Variable Threshold Research Articles

Multiscale Sensitivity Analysis of Landscape Fragmentation in Plantation Forests on the Loess Plateau

ABSTRACTEffectively quantifying plantation forest fragmentation is crucial for vegetation restoration and management on the Loess Plateau. Forest area density (FAD) effectively measures fragmentation, with multiscale sensitivity analysis essential for determining the appropriate window scale threshold. This study uses plantation forest data from different geomorphological types (hilly gully and plateau gully region) of the Loess Plateau. Various window sizes are applied to evaluate the FAD threshold curve and its derivative for determining a stable fragmentation threshold, while a structural equation model is employed to analyze the drivers of forest fragmentation. The results are as follows: (1) FAD variability decreases with increasing window size, leveling off after reaching the threshold. (2) Thresholds vary by forest type and county. (3) Two principal components explain 81.27% of the window stabilization threshold variation, with strong correlations between similar fragmentation types. (4) Fragmentation slowed between 2000 and 2022, with hilly gully region more fragmented than plateau gully region. (5) Stand structure mitigates fragmentation, with topographic and climate change influences showing heterogeneity. “Core” maintain stability, while “Islet” drive fragmentation. This study emphasizes the importance of scale‐specific analysis and adaptive management strategies in mitigating forest fragmentation. This study supports optimizing ecological restoration and targeted conservation strategies, promoting sustainable forest management on the Loess Plateau.

Comparison of Feature Extraction Methods in High Dimensional Time Series

Working with high-dimensional datasets increases the workload on machine learning models. Therefore, before making predictions, the most meaningful data points in the entire data set must be determined. It is highly important to improve model performance, especially in the field of machine learning. For this reason, five feature selection methods—Mutual Information, Principal Component Analysis, Chi-square, Information Gain, and Variance Thresholding—commonly used in the literature, were tested on the 14400 feature data set obtained with a system previously proposed to determine the sand, silt and clay ratios in the soil. The success of these five methods is presented comparatively using R-square (R²) and Mean Absolute Error (MAE) metrics. The best results were obtained with the Information Gain method for sand (R2 = 0.44), with Chi-square for silt (R2 = 0.17), and with Variance Thresholding for clay (R2 = 0.61).

Phenytoin-induced cerebellar symptoms – A case report

Phenytoin, a hydantoin derivative, is an effective anticonvulsant drug widely used to treat seizure disorder. The wide pharmacokinetic variability and low toxicity threshold of phenytoin often contribute to its toxicity. We now report the case of a 43-year-old woman who was hospitalized with ataxia, nystagmus, external ophthalmoplegia, drowsiness, gingival hypertrophy, vomiting, and generalized weakness for 10 days while she was on treatment with T. phenytoin 200 mg/day for the past 6 months for seizure disorder. Two days before the onset of these symptoms, the dose of T. phenytoin was increased from 200 mg/day to 300 mg/day. Based on her signs, symptoms, and laboratory tests, she was diagnosed with phenytoin-induced poisoning. After stopping the medication, the symptoms improved. She started using oral levetiracetam instead. Based on the Naranjo and the World Health Organization, a causal analysis of the relationship between the patient’s symptoms and phenytoin use was done. The severity of this adverse drug reaction is moderate at level 4 on the Hartwig severity rating scale. These data highlight the adverse effects of phenytoin and the importance of clinical monitoring for a drug with a narrow therapeutic index.

A Snow-Based Hydroclimatic Aggregate Drought Index for Snow Drought Identification

Climate change has increased the risk of snow drought, which is associated with a deficit in snowfall and snowpack. The objectives of this research are to improve drought identification in a warming climate by developing a new snow-based hydroclimatic aggregate drought index (SHADI) and to assess the impacts of snowpack and snowmelt in drought analyses. To derive the SHADI, an R-mode principal component analysis is performed on precipitation, snowpack, surface runoff, and soil water storage. Then, a joint probability distribution function of drought frequencies and drought classes, conditional expectation, and k-means clustering are used to categorize droughts. The SHADI was applied to the Red River of the North Basin (RRB), a typical cold climate region, to characterize droughts in a mostly dry period from 2003 to 2007. The SHADI was compared with the hydroclimatic aggregate drought index (HADI) and U.S. drought monitor (USDM) data. Cluster analysis was also utilized as a benchmark to compare the results of the HADI and SHADI. The SHADI showed better alignment with cluster analysis results than the HADI, closely matching the identified dry/wet conditions in the RRB. The major differences between the SHADI and HADI were observed in cold seasons and in transition periods (dry to wet or wet to dry). The derived variable threshold levels for different categories of drought based on the SHADI were close to, but different from, those of the HADI. The SHADI can be used for short-term lead prediction of droughts in cold climate regions and, in particular, can provide an early warning for drought in the warming climate.

Online Arabic and English Digits-in-Noise Tests: Effects of Test Language and At-Home Testing.

The Digits-in-Noise (DIN) test is used widely in research and, increasingly, in remote hearing screening. The reported study aimed to provide basic evaluation data for browser-based DIN software, which allows remote testing without installation of an app. It investigated the effects of test language (Arabic vs. English) and test environment (lab vs. home) on DIN thresholds and test-retest reliability. It also examined the effects of test language on the correlations between DIN and audiometric thresholds. Fifty-two bilingual adults with normal hearing aged 18-35 years completed Arabic and English diotic DIN tests (two sessions in the lab and two sessions at home via the web). Effects of language and environment on DIN thresholds were assessed via paired t tests, while intraclass and Pearson's/Spearman's correlation coefficients quantified test-retest reliability and relations to audiometric thresholds. DIN thresholds were 0.74 dB higher (worse) for Arabic than English stimuli. Thresholds were 0.52 dB lower in the lab than at home, but the effect was not significant after correction for multiple comparisons. Intraclass and Pearson's correlation coefficients were too low for meaningful analysis due to the use of a normal-hearing sample with low between-subject variability in DIN and audiometric thresholds. However, exploratory analysis showed that absolute test-retest differences were low (< 1.2 dB, on average) for both languages and both test environments. Arabic DIN thresholds were a little higher than English thresholds for the same listeners. Employing home-based rather than lab-based testing may slightly elevate DIN thresholds, but the effect was marginal. Nonetheless, both factors should be considered when interpreting DIN data. Test-retest differences were low for both languages and environments. To support hearing screening, subsequent research in audiometrically diverse listeners is required, testing the reliability of DIN thresholds and relations to hearing loss.

Clinical evaluation of different types of teeth in in-office whitening.

This prospective case series aimed to clinically evaluate the bleaching effect, spontaneous tooth sensitivity, and variation in the thermal sensation threshold of different groups of teeth undergoing in-office bleaching. Ten patients received conventional bleaching treatment: 35% hydrogen peroxide with three bleaching sessions of 45 minutes, evaluating color change (∆E and ∆E00), Whitening Index (WID), and tooth sensitivity (visual analog scale). Thermal stimulus-generating devices were used to simulate sensitivity caused by low temperatures through quantitative sensory tests. Analyses were conducted individually on different teeth groups (n = 20) (mandibular incisors, maxillary incisors, canines, maxillary first premolars). Regarding color change, mandibular and maxillary incisors did not statistically differ from each other but showed significant difference and greater bleaching potential compared to canines and maxillary first premolars (P = .018). Regarding sensitivity, mandibular and maxillary incisors presented the highest spontaneous sensitivity values (P = .032), while maxillary first premolars did not display painful symptoms, also observed in provoked sensitivity analysis (P = .025). The general analysis of the results indicates that the tooth type affects the response to the whitening treatment, both in relation to the esthetic benefit and the occurrence of tooth sensitivity. It was observed that mandibular incisors reach the degree of chromatic saturation before canines and premolars, in addition to presenting greater bleaching sensitivity. Personalizing the treatment, based on prior knowledge of the degree of saturation, anatomical factors, and the risk of sensitivity, can provide considerable advantages in the whitening technique.

Genome-wide epistasis analysis reveals significant epistatic signals associated with Parkinson's disease risk.

Genome-wide association studies (GWAS) have increased our understanding of Parkinson's disease (PD) genetics by identifying common disease-associated variants. However, much of the heritability remains unaccounted for and we hypothesized that this could be partly explained by epistasis, the statistical interaction between two or more genetic variants. Here, we developed a genome-wide non-exhaustive epistasis screening pipeline called Variant-variant interaction through variable thresholds (VARI3) and applied it to diverse PD GWAS cohorts. We used 14 cohorts of European ancestry (14,671 cases and 17,667 controls) as a discovery stage, identifying 14 significant candidate variant-variant interactions. We then used four independent cohorts (13,377 cases and 413,789 controls) as replication stage, successfully replicating three epistasis signals located nearby SNCA and within MAPT and WNT3. Admixture analysis showed that the epistatic effect on PD of those variants at these loci was observed in both European ancestry and Native American ancestry individuals. We assessed the functional impact of the epistasis signals across a range of functional/-omics datasets identifying significant single-variant eQTLs across brain tissues, epistasis eQTL signals in whole-blood, PD-relevant pathways and ontologies, and chromatin interactions between the regions of the interacting SNPs. In conclusion, we identified and replicated novel epistatic signals associated with PD risk across multiple diverse genetic ancestry cohorts, highlighting their enrichment in pathways relevant to Parkinson's disease.

Room‐Temperature‐Integrated Flexible Diffusive Oxide Memristors for Artificial Nociceptive Systems

AbstractNovel areas of human–computer interaction and intelligent robotics are rapidly boosted by flexible electronic devices, among which artificial nociceptors for pain perception are considered essential sensory devices. Recently, flexible diffusive memristors with volatile behavior are extensively studied to emulate biological synapses and neurons because of their rich threshold–switching dynamics, which are crucial for nociceptive functions. Herein, flexible diffusive memristors with an Ag/TaO1.8/TaO2.7/TaO1.8/Ag (ATTTA) structure are fabricated at room temperature, demonstrating a high switching ratio of 107, an ultra‐fast turn‐on speed of 40 ns, a long endurance up to 104 sweep cycles, low positive and negative threshold variation rates (6.7%/7.8%) for 50 devices, a good thermal stability at an 85 °C measurement temperature, and a high flexibility under an 8 mm bending radius. These high performances are attributed to the oxygen‐rich TaO2.7 layer, which can prevent the excessive migration of Ag atoms. Moreover, multiple biological nociceptor functions, including threshold firing, no adaptation, and sensitization, are successfully emulated in the flexible ATTTA diffusive memristor. Accordingly, the human–skin nociceptive system is successfully emulated by integrating ATTTA diffusive memristor array with force–sensing resistors (FSRs), which enables stereoscopic injury perception of inhomogeneous stimuli.

Simulating Organic Thin Film Transistors Using Multilayer Perceptron Regression Models to Enable Circuit Design

AbstractThere is increasing interest in using specialized circuits based on emerging technologies to develop a new generation of smart devices. The process and device variability exhibited by such materials, however, can present substantial challenges for designing circuits. Three models are considered here: a physical compact model, an empirical look‐up table, and an empirical surrogate model based on a multilayer perceptron (MLP) regression. Each one is fit to measurements of discrete organic thin film transistors in the low voltage regime. It is shown that the models provide consistent results when designing artificial neuron circuits, but that the MLP regression provides the highest accuracy and is much simpler to fit compared to the compact model. The targeted technology exhibits non‐ideal behavior such as variable threshold voltage and hysteresis. Using the MLP regression model, the effect of such variability on the performance of an artificial neuron circuit is compared. It is found that these effects alter the neuron firing rate and change the time spent in the on/off states but do not change the basic operation.

Genotypic variability in cotton's transpiration response under progressive soil drying.

Crop yields in food and fibre production systems throughout the world are significantly limited by soil water deficits. Identifying water conservation mechanisms within existing genotypes is pivotal in developing varieties with improved performance in water-limited conditions. The objective of this study was to screen Australian germplasm for variability in the transpiration response to progressive soil drying using a glasshouse dry-down experiment. It tests the hypothesis that water conservation traits may provide tolerance to water stress, particularly when combined with other drought stress traits. Three glasshouse experiments were conducted to identify whether there are differences in the fraction of transpirable soil water (FTSW) threshold values for transpiration decline among six cotton genotypes. We also assessed whether genotype dependent responses to progressive soil drying are evident from leaf-level physiology, by measurement of gas exchange parameters. Significant variation in the FTSW threshold for transpiration decline between six genotypes was found, ranging from 0.13 to 0.29. Genotypic variation in the response to soil drying was also observed from leaf level physiology, with reductions in stomatal conductance and photosynthetic rate coinciding with when the FTSW threshold was reached. Genotypes that limit transpiration at high FTSW can conserve water earlier in the season to maintain productivity during extended dry periods. Therefore, these genotypes may provide physiological traits that improve productivity in water-limited environments. This research is important as rainfall and water resources for irrigated agriculture are predicted to decline. The development of drought tolerant germplasm for the Australian cotton industry will be beneficial in the projected increasingly frequent limited water environments resulting from a changing climate.

Effect of depth ratio on frozen wave instability in immiscible liquids

In the present experimental work, a two-liquid system with comparable densities (density ratio = 0.543) undergoing lateral sinusoidal oscillation [along the length (L) of the container] has been considered. Under high forcing frequency condition (ω≫ν/L2), frozen waves appear at the interface of two-liquid system. Different depth ratios (hr) (or filling levels of liquid-1 and liquid-2) have been used to investigate the variations in the instability thresholds, wave amplitudes, wavelengths, and wave profiles of the frozen waves. Instability threshold of frozen waves shows unidirectional nature with varying depth ratios. A coefficient of relative velocity (c1) has been used to non-dimesionalize the variation of threshold amplitude (Aoc) and wave amplitude (Ar) with respect to depth ratios. Variation of normalized wave amplitude with modified Froude number shows existence of two regimes viz. gravity and surface tension dominated beyond which waves become three dimensional. A significant variation in wave profiles has been observed at high amplitude of forcing and reported in terms of radius of curvature. The three-dimensional frozen waves with temporally and spatially periodic doubling have been discussed.

Small-scale spatial variability in erosion threshold and bedform for cohesive sediment measured by 3D Sonar

Small-scale spatial variability in erosion threshold and bedform for cohesive sediment measured by 3D Sonar

Constrained Independent Vector Analysis with Reference for Multi-Subject fMRI Analysis.

Independent component analysis (ICA) is now a widely used solution for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Independent vector analysis (IVA) generalizes ICA to multiple datasets (multi-subject data). Along with higher-order statistical information in ICA, it leverages the statistical dependence across the datasets as an additional type of statistical diversity. As such, IVA preserves variability in the estimation of single-subject maps but its performance might suffer when the number of datasets increases. Constrained IVA is an effective way to bypass computational issues and improve the quality of separation by incorporating available prior information. Existing constrained IVA approaches often rely on user-defined threshold values to define the constraints. However, an improperly selected threshold can have a negative impact on the final results. This paper proposes two novel methods for constrained IVA: one using an adaptive-reverse scheme to select variable thresholds for the constraints and a second one based on a threshold-free formulation by leveraging the unique structure of IVA. Notably, the proposed algorithms do not require all components to be constrained, utilizing free components to model interferences and components that might not be in the reference set. We demonstrate that our solutions provide an attractive solution to multi-subject fMRI analysis both by simulations and through analysis of resting state fMRI data collected from 98 subjects - the highest number of subjects ever used by IVA algorithms. Our results show that both proposed approaches obtain significantly better separation quality and model match while providing computationally efficient and highly reproducible solutions.

Using machine learning to reveal seasonal nutrient dynamics and their impact on chlorophyll-a levels in lake ecosystems: A focus on nitrogen and phosphorus

Chlorophyll-a (Chl-a) is a pivotal indicator of lake eutrophication. Studies examining nutrients limiting lake eutrophication at large scales have traditionally focused on summer and autumn, potentially limiting the applicability of their findings. This study encompasses 86 state-controlled points in the Eastern China Basin, spanning data collected from January 2020 to July 2023. Furthermore, we focus on the application of three machine-learning models (i.e., eXtreme Gradient Boosting, Support Vector Machines, and Naive Bayes Classifier) to analyze the seasonal nutrient dynamics in lake ecosystems. We categorized the monitoring data by season to eliminate outliers and employed adaptive synthetic sampling to address data imbalance issues. The results reveal that the direct correlations between total nitrogen (TN), total phosphorus (TP), and TP in conjunction with turbidity and Chl-a are broadly weak, possibly because of geographic variations, nutrient lag effects on algae, and differences in algal community composition. However, probabilistic analyses revealed that as TP or TN levels transitioned from oligo-mesotrophic (O) to eutrophic (E), TP exhibited a greater influence on the variation in Chl-a status than TN during spring and winter (p < 0.05). Conversely, the effects of TP and TN on Chl-a (O-E) were comparable during summer and autumn. Seasonal variations in TN and TP thresholds derived from XGBoost modeling for O and E states of Chl-a suggest the need for stricter control measures during periods of high-nutrient levels and cost-effective management strategies to employ during low-nutrient periods. These findings should enhance our understanding of trophic shifts in lakes and provide a foundation for optimizing eutrophication management strategies across all seasons.

Global Terrestrial Water–Energy Coupling Across Scales

ABSTRACTTerrestrial water–energy coupling (WEC), in the form of a non‐linear relationship between Soil Moisture (SM) and evaporative fraction (EF, ratio of actual and potential evapotranspiration), controls critical ecohydrological processes. We investigate and parameterize the evolution of global SM–EF coupling from the field to remote‐sensing (RS)‐pixel. The field‐scale EF and SM were obtained from eddy covariance (EC) and SM sensors at the global FLUXNET and Texas Water Observatory sites. RS‐pixel‐scale EF and SM estimates were obtained from Moderate‐resolution Imaging Spectroradiometer (MODIS) and Soil Moisture Active Passive (SMAP) sensors, respectively. We estimate the effective thresholds of the WEC regimes from both EC and satellite datasets to highlight the influence of sub‐pixel‐scale heterogeneity, and, scaling and observational constraints on the evolution of WEC regimes from the field to RS‐pixel scale. We argue that the changes in land surface conditions add a temporal variability in the critical thresholds of terrestrial WEC at RS pixel scale. We compare the critical WEC thresholds of the water‐ and energy‐limited regimes with a SM drydown‐based approach and highlight the similarities between both methods in partitioning dominant WEC regimes. EF and SM are strongly coupled in dryland arid and semi‐arid regions compared to humid climates. WEC regimes and thresholds have strong interseason variability due to dynamic interactions between soil, vegetation and atmosphere at the RS‐pixel scale. In contrast, field‐scale SM‐EF coupling is influenced predominantly by soil conditions and land‐use/management practices. Hence, future development of Earth‐system/Land‐surface models must account for the inter‐scale differences in the coupling between terrestrial water and energy fluxes representative of the ‘effective’ processes at large spatial scales.

The Protocol for Active Movement Extent Discrimination Assessment (AMEDA) is Reliable When Shortened From 50 to 25 Stimuli to Reduce Testing Fatigue.

Active movement extent discrimination assessment (AMEDA) is a psychophysical task that evaluates proprioception and tactile acuity of the lower limbs, and it is a method of determining sensorimotor ability. Sensorimotor ability is the ability to judge small differences in movement tasks through the process of receiving sensory messages (sensory input) and producing a response (motor output). Participant attention lapses in prior psychophysical studies have been implicated as a cause for increased measurement variance thresholds in these types of assessments. Since minimizing the time needed for the AMEDA may help to reduce attention lapses, we compared the reliability of the 50-repetition AMEDA protocol (Group 1) with that of a 25-repetition protocol (Group 2). We assessed the split half reliability of these two approaches, using the Spearman-Brown Adjusted Pearson correlation (r). For each method, we calculated Bland-Altman Plots and Intra Class Correlation Coefficients to compare the reliability of the two data sets and determine the 95% confidence intervals. Split-half test re-test Spearman-Brown Adjusted Pearson r (rfull) was Group 1 rfull = 0.83 and Group 2 rfull = 0.85. The Bland-Altman Plots indicated only a small degree of bias from the zero-difference line, with 95% of the difference points lying within the limits of agreement. For Group 1, the intraclass correlation coefficient (ICC) two-way, agreement was 0.83 (95% CI 0.54-0.93) and for Group 2, the ICC, two-way, agreement, was 0.85 (95% CI 0.66-0.93). The MDC90 for Group 1 was 0.082 AUC units and for Group 2, it was 0.086 AUC units. The combined data for Group 1 plus Group 2 Bland-Altman Plot indicated only a small degree of bias from the zero-difference line, with 95% of the difference points lying within the limits of agreement. The MDC90 for the combined groups was 0.08 AUC units. The multiple methods from previous research assessing test re-test reliability that we applied to our two data sets indicate that the 25-response AMEDA was a reliable system for evaluating sensorimotor function in the lower limbs and may be an alternative for the more traditional 50-response protocol in which lapses in participant attention from fatigue or other biases may be a concern. There are also practical advantages in time restricted athletic screenings to a shorter administration of this assessment.

Functional biogeography of the thermal thresholds for post-dispersal embryo growth in Conopodium majus.

Plant regeneration by seeds is driven by a set of physiological traits, many of which show functional intraspecific variation along biogeographic gradients. In many species, germination phenology depends on a germination delay imposed by the need for post-dispersal embryo growth (a.k.a. morphological dormancy). Such growth occurs as a function of environmental temperatures and shows base, optimum and ceiling temperatures (i.e. cardinal temperatures or thermal thresholds). However, the biogeographical variation in such thresholds has not been tested. We used a thermal time approach and field experiments to assess intraspecific variation at the continental scale in the embryo growth thermal thresholds of the geophyte Conopodium majus (Apiaceae) across its distribution from the Iberian Peninsula to Scandinavia. Thermal thresholds for embryo growth varied across the latitudinal gradient, with the estimated optimum temperatures between 2.5 and 5.2 ºC, ceiling temperatures between 12 and 20.5 ºC and base temperatures between -6.6 and -2.7 ºC. Germination in the field peaked in the months of January and February. The limiting factor for embryo growth was the ceiling temperature, which was negatively correlated with latitude and the bioclimatic environment of each population. In contrast, the optimal and base temperature were independent of local climate. These results indicate that thermal thresholds for embryo growth are functional ecophysiological traits that drive seed germination phenology and seed responses to soil climatic environment. Therefore, post-dispersal embryo growth can be a key trait impacting climate change effects on phenology and species distributions.

Influence of pump spectrum on stimulated Brillouin scattering part 1: analysing SBS threshold

An expression for the threshold power of stimulated Brillouin scattering (SBS) in optical fibres is derived for pumps with arbitrary power spectra. It is compared to spectrally independent approximations for the SBS threshold and is found to be in close agreement when the assumptions used for the approximations are applied. This expression is then used to examine the variation of the SBS threshold with pump power spectrum. The analysis of the results shows a significantly lower SBS threshold than that predicted by the more commonly used expressions. Additionally, a connection is made between decreasing the pump power spectral density, which is dependent on spectral shape of the pump, and increasing the SBS threshold power.

Prevent hypoglycaemia when using automated insulin delivery systems in type 1 diabetes requires near normal glycaemic variability

AimAlthough newer technologies of insulin delivery in type 1 diabetes have facilitated an improvement in glycaemic control the risk of hypoglycaemia remains a threat. Therefore, it is important to define the thresholds of glycaemic variability below which the risk of hypoglycaemia can be eliminated or at least minimized. MethodsRandomized controlled trials conducted from 2017 to 2023 comparing Sensor-Augmented-Pumps and Augmented Insulin Delivery Systems (n = 16 and 22 studies, respectively) were selected. A weighted linear model of regression was used to compute the relationship between glycaemic variability and times spent below glucose range. The intercepts of regression lines with the abscissa axis (time below range = 0 %) defined the glycaemic variability thresholds. ResultsPositive relationships were observed between the 2 metrics. The scatter plots indicated that the times spent below range never reached the value of 0 % and that the glycaemic variability never fell below 28 %. By extrapolating the regression lines, the glycaemic variability at intercepts with time below range < 70 mg/dL of 0 % was 30.1 % with sensor augmented pumps and 18.9 % with automated insulin delivery. For a time below range < 54 mg/dL of 0 % the respective glycaemic variability values were 32.7 % and 19.9 % (with sensor augmented pumps and automated insulin delivery, respectively). ConclusionsImportantly, glycaemic variability targets and ambient hyperglycaemia are interdependent. Users of automated insulin delivery need to reach a glycaemic variability of 18 % to 20 % to minimize or eradicate the risk of hypoglycaemia. Such values are those observed in healthy non-diabetic people.

Top 10+1 indicators for assessing forest ecosystem conditions: A five-decade fragmentation analysis

Globally, land use change has consistently resulted in greater losses than gains in aboveground biomass (AGB). Forest fragmentation is a primary driver of biodiversity loss and the depletion of natural capital. Measuring landscape characteristics and analyzing changes in forest landscape patterns are essential for accounting for the contributions of forest ecosystems to the economy and human well-being. This study predicts national forest distribution for 2036 and 2054 using a Cellular Automata (CA) system and assesses ecosystem conditions through landscape metrics at the patch, class, and landscape levels. We calculated 130 metrics and applied a Variance Threshold method to remove features with low variance, testing different thresholds. The first filtered-out metrics were further analysed through Principal Component Analysis combined with a Feature Importance technique to select and rank the top 10 indicators: effective mesh size, splitting index, mean radius of gyration, largest patch index, mean core area, core area percentage, Simpson's evenness index, mutual information, Simpson's diversity index, and mean contiguity index. The eleventh selected indicator is the AGB density, a structural measurement for ecosystem condition and a proxy for forest carbon storage and sequestration assessments. From 2000 to 2018, the national AGB forest carbon stock decreased from 131.5 to 91.3 Megatons (Mt) with expected values for 2036 and 2054 being 71.8 and 55.3 Mt., respectively. Landscape measurements quantitatively describe forest dynamics, providing insights into the structure, configuration, and changes characterizing landscape evolution. This research underscores the capability of CA models to map large-scale forest resources and predict future development scenarios, offering useful information for conservation and environmental management decisions. Additionally, it provides measurements to support Ecosystem Accounting by assessing forest extent and indicators of its conditions.