Search Results (1,773)

Soil salinization is a widespread form of land degradation that severely constrains agricultural productivity and ecosystem stability. Efficient and transferable monitoring methods are therefore essential for large-scale salinization assessment. Remote sensing provides timely and synoptic observations, while the integration of multi-source datasets offers complementary spectral and spatial information. In this study, we developed a cross-platform spectral index specifically for soda saline–alkali (carbonate/bicarbonate-dominated) soils by integrating laboratory spectra and hyperspectral satellite observations through a collaborative, cross-dataset spectral feature selection framework. Dual-band spectral indices were constructed from transformed reflectance spectra, and a stepwise coupled correlation analysis was applied to identify representative candidates that consistently exhibited strong associations with log-transformed soil electrical conductivity (logEC) across datasets. An optimal central-wavelength analysis was then performed to determine a stable and transferable band pair. The study was conducted in the Songnen Plain of Northeast China using laboratory-measured soil spectra and Ziyuan-1 02D Advanced Hyperspectral Imager data, and the proposed index was further validated using Landsat-8 and Sentinel-2 Multispectral data. Results show that the proposed Difference Index based on Square Root Reflectance at 520 nm and 900 nm (DISRR520900) exhibited consistent relationships with logEC (R = 0.60 for hyperspectral satellite data and R = 0.82 for laboratory spectral data), outperforming commonly used salinity indices in terms of cross-sensor stability. The spatial distribution of soil salinization derived from DISRR520900 is highly consistent with true-color imagery, and multi-source data fusion further improves mapping continuity and spatial coverage. It should be noted that the proposed index is primarily applicable to bare or sparsely vegetated soil surfaces in soda saline–alkali regions. Under dense vegetation cover, substantial crop residue, or wet surface conditions, additional masking or correction may be required. These results demonstrate that DISRR520900 provides a stable cross-sensor solution for large-scale soil salinization mapping within comparable soil chemical contexts. Full article

Under the dual pressures of climate change and intensive urban expansion, which jointly exacerbate urban heat risks, optimizing the urban thermal environment through vegetation has become a core pathway for climate adaptation. However, accurately quantifying the nonlinear cooling responses of vegetation under complex urban morphologies and diverse geomorphic conditions remains a major scientific challenge in achieving efficient heat-resilient urban planning. This study takes three representative megacities in China—Beijing, Shanghai, and Shenzhen—as case studies. By integrating multi-source datasets, an urban spatial morphology indicator system was constructed that encompasses key dimensions of the natural environment, urban morphology, and socioeconomic factors. Eleven machine learning models were applied to model and compare urban land surface temperature (LST). The results demonstrate that the CatBoost model exhibited superior performance in simulating complex urban thermal environments (R² = 0.683–0.873), effectively capturing the interactive effects among multidimensional factors. The findings reveal a dual differentiation pattern of “topographic constraint–morphological dominance” in urban thermal environments: in mountainous cities, elevation and mountain forests act as rigid cooling barriers that restrict the spread of heat islands; whereas in plain cities, thermal conditions are primarily governed by the synergistic warming effects of impervious surface expansion and intensive human–economic activities. More importantly, the study identifies a significant nonlinear threshold effect of vegetation cover (NDVI) on LST reduction—only when vegetation coverage exceeds a critical threshold can large-scale cooling benefits be activated to effectively offset the thermal accumulation associated with high GDP intensity. Based on these insights, the study proposes differentiated climate-adaptive spatial planning strategies: mountainous cities should strictly maintain ecological redlines at mountain fronts to safeguard macro-scale cooling sources, while high-density plain cities should focus on integrating green space patches to surpass the “cooling threshold” and enhance vertical greening systems. These findings provide a quantitative scientific basis for improving urban thermal resilience. Full article

The Water Cloud Model (WCM) provides a simple and effective framework for quantifying vegetation canopy absorption and scattering components and has been widely applied to active microwave-based soil moisture retrieval on vegetated areas. However, under conditions of dense vegetation, the WCM neglects soil–vegetation interaction scattering, which limits its retrieval accuracy. To mitigate this limitation, this study analyzes the active microwave radiative transfer process under vegetated conditions and proposes an approach to explicitly quantify soil–vegetation interaction scattering by incorporating the first-order soil–vegetation-scattering component into the WCM, thereby enhancing the performance of the WCM at high vegetation coverage. The effectiveness of the proposed model is validated using in situ observations from three study areas with different vegetation characteristics: (a) a pure farmland area, (b) a mixed landscape with small forest and shrubland patches and large cropland areas, and (c) a mixed landscape with large forest and shrubland patches and small cropland areas. Data from 2020–2022 were used for model training and parameter calibration, while independent datasets from 2023 and 2024 were employed to validate the model performance. In both the model training and validation phases, the proposed model improved the soil moisture retrieval accuracy across all study areas while exhibiting slight differences in the backscatter simulation performance. During the model training period, the root-mean-square error (RMSE) between simulated and measured backscatter in study area (a) increased slightly by 1.9%, whereas it decreased by 2.79% and 2.0% in study areas (b) and (c), respectively. In terms of soil moisture retrieval, the RMSEs in study areas (a), (b), and (c) decreased by 6.66%, 1.18%, and 6.03%, respectively. In the validation experiments, for the year 2023, the RMSEs of simulated versus observed backscatter in study areas (a), (b), and (c) were reduced by 9.6%, 1.51%, and 4.35%, respectively, while the corresponding soil moisture retrieval RMSEs decreased by 12.6%, 4.53%, and 7.24%. For the year 2024, the backscatter RMSE in study area (a) increased by 6.07%, whereas it decreased by 2.17% and 6.47% in study areas (b) and (c), respectively; meanwhile, the soil moisture retrieval RMSEs were reduced by 2.81%, 3.69%, and 9.45%, respectively. In summary, this study improves the accuracy of active microwave remote sensing-based soil moisture retrieval in areas with different vegetation cover by explicitly quantifying soil–vegetation interaction scattering. Full article

Urban green spaces (UGSs) are crucial for public health by supporting leisure-time physical activities (LTPAs), but the mechanisms by which micro-scale UGS features shape different LTPA types remain unclear. In this study, the relationship between the micro-scale features of UGSs and LTPAs was investigated in 63 sample plots of nine comprehensive parks in downtown Shanghai. Using the behavior annotation method and multiple linear regression analysis, we identified significant correlations between the UGS features and LTPA types. The results showed that sitting and chatting (SC) activities had the highest participation rate at 46.84%, while sports and fitness (SF) activities had the lowest at 9.82%. Walking and sightseeing (WS) activities and culture and entertainment (CE) activities accounted for 19.99% and 23.35% of participants, respectively. Spatial accessibility (SA) and canopy coverage ratios (CCRs) were significantly negatively correlated with SC, while seat number (SN), ground-cover density (D_GNC), and three-dimensional green quantity (TGQ) were positively correlated. For WS, SN and tree density (D_TREE) were positively correlated, while TGQ was negatively correlated. CE activities were positively associated with SN, D_TREE, and Shannon’s diversity index of ground-cover (SHI_GNC) but negatively associated with Shannon’s diversity index of trees (SHI_TREE). The regression models explained 65.9%, 38.3%, and 44.3% of the variance in SC, WS, and CE, respectively, while the overall model was not significant for SF. These findings highlight the need to optimize rest facilities, vegetation diversity, and spatial layout in UGS design to accommodate diverse LTPA needs and foster health-oriented environments. The conclusions are mainly applicable to seasons with mild climates, and LTPA characteristics in different seasons require further verification. Full article

Monitoring vegetation and land cover changes over time in oil-impacted regions is crucial for assessing ecological degradation and informing remediation options. This study aimed to identify the challenges encountered when using Landsat imagery to detect changes in vegetation health and land cover in Bodo, a hydrocarbon-impacted community in the Niger Delta region of Nigeria, over a 20-year period. Landsat 7 ETM+ and Landsat 8 OLI imagery were used to derive the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) from 2003 to 2023. Data continuity was affected by the Landsat 7 Scan Line Corrector malfunction in the 2008 images and by high cloud coverage in the Landsat 8 OLI 2013 images. Hence, 2008 and 2013 were excluded from the analysis, limiting multi-year comparisons. Results from the available years indicated that NDBI values increased gradually, suggesting minor urban expansion. Stable but low NDWI levels suggest water stress, while changing NDVI values indicate alterations in vegetative health. However, this study highlights observable environmental changes and the challenges involved in using satellite imagery for environmental monitoring in oil-impacted regions, underscoring the need for improved cloud-masking methodologies and radar datasets to enhance long-term environmental assessment. Full article

Urban sky gardens—elevated green spaces on buildings, encompassing rooftop gardens and podium gardens—are critical to the improvement of urban ecosystem services and functions. Understanding the spatial patterns and the influencing factors of sky gardens is essential for the precise allocation of elevated spaces in urban development. Taking the four central urban districts of Lanzhou in China as the study region, a GIS database of 508 sky gardens was established by identifying high-definition image maps and on-site investigations. The spatial patterns and influencing factors, such as building height, ground-level green area, and population density, were analyzed. The development of sky gardens was also compared in Lanzhou and Guangzhou, China. The distribution of sky gardens in Lanzhou exhibited spatial heterogeneity. Most sky gardens were distributed along the Yellow River. Chengguan District had more sky gardens than Xigu District. In terms of structural characteristics, 82% of sky gardens were rooftop gardens, 73% were located in residential buildings, and 63% were attached to mid- and low-rise buildings. Most sky gardens were one floor, characterized by no public accessibility, a location in high-density plots, and low vegetation coverage. Sky garden area was negatively correlated with building height, ground-level green area, and green plot ratio in sky gardens. There were positive associations between sky garden area and higher plot ratio, building density, and population density based on Multiscale Geographically Weighted Regression. Due to the proper climate conditions and economy, Guangzhou had more sky gardens than Lanzhou. Our study suggests that the utilization of rooftops and podiums is relatively low, and the development of sky gardens exhibits spatial clustering. A suite of optimizing strategies should be implemented to enhance the accessibility and usability of sky gardens. Full article

Urban waterfront walkways are everyday public built environments where people commonly engage in slow walking, yet evidence remains limited that links what pedestrians see to immediate psychophysiological responses under controlled first-person dynamic exposure. To address this gap, we developed a fixed-speed, fixed-duration VR walk-through model using real-world 360° panoramic video and quantified scene visual composition via computer vision-based image analysis. Based on the visible shares of key components (greenery, water, sky, hardscape, and built structures), clips were grouped into four interpretable waterfront typologies: Vegetation-Enclosed, Built-Dominant, Hardscape-Plaza, and Blue-Open. Fifty healthy adults completed within-subject VR exposures to the four typologies (50 s per clip), while multimodal physiological signals and brief affect and landscape ratings were collected before and after exposure. The results showed that scenes with more water and vegetation coverage, along with expansive views, were associated with promoted autonomic nervous system calming responses, whereas scenes with fewer natural elements and higher built structure density were more likely to induce tension responses. Negative emotions decreased significantly across all four scene experiences, though artificial scenes concurrently exhibited emotional improvement alongside physiological tension. Overall, brief first-person dynamic VR exposure can yield immediate emotional benefits, and waterfront designs combining water proximity, abundant greenery, and expansive vistas may maximize short-term restorative potential, offering quantitative targets for health-supportive planning and retrofitting. Full article

China possesses 400 million hectares of grasslands that provide regulating ecosystem services (ESs), including wind erosion control, water conservation, and carbon sequestration. The central government implemented the Grassland Ecological Protection Subsidy and Reward Policy (GERCP) in 2011, allocating 150 billion yuan (approximately $23 billion) through 2020, while national vegetation coverage increased from 51.0% in 2011 to 56.1% in 2020. Existing valuation studies emphasize total economic value but rarely quantify the concentration of ES values across space or their alignment with fiscal allocation. We compiled 734 grassland ES valuation observations from 186 studies published between 2000 and 2024, and estimated a multi-level mixed-effects meta-regression model for benefit transfer. We projected standardized county-level ES values, decomposed spatial inequality using the Gini coefficient and Theil index, and assessed the mismatch between value-informed allocation weights and observed GERCP transfers. Predicted values exhibit high concentration (Gini coefficient = 0.58), and between-zone differences explain 52% of total Theil inequality. The mismatch analysis identifies 94 high-value and low-compensation counties concentrated in southern Qinghai and northern Tibet, where per-hectare values are 180 to 240% above national medians, and compensation is 35 to 55% below the median. The results support value-informed targeting and redistribution of fiscal weights across regions, while payment levels require pricing benchmarks based on opportunity cost or conservation cost rather than total economic value. We propose calibrating compensation rates through a tiered schedule based on ESV quantiles or standardized ecosystem-service bundles, and implementing county-level differentiated payments with periodic updating tied to monitoring and evaluation. As a minimum viable step, we recommend piloting this scheme in counties with high ESV yet low current compensation, and integrating it into existing ecological compensation funding channels to reduce administrative frictions. Full article

Cotton spider mites pose a significant threat to cotton production, while traditional manual investigation and blanket pesticide application are inefficient for precision pest management in large-scale cotton fields. To address this challenge, this study developed an integrated UAV multispectral remote sensing system for spider mite monitoring and precision spraying. Multispectral imagery was acquired from cotton fields in Shaya County, Xinjiang using UAV-mounted cameras, and vegetation indices including RDVI, MSAVI, SAVI, and OSAVI were selected through feature optimization. Comparative evaluation of three machine learning models (Logistic Regression, Random Forest, and Support Vector Machine) and two deep learning models (1D-CNN and MobileNetV2) was conducted. Considering classification performance and computational efficiency for real-time UAV deployment, Random Forest was identified as optimal, achieving 85.47% accuracy, an 85.24% F1-score, and an AUC of 0.912. The model generated centimeter-level spatial distribution maps for precise spray zone delineation. An improved NSGA-III multi-objective path optimization algorithm was proposed, incorporating PCA-based heuristic initialization, differential evolution operators, and co-evolutionary dual population strategies to optimize deadheading distance, energy consumption, operation time, turning frequency, and load balancing. Ablation study validated the effectiveness of each component, with the fully improved algorithm reducing IGD by 59.94% and increasing HV by 5.90% compared to standard NSGA-III. Field validation showed 98.5% coverage of infested areas with only 3.6% path repetition, effectively minimizing pesticide waste and phytotoxicity risks. This study established a complete technical pipeline from monitoring to application, providing a valuable reference for precision pest control in large-scale cotton production systems. The framework demonstrated robust performance across multiple field sites, though its generalization is currently limited to one geographic region and growth stage. Future work will extend its application to additional cotton varieties, growth stages, and geographic regions. Full article

Remote sensing has revolutionized monitoring landscapes that are inaccessible or impractical to survey on the ground. Satellite platforms such as Sentinel-2 enable assessment of ecosystem changes over extensive areas with high temporal frequency, while Unmanned Aerial Systems (UAS) offer flexible, ultra-high-resolution observations ideal for site-specific analysis and sensitive environments. This study compares the performance of Sentinel-2 and Phantom 4 multispectral RTK data for monitoring vegetation dynamics in Mediterranean shrubland ecosystems, focusing on the Normalized Difference Vegetation Index (NDVI). Both platforms produced broadly consistent patterns in seasonal and interannual vegetation dynamics. However, UAS outperformed satellite data in capturing fine-scale heterogeneity, regeneration patches, and subtle disturbance responses, particularly in sparsely vegetated or heterogeneous terrain where satellite metrics may be insensitive. The comparison of NDVI across platforms accounted for standardized processing, harmonization, radiometric and atmospheric correction, and spatial resolution differences. Results show platform selection can be optimized according to monitoring objectives: satellite data are well suited for long-term monitoring of landscape-level vegetation dynamics, as both platforms capture consistent patterns when evaluated at comparable, spatially aggregated scales, while UAS data provide critical detail for localized management, early stress detection, and restoration prioritization by resolving fine-scale features. A combined approach enhances ecosystem disturbance assessments and resource management by binding the strengths of both wide-area coverage and precise spatial detail. Full article

This research designed a high-performance superabsorbent gel aligned on the integration of lignite humic residue (LHR) with a polymeric organic network in order to address ecological restoration challenges in the arid mining area in Xinjiang. This water-retaining agent was synthesized by employing solution polymerization techniques using acrylic acid (AA) and acrylamide (AM) as monomers, lignite hydrothermal residue (LHR) as a functional additive, and ammonium persulphate (APS) as the initiator. The resulting lignite hydrothermal residue–polyacrylic gel composite material was obtained by using N,N′-methylene-bisacrylamide (MBA) as the primary crosslinking agent. The water absorption capacity and mechanical strength of the acrylic gel were further enhanced by specifically incorporating low-cost, safe, and non-toxic lignite humic residue (LHR). The performance test indicated that this gel achieved a maximum water absorption of 522 g·g⁻¹ in distilled water and 65.5 g·g⁻¹ in 0.9% sodium chloride solution. Its reusability and water absorption capacity remained above 81.8% even after five cycles of natural dehydration and reabsorption. The method for synthesizing this superabsorbent gel effectively constructs a soil water retention network structure, improving the soil microenvironment, and enhancing plant salt tolerance. The field trial results showed that the application of this LHR-AA-AM superabsorbent gel considerably improved vegetation coverage in mining areas. Hence, this study provides an efficient and economical superabsorbent material for ecological restoration of saline–alkali land in arid regions without soil replacement, demonstrating promising application prospects. Full article

This study investigates the interplay between urban morphology, vegetation, and thermal environments by integrating mobile air temperature (AT) measurements with satellite-derived land surface temperature (LST). The case study is the city of Bologna (Italy). Correlation analysis revealed strong multicollinearity among morphological indicators, with building density and floor area ratio nearly collinear, while vegetation cover (PV) remained the most independent predictor. A composite urban density indicator (CUDI), derived through principal component analysis, was introduced to address redundancy among morphological metrics. Ordinary least squares regressions demonstrated significant associations, with PV exerting a pronounced cooling effect and CUDI amplifying both AT and LST. Model diagnostics confirmed statistical robustness, though residual spatial autocorrelation necessitated spatial regression approaches. Spatial lag models (SLMs) substantially improved explanatory power, highlighting spatial spillovers and neighborhood effects as central to understanding urban heat dynamics. Comparative analysis with spatial error models reinforced the dominance of SLM in capturing localized dependencies. Despite limitations in spatial coverage, temporal scope, and indicator transferability, findings emphasize the critical roles of vegetation and urban compactness in shaping thermal environments. This work underscores the necessity of integrating greening strategies with urban form management for effective heat mitigation and provides a methodological framework for analyzing urban heat islands through multi-source thermal and morphological data. Full article

Progressive climate change, intensified urbanization, and deteriorating urban environmental quality pose significant challenges for compact mid-sized city centers, where limited land availability and strong investment pressure hinder the development of green spaces. In this context, green and blue infrastructure (GBI) is increasingly seen as a key element of climate change adaptation strategies and strengthening the resilience of cities. This study aims to assess the state of GBI in the city center of Rzeszów and identify the opportunities for its integration into a coherent and multifunctional public space system. The research was conducted using a case study method combining GIS spatial analyses, remote sensing data (NDVI index), an assessment of the accessibility of green spaces according to the 3–30–300 rule, an expert assessment of the quality of public spaces, and field visits to the selected areas. An analysis of changes in vegetation cover between 2016 and 2024 showed a systematic decline in the proportion of green areas and insufficient tree cover and continuity in the GBI system. The results indicate that, despite the relatively good accessibility of larger green areas within a 300 m radius, the city center does not meet the key criteria for tree visibility, tree canopy coverage, and the creation of a coherent GBI system. The areas with the greatest integration potential were identified as the Wisłok River valley, marginal spaces, interiors between blocks, and green microforms, such as pocket parks, rain gardens, and linear greenery. The results obtained form the basis for formulating planning recommendations to support the development of GBI in densely built-up city centers. Full article

This work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization. More specifically, a visible-band vegetation index (RGBVI) is calculated to enhance canopy regions, followed by morphological filtering to delineate individual tree crowns. The Euclidean Distance Transform is then applied, and the local maxima of the smoothed distance map are extracted as candidate tree locations. The final detections are iteratively refined using the AIC to optimize the number of trees with respect to canopy coverage efficiency. Additionally, this work introduces DTCD-PC, a modified algorithm tailored for point clouds, which significantly enhances detection accuracy in complex environments. This work makes a significant contribution to tree detection in the following ways: (1) by creating a tree detection framework entirely based on an unsupervised technique, which outperforms state-of-the-art unsupervised and supervised tree detection methods; (2) by introducing a new urban dataset, named AgiosNikolaos-3, that consists of orthomosaics and photogrammetrically reconstructed 3D point clouds, allowing the assessment of the proposed method in complex urban environments. The proposed DTCD approach was evaluated on the Acacia-6 dataset, consisting of UAV images of six-month-old Acacia trees in Southeast Asia, demonstrating superior detection performance compared to existing state-of-the-art techniques, both unsupervised and supervised. Additional experiments were conducted in the custom-developed Urban Dataset, confirming the robustness and generalizability of the DTCD-PC method in heterogeneous environments. Full article

Leaf area index (LAI) is a key indicator of crop growth and development and is widely used in both agricultural research and precision farming applications. PlanetScope imagery is generally used for monitoring crop growth due to its high revisit frequency, broad spatial coverage, and cost-effective access to consistent high-resolution multispectral data. Therefore, we developed regression models to estimate peanut LAI, combining PlanetScope spectral bands and vegetation indices (VIs). Specifically, we compared the performance of random forest (RF), eXtreme Gradient Boosting (XGBoost), and Partial Least Squares Regression (PLSR) regression algorithms for peanut LAI estimation. Our results showed that most of the VIs exhibited strong relationships with LAI. Thirteen VIs were individually evaluated for estimating LAI using the aforementioned algorithms, and our results showed that the best single predictors of LAI are: TSAVI (RF: R² = 0.87, RMSE = 0.83 m²/m², RRMSE = 24.20%; XGBoost: R² = 0.77, RMSE = 0.95 m²/m², RRMSE = 27.96%); and RTVIcore (PLSR: R² = 0.68, RMSE = 1.12 m²/m², RRMSE = 32.88%). The top six ranked VIs were used to calibrate the RF, XGBoost, and PLSR algorithms. Model validation indicated that RF achieved the highest accuracy (R² = 0.844, RMSE = 0.858 m²/m², RRMSE = 25.17%), followed by XGBoost (R² = 0.808, RMSE = 0.92 m²/m², RRMSE = 26.99%), whereas PLSR showed comparatively lower performance (R² = 0.76, RMSE = 0.983 m²/m², RRMSE = 28.85%). Further results showed that PlanetScope VIs provided superior model accuracy in estimating peanut LAI compared to the use of spectral bands alone. Additionally, integrating spectral bands with VIs reduced LAI estimation accuracy, underscoring the importance of selecting predictor variables in ensuring optimal model performance. Overall, the presented results are significant for future crop monitoring using RF to reduce overreliance on multiple models for peanut LAI estimation. Full article