetic diversity of local and commercial bean varieties and potentially be included in future studies examining diversity in a larger collection of local and commercial bean varieties from different regions. Full article

Deep learning is a promising method for image classification, including satellite images acquired by various sensors. However, the synergistic use of geospatial data for water body extraction from Sentinel-1 data using deep learning and the applicability of existing deep learning models have not been thoroughly tested for operational flood monitoring. Here, we present a novel water body extraction model based on a deep neural network that exploits Sentinel-1 data and flood-related geospatial datasets. For the model, the U-Net was customised and optimised to utilise Sentinel-1 data and other flood-related geospatial data, including digital elevation model (DEM), Slope, Aspect, Profile Curvature (PC), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Buffer for the Southeast Asia region. Testing and validation of the water body extraction model was applied to three Sentinel-1 images for Vietnam, Myanmar, and Bangladesh. By segmenting 384 Sentinel-1 images, model performance and segmentation accuracy for all of the 128 cases that the combination of stacked layers had determined were evaluated following the types of combined input layers. Of the 128 cases, 31 cases showed improvement in Overall Accuracy (OA), and 19 cases showed improvement in both averaged intersection over union (IOU) and F1 score for the three Sentinel-1 images segmented for water body extraction. The averaged OA, IOU, and F1 scores of the ‘Sentinel-1 VV’ band are 95.77, 80.35, and 88.85, respectively, whereas those of ‘band combination VV, Slope, PC, and TRI’ are 96.73, 85.42, and 92.08, showing improvement by exploiting geospatial data. Such improvement was further verified with water body extraction results for the Chindwin river basin, and quantitative analysis of ‘band combination VV, Slope, PC, and TRI’ showed an improvement of the F1 score by 7.68 percent compared to the segmentation output of the ‘Sentinel-1 VV’ band. Through this research, it was demonstrated that the accuracy of deep learning-based water body extraction from Sentinel-1 images can be improved up to 7.68 percent by employing geospatial data. To the best of our knowledge, this is the first work of research that demonstrates the synergistic use of geospatial data in deep learning-based water body extraction over wide areas. It is anticipated that the results of this research could be a valuable reference when deep neural networks are applied for satellite image segmentation for operational flood monitoring and when geospatial layers are employed to improve the accuracy of deep learning-based image segmentation. Full article

Urban building morphology has a significant impact on the urban thermal environment (UTE). The sky view factor (SVF) is an important structure index of buildings and combines height and density attributes. These factors have impact on the land surface temperature (LST). Thus, it is crucial to analyze the relationship between SVF and LST in different spatial-temporal scales. Therefore, we tried to use a building vector database to calculate the SVF, and we used remote sensing thermal infrared band to retrieve LST. Then, we analyzed the influence between SVF and LST in different spatial and temporal scales, and we analyzed the seasonal variation, day–night variation, and the impact of building height and density of the SVF–LST relationship. We selected the core built-up area of Beijing as the study area and analyzed the SVF–LST relationship in four periods in 2018. The temporal experimental results indicated that LST is higher in the obscured areas than in the open areas at nighttime. In winter, the maximum mean LST is in the open areas. The spatial experimental results indicate that the SVF and LST relationship is different in the low SVF region, with 30 m and 90 m pixel scale in the daytime. This may be the shadow cooling effect around the buildings. In addition, we discussed the effects of building height and shading on the SVF–LST relationship, and the experimental results show that the average shading ratio is the largest at 0.38 in the mid-rise building area in winter. Full article

The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields. Full article

Background: It is well known that the cerebrospinal fluid (CSF) concentrations of free light chains (FLC) and immunoglobulin G (IgG) are elevated in multiple sclerosis patients (MS). Therefore, in this study we aimed to develop a model based on the concentrations of free light chains and IgG to predict multiple sclerosis. We tried to evaluate the diagnostic usefulness of the novel κIgG index and λIgG index, here presented for the first time, and compare them with the κFLC index and the λFLC index in multiple sclerosis patients. Methods: CSF and serum samples were obtained from 76 subjects who underwent lumbar puncture for diagnostic purposes and, as a result, were divided into two groups: patients with multiple sclerosis (n = 34) and patients with other neurological disorders (control group; n = 42). The samples were analyzed using turbidimetry and isoelectric focusing. The κIgG index, λIgG index, κFLC index, and λFLC index were calculated using specific formulas. Results: The concentrations of CSF κFLC, CSF λFLC, and serum κFLC and the values of κFLC index, λFLC index, and κIgG index were significantly higher in patients with multiple sclerosis compared to controls. CSF κFLC concentration and the values of κFLC index, λFLC index, and κIgG index differed in patients depending on their pattern type of oligoclonal bands. κFLC concentration was significantly higher in patients with pattern type 2 and type 3 in comparison to those with pattern type 1 and type 4. The κFLC index, λFLC index, and κIgG index were significantly higher in patients with pattern type 2 in comparison to those with pattern type 4. The κFLC index and κIgG index were significantly higher in patients with pattern type 2 in comparison to those with pattern type 1, and in patients with pattern type 3 compared to those with pattern type 4. The κIgG index was markedly elevated in patients with pattern type 3 compared to those with pattern type 1. In the total study group, κFLC, λFLC, κFLC index, λFLC index, κIgG index, and λIgG index correlated with each other. The κIgG index showed the highest diagnostic power (area under the curve, AUC) in the detection of multiple sclerosis. The κFLC index and κIgG index showed the highest diagnostic sensitivity, and the κIgG index presented the highest ability to exclude multiple sclerosis. Conclusion: This study provides novel information about the diagnostic significance of four markers combined in the κIgG index. More investigations in larger study groups are needed to confirm that the κIgG index can reflect the intrathecal synthesis of immunoglobulins and may improve the diagnosis of multiple sclerosis. Full article

Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R² = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R² = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred. Full article

Digital Elevation Models (DEMs) contribute to geomorphological and hydrological applications. DEMs can be derived using different remote sensing-based datasets, such as Interferometric Synthetic Aperture Radar (InSAR) (e.g., Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) and Shuttle Radar Topography Mission (SRTM) DEMs). In addition, there is also the Digital Surface Model (DSM) derived from optical tri-stereo ALOS Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) imagery. In this study, we evaluated satellite-based DEMs, SRTM (Global) GL1 DEM V003 28.5 m, ALOS DSM 28.5 m, and PALSAR DEMs 12.5 m and 28.5 m, and their derived channel networks/orders. We carried out these assessments using Light Detection and Ranging (LiDAR) Digital Surface Models (DSMs) and Digital Terrain Models (DTMs) and their derived channel networks and Strahler orders as reference datasets at comparable spatial resolutions. We introduced a pixel-based method for the quantitative horizontal evaluation of the channel networks and Strahler orders derived from global DEMs utilizing confusion matrices at different flow accumulation area thresholds (ATs) and pixel buffer tolerance values (PBTVs) in both ±X and ±Y directions. A new Python toolbox for ArcGIS was developed to automate the introduced method. A set of evaluation metrics—(i) producer accuracy (PA), (ii) user accuracy (UA), (iii) F-score (F), and (iv) Cohen’s kappa index (KI)—were computed to evaluate the accuracy of the horizontal matching between channel networks/orders extracted from global DEMs and those derived from LiDAR DTMs and DSMs. PALSAR DEM 12.5 m ranked first among the other global DEMs with the lowest root mean square error (RMSE) and mean difference (MD) values of 4.57 m and 0.78 m, respectively, when compared to the LiDAR DTM 12.5 m. The ALOS DSM 28.5 m had the highest vertical accuracy with the lowest recorded RMSE and MD values of 4.01 m and −0.29 m, respectively, when compared to the LiDAR DSM 28.5 m. PALSAR DEM 12.5 m and ALOS DSM 28.5 m-derived channel networks/orders yielded the highest horizontal accuracy when compared to those delineated from LiDAR DTM 12.5 m and LiDAR DSM 28.5 m, respectively. The number of unmatched channels decreased when the PBTV increased from 0 to 3 pixels using different ATs. Full article

In situ and remote-sensing measurements have been used to characterize the run-up phase and the phenomena that occurred during the August–November 2014 flank eruption at Stromboli. Data comprise videos recorded by the visible and infrared camera network, ground displacement recorded by the permanent-sited Ku-band, Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) device, seismic signals (band 0.02–10 Hz), and high-resolution Digital Elevation Models (DEMs) reconstructed based on Light Detection and Ranging (LiDAR) data and tri-stereo PLEIADES-1 imagery. This work highlights the importance of considering data from in situ sensors and remote-sensing platforms in monitoring active volcanoes. Comparison of data from live-cams, tremor amplitude, localization of Very-Long-Period (VLP) source and amplitude of explosion quakes, and ground displacements recorded by GBInSAR in the crater terrace provide information about the eruptive activity, nowcasting the shift in eruptive style of explosive to effusive. At the same time, the landslide activity during the run-up and onset phases could be forecasted and tracked using the integration of data from the GBInSAR and the seismic landslide index. Finally, the use of airborne and space-borne DEMs permitted the detection of topographic changes induced by the eruptive activity, allowing for the estimation of a total volume of 3.07 ± 0.37 × 10⁶ m³ of the 2014 lava flow field emplaced on the steep Sciara del Fuoco slope. Full article

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Therefore, the monitoring and estimation of surface water is an essential task. In Nepal, surface water has different characteristics such as varying temperature, turbidity, depth, and vegetation cover, for which remote sensing technology plays a vital role. Single or multiple water index methods have been applied in the classification of surface water with satisfactory results. In recent years, machine learning methods with training datasets, have been outperforming different traditional methods. In this study, we tried to use satellite images from Landsat 8 to classify surface water in Nepal. Input of Landsat bands and ground truth from high resolution images available form Google Earth is used, and their performance is evaluated based on overall accuracy. The study will be will helpful to select optimum machine learning methods for surface water classification and therefore, monitoring and management of surface water in Nepal. Full article

Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying crop health. This paper reports some experiences related to the analysis of cultivations (vineyards and tomatoes) with Tetracam multispectral data. The Tetracam camera was mounted on a multi-rotor hexacopter. The multispectral data were processed with a photogrammetric pipeline to create triband orthoimages of the surveyed sites. Those orthoimages were employed to extract some Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Soil Adjusted Vegetation Index (SAVI), examining the vegetation vigor for each crop. The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications. Full article