Search Results (4,013)

Accurate identification of built-up land from remotely sensed imagery is essential for urban planning, environmental monitoring, and disaster response. However, binary built-up maps derived from single-date classifications often contain semantic noise—misclassified pixels resulting from shadows, bare soil confusion, or seasonal conditions. Common denoising methodologies, such as smoothing or filtering, are designed for continuous imagery and can distort small or fragmented features and fail to correct underlying classification errors. To overcome these limitations, this study evaluated a multi-date summation and thresholding workflow as a denoising alternative. Five Sentinel-2 images per site were classified as built-up maps, summed into a composite “built-up frequency” raster, and thresholded using Otsu, adaptive, and voting methods to produce refined binary maps. The results across nine international study sites show that the Otsu thresholding method outperformed the other methods in most locations when comparing their accuracies using the Matthews Correlation Coefficient (MCC), showing that using multiple images can improve identification of built-up land. Full article

This study developed an integrated vision–actuation system for precision weeding in indoor soil bin environments, with cabbage as a case example. The system integrates lightweight object detection, 3D co-ordinate mapping, path planning, and a three-axis synchronized conveyor-type actuator to enable precise weed identification and automated removal. By integrating ECA and CBAM attention mechanisms into YOLO11, we developed the YOLO11-WeedNet model. This integration significantly enhanced the detection performance for small-scale weeds under complex lighting and cluttered backgrounds. Based on the optimal model performance achieved during experimental evaluation, the model achieved 96.25% precision, 86.49% recall, 91.10% F1-score, and a mean Average Precision (mAP@0.5) of 91.50% calculated across two categories (crop and weed). An RGB-D fusion localization method combined with a protected-area constraint enabled accurate mapping of weed spatial positions. Furthermore, an enhanced Artificial Hummingbird Algorithm (AHA⁺) was proposed to optimize the execution path and reduce the operating trajectory while maintaining real-time performance. Indoor soil bin tests showed positioning errors of less than 8 mm on the X/Y axes, depth control within ±1 mm on the Z-axis, and an average weeding rate of 88.14%. The system achieved zero contact with cabbage seedlings, with a processing time of 6.88 s per weed. These results demonstrate the feasibility of the proposed system for precise and automated weeding at the cabbage seedling stage. Full article

Soil organic matter (SOM) is a key indicator of arable land quality and the global carbon cycle; accurate regional-scale SOM estimation is vitally significant for sustainable agricultural development and climate change research. This study evaluates a multisource data-fusion approach for improving cropland SOM prediction in Yucheng City, Shandong Province, China. We applied a Local Energy Weighted Discrete Wavelet Transform (LEW-DWT) to fuse multi-temporal Landsat 8 imagery (2014–2023). Quantitative analysis (e.g., Information Entropy and Average Gradient) demonstrated that LEW-DWT effectively preserved high-frequency spatial details and texture features of fragmented croplands better than traditional DWT and simple splicing methods. These were combined with 41 environmental predictors to construct composite Ev–Tn–Mm features (environmental variables, temporal NDVI features, and multi-temporal multispectral information). Random Forest (RF) and Convolutional Neural Network (CNN) models were trained and compared to assess the contribution of the fused data to SOM mapping. Key findings are: (1) Comparative analysis showed that the LEW-DWT fusion strategy achieved the lowest spectral distortion and highest spatial fidelity. Using the fused multitemporal dataset, the CNN attained the highest predictive performance for SOM (R² = 0.49). (2) Using the Ev–Tn–Mm features, the CNN achieved R² = 0.62, outperforming the RF model (R² = 0.53). Despite the limited sample size, the optimized shallow CNN architecture effectively extracted local spatial features while mitigating overfitting. (3) Variable importance analysis based on the RF model reveals that mean soil moisture is the primary single variable influencing the SOM, (relative importance 15.22%), with the NDVI phase among time-series features (1.80%) and the SWIR1 band among fused multispectral bands (1.38%). (4) By category, soil moisture-related variables contributed 45.84% of total importance, followed by climatic factors. The proposed multisource fusion framework offers a practical solution for regional SOM digital monitoring and can support precision agriculture and soil carbon management. Full article

Current agriculture faces the challenge of producing sufficient food from diminishing land resources, due to deteriorating soil quality and accelerated population growth. Numerous studies have demonstrated that biochar/hydrochar can serve as efficient soil amendments by improving soil fertility and enhancing crop productivity. Various food wastes are promising raw materials for biochar/ hydrochar production due to their abundant organic matter. Recently, thermochemical techniques such as pyrolysis, hydrothermal carbonization (HTC), and microwave-assisted pyrolysis (MAP) have been widely proposed for converting food waste into biochar/hydrochar for soil amendment. However, the composition of food waste is complex and the parameters for its thermal treatment are highly variable, leading to uncertainties in the performance of the derived biochar/hydrochar for soil applications. This study aims to establish a structure–activity relationship linking food waste carbonization technology, the properties of the obtained biochar/hydrochar, and its functions as a soil amendment. Furthermore, the detailed mechanisms by which biochar improves plant growth or poses potential ecological risks to agricultural land are discussed. This review is intended to provide a guideline for the large-scale application of food waste-derived char for soil amendment. Full article

This article presents a prospective analysis of the corn agro-industrial chain in Colombia up until 2035, using a mixed-methods approach that integrates technological surveillance, two rounds of the Delphi method, S-curve analysis, and patent–publication matrices and quadrants. Text-mining analysis was conducted using VantagePoint^® v15.1 software, enabling the generation of multiple analytical outputs, including cluster maps, co-occurrence networks, and relational matrices. The study examines the dynamics of scientific and technological production related to the utilization of corn by-products and residues over the period 2003–2025. A total of 30 Delphi responses were collected from experts representing academia, industry, and government institutions in Argentina, Ecuador, Portugal, and Colombia. Based on expert consensus, the Delphi process identified 23 priority topics and 40 additional topics for discussion. Six priority themes were highlighted: (i) antioxidant and antimicrobial packaging derived from bioactive compounds extracted from corn by-products; (ii) bioethanol production; (iii) biodegradable straw manufactured from basket fibers; (iv) bioactive extracts for application in anti-aging cosmetic formulations; (v) modified biochar for the adsorption of ammonium and phosphate ions from aqueous systems; and (vi) the use of corn stover to enhance soil nitrogen content and grain yield. Finally, patent-based S-curve analysis and patent–publication matrices revealed notable asymmetries between scientific knowledge production and patenting activity, underscoring structural gaps in the translation of research into technological innovation within the corn agro-industrial sector. Full article

Chile’s livestock industry faces growing demands for emissions reduction, animal welfare, and value creation, while continuing to play a key role in rural food security and pasture-based production systems. In light of Chile’s varied agroclimatic conditions, a diminishing national herd, and shifting market signals, such as alternative proteins and distinctive meat products, this narrative review explores four complementary transition pathways: sustainable intensification, organic and agroecological systems, heritage livestock, and regenerative practices. We map the structural challenges, including grazing dairy and beef herds, fragmented producer organization, and the absence of unified, farm-scale greenhouse-gas measurements. We assess the management strategies that have the strongest support; viz., efficiency gains at the animal/herd level, adaptive grazing and silvopastoral designs, nutrient cycling via manure management and local by-products, and welfare frameworks that are aligned with national law and World Organisation for Animal Health guidance. Heritage systems (e.g., Chilota sheep breed in the Chiloé archipelago) provide resilience, cultural identity, and low-input baselines for stepwise transitions. Regenerative procedures can improve soil function and drought buffering but require context-specific designs and credible outcome-based verification to avoid greenwashing. Key enabling policies include coordinated certification and labeling covering animal welfare and origin. Additional elements are cooperative and territorial governance, targeted R&D and extension services for smallholders, and a transparent, standardized greenhouse-gas measurement framework linking farm-level actions to national inventories. Chile’s pathway is not a single model but a practical combination shaped by regional conditions that can deliver long-term economic sustainability, ecosystem services, and nutrition. Full article

This study applies a qualitative Geographic Information Systems model that integrates satellite-derived wind and rainfall data to predict potential debris-flow locations in Puerto Rico triggered by Hurricane Maria (2017). A key innovation of the model is the use of wind-driven rainfall (WDR), calculated at multiple elevation levels using satellite wind data and Global Precipitation Measurement (GPM) precipitation at three time steps. WDR replaces the conventional use of total rainfall commonly applied in landslide modeling. A second innovation is the use of WDR slope exposure to hurricane direction in place of a standard aspect parameters. The model assumes that WDR was the primary trigger of debris flows during the hurricane. Predicted debris-flow locations were compared with mapped debris-flow inventories using threshold distances of 1000, 500, and 250 m. Prediction rates ranged from 30 to 100%, and success ratios from 10 to 90%, depending on elevation and distance thresholds, with the best performance at 500 and 1000 m ranges. Model performance could be enhanced through higher-resolution satellite observations of wind, soil moisture, and precipitation, supporting potential real-time hazard applications. Model limitations include its empirical nature, qualitative structure, and current applicability to equatorial or sub-equatorial regions affected by hurricanes or typhoons. Further testing and regional calibration are recommended. Full article

Al-Musk Lake, an artificial waterbody of 2.9 km² formed by illegal dumping of 9.5 million cubic meters of raw sewage near Jeddah, Saudi Arabia, remains a significant subsurface environmental hazard after drainage activities in 2010. The current research employs a multidisciplinary approach, integrating geological mapping, aeromagnetic and electromagnetic surveys, Landsat imagery, and chemical analyses, to investigate contamination migration and accumulation. The objective is to delineate subsurface contamination pathways and assess their impact on soil and groundwater quality. Frequency-domain electromagnetic (FDEM) surveys identified areas of high apparent conductivity (up to 200 mS/m at 2000 kHz), indicative of deep contamination saturation. Chemical analysis of water and soil samples revealed distressing levels of heavy metals, Na⁺ up to 2400 mg/L, Ca²⁺ up to 3648 mg/L, and Fe up to 4150 mg/L, far exceeding irrigation safe standards. Findings locate two at-risk areas several kilometers from the lake, where contaminants accumulate through basement depressions controlled by faults. These pose immediate risks to adjacent residential areas and expanding agricultural belts. In short, subsurface contamination continues to spread westward. Short-term remedies include halting agricultural activities, treating in-storage water, and paving infiltration zones. A larger-scale geophysical survey, along with denser geochemical sampling and analysis, is necessary to guide long-term remediation and to protect public health. Full article

Landslides in mountainous regions threaten infrastructure and human safety, making high-accuracy landslide inventories crucial for disaster management. However, fine-grained identification using high-resolution remote sensing imagery is hindered by low small-landslide detection accuracy and bare soil spectral interference. The aim of this study is to propose a lightweight UCTransNet with Triplet Attention and Pyramid Kernel Interaction (UCTransNet-TPKI) deep learning model for accurate multi-scale landslide extraction. The study area is located in Wushan County, Chongqing. GF-2 imagery from 2022 was collected, along with field sampling data and Mengdong dataset as validation data. The model proposed in this study, named UCTransNet-TPKI, is based on an improved UCTransNet architecture. Its key innovations include the introduction of two critical modules: the Pyramid Kernel Interaction module and the Triplet Attention mechanism. The PKI module captures multi-scale local contextual information in parallel under different receptive fields, significantly enhancing the network’s ability to extract landslide features. Concurrently, the Triplet Attention mechanism effectively refines feature representations by capturing the interaction dependencies across the three dimensions of a feature map. This enables the model to focus more precisely on key areas, such as the main body and edges of a landslide, while simultaneously suppressing interference from background noise. The experimental results show that UCTransNet-TPKI achieved the highest F1-score of 0.9008 and an IoU of 0.8252, outperforming MFFENet, TransLandSeg, and Segformer++. Ablation studies confirmed the contributions of each component, with the PKI module improving IoU by 0.72%, the Triplet Attention mechanism increasing IoU by 0.9%, and their combination yielding a clear synergistic enhancement of overall performance. Furthermore, UCTransNet-TPKI demonstrated strong generalization on the Mengdong dataset, achieving an F1-score of 0.9230 and an IoU of 0.8560. These results demonstrate that UCTransNet-TPKI provides an accurate automated landslide mapping solution, offering significant value for post-disaster emergency response and geological hazard management. Full article

The growing global population intensifies food demand, challenging the agricultural sector to increase efficiency. Precision agriculture (PA) addresses this challenge by leveraging advanced technologies, such as the Internet of Things (IoT) and sensor networks, to collect and analyze field data. However, accessible tools for storing, managing, and analyzing these data are often limited. This study presents AgDataBox-IoT (ADB-IOT), a novel web application designed to fill this gap by providing a user-friendly platform for optimizing agricultural management. ADB-IOT integrates into the existing AgDataBox ecosystem, extending its capabilities with dedicated IoT functionalities. The application enables farmers to plan IoT networks, visualize and analyze field-collected data through thematic maps and graphs, and monitor and control IoT devices. This integrated approach facilitates informed decision-making, improves control over sustainable soil management, and enhances the overall efficiency of agricultural operations. As a freely accessible tool, ADB-IOT lowers the barrier to adopting precision agriculture technologies. Full article

As a pivotal component of the global carbon cycle, the spatial variation in soil respiration (Rs) is crucial for forecasting climate change trajectories. Despite extensive research on the spatial patterns of total Rs, the distinct drivers of its two components, heterotrophic respiration (Rh) and autotrophic respiration (Ra), are still not well defined. We compiled a global dataset from studies published between 2007 and 2023 to investigate the drivers of spatial variations in Rs, Ra, and Rh. This dataset comprises 308 annual flux measurements from 172 sites. The results showed that Rh contributed 63% and 60% to Rs in forest and grassland ecosystems, respectively. Further analyses using structural equation modelling (SEM) showed that the spatial variation in Rh and Ra exhibited divergent responses to climatic factors and plant community structure (mostly driven by gross primary production, GPP). Rh was more affected by mean annual temperature (MAT) than by mean annual precipitation (MAP), with standardized total effects of 0.17 (forests) and 0.57 (grasslands) for MAT versus 0.10 and 0.07 for MAP, respectively. In contrast, Ra exhibited greater sensitivity to MAP (0.08 and 0.18) than to MAT (−0.01 and 0.04). GPP exerted biome-specific effects: in forests, high GPP enhanced Rh (0.18) more substantially than Ra (0.08), while in grasslands, elevated GPP significantly increased Ra (0.34) but suppressed Rh (−0.30). Moreover, these variables incorporated into the SEMs accounted for a greater proportion of the variation in Rh and Ra in grasslands (R² = 0.73 for Rh, 0.48 for Ra) as compared to forests (R² = 0.21 for Rh, 0.22 for Ra), suggesting the greater complexity in forest soil C dynamics. By using the whole yearly measured soil respiration data around the world, this study highlights the differential environmental regulation of Rh and Ra, providing critical insights into the mechanisms governing Rs variations under climate change. Full article

The flavor and color profiles of vegetables are crucial in determining their nutritional value, health benefits, taste, and visual appeal. The genomic characteristics of plants control these traits. Components such as sugars, organic acids, amino acids, phenolic compounds, and essential oils, as well as color pigments like anthocyanin, chlorophyll, carotenoid, and betalain, are synthesized in plants based on their genetic structure. Environmental factors like temperature, water, light, and soil can affect the production and intensity of these components. Long-term environmental changes, such as climate change, can significantly alter the dynamics of these components. This comprehensive review focuses on the genetic and environmental interactions underlying the flavor and color profiles of vegetables, with particular emphasis on the analysis of quantitative trait loci (QTL) associated with these traits. The article discusses the identification of genes that regulate taste and color in vegetables and how these genes have been localized in QTL mapping studies. It also discusses the influence of environmental factors on taste and color, as well as gene–environment interactions. Furthermore, it focuses on how this information can be used to improve plant breeding and sustainable agriculture and emphasizes that data from QTL analyses provide valuable insights into the integration of genetic and environmental approaches to improve vegetable quality and meet consumer preferences. In conclusion, the review aims to be a valuable resource for both researchers and professionals interested in the genetic and environmental aspects of taste and color in vegetables. Full article

Detecting trace metals in soil across geologically diverse terrains remains challenging due to complex mineral–metal interactions and the limited spatial coverage of traditional geochemical tests. This study develops a scalable VIS–NIR–SWIR spectroscopy and machine learning (ML) framework to predict and map soil concentrations of Cr, As, Cu, and Cd in the Aligudarz District, located within the geotectonically complex Sanandaj–Sirjan Zone of western Iran. Laboratory reflectance spectra (~350–2500 nm) obtained from 110 soil samples were pre-processed using derivative filtering, scatter-correction techniques, and genetic algorithm (GA)-based wavelength optimisation to enhance diagnostic absorption features linked to Fe-oxides, clay minerals, and carbonates. Multiple ML-based approaches, including artificial neural networks (ANNs), support vector regression (SVR), and partial least squares regression (PLSR), as well as stepwise multiple linear regression (SMLR), were compared using nested, spatial, and external validation. Nonlinear models, particularly ANNs, exhibited the highest predictive accuracy, with strong generalisation confirmed via an independent test set. GA-selected wavelengths and derivative-enhanced spectra revealed mineralogical controls on metal retention, confirming that spectral predictions reflect underlying geological processes. Ordinary kriging of spectral-ML residuals generated spatially consistent metal-distribution maps that aligned well with local and regional geological features. The integrated framework demonstrates high predictive accuracy and operational scalability, providing a reproducible, field-ready method for rapid geochemical assessment. The findings highlight the potential of VIS–NIR–SWIR spectroscopy, combined with advanced modelling and geostatistics, to support environmental monitoring, mineral exploration, and risk assessment in geologically complex terrains. Full article

This paper presents the results of assessing the influence of siltation factors in 23 ponds in one of the most agriculturally developed macro-regions of European Russia—the Central Russian Upland. Key natural and anthropogenic factors determining the intensity of pond siltation have been identified, and a typification of ponds has been developed to predict the rate of accumulation of bottom sediments in them. For the typification, statistical methods such as correlation analysis (Spearman’s coefficient), cluster and factor analysis, and the Random Forest machine learning algorithm were used. Correlation analysis revealed that the percentage of catchment cultivation has a significant effect (r = 0.55, p < 0.01) on the volume of bottom sediments, while soil loss (r = 0.47, p < 0.05) and vertical terrain dissection (r = 0.43, p < 0.05) have a moderate effect. The most important factors in the siltation process are the average slope of the catchment (24.5%), the percentage of cultivated soils (18.8%), and the average annual soil loss (14.1%). All factors were grouped into three clusters, which explained 77.8% of the variance. As a result, four pond types were identified, differing in their dominant limiting factors: pond hydrological characteristics, catchment morphometry, and the degree of anthropogenic transformation of the catchment. Verification of the typification was carried out based on the calculation of annual soil losses considering the sediment delivery coefficient; the discrepancies between the calculated and actual pond sediment volumes were 1.2–10.0%. The proposed approach, which recommends a multi-scale assessment of potential sediment formation volumes using remote sensing data and thematic mapping, offers heuristic potential for identifying the most degraded water bodies. This enables the planning of priority sites and rehabilitation measures for their restoration within the framework of regional soil and water conservation programs. Full article

Climate warming impacts arctic and subarctic lands, subjecting it to a generalized rise in soil temperature and causing changes in the surface cover. Land cover is a key control parameter for soil hydrothermal states, and its study by satellite imagery is necessary for monitoring boreal surface changes over time at large scales. Understanding the links between land cover and environmental conditions is also crucial to anticipate the impacts of atmospheric changes on continental surfaces. Sentinel-1 and Sentinel-2 data combined with a field campaign in July 2024 were used to produce a 10 m spatial resolution land cover map in the Abisko region, northern Sweden, covering 2180 km² and including three watersheds with an overall accuracy exceeding 94%. In parallel, temperature and precipitation fields were statistically downscaled at 100 m spatial resolution using topography, ordinary kriging based on weather stations and reanalysis. The relationships between surface areas and average summer temperature–precipitation clusters reveal that the vegetation distribution closely reflects the recent atmospheric conditions with the treeline following the 10.2 °C July–August isotherm in the considered area. This study provides a spatial basis for investigating the complex atmosphere–surface interactions and for assessing the sensitivity of boreal landscapes to ongoing climate warming. Full article