Search Results (11)

Climate change has increased the frequency of hotter droughts and forest decline processes, highlighting the need for monitoring methodologies capable of detecting loss of vigor on the individual-tree scale. Pinus pseudostrobus is an economically important species in the temperate forests of the indigenous community of Nuevo San Juan Parangaricutiro, Michoacán. The aim of this study was to evaluate the elevational and thermal factors associated with the spatial and temporal variability of P. pseudostrobus vigor by integrating multispectral, LiDAR, and land surface temperature (LST) data. Five multispectral flights were conducted using unmanned aerial vehicles (UAVs) between June 2023 and June 2024, along with one LiDAR flight in April 2024, across an elevational gradient ranging from 2150 to 2920 m. High-resolution orthomosaics, NDVI and LCI indices, a canopy height model, and an object-based classification using Random Forest were generated. LST was obtained from Landsat 8–9 imagery in Google Earth Engine using the Statistical Mono-Window algorithm. Spectral, thermal, and elevation values were extracted at the individual-tree level. The classification achieved an overall accuracy of 69.6% and enabled the identification of 28,585 P. pseudostrobus individuals. NDVI and LCI varied significantly among periods, elevations, and their interaction. The lowest values of vigor were recorded in lower elevation areas, particularly in June 2023, April 2024, and June 2024. In addition, both indices decreased as LST increased, showing strong negative relationships for NDVI (R² = 0.96) and LCI (R² = 0.86). Individuals located at the lower elevational limit showed greater vulnerability to thermal stress. Full article

Monitoring shoreline movements is essential for understanding the impact of anthropogenic activities and climate change on the coastal zone dynamics. The use of remote sensing allows for large-scale spatial and temporal studies to better comprehend current trends. This study used Landsat 5 (TM), Landsat 8 (OLI), and Sentinel-2 (MSI) remote sensing images, together with the Otsu algorithm, marching squares algorithm, and tidal correction algorithm, to extract and correct the coastline positions of the east coast of Laizhou Bay in China from 1984 to 2022. The results indicate that 89.63% of the extracted shoreline segments have an error less than 30 m compared to the manually drawn coastline. The total length of the coastline increased from 166.90 km to 364.20 km, throughout the observation period, with a length change intensity (LCI) of 3.11% due to the development of coastal protection and engineering structures for human activities. The anthropization led to a decrease in the natural coastline from 83.33% to 13.89% and a continuous increase in the diversity and human use of the coastline. In particular, the index of coastline diversity (ICTD) and the index of coastline utilization degree (ICUD) increased from 0.39 to 0.79, and from 153.30 to 390.37, respectively. Over 70% of the sandy beaches experienced erosional processes. The shoreline erosion calculated using the end point rate (EPR) and the linear regression rate (LRR) is 79.54% and 85.58%, respectively. The fractal dimension of the coastline shows an increasing trend and is positively correlated with human activities. Coastline changes are primarily attributed to interventions such as land reclamation, aquaculture development, and port construction resulting in the creation of 10,000.20 hectares of new coastal areas. Finally, the use of Kalman filtering for the first time made it possible to predict that approximately 84.58% of the sandy coastline will be eroded to varying degrees by 2032. The research results can provide valuable reference for the scientific planning and rational utilization of resources on the eastern coast of Laizhou Bay. Full article

Future climate forecasts predict major changes that will have negative impacts on the distribution, abundance, and dynamics of forest ecosystems. In Mexico, there is evidence of symptoms of massive forest decline; however, there is no consensus in terms of attributing these symptoms to climate change. This study aimed to provide evidence of forest decline possibly associated with climatic change in the highland pine (Pinus hartwegii Lindl.) populations of the Nevado de Toluca Flora and Fauna Protection Area. Using unmanned aerial vehicles (UAV) equipped with multispectral sensors, the study applied digital photogrammetry techniques, automated tree crown detection algorithms, and calculation of the normalized difference vegetation index (NDVI) and leaf chlorophyll index (LCI) to assess forest health across an altitudinal transect (from 3300 m to the timberline at 4040 m elevation). Climate analysis was conducted with TerraClimate data using mean annual temperature (MAT), April temperature, and Palmer Drought Severity Index (PDSI) from the studied altitudinal transect and its xeric limit. We found that lower altitude populations had significantly higher stress levels, indicating forest decline phenomena, while intermediate altitude populations showed greater vigor of the detected trees. Statistically significant differences in the NDVI and LCI values along the altitudinal gradient provided evidence of forest decline in terms of forest vigor and productivity, with the greatest disturbance found at the lower altitude of the examined forest species. The analysis of the climatic data revealed an increase in April temperature +1.4 °C of the xeric limit of the transect (low altitude) when comparing the reference period, 1961–1990 (mean: 12.17 °C), with the decade prior to our study (2011–2020; mean: 13.57 °C). This would be equivalent to an upward shift in elevation of 280 m of the xeric limit. In addition, the PDSI analysis revealed that droughts are becoming increasingly intense at a rate of 0.06 PDSI units per decade, with greater intensity in the last five years. These findings highlight the negative impacts of climate change on forest ecosystems and the urgent need for alternative forest management and conservation practices to increase resilience and adaptation in the temperate forests of Mexico. This study sets a precedent for further research to improve our understanding of the impacts of climate change on forest ecosystems and the development of sustainable management practices. Full article

Infrared small target detection (ISTD) plays a crucial role in precision guidance, anti-missile interception, and military early-warning systems. Existing approaches suffer from high false alarm rates and low detection rates when detecting dim and small targets in complex scenes. A robust scheme for automatically detecting infrared small targets is proposed to address this problem. First, a gradient weighting technique with high sensitivity was used for extracting target candidates. Second, a new collection of features based on local convergence index (LCI) filters with a strong representation of dim or arbitrarily shaped targets was extracted for each candidate. Finally, the collective set of features was inputted to a random undersampling boosting classifier (RUSBoost) to discriminate the real targets from false-alarm candidates. Extensive experiments on public datasets NUDT-SIRST and NUAA-SIRST showed that the proposed method achieved competitive performance with state-of-the-art (SOTA) algorithms. It is also important to note that the average processing time was as low as 0.07 s per frame with low time consumption, which is beneficial for practical applications. Full article

Nitrogen balance index (NBI) is an important indicator for scientific diagnostic and quantitative research on crop growth status. The quick and accurate assessment of NBI is necessary for farmers to make timely N management decisions. The objective of the study was to estimate winter wheat NBI based on canopy hyperspectral features between 400–1350 nm combined with machine learning (ML) methods in the individual and whole growth stages. In this study, 3 years of winter wheat plot experiments were conducted. Ground-level canopy hyperspectral reflectance and corresponding plant NBI values were measured during the jointing, booting, flowering and filling stages. Continuous removal spectra (CRS) and logarithmic transformation spectra (LOGS) were derived from the original canopy spectra. Sensitive bands and vegetation indices (VIs) highly correlated with NBI under different spectral transformations were selected as hyperspectral features to construct the NBI estimation models combined with ML algorithms. The study indicated that the spectral transformation significantly improved the correlation between the sensitive bands, VIs and the NBI. The correlation coefficient of the sensitive band in CRS in the booting stage increased by 27.87%, reaching −0.78. The leaf chlorophyll index (LCI) in LOGS had the highest correlation with NBI in the filling stage, reaching a correlation coefficient of −0.96. The NBI prediction accuracies based on the sensitive band combined with VIs were generally better than those based on the univariate hyperspectral feature, and the prediction accuracy of each growth stage was better than that of the whole growth stage. The random forest regression (RFR) method performed better than the support vector regression (SVR) and partial least squares regression (PLS) methods. The NBI estimation model based on the LOGS-RFR method in the filling stage could explain 95% of the NBI variability with relative prediction deviation (RPD) being 3.69. These results will provide a scientific basis for better nitrogen nutrition monitoring, diagnosis, and later for field management of winter wheat. Full article

Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms. Full article

Type-1 diabetes mellitus is a chronic disease that is constantly monitored worldwide by researchers who are strongly determined to establish mathematical and experimental strategies that lead to a breakthrough toward an immunological treatment or a mathematical model that would update the UVA/Padova algorithm. In this work, we aim at a nonlinear mathematical analysis related to a fifth-order ordinary differential equations model that describes the asymmetric relation between C-peptides, pancreatic cells, and the immunological response. The latter is based on both the Localization of Compact Invariant Set (LCIS) appliance and Lyapunov’s stability theory to discuss the viability of implementing a possible treatment that stabilizes a specific set of cell populations. Our main result is to establish conditions for the existence of a localizing compact invariant domain that contains all the dynamics of diabetes mellitus. These conditions become essential for the localizing domain and stabilize the cell populations within desired levels, i.e., a state where a patient with diabetes could consider a healthy stage. Moreover, these domains demonstrate the cell populations’ asymmetric behavior since both the dynamics and the localizing domain of each cell population are defined into the positive orthant. Furthermore, closed-loop analysis is discussed by proposing two regulatory inputs opening the possibility of nonlinear control. Additionally, numerical simulations show that all trajectories converge inside the positive domain once given an initial condition. Finally, there is a discussion about the biological implications derived from the analytical results. Full article

In the arid climate area north of Tenth of Ramadan City, southeast of the Nile Delta, Egypt, it is necessary to search for additional water resources for sustainable developments such as agricultural and industrial activities. Thirty two vertical electrical soundings (VES) of a electrical resistivity (DC) survey were carried out along four main profiles by using the Schlumberger array with electrode distances (AB/2) up to 500 m, to explore the shallow Pleistocene groundwater aquifer. The collected data was interpreted by a one-dimensional laterally constrained inversion (1D-LCI) and two-dimensional inversion algorithms to derive a best fit layered-earth resistivity model. The derived resistivity sections are geologically well interpreted based on information taken from the available water boreholes (P2-Well and P3-Well). The lateral constraints are part of the inversion where all data sets are inverted simultaneously, and consequently the output models are balanced between the constraints and the data-model fit. The 1D-LCI offers good analysis of the model parameters, which was successfully used to characterize a zone of groundwater aquifer, as it produces a laterally smooth model with sharp layer boundaries. The 1D-LCI inversion results show that the study area is subdivided into five geo-electrical layers of varied resistivity and thickness. In particular, the resistivity values of the last layer range between 9.3 and 110 Ωm representing the existing shallow Pleistocene aquifer located at depths between 134.5 and 118.4 m. Such results are tied and confirmed well with the results of the 2D inversion of the DC data. It reveals three interpreted geo-electric layers along the four profiles and shows that the area is affected by some normal faults striking nearly in the E–W direction. The very low resistivities of the groundwater aquifer beneath the agricultural part of the survey area probably indicates contamination due to the possible effect of irrigation operated in the cultivated lowlands. The results obtained could help the stakeholder to find additional information about the ground water aquifers in the newly reclaimed arid area and possible locations of new sites for drilling new water wells as additional water resources. Full article

Life cycle assessment (LCA) is increasingly becoming a common technique to assess the embodied energy and carbon of buildings and their components over their life cycle. However, the vast majority of existing LCAs result in very definite, deterministic values which carry a false sense of certainty and can mislead decisions and judgments. This article tackles the lack of uncertainty analysis in LCAs of buildings by addressing the main causes for not undertaking this important activity. The research uses primary data for embodied energy collected from European manufacturers as a starting point. Such robust datasets are used as inputs for the stochastic modelling of uncertainty through Monte Carlo algorithms. Several groups of random samplings between 10¹ and 10⁷ are tested under two scenarios: data are normally distributed (empirically verified) and data are uniformly distributed. Results show that the hypothesis on the data no longer influences the results after a high enough number of random samplings (10⁴). This finding holds true both in terms of mean values and standard deviations and is also independent of the size of the life cycle inventory (LCI): it occurs in both large and small datasets. Findings from this research facilitate uncertainty analysis in LCA. By reducing significantly the amount of data necessary to infer information about uncertainty, a more widespread inclusion of uncertainty analysis in LCA can be encouraged in assessments from practitioners and academics alike. Full article

This study developed an IPCC (Intergovernmental Panel on Climate Change) compliant method for the estimation of above-ground carbon (AGC) in forest stands using remote sensing technology. A multi-level morphological active contour (MMAC) algorithm was employed to obtain tree-level metrics (tree height (LH), crown radius (LCR), competition index (LCI), and stem diameter (LDBH)) from an airborne LiDAR-derived canopy height model. Seven biomass-based AGC models and 13 volume-based AGC models were developed using a training dataset and validated using a separate validation dataset. Four accuracy measures, mean absolute error (MAE), root-mean-square error (RMSE), percentage RMSE (PRMSE), and root-mean-square percentage error (RMSPE) were calculated for each of the 20 models. These measures were transformed into a new index, accuracy improvement percentage (AIP), for post hoc testing of model performance in estimating forest stand AGC stock. Results showed that the tree-level AGC models explained 84% to 91% of the variance in tree-level AGC within the training dataset. Prediction errors (RMSEs) for these models ranged between 15 ton/ha and 210 ton/ha in mature forest stands, which is equal to an error percentage in the range 6% to 86%. At the stand-level, several models achieved accurate and reliable predictions of AGC stock. Some models achieved 90% to 95% accuracy, which was equal to or superior to the R-squared of the tree-level AGC models. The first recommended model was a biomass-based model using the metrics LDBH, LH, and LCI and the others were volume-based models using LH, LCI, and LCR and LDBH and LH. One metric, LCI, played a critical role in upgrading model performance when banded together with LH and LCR or LDBH and LCR. We conclude by proposing an IPCC-compatible method that is suitable for calculating tree-level AGC and predicting AGC stock of forest stands from airborne LiDAR data. Full article

The Wuppertal Institute developed, in the early 1990s, an input-oriented lifecycle-wide resource accounting method, the “Material Input per Service-Unit” concept (MIPS), today also referred to as “Material Footprint”. The official handbook applicable to products, services, and processes describes a MS Excel-based sequential approach for calculating MIPS. Today’s computing power, available to every researcher, and access to software and databases dedicated to lifecycle analysis make calculating MIPS using matrix inversion possible. This also opens up possibilities for enhancing MIPS-models programmatically: parameterizing the foreground and background systems, batch modeling for producing time series, and computational algorithms enhancing interpretation. The article provides (1) an overview of the methods and tools used for calculating MIPS from its origins to today, and (2) demonstrates some of the programmatically enhanced capabilities offered to MIPS-practitioners. Full article