Search Results (126)

Land use/cover (LUCC) change in arid oasis–desert ecotones has significant implications for spatial governance in ecologically fragile regions. To better capture the temporal and spatial complexity of land transitions, this study developed a vector tracing method by integrating time-series remote sensing data with vector-based transfer pathways. Analysis of northern Oman from 1995 to 2020 revealed the following: (1) Arable land and impervious surfaces expanded from 0.51% to 1.09% and from 0.31% to 0.98%, respectively, while sand declined from 99.03% to 97.01%. Spatially, arable land was concentrated in piedmont irrigation zones, impervious surfaces near coastal cities, and shrubland and grassland along the Al-Hajar Mountains, forming a complementary land use mosaic. (2) Human activities were the dominant driver, with typical one-way chains accounting for 69.76% of total change. Sand was mainly transformed into arable land (7_C1, 7_D1, 7_E1; where the first part denotes the original type, the letter denotes the year of change, and the last digit denotes the new type), impervious surfaces (7_C6, 7_D6, 7_E6), and shrubland (7_E4). (3) Water scarcity and an arid climate remained primary constraints, manifested in typical reciprocating chains in the oasis–desert interface (7_D1_E7, 7_A1_B7, 7_C1_D7) and in the arid vegetation zone along the Al-Hajar Mountain foothills (7_D3_E7, 7_C3_D7), together accounting for 24.50% of total change. (4) The region exhibited coordinated transitions among oasis, urban, and ecological land, avoiding the common conflict of cropland loss to urbanization. During the study period, transitions among arable land, impervious surfaces, forest, shrubland, and wetland were rare (Type 16: 3.31%, Type 82: 2.89%, Type 12: 0.04%, Type 18: 0.01%). The case of northern Oman provides a valuable reference for collaborative spatial governance in ecologically fragile arid zones. Future research should integrate socio-economic drivers, climate change projections, and higher-temporal-resolution data to enhance the applicability of the chain-spectrum method in other arid regions. Full article

Burned area (BA) mapping and fire severity assessment are essential for understanding fire occurrence patterns, formulating post-fire restoration strategies and evaluating vegetation recovery processes. However, existing BA datasets are primarily derived from coarse-resolution satellite imagery and often lack sufficient consideration of fire severity. To address these limitations, this study utilized dense time-series Landsat imagery available on the Google Earth Engine, applying the qualityMosaic method to generate annual composites of minimum normalized burn ratio values. These composites imagery enabled the rapid identification of fire sample points, which were subsequently used to train a random forest classifier for estimating per-pixel burn probability. Pixels with a burned probability greater than 0.9 were selected as the core of the BA, and used as candidate seeds for region growing to further expand the core and extract complete BA. This two-stage extraction method effectively balances omission and commission errors. To avoid the repeated detection of unrecovered BA, this study developed distinct correction rules based on the differing post-fire recovery characteristics of forests and grasslands. The extracted BA were further categorized into four fire severity levels using the delta normalized burn ratio. In addition, we conducted a quantitative validation of the BA mapping accuracy based on Sentinel-2 data between 2015 and 2023. The results indicated that the BA mapping achieved an overall accuracy of 93.90%, with a Dice coefficient of 82.04%, and omission and commission error rates of 26.32% and 5.25%, respectively. The BA dataset generated in this study exhibited good spatiotemporal consistency with existing products, including MCD64A1, FireCCI51, and GABAM. The BA fluctuated significantly between 1985 and 2010, with the highest value recorded in 1987 (13,315 km²). The overall trend of BA showed a decline, with annual burned areas remaining below 2000 km² after 2010 and reaching a minimum of 92.8 km² in 2020. There was no significant temporal variation across different fire severity levels. The area of high-severity burns showed a positive correlation with the annual total BA. High-severity fire-prone zones were primarily concentrated in the northeastern, southeastern, and western parts of the study area, predominantly within grasslands and forest–grassland ecotone regions. Full article

Biodiversity is crucial for humankind. It encompasses three main levels: ecosystem, species, and intraspecific genetic diversity. Species consist of populations that exhibit deoxyribonucleic acid (DNA) variability, which is a key component of intraspecific genetic diversity. In turn, intraspecific genetic diversity is directly linked with the term population genetic structure (PGS). There is a great deal of uncertainty and confusion surrounding the concept of the PGS of species in the scientific literature, yet the term PGS is central to population genetics, and future research is expected to focus on the evolutionary continuum from populations to species. Therefore, it is necessary for current biologists and the next generation of scientists to acquire a better understanding of a PGS, both as a term and a concept, as well as the various roles PGSs play within a biodiversity context. This knowledge can then be applied to the expansion of both practical and theoretical science. Finding answers and reaching a consensus among the scientific community on certain questions regarding PGSs could expand the horizons of population genetics and related research disciplines. The major areas of interest and research are PGSs’ roles in the processes of microevolution and speciation, the sustainable use of natural resources, and the conservation of genetic diversity. Other important aspects of this perspective review include proposals for scientific definitions of some terms and concepts, as well as new perspectives and explanations that could be used as a basis for future theoretical models and applied research on PGSs. In conclusion, a PGS should be viewed as a fragile genetic mosaic encompassing at least three spatial dimensions and one temporal dimension. Full article

Mapping Soil Organic Carbon (SOC) at a regional scale is essential for assessing soil health and supporting sustainable land management. This study evaluates the potential of using Sentinel-2 imagery and regional calibration to predict SOC in salt-affected agricultural lands in Portugal while also assessing the influence of soil properties, such as texture and salinity, on SOC prediction. A per-pixel mosaicking approach was set to analyze the relationship of spectral reflectance indices linked to bare soil conditions with SOC. SOC prediction models were developed using linear regression (LR) and Partial Least Squares Regression (PLSR). Among the tested approaches, the combination of the maximum Bare Soil Index (maxBSI) with LR produced the most accurate SOC predictions, achieving moderate prediction performance (R² = 0.52; RMSE = 0.16%; LCCC = 70%). This approach slightly outperformed the application of the 90th percentile of bare soil pixels (R90 reflectance) and the median approaches with PLSR. Notably, our findings indicate that soil salinity did not significantly affect SOC predictions within the observed salinity range of EC_e between 1.2 and 10.4 dS m⁻¹ in topsoil. However, further case studies are needed to validate this observation across diverse agricultural conditions. In contrast, soil texture and moisture content emerged as the dominant factors influencing soil reflectance. The combination of per-pixel mosaicking and regional calibration provides a practical, scalable, and cost-effective method for generating SOC maps using open access satellite imagery. To support wider adoption and improve model generalizability, future studies should incorporate a larger number of fields with a wider range of soil properties, crop types, and management practices. Full article

Since 2005, landscape ecology has emerged as a structured scientific field in the Democratic Republic of Congo, notably shaped by the contributions of Professor Jan Bogaert. The evolution of research in this field can be divided into three main phases. The first phase (2005–2012) focused on the quantitative analysis of forest fragmentation using Geographic Information Systems and landscape metrics. From 2013 to 2019, research approaches broadened to include the social sciences, marking a shift toward a socio-ecological perspective on landscapes. Since 2020, the field has increasingly adopted holistic frameworks that integrate climatic factors and forward-looking modeling. Key research themes now include ecological flows across landscape mosaics, land-use dynamics, and the anthropogenic transformation of ecosystems. However, several challenges persist, including the lack of long-term temporal datasets, uneven geographic coverage, and limited integration of local knowledge systems. Notable advances have been made through high-resolution remote sensing and participatory methods, although their application is still limited by technical and financial constraints. This manuscript advocates for stronger interdisciplinary collaboration, improved field methodologies, and the development of context-appropriate tools to support sustainable and locally grounded landscape management in the Congolese context. Full article

The downwelling light sensor (DLS) is the industry-standard solution for generating UAV-based digital orthophoto maps (DOMs). Current mainstream DLS correction methods primarily rely on angle compensation. However, due to the temporal mismatch between the DLS sampling intervals and the exposure times of multispectral cameras, as well as external disturbances such as strong wind gusts and abrupt changes in flight attitude, DLS data often become unreliable, particularly at UAV turning points. Building upon traditional angle compensation methods, this study proposes an improved correction approach—FIM-DC (Fitting and Interpolation Model-based Data Correction)—specifically designed for data collection under clear-sky conditions and stable atmospheric illumination, with the goal of significantly enhancing the accuracy of reflectance retrieval. The method addresses three key issues: (1) field tests conducted in the Qingpu region show that FIM-DC markedly reduces the standard deviation of reflectance at tie points across multiple spectral bands and flight sessions, with the most substantial reduction from 15.07% to 0.58%; (2) it effectively mitigates inconsistencies in reflectance within image mosaics caused by anomalous DLS readings, thereby improving the uniformity of DOMs; and (3) FIM-DC accurately corrects the spectral curves of six land cover types in anomalous images, making them consistent with those from non-anomalous images. In summary, this study demonstrates that integrating FIM-DC into DLS data correction workflows for UAV-based multispectral imagery significantly enhances reflectance calculation accuracy and provides a robust solution for improving image quality under stable illumination conditions. Full article

Often thought of as a mesic paradise, forest ecosystems are a mosaic of microhabitats with temporal oscillations that cause significant environmental stresses, providing habitats for extremophilic and extremotolerant fungi. Adapted to survive and thrive under conditions lethal to most mesophiles (e.g., extreme temperatures, pH, water potential, radiation, salinity, nutrient scarcity, and pollutants), these species are increasingly recognized as vital yet underappreciated elements of forest biodiversity and function. This review examines the current understanding of the roles of extremophilic fungi in forests, scrutinizing their presence in these ecosystems with a critical eye. Particularly under severe environmental conditions, extremophilic fungi play a crucial role in forest ecosystems, as they significantly enhance decomposition and nutrient cycling, and foster mutualistic interactions with plants that increase stress resilience. This helps to maintain ecosystem stability. We examine the definition of “extreme” within forest settings, survey the known diversity and distribution of these fungi across various forest stress niches (cold climates, fire-affected areas, acidic soils, canopy surfaces, polluted sites), and delve into their possible ecological functions, including decomposition of recalcitrant matter, nutrient cycling under stress, interactions with plants (pathogenesis, endophytism, perhaps mycorrhizae), bioremediation, and contributions to soil formation. However, the review stresses significant methodological difficulties, information gaps, and field-based natural biases. We recommend overcoming cultural constraints, enhancing the functional annotation of “omics” data, and planning investigations that clarify the specific activities and interactions of these cryptic creatures within the forest matrix to further advance the field. Here, we demonstrate that moving beyond simple identification to a deeper understanding of function will enable us to more fully appreciate the value of extremophilic fungi in forest ecosystems, particularly in relation to environmental disturbances and climate change. Full article

This study presents a multi-temporal analysis of vegetation cover changes in the Guayepo stream watershed (Cartagena de Indias, Colombia) for 2000, 2010, and 2020 and their impact on surface runoff generation. Hydrological data from 1974 to 2019 were processed to model intensity–duration–frequency (IDF) curves and simulate heavy rainfall events using six storms of nine-hour duration. Following the Soil Conservation Service guidelines, these were used to estimate runoff flows for return periods of 25, 50, and 100 years via the curve number method in HEC-HMS. Vegetation cover was assessed using the CORINE land cover methodology applied to official land use maps. The analysis revealed a significant loss of natural vegetation: dense forest cover declined dramatically from 14.38% in 2000 to 0% in 2020, and clean pastures were reduced by 46%. In contrast, weedy pastures and pasture mosaics with natural areas increased by 299% and 136%, respectively, reflecting a shift towards more degraded land cover types. As a result of these changes, total runoff flows of the model increased by 9.7% and 4.3% under antecedent moisture conditions I and II, respectively, for the 100-year return period. These findings reveal ongoing degradation of the watershed’s natural cover, linked to expanding agricultural uses and changes in vegetation structure. The decline in forested areas has increased surface runoff, elevating flood risk and compromising the watershed’s hydrological regulation. The study suggests that integrated land management and ecological restoration strategies could be key in preserving hydrological ecosystem services and reducing the negative impacts of land use change. Full article

Over 400 articles on the pathophysiology of brain aging, neuroaging, and neurodegeneration were reviewed, with a focus on epigenetic mechanisms and numerous non-coding RNAs. In particular, this review the accent is on microRNAs, the discovery of whose pivotal role in gene regulation was recognized by the 2024 Nobel Prize in Physiology or Medicine. Aging is not a gradual process that can be easily modeled and described. Instead, multiple temporal processes occur during aging, and they can lead to mosaic changes that are not uniform in pace. The rate of change depends on a combination of external and internal factors and can be boosted in accelerated aging. The rate can decrease in decelerated aging due to individual structural and functional reserves created by cognitive, physical training, or pharmacological interventions. Neuroaging can be caused by genetic changes, epigenetic modifications, oxidative stress, inflammation, lifestyle, and environmental factors, which are especially noticeable in space environments where adaptive changes can trigger aging-like processes. Numerous candidate molecular biomarkers specific to neuroaging need to be validated to develop diagnostics and countermeasures. Full article

Data augmentation methods offer a cost-effective and efficient alternative to the acquisition of additional data, significantly enhancing data diversity and model generalization, making them particularly favored in object detection tasks. However, existing data augmentation techniques primarily focus on the visible spectrum and are directly applied to RGB-T object detection tasks, overlooking the inherent differences in image data between the two tasks. Visible images capture rich color and texture information during the daytime, while infrared images are capable of imaging under low-light complex scenarios during the nighttime. By integrating image information from both modalities, their complementary characteristics can be exploited to improve the overall effectiveness of data augmentation methods. To address this, we propose a cross-modality data augmentation method tailored for RGB-T object detection, leveraging masked image modeling within representation learning. Specifically, we focus on the temporal consistency of infrared images and combine them with visible images under varying lighting conditions for joint data augmentation, thereby enhancing the realism of the augmented images. Utilizing the masked image modeling method, we reconstruct images by integrating multimodal features, achieving cross-modality data augmentation in feature space. Additionally, we investigate the differences and complementarities between data augmentation methods in data space and feature space. Building upon existing theoretical foundations, we propose an integrative framework that combines these methods for improved augmentation effectiveness. Furthermore, we address the slow convergence observed with the existing Mosaic method in aerial imagery by introducing a multi-scale training strategy and proposing a full-scale Mosaic method as a complement. This optimization significantly accelerates network convergence. The experimental results validate the effectiveness of our proposed method and highlight its potential for further advancements in cross-modality object detection tasks. Full article

We investigated sea turtle landing and spawning behavior along 1.4 km of Kili-Kili Beach in East Java, Indonesia, by combining aerial photographs and field survey data. In the study, we surveyed marks of sea turtles landing and spawning on the beach and utilized aerial photographs, beach profile survey records, grain size measurements of the beach material, and tide records to understand the behavior of the turtles. Firstly, aerial photographs are processed into ortho-mosaics, and beach surfaces are classified into land cover categories. Then, we calculate the number of spawning and non-spawning instances for each category, visualizing landing positions to identify local concentrations. Spawning distances from the waterline are estimated, and beach stability is evaluated by analyzing the temporal elevation change through standard deviation. Our findings reveal preferred spawning locations on bare sand surfaces, around 8 to 45 m from the waterline, with beach elevations ranging from 1 to 5 m. The standard deviations of beach elevation were between 0.0 and 0.7 m, with a mean slope of 0.07. This information is important for effectively conserving sandy beaches that serve as spawning sites for sea turtles. Full article

The semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiveness of a fire detection model and analyze the spatial and temporal patterns of burned areas, providing essential insights for fire management and prevention strategies. Utilizing deep neural network (DNN) models, we mapped burned areas across the Caatinga biome from 1985 to 2023, based on Landsat-derived annual quality mosaics and minimum NBR values. Over the 38-year period, the model classified 10.9 Mha (12.7% of the Caatinga) as burned, with an average annual burned area of approximately 0.5 Mha (0.56%). The peak burned area reached 0.89 Mha in 2021. Fire scars varied significantly, ranging from 0.18 Mha in 1985 to substantial fluctuations in subsequent years. The most affected vegetation type was savanna, with 9.8 Mha burned, while forests experienced only 0.28 Mha of burning. October emerged as the month with the highest fire activity, accounting for 7266 hectares. These findings underscore the complex interplay of climatic and anthropogenic factors, highlighting the urgent need for effective fire management strategies. Full article

Forest fires are disturbance events that can impact biological assemblages at multiple scales. In this study, the structures of breeding bird communities in cork oak patches located in an agro-mosaic suburban landscape of central Italy (Rome) were compared at the local scale with a fine-grained mapping method before (2018) and after (2023) a fire event occurred in July 2022. The analyses did not reveal any significant changes in the density of territorial pairs or in the diversity metrics, both univariate (Shannon–Wiener index, evenness, Margalef normalized richness) and bivariate (Whittaker and k-dominance plots, abundance/biomass curves) of diversity. Even when the guilds of strictly forest-related species were compared, no differences emerged before and after the fire. This counterintuitive phenomenon may be due to the characteristics of the dominant tree, the cork oak (Quercus suber), a sclerophilous tree that is very resilient to fires and able to recover foliage in the following spring season, thus allowing rapid bird recolonization. However, other small-scale phenomena (e.g., the ‘crowding effect’ and local dispersal of territorial pairs from remnant wood patches not affected by fire) may explain this lack of change in breeding bird density and diversity. Further studies should be carried out at larger spatial and temporal scales and at different levels of fire frequency and intensity to confirm these responses at the guild/community level in suburban cork oak wood patches. Full article

UAV thermal infrared remote sensing technology, with its high flexibility and high temporal and spatial resolution, is crucial for understanding surface microthermal environments. Despite DJI Drones’ industry-leading position, the JPG format of their thermal images limits direct image stitching and further analysis, hindering their broad application. To address this, a format conversion system, ThermoSwitcher, was developed for DJI thermal JPG images, and this system was applied to surface microthermal environment analysis, taking two regions with various local zones in Nanjing as the research area. The results showed that ThermoSwitcher can quickly and losslessly convert thermal JPG images to the Geotiff format, which is further convenient for producing image mosaics and for local temperature extraction. The results also indicated significant heterogeneity in the study area’s temperature distribution, with high temperatures concentrated on sunlit artificial surfaces, and low temperatures corresponding to building shadows, dense vegetation, and water areas. The temperature distribution and change rates in different local zones were significantly influenced by surface cover type, material thermal properties, vegetation coverage, and building layout. Higher temperature change rates were observed in high-rise building and subway station areas, while lower rates were noted in water and vegetation-covered areas. Additionally, comparing the temperature distribution before and after image stitching revealed that the stitching process affected the temperature uniformity to some extent. The described format conversion system significantly enhances preprocessing efficiency, promoting advancements in drone remote sensing and refined surface microthermal environment research. Full article

Habitat disturbance driven by human activities poses a major threat to biodiversity and can disrupt ecological interactions. Butterfly–plant mutualisms represent an ideal model system to study such anthropogenic impacts, as butterflies exhibit intimate dependencies on larval host plants and adult nectar sources, rendering them highly sensitive to habitat changes affecting the availability of these floral resources. This study examined flower-visiting butterfly communities and their associations with flowering plants in a landscape altered by anthropogenic factors in central Mexico. The study area encompassed a mosaic of vegetation types, including native juniper forests, agricultural lands, and introduced eucalyptus plantations, representing different degrees of human-induced habitat modification. Monthly surveys were conducted over a single year, covering both rainy and dry seasons, to analyze butterfly and plant diversity, community composition, and interactions. Results showed the highest diversity in juniper forests, followed by eucalyptus and agricultural sites. Seasonal turnover was the primary driver of community changes, with habitat-based segregation persisting within seasons. Butterfly diversity strongly correlated with flower abundance, while plant richness played a secondary role. SIMPER and indicator species analyses identified key taxa contributing to compositional dissimilarities among habitats and associated with specific vegetation types and seasons. Our research provides insights into temporal dynamics structuring butterfly–plant interactions across this forest disturbance spectrum, highlighting how habitat changes and seasonality shape these mutualistic communities in changing landscapes. Full article