Search Results (5,801)

Research Highlights: This study quantifies aboveground biomass for Oreopanax oroyanus and Escallonia resinosa in an Andean relict forest and examines their conservation relevance related to the scale of emissions voluntarily reported by small corporate emitters. Background and Objectives: Andean relict forests face severe fragmentation and degradation. This research evaluates carbon stocks in the Bosque de Zárate Reserved Zone (Peru) and explores how these findings may inform climate mitigation and conservation initiatives by examining their potential alignment with emissions voluntarily reported by Peruvian firms participating in a carbon disclosure system. Materials and Methods: A total of 27 plots were evaluated between 3034 and 3200 m a.s.l., tree height and diameter (DBH ≥ 10 cm) were measured for key species, and biomass was estimated using a pantropical allometric equation. Landsat imagery (1985–2025) was analyzed to assess long-term vegetation conditions, while Dynamic World land cover and Sentinel-1 radar (2018–2025) were used to assess forest cover and canopy structure changes. Voluntarily reported emissions of Peruvian firms participating in the “Carbon Footprint Peru” system (2012–2024) were analyzed to contextualize the forest results in the potential corporate interest in climate mitigation in Peru. Results: Total aboveground carbon stock for the altitudinal belt in the study area was 919.4 Mg C (18.6 Mg C ha⁻¹), equivalent to 3374.2 Mg CO₂, with Escallonia resinosa accounting for approximately 71% of the estimated stock. Multi-decadal satellite observations indicated persistent forest cover within the evaluated belt, while analysis of voluntarily reported corporate emissions identified numerous service-sector firms with annual emissions below 100 Mg CO₂ eq, providing context for the potential scale of future conservation-financing initiatives. Conclusions: Relict forests offer relevant localized carbon storage linked to other ecosystem services. Providing field-based carbon data may support the development of locally relevant community-led initiatives meaningful to climate-financing initiatives. However, the existing carbon stock does not by itself represent a source of carbon credits, and carbon capture-specific studies would need to be implemented to fully assess the mitigation capacity of these ecosystems. Full article

Black-sand mining is an under-studied anthropogenic pressure on arid coastal environments, where sparse vegetation and slow natural recovery limit conventional impact assessment. Despite the recent expansion of heavy-mineral extraction along the Mauritanian coast, no spatio-temporal analysis has quantified its effects on land cover and surface-water dynamics in the N’Diago region. We analysed the N’Diago–LGUWYCHICH coastal sector (south-western Mauritania) using three Landsat scenes (2014, 2020, and 2026) in QGIS (free and open-source Geographic Information System), four spectral indices (NDVI-Normalized Difference Vegetation Index, NDWI-Normalized Difference Water Index, BSI- Bare Soil Index, and CI -Coloration Index), supervised Support Vector Machine classification and a 30 m SRTM Digital Elevation Model over a 97.82 ha area of interest. Bare soil dominated the landscape at every date (92.6–95.7%) and vegetation stayed below 7%, indicating that canopy-based metrics underestimate disturbance in this setting. The clearest change was hydrological: an inland water body shrank from 1.02 ha in 2020 to 0.40 ha in 2026, a 60.6% loss. This individual inland water body, measured directly and independently from the NDWI index, is our primary hydrological observation: the wet feature was smaller in the 2026 image than in the 2020 image and was located near the mapped concession areas, but the available data do not establish the cause of this change. Similar bare-soil and colour index values (BSI ≈ 0.23, CI ≈ 0.79–0.80) were observed in the processed images, while residual seasonal and radiometric differences between the Landsat 8 and DOS-corrected Landsat 9 products cannot be excluded; BSI and CI are treated only as candidate or contextual spectral patterns, so any delineation of the disturbed footprint is provisional and requires confirmation from independent field data. This study illustrates a low-cost exploratory workflow that may support preliminary monitoring in data-scarce arid coastal settings, pending validation with denser time series and field observations. Full article

Understanding how urbanization modifies stream thermal regimes is essential for assessing freshwater ecosystem responses to climate variability and land-use and land-cover (LULC) change. This study investigated air temperature (AT)–water temperature (WT) relationships at annual, monthly, and diurnal timescales in two adjacent, relatively flat watersheds with contrasting urbanization levels in North Central Texas: the urbanized Doe Branch and less urbanized Little Elm Creek during 2012–2021. A single harmonic analysis was applied to characterize annual and diurnal thermal patterns, including mean temperature, amplitude, and phase, while statistical analyses were used to evaluate seasonal and daily thermal variability and peak timing. At the annual scale, AT and WT metrics were strongly correlated at both sites (r = 0.85–0.94, p < 0.01) indicating that atmospheric conditions were the dominant control of annual stream temperature variability. Annual mean WT increased with AT, suggesting strong air–water thermal coupling and the potential for warmer stream temperatures under future climate warming. However, Doe Branch exhibited higher annual mean WTs, delayed seasonal peak WTs, and reduced annual temperature ranges compared with Little Elm Creek, reflecting the influence of urban watershed characteristics on seasonal thermal responses. At the diurnal scale, daily mean WT remained strongly coupled with daily mean AT, whereas daily temperature range and peak timing showed weaker relationships with AT. The greater variability in peak WT timing at Doe Branch suggests that short-term stream thermal dynamics were influenced by additional watershed characteristics beyond atmospheric forcing alone. Full article

Land-use and land-cover (LULC) change is a major driver of ecosystem degradation and ecosystem service loss in tropical landscapes. This study assessed the impacts of LULC changes between 1985 and 2023 on the potential of different LULC classes to supply ecosystem services in the Upper Rio Pardo Basin (Rio Pardo and Rio São João do Paraíso watersheds), northern Minas Gerais, Brazil. LULC data from the MapBiomas Project were used to quantify transitions among LULC classes within the study area. The potential supply of ecosystem services was assessed using the Burkhard matrix, adapted to local environmental conditions and informed by expert knowledge, while the relative importance of ecosystem services was evaluated through a participatory assessment involving local communities. The expansion of pasturelands and commercial forest plantations was identified as the primary driver of ecosystem service loss in the study region. Native ecosystems showed the highest potential to provide regulating, supporting, provisioning, and cultural ecosystem services, particularly water regulation, soil protection, carbon sequestration, and biodiversity conservation. In contrast, anthropogenic land uses were primarily associated with provisioning services and showed limited capacity to sustain regulating and supporting services, indicating clear trade-offs between production and ecosystem functioning. Local communities identified water-related services as the highest priority, followed by climate regulation, soil fertility, and food provision. These findings demonstrate the value of integrating expert-based and participatory approaches to ecosystem service assessment and provide a basis for territorial planning strategies that prioritize the conservation and restoration of native vegetation, particularly in hydrologically sensitive areas. Full article

CORINE Land Cover (CLC) is one of the most used land databases intended for pan-European-scale analysis. Despite periodic updates, the data suffer from generalization, subjectivity, and inconsistent local knowledge, leading to a distorted representation of reality. In this study, we raise awareness of the use of CLC data in local analysis, for which it was never intended. Our study investigates whether incorporating auxiliary spatial data and local geographical knowledge can yield a higher-accuracy CLC product, without departing from the official CLC definitions and standards. We critically remapped polygon by polygon the 1990, 2000, and 2006 CLC layers, for Iași County (NE Romania), using topographic maps (1972–1989), aerial imagery (1978–2008), and satellite data (1980–2006), and compared it to the original CLC, through change detection analysis. The new maps revealed several issues imposed by (a) generalization—cartographical omissions among settlements, due to the application of a 25 ha minimum mapping unit and a 100 m minimum mapping width; (b) confusions between land cover and land-use classes, such as pasture and wetlands, especially under varying climatic conditions, or imposed by landforms, where we suggest the use of complementary data, such as a Digital Elevation Model (DEM); and (c) the inconsistencies of mapping between successive CLC editions. Our results indicate that the CLC should not be used for downscaling analysis. Therefore, the authors advocate integrating multiple temporal remote sensing layers to achieve a more accurate assessment of land cover classes, thereby compensating for the data’s top-down character. Based on these findings, we propose an error classification approach to serve as a reference for risk mitigation in downscaled spatial analysis. We emphasize the need for CLC data users to validate their data against ground truth to mitigate analytical uncertainties. Full article

Compound hot–humid events are placing increasing pressure on urban health and land-use governance as climate warming and urban expansion continue. This study examined urban built-up areas in 21 prefecture-level cities and prefectures of Sichuan Province for 2000, 2005, 2010, 2015, 2020, and 2024. Using the IPCC hazard–exposure–vulnerability framework, ERA5-Land, MODIS land-cover, GHSL population, and socioeconomic data were combined to construct the Humid Heat Hazard Index (HI), Population-Density Exposure Index (EI), Socioeconomic Vulnerability Index (VI), and Comprehensive Urban Heat–Humidity Risk Index (HHRI). Contribution decomposition, LISA, and standard deviation ellipse analysis were then used to examine risk changes and spatial patterns. The mean HHRI decreased from 0.42 to 0.24, but rebounds appeared in 2010 and 2024. HI varied strongly among years and drove short-term risk changes, EI declined continuously, and VI dropped from 0.87 to 0.30, making it the main factor behind long-term risk reduction. Higher-risk areas were mainly located in southern Sichuan, southeastern Sichuan, and the central-eastern Sichuan Basin. LISA results showed high-risk clusters in basin cities and low-risk clusters on the western Sichuan Plateau. The standard deviation ellipse remained mostly within the basin and followed the basin urban belt. Future adaptation should combine public-service improvement with heat warnings, cooling services, and emergency preparedness. Full article

Megacities represent an advanced stage of urban development where expansion-induced carbon debt increases environmental pressure. This study examined the spatial response of carbon stock change to urban expansion in megacities. Based on 2000–2020 land use/land cover data for 11 Chinese megacities, concentric ring analysis and spatial exploratory methods were employed to quantify urban expansion intensity and carbon stock change and to infer stage transition characteristics. The results indicate that: (1) urban expansion intensity declined over time as growth shifted from rapid edge or infill expansion toward smart-growth-oriented development; (2) carbon budget fluctuations weakened, and emission patterns evolved toward improved carbon balances and enhanced sink effects, with Beijing and Tianjin emerging as local carbon sinks; (3) the carbon effects of LUCC showed a stage transition from rapid expansion–intensive emission to slow expansion–moderate emission, and policy intervention reshaped this relationship. The carbon effects of megacity expansion are stage-dependent rather than linear, and policy intervention can shift this relationship before economic maturity, offering guidance for low-carbon planning in developing-country contexts. Full article

Single-source remote sensing image (RSI) cannot simultaneously meet high-spatial and high-temporal resolution requirements, failing to provide decision-makers with timely and accurate monitoring data. Spatiotemporal fusion (STF) of multi-source RSIs represents an efficient and convenient means of producing land-cover observations with high-temporal and high-spatial resolutions. However, current STF approaches still suffer from severe prediction distortion under abrupt changes, long-interval temporal variations, and land-cover type transitions, as well as poor robustness against disturbances in prior data. To address these challenges, a temporal-variation-resistant bidirectional convolution-Transformer generative adversarial network (TRB-GAN) for RSI STF, which comprises a temporal-variation-resistant bidirectional convolution-Transformer generator (TRBG) and a multiresolution input convolution-Transformer discriminator (MICTD), is devised to improve the robustness in predicting time-varying information and enhance STF capability. First, the TRBG designs a temporal-variation-resistant bidirectional encoder to capture prior information and arbitrary time-varying local–global features, enhancing prediction robustness and representation capability for time-varying information. Second, the TRBG designs a dual-guided triple-attention fusion decoder (DTAFD), incorporating dual-guided cross convolution-attention fusion and decision attention fusion. DTAFD dynamically calculates correlations among spectral, spatial, and time-varying information to aggregate heterogeneous features and adaptively performs stepwise weighting and integration, effectively mitigating the adverse impacts from heterogeneous imaging mechanisms and significant resolution gaps. Finally, MICTD and deep supervision enable adversarial learning of local–global structures and spectra across resolutions, providing feedback to the TRBG for producing finer images. Ablation and comparative experiments demonstrate the TRB-GAN achieves superior STF performance and stronger robustness to time-varying disturbances for the widely used CIA and LGC datasets. Full article

The Hetao Irrigation District in arid northwestern China presents a significant challenge in balancing agricultural intensification and water conservation, particularly in its terminal lake, Wuliangsu Lake. This study examined how changes in Land Use/Land Cover (LULC) and cropping structures influenced the lake’s water quality. By using remote sensing data for LULC classification and agricultural statistics for crop composition, we analyzed the spatio-temporal variation in LULC and cropping structure in the irrigation district and quantified the associated agricultural non-point source pollution loads (total nitrogen, total phosphorus, and chemical oxygen demand) entering the lake. A calibrated Environmental Fluid Dynamics Code model was applied to evaluate water quality responses to cropping structure optimization. Our findings revealed significant shifts in LULC and cropping structure during the study period, driven by agricultural intensification, ecological restoration policies, urbanization, market forces, and national food security strategies. Concurrently, agricultural non-point source pollution loads into the lake showed a steady declining trend from 2018 to 2023, with total nitrogen (TN) decreasing by 15%, total phosphorus (TP) by 16.9%, and chemical oxygen demand (COD) by 19.4%. Model simulations demonstrated that optimizing the cropping structure, specifically by reducing the area of high-fertilizer crops (sunflower) and expanding low-fertilizer crops (spring wheat) and forage crops for ecological purposes, could further improve lake water quality. Under the intensive adjustment scenario, the inflow loads of TN, TP, and COD decreased by 10%, 11.7%, and 10.9%, respectively, while the corresponding in-lake concentrations decreased by 22.1%, 19.8%, and 18.7%, respectively. TP exhibited the highest sensitivity to such adjustments. By linking cropping structure adjustments with hydrodynamic-water quality modeling, this study provides a quantitative framework for assessing water quality responses in arid irrigated systems, offering a scientific basis for balancing agricultural production and water ecosystem protection in the Hetao district and similar regions. Full article

Climate change poses increasing threats to urban populations, with rising temperatures exacerbating heat-related health risks and reducing outdoor thermal comfort. Although cities occupy only a small fraction of the Earth’s surface, they function as complex ecosystems where human transformations significantly influence environmental quality and public health. This research introduces a trans-scalar methodological framework to assess the impacts of climate change on human well-being in urban areas, integrating qualitative and quantitative approaches. The methodology combines direct field observations, instrumental surveys using infrared thermography, quantitative land cover analysis (Runoff and RIE indices), and predictive microclimatic simulations using ENVI-met 5.8. The framework is applied to two case studies in Italy, the municipalities of Bibbiena and Borgo San Lorenzo, focusing on pedestrian accessibility to community healthcare facilities, known as Community Houses. Results reveal critical environmental vulnerabilities, including surface temperatures reaching up to 60 °C on impervious materials, runoff coefficients exceeding 80%, and Physiological Equivalent Temperature (PET) values exceeding 56 °C during summer peak hours, indicating extreme heat stress conditions. The proposed framework enables the identification of priority intervention areas for climate adaptation strategies, supporting evidence-based urban design that prioritises public health and thermal comfort. The methodology offers replicability for other Mediterranean cities facing similar climate challenges, providing a decision-support tool for planners and public administrations. Full article

Understanding the spatiotemporal dynamics of land-use and cover change (LUCC) and ecosystem service (ES) responses is essential for assessing ecological functions in regional landscapes. However, conventional LUCC simulations often rely on historical trends and inadequately represent the spatially heterogeneous effects of top-down policy constraints. Taking the Henan section of the Yellow River Basin (HYRB) as a case study, we developed a policy-to-rule framework that translated ecological redlines, urban development boundaries, and restoration requirements into explicit spatial constraints and land-use transition rules in the PLUS model. A policy-constrained High-Quality Development Scenario (HQDS) was established, with the Natural Growth Scenario (NGS) as a reference. Five ESs were assessed using InVEST from 1985 to 2050, and the results were integrated with the Minimum Cumulative Resistance (MCR) model and circuit theory to construct an ecological security pattern (ESP). Historical reconstruction of the 2022 land-use pattern achieved an overall accuracy of 90.18% and a Kappa coefficient of 86.39%. The five ESs remained relatively stable overall: water yield, soil conservation, and the sediment-related indicator increased, whereas habitat quality and carbon storage declined slightly. Ecological source areas expanded from 7140.54 km² in 1985 to 12,039.17 km² under the HQDS in 2050, a 68.6% increase. Compared with the NGS, the HQDS increased source areas by 562.42 km² (4.9%), reduced ecological corridors from 26 to 24, and increased their total length from 1068.89 to 1099.61 km. These differences represent the projected, scenario-conditioned consequences of the specified policy constraints and provide quantitative decision support for future ecological management. Full article

Rapid urbanization is reshaping land surface conditions and local thermal environments in fast-growing coastal cities. This study examines how Land Use Land Cover (LULC) transformation influenced seasonal land surface temperature (LST) dynamics in Chattogram City Corporation (CCC), Bangladesh, over 2004–2024. Multi-temporal Landsat imagery was analyzed using a Random Forest classifier, and spectral indices (NDVI, NDBI, NDBaI, MNDWI) were derived to characterize surface biophysical conditions. Built-up land expanded by 27.71 km², largely replacing agricultural and vegetated areas. Summer mean LST rose from 36.08 °C to 36.50 °C, while winter LST rose from 25.25 °C to 26.97 °C. Only the winter warming trend is statistically significant; the summer change falls within the ±1–2 °C retrieval uncertainty of Landsat-derived LST. The summer–winter thermal gap consequently narrowed from 10.83 °C to 9.53 °C, indicating that urbanization-driven warming in this tropical coastal city is disproportionately concentrated in the cool dry season. Partial correlation and multiple regression analyses confirm that built-up intensity (NDBI) is the dominant driver of surface warming, while vegetation (NDVI) exerts a consistent cooling influence. Water bodies showed contrasting seasonal trends, with winter extent declining alongside a slight summer increase. These findings highlight the critical role of vegetation and water bodies in moderating urban heat and provide data-driven insights for climate-responsive planning in rapidly urbanizing coastal cities. Full article

Climate variability and land use/land cover changes (LULCC) can intensify soil erosion. This study evaluated the relative influence of LULCC and climate change on soil water erosion in an Andean watershed in Ecuador. Soil erosion was assessed using the RUSLE model for 1996, 2023, and 2040. Future erosion was predicted for 2040 using projected land use/land cover (LULC) and two climate scenarios under the Shared Socioeconomic Pathways SSP585 (pessimistic) and SSP126 (optimistic). Meanwhile, LULC was projected until 2040 through a Cellular Automata Markov model (CA-Markov). Climate scenarios for 2021–2041 were generated through statistical downscaling of the MPI-ESM-1-2-HR climate model. Results showed that erosion increased by 17.8% from 1996 to 2023; meanwhile, by 2040, it is projected to increase by up to 65.1% under both climate scenarios. A factorial sensitivity analysis revealed that LULCC is the primary driver of erosion, accounting for over 82% of the total variation in erosion, while the contribution of climate change remains secondary. These findings suggest that in tropical mountain zones, loss of vegetation cover has a greater impact on erosion rates than climatic variability, regardless of the climate scenario, confirming that the effect of climate change on soil erosion remains marginal. Full article

The rapid expansion of road construction has significantly contributed to economic development and regional connectivity. However, linear infrastructure such as roads, railways, and utility corridors has also introduced considerable ecological disruptions. Although increasing global attention is being paid to mitigating these effects, most existing research primarily focuses on quantifying and monitoring external environmental changes (e.g., landscape structure or vegetation coverage) while often neglecting internal ecological dynamics such as ecosystem resilience. Previous road-ecology studies have extensively examined road-buffer effects, land-use/land-cover changes, vegetation-index dynamics, and landscape fragmentation. Therefore, the contribution of this study does not lie in proposing an entirely new class of indicators. Rather, it lies in applying a combined external–internal assessment framework to a recently constructed expressway corridor by jointly examining annual land-cover transitions and leaf area index (LAI)-derived temporal variability/resilience indicators across multiple distance buffers and spatial resolutions. This design allows us to compare whether structural land-cover changes and vegetation time-series responses show similar distance–decay patterns around the expressway corridor. The results show that: (1) Land-cover transformation was mainly concentrated within the first 500–1000 m from the expressway, especially for impervious surface expansion and vegetation loss. Multi-indicator distance-gradient analysis showed that land-cover change intensity and LAI-derived variability indicators gradually approached the distal reference condition at approximately 2000 m, which was therefore used as an empirical corridor-analysis boundary rather than a definitive ecological impact threshold. (2) Within the 2000 m buffer zone, forest area increased from 21.866 km² in 2001 to 45.370 km² in 2023, while impervious surface area increased from 3.016 km² to 6.869 km². During the construction and early operation period from 2018 to 2023, impervious surface area increased from 6.268 km² to 6.869 km², indicating localized artificial surface expansion along the expressway corridor. (3) During 2018–2023, the 30 m LAI product showed a 22.3% increase in coefficient of variation (CV), indicating enhanced relative LAI variability. In contrast, temporal autocorrelation (TAC) did not show the consistent increase expected under classical critical slowing down theory, suggesting that TAC-based evidence for resilience decline was weak or inconclusive during this short period. The observed TAC/CV changes were interpreted as critical slowing down (CSD)-related vegetation variability signals, rather than as a distinct or definitive critical slowing down signature. (4) The multi-resolution comparison showed weak pixel-level correspondence between the 30 m and 250 m LAI products, indicating clear scale dependence rather than robust multi-scale consistency. The 250 m data were useful for characterizing long-term regional background trends, whereas the 30 m data were more suitable for detecting localized corridor-scale vegetation variability. Thus, the multi-resolution analysis should be regarded as a scale-sensitivity assessment rather than as direct cross-scale validation. Full article

High-resolution Normalized Difference Vegetation Index (NDVI) data are essential for monitoring fine-scale coastal environmental dynamics, yet persistent cloud cover, rapid geomorphic change, and strong spatial heterogeneity limit the availability of temporally continuous observations. Existing spatiotemporal fusion approaches can partially address these limitations, but many rely primarily on data-driven feature learning and do not explicitly incorporate geographic information, leading to boundary blurring, structural inconsistency, and sensitivity to background noise in complex coastal environments. This study presents a geographically constrained Transformer-based framework for 2 m NDVI spatiotemporal reconstruction in coastal landscapes named Coastal-Prior-Embedded Global–Local Fusion Transformer (Coastal-GLFT). The approach integrates high-resolution Gaofen-6 panchromatic and multispectral imagery with high-frequency wide-field-view observations and auxiliary geographic datasets describing elevation, coastline proximity, and land use/land cover. Geographic priors were incorporated as explicit spatial constraints, while a spatiotemporal gating mechanism and global–local fusion architecture were used to improve the representation of temporal variation and multi-scale spatial structure. The method was evaluated using a multi-temporal dataset for the Yellow River Delta comprising 49 high-resolution scenes and 137 coarse-resolution scenes acquired between 2020 and 2025. Compared with representative physics-based, convolutional neural network, generative adversarial network, and Transformer-based fusion methods, the proposed approach reduced reconstruction error by approximately 5–72%, increased signal fidelity by approximately 1–12%, and improved structural similarity by approximately 2–52%. Compared with the strongest Transformer-based baseline, SwinSTFM, Coastal-GLFT reduced RMSE from 0.0896 to 0.0855, increased PSNR from 36.19 dB to 37.09 dB, and improved SSIM from 0.8551 to 0.8742. Qualitative analysis further demonstrated improved preservation of boundary structure, spatial continuity, and heterogeneous coastal features, including aquaculture ponds, tidal creeks, and fragmented wetlands. These results indicate that integrating geographic constraints with multi-scale Transformer-based reconstruction can improve the fidelity and structural consistency of high-resolution NDVI reconstruction in complex coastal environments. The framework provides a basis for fine-scale coastal vegetation monitoring and land-cover analysis, while future work should assess transferability across diverse coastal systems and improve computational scalability. Full article