MotivationGroundwater ecosystems sustain a unique and globally important biodiversity but remain understudied due to sampling and exploration challenges, as well as a shortage of taxonomic experts. Groundwater ostracods, like other groundwater taxa, exhibit a high degree of endemism, rarity and subterranean specialisation, positioning them as potentially vulnerable organisms. To better understand biodiversity

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AimEcological networks describe the complex set of interconnections among species and their environment, and network structure can inform the stability, resilience, and functioning of ecosystems. Increasing attention is being paid to the mechanisms that determine species interactions. Phylogeny has informed our understanding of connections within networks, mostly by describing the strength of phylogenetic

AimWe aimed to examine the abiotic, biotic and anthropogenic drivers of non‐native plant species distribution along hiking trails in mountainous regions.LocationNine mountain regions across six continents, including North America (USA), South America (Argentina and Chile), Europe (Sweden, Norway, Czech Republic), Africa (South Africa), Asia (China) and Oceania (Australia).Time PeriodData were collected

AimDeep soils (> 30 cm) store considerable amounts of carbon and are often assumed to be less responsive to warming than topsoil. However, recent evidence indicates that deep soils are more sensitive to climate change in alpine grasslands, yet their influence on ecosystem productivity is not well understood. Here, we tested the key environmental drivers, particularly the roles of deep soil moisture

Understanding the relationship between intercity travel modes and the built environment from a functional perspective is essential for supporting and improving transportation services, coordinating regional development, and promoting regional sustainability. In urban regions, residents often engage in intercity travel to seek specific functions and services, and the choice of travel mode is closely

In an era, where, on the one hand, climate change constitutes a planet-defining threat but, on the other, technology provides unprecedented opportunities for connectivity, sharing and intelligence in transport, mobility hubs have emerged as a potential cornerstone for less car-dominated societies. Still in their infancy as a brand, mobility hubs promise to be visible, accessible, and integration-enabling

Measuring dynamic accessibility in the metro system is important for improving operations and for allowing passengers to plan their trips in advance under different circumstances. Yet, due to the large amount of data required, most accessibility studies do not consider the impacts of travel time uncertainty and individual preferences. In metro systems, smart cards record accurate travel information

Remote sensing is an essential tool for monitoring the extent and biophysical attributes of vegetation. Multi-sensor approaches, that can reduce the costs of developing high-quality datasets and improve predictive performance, are increasingly common. Despite this trend, the advantages of these data-fusion techniques are rarely reported beyond statistical performance. We use airborne lidar-derived

Building height serves as fundamental information for characterizing urban landscapes and morphology, as influencing various aspects of the urban environment. While traditional methods of obtaining building height are often limited by spatial coverage and proprietary constraints, remote sensing data provides an alternative for indirect estimation. Several height products developed across different

Vicarious calibration/validation (Cal/Val) is essential for ensuring the radiometric accuracy of spaceborne thermal infrared (TIR) sensors. Anticipating an increase in the number of TIR sensors in the near future, this study developed an automated vicarious radiometric validation method based on in situ water temperature measurements from 14 sites in lakes and bays across Japan, which are not specifically

Straw burning poses significant threats to local air quality and nearby public health by emitting harmful pollutants during specific seasons. Traditional satellite-based remote sensing techniques encounter difficulties in monitoring small-scale straw-burning events due to long revisit intervals and low spatial resolution. To address this challenge, unmanned aerial vehicles (UAVs) equipped with imaging

This paper examines the nonlinear influences of the quantity and quality of street-level greenery on active travel among older adults. The active travel information was obtained from the Study on Global Ageing and Adult Health conducted in Shanghai, China. Street-level greenery was assessed based on street view data and a deep learning approach, namely street view greenery quantity (SVG-quantity) and

The substitution relationship between ride-hailing and public transit significantly impacts urban transport carbon emissions. However, a quantitative analysis exploring the spatio-temporal dimensions of the substitution effect between these two modes of transport is still lacking. Furthermore, the motivations for travel and the associated activity networks remain unclear. This study proposes a spatio-temporal

There is a tendency in global production network (GPN) analyses to primarily focus on manufacturing and distribution, rather than tracking back to their raw material origins. This risks missing important insights at a time when the sourcing of key energy transition materials (ETMs) has become an area of acute geopolitical-economic importance. This article investigates efforts to facilitate the production

Accurate spatial distribution information of field features is critical for enabling autonomous agricultural machinery navigation. However, current perception systems exhibit limited segmentation performance in complex farm environments due to illumination variations and mutual occlusion among various regions. This paper proposes a low-cost UAV photogrammetry framework for centimeter-level 3D semantic

The rapid expansion of rubber plantations has led to significant ecological impacts, including deforestation and reduced carbon storage capacity, such as rubber-induced 4.1 million ha of forest loss in Southeast Asia (SEA). Remote sensing, essential for monitoring rubber expansion and supporting initiatives like REDD+ and EU deforestation regulations. However, due to the heterogeneity, dynamism, and

The physical dimension of cities and its spatial patterns play a crucial role in shaping society and urban dynamics. Understanding the complexity of urban systems requires a detailed assessment of their physical structure. Urban geography has long focused on framing typologies to represent common patterns in the urban fabric using various methodologies. However, only recent advancements in computational

Spatial and temporal data gaps present a significant challenge to high-frequency surface water mapping using satellite imagery. Utilizing observations from temporally close periods and multi-modal sensors for gap-filling is of critical importance. However, discontinuous pixel values inherent to conventional water maps hinder the application of deep learning methods, which are effective and popular

The increasing complexity and scale of remote sensing datasets, coupled with the challenges of accurately estimating algorithmic time efficiency, often lead to significant resource waste or even failure when using machine learning algorithms in urgent or resource-constrained scenarios. Accurate time efficiency estimation is critical for deploying effective algorithms, yet it remains challenging due

This study examines the global Crude Oil Maritime Transportation Network (COMTN) in the context of the Russia-Ukraine conflict, focusing on its key structural elements, port communities and interdependencies. The COMTN is constructed using AIS data, along with a trade network built from the UNComtrade dataset. An analysis of changes in community structures within the trade network is then conducted

The causal relationship between the built environment, car ownership, and travel-induced pollutant emissions remains obscured by residential self-selection (RSS) bias. This study leverages China's unique housing demolition and resettlement program to conduct a quasi-experiment, analyzing how the built environment impacts car ownership and travel-induced vehicle-related PM2.5 emissions in Shanghai.

Identifying urban villages (UVs) is essential for supporting urban renewal and governance, as these informal areas present significant challenges to urban sustainability. Existing multi-modal deep-learning studies have accurately identified UVs in individual cities; however, their scalability is constrained by substantial computational demands and data availability. Currently, efficient deep-learning

Accurate building damage assessment is crucial for post-disaster response, yet existing methods struggle to capture complex spatial relationships and contextual features needed for distinguishing damage levels. To address this, we propose the Gated Adaptive Multi-scale Spatial-frequency Fusion Network (GAMSF), a two-phase framework for building localization and damage classification. GAMSF integrates

The wheat powdery mildew (WPM) is one of the most severe crop diseases worldwide, affecting wheat growth and causing yield losses. The WPM was a bottom-up disease that caused the loss of cell integrity, leaf wilting, and canopy structure damage with these symptoms altering the crop’s functional traits (CFT) and canopy spectra. The unmanned aerial vehicle (UAV)-based hyperspectral analysis became a

Upright maize straw left in the field during autumn and winter significantly contributes to severe air pollution in agricultural ecosystems due to burning. It is essential to obtain the spatial distribution of upright maize straw quickly and accurately for effective management and environmental protection. However, identifying upright maize straw using remote sensing is difficult because its spectral

This study envisions a future of operating shared autonomous vehicles (SAVs) to provide mobility services in 374 small and medium-sized urban areas (population < 375,000) in the United States. Specifically, this study 1) generates large-scale synthetic commuting trip data for 374 urban areas, 2) employs an agent-based modeling framework to simulate the operations of SAV fleets serving commuters in

“Ride-pooling” services provided by transportation network companies have gained substantial popularity and demonstrate significant potential for integration with mass rapid transit to form competitive multimodal transportation options. However, data-driven studies on these services, particularly those using spatial-temporal analysis, remain complex and underexplored. This study examines the intricate

Vulnerability envelope is a recently proposed systematic tool for analyzing transportation network vulnerability, which evaluates the lower and upper bounds of vulnerability and reveals the possible range of network performance degradations under various disruption severities. However, in urban transit systems, vulnerability envelopes are typically evaluated using a simplified node-link topology. While

The environmental polycrisis, including climate change, biodiversity loss, and water scarcity, touches down in agricultural global production networks (GPNs) through different environmental interactions. Drawing on environmental economic geography and critical systems thinking, we propose a novel conceptualization to trace environmental interactions of influence, disruption, and synergy, and their

Understanding the spatial and temporal distribution of irrigated cropland at the field scale is essential for managing irrigation water use and addressing the water-food nexus. While global and regional irrigation products exist, they often classify irrigated crops based on machine learning principles, where irrigated crops outperform rainfed ones. However, these methods typically lack mechanistic

AimNitrogen (N) deposition and climate warming are the two most important factors driving soil N2O emissions in terrestrial ecosystems. Both biotic and abiotic factors impact N cycling processes and functional gene abundances, but their global responses and patterns to mineral N addition and warming and the regulatory factors affecting N2O emissions remain unclear.LocationGlobal.Time Period1986–2022

AimSpecies are often expected to track climate change via changes in their abundances and distributions. This climate tracking hypothesis generates the prediction that warming temperatures should cause species to show population increases at their poleward range limits and population decreases at their equatorward range limits. We tested these predictions for 516 species of North and Central American

AimAnthropogenic phosphorus (P) input profoundly affects carbon (C) and nutrient dynamics in terrestrial ecosystems, which poses a threat to soil health and nutrient sustainability. Litter decomposition is crucial for maintaining soil C and nutrient pools, yet there is a significant knowledge gap regarding the effects of anthropogenic P input on terrestrial litter decomposition.LocationTerrestrial

AimAnthropogenic noise is a global pollutant that threatens biodiversity. However, we currently lack effective methods to assess and compare the impacts of anthropogenic noise on extended terrestrial species. This can be critical for the majority of species that lack conservation attention and empirical measurements.LocationGlobal.Time Period1963–2023.Major Taxa StudiedBats.MethodsWe leverage the conserved

Phase filtering is crucial for ensuring accurate phase unwrapping in the processing of Interferometric Synthetic Aperture Radar (InSAR) data. Traditional filtering methods often struggle to effectively suppress noise and accurately preserve phase edge information when processing InSAR interferometric phases with complex noise and large gradient deformations. In response to this challenge, we have proposed

Canadian prairies areamong the world’s most endangered ecosystems, and the identification of native grasslands is crucial for supporting grassland management and wildlife conservation. However, the current amount, distribution, and dynamic changes of native grassland remain uncertain, partly due to the difficulty of separating native grassland with other land cover types, especially tame grassland

AimPlant senescence largely influences the global carbon cycle by regulating the growing season length. However, the driving mechanisms of plant senescence remain unclear, particularly the role of developmental factors. This study aims to investigate how environmental and developmental factors drive autumn senescence and evaluate whether woody and herbaceous plants exhibit divergent responses to these

The association of air pollution with subjective wellbeing and mental health is a topic of growing discussion in the fields of environment science, public health, psychology and geography of health. However, short-term (activity/trip episode and momentary) to medium-term (day and week) air pollution exposure, however, has not been fully studied in relation to long-term (month and year) subjective wellbeing

Slow-moving landslides pose a substantial threat to communities and infrastructure, with annual creeping distances ranging from a few mm to hundreds of meters. To protect local communities from the landslide motion, landslide displacement retrieving or monitoring is necessary. However, traditional field investigations are time- and labor-consuming, which may limit the understanding of the landslide

The “15-minute city” concept provides a framework for livable, sustainable cities but often overlooks human mobility, public transit, and factors beyond proximity when measuring accessibility. This study introduces an interpretable, individual-level, trajectory-based accessibility measure considering connections to amenities through the transportation system, affordability, and operating hours in addition

Accurate estimation of individual tree above-ground biomass (AGB) is crucial for regional forest AGB measurement. In this study, 64 individual trees of Pinus kesiya var. langbianensis, exhibiting a range of diameters, were felled from natural forests in mountainous regions to develop region-specific allometric equations for AGB. To enhance AGB estimation accuracy, we integrated unmanned aerial vehicle

Urban agglomerations, formed by multiple cities connected through transportation and information networks, play a pivotal role in the sustainable development of human settlements and communities. This study introduces a data-driven approach to analyze urban connectivity using high-precision 10-meter panchromatic nighttime light (NTL) data from SDGSAT-1, integrated with OpenStreetMap (OSM) road data

With the surge in cultural heritage tourism, personalized tourism route planning has emerged as a crucial strategy to enrich tourists’ experiences. Our objective is to establish a personalized tourism route recommendation and evaluation system. This system will leverage the Analytic Hierarchy Process (AHP) and Delphi method to assess the factors influencing the travel decisions of city-life lovers

Ground surface displacements monitored by interferometric synthetic aperture radar (InSAR) techniques are a useful indicator for revealing precursors and assessing risks of tailings dam failures. However, the long-term and large-magnitude consolidation settlements, which impose low risks on dam collapses, usually dominate InSAR displacement observations. The phenomenon would cause misleading in failure

The integration of machine learning (ML) algorithms with remote sensing (RS) data offers significant potential for enhancing dynamic forest characterization and change detection through high temporal density mapping. This study evaluates the effectiveness of RS-ML models for forest mapping in parts of southern Nigeria, with particular emphasis on assessing their temporal scalability using multi-sensor

Accurately monitoring impervious surface (IS) changes is vital for assessing the effects of urbanization on human activities and its broader environmental, economic, and social sustainability. However, optical building shadows pose significant challenges for IS extraction in high-density urban core areas. Even though combining optical and synthetic aperture radar (SAR) imagery makes it easier to identify

Coal fires present significant global environmental and energy challenges, posing substantial barriers to achieving carbon-neutral goals. Thermal Infrared Remote Sensing (TIRS) technology, which is used to retrieve land surface temperatures, plays a crucial role in detecting coal fires. However, its accuracy suffers from solar radiation interference. In addition, there is limited research focused specifically