Search Results (1,464)

This study analyzes the establishment of Local Agri-Food Systems (LAFSs) in the triple-border region between the states of Minas Gerais, Rio de Janeiro, and São Paulo, by identifying and mapping potential areas of primary peasant agri-food production. An integrated analysis of data sources was treated, processed, and integrated into a common spatial support. Land use and land cover data were used from demographic and agricultural censuses, from the Rural Environmental Registry, agrarian reform settlement projects and conservation units. Our study revealed that 23.73% of the regional area has potential for peasant production, identifying four regions that stand out in terms of this potential. The area presented livestock and animal husbandry as the main agri-food chain, with potential for processing within the territory itself, in addition to extractive activities in the Atlantic Forest biome. The results indicate that there are possibilities for the establishment of LAFSs as a local development strategy associated with social inclusion and environmental responsibility, although there is a need to expand and strengthen the transportation and marketing channels for products from these short chains. The cartographies produced aim to contribute as auxiliary instruments to land use planning and management, seeking to strengthen LAFSs at different scales of governance. Full article

Forested areas in Poland comprise numerous post-mining sites that hinder effective forest management. Such mining remnants may pose a threat to humans, animals, and operating forest machines. This study aimed to determine the feasibility of inventorying such man-made landforms as mining waste heaps, excavations, remnants of shallow shafts, adits, etc., using the Digital Elevation Model (DEM) based on Airborne Laser Scanning (ALS) data provided by the national agency (the Head Office of Geodesy and Cartography—HOGC) as open data. The DEM, when combined with other cartographic materials using GIS, accurately reflects the anthropogenic transformation evident in the topography. This paper presents the results of inventorying remnants of iron ore mining in the present-day forested area located between Krzepice, Kłobuck, and Częstochowa in southern Poland. The identification and inventory of post-mining landforms, mainly mounds resulting from shallow shaft mining operations, were supplemented by their digitization, automatically providing information on parameters such as perimeter (ranged in most cases from 24.3 to 159 m), surface area (46.9 to 1656 m²), length and width (7.8 to 59.2 m). The heights of the investigated structures were also read from the DEM, ranging from 0.3 to 4.1 m. Much larger structures were also identified, but they occurred accidentally (up to 23.5 m in height). In this manner, approximately 823 morphological forms were characterized, resulting in a database. Test fieldwork was then conducted to verify the DEM readings. It was proposed to calculate deformation indexes (Id [%]) for forested areas and apply them when estimating the forest management hindrance index used by the State Forests. The studied forest compartments managed by State Forests were characterized by an Id value from 0.1 to 55.5%. This type of measure provides a helpful tool in planning forestry operations in areas with diverse topography, including those transformed by mining activities. The actual environmental impact is highlighted. Forest management practices in the study area must take into consideration, in particular, topography, as well as geology and hydrology. Studies have shown that the DEM based on the ALS data is sufficiently accurate to detect even minor post-mining deformations (which may be important, in particular, in inaccessible areas). The recorded parameters can be considered when planning management, protection interventions, or reclamation activities. Full article

Despite the growing interest in physiological stress monitoring, an objective measure of stress is currently lacking, especially in clinical and rehabilitation contexts. With the emerging integration of artificial intelligence (AI) in data analytics, heart rate variability (HRV) has gained attention as an effective biomarker; however, the literature remains fragmented across disciplines, stress types, and methodological approaches. This scoping review aims to investigate how AI techniques are applied to HRV analysis for stress detection and prediction in adult populations. Although this review does not focus on a specific subtype of stress, its primary objective is to explore the current methodological state of the art as reported in the literature, without restrictions on stress typology. Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Scopus, and Google Scholar for studies published between 2005 and 2025, using MeSH terms including “HRV”, “Rehabilitation”, “SCI” (for Spinal Cord Injury), “Stress”, “Sympathetic”, “Parasympathetic”, “Non-linear”, “Gamification”, “AI” and “Machine Learning”. Inclusion criteria targeted adult human populations and studies employing HRV features as input for AI and machine learning techniques for psychophysical stress assessment. Of the 566 records identified, 15 studies met the eligibility criteria. The reviewed studies exhibit substantial heterogeneity in terms of settings, populations, sensors, and algorithms with most employing supervised methods (e.g., random forest, support vector machine), alongside several applications of deep learning and explainable AI. Only one study focused specifically on physiological stress, none focused on SCI populations, and rehabilitation-related research was scarce, thereby underscoring important gaps in the current literature. Overall, HR variability analysis, especially when combined with artificial intelligence techniques, represents a promising approach for stress assessment; however, the field is methodologically fragmented and clinically underdeveloped in critical areas, underscoring the need for a multidisciplinary methodological framework. Full article

SoilMERGE (SMERGE) is a 0.125-degree root zone soil moisture (RZSM) product (0 to 40 cm depth) covering the contiguous United States. The study area included most of Oklahoma and Kansas, a region where SMERGE exhibited superior performance. The time frame examined was the warm season from 2008 to 2019. In this study, evaluation of a prototype downscaled (500 m) version of SMERGE was made using (1) Ranked correlation (R²) benchmarking against Normalized Difference Vegetation Index (NDVI) datasets and (2) Ranked correlation (R²) analysis of antecedent RZSM with storm-event streamflow across a range of precipitation intensities (5 to >35 mm/day) at a watershed scale. In the NDVI benchmarking, all three downscaled products outperformed (0.52 to 0.59) default SMERGE (0.44). EXtreme Gradient Boosting (XGB) and Gradient Boost recorded a higher ranked correlation (0.59) than Random Forest (0.52). Within the study area, ranked correlation analysis of antecedent RZSM with storm-event United States Geological Survey streamflow was examined in five watersheds. For the most intense storm events (>35 mm), antecedent XGB downscaled SMERGE (0.64) outperformed antecedent streamflow (0.43) and all other versions of SMERGE (0.52 to 0.56) as a predictor of storm event response. The results of this study demonstrated broad-scale benefits of Machine Learning-assisted downscaling, providing proof of concept for the development of state-based SMERGE products across the US Great Plains. Full article

Leaf area index (LAI) is a pivotal biophysical parameter linking vegetation physiological processes and macro-ecological functions. Accurate large-scale LAI estimation is indispensable for agricultural management, climate change research, and ecosystem modeling. However, existing methods fail to efficiently extract integrated spatial-spectral-temporal features and lack targeted modeling of spatio-temporal dependencies, compromising the accuracy of LAI products. To address this gap, we propose STC-DeepLAINet, a Transformer-GCN hybrid deep learning architecture integrating spatio-temporal correlations via the following three synergistic modules: (1) a 3D convolutional neural networks (CNNs)-based spectral-spatial embedding module capturing intrinsic correlations between multi-spectral bands and local spatial features; (2) a spatio-temporal correlation-aware module that models temporal dynamics (by “time periods”) and spatial heterogeneity (by “spatial slices”) simultaneously; (3) a spatio-temporal pattern memory attention module that retrieves historically similar spatio-temporal patterns via an attention-based mechanism to improve inversion accuracy. Experimental results demonstrate that STC-DeepLAINet outperforms eight state-of-the-art methods (including traditional machine learning and deep learning networks) in a 500 m resolution LAI inversion task over China. Validated against ground-based measurements, it achieves a coefficient of determination (R²) of 0.827 and a root mean square error (RMSE) of 0.718, outperforming the GLASS LAI product. Furthermore, STC-DeepLAINet effectively captures LAI variability across typical vegetation types (e.g., forests and croplands). This work establishes an operational solution for generating large-scale high-precision LAI products, which can provide reliable data support for agricultural yield estimation and ecosystem carbon cycle simulation, while offering a new methodological reference for spatio-temporal correlation modeling in remote sensing inversion. Full article

Wildfires can drastically alter ecological landscapes in just a few days, while it takes years of post-fire recovery for vegetation to return to its former pre-fire state. Assessing changes in vegetation can help with understanding how the hydrological components in the wildfire-affected areas contribute to potential vegetation shifts. This case study of the Los Angeles Bobcat Fire in 2020 uses Google Earth Engine (GEE) and Python 3.10.18 to access and visualize variations in Difference Normalized Burn Ratio (dNBR) area, Normalized Difference Vegetation Index (NDVI), and OpenET’s evapotranspiration (ET) across three dominant National Land Cover Database (NLCD) vegetation classes and dNBR classes via monthly time series and seasonal analysis from 2016 to 2024. Burn severity was determined based on Landsat-derived dNBR thresholds defined by the United Nations Office for Outer Space Affairs UN-Spider Knowledge Portal. Our study showed a general reduction in dNBR class area percentages, with High Severity (HS) dropping from 15% to 0% and Moderate Severity (MS) dropping from 45% to 10%. Low-Severity (LS) areas returned to 25% after increasing to 49% in May of 2022, led by vegetation growth. The remaining area was classified as Unburned and Enhanced Regrowth. Within our time series analysis, HS areas showed rapid growth compared to MS and LS areas for both ET and NDVI. Seasonal analysis showed most burn severity levels and vegetation classes increasing in median ET and NDVI values while 2024’s wet season median NDVI decreased compared to 2023’s wet season. Despite ET and NDVI continuing to increase post-fire, recent 2024 NLCD data shows most Forests and Shrubs remain as Grasslands, with small patches recovering to pre-fire vegetation. Using GEE, Python, and available satellite imagery demonstrates how accessible analytical tools and data layers enable wide-ranging wildfire vegetation studies, advancing our understanding of the impact wildfires have on ecosystems. Full article

Biological invasions are major threats to global biodiversity, and mapping their distribution is essential to prioritizing management efforts. The Pinaceae family (hereafter pines) includes invasive trees, particularly in Southern Hemisphere regions where they are non-native. These invasions can increase the severity of fires in wildland–urban interfaces (WUIs). We mapped pine invasion in the Bariloche WUI (≈150,000 ha, northwest Patagonia, Argentina) using supervised land cover classification of Sentinel-2 imagery with a Random Forest algorithm on Google Earth Engine, achieving 90% overall accuracy but underestimating the pine invasion area by about 25%. We then assessed in which main vegetation context pine invasions occurred relying on major vegetation units across the precipitation gradient of our study area. Invasions cover 2% of the study area, mainly in forests (61%), steppes (25.4%), and shrublands (13.4%). Most invaded areas (89.1%) are on private land; nearly 70% are on large properties (>10 ha), where state financial incentives could support removal. Another 13.5% occur on many small properties (<1 ha), where awareness campaigns could enable decentralized, low-effort control. Our land cover map can be developed further to integrate invasion dynamics, inform fire risk and behavior models, optimize management actions, and guide territorial planning. Overall, it provides a valuable tool for targeted, scale-appropriate strategies to mitigate ecological and fire-related impacts of invasive pines. Full article

For the development of a green future, managerial decision making at the local level plays an important role. The study is dedicated to the analysis of the current state of green areas, and development and rehabilitation of green areas in the territorial communities of Ukraine. The goal of the study is the development of a set of measures to create a sustainable green infrastructure at the local level in Ukraine. The main trends of green land policies by territorial communities were substantiated: keeping the natural afforestation of agricultural land; the development and rehabilitation of water conservation zones, windbreak belts, anti-erosion forests, green belts of inhabited areas, and nature conservation or recreation areas; and promoting gardening. A land reallotment methodology, which allows for the expansion of a spatial environment for the development and rehabilitation of green areas was suggested. The methods and approaches presented were tested in the Petrivska Territorial Community of Kyiv Region. The presented measures allow for an increase the green area of a territorial community by 1,084,352 m². The approach allows for the minimization of the condemnation of land from landowners, creates a more comfortable environment for the population, facilitates the effectiveness of agriculture due to containing the erosion, and conservation of natural landscapes. The research findings approved that the main challenges for the implementation of green policies are the acquisition of land for green areas in the environment of the historically established land use, and controlling the sustainable use of green areas and their surroundings responsibly to prevent their violation. Full article

Thinning is a key silvicultural practice for managing forests; however, its effects on stand growth and yield remain debated. This study evaluated the growth and yield responses to thinning in even-aged Quercus robur stands in Galicia (NW Spain) using data from three long-term thinning trials established between 1998 and 1999. A randomised complete block design was applied with four thinning intensities from below: control (C, 0% basal area removal), light (L, 15%), moderate (M, 35%), and heavy (H, 55%). Two complementary analytical approaches were implemented using linear mixed-effects models: a state-space approach examining post-thinning stand dynamics and a thinning-effect approach assessing the cumulative stand growth and yield, accounting for both standing and harvested components. The state-space analysis confirmed that thinning produced distinct stand structures in moderate and heavy treatments (M and H), with the largest differences observed in the stand basal area and trees per hectare, while the dominant height remained unaffected. In the thinning-effect approach, the cumulative basal area and volume—excluding and including mortality—followed the pattern L > C > M > H. Overall, the results indicate that light or moderate thinning intensities maintain stand yield and enable intermediate harvests. At the same time, although the mean diameter increased under more intensive thinning, differences in the dominant diameter—approximating potential future crop trees—were not significant, indicating that stronger thinning from below did not necessarily enhance the development of the dominant trees. Full article

The study examines the extent to which the concept of ecosystem services is reflected in Estonian forestry legislation and how local communities and interest groups perceive and prioritise these services. Using a three-step methodology, the analysis combined (1) a content analysis of key legal acts—including the Forest Act, Nature Conservation Act, and related regulations; (2) a qualitative review of 26 forest management proposals submitted by communities to the State Forest Management Centre between 2021 and 2024; and (3) a comparative synthesis of legislative and community perspectives in order to identify their main areas of convergence and divergence. The findings reveal that provisioning services, particularly timber production, are most explicitly regulated, while regulating and cultural services appear mainly through indirect references. Community expectations, however, emphasise regulating (44%) and cultural (30%) services—especially habitat conservation, recreation, and landscape aesthetics—over provisioning benefits (26%). This discrepancy highlights a structural imbalance between legal framework and societal values. The study concludes that a more systematic integration of ecosystem services into forest management practice and regulations is required to achieve a balanced approach that accounts for ecological, social, and economic dimensions. Full article

This study presents the development and validation of an in situ monitoring method for the laser direct energy deposition (DED) process, utilizing an integrated optical camera (720 HD, 60 fps) to analyze melt pool imagery. The approach is grounded in an experimental framework employing Taguchi orthogonal arrays, which ensures a stable dataset by controlling process variability and enabling reliable extraction of relevant features. The monitoring system focuses on analyzing brightness distribution regions within the melt pool image, identified as specific clusters that reflect external process conditions. The method emphasizes precise segmentation of the melt pool area, combined with automatic detection and classification of cluster features associated with key process parameters—such as focus distance, the number of deposited layers, powder feed rate, and scanning speed. The main contribution of this work is demonstrating the effectiveness of using an optical camera for DED monitoring, based on an algorithm that processes a set of melt pool identification features through computer vision and machine learning techniques, including Random Forest and HistGradient Boosting, achieving classification accuracies exceeding 95%. By continuously tracking the evolution of these features within a closed-loop control system, the process can be maintained in a stable, defect-free state, effectively preventing the formation of common process defects. Full article

Woody encroachment (WE) refers to the expansion of woody vegetation, particularly scrubs, into grasslands, altering ecosystem structure, function, and vegetation phenology. WE is especially pronounced in arid and semi-arid regions, where climate variability, land use, and ecological resilience interact strongly. Even though long-term monitoring of these dynamics in protected areas is essential to understanding landscape change and guiding conservation strategies, a few studies address this. The Flora and Fauna Protection Area (FFPA) Bavispe, a sky island in northwestern Mexico, provides an ideal setting to examine WE. Using remote sensing, we analyzed 30 years of land cover change (Landsat 5 TM and Landsat 8 OLI) in two reserve zones, Los Ajos and La Madera, and their 5 km buffer areas. Additionally, NDVI-based regressions (MODIS MOD13Q1) were applied to assess phenological responses across vegetation types. Classifications showed high accuracy (Kappa > 0.75) and revealed notable woody expansion: 960 ha of oak forest and 1322 ha of scrubland gained in Los Ajos, and 1420 ha of scrubland in La Madera. Grasslands declined by 2234 ha in Los Ajos and 1486 ha in La Madera, with stronger trends in surrounding buffers. Phenologically, the onset of the growing season was delayed by ~2 days per year in Los Ajos and ~3 days in La Madera. A generalized increment of woody vegetation in the region and the observed change in phenophases in selected land cover types indicated a shift in regional drivers (human or other ecological state factor) related to land cover distribution. Full article

Fire is a key driver of ecosystem dynamics under global change, and understanding its complex relationship with the climate system is crucial for regional wildfire risk management and the development of ecological adaptation strategies. The western United States is a critical region for studying fire–climate interactions due to its pronounced environmental gradients, diverse fire regimes, and high vulnerability to climate change, which together provide a robust natural laboratory for examining spatial variability in fire responses. Based on tree-ring fire-scar records systematically collected from five major ecoregions in the western United States via the International Tree-Ring Data Bank (ITRDB), this study reconstructed fire history sequences spanning 430–454 years. By integrating methods such as correlation analysis, random forest regression, superposed epoch analysis, and effect size assessment, we systematically revealed the spatial differentiation patterns of fire frequency and fire spatial extent across different ecoregions, quantified the relative contributions of key climatic drivers, and identified climatic anomaly characteristics during extreme fire years. The results indicate that: (1) there are significant differences in fire frequency between different ecological areas; (2) summer drought conditions (PDSI) are the most consistent and strongest driver of fire across all ecoregions, and ENSO (NINO3) also shows a widespread negative correlation; (3) random forest models indicate that the Sierra Nevada and Madrean Archipelago ecoregions are the most sensitive to multiple climatic factors, while fire in regions such as the Northern Rockies may be more regulated by non-climatic processes; (4) extreme fire years across all ecoregions are associated with significant negative PDSI anomalies with prominent effect sizes, confirming that severe drought is the dominant cross-regional precondition for extreme fire events. This study emphasizes the region-specific nature of fire–climate relationships and provides a scientific basis for developing differentiated, ecoregion-specific fire prediction models and prevention strategies. The methodological framework and findings offer valuable insights for fire regime studies in other global forest ecosystems facing similar climate challenges. Full article

Many Australian suburbs are threaded with open drainage networks. However, a preoccupation with drainage functions means that most of this drainage land delivers few liveability benefits to surrounding communities. As a result, numerous Local and State Governments are engaged in providing Living Stream upgrades to drainage land. Nonetheless, questions remain about where such improvements should be targeted for maximum benefit. In response, this paper documents a Delphi survey of experts and a related geospatial suitability analysis using a wide-ranging set of urban, societal, and environmental criteria to determine which areas of drainage land are most suitable for upgrades in Perth, Western Australia, a city which experiences a Mediterranean climate. The novelty of the paper’s contribution stems from the highly seasonal rainfall and related ephemeral summer hydrology distinguish Perth from many other cities where Water-Sensitive Urban Design is well-established. Moreover, the inclusion and evaluation of both tangible criteria (e.g., areas with a shortage of Public Open Space) and more intangible criteria (e.g., areas with population experiencing psychological distress) in the suitability analysis are comparatively rare. The results indicate that Living Stream-oriented Public Open Space should be deployed in areas with limited Public Open Space reserves, urban forest degradation, increasing urban densification, and Urban Heat Island challenges. Full article

Soil moisture is an essential environmental parameter affecting hydrological cycles, agricultural productivity, and climate systems. Conventional in situ measurements are precise but do not provide the spatiotemporal coverage for large applications. This research provides an extensive framework for estimating and mapping surface soil moisture by integrating Sentinel-1 Synthetic Aperture Radar (SAR) data with machine learning in the Google Earth Engine (GEE) cloud platform. The study area is the agricultural region of Perambalur district in Tamil Nadu State, India. The research took place between September 2018 and January 2019. The dual-polarized (VV and VH) Sentinel-1 C-band images were collected in tandem with ground truth soil moisture data collected through the gravimetric method. A set of SAR indices and engineered features were extracted from the backscattering coefficients (σ°). A random forest (RF) machine learning model was used in this study to estimate soil moisture. The RF model incorporating the complete set of engineered features showed a coefficient of determination (R²) of 0.694 and a root mean square error (RMSE) of 1.823 (Soil moisture %). The complete processing and modeling workflow was encapsulated in the GEE-based software tool (version 1) providing an accessible, user-friendly platform for generating near-real-time maps of soil moisture. This research proves that the combination of Sentinel-1 data with clever machine-learning algorithms in the GEE cloud platform provides a scalable, efficient, and potent tool for operational soil moisture mapping serving applications in precision agriculture and in the management of the water resource. Full article