Search Results (1,159)

Agricultural intensification in the steppe zone of Central Asia has increasingly exacerbated the tension between food security and the conservation of natural ecosystems. This study proposes an integrated landscape planning methodology for Kazakhstan by adapting the German Landschaftsplanung approach and the Chinese concept of “ecological red lines” to the conditions of post-Soviet land use. The focus is on the analysis of soil degradation and biodiversity loss. It is applied to the Ayyrtau district of the North Kazakhstan region, an area characterized by a heterogeneous landscape mosaic composed of arable land, a substantial share of degraded land, vulnerable steppe ecosystems, and woodlands. Using GIS analysis and remote sensing data, we identify nine types of landscape units, which are operational units for evaluating landscape functions and planning priorities. The result of this work is a map of the zoning of the planning area, which defines seven modes of eco-oriented management, ranging from strict protection to active agricultural production. The study demonstrates that the transition from an extensive monocultural system to a landscape-adaptive strategy can improve the spatial coordination between agricultural use and ecological protection, strengthen the regional ecological framework, and enhance ecotourism potential. For the first time, an integrated zoning system is presented, designed for use by local authorities as a decision support tool aimed at preventing land degradation. Full article

Effective monitoring of inland waters requires approaches capable of capturing high spatial and temporal variability. Traditional in situ sampling provides accurate point measurements but lacks spatial coverage, while satellite remote sensing is often limited by coarse spatial resolution and cloud cover. To address these limitations, this study developed and validated an integrated monitoring platform combining an Autonomous Surface Vehicle (ASV) and a drone-based multispectral imaging system for flexible, high-resolution water quality monitoring. The study was conducted in two contrasting aquatic environments in Alabama: the North River–Lake Tuscaloosa system and the Sardine Pass and Duck Skiff Pass tidal inlets in Mobile Bay. A HyCAT ASV equipped with a YSI EXO2 multiparameter sonde collected continuous in situ measurements of turbidity, chlorophyll-a (Chl-a), and fluorescent dissolved organic matter (fDOM), which served as water-truth for a MicaSense Dual multispectral camera onboard a DJI Inspire-2 drone platform acquiring imagery in 10 spectral bands at ~8 cm spatial resolution. Machine learning models, including ensemble and Random Forest approaches, were developed and compared with traditional empirical algorithms. Ensemble models consistently outperformed empirical approaches, while Random Forest models achieved the highest accuracy and best generalization across variable environmental conditions. Compared with Sentinel-2 and Landsat-8 imagery, the drone-derived maps resolved fine-scale spatial variability, including sediment plumes and near-shore gradients, that could not be detected by satellite sensors. To facilitate operational implementation, the RS-WaterQuality Mapper software tool was expanded to support ensemble and Random Forest analyses for MicaSense imagery. Overall, the integrated ASV–drone system demonstrated substantial advantages over traditional sampling and satellite remote sensing, including rapid deployment, user-controlled acquisition timing, high spatial resolution, and improved monitoring of small and optically complex water bodies, highlighting its potential for adaptive water resource management and early warning applications. Full article

Accurate digital elevation models (DEMs) are essential for hydrological modelling and floodplain analysis, particularly in low-relief floodplains where small elevation errors can significantly affect flow routing and inundation extent. This study evaluated the vertical accuracy of six freely available global DEMs across the Flinders River catchment, North Queensland, Australia, using 30,916,100 quality-filtered ICESat-2 ATL06 elevation points for regression-based bias correction and airborne LiDAR datasets from five benchmark regions for independent validation. The evaluated DEMs included TANDEM-X, Copernicus DEM, ALOS AW3D30, SRTM, ASTER GDEM, and the Hydrological DEM. Vertical accuracy was assessed using mean error (ME), root mean square error (RMSE), and residual dispersion before and after calibration. Results showed substantial pre-calibration bias in the Hydrological DEM (ME = −2.93 m) and SRTM (ME = −2.66 m), whereas Copernicus DEM showed minimal initial bias (ME = −0.01 m). Regression-based correction reduced mean errors to within ±0.13 m across all DEMs. SRTM showed the largest improvement, with RMSE decreasing from 3.20 m to 0.55 m, while TANDEM-X achieved the highest post-calibration accuracy (RMSE = 0.14 m). Independent LiDAR validation confirmed improved vertical accuracy while preserving terrain morphology and river gradients. Full article

This study proposes a Variational AutoEncoder (VAE)-based Quality Controlled (QC) generative augmentation framework for North Atlantic right whale (NARW) upcall detection. Existing generative augmentation methods can generate synthetic samples; however, they do not provide a sample-level criterion for determining whether each generated sample is a positive sample that contributes to improved detector performance or a synthetic outlier that should be removed. This study learns manually extracted upcall frequency contours using a 1D-VAE and evaluates generated contour candidates in a 10-dimensional acoustic morphology feature space. The QC score is computed with respect to the reference distribution of manually extracted real upcall contours, and stochastic acceptance probabilities for borderline samples around the hard threshold are calibrated using same-call manual re-extraction variability. QC-passed contours are converted into detector training spectrograms using smooth amplitude modulation and Gaussian noise injection based on SNR statistics. Using 30,000 acoustic segments from the Kaggle NARW dataset, Original, Denoising Diffusion Probabilistic Models (DDPM), Contour-VAE without QC, and VAE-QC conditions were compared under the same detector and augmentation budget. VAE-QC with α = 0.97 achieved the highest mean AUC of 0.901, outperforming Original training (0.802), DDPM (0.845), and Contour-VAE without QC (0.810). Feature distribution and QC-score analyses further showed that VAE-QC suppresses morphology outlier tails observed in unfiltered generation. These results indicate that the key factor in generative augmentation is the QC process that defines feature boundaries useful for detector learning and selects synthetic positive samples accordingly. Full article

Precision agriculture (PA) has emerged as a transformative approach for improving agricultural productivity, resource-use efficiency, and environmental sustainability through the integration of digital technologies, including Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and autonomous systems. Although previous reviews have primarily emphasized technological innovation, adoption trends, or environmental outcomes, they have provided limited synthesis of the economic mechanisms linking technology adoption, resource allocation, production efficiency, investment performance, and long-term sustainability. A structured narrative–systematic review was conducted using peer-reviewed research retrieved from Scopus, Web of Science, and Google Scholar, covering studies published between 2004 and 2026. An integrated analytical framework combining technology adoption theory, resource economics, and production-efficiency models was employed to explain how digital technologies generate economic value while identifying methodological limitations, geographical bias, unresolved research questions, and future research priorities. The review demonstrates that GPS-guided machinery, variable-rate technologies, smart irrigation systems, AI-driven decision-support tools, and integrated digital platforms improve water- and nutrient-use efficiency, labor productivity, production efficiency, and farm profitability. However, economic performance remains highly context-dependent, varying according to farm size, crop type, climatic conditions, institutional support, digital infrastructure, resource scarcity, and policy environments. Methodological inconsistencies in return on investment (ROI), net present value (NPV), lifecycle costing, ecosystem-service valuation, and environmental externality assessment reduce comparability among studies and complicate evidence-based policymaking. The review further identifies a pronounced geographical concentration of evidence in North America, Europe, and Australia, with comparatively limited understanding of PA economics in China, India, Brazil, Sub-Saharan Africa, and Southeast Asia. Persistent challenges include high capital costs, unequal access among smallholder farmers, data governance concerns, interoperability limitations, uncertainty in long-term investment performance, and limited integration of agricultural insurance, climate-risk management, and digital finance. By integrating economic theory, methodological comparison, geographical analysis, sustainability valuation, and policy perspectives within a unified conceptual framework, this review highlights the need for standardized economic evaluation methodologies, broader geographical representation, and interdisciplinary research to support evidence-based policy and the sustainable digital transformation of global agriculture. Full article

Influenza A virus is a segmented, negative-sense RNA virus. Since the early 2020s, H5 clade 2.3.4.4b viruses have spread widely across Europe, Africa, and Asia, affecting wild birds and poultry. Costa Rica reported its first H5 clade 2.3.4.4b avian influenza virus (AIV) case on 19 January 2023. This study describes an outbreak in backyard chickens and ducks. Initial serum samples collected on 24 January showed three chickens negative for AIV, while one duck tested positive by ELISA and agar gel immunodiffusion (AGID). During a second visit on 27 January, three of four chicken sera collected tested positive by ELISA and AGID. Tissue samples were positive for influenza A by qRT-PCR. Next-generation sequencing recovered five of the eight viral genomic segments, and the hemagglutinin cleavage site sequence (REKRRKR↓G) confirmed a highly pathogenic avian influenza virus (HPAIV) H5 strain. The samples were submitted to the National Veterinary Services Laboratories for confirmation. Serological testing showed reactivity to North American low pathogenic H5 antigens, and qRT-PCR amplified influenza A and N1 genes. Virus isolation and next-generation sequencing (NGS) of all eight viral genome segments were successfully performed at the WHO Collaborating Centre at St. Jude Children’s Research Hospital (SJCRH). Full article

Rapid urbanization presents a fundamental challenge to global sustainable development, as urban expansion increasingly conflicts with resource constraints, ecological carrying capacity, and carbon emission mandates. Serving as a highly spatially heterogeneous laboratory, China offers a critical context for embedding green development into urban efficiency assessments. This study reconceptualizes new urbanization efficiency (NUE) through a multidimensional framework encompassing resource inputs, coordinated development processes, and sustainable outcomes. Using a panel of 281 prefecture-level cities, we evaluated NUE via a remote-sensing-constrained Super-SBM model, utilizing LISA time paths and an interpretable spatial machine learning framework (GWR-XGBoost-SHAP) to unpack its spatiotemporal dynamics. Key findings indicate: (1) China’s NUE exhibited a fluctuating upward trajectory, transitioning from an east-high/west-low to a south-high/north-low spatial pattern. (2) Spatiotemporal analysis revealed strong spatial inertia and profound path dependency in North China, characterizing it as a persistent low-value basin, whereas southeastern coastal cities demonstrated dynamic, path-breaking trajectories. (3) While green development intensity, industrial upgrading, and technological innovation emerged as primary drivers, they operate through complex nonlinear mechanisms. Specifically, green development and innovation exhibit threshold-triggered synergies, population agglomeration acts as a nonlinear amplifier, and external openness presents context-dependent negative interactions. These findings refine NUE measurement methodologies and provide a transferable analytical framework to inform differentiated, place-based urbanization policies for regions navigating the friction between urban growth and ecological limits. Full article

Drought is one of the major factors affecting terrestrial ecosystem functioning under climate change. However, the interactions among different drought indices and the response of gross primary productivity (GPP) to them remain unclear. This study used multi-source remote sensing data of China from 2003 to 2020, employing the standardized precipitation evapotranspiration index (SPEI), temperature condition index (TCI), soil moisture condition index (SMCI), and vegetation condition index (VCI) to characterize the spatiotemporal dynamics of drought and quantify the response mechanism between drought indices and GPP. Partial correlation analysis and structural equation modeling were used to distinguish the direct and indirect effects of different drought types on GPP. The results show that GPP exhibited a fluctuating upward trend from 2003 to 2020, ranging from 163.71 to 458.56 g C·m⁻². In 2005, GPP declined significantly across most regions of China relative to 2004, decreasing by 14.47% in North China, 13.93% in Central China, 12.05% in East China, 8.45% in South China, 4.21% in Northwest China, and 4.15% in Northeast China, whereas Southwest China exhibited a slight increase of 0.34%. Different types of drought exhibited distinct occurrence characteristics. SMCI exhibited a drought frequency of 44.35% and a mean duration of 75.64 days. Among the four indices, SPEI showed the lowest frequency (12.54%) and the highest intensity (0.14), whereas TCI exhibited both the highest frequency (60.22%) and the longest duration (98.46 days), and VCI yielded the lowest drought severity (1.96). The TCI was considered a major driver of GPP variations, showing a significant positive correlation with GPP in all regions (r = 0.45–0.88, p < 0.001). In contrast, the SPEI had a relatively weak impact on GPP. The “TCI→VCI → GPP” pathway represented the main pathway of drought–GPP interaction at the national scale. This study provides methods for assessing the response characteristics of GPP under future climate change. Full article

Accurate characterization of multi-band solar radiation is essential for optimizing photovoltaic (PV) site selection and supporting carbon neutrality targets. Shandong Province, a major economic and energy-consuming province in eastern China, possesses abundant solar resources but exhibits pronounced spatiotemporal heterogeneity driven by complex terrain, rapid urbanization, and variable cloud cover. Based on high-resolution CARE (Cloud Remote Sensing, Atmospheric Radiation and Renewable Energy Application) satellite products (0.1°, hourly, 2016–2020) combined with SRTM DEM, CLCD land use, and ERA5 cloud data, this study systematically analyzes the spatiotemporal distribution and driving factors of four solar radiation components—shortwave radiation (SWR), photosynthetically active radiation (PAR), UVA, and UVB—across Shandong Province. Key findings are as follows: (1) All four radiation components exhibit a consistent spatial pattern characterized by higher radiation intensities in the eastern coastal and northern plain regions, which gradually decrease toward the western inland and southern mountainous areas. Provincial five-year means are SWR 186.6 W/m², PAR 86.3 W/m², UVA 11.4 W/m², and UVB 0.3 W/m², with high-value zones concentrated in the Jiaodong Peninsula coast and the North Shandong Plain. (2) During 2016–2020, short-term increasing tendencies were observed across 80.4% (SWR), 78.0% (PAR), 85.1% (UVA), and 91.3% (UVB) of the province, while all components declined in winter. (3) STL decomposition reveals a “down-up-down” multi-year trend, a unimodal annual seasonal cycle peaking in May, and residuals closely associated with extreme weather events. (4) Geodetector analysis identifies cloud cover as the dominant factor (q = 0.332), followed by elevation (q = 0.100); and nonlinear enhancement characterizes all factor interactions, especially cloud cover × elevation (q = 0.393) and cloud cover × land-use (q = 0.347), revealing a “climate–topography–human activity” multi-level coupling mechanism. Built-up land records the lowest SWR (172.4 W/m²) and spatially coincides with radiation low-value zones. These results provide a scientific basis for PV site optimization and the realization of carbon neutrality goals in Shandong Province. Full article

A comprehensive understanding of the spatiotemporal evolution of water use efficiency (WUE) and its driving mechanisms is essential for sustainable water resource management in the Huang–Huai–Hai Plain (HHHP), a critical agricultural production base in northern China. Based on multi-source remote sensing datasets from 2001 to 2024, this study adopted the Sen-MK trend test and optimized XGBoost-SHAP framework to characterize the spatiotemporal variations in WUE and quantify corresponding driving forces: (1) WUE exhibited a predominantly rising trend across 78.53% of the study area, with significant increases concentrated in the southwestern Shandong hilly region; high WUE values (1.68–1.74 g C·m⁻²·mm⁻¹) occurred in the eastern Shandong hills and southwestern North China Plain, while low values (1.50–1.56 g C·m⁻²·mm⁻¹) were found in the northern North China Plain and southeastern Bohai Bay area. (2) Among vegetation types, shrubland showed the highest multi-year mean WUE (1.75 g C·m⁻²·mm⁻¹) and wetland the lowest (1.34 g C·m⁻²·mm⁻¹); cropland displayed the most rapid increasing trend (0.0045 g C·m⁻²·mm⁻¹·a⁻¹), while wetland showed a decreasing trend. (3) The HHHP showed low interannual volatility and strong persistence of WUE, with stable interannual WUE and historical persistence suggesting that the increasing trend may continue in most areas. (4) Spatial variation in WUE is primarily driven by NDVI and temperature, with NDVI having the greatest influence, accounting for approximately 40.36% of the vegetated area, while temperature accounts for 26.43%, ranking second; precipitation and the aridity index exerted secondary regulatory effects, with positive effects mainly in the Huang–Huai Plain and negative effects in the North China Plain. This study elucidates the evolutionary mechanisms of WUE under coupled climate–vegetation influences, providing critical scientific evidence for optimizing regional water resource allocation and management and promoting sustainable agricultural practices in this water-limited region. Full article

Most remote-sensing assessments within China’s large-scale forestry engineering regions have relied primarily on greenness indicators, leaving productivity recovery and remaining restoration potential insufficiently characterized. Here, we assessed vegetation greenness and productivity recovery status, together with habitat-constrained remaining recovery potentials, within China’s Eight Forestry Engineering Regions from 2001 to 2025 using long-term Normalized Difference Vegetation Index (NDVI) and net primary productivity (NPP) datasets and climatic, topographic, and soil variables. Both NDVI and NPP increased significantly across all regions, with overall trends of 0.0029 yr⁻¹ and 3.38 g C m⁻² yr⁻¹ per year, respectively. The middle Yellow River shelterbelt region showed the strongest increasing trend, with 94.4% and 98.4% of vegetated pixels exhibiting significant increases in NDVI and NPP, respectively. The sliding-window similar-habitat model revealed that most regions have already approached their habitat-constrained potential states, though substantial remaining potential persisted in parts of the Three-north shelterbelt program and ecotonal areas. Greenness and productivity recovery potentials were positively correlated (Pearson r = 0.637), yet 14.0% of the vegetated area exhibited low greenness but high-productivity remaining potential, indicating that apparent greening does not necessarily translate into equivalent productivity recovery. These findings highlight the importance of jointly evaluating vegetation structural and functional recovery using greenness and productivity indicators. They also provide a scientific basis for differentiated restoration assessment and management within China’s large-scale forestry engineering regions. Full article

Flash drought, an abruptly intensifying meteorological anomaly, poses a growing threat to agricultural production, ecosystem stability, and regional carbon cycling, particularly in croplands of monsoon regions. Existing studies have largely focused on point-scale identification or conventional vegetation indices, whereas the regional spatiotemporal evolution of flash droughts and crop-specific differences in photosynthetic recovery remain poorly understood. Using multi-source remote sensing data for the North China Plain and the Middle–Lower Yangtze Plain during 2001–2024, this study integrated triple-collocation error assessment, root-zone soil-moisture percentile identification, connected-component tracking, and Random Forest–SHAP analysis to characterize flash drought trajectories and their vegetation impacts. The results showed that the southern Middle–Lower Yangtze Plain exhibited a high-frequency but low-intensity pattern, whereas the central North China Plain was characterized by lower frequency yet higher intensity and longer duration. Rice-based systems were more vulnerable to frequent flash drought shocks, whereas rainfed and rotation systems faced stronger cumulative risks. Solar-induced chlorophyll fluorescence (SIF) responded to flash droughts 6–9 days earlier than gross primary productivity (GPP), and all cropping systems displayed a “rapid physiological response–lagged carbon-assimilation recovery” pattern. The month of occurrence, drought duration, and decline rate were identified as the dominant factors governing photosynthetic recovery. These findings extend the flash drought monitoring framework to incorporate regional connectivity and crop recovery mechanisms, providing a remote-sensing basis for agricultural early warning, drought mitigation, and food-security management. Full article

Background: Preterm birth affects about 10% of births globally, often resulting in neurodevelopmental delays and disrupted parent-infant bonding. In low-resource settings, such as South Africa, neonatal intensive care unit (NICU) interventions require contextual adaptation. The NeuroSense PremmieEd Parenting Educational Intervention (PremmieSense) was developed to strengthen parent-infant bonding and promote neuroprotective care during NICU admission. Objectives: To describe the development, components, and pilot delivery of the PremmieSense intervention using the Template for Intervention Description and Replication (TIDieR) framework and document contextual adaptations and implementation lessons in low-resource NICUs. Approach: This project report outlines PremmieSense according to TIDieR. The programme comprises a picture-based booklet in English and Setswana, supplemented by facilitator-led group sessions delivered by trained healthcare professionals. It was piloted at two public-sector NICUs in the North West province of South Africa (N = 60 mothers; 30 per site). Parent knowledge was measured using the Knowledge of Preterm Infant Behavior (KPIB) scale, and stress was measured using the Parental Stress Scale: NICU (PSS:NICU tool). Quantitative outcomes are reported separately in a companion paper. Findings: PremmieSense was feasible and acceptable in low-resource NICUs. Logistical challenges including early discharges, staff constraints and language needs required pragmatic adaptations. The modular, multilingual design supported flexible delivery. TIDieR reporting facilitates replication and contextual adaptation. Conclusions and Recommendations: PremmieSense shows promise as a culturally appropriate and adaptable intervention for resource-constrained NICUs. Future work should tailor content to gestational age, prior parenting experience, and literacy, expand implementation, and assess long-term outcomes. Full article

This study evaluates the spatial variability of hydric stress in the State of Sinaloa, northwestern Mexico, through the integrated analysis of hydroclimatic variables, multispectral remote sensing indicators, and environmental factors. Historical hydroclimatic conditions were analyzed using meteorological records from 1961 to 2020, whereas Landsat 8 imagery acquired on 7 July 2025, was used to evaluate the spatial expression of hydric stress. Reference evapotranspiration (ETo) was estimated using the FAO-56 Penman–Monteith methodology, and hydrological deficit conditions were determined from the relationship between precipitation (P) and ETo. Spectral indicators including land surface temperature ($\overline{T}$_a), the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and the NDWI/MNDWI relationship were used to evaluate vegetation response, surface moisture conditions, and thermal anomalies associated with hydric stress. The results revealed persistent conditions where ETo systematically exceeded P, with hydrological deficit values ranging from approximately −1600 mm·year⁻¹ to localized positive values near 50 mm·year⁻¹. The most severe deficits were concentrated within the northwestern and north-central agricultural valleys of Sinaloa. Statistical validation revealed significant negative relationships between hydrological deficit and all evaluated spectral indicators. The strongest association was observed for MNDWI (R² = 0.387), followed by NDWI/MNDWI (R² = 0.277), NDWI (R² = 0.220), and NDVI (R² = 0.134), confirming the sensitivity of vegetation and moisture-related indicators to long-term hydrological stress conditions. Spatial analyses revealed a strong correspondence among low NDVI, negative NDWI and MNDWI responses, elevated $\overline{T}$_a, and regions characterized by high atmospheric evaporative demand. Additional spatial validation integrating land-use and vegetation-cover changes (1993–2011), regional geology, topography, and the distribution of highly productive agricultural valleys demonstrated that the most severe hydrological deficits coincided with areas affected by vegetation-cover loss, agricultural expansion, and intensive land use. Although these datasets correspond to different observation periods, they collectively reflect the cumulative environmental effects associated with persistent hydrological stress across the region. The combined effects of hydrological imbalance, forest-cover reduction, and agricultural intensification have progressively reduced ecosystem resilience and increased environmental vulnerability throughout one of the most productive agricultural regions of northwestern Mexico. These findings provide a scientific basis for water-resource management, territorial planning, ecosystem restoration, and climate-adaptation strategies under increasing water-scarcity conditions. Full article

Deforestation remains a crucial Anthropocene challenge, driving biodiversity loss, carbon emissions, and socio-ecological disruption. Despite extensive study, the long-term structure, thematic evolution, and collaborative patterns of deforestation research remain insufficiently synthesized. This bibliometric analysis examines 5091 publications from WoS and Scopus (1974–2025), using RStudio (version 4.5.2 (31 October 2025)), VOSViewer (version 1.6.20), and Excel to analyze publication trends, citation patterns, thematic clusters, and collaboration networks. Results show rapid growth after 2000, with citation peaks in 2010 and 2020. Major thematic clusters include deforestation, climate change, agriculture, governance, REDD+, and remote sensing. Environmental Research Letters is the most influential journal; Fearnside, P., is the leading author, and the UC system is a top institution. The USA and Brazil lead nationally, with the Amazon, Congo Basin, and Southeast Asia as primary geographic foci, reflecting persistent North–South collaboration dynamics. Limitations include reliance on English-language publications and title-only search criteria, which may underrepresent non-Anglophone research. Future research should expand to multiple languages, incorporate gray literature, and examine the policy impacts of deforestation-free supply chain regulations, such as the EUDR. This review underscores deforestation science as a growing, multidisciplinary field that requires the integration of social and ecological sciences, AI, and geospatial tools, alongside stronger research-policy linkages and enhanced capacity in forest-affected regions. Full article