Fractal Analysis of the Cerebrovascular System Pathophysiology

The cerebrovascular system is characterized by parameters such as arterial blood pressure (ABP), cerebral perfusion pressure (CPP), and cerebral blood flow velocity (CBFV). These are regulated by interconnected feedback loops resulting in a fluctuating and complex time course. They exhibit fractal characteristics such as (statistical) self-similarity and scale invariance which could be quantified by fractal measures. These include the coefficient of variation, the Hurst coefficient H, or the spectral exponent α in the time domain, as well as the spectral index ß in the frequency domain. Prior to quantification, the time series has to be classified as either stationary or nonstationary, which determines the appropriate fractal analysis and measure for a given signal class. CBFV was characterized as a nonstationary (fractal Brownian motion) signal with spectral index ß between 2.0 and 2.3. In the high-frequency range (>0.15 Hz), CBFV variability is mainly determined by the periodic ABP variability induced by heartbeat and respiration. However, most of the spectral power of CBFV is contained in the low-frequency range (<0.15 Hz), where cerebral autoregulation acts as a low-pass filter and where the fractal properties are found. Cerebral vasospasm, which is a complication of subarachnoid hemorrhage (SAH), is associated with an increase in ß denoting a less complex time course. A reduced fractal dimension of the retinal microvasculature has been observed in neurodegenerative disease and in stroke. According to the decomplexification theory of illness, such a diminished complexity could be explained by a restriction or even dropout of feedback loops caused by disease.

Effects of water stress and fertilizer stress on maize growth and spectral identification of different stresses

Water stress and fertilizer stress have a significant impact on the growth and yield of maize. In order to improve the timeliness and accuracy of irrigation and fertilizer application, it is crucial to monitor water stress and fertilizer stress rapidly and accurately. This would help in conserving water and fertilizer resources and ensuring a stable maize yield. To this end, pot experiments were set up to explore the growth differences and photosynthetic properties of maize under water stress and fertilizer stress. The hyperspectral technology was used to construct the spectral indexes that can distinguish stress types, and the classification algorithm was combined to identify stress types. The research has shown that the plant height, basal diameter, leaf area, and photosynthetic properties of maize decreased with an increase in drought stress. However, rewatering could compensate for drought stress. Furthermore, fertilizer stress also affected water uptake by plants, and high nitrogen stress had a significant negative effect on the growth of maize plants. We employed a combination of spectral indexes and the support vector machine (SVM) classification algorithm in a stepwise manner to identify stress types. Using the training dataset, we constructed six classifiers for distinguishing stress types, including the SVM classifier, K-nearest neighbor (KNN) classifier, naive Bayes (NB) classifier, decision tree (DT) classifier, random forest (RF) classifier, and AdaBoost classifier. Our results showed that the RF and AdaBoost classifiers obtained stable results in stress type differentiation, achieving accurate identification of unstressed, water stressed, and fertilizer stressed maize plants. This is expected to provide a solid basis and reference for monitoring crop stress types in agricultural fields.

Potential linkage between WWTPs-river-integrated area pollution risk assessment and dissolved organic matter spectral index

The direct discharge of wastewater can cause severe damage to the water environment of the surface water. However, the influence of dissolved organic matter (DOM) present in wastewater on the allocation of DOM, nitrogen (N), and phosphorus (P) in rivers remains largely unexplored. Addressing the urgent need to monitor areas affected by direct wastewater discharge in a long-term and systematic manner is crucial. In this paper, the DOM of overlying water and sediment in the WWTPs-river-integrated area was characterized by ultraviolet-visible absorption spectroscopy (UV-vis), three-dimensional excitation-emission matrix combined with parallel factor (PARAFAC) method. The effects of WWTPs on receiving waters were investigated, and the potential link between DOM and N, P pollution was explored. The pollution risk was fitted and predicted using a spectral index. The results indicate that the improved water quality index (IWQI) is more suitable for the WWTPs-river integration zone. The DOM fraction in this region is dominated by humic-like matter, which is mainly influenced by WWTPs drainage as well as microbial activities. The DOM fractions in sediment and overlying water were extremely similar, but fluorescence intensity possessed more significant spatial differences. The increase in humic-like matter facilitates the production and preservation of P and also inhibits nitrification, thus affecting the N cycle. There is a significant correlation between DOM fraction, fluorescence index, and N, P. Fluorescence index (FI) fitting of overlying water DOM predicted IWQI and trophic level index, and a(254) fitting of sediment DOM predicted nitrogen and phosphorus pollution risk (FF) with good results. These results contribute to a better understanding of the impact of WWTPs on receiving waters and the potential link between DOM and N and P pollution and provide new ideas for monitoring the water environment in highly polluted areas.

Identification for the species of aquatic higher plants in the Taihu Lake basin based on hyperspectral remote sensing

Aquatic plants are crucial for aquatic ecosystems and their species and distribution reflect aquatic ecosystem health. Remote sensing technology has been used to monitor plant distributions over large scales. However, the fine identification of the species of aquatic higher plants is challenging due to large temporal-spatial changes in optical water body properties and small spectral differences among plant species. Here, an aquatic plant identification method was developed by constructing a decision tree file in the C4.5 algorithm based on the canopy spectra of eight plants in the Changguangxi Wetland water area from hyperspectral remote sensing technology. The method was used to monitor the distribution of different plants in the Changguangxi Wetland area and two other water areas. The results showed that the spectral characteristics of plants were enhanced by calculating their spectral index, thereby improving the comparability among different species. The total recognition accuracy of the constructed decision tree file for eight types of plants was 85.02%. Nymphaea tetragona, Pontederia cordata, and Nymphoides peltatum had the highest recognition accuracy and Eichhornia crassipes was the lowest. The specific species and distributions of aquatic plants were consistent with the water quality in the area. The results can provide a reference for the accurate identification of aquatic plants in the same type of water area.

Impact of potentially toxic elements on pines in a former ore processing mine: Exploitation of hyperspectral response from needle and canopy scales

Anthropic potentially toxic element (PTE) releases can lead to persistent pollution in soil. Monitoring PTEs by their detection and quantification on large scale is of great interest. The vegetation exposed to PTEs can exhibit a reduction of physiological activities, structural damage … Such vegetation trait changes impact the spectral signature in the reflective domain 0.4-2.5 μm. The objective of this study is to characterize the impact of PTEs on the spectral signature of two pine species (Aleppo and Stone pines) in the reflective domain and ensure their assessment. The study focuses on nine PTEs: As, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Zn. The spectra are measured by an in-field spectrometer and an aerial hyperspectral instrument on a former ore processing site. They are completed by measurements related to vegetation traits at needle and tree scales (photosynthetic pigments, dry matter, morphometry …) to define the most sensitive vegetation parameter to each PTE in soil. A result of this study is that chlorophylls and carotenoids are the most correlated to PTE contents. Context-specific spectral indices are specified and used to assess metal contents in soil by regression. These new vegetation indices are compared at needle and canopy scales to literature indices. Most of the PTE contents are predicted at both scales with Pearson correlation scores between 0.6 and 0.9, depending on species and scale.

A study of cyanobacterial bloom monitoring using unmanned aerial vehicles, spectral indices, and image processing techniques

Last 5 years, the deterioration of water quality caused by algal bloom has emerged as a serious issue in Korea. The method of on-site water sampling to check algal bloom and cyanobacteria is problematic by only partially measuring the site and not fully representing the field, while at the same time, consuming a lot of time and manpower to complete it. In this study, the different spectral indices reflecting the spectral characteristics of photosynthetic pigments were compared. We monitored harmful algal bloom and cyanobacteria in Nakdong rivers with multispectral sensor images from unmanned aerial vehicles (UAVs). The multispectral sensor images were used to assess the applicability of estimating cyanobacteria concentration based on field sample data. Several wavelength analysis techniques were conducted in June, August, and September 2021, when algal bloom intensified, including the analysis of images from multispectral cameras using normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), blue normalized difference vegetation index (BNDVI), and normalized difference red edge index (NDREI). Radiation correction was performed using the reflection panel to minimize interference that could distort the analysis results of the UAVs image. Regarding field application and correlation analysis, correlation value of NDREI was the highest at 0.7203 in June. And NDVI was the highest at 0.7607 and 0.7773 in August and September, respectively. Based on the results obtained from this study, it is found that it is possible to quickly measure and judge the distribution status of cyanobacteria. In addition, the multispectral sensor installed to the UAV can be considered as a basic technology for monitoring the underwater environment.

Spectral characteristics coupled with self-organizing maps analysis on different molecular size-fractionated water-soluble organic carbon from biochar

Biochar-derived water-soluble organic carbon (BWSOC) plays important roles in the environmental effect of biochar. The environmental behavior and fate of BWSOC are closely related to its size distribution and chemical components. However, the molecular size-dependent BWSOC components and properties remain little known. To evaluate molecular size-dependent BWSOC characteristics, BWSOC samples were prepared by pyrolyzing biomasses in air-limitation and N₂-flow atmospheres at 300-600 °C and fractionated through a series of membranes with different pore sizes including 0.7 μm, 0.45 μm, 100 kDa, 10 kDa, 3 kDa, and 1 kDa. In all BWSOCs, <1 kDa and 0.45-0.7 μm fractions had the maximum abundance (mean: 40.6 %) and the minimum abundance (mean: 4.4 %), respectively. The spectral characteristics of BWSOC including polarity index, spectral slope, and humification index varied significantly with molecular size. The fluorescence excitation-emission matrix parallel factor (EEM-PARAFAC) analysis indicated that BWSOC was mainly composed of three organic components (humic-like, fulvic-like, and aromatic protein/polyphenol-like substances). Humic-like and fulvic-like substances mainly existed in <1 kDa fraction, while aromatic protein/polyphenol-like substances mainly existed in medium-size fractions (3 kDa-0.45 μm). The different locations of <1 kDa, 1 kDa-0.45 μm, and 0.45-0.7 μm fractions in EEM and PARAFAC self-organizing maps indicated self-organizing maps could effectively distinguish 0.45-0.7 μm, 1 kDa-0.45 μm, and < 1 kDa fractions via the variations of fluorescence intensity and organic components. Additionally, the distribution ratio of different molecular size fractions as well as the abundances of organic components in different molecular size fractions were strongly controlled by pyrolysis atmospheres (air-limitation and N₂-flow). This study systematically clarified the organic components and properties of different molecular size fractions in BWSOC, and the results are helpful to understand the possible environmental behavior and fate of BWSOC.

Imaging particulate matter exposed pine trees by vehicle exhaust experiment and hyperspectral analysis

This study analyzed spectral variations of the particulate matter (PM hereafter)-exposed pine trees using a spectrometer and a hyperspectral imager to derive the most effective spectral indices to detect the pine needle exposure to PM emission. We found that the spectral variation in the near-infrared (NIR hereafter) bands systemically coincided with the variations in PM concentration, showing larger variations for the diesel group whereas larger dust particles showed spectral variations in both visible and NIR bands. It is because the PM adsorption on needles is the main source of NIR band variation, and the combination of visible and NIR spectra can detect PM absorption. Fourteen bands were selected to classify PM-exposed pine trees with an accuracy of 82% and a kappa coefficient of 0.61. Given that this index employed both visible and NIR bands, it would be able to detect PM adsorption. The findings can be transferred to real-world applications for monitoring air pollution in an urban area.

Development of a soil heavy metal estimation method based on a spectral index: Combining fractional-order derivative pretreatment and the absorption mechanism

Visible and near-infrared (Vis-NIR) reflectance is an effective way to estimate soil heavy metal content. In this study, in order to magnify the spectral information of the soil heavy metals and solve the collinearity and redundancy of hyperspectral datasets, we aimed to explore the potential of the fractional-order derivative (FOD) spectral pretreatment method and the band combination algorithm in soil heavy metal estimation. A total of 120 soil samples were collected in Xuzhou city, Jiangsu province, China, and their heavy metal contents and spectra were measured. The FOD (intervals of 0.25, range of 0-2) and a new three-band spectral index which take into account the electronic transition of metal ions in the visible region and organic matter and clay minerals in the near-infrared region were utilized for the spectral pretreatment and the selection of characteristic bands, respectively. FOD with an order of 0.75 exhibited the best model performance for estimating Cr and Zn, yielding R_P² values of 0.74 and 0.81, respectively. As regards Pb, the highest estimation accuracy was achieved with the 0.5-order reflectance, yielding R_P² values of 0.56. The three-band spectral indices with the best performance were then combined for a better estimation. To improve the estimation accuracy and generalization, partial least squares (PLS), support vector machine (SVM), random forest (RF), ridge regression (RR), XGBoost and extreme learning machine (ELM) were used to estimate the heavy metals by incorporating multiple spectral indices, and it was found that ELM outperformed other counterparts (the highest R_P² = 0.77 for Cr, the highest R_P² = 0.86 for Zn, the highest R_P² = 0.63 for Pb). The main spectral absorption mechanisms and modes of heavy metals were also analyzed. This estimation method combining FOD and a three-band index will provide a reference to estimate soil heavy metals using Vis-NIR spectra over a large scale.

A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest

Massive plant hyperspectral images (HSIs) result in huge storage space and put a heavy burden for the traditional data acquisition and compression technology. For plant leaf HSIs, useful plant information is located in multiple arbitrary-shape regions of interest (MAROIs), while the background usually does not contain useful information, which wastes a lot of storage resources. In this paper, a novel hyperspectral compressive sensing framework for plant leaves with MAROIs (HCSMAROI) is proposed to alleviate these problems. HCSMAROI only compresses and reconstructs MAROIs by discarding the background to achieve good reconstructed performance. But for different plant leaf HSIs, HCSMAROI has the potential to be applied in other HSIs. Firstly, spatial spectral decorrelation criterion (SSDC) is used to obtain the optimal band of plant leaf HSIs; Secondly, different leaf regions and background are distinguished by the mask image of the optimal band; Finally, in order to improve the compression efficiency, after discarding the background region the compressed sensing technology based on blocking and expansion is used to compress and reconstruct the MAROIs of plant leaves one by one. Experimental results of soybean leaves and tea leaves show that HCSMAROI can achieve 3.08 and 5.05 dB higher PSNR than those of blocking compressive sensing (BCS) at the sampling rate of 5%, respectively. The reconstructed spectra of HCSMAROI are especially closer to the original ones than that of BCS. Therefore, HCSMAROI can achieve significantly higher reconstructed performance than that of BCS. Moreover, HCSMAROI can provide a flexible way to compress and reconstruct different MAROIs with different sampling rates, while achieving good reconstruction performance in the spatial and spectral domains.

Assessing optical remote sensing for grave detection

The application of an effective and ready-to-use tool for discovering clandestine graves is crucial for solving a number of cases where disappearance of people is involved. This is the case in Mexico, where the government drug war has resulted in a large number of missing people that has been estimated to be over 40,000 since the year 2006. In this article, we report results from an experimental study on simulated animal graves detection using several techniques from optical remote sensing. Results showed that several spectral indices from hyperspectral and/or multispectral sensors may be used to detect N-enriched vegetation. Thermal imagery was also effective to detect underground voids through differential thermography, although this was only effective for detecting large graves with bare terrain. Lastly, while dense pointclouds reconstructed from oblique aerial photography was able to detect vegetation regrowth over the pits, the terrain subsidence was not sufficiently large to be detected with this technique, even in the case of mechanical removal of vegetation.

Moisture spectral characteristics and hyperspectral inversion of fly ash-filled reconstructed soil

To explore problems with the fast estimation method of moisture content (MC) in reconstructed soil under human disturbance, this paper used a fly ash-filled reconstructed soil region as the research object and obtained experimental data by Fieldspec4 high spectrometry and the laboratory drying method. The response characteristics of MC were analyzed from the original spectral data that underwent mathematical transformation and the spectral index data, and a corresponding inversion model was established. Combined with the successive projections algorithm (SPA), the model was optimized with a better fitting effect, and the optimal inversion model was obtained. The results showed that the composition of soil and fly ash were different, resulting in obvious differences in the shape of the spectral curve, but both had large moisture absorption peaks near 1420 nm and 1920 nm. After mathematical transformation, the correlation between the spectral reflectance and MC was enhanced, in which the absolute value of the maximum correlation between the soil moisture content (SMC) was 0.839, and the absolute value of the maximum correlation between the fly ash moisture content (FMC) was 0.801. Among them, the first-order differential of multivariate scattering correction (MSC') and the first-order differential of logarithm ((lgR)') had higher fitting accuracy for FMC and SMC, respectively. The scale and sensitivity of significance variables were greatly improved based on the spectral index of two-band operation. Better FMC and SMC models were constructed based on the difference soil index (DSI) under mathematical transformation, and R² were 0.73 and 0.87, respectively. After SPA optimization, the predictive ability of the model was further improved, in which the predictive accuracy R² of FMC and SMC reached up to 0.87 and 0.96, respectively, and the RPD was greater than 3. This shows that the DSI model based on MSC' and (lgR)' combined with the SPA method can be used as an effective means of predicting the MC in fly ash-filled reconstructed soil. These research results provide the theoretical basis and technical support for the application of soil near-earth sensing technology and rapid estimation of the MC of reconstructed soil under human disturbance.

Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content

BACKGROUND

The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation.

METHODS

In this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018-2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models.

RESULTS

The results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750-1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R² of 0.85 and R² of 0.78 for the calibration and validation, respectively. Due to a large amount of hyperspectral data, the correlation coefficient method (CA) and x-Loading weight (x-Lw) were used to select the water characteristic bands (100 and 28 characteristic bands, respectively) from the full spectrum. We found that the accuracy of the model based on the characteristic bands was not significantly lower than that of the full spectrum-based models. Among these models, the ERT- x-Lw model performed the best (R² and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively). In addition, the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307).

CONCLUSION

The two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions.

Properties of GPS noise at Japan islands before and after Tohoku mega-earthquake

The field of 3-components GPS signals is analyzed for the network of 1203 stations at the Japanese islands from January 30 up to March 26, 2011. This time interval includes just over 40 days of observation before the Tohoku mega-earthquake on March 11, 2011 (M = 9.0) and nearly 16 days of observation following this event. The signals from each station are three-component time series with time step 30 minutes. We study the statistical properties of the random fluctuations of GPS signals before and after the seismic catastrophe after transition to increments. The values of wavelet-based spectral index for GPS noise components for each station were estimated separately for pieces of records before and after seismic event. The maps of the noise spectral index are constructed as the values for grid size of 50 × 50 nodes covering the region under study, based on information from 10 stations closest to each node. These maps clearly extract the region of future seismic catastrophe by relatively high noise spectral index. The using of principal components method distinguished this spatial anomaly more explicitly. These results support the hypothesis that statistical properties of random fluctuations of geophysical fields carry important information about earthquake preparation.

Visible and near-infrared reflectance spectroscopy-an alternative for monitoring soil contamination by heavy metals

Soil contamination by heavy metals is an increasingly important problem worldwide. Quick and reliable access to heavy metal concentration data is crucial for soil monitoring and remediation. Visible and near-infrared reflectance spectroscopy, which is known as a noninvasive, cost-effective, and environmentally friendly technique, has potential for the simultaneous estimation of the various heavy metal concentrations in soil. Moreover, it provides a valid alternative method for the estimation of heavy metal concentrations over large areas and long periods of time. This paper reviews the state of the art and presents the mechanisms, data, and methods for the estimation of heavy metal concentrations by the use of visible and near-infrared reflectance spectroscopy. The challenges facing the application of hyperspectral images in mapping soil contamination over large areas are also discussed.