Search Results (3,138)

Farmland abandonment is increasingly prevalent, especially in the Central Himalaya. Precise mapping of abandoned areas is crucial for understanding their status and socioecological impacts. However, distinguishing abandoned farmland from transitional classes like fallow land and barren land is challenging without high-resolution satellite imagery and field verification. In this context, this work analyzes farmland abandonment in three ecological villages of the Nepal Himalaya using high-resolution satellite imagery and a people-to-pixel approach. First, the study villages were divided into grids based on their areas, and satellite imagery was printed for ground truthing. Second, ground truthing was conducted to identify active and abandoned farmland areas using the Field Area Measure App and satellite imagery. We measured the extent of abandoned farmland and assessed its current conditions. Third, the measured abandoned farmland shapefiles were exported for precise on-screen mapping using the Geographic Information System, alongside detailed land-cover mapping. Next, the accuracy assessment was performed using Google Earth satellite imagery, and the overall mapping accuracy was found to be 95.8%. Mapping results show that the highest areas of abandoned farmland were found in the Mountain region with 19.2% of total farmland, followed by the Hill region (12.7%) and the Tarai region (2.6%). Out of the total abandoned farmland, 49.2% is currently covered with bushes and shrubs, 42.9% with weeds and grasses, and the remaining 7.9% with woodlands. The findings emphasize the importance of integrating satellite technology with people engagement to address complex land-use challenges and offer critical insights for sustainable land management in the Nepal Himalaya and similar regions worldwide. Full article

This research presents a comprehensive analysis of the spatial extent and temporal change in benthic habitats within the Puck Lagoon in the southern Baltic Sea, utilizing integrated machine learning classification and multi-sourced remote sensing. Object-based image analysis was integrated with Random Forest, Support Vector Machine, and K-Nearest Neighbors algorithms for benthic habitat classification based on airborne bathymetric LiDAR (ALB), multibeam echosounder (MBES), satellite bathymetry, and high-resolution aerial photography. Ground-truth data collected by 2023 field surveys were supplemented with long temporal datasets (2010–2023) for seagrass meadow analysis. Boruta feature selection showed that geomorphometric variables (aspect, slope, and terrain ruggedness index) and optical features (ALB intensity and spectral bands) were the most significant discriminators in each classification case. Binary classification models were more effective (93.3% accuracy in the presence/absence of Zostera marina) compared to advanced multi-class models (43.3% for EUNIS Level 4/5), which identified the inherent equilibrium between ecological complexity and map validity. Change detection between contemporary and 1957 habitat data revealed extensive Zostera marina loss, with 84.1–99.0% cover reduction across modeling frameworks. Seagrass coverage declined from 61.15% of the study area to just 9.70% or 0.63%, depending on the model. Seasonal mismatch may inflate loss estimates by 5–15%, but even adjusted values (70–94%) indicate severe ecosystem degradation. Spatial exchange components exhibited patterns of habitat change, whereas net losses in total were many orders of magnitude larger than any redistribution in space. These findings recorded the most severe seagrass habitat destruction ever described within Baltic Sea ecosystems and emphasize the imperative for conservation action at the landscape level. The methodology framework provides a reproducible model for analogous change detection analysis in shallow nearshore habitats, creating critical baselines to inform restoration planning and biodiversity conservation activities. It also demonstrated both the capabilities and limitations of automatic techniques for habitat monitoring. Full article

Soybeans (Glycine max (L.) Merr.) are a vital global crop; however, Thailand currently imports 99% of its domestic requirement, highlighting the critical need for enhanced domestic production. Morkhor 60, a new high-yielding variety, lacks optimized agronomic management for cultivation in the challenging sandy soils of Northeast Thailand. This study evaluated the effects of NPK fertilizer rates and plant spacing on Morkhor 60 growth and yield through two independent experiments conducted in sandy soils over a four-season period (2022–2023). Results demonstrated that 23.44 kg ha⁻¹ NPK provided optimal cost-effectiveness for Morkhor 60, achieving yields of 1238 kg ha⁻¹ statistically comparable to higher rates (1286 kg ha⁻¹) while reducing input costs by 50%. Plant spacing significantly affected productivity, with 30 × 20 cm spacing producing the highest yield (1775 kg ha⁻¹), representing 41% improvement over the narrow spacing (20 × 20 cm: 1257 kg ha⁻¹). The integrated management system (23.44 kg ha⁻¹ NPK with 30 × 20 cm spacing) achieved 87.6% ground cover for moisture conservation and delivered net profits of 29,850 THB ha⁻¹, with a benefit–cost ratio of 3.1. This research provides evidence-based agronomic recommendations for Morkhor 60 cultivation in sandy soil environments, contributing to Thailand’s soybean self-sufficiency through sustainable and economically viable production practices. Full article

High-resolution Fourier transform infrared (FTIR) spectra of methyl fluoride (CH₃F) were recorded in the mid- and far-infrared regions using the Bruker IFS 125HR spectrometers at GSMA (Reims, France) and at the SOLEIL synchrotron facility (Saint-Aubin, France). The measurements cover both the pure rotational transitions of the ground state (10–100 cm⁻¹) and the vibrational triad region (1950–2450 cm⁻¹), which includes the 2${\nu }_3$, ${\nu }_3+{\nu }_6$, and 2${\nu }_6$ bands. Spectra were recorded under various pressure conditions to optimize line visibility, with a high resolution. Line assignments were performed using predictions from the tensorial effective Hamiltonian implemented in the MIRS package, together with a newly developed automated assignment tool, SpectraMatcher, which facilitates line matching and discrimination of CH₃F transitions from overlapping CO₂ features. More than 5000 transitions (up to $J=52$ in the ground state and up to $J=45$ in the triad and $K=19$) were assigned and included in a global fit. The sixth-order tensorial effective Hamiltonian model yielded excellent agreement with experiment, with root mean square (RMS) deviations better than 7 × 10⁻⁴ cm⁻¹ across all regions. This paper presents the first continuous rovibrational study of CH₃F over both the triad and far-infrared ground state regions. The improved accuracy from previous studies stems from the improved set of effective Hamiltonian parameters which will also form a good basis from future applications in atmospheric modelling and spectroscopic databases. Full article

A Global Navigation Satellite System (GNSS) and Long Range (LoRa) technology play a crucial role in connected vehicles. The demand for antennas that cover both LoRa and GNSS bands is increasing. This work has developed a novel dual-band coplanar waveguide (CPW)-fed interleaved meander line antenna, incorporating a radiating element, ground plane, and feed. The antenna dimension is 90 × 90 × 1.635 mm³. The design employs a planar meander line configuration to effectively cover the 868 MHz LoRa and 1248 MHz GNSS bands. The antenna was integrated with a Frequency Selective Structure (FSS) to improve the parameters. The designed antenna provides sufficient bandwidth of 40 and 110 MHz for the LoRa and GNSS frequency bands, respectively. The CPW-interleaved meander line antenna attains a gain of −0.12 dBi at LoRa and 3.5 dBi at GNSS frequency. It achieves a voltage standing wave ratio of <2 and impedance of 50 Ω. The novelty of the proposed work is integrating FSS with a CPW-interleaved meander line antenna, which achieves dual-band operation. This dual-band low-profile configuration is suitable for connected vehicle communication. Full article

Planned capitals across the Global South frequently experience unplanned land use transitions that contradict their founding visions. Despite six decades of planning and academic inquiry, Islamabad’s research remains fragmented. Environmental studies have documented land use and land cover changes through remote sensing, while governance-oriented analyses have highlighted institutional weaknesses and policy failures. However, these domains rarely intersect, and few studies systematically link spatial transformations with the underlying governance structures and political–economic processes that drive them. Consequently, the existing literature provides valuable but partial explanations for why Islamabad’s planned order unraveled. This study examines Islamabad, conceived in 1960 as a model of order and green balance, where the built-up area expanded by 377 km² (from 88 to 465 km²; +426%) and forest cover declined by 83 km² (−40%) between 1979 and 2019. Using a PRISMA-guided systematic review integrating spatial, governance, and policy data, we synthesized 39 peer-reviewed and gray literature sources to explain why Islamabad’s planned order unraveled. The findings reveal that governance fragmentation between the Capital Development Authority (CDA) and Metropolitan Corporation Islamabad (MCI), combined with elite capture and weak enforcement of the 2020–2040 Master Plan, has produced enduring contradictions between policy intent and urban reality. These conditions mirror those of other planned capitals, such as Brasília and Abuja. Grounded in Pakistan’s institutional context, the study proposes four actionable reforms: (1) regularization frameworks for informal settlements, (2) cross-agency spatial and fiscal coordination, (3) ecological thresholds within zoning by-laws, and (4) participatory master-plan reviews. Islamabad’s experience illustrates how planned capitals can evolve toward inclusive and ecologically resilient futures through governance reform and adaptive planning. Full article

Blue carbon ecosystems, particularly mangroves, seagrasses, and salt marshes, play a crucial role in climate regulation by capturing and storing huge stocks of carbon. Together with supporting fisheries production, protecting shorelines from erosion, and supplying timber and non-timber products to communities, blue carbon ecosystems offer investment opportunities through carbon markets, thus supporting climate change mitigation and sustainable livelihoods. The current study assessed above- and below-ground biomass, sediment carbon, and the capacity of the blue carbon ecosystems in Kwale and Lamu Counties, Kenya, to capture and store carbon. This was followed by mapping of hotspot areas of degradation and the identification of investment opportunities in blue carbon credits. Carbon densities in mangroves were estimated at 560.23 Mg C ha⁻¹ in Lamu and 526.34 Mg C ha⁻¹ in Kwale, with sediments accounting for more than 70% of the stored carbon. In seagrass ecosystems, carbon densities measured 171.65 Mg C ha⁻¹ in Lamu and 220.29 Mg C ha⁻¹ in Kwale, values that surpass the national average but are consistent with global figures. Mangrove cover is declining at 0.49% yr⁻¹ in Kwale and 0.16% yr⁻¹ in Lamu, while seagrass losses in Lamu are 0.67% yr⁻¹, with a 0.34% yr⁻¹ increase in Kwale. Under a business-as-usual scenario, mangrove loss over 30 years will result in emissions of 4.43 million tCO₂e in Kwale and 18.96 million tCO₂e in Lamu. Effective interventions could enhance carbon sequestration from 0.12 to 3.86 million tCO₂e in Kwale and 0.62 to 19.52 million tCO₂e in Lamu. At the same period, seagrass losses in Lamu would emit 5.21 million tCO₂e. With a conservative carbon price of 20 USD per tCO₂e, projected annual revenues from mangrove carbon credits amount to USD 3.59 million in both Lamu and Kwale, and USD 216,040 for seagrass carbon credits in Lamu. These findings highlight the substantial climate and financial benefits of investing in the restoration and protection of the two ecosystems. Full article

Understanding long-term cloud cover variability is essential for assessing past climate dynamics and human influences on atmospheric conditions. In Padua, instrumental weather records (temperature, precipitation, pressure) and descriptive sky observations date back to 1725, but quantitative cloud cover data, expressed as tenths of the sky covered by clouds, began in 1872 at the Astronomical Observatory. From 1920 to 1989, observations continued under the authority of the Meteorological Observatory of the Water Magistrate, and from 1951 to 1990, additional records by the Italian Air Force expressed in eighths of sky are available. These visual datasets—based on multiple daily observations—are complemented by satellite records (from 1983) and reanalysis such as ERA5 (from 1940) and NOAA 20CRv3 (from 1872 to 2015). The aim of this study is to reconstruct a homogenized, long-term total cloud cover (TCC) time series for Padua from 1872 to 2024, integrating all available observational sources. By comparing overlapping periods across different subseries and nearby ground-based stations, the analysis not only investigates consistency and potential discontinuities across datasets but also quantifies the reliability and limitations of historical visual observations. This work provides one of the few centennial-scale reconstructions of cloud cover in Europe, offering a valuable contribution to historical climatology and climate change studies. Full article

This study presents a novel top-down approach to quantify diffuse methane (CH₄) emissions at oil and gas well sites. It uses an unmanned aerial vehicle (UAV) equipped with a scanning–sampling tunable diode laser absorption spectroscopy (TDLAS) CH₄ measurement instrument. By integrating the top-down emission rate retrieval algorithm (TERRA) and adopting concentric circular sampling, the method aims to overcome the limitations of traditional ground-based measurements. The UAV system was deployed at 11 oil and gas sites in the Changqing Oilfield. The results show that the average CH₄ emission rate detected by the UAV is 1.425 kg/h (excluding non-detected samples), which is larger than the 1.061 kg/h obtained from ground-based onsite direct measurement. This discrepancy may be because the UAV’s scanning–sampling capability can cover a larger area, capturing scattered or hidden diffuse emission sources that might be missed by ground-based onsite direct measurement. The study demonstrates that the UAV-based approach with a scanning–sampling TDLAS CH₄ measurement instrument, integrated with the TERRA and concentric circular sampling, is effective in capturing diffuse CH₄ emissions at oil and gas well sites, providing a viable method for large-scale and efficient monitoring of such emissions. This approach could provide an effective pathway for large-scale, efficient, and cost-effective monitoring of methane emissions. Full article

The soil freeze–thaw process is a dominant disturbance in the seasonally frozen ground and the active layer of permafrost, which plays a crucial role in the surface energy balance, water cycle, and carbon exchange and has a pronounced influence on vegetation phenology. This study proposes a novel density-based Freeze–Thaw Disturbance Index (FTDI) based on the identification of the freeze–thaw disturbance region (FTDR) over the Qinghai–Tibet Plateau (QTP). FTDI is defined as an areal density metric based on geomorphic disturbances, i.e., the proportion of FTDRs within a given region, with higher values indicating greater areal densities of disturbance. As a measure of landform clustering, FTDI complements existing freeze–thaw process indicators and provides a means to assess the geomorphic impacts of climate-driven freeze–thaw changes during permafrost degradation. The main conclusions are as follows: the FTDR results that are identified by the random forest model are reliable and highly consistent with ground observations; the FTDRs cover 8.85% of the total area of the QTP, and mainly in the central and eastern regions, characterized by prolonged freezing durations and the average annual ground temperature (MAGT) is close to 0 °C, making the soil in these regions highly susceptible to warming-induced disturbances. Most of the plateau exhibits low or negligible FTDI values. As a geomorphic indicator, FTDI reflects the impact of potential freeze–thaw dynamic phase changes on the surface. Higher FTDI values indicate a greater likelihood of surface thawing processes triggered by rising temperatures, which impact surface processes. Regions with relatively high FTDI values often contain substantial amounts of organic carbon, and may experience delayed vegetation green-up despite general warming trends. This study introduces the FTDI derived from the FTDR as a novel index, offering fresh insights into the study of freeze–thaw processes in the context of climate change. Full article

Background/Objectives: BrdU (5′-bromo-2′-deoxyuridine), a synthetic thymidine (T) analog, is widely used to study cell proliferation and DNA synthesis. To precisely identify where and when DNA replication starts and terminates, it is essential to determine the BrdU incorporation rate and sites at a single-nucleotide resolution. Although several deep learning-based methods have been developed for detecting BrdU using Oxford nanopore sequencing data, there is a lack of accessible, easy-to-follow tutorials to guide researchers in preparing training data and implementing deep learning approaches as the nanopore sequencing technologies continue to evolve. Methods: Due to the lack of ground truth BrdU-positive data generated on the latest R10 flow cells, we prepared model training data from legacy R9 flow cells, consistent with existing tools. We processed publicly available synthetic and real nanopore DNA sequencing datasets, with and without BrdU incorporation, using a combination of open-source and custom software tools. Subsequently, we trained bidirectional gated recurrent unit (BiGRU)-based recurrent neural networks (RNNs) for BrdU detection using the TensorFlow library on the Google Colab platform. Results: We trained BiGRU-based RNNs for BrdU detection with a high specificity (>94%) but a moderate sensitivity due to limited BrdU-positive data. We detail the setup, training, testing, and fine-tuning of the model using both synthetic and real DNA sequencing data. Conclusions: Though the models were trained with data generated on legacy flow cells, we believe that this detailed protocol, covering both data preparation and model development, can be readily extended to R10 flow cells and basecallers for other base modifications. This work will facilitate the broader adoption of deep learning neural networks in biological research, particularly RNNs, which are well suited for modeling sequential and time-series data. Full article

This paper compares three nondestructive methods used to detect and locate defects such as delaminations or voids in externally bonded fiber reinforced polymer (FRP) concrete structures: infrared thermography, ground-penetrating radar, and measurement of acoustic wave velocity. One of the main goals was to check whether it was possible to distinguish overlapping defects. For this purpose, eight concrete samples were made with a bonded carbon fiber reinforced polymer (CFRP) strip with the following dimensions 100 × 100 × 500 mm. Two samples had no defects, four had single defects varying in location (at the edge of the strip or in the centre) simulating delamination or voids in the concrete cover, and the remaining samples had overlapping defects. Both infrared thermography and acoustic wave velocity measurement methods allow the detection of defects/voids in the adhesive layer and a concrete defect (void in the concrete cover). However, ground penetration failed to detect defects in the adhesive layer. Only infrared thermography allows for the differentiation of overlapping defects. On the basis of the conducted research, the methodology, differences, advantages, and limitations of each method were described, along with recommendations based on the authors’ experience. Full article

The identification of strategies to mitigate climate change and address urban challenges is nowadays a priority for urban planners. The installation of green roofs (GR), as a natural-based solution, is widely promoted. Despite this recognition, most installations result from individual initiatives, and their mapping and monitoring remains absent. Over time, the installation of green roofs has followed the building construction sector, moving from individual to groups of buildings organ, grouped in condominiums, on which common shared areas at ground level are covered with GR. The identification of those GRs is important, as they represent the majority of the GR installations in urban areas; however, this task is still very challenging due to the lack of information about the condominium boundaries. This work proposes a methodology for mapping GR at a top and ground level, and monitoring them, through the use of Support Vector Machine classification process, deep learning models, and GIS-based spatial analysis. Applied to the Lisbon Municipality, the methodology enabled the identification and validation of 196 GR. The results demonstrate the effectiveness and scalability of the proposed approach, which surpasses existing methods and is adaptable to diverse urban contexts without reliance on location-specific characteristics. Full article

Autonomous driving systems encounter various uncertainties in real-world environments, many of which are difficult to represent in maps. Among them, accumulated snow poses a unique challenge since its shape and volume gradually change over time. If accumulated snow is included in a map, it leads to two main problems. First, during long-term driving, discrepancies between the actual and mapped environments, caused by melting snow, can significantly degrade localization performance. Second, the inclusion of large amounts of accumulated snow in the map can cause registration errors between sessions, thereby hindering accurate map updates. To address these issues, we propose a mapping strategy specifically designed for snow-covered environments. The proposed method first detects and removes accumulated snow using a deep learning-based approach. The resulting snow-free data are then used for map updating, and the ground information occluded by snow is subsequently restored. The effectiveness of the proposed method is validated with data collected in real-world snow-covered environments. Experimental results demonstrate that the proposed method achieves 78.6% IoU for snow detection and reduces map alignment errors by 12.5% (RMSE) and 15.6% (Chamfer Distance) on average, contributing to maintaining map quality and enabling long-term autonomous driving in snow-covered environments. Full article

Plastic mulch is widely used in organic strawberry production but raises sustainability concerns due to its persistence, disposal challenges, and contribution to microplastic pollution. This study evaluated the potential of high-residue cover crops and living mulches as alternatives to plastic mulch in coastal California. Over two seasons (2022–2024), we compared five mulching treatments: black polyethylene mulch (Plastic); a white clover (Trifolium repens) living mulch (Clover); two roller-crimped sorghum–sudangrass and field pea mixtures (Sorghum 1, Sorghum 2); and a roller-crimped buckwheat–pea mixture (Buckwheat). The objectives were to evaluate the effectiveness of these treatments on (i) soil properties and biological indicators, (ii) weed suppression, and (iii) strawberry yield in organic systems. A schematic timeline was developed to depict cover-crop growth, termination, and strawberry production across both years. Compost (10 t·ha⁻¹) and fish emulsion (5–1–1 NPK, 4 L·ha⁻¹ biweekly) were applied to all treatments during fruiting. Sorghum residues produced the highest biomass (up to 23 t·ha⁻¹) and supported yields comparable to plastic mulch in 2023. Under lower-yield conditions in 2024, sorghum-based treatments outperformed plastic. Soil responses were modest and time-point specific: Sorghum 1 showed higher organic C and organic N pre-harvest in 2023, and both sorghum treatments increased soil organic matter pre-harvest in 2024. Biological indicators such as CO₂–C and microbially active carbon declined seasonally across all treatments, indicating strong temporal control. Weed outcomes diverged by system—Clover suppressed weeds effectively but reduced yield by >50% due to competition, while Buckwheat decomposed rapidly and provided limited late-season suppression. These results demonstrate that rolled high-residue cover crops, particularly sorghum-based systems, can reduce dependence on plastic mulch while maintaining yields and enhancing soil cover. Living mulches and short-lived covers may complement residue systems when managed to minimize competition and extend ground cover. Full article