Search Results (36)

Abies nebrodensis L. is a critically endangered conifer endemic to Sicily (Italy). Its residual population is confined to the Madonie mountain range under challenging climatological conditions. Despite the good adaptation shown by the relict population to the environmental conditions occurring in its habitat, Abies nebrodensis is subject to a series of threats, including climate change. Effective conservation strategies require reliable and versatile methods for monitoring its health status. Combining high-resolution remote sensing data with reanalysis of climatological datasets, this study aimed to identify correlations between vegetation indices (NDVI, GreenDVI, and EVI) and key climatological variables (temperature and precipitation) using advanced machine learning techniques. High-resolution RGB (Red, Green, Blue) and IrRG (infrared, Red, Green) maps were used to delineate tree crowns and extract statistics related to the selected vegetation indices. The results of phytosanitary inspections and multispectral analyses showed that the microclimatic conditions at the site level influence both the impact of crown disorders and tree physiology in terms of water content and photosynthetic activity. Hence, the correlation between the phytosanitary inspection results and vegetation indices suggests that multispectral techniques with drones can provide reliable indications of the health status of Abies nebrodensis trees. The findings of this study provide significant insights into the influence of environmental stress on Abies nebrodensis and offer a basis for developing new monitoring procedures that could assist in managing conservation measures. Full article

In this study, we investigate the impact of soil moisture at varying depths on the stability of Chinese ecosystems, with ecosystem stability assessed using the Enhanced Vegetation Index (EVI) and Gross Primary Productivity (GPP). A multi-perspective analysis is conducted across different climatic zones and land cover types. Sen’s Slope Estimation and the Mann–Kendall trend test, combined with linear regression and correlation analyses, are employed to analyze the long-term trends of EVI and GPP in different climatic zones and land cover types and to assess the effects of soil moisture changes on ecosystem stability. The research reveals the following findings: (1) On a national scale, both EVI and GPP exhibit positive growth trends, with more significant increases in humid areas and relatively slower growth in arid areas. In addition, EVI and GPP of different land cover types exhibit positive inter-annual variation trends, reflecting a gradual enhancement in ecosystem productivity. (2) Cluster analysis shows that EVI has strong spatial correlation, with a distribution pattern of low–low (L-L) clusters in the north and high–high (H-H) clusters in the south. L-H clusters are concentrated in the Huaihai, Southwest Rivers, and Pearl River basins, while H-L clusters are scattered along the eastern coast. The spatial correlation of GPP is mainly concentrated in the south and the northeast, with a distribution pattern of L-L in the northeast, L-H in the Yangtze River basin, and H-H in the south. H-L clusters are dispersed in the downstream area of the Yangtze River. Both EVI and GPP show a tendency for high-value aggregation in space, with high-value areas of EVI located in the south and low-value areas in the central and western regions. High-value areas of GPP are in the south, while low-value areas are in the northeast, particularly in the Yangtze River Delta. (3) The correlation between EVI, GPP, and soil moisture varies significantly across different climatic regions. Arid and semi-humid regions show significant correlations between specific soil moisture depths and EVI and GPP, while such correlations are not significant in humid regions. The EVI and GPP values of croplands and grasslands are significantly and negatively correlated with soil moisture at depths of 150–200 cm (SM4). Conversely, wetland GPP values increase significantly with increasing soil moisture. Other vegetation types do not show significant correlations with soil moisture. The results of this study provide an important basis for understanding the impact of climate change on ecosystem stability and offer scientific guidance for ecological protection and water resource management. Full article

Predicting the evolution of financial data, if at all possible, would be very beneficial in revealing the ways in which different aspects of a global environment can impact local economies. We employ an iterative stochastic differential equation that accurately forecasts an economic time series’s next value by analysing its past. The input financial data are assumed to be consistent with an $\alpha$-stable Lévy motion. The computation of the scaling exponent and the value of $\alpha$, which characterises the type of the $\alpha$-stable Lévy motion, are crucial for the iterative scheme. These two indices can be determined at each iteration from the form of the structure function, for the computation of which we use the method of generalised moments. Their values are used for the creation of the corresponding $\alpha$-stable Lévy noise, which acts as a seed for the stochastic component. Furthermore, the drift and diffusion terms are calculated at each iteration. The proposed model is general, allowing the kind of stochastic process to vary from one iterative step to another, and its applicability is not restricted to financial data. As a case study, we consider Greece’s stock market general index over a period of five years, from September 2019 to September 2024, after the completion of bailout programmes. Greece’s economy changed from a restricted to a free market over the chosen era, and its stock market trading increments are likely to be describable by an $\alpha$-stable L’evy motion. We find that $\alpha =2$ and the scaling exponent H varies over time for every iterative step we perform. The forecasting points follow the same trend, are in good agreement with the actual data, and for most of the forecasts, the percentage error is less than 2%. Full article

Heilongjiang Province, a major grain-producing region in China, faces ecological vulnerabilities that directly affect its sustainable development. A scientific assessment of the spatiotemporal characteristics of ecological vulnerability and its influencing factors in Heilongjiang is crucial for a deeper understanding of environmental issues and provides theoretical support for enhancing regional ecological governance capabilities. The SRP model, combined with the AHP-CRITIC weighting method, was employed to assess Heilongjiang Province’s ecological vulnerability’s temporal and regional differentiation trends between 2000 and 2020. The aggregation kinds of ecological vulnerability were examined using spatial autocorrelation. GeoDetector was used to determine the main elements affecting ecological vulnerability in the province. Additionally, the ecological vulnerability status in 2030 was predicted using the CA-Markov model. The findings indicate that (1) the average EVI values for Heilongjiang Province during the three periods were 0.323, 0.317, and 0.347, respectively, indicating a medium level of ecological vulnerability across the province; the ecological vulnerability initially decreased and then worsened. Spatially, the distribution followed a pattern of “high in the east and west, and low in the north and south”. (2) Spatial agglomeration is evident, with high-high (H-H) aggregation primarily occurring in heavily and extremely vulnerable areas characterized by high human activity, while low–low (L-L) aggregation is mainly found in mildly and marginally vulnerable areas with a favorable natural background. (3) Biological abundance, net primary productivity, dry degree, and PM_2.5 were the main drivers of ecological vulnerability, with interactions between these factors amplifying their impact on ecological vulnerability. (4) The CA-Markov model prediction results indicated an upward trend in the overall ecological vulnerability of Heilongjiang Province by 2030, reflecting a decline in the ecological environment. The study indicates that the ecological vulnerability of Heilongjiang Province is closely linked to its natural geographic conditions and is influenced through the interplay of several environmental elements. Based on the vulnerability zoning results, this paper proposes governance recommendations for regions with different vulnerability levels, aiming to provide theoretical support for future ecological restoration and sustainable development. Full article

Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely and accurate yield prediction is essential for ensuring food security. There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear. This study was conducted to (1) assess the performance of widely recognized remote sensing indices to predict wheat yield at different growth stages, (2) evaluate the predictive accuracy of different yield predictive machine learning models, (3) determine the appropriate growth period for wheat yield prediction in arid regions, and (4) evaluate the impact of climate parameters on model accuracy. The vegetation indices, widely recognized due to their proven effectiveness, used in this study include the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Atmospheric Resistance Vegetation Index (ARVI). Moreover, four machine learning models, viz. Decision Trees (DTs), Random Forest (RF), Gradient Boosting (GB), and Bagging Trees (BTs), were evaluated to assess their predictive accuracy for wheat yield in the arid region. The whole wheat growth period was divided into three time windows: tillering to grain filling (December 15–March), stem elongation to grain filling (January 15–March), and heading to grain filling (February–March 15). The model was evaluated and developed in the Google Earth Engine (GEE), combining climate and remote sensing data. The results showed that the RF model with ARVI could accurately predict wheat yield at the grain filling and the maturity stages in arid regions with an R² > 0.75 and yield error of less than 10%. The grain filling stage was identified as the optimal prediction window for wheat yield in arid regions. While RF with ARVI delivered the best results, GB with EVI showed slightly lower precision but still outperformed other models. It is concluded that combining multisource data and machine learning models is a promising approach for wheat yield prediction in arid regions. Full article

Timely access to soil moisture distribution is critical for agricultural production. As an in-orbit L-band synthetic aperture radar (SAR), SAOCOM offers high penetration and full polarization, making it suitable for agricultural soil moisture estimation. In this study, based on the single-temporal coupled water cloud model (WCM) and Oh model, we first modified the WCM (MWCM) to incorporate bare soil effects on backscattering using SAR data, enhancing the scattering representation during crop growth. Additionally, the Oh model was revised to enable retrieval of both the surface layer (0–5 cm) and underlying layer (5–10 cm) soil moisture. SAOCOM data from 19 June 2022, and 23 June 2023 in Bei’an City, China, along with Sentinel-2 imagery from the same dates, were used to validate the coupled MWCM-Oh model individually. The enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and leaf area index (LAI), together with the radar vegetation index (RVI) served as vegetation descriptions. Results showed that surface soil moisture estimates were more accurate than those for the underlying layer. LAI performed best for surface moisture (RMSE = 0.045), closely followed by RVI (RMSE = 0.053). For underlying layer soil moisture, RVI provided the most accurate retrieval (RMSE = 0.038), while LAI, EVI, and NDVI tended to overestimate. Overall, LAI and RVI effectively capture surface soil moisture, and RVI is particularly suitable for underlying layers, enabling more comprehensive monitoring. Full article

Corn (Zea mays L.) silage yield monitor data offer crucial insights into spatial and temporal yield variability. However, equipment’s sensor malfunctioning can result in data loss, and yield sensor systems are expensive to purchase and maintain. In this study, we analyzed corn silage yield data from two fields and three years each for two dairy farms (Farm A and B). We aimed to explore the potential of integrating high-resolution satellite data, topography, and climate data with machine learning models to estimate missing yield data for a field or a year. Our objectives were to identify key yield-explaining features and assess the accuracy of different machine learning models in estimating silage yield. Results showed that the features differed among farms with a Two-Band Enhanced Vegetation Index, EVI2 (Farm A), and elevation (Farm B) emerging as the most prominent predictors. Ensemble-based models like XGBoost, Random Forest, and Extra Tree regressors exhibited superior predictive performance. However, XGBoost performed poorly when applied to unseen fields or years, whereas Extra Tree regressor, followed closely by Random Forest, emerged as a more reliable model for predicting missing data. Despite achieving reasonable accuracy, the best performance for estimating data for a missing field (6.46 Mg/ha) and year (5.51 Mg/ha) fell short of the acceptable error threshold of 4.9 Mg/ha currently used in state policy to evaluate if a management change resulted in a yield increase. These findings emphasize the need for higher-resolution data and extended years of yield records to better capture the trends in farm-scale yield applications. Full article

Acute myelogenous leukemia (AML) is one of the most lethal cancers, lacking a definitive curative therapy due to essential constraints related to the toxicity and efficacy of conventional treatments. This study explores the co-adjuvant potential of Lippia alba essential oils (EO) for enhancing the effectiveness and selectivity of two chemotherapy agents (cytarabine and clofarabine) against AML cells. EO derived from L. alba citral chemotype were produced using optimized and standardized environmental and extraction protocols. Rational fractionation techniques were employed to yield bioactive terpene-enriched fractions, guided by relative chemical composition and cytotoxic analysis. Pharmacological interactions were established between these fractions and cytarabine and clofarabine. The study comprehensively evaluated the cytotoxic, genotoxic, oxidative stress, and cell death phenotypes induced by therapies across AML (DA-3ER/GM/EVI1+) cells. The fraction rich in citral (F2) exhibited synergistic pharmacological interactions with the studied chemotherapies, intensifying their selective cytotoxic, genotoxic, and pro-oxidant effects. This shift favored transitioning from necrosis to a programmed cell death phenotype (apoptotic). The F2-clofarabine combination demonstrated remarkable synergistic anti-leukemic performance while preserving cell integrity in healthy cells. The observed selective antiproliferative effects may be attributed to the potential dual prooxidant/antioxidant behavior of citral in L. alba EO. Full article

Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS’s blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R² = 0.62, p < 0.01), reducing RMSE to 579 kg·ha⁻¹. Using only optical or SAR data yielded R² values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management. Full article

Efficiently monitoring and quantifying the response of genotypes to water stress is critical in developing resilient crop cultivars in water-limited environments. The objective of this study was to assess water stress in cotton (Gossypium hirsutum L.) using high-resolution unmanned aerial system (UAS) images and identify water-stress-resistant cultivars in plant breeding. Various vegetation indices (VIs) and the crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars under four irrigation treatments (90%, 60%, 30%, and 0% ET). The enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), normalized difference vegetation index (NDVI), and crop water stress index (CWSI) were effective in detecting the effects of the irrigation treatments during the growing season. These VIs effectively differentiated cultivars in the middle and late seasons, while the CWSI detected cultivar differences in the mid–late growing season. The NDVI, GNDVI, NDRE, and EVI had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R² ranged from 0.90 to 0.95). Cultivars under each irrigation treatment were clustered into high-, medium-, and low-yielding groups based on the VIs at the mid–late growing seasons using hierarchical cluster analysis (HCA). The EVI derived from UAS images with high temporal and spatial resolutions can effectively screen drought-resistant cotton varieties under 30% and 60% irrigation treatments. The successful classification of cultivars based on UAS images provides critical information for selecting suitable varieties in plant breeding to optimize irrigation management based on water availability scenarios. This technology enables the targeted selection of water-stress-resistant cotton cultivars and facilitates site-specific crop management and yield prediction, ultimately contributing to precision irrigation and sustainable agriculture in water-limited environments. Full article

The leaf area index (LAI) is a crucial physiological indicator of crop growth. This paper introduces a new spectral index to overcome angle effects in estimating the LAI of crops. This study quantitatively analyzes the relationship between LAI and multi-angle hyperspectral reflectance from the canopy of winter oilseed rape (Brassica napus L.) at various growth stages, nitrogen application levels and coverage methods. The angular stability of 16 traditional vegetation indices (VIs) for monitoring the LAI was tested under nine view zenith angles (VZAs). These multi-angle VIs were input into machine learning models including support vector machine (SVM), eXtreme gradient boosting (XGBoost), and Random Forest (RF) to determine the optimal monitoring strategy. The results indicated that the back-scattering direction outperformed the vertical and forward-scattering direction in terms of monitoring the LAI. In the solar principal plane (SPP), EVI-1 and REP showed angle stability and high accuracy in monitoring the LAI. Nevertheless, this relationship was influenced by experimental conditions and growth stages. Compared with traditional VIs, the observation perspective insensitivity vegetation index (OPIVI) had the highest correlation with the LAI (r = 0.77–0.85). The linear regression model based on single-angle OPIVI was most accurate at −15° (R² = 0.71). The LAI monitoring achieved using a multi-angle OPIVI-RF model had the higher accuracy, with an R² of 0.77 and with a root mean square error (RMSE) of 0.38 cm²·cm⁻². This study provides valuable insights for selecting VIs that overcome the angle effect in future drone and satellite applications. Full article

Modeling financial markets based on empirical data poses challenges in selecting the most appropriate models. Despite the abundance of empirical data available, researchers often face difficulties in identifying the best fitting model. Long-range memory and self-similarity estimators, commonly used for this purpose, can yield inconsistent parameter values, as they are tailored to specific time series models. In our previous work, we explored order disbalance time series from the broader perspective of fractional L’evy stable motion, revealing a stable anti-correlation in the financial market order flow. However, a more detailed analysis of empirical data indicates the need for a more specific order flow model that incorporates the power-law distribution of limit order cancellation times. When considering a series in event time, the limit order cancellation times follow a discrete probability mass function derived from the Tsallis q-exponential distribution. The combination of power-law distributions for limit order volumes and cancellation times introduces a novel approach to modeling order disbalance in the financial markets. Moreover, this proposed model has the potential to serve as an example for modeling opinion dynamics in social systems. By tailoring the model to incorporate the unique statistical properties of financial market data, we can improve the accuracy of our predictions and gain deeper insights into the dynamics of these complex systems. Full article

Remote sensing approaches are often used to monitor land cover change. However, the small physical size (about 1–2 hectare area) of smallholder orchards and the cultivation of cocoa (Theobroma cocoa L.) under shade trees make the use of many popular satellite sensors inefficient to distinguish cocoa orchards from forest areas. Nevertheless, high-resolution satellite imagery combined with novel signal extraction methods facilitates the differentiation of coconut palms (Cocos nucifera L.) from forests. Cocoa grows well under established coconut shade, and underplanting provides a viable opportunity to intensify production and meet demand and government targets. In this study, we combined grey-level co-occurrence matrix (GLCM) textural features and vegetation indices from Sentinel datasets to evaluate the sustainability of cocoa expansion given land suitability for agriculture and soil capability classes. Additionally, it sheds light on underexploited areas with agricultural potential. The mapping of areas where cocoa smallholder orchards already exist or can be grown involved three main components. Firstly, the use of the fine-resolution C-band synthetic aperture radar and multispectral instruments from Sentinel-1 and Sentinel-2 satellites, respectively. Secondly, the processing of imagery (Sentinel-1 and Sentinel-2) for feature extraction using 22 variables. Lastly, fitting a random forest (RF) model to detect and distinguish potential cocoa orchards from non-cocoa areas. The RF classification scheme differentiated cocoa (for consistency, the coconut–cocoa areas in this manuscript will be referred to as cocoa regions or orchards) and non-cocoa regions with 97 percent overall accuracy and over 90 percent producer’s and user’s accuracies for the cocoa regions when trained on a combination of spectral indices and GLCM textural feature sets. The top five variables that contributed the most to the model were the red band (B4), red edge curve index (RECI), blue band (B2), near-infrared (NIR) entropy, and enhanced vegetation index (EVI), indicating the importance of vegetation indices and entropy values. By comparing the classified map created in this study with the soil and land capability legacy information of Bougainville, we observed that potential cocoa regions are already rated as highly suitable. This implies that cocoa expansion has reached one of many intersecting limits, including land suitability, political, social, economic, educational, health, labour, and infrastructure. Understanding how these interactions limit cocoa productivity at present will inform further sustainable growth. The tool provides inexpensive and rapid monitoring of land use, suitable for a sustainable planning framework that supports responsible agricultural land use management. The study developed a heuristic tool for monitoring land cover changes for cocoa production, informing sustainable development that balances the needs and aspirations of the government and farming communities with the protection of the environment. Full article

Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans’ exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm’s ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L’evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments’ outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms. Full article

Safflower (Carthamus tinctorius L.) is a highly adaptable but underutilized oilseed crop capable of growing in marginal environments, with crucial agronomical, commercial, and industrial uses. Considerable research is still needed to develop commercially relevant varieties, requiring effective, high-throughput digital phenotyping to identify key selection traits. In this study, field trials comprising a globally diverse collection of 350 safflower genotypes were conducted during 2017–2019. Crop traits assessed included phenology, grain yield, and oil quality, as well as unmanned aerial vehicle (UAV) multispectral data for estimating vegetation indices. Phenotypic traits and crop performance were highly dependent on environmental conditions, especially rainfall. High-performing genotypes had intermediate growth and phenology, with spineless genotypes performing similarly to spiked genotypes. Phenology parameters were significantly correlated to height, with significantly weak interaction with yield traits. The genotypes produced total oil content values ranging from 20.6–41.07%, oleic acid values ranging 7.57–74.5%, and linoleic acid values ranging from 17.0–83.1%. Multispectral data were used to model crop height, NDVI and EVI changes, and crop yield. NDVI data identified the start of flowering and dissected genotypes according to flowering class, growth pattern, and yield estimation. Overall, UAV-multispectral derived data are applicable to phenotyping key agronomical traits in large collections suitable for safflower breeding programs. Full article