Search Results (485)

As an important component of forest ecosystem processes, leaf litterfall plays a key role in nutrient cycling and ecosystem functioning. However, monitoring litterfall dynamics in subtropical forests remains challenging due to complex community structures and asynchronous leaf phenology, which limit the applicability of remote sensing approaches developed for temperate forests. As a critical linkage between vegetation and soil carbon pools, leaf litterfall directly influences forest carbon sequestration by providing carbon inputs in the form of litter. Unlike the concentrated autumn leaf fall in temperate forests, subtropical forests exhibit complex community structures with concurrent leaf abscission and new leaf growth, limiting the applicability of temperate-focused remote sensing techniques. To address this, we collected annual leaf litterfall data from 18 plots in eastern China’s subtropical forests and integrated these with high-resolution Sentinel-2 imagery using supervised machine learning models to develop a novel monitoring method. Our results indicated that subtropical forests exhibited clear seasonal leaf litterfall peaks during spring, summer, and autumn. Sentinel-2 satellite imagery combined with supervised machine learning algorithms can effectively monitor forest leaf litterfall dynamics. Temporal models, which use multi-date monthly spectral differences (R²_adj = 0.70, RMSE = 0.46, RPD = 1.86), significantly outperformed instantaneous models based on single-date canopy states (R²_adj = 0.33, RMSE = 0.85, RPD = 1.24). Following variable selection, model performance improved, with R² increasing by more than 2% in most models and the number of variables reduced by over 44%. Robustness analysis indicated that the model was spatially robust (no significant bias among sites), and despite seasonal intercept differences, the slopes were consistent, enabling reliable tracking of litterfall dynamics. Among the examined spectral indices and canopy characteristics, those reflecting canopy greenness, pigments, and structure contributed over 65%, with WV-VI, MCARI2, and LAI being most influential. Incorporating drought-sensitive water indices and soil exposure-related mineral indices further enhanced model performance. These indices may partially reflect drought stress or seasonal canopy opening. Our findings provide a new method for monitoring leaf litterfall dynamics in structurally complex subtropical forests and offer a critical theoretical basis for accurately assessing leaf fall dynamics. Our findings provide a novel and effective method for monitoring leaf litterfall dynamics in structurally complex subtropical forests, improving seasonal litterfall assessment and supporting vegetation monitoring, with potential implications for ecosystem- and carbon-related studies. Full article

Traditional urban landscape evaluations have primarily relied on either objective spatial metrics, such as the Green View Index (GVI), or subjective human surveys, often failing to capture the complex mechanisms of human environmental perception. This study proposes a novel Explainable Artificial Intelligence (XAI) framework that integrates objective physical configuration with subjective cognitive assessment to predict human landscape preference. Utilizing 159 urban landscape images, we extracted physical features via semantic segmentation (SegFormer) and psychological perceptions via a zero-shot vision-language model (CLIP). Our hybrid Random Forest model successfully bridged these dimensions, achieving moderate yet promising predictive performance (Rsquare = 0.442). SHAP (Shapley Additive exPlanations) analysis revealed that psychological perceptions—specifically Safety (0.104), Fascination (0.096), and Tranquility (0.080)—outperformed traditional objective metrics like GVI (0.067) in determining overall preference, while sub-model interpretation linked these psychological responses to specific physical elements such as buildings, sky openness, low vegetation, and water bodies. The findings suggest that urban green space design should move beyond maximizing greenery quantity and instead prioritize spatial compositions that induce psychological security, visual interest, and restoration. The proposed framework offers a scalable and interpretable tool for human-centered landscape assessment, while acknowledging limitations related to sample size, cultural generalizability, pretrained model bias, and reliance on static two-dimensional imagery. Full article

Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in inconsistent and often unreliable assessments. While available accurate tissue analysis is destructive and costly. Nondestructive, in-field imaging techniques such as the normalized difference vegetation index (NDVI) exist but require expensive multispectral imaging systems. To address these limitations, this study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images. The experiment utilized ‘Yotsuboshi’ strawberries in a randomized complete block design with sufficient nitrogen (T1) and deficient nitrogen (T2) treatments. To mitigate ambient light variability, a key challenge in open-field phenotyping, a low-cost phenotyping cylinder was developed for standardized smartphone image acquisition. Rigorous four-stage annotation criteria were also introduced to classify the nitrogen status in strawberry leaves as NormalN, LowN, or AdvancedLowN, ensuring a high-quality novel dataset. A YOLO11 model trained on this dataset achieved precision, recall, and mAP50 values exceeding 99%. Subsequent testing using the phenotyping cylinder yielded a mAP50 of 87%. In-field validation without a phenotyping cylinder also demonstrated robust performance under diffuse cloudy conditions (82.7% mAP50), outperforming direct sunlight (79% mAP50). Moreover, the model’s classifications of ‘NormalN’ and ‘LowN’ statuses strongly corresponded with NDVI measurements, validating the accuracy of the RGB-based approach. This research demonstrates the significant potential of combining deep learning and phenotyping cylinder to create a rapid, low-cost, nondestructive and reliable tool for in-field nitrogen detection, with possible application across different crops and environmental conditions. Full article

Responsible resource development in Alberta requires the reclamation of disturbed lands to achieve equivalent land capability to pre-disturbance conditions. Vegetation assessments on reclaimed wellsites and oil sand exploration (OSE) sites currently rely on plots placed in areas deemed representative using professional judgement, which may introduce sampling bias. This study compared woody vegetation attributes derived from conventionally placed plots with those from randomly placed plots on certified reclaimed sites. Furthermore, increased sampling intensity was evaluated on a subset of sites. Site-level plot-based estimates were also compared with estimates from uncrewed aerial vehicle light detection and ranging (UAV-LiDAR) and airborne laser scanning (ALS). Woody stem density and height estimates from random and judgment-based plots were generally comparable; however, on sites with spatially heterogeneous recovery, judgment-based placement tended to overestimate woody stem density relative to larger-area sampling. LiDAR data captured spatial patterns of woody vegetation but underestimated stem densities, particularly on high-density, clustered sites. Full article

Spaceborne LiDAR systems, such as ICESat-2, provide critical data for global land cover and topography; however, their performance in rugged, vegetated landscapes requires rigorous local validation. This study evaluates the vertical accuracy of ICESat-2 ATL08 terrain height metrics in the complex Turkish Western Black Sea region, utilizing a reference dataset of high-precision terrestrial GNSS measurements. Following strict IQR-based outlier detection and photon density filtering, 1637 spatially matched segments were analyzed. The h_te_best_fit terrain height metric showed the best agreement with the terrestrial GNSS reference data, yielding an RMSE of 3.37 m and a mean bias of −0.42 m, indicating a slight underestimation of the terrain surface. The univariate analysis revealed a strong positive correlation between terrain slope and vertical error, indicating that slope is the prominent degradation factor contributing to pulse broadening. Additionally, dense forest cover was found to limit ground photon retrieval, leading to increased error margins, whereas nighttime acquisitions offered slightly improved precision. These findings suggest that while ATL08 is a valuable topographic source, slope-dependent corrections are essential for applications in mountainous environments. Full article

Plant diversity plays a fundamental role in ecosystem functioning and is essential for sustaining ecosystem services. National forest inventories are key instruments for assessing floristic diversity. However, their measurement protocols may introduce bias by omitting smaller individuals because of the stem diameter criterion used or the minimum plant size threshold applied. Such bias is exacerbated in dryland ecosystems where small-statured plants with low-branching stems are particularly abundant. In this study, we evaluated the effects of using basal diameter (BD) instead of diameter at breast height, and of sampling small individuals (BD ≥ 2.5 cm), on the estimation of abundance, alpha and gamma diversity and community composition in different vegetation types in NW Mexico. We found substantial underestimation due to the omission of smaller individuals in xeric shrubland and tropical dry forest, where gamma diversity may be underestimated by up to 209% and 139%, respectively. Broadleaf forest also showed strong underestimation (133%), whereas mixed conifer–broadleaf forests were unaffected. We discuss these differential effects and propose a methodology to attenuate this underestimation and achieve more accurate floristic diversity estimates from national forest inventories in dryland vegetation, which encompasses roughly one-third of the Earth’s surface and more than half of Mexico’s territory. Full article

Digital elevation models (DEMs) are foundational for critical tasks such as flood inundation simulation, disaster risk assessment, and ecosystem monitoring in coastal zones, yet their vertical accuracy is significantly compromised by complex terrain and surface characteristics. This study quantitatively decomposes the vertical errors of three 30 m global DEMs (COP30, NASADEM, and AW3D30) across the subtropical coastal region of Southeast China using ICESat-2 ATL08 data as a reference. By integrating an eXtreme Gradient Boosting (XGBoost) model with SHapley Additive exPlanations (SHAP), we successfully decoupled systematic biases from random noise. The results show that NASADEM achieved the lowest RMSE (7.775 m), followed by COP30 and AW3D30. While the Terrain Ruggedness Index (TRI) and categorically encoded Land Cover were identified as the universally dominant error drivers across all datasets, explainable analysis revealed distinct secondary mechanisms: X-band COP30 is notably susceptible to canopy height, exhibiting significant positive bias in forests exceeding 15 m; C-band NASADEM shows a systematic bias related to topographic position, typically overestimating ridges and underestimating valleys; and optical AW3D30 is significantly affected by stereo-matching errors. Furthermore, the analysis quantified a systematic error component of ~40%. These findings provide a data-driven basis for DEM selection and highlight that accuracy improvements should prioritize vegetation removal for radar DEMs and enhanced stereo-matching for optical models. Full article

Promoting pesticide reduction is a key step toward green vegetable production and ecological safety. Based on survey data collected from 356 leek growers in Weifang City—the largest facility-based vegetable production base in Shandong Province—this study empirically estimates the ecological compensation standard associated with pesticide-reduction behavior. The estimation employs a contingent valuation method (CVM) using non-parametric kernel density estimation for conditional value assessment, combined with the Heckman two-step model to address potential sample selection bias. The results show that 79.3% of respondents are willing to participate in an eco-compensation program for pesticide reduction; the main reason for refusal is “the higher reduction costs and lower profits”. The expected compensation level ranges from 614.94 to 620.57 yuan per mu (1 mu is approximately 0.165 acres) per year. Gender, share of Chinese chives (Allium tuberosum) income, trust in extension agents, and government penalties for excessive spraying significantly raise the required compensation, whereas age and knowledge of eco-compensation significantly lower it. Therefore, a sustainable compensation scheme co-driven by government and market should be established, combining cash, technical and in-kind support, and adopting tiered compensation schemes that reflect different reduction intensities. Full article

Leaf Area Index (LAI) is a fundamental parameter for characterizing the growth of tea (Camellia sinensis L.). However, in rugged mountainous regions, the combined effects of topographic relief and canopy structural heterogeneity severely constrain the accuracy of UAV-based multispectral LAI retrieval. This study develops an integrated framework combining topographic correction with interpretable machine learning to improve LAI estimation. We utilized a UAV multispectral dataset collected during the peak growing season from a typical tea-growing region in Fujian Province, China (altitude range: 58–186 m), comprising a total of 90 samples. Three topographic correction methods, including Sun–Canopy–Sensor (SCS), SCS with C correction (SCS+C), and Minnaert+SCS, were evaluated in combination with Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models. Results indicated that the SCS+C algorithm outperformed other methods by effectively accounting for direct and diffuse radiation components, thereby reducing topographic dependence while maintaining radiometric consistency across heterogeneous surfaces. The XGBoost model combined with SCS+C correction achieved the highest performance (R² = 0.8930, RMSE = 0.6676, nRMSE = 7.93%, MAE = 0.4936, Bias = −0.0836). SHapley Additive exPlanations (SHAP) analysis revealed a structure-dominated retrieval mechanism, in which red-band textural features (Correlation_R) exhibited higher importance than conventional vegetation indices. Compared with previous studies that primarily focus on either topographic correction or model development, this study provides quantitative insights into the underlying retrieval mechanisms. This framework improves the precision of tea LAI retrieval in complex terrains and provides a robust methodological basis for digital management in mountainous agriculture. Full article

This study examines land surface temperature (LST) variability over Malta, a small island in the central Mediterranean, using satellite observations compared with in situ near-surface air temperature (NSAT) measurements. The analysis focuses on the comparison between satellite-derived LST and local atmospheric thermal conditions for urban and rural land cover types. LST data from Landsat-8, MODIS (Terra and Aqua), and Sentinel-3A and 3B were analysed over a six-month period (September 2024 to February 2025). Monthly morning and evening field campaigns were conducted at 19 monitoring sites distributed across the island, during which NSAT, relative humidity, wind speed, and wind direction were recorded. Morning comparisons showed strong correlations between satellite-derived LST and in situ NSAT, i.e., Pearson’s correlation coefficient, r, in the range of 0.82–0.85. Landsat-8 exhibited a slight positive bias (+1.04 °C), while MODIS and Sentinel-3 Level-2 products showed negative biases (−3.82 °C and −1.89 °C, respectively). Nighttime comparisons revealed larger negative biases for MODIS (−6.91 °C) and Sentinel-3 (−6.89 °C). After empirical-based harmonisation, these discrepancies were reduced to near-zero mean bias, maintaining strong correlations. Spatial analysis indicated a persistent nocturnal urban heat island (UHI) effect, with urban areas retaining more heat than rural zones. Morning patterns showed seasonal modulation: during late summer and early autumn, rural areas exhibited higher surface temperatures due to sparse vegetation and exposed soils, whereas during cooler months the urban signal became more pronounced as vegetation recovery enhanced rural cooling. Overall, the results demonstrate the usefulness of multi-sensor satellite observations, interpreted alongside ground-based measurements for characterising thermal behaviour in small island environments. Full article

Forest fires release various chemical compounds that directly degrade air quality and endanger human health. This study examines the occurrence of forest fires in six Brazilian biomes over a 22-year period (1999–2021). The primary purpose is to identify significant meteorological predictors for the monthly number of hot spots using a standardized statistical framework. Fire hotspots were identified using satellite thermal sensors (AVHRR and MODIS), and we employed a standardized negative binomial regression modeling approach to analyze the relationship between meteorological variables and fire hotspots in all six Brazilian biomes simultaneously, providing a comprehensive comparative perspective often lacking in studies focused on isolated regions. The results show that the Amazon and Cerrado biomes have the highest absolute number of fires, which is consistent with their size and vegetation structure. To avoid bias associated with biome size, fire occurrence was additionally estimated using hotspot density normalized by biome area (hotspots per km²). Using these models, significant factors for fire occurrence were identified, namely the main meteorological variables—temperature, precipitation and wind speed. By comparing the performance of the models in different biomes, we aimed to better understand regional fire dynamics. The model’s ability to predict the expected number of fires based on these variables provides a key tool for preventive air quality monitoring. Such a predictive model serves as a basis for developing early warning systems, assessing potential health risks for the population, and adopting targeted fire management policies. Full article

Accurate wind field simulation in forest areas is crucial for wind energy development but remains challenging for traditional WRF models due to complex terrain and vegetation heterogeneity. This study proposes a multi-source optimization framework integrating seasonal PBL scheme selection, localized leaf area index (LAI) adjustment, and 3DVAR data assimilation to improve WRF performance in forested terrain. The framework was validated using observations at 20 m, 50 m, and 100 m heights in Maoershan forest area. Results show that: (1) PBL schemes exhibit significant seasonal dependence—YSU performs best in spring (unstable conditions), while MYJ shows slight advantages near the surface in winter (stable conditions). (2) Localized LAI correction reduces near-surface wind speed bias by 35% and improves wind direction accuracy by 28%, with stronger effects in summer. (3) 3DVAR assimilation further enhances accuracy, achieving correlation coefficients of 0.869 for wind speed and 0.813 for wind direction, with greater improvements in summer and near the surface. (4) Winter wind power density at 100 m reaches 475 W/m², 38% higher than summer, indicating stable exploitable resources. The proposed framework provides a replicable methodology for wind field simulation in forest regions worldwide. Full article

Background/Objectives: Disorders of Gut–Brain Interaction (DGBIs), particularly irritable bowel syndrome (IBS), are frequently underdiagnosed in clinical practice, contributing to a substantial hidden burden of disease. This study aimed to quantify this “symptomatic iceberg” by comparing the prevalence of formal IBS diagnoses with a broader symptom-based case definition in a clinical cohort. Methods: We conducted a cross-sectional analysis of 194 adult subjects from a gastroenterology clinic in Western Romania. Data on demographics, clinical diagnoses, self-reported symptoms, and eating behaviors were collected. For the case–control analysis, patients with confirmed organic gastrointestinal pathology or incomplete data were excluded. The final analytical sample consisted of 52 patients classified as having a functional DGBI phenotype and 84 asymptomatic controls without organic disease, while 58 were excluded from the analysis. Results: While only 4.4% (95% CI: 2.0–9.3%) of the cohort (N = 136) had a formal IBS diagnosis, 47.8% (95% CI: 39.6–56.1%) met criteria for an IBS-compatible symptom cluster, yielding an underdiagnosis ratio of 10.8. Neuro-vegetative symptoms such as sweating (19.1%) and dizziness (11.8%) were highly prevalent. In the case–control analysis, patients with a functional DGBI phenotype had a significantly higher mean BMI compared to controls (28.15 ± 6.49 vs. 24.47 ± 4.60 kg/m²; p = 0.001). DGBI cases were less likely to report regular snacking behavior (OR = 0.36; 95% CI: 0.18–0.74; p = 0.009), suggesting behavioral adaptation. A sensitivity analysis excluding participants with CRP > 10 mg/L (n = 98) confirmed the robustness of these associations, indicating that minor systemic inflammation did not bias the primary findings. Full article

Tropical cyclones rank among the most destructive natural hazards globally, posing significant threats to coastal ecosystems and communities. Mangrove forests, renowned for their ecological importance and coastal protection services, are vulnerable to these disturbances, suffering structural damage, habitat loss, and disruption of vital ecosystem functions. Conventional field-based assessment methods often fall short in capturing the rapid and widespread impacts of cyclones, particularly in remote or cloud-obscured regions. This review aims to provide a comprehensive synthesis of remote sensing applications for monitoring cyclone-induced impacts on mangrove and coastal ecosystems worldwide. Through a systematic literature review of 74 peer-reviewed articles from 1990 to 2025, the study evaluates the utility of optical sensors, radar systems, and multi-sensor platforms in assessing inundation, vegetation damage, and ecosystem service loss. Key methodological advances such as time-series analysis, machine learning, and UAV-based validation are highlighted, alongside critical gaps including limited geographic coverage, weak validation practices, and minimal socio-economic integration. Notably, 75.4% of reviewed studies are concentrated in Asia, with Bangladesh and India alone accounting for 44.6% of the total literature, underscoring a pronounced geographic bias. The findings underscore the need for robust, near-real-time monitoring frameworks that combine satellite technologies with ground data and community engagement. Ultimately, the review advocates for an integrated, multi-sensor, and participatory approach to cyclone resilience, offering valuable insights for future research, disaster response planning, and sustainable mangrove management. Full article

Active Channel Width (ACW) provides a robust indicator for tracking river corridor dynamics, yet automated extraction from multisensory imagery remains limited by spatial and temporal variability in spectral conditions. We developed and validated a workflow in Google Earth Engine (GEE) to delineate the active channel using multispectral indices derived from annual composite Landsat and Sentinel-2 imagery. The indices include the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI). The 34 km study segment of the Lircay River (Chile) served as a demonstration site undergoing substantial geomorphic change over a 20-year period (2003–2023) that spanned a decade-long mega drought (2010–2023) and two major floods (2006, 2023). Multispectral index thresholds were calibrated using manually digitized active channel polygons for a reference year and validated for five different years within the study period to assess their spatial transferability across reaches and temporal stability under varying hydrologic regimes. Sentinel-2 annual composites with the MNDWI-EVI pairing achieved the highest overall accuracy in estimating ACW (mean Kling-Gupta Efficiency = 0.72; Percent Bias = 12.69 across study reaches). Threshold values were tested at the cross-sectional and reach scales. Using cross-section-specific thresholds enhanced the accuracy of ACW estimation, indicating that threshold performance is strongly conditioned by the local characteristics present in the immediate surroundings of each cross section. These results suggest that spectral threshold selection is sensitive to small scale factors that vary across the river corridor, underscoring the need to explicitly consider local geomorphic and ecological conditions when defining thresholds. This reproducible, open-source workflow links automated channel delineation with cross-section-based morphology and explicitly quantifies uncertainty from spatiotemporal spectral variability. It enables high-resolution, repeatable measurements of river corridor change and underscores the need to consider evolving spectral and vegetation conditions when interpreting remotely sensed geomorphic indicators. Full article