Search Results (786)

Vegetation is often retained in river channels for habitat conservation and restoration and to facilitate nature-based flood management, and its hydraulic effects are typically represented using flow resistance coefficients in two-dimensional (2D) models. This study evaluated two Manning’s roughness parameterizations in HEC-RAS 2D against large-scale experimental data for grouped and isolated willow patches under high- and low-flow conditions. A distributed approach, which represents spatially varying total resistance using a momentum-based resistance formulation, was compared with a spatially uniform bulk coefficient applied to the entire reach. Because the bulk coefficient was back-calculated from the measured experimental data, it served as an observation-based benchmark rather than an independently derived prediction. A sensitivity analysis established a terrain resolution of 0.001 m and a mesh size of 0.25 m as appropriate for the simulations. The distributed approach consistently underestimated water levels, exhibited an incomplete but directionally correct response to changes in the drag coefficient for grouped patch configurations, and was negligibly sensitive to changes in the drag coefficient for isolated patch configurations. In contrast, the bulk approach reproduced water levels more closely but overpredicted them for grouped patch configurations and could not resolve local flow structures. Patch arrangement appeared more influential than vegetation density; however, their individual effects could not be separated because the grouped layout was also the densest. These findings provide guidance for selecting resistance parameterizations in 2D models of partly vegetated channels. Full article

Under the background of urban intensification, the moderating role of green space patterns on Urban Heat Island (UHI) effects has become a significant topic. This study aims to systematically explore the moderating mechanisms of green space patterns on the UHI effect and their spatiotemporal evolution using Nanjing as a case study. We utilized data including the Urban Density Index (UDI), Land Surface Temperature (LST), Green Space Index (GSI), and landscape pattern indices (Patch Density (PD), Largest Patch Index (LPI), and Landscape Shape Index (LSI)) for the years 2012, 2016, and 2020. Pearson correlation analysis, Moran’s I spatial autocorrelation, and a Coupling Coordination Degree Model (CCDM) were employed to quantify these interactions. Urban intensification was associated with a more pronounced UHI effect, as indicated by positive spatial autocorrelation (Moran’s I > 0.4) and a positive correlation between UDI and LST (r = 0.19 in 2020). Conversely, contiguous green spaces demonstrated cooling potential; the correlation between LPI and LST became more negative from r = −0.12 in 2012 to r = −0.25 in 2020, while positive PD-LST correlations were observed in some study years. The spatial mean coupling coordination degree (D) across valid 500 m × 500 m grid cells increased from 0.50 in 2012 to 0.60 in 2020, although coordination in high-density central urban areas remained low (D < 0.3). The fragmentation of green spaces restricts their capacity to regulate urban thermal environments. To optimize green space layout and effectively mitigate the UHI effect, urban planning should prioritize improving green connectivity, expanding contiguous large green areas, and promoting green roofs. Full article

This paper proposes Sensor-Initialized Gaussian Splatting (SIGS), a method that uses panoramic depth data acquired by the LiDAR sensor in a smartphone for three-dimensional (3D) scene reconstruction and VR space generation. Traditionally, 3D scene generation has required specialized knowledge and significant time, posing challenges for its application in VR. In particular, point clouds estimated by Structure from Motion (SfM), which are used for initializing 3D Gaussian Splatting (3DGS), have had limitations in density and accuracy. SIGS addresses these challenges by utilizing high-accuracy point clouds directly acquired from an iPhone’s LiDAR sensor for 3DGS initialization. For this research, a dedicated smartphone application called Panoramic Depth Recorder (PDR) was developed to simultaneously capture RGB images, depth images, point cloud data, and camera position and rotation information while the iPhone is rotated. These point clouds are then integrated into a common world coordinate system. For outdoor scenes, geometric information over a wider range is supplemented by combining near-field LiDAR data with far-field point clouds estimated by SfM. Experiments demonstrated that SIGS improved rendering accuracy (Structural Similarity Index Measure and Learned Perceptual Image Patch Similarity) and visual quality compared to conventional methods initialized solely with SfM. The generated scenes exhibited fewer artifacts and reproduced more faithful geometric shapes, with improved floor surfaces and ceiling irregularities. The mesh data of the generated 3D scenes is designed for use in VR environments. After conversion from PLY to the FBX format using Blender, they can be imported into Unity, enabling collision detection and user movement control within the VR space. This opens up possibilities for applications such as creating digital twins of robot training environments to reproduce real-world spaces in VR applications. Full article

This paper examines the influence of urbanisation on avifaunal communities along an urban–rural gradient in Kannur District, Kerala. Urbanisation and associated anthropogenic activities are known to alter habitat structure, reduce environmental quality, and promote biotic homogenisation by favouring a limited set of disturbance-tolerant species. Birds, being widely distributed, ecologically diverse, and sensitive to habitat modification, serve as effective bioindicators for assessing environmental change. This study aimed to evaluate whether urbanisation functions as an ecological filter influencing bird species richness, abundance, and diversity, and to test the applicability of the Intermediate Disturbance Hypothesis along the urban–suburban–rural gradient. The study area was classified into three habitat categories—urban, suburban, and rural—based on building density using ArcGIS. A total of 60 sampling sites were selected, with 20 point-count locations representing each habitat category. Bird monitoring was carried out using standard point-count methods across two seasons. Species diversity and abundance were analysed using PAST (PAleontological STatistics) software. During the January–February season, a total of 428 individual birds representing 44 species were recorded, whereas the March–April season yielded 309 individuals belonging to 39 species. The results did not support the Intermediate Disturbance Hypothesis, as avifaunal diversity was consistently highest in rural habitats, followed by suburban and urban habitats in both seasons. The findings indicate that urbanisation acts as a strong ecological filter, leading to a marked reduction in bird species richness and diversity in highly built-up areas. This pattern is likely driven by habitat simplification, loss of native vegetation, increased anthropogenic disturbance, and the dominance of a few urban-adapted species. Overall, the study highlights the importance of conserving semi-natural and rural habitat patches within rapidly urbanising landscapes to sustain avifaunal diversity and ecological integrity. Full article

Mangrove forests play a crucial role in coastal environments by providing ecosystem services, supporting biodiversity, and contributing to nature-based solutions for climate change mitigation and adaptation. This study investigated spatio-temporal changes in mangrove extent and assessed mangrove canopy condition in the Welu and Chanthaburi Estuaries, Chanthaburi Province, Thailand, using Sentinel-2 Level-2A imagery gathered from 2015 to 2024. Mangrove extent was extracted using the mangrove vegetation index (MVI). Mangrove canopy condition was assessed through two key dimensions: mangrove greenness and mangrove forest density. Greenness was evaluated using the chlorophyll vegetation index (CVI), normalized difference moisture index (NDMI), and normalized difference chlorophyll index (NDCI). Density was assessed using the optimized soil-adjusted vegetation index (OSAVI) and the renormalized difference vegetation index (RDVI). These indices were integrated using the entropy weight method to construct composite mangrove condition indices, and their accuracy was assessed using a confusion matrix based on validation points, Google Earth historical imagery, and field survey observations. The MVI-based extraction achieved high accuracy, ranging from 93% to 95% for the Welu Estuary and 94% to 96% for the Chanthaburi Estuary, with Kappa coefficients ranging from 0.81 to 0.85 in both cases. The Welu Estuary showed an increasing mangrove extent, from approximately 91.4 km² in 2015 to 103.9 km² in 2024; over the same period, in the Chanthaburi Estuary, it increased slightly from 29.9 km² to 31.7 km². The mangrove canopy assessment results indicated that both estuaries were generally dominated by moderate-to-high canopy density and greenness. However, the Chanthaburi Estuary exhibited greater spatial heterogeneity and higher fragmentation, particularly near aquaculture areas and pond margins, whereas the Welu Estuary maintained more continuous and structurally stable mangrove patches. These findings demonstrate that integrating Sentinel-2-derived MVI, composite vegetation indices, and spatial interpretation provides an effective framework for long-term mangrove monitoring and supports restoration planning, coastal resource management, and nature-based solutions. Full article

Restricting the $\left[[3L^3,2L^3+2,3]\right]$ FCC lattice code to a single triad sheet gives the $\left[[L^3,2L,L]\right]$ sheet code, with uniform weight-4 stabilizers and $K=4$ active connectivity. This code is isomorphic to L disjoint rotated 2D toric codes, so its memory is the toric code’s, and no density advantage is claimed. The contribution is a logical interconnect: native fault-tolerant joint Pauli measurement between selected logical qubits in independently encoded, co-located code blocks, with no dedicated routing region. Its natural use is entanglement generation and parity measurement between such blocks, since the $ZZ$ and $XX$ merges characterized here are exactly the two measurements a logical Bell measurement requires. Every FCC triangle has one edge in each triad sheet, so products of triangle measurements implement joint Pauli measurements across sheets while the merged code retains $d=L$. Finite-size crossing estimates under circuit-level depolarizing noise at $L\in \{4,6,8\}$ are $1.07\pm 0.05\%$ for the $ZZ$-merge, $0.76\pm 0.05\%$ with planar boundaries, and $0.91\pm 0.05\%$ for the $XX$-merge. The value of the primitive is that, for the pairs it reaches, it needs no routing region. Surface-code surgery merges only adjacent patches, so joining arbitrary pairs costs a reservation of roughly $1.5$ tiles per logical qubit; within the directly reachable set, the sheet architecture avoids that reservation, using $L^2$ physical qubits per logical, against $1.5L^2$ for a bussed toric array. That set is structured and limited: at the verified sizes of $L\in \{4,6,8\}$, each basis logical reaches L partners in one other sheet, giving $3L^2$ directly measurable pairs, corresponding to about $17\%$ of all logical pairs, and the same pattern is conjectured for general even L values. Three limits are stated rather than deferred: only the joint measurements are fault-tolerant primitives, the composed three-sheet CNOT being verified but not yet fault-tolerantly characterized; all thresholds are finite-size crossings at three lattice sizes, not asymptotic values; and pairs outside the reachable set would need routing as in any other architecture. Full article

Forest ecological connectedness is defined as the spatial continuity of forested patches and landscapes that facilitate the exchange of materials, biodiversity, and ecosystem services; it has become an important criterion for environmental quality and economic productivity in China. Although forest cover in China has been rapidly increasing since the 1990s, forest ecosystems remain fragmented across administrative regions, resulting in complex environmental effects. This study develops a robust provincial-level Forest Ecological Connectivity Index (FECI) by merging forest cover, fragmentation, functional corridor density, and carbon stock data for 30 Chinese provinces (excluding Tibet, Hong Kong, Macao, and Taiwan) between 2013 and 2023, using data from national forest inventory records, satellite-based forest-cover products, and published databases. We use panel data econometrics (fixed-effects models, spatial Durbin models (SDMs), mediation analysis) to analyze the effects of within-province FECI improvements and ecological spillovers across provinces on (i) air quality (PM_2.5 concentrations), (ii) hydrological regulation (annual surface runoff variability), (iii) regional GDP growth and green total factor productivity (GTFP), and (iv) carbon sequestration in neighboring provinces. Results indicate that a one-standard-deviation increase in FECI is associated with a 4.7 μg/m³ reduction in PM_2.5 in the same province (p < 0.01) and an 23.4% reduction in annual runoff variability (relative to sample mean) in the same province (p < 0.05), with a statistically significant positive impact within a spatial bandwidth of 300–500 km. Provinces with higher forest connectivity have lower per-unit-GDP carbon emissions and higher GTFP growth (β = 0.18, p < 0.01). Mediation analysis shows that forest connectivity’s overall economic impacts are driven by ecosystem services, with carbon sequestration and water-yield regulation as the main ones, accounting for about 42%. These empirical, data-driven insights are expected to strengthen spatial governance and carbon-neutrality policy frameworks in China by incorporating ecological connectivity planning. Full article

Fringe belts serve as the “growth rings” of urban expansion, bearing witness to the historical development and evolution of cities. Previous studies on urban morphology often focused on qualitative research, lacking quantitative measurement of various values in the context of high-density megacities. This study uses the main urban area of Guangzhou as a case study, reconstructs morphological periods based on the city’s history of over 2000 years, combines multi-source spatiotemporal data to accurately identify natural and artificial fixation lines, and scientifically defines and divides the spatial ranges of four urban fringe belts: inner, middle, middle-outer, and outer. This study has constructed a quantitative measurement framework integrating function and space, relying on remote sensing land surface temperature inversion, land use models, nighttime lights, and POI data to determine the ecological, economic, and social functions of the fringe belts in Guangzhou. The results demonstrate that (1) Guangzhou fringe belts exhibit distinct hierarchical spatial expansion pathways: leapfrog expansion, linear extension and stratified renewal. (2) Fringe belts constitute composite spaces with notable ecological value, exhibiting relatively favorable local thermal environmental characteristics within high-density built-up environments. Moreover, the natural patches contained within fringe belts demonstrate strong morphological resilience. (3) The economic and social functions of fringe belts strengthened while exhibiting uneven spatial changes, while spatial functions shifted from predominantly production and institutional uses toward more diverse urban functions. Nighttime light intensity increased overall, with widening absolute differences among units but declining relative dispersion, indicating overall brightening alongside persistent spatial variation in nighttime illumination. This work creates a novel paradigm for value evaluation, adapting Western urban morphological theory to a Chinese megacity while offering an effective tool for urban resilience and landscape regulation. Full article

Accurate estimation of forage aboveground biomass (AGB) from unmanned aerial vehicle (UAV) imagery is essential for monitoring alpine grassland productivity and supporting sustainable grassland management. However, the biomass of non-palatable species may be included in remote sensing-based estimates when their spatial distribution is not explicitly considered, potentially affecting spatial assessments of forage biomass. This study developed a UAV RGB-based framework for spatially constrained grassland AGB assessment by integrating the extraction of the growing-season non-palatable species Achnatherum splendens, mask-based spatial exclusion, feature optimization, and AGB inversion using field measurements from the northwestern shore of Qinghai Lake. Among tested semantic segmentation models, the Attention U-Net achieved the highest segmentation accuracy and was selected to identify A. splendens, while vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features were optimized using the minimum redundancy maximum relevance (mRMR) algorithm. Random forest regression (RFR), support vector regression (SVR), and partial least squares regression (PLSR) models were subsequently evaluated. The Attention U-Net achieved high segmentation accuracy (PA = 89.98%, mIoU = 88.89%, and F1 = 93.88%). Feature optimization improved the performance of all regression models, with SVR providing the highest estimation accuracy (R² = 0.72; RMSE = 23.73 g m⁻²). The estimated AGB ranged from 67.88 to 205.85 g m⁻², revealing pronounced spatial heterogeneity. In a typical sample area with high-density A. splendens, excluding pixels classified as A. splendens reduced the estimated AGB by 15.45%, demonstrating the influence of dense A. splendens patches on spatial AGB assessment. These results demonstrate that integrating deep learning-based species masking with feature optimization provides a spatially explicit approach for grassland AGB assessment after excluding areas classified as A. splendens and provides useful spatial information for fine-scale grassland monitoring and management. Full article

Mamba-based point cloud networks process serialized point tokens with state space layers and offer efficient classification, yet their performance is largely determined by the quality of local tokens produced before serialization. Existing tokenization compresses local patches via symmetric pooling, thereby discarding fine-grained geometric relationships and introducing aggregation bias under non-uniform sampling. To handle this, we propose a density-aware multi-level geometry enhancement method. A density-weighted token encoder estimates the local point density within each patch and adaptively calibrates point-wise contributions before aggregation, thus reducing the dominance of redundant dense samples. A multi-level local geometry branch extracts hierarchical coordinate-based neighborhood features directly from raw points and injects them into serialized tokens through residual fusion, compensating for geometric details lost during patch compression. Supervised contrastive learning is further adopted as an auxiliary regularizer to improve intra-class compactness and inter-class separability. Experiments on ScanObjectNN and ModelNet40 confirm the effectiveness of the approach: our method achieves 94.42 ± 0.09%, 92.11 ± 0.13%, and 87.89 ± 0.11% on the OBJ_BG, OBJ_ONLY, and PB_T50_RS variants, respectively, while ModelNet40 accuracy reaches 93.04 ± 0.12%. These results, obtained with only 12.57 M parameters, indicate a favorable accuracy–efficiency trade-off on the evaluated benchmarks. Full article

How landscape configuration relates to carbon emissions at the county scale, including spatial spillovers and local heterogeneity, remains insufficiently resolved. Here, we analyze 199 county-level units in the Beijing–Tianjin–Hebei (BTH) region using data from 2000, 2005, 2010, 2015, and 2020. We combine landscape pattern metrics derived from 30 m land-use data with ODIAC carbon emissions and socioeconomic indicators. Spatial autocorrelation analysis, two-way fixed-effects ordinary least squares, the Spatial Durbin Model, and Geographically Weighted Regression are used to quantify average associations, spatial spillovers, and local variation. County-scale carbon emissions increased continuously from 2000 to 2020, although growth slowed after 2010, and high-emission areas remained concentrated in urban cores. Emissions showed significant positive spatial autocorrelation, with stable High–High clusters after 2010. Landscape configuration was associated with carbon emission density. Patch Density generally showed negative associations, whereas the Contagion Index showed positive associations in the global and spatial models. The Largest Patch Index and Patch Cohesion Index also exhibited spillover effects, while GWR revealed substantial local variation in the direction and magnitude of these relationships. These findings demonstrate that landscape patterns are linked to county-scale carbon emissions through both local and cross-county pathways, highlighting the need for spatially differentiated low-carbon governance and territorial spatial planning. Full article

When national-level market-oriented agricultural reforms enter regional agricultural systems with different land-use structures, production infrastructure, and degrees of institutional dependence, they may generate spatially uneven political responses. Focusing on Punjab and Madhya Pradesh, India, this study integrates agricultural reform policy documents, public procurement statistics, MODIS land-cover data for 2013, 2017, 2020, and 2023, irrigation statistics for the 2013–2014 to 2022–2023 agricultural years, and protest-event records from the Global Database of Events, Language, and Tone (GDELT) Event Database for 25 September 2020 to 11 December 2021. We examine agricultural land-use intensity, cropland fragmentation, gross irrigated area, and the district-level coverage, spatial concentration, and cross-quarter persistence of protest events. Punjab maintained an agricultural land-use intensity of approximately 95%, a cropland patch density of 2.50–4.27 patches per 1000 km², and only a 0.56% increase in gross irrigated area, indicating a mature agricultural space characterized by intensive land use, continuous cropland, and stable irrigation. By contrast, agricultural land-use intensity in Madhya Pradesh increased from 75.24% to 77.63%, cropland patch density declined from 17.40 to 13.15 patches per 1000 km², and gross irrigated area increased by 70.87%, indicating a transitional agricultural space marked by gradual land expansion, increasing cropland connectivity, and rapid irrigation growth. Protest events were more widespread and persistent in Punjab but more concentrated in Madhya Pradesh. These findings show that agricultural land expansion, cropland fragmentation, or irrigation growth alone cannot explain regional differences in farmer protests. Rather, the observed regional differences are more consistent with an interpretation emphasizing the interaction between differentiated agricultural spaces and existing institutional arrangements, including minimum support prices, public procurement, and Agricultural Produce Market Committee markets. The study develops an integrated institutional–spatial framework for explaining uneven regional responses to uniform agricultural reform. Full article

Recent research has advanced fine-scale mapping and driver analysis of cropland fragmentation, but composite indices may mask structurally different fragmentation configurations, and evidence on how terrain and urban-system location jointly relate to fragmentation remains limited in rapidly urbanizing delta regions. This study quantified cropland fragmentation in the Yangtze River Delta (YRD), China, between 2000 and 2020. A Cropland Fragmentation Index (CFI) integrating edge density (ED), patch density (PD), and mean patch area (MPA) was calculated at a 1 km grid scale, and K-means clustering was used to identify fragmentation configurations. Pearson correlation and random-forest regression were used to examine spatial associations with selected 2020 natural and socioeconomic variables. Cropland area declined by 7.97%, while the regional-mean CFI increased from 0.29 to 0.32. Four configurations were identified, with the largest type (38.45% of grids) characterized by small patches and complex boundaries. Elevation and slope showed the strongest bivariate correlations with CFI, whereas distance to urban areas had the highest random-forest importance. These results reveal distinct fragmentation pathways and support differentiated cropland management in rapidly urbanizing regions. Full article

LULCC is a major driver of habitat fragmentation, biodiversity loss, and declining landscape connectivity, particularly in rapidly urbanizing regions. Although Islamabad has experienced substantial urban expansion and forest-cover change, long-term city-scale assessments linking land-cover dynamics with landscape connectivity remain limited. This study examined spatiotemporal LULCC in Islamabad from 1991 to 2021 and assessed whether recent forest recovery improved landscape structural connectivity. Landsat images acquired in 1991, 2001, 2011, and 2021 were classified into five land-cover categories: water, forest, built-up area, bare land, and agricultural land. Classification was performed using the Random Forest (RF) algorithm in Google Earth Engine (GEE). Landscape composition and spatial configuration were quantified using FRAGSTATS 4.3, while forest fragmentation was evaluated using the Landscape Fragmentation Tool v2.0 (LFT) with a 100 m edge threshold. The classifications achieved overall accuracies above 90%, with Kappa coefficients (K) greater than 0.85. Built-up area increased from 76.31 km², representing 7.55% of the study area, in 1991, to 258.62 km², or 25.60%, in 2021, demonstrating rapid urban expansion and associated habitat conversion. Forest cover increased to 340.86 km² in 2001, declined to 271.96 km² in 2011, and subsequently recovered to 409.22 km² in 2021. Despite this increase in forest extent, fragmentation metrics indicated persistent spatial subdivision and limited structural connectivity. High patch density (PD), reduced landscape aggregation, and changes in the largest patch index (LPI) indicated persistent spatial subdivision and limited habitat continuity. These findings highlight the value of integrating RF-based land-cover classification, multitemporal remote sensing, and landscape metrics for urban environmental monitoring. The findings suggest that future land-use planning should consider landscape connectivity, protection of existing forest patches, and spatially coordinated restoration alongside continued reforestation. Full article

Effective conservation of tropical forests under sustained anthropogenic pressure requires reliable, up-to-date knowledge of species presence, yet local ecological knowledge (LEK), camera trapping (CT), and passive acoustic recording (PAR) are rarely compared and used jointly against the same species list at the same site, simultaneously. Kibale National Park (KNP) in Uganda is a well-known biodiversity hotspot and the local culture is deeply shaped by wildlife, with social life and cultural identity rooted in a system of clans and totems. Sebitoli, in the northern sector of KNP, was logged in the 1970s and was part of a park-wide census in 2005. We used the three approaches for 54 vertebrate taxa (32 mammals, 15 birds, 7 reptiles) in this regenerating forest patch twenty years after. Over six months, from 1 February to 30 July 2025, 20 paired camera-trap/acoustic-recorder stations using automated deep-learning classifiers to detect species from camera-trap footage and audio recordings were combined with a structured LEK survey of 34 local research and conservation staff. The 2005 park-wide census provides a historical reference for interpreting present-day detections, although differences in survey design and metrics preclude direct inference about changes in density or abundance. All seven taxa for which non-zero density estimates were reported in Sebitoli in 2005 were detected by at least one method in the present study, the endangered elephants and chimpanzees being among the most frequently detected by the three methods. Camera trapping also detected African golden cat Caracal aurata and African Buffalo Syncerus caffer, which were not recorded during the 2005 transects. No single method captured the full community: CT and PAR combined detected 61% of taxa within their joint taxonomic scope, against 50% for the best single sensor, while LEK alone returned a non-zero score for all 54 species, including every reptile and most taxa currently outside classifier coverage. LEK-sensor correlation weakened substantially once classifier scope was accounted for ($\rho$ = 0.29–0.30), and naming consensus among respondents tracked visual familiarity rather than totemic or cultural salience. Our findings show the detections of species rare or absent twenty years before and highlight how integrating LEK, CT, and PAR can provide a broader and more complementary assessment of biodiversity than any single monitoring approach. Full article