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Search Results (517)

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Keywords = tree-canopy cover

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12 pages, 9023 KiB  
Article
The Impact of Vegetation Structure on Shaping Urban Avian Communities in Chaoyang District Beijing, China
by Anees Ur Rahman, Kamran Ullah, Shumaila Batool, Rashid Rasool Rabbani Ismaili and Liping Yan
Animals 2025, 15(15), 2214; https://doi.org/10.3390/ani15152214 - 28 Jul 2025
Viewed by 268
Abstract
This study examines the impact of vegetation structure on bird species richness and diversity across four urban parks in Chaoyang District, Beijing. Throughout the year, using the Point Count Method (PCM), a total of 68 bird species and 4279 individual observations were recorded, [...] Read more.
This study examines the impact of vegetation structure on bird species richness and diversity across four urban parks in Chaoyang District, Beijing. Throughout the year, using the Point Count Method (PCM), a total of 68 bird species and 4279 individual observations were recorded, with surveys conducted across all four seasons to capture seasonal variations. The parks with more complex vegetation, such as those with a higher tree canopy cover of species like poplars, ginkgo, and Chinese pines, exhibited higher bird species richness. For example, Olympic Forest Park, with its dense vegetation structure, hosted 42 species, whereas parks with less diverse vegetation supported fewer species. An analysis using PERMANOVA revealed that bird communities in the four parks were significantly different from each other (F = 2.76, p = 0.04075), and every comparison between parks showed significant differences as well (p < 0.001). Variations in the arrangement and level of disturbance within different plant communities likely cause such differences. Principal component analysis (PCA) identified tree canopy cover and shrub density as key drivers of bird diversity. These findings underscore the importance of preserving urban green spaces, particularly those with a diverse range of native tree species, to conserve biodiversity and mitigate the adverse effects of urbanisation. Effective vegetation management strategies can enhance avian habitats and provide ecological and cultural benefits in urban environments. Full article
(This article belongs to the Section Birds)
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19 pages, 3568 KiB  
Article
Heat Impact of Urban Sprawl: How the Spatial Composition of Residential Suburbs Impacts Summer Air Temperatures and Thermal Comfort
by Mahmuda Sharmin, Manuel Esperon-Rodriguez, Lauren Clackson, Sebastian Pfautsch and Sally A. Power
Atmosphere 2025, 16(8), 899; https://doi.org/10.3390/atmos16080899 - 23 Jul 2025
Viewed by 275
Abstract
Urban residential design influences local microclimates and human thermal comfort. This study combines empirical microclimate data with remotely sensed data on tree canopy cover, housing lot size, surface permeability, and roof colour to examine thermal differences between three newly built and three established [...] Read more.
Urban residential design influences local microclimates and human thermal comfort. This study combines empirical microclimate data with remotely sensed data on tree canopy cover, housing lot size, surface permeability, and roof colour to examine thermal differences between three newly built and three established residential suburbs in Western Sydney, Australia. Established areas featured larger housing lots and mature street trees, while newly developed suburbs had smaller lots and limited vegetation cover. Microclimate data were collected during summer 2021 under both heatwave and non-heatwave conditions in full sun, measuring air temperature, relative humidity, wind speed, and wet-bulb globe temperature (WBGT) as an index of heat stress. Daily maximum air temperatures reached 42.7 °C in new suburbs, compared to 39.3 °C in established ones (p < 0.001). WBGT levels during heatwaves were in the “extreme caution” category in new suburbs, while remaining in the “caution” range in established ones. These findings highlight the benefits of larger green spaces, permeable surfaces, and lighter roof colours in the context of urban heat exposure. Maintaining mature trees and avoiding dark roofs can significantly reduce summer heat and improve outdoor thermal comfort across a range of conditions. Results of this work can inform bottom-up approaches to climate-responsive urban design where informed homeowners can influence development outcomes. Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
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20 pages, 25345 KiB  
Article
Mangrove Damage and Early-Stage Canopy Recovery Following Hurricane Roslyn in Marismas Nacionales, Mexico
by Samuel Velázquez-Salazar, Luis Valderrama-Landeros, Edgar Villeda-Chávez, Cecilia G. Cervantes-Rodríguez, Carlos Troche-Souza, José A. Alcántara-Maya, Berenice Vázquez-Balderas, María T. Rodríguez-Zúñiga, María I. Cruz-López and Francisco Flores-de-Santiago
Forests 2025, 16(8), 1207; https://doi.org/10.3390/f16081207 - 22 Jul 2025
Viewed by 1222
Abstract
Hurricanes are powerful tropical storms that can severely damage mangrove forests through uprooting trees, sediment erosion, and saltwater intrusion, disrupting their critical role in coastal protection and biodiversity. After a hurricane, evaluating mangrove damage helps prioritize rehabilitation efforts, as these ecosystems play a [...] Read more.
Hurricanes are powerful tropical storms that can severely damage mangrove forests through uprooting trees, sediment erosion, and saltwater intrusion, disrupting their critical role in coastal protection and biodiversity. After a hurricane, evaluating mangrove damage helps prioritize rehabilitation efforts, as these ecosystems play a key ecological role in coastal regions. Thus, we analyzed the defoliation of mangrove forest canopies and their early recovery, approximately 2.5 years after the landfall of Category 3 Hurricane Roslyn in October 2002 in Marismas Nacionales, Mexico. The following mangrove traits were analyzed: (1) the yearly time series of the Combined Mangrove Recognition Index (CMRI) standard deviation from 2020 to 2025, (2) the CMRI rate of change (slope) following the hurricane’s impact, and (3) the canopy height model (CHM) before and after the hurricane using satellite and UAV-LiDAR data. Hurricane Roslyn caused a substantial decrease in canopy cover, resulting in a loss of 47,202 ha, which represents 82.8% of the total area of 57,037 ha. The CMRI standard deviation indicated early signs of canopy recovery in one-third of the mangrove-damaged areas 2.5 years post-impact. The CMRI slope indicated that areas near the undammed rivers had a maximum recovery rate of 0.05 CMRI units per month, indicating a predicted canopy recovery of ~2.5 years. However, most mangrove areas exhibited CMRI rates between 0.01 and 0.03 CMRI units per month, anticipating a recovery time between 40 months (approximately 3.4 years) and 122 months (roughly 10 years). Unfortunately, most of the already degraded Laguncularia racemosa forests displayed a negative CMRI slope, suggesting a lack of canopy recovery so far. Additionally, the CHM showed a median significant difference of 3.3 m in the canopy height of fringe-type Rhizophora mangle and Laguncularia racemosa forests after the hurricane’s landfall. Full article
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19 pages, 2791 KiB  
Article
Combining Open-Source Machine Learning and Publicly Available Aerial Data (NAIP and NEON) to Achieve High-Resolution High-Accuracy Remote Sensing of Grass–Shrub–Tree Mosaics
by Brynn Noble and Zak Ratajczak
Remote Sens. 2025, 17(13), 2224; https://doi.org/10.3390/rs17132224 - 28 Jun 2025
Viewed by 619
Abstract
Woody plant encroachment (WPE) is transforming grasslands globally, yet accurately mapping this process remains challenging. State-funded, publicly available high-resolution aerial imagery offers a potential solution, including the USDA’s National Agriculture Imagery Program (NAIP) and NSF’s National Ecological Observatory Network (NEON) Aerial Observation Platform [...] Read more.
Woody plant encroachment (WPE) is transforming grasslands globally, yet accurately mapping this process remains challenging. State-funded, publicly available high-resolution aerial imagery offers a potential solution, including the USDA’s National Agriculture Imagery Program (NAIP) and NSF’s National Ecological Observatory Network (NEON) Aerial Observation Platform (AOP). We evaluated the accuracy of land cover classification using NAIP, NEON, and both sources combined. We compared two machine learning models—support vector machines and random forests—implemented in R using large training and evaluation data sets. Our study site, Konza Prairie Biological Station, is a long-term experiment in which variable fire and grazing have created mosaics of herbaceous plants, shrubs, deciduous trees, and evergreen trees (Juniperus virginiana). All models achieved high overall accuracy (>90%), with NEON slightly outperforming NAIP. NAIP underperformed in detecting evergreen trees (52–78% vs. 83–86% accuracy with NEON). NEON models relied on LiDAR-based canopy height data, whereas NAIP relied on multispectral bands. Combining data from both platforms yielded the best results, with 97.7% overall accuracy. Vegetation indices contributed little to model accuracy, including NDVI (normalized digital vegetation index) and EVI (enhanced vegetation index). Both machine learning methods achieved similar accuracy. Our results demonstrate that free, high-resolution imagery and open-source tools can enable accurate, high-resolution, landscape-scale WPE monitoring. Broader adoption of such approaches could substantially improve the monitoring and management of grassland biodiversity, ecosystem function, ecosystem services, and environmental resilience. Full article
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15 pages, 6206 KiB  
Article
Analyzing the Relationship Between Tree Canopy Coverage and Snowpack in the Great Salt Lake Watershed
by Kyle J. Bird, Grayson R. Morgan, Benjamin W. Abbott and Samuel M. Otterstrom
Sustainability 2025, 17(13), 5771; https://doi.org/10.3390/su17135771 - 23 Jun 2025
Viewed by 304
Abstract
Utah, USA, relies heavily on snowpack for water to sustain its growing population. Scientists and policy makers are exploring and proposing several potential sustainable solutions to improving flow to the Great Salk Lake as it recently has significantly declined in size, including removing [...] Read more.
Utah, USA, relies heavily on snowpack for water to sustain its growing population. Scientists and policy makers are exploring and proposing several potential sustainable solutions to improving flow to the Great Salk Lake as it recently has significantly declined in size, including removing tree canopy. This study examines the influence of tree canopy coverage, climate, and topography on snow water equivalent (SWE) within the Great Salt Lake Watershed. Using SNOTEL data, NLCD land use/land cover rasters, t-tests, and multiple linear regression (MLR), the study analyzed SWE variability in relation to canopy density, winter precipitation, elevation, temperature, and latitude. Initial t-tests showed significant differences in SWE between sites with canopy coverage below and above 70%, yet tree canopy was excluded as a significant predictor in the MLR model. Instead, SWE was primarily explained by mean winter precipitation, elevation, average winter high temperatures, and latitude. Additionally, canopy change analysis of the 2018 Pole Creek Fire in the Jordan River watershed showed no significant changes in SWE following canopy loss. This study highlights the dominant role of climatic factors in influencing snowpack dynamics on a watershed scale. It also provides important data for sustainable watershed and forestry management and a framework for understanding snowpack responses to climate and land cover changes in saline lake ecosystems. Full article
(This article belongs to the Section Sustainable Forestry)
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29 pages, 37426 KiB  
Article
Support for Subnational Entities to Develop and Monitor Land-Based Greenhouse Gas Reduction Activities
by Erin Glen, Angela Scafidi, Nancy Harris and Richard Birdsey
Land 2025, 14(7), 1336; https://doi.org/10.3390/land14071336 - 23 Jun 2025
Viewed by 448
Abstract
Land managers across the United States (U.S.) are developing plans to mitigate climate change. Effective implementation and monitoring of these climate action plans require standardized methods and timely, accurate geospatial data at appropriate resolutions. Despite the abundance of geospatial and statistical data in [...] Read more.
Land managers across the United States (U.S.) are developing plans to mitigate climate change. Effective implementation and monitoring of these climate action plans require standardized methods and timely, accurate geospatial data at appropriate resolutions. Despite the abundance of geospatial and statistical data in the U.S., a significant gap remains in translating these data into actionable insights. To address this gap, we developed the Land Emissions and Removals Navigator (LEARN), an online tool that automates subnational greenhouse gas (GHG) inventories of forests and trees in nonforest lands using a standardized analytical framework consistent with national and international guidelines. LEARN integrates multiple datasets to calculate land cover and tree canopy changes, delineate areas of forest disturbance, and estimate carbon emissions and removals. To demonstrate the application of LEARN, this paper presents case studies in Jefferson County, Washington; Montgomery County, Maryland; and federally owned forests across the conterminous U.S. Our results highlight LEARN’s capacity to provide localized insights into carbon dynamics, enabling subnational entities to develop tailored climate strategies. By enhancing accessibility to standardized data, LEARN empowers community land managers to more effectively mitigate climate change. Future developments aim to expand LEARN’s scope to cover nonforest landscapes and incorporate additional decision-making functionalities. Full article
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16 pages, 9522 KiB  
Article
Tabonuco and Plantation Forests at Higher Elevations Are More Vulnerable to Hurricane Damage and Slower to Recover in Southeastern Puerto Rico
by Michael W. Caslin, Madhusudan Katti, Stacy A. C. Nelson and Thrity Vakil
Land 2025, 14(7), 1324; https://doi.org/10.3390/land14071324 - 21 Jun 2025
Viewed by 1409
Abstract
Hurricanes are major drivers of forest structure in the Caribbean. In 2017, Hurricane Maria caused substantial damage to Puerto Rico’s forests. We studied forest structure variation across 75 sites at Las Casas de la Selva, a sustainable forest plantation in Patillas, Puerto Rico, [...] Read more.
Hurricanes are major drivers of forest structure in the Caribbean. In 2017, Hurricane Maria caused substantial damage to Puerto Rico’s forests. We studied forest structure variation across 75 sites at Las Casas de la Selva, a sustainable forest plantation in Patillas, Puerto Rico, seven years after Hurricane Maria hit the property. At each site we analyzed 360° photos in a 3D VR headset to quantify the vertical structure and transformed them into hemispherical images to quantify canopy closure and ground cover. We also computed the Vertical Habitat Diversity Index (VHDI) from the amount of foliage in four strata: herbaceous, shrub, understory, and canopy. Using the Local Bivariate Relationship tool in ArcGIS Pro, we analyzed the relationship between forest recovery (vertical structure, canopy closure, and ground cover) and damage. Likewise, we analyzed the effects of elevation, slope, and aspect, on damage, canopy closure, and vertical forest structure. We found that canopy closure decreases with increasing elevation and increases with the amount of damage. Higher elevations show a greater amount of damage even seven years post hurricane. We conclude that trees in the mixed tabonuco/plantation forest are more susceptible to hurricanes at higher elevations. The results have implications for plantation forest management under climate-change-driven higher intensity hurricane regimes. Full article
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20 pages, 5980 KiB  
Article
Remote-Sensed Evidence of Fire Alleviating Forest Canopy Water Stress Under a Drying Climate
by Thai Son Le, Bernard Dell and Richard Harper
Remote Sens. 2025, 17(12), 1979; https://doi.org/10.3390/rs17121979 - 6 Jun 2025
Viewed by 548
Abstract
Fire is a distinctive factor in forest ecosystems. While uncontrolled wildfires can cause significant damage, prescribed burning is widely used as a management tool. However, despite the growing threat of forest water stress under climate change, there is a lack of concrete evidence [...] Read more.
Fire is a distinctive factor in forest ecosystems. While uncontrolled wildfires can cause significant damage, prescribed burning is widely used as a management tool. However, despite the growing threat of forest water stress under climate change, there is a lack of concrete evidence on the impact of fire on water stress in forest ecosystems. This study utilized Landsat time-series remote sensing data combined with the Infrared Canopy Dryness Index (ICDI) to monitor changes in canopy dryness patterns across the eucalyptus-dominated Northern Jarrah Forest of southwestern Australia. The forest was chosen due to its exposure to a changing climate characterized by decreasing rainfall and more frequent droughts, signs of water stress in otherwise drought-resilient trees, and its well-documented fire management history. Analysis of ICDI patterns over the period from 1988 to 2024 revealed a clear overall trend of increasing water stress, coinciding with a small overall decline in annual rainfall in the 10,000 km2 study area. Furthermore, by examining five prescribed burns and five wildfires, we found that NDVI-assessed canopy cover recovered rapidly in fire-affected areas, typically within one to three years, depending on fire severity. However, ICDI water stress levels were reduced for approximately 7–8 years following low-severity prescribed burns and more than 20 years after high-severity wildfires. These findings suggest the potential of prescribed burning as a tool to mitigate water stress in vulnerable forest landscapes, particularly in regions prone to drought and climate change. Additionally, the study underscores the effectiveness of the ICDI in monitoring forest water stress and its potential for broader applications in forest management and climate adaptation strategies. Full article
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26 pages, 2906 KiB  
Article
Street-Scale Urban Air Temperatures Predicted by Simple High-Resolution Cover- and Shade-Weighted Surface Temperature Mosaics in a Variety of Residential Neighborhoods
by Katarina Kubiniec, Kevan B. Moffett and Kyle Blount
Remote Sens. 2025, 17(11), 1932; https://doi.org/10.3390/rs17111932 - 3 Jun 2025
Viewed by 1122
Abstract
A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is [...] Read more.
A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is difficult due to (1) the coarse scale of common remote sensing data, which do not observe the key environments beneath urban tree canopies, and, (2) conversely, the immense labor of intense, location-specific, ground-based survey campaigns. This work tested whether remotely sensed urban heat merged with land cover heterogeneity and shade/sun fractions, if combined at a sufficiently fine scale so as to be linearly additive, would enable simple and accurate statistical modeling of street-scale urban air temperatures with minimal empirical fitting. We used ground-based thermography of a sample of 12 residential streetscapes in Portland, Oregon, to characterize the land surface temperatures (LSTg) of eleven common urban surface cover types when sun-exposed and in shade. Surfaces were cooler in shade than sun, but with surface-specific differences not explained by greenery nor (im)perviousness. Also, surfaces on streetscapes with more canopy cover, even when sun-exposed at midday, remained significantly cooler than comparable sun-exposed surfaces on streets with less canopy cover, indicating the key significance of partial diurnal shading, not typically accounted for in urban thermal statistical models. We used high-resolution orthoimagery to quantify the area of each surface cover type within each streetscape and computed an area-weighted average surface temperature (Ts), accounting for sun/shade heterogeneity. The data revealed a significant, nearly 1:1 relationship between calculated Ts values and sun-shielded air temperatures (Ta). In contrast, relationships of Ta to tree coverage, impervious area, or the LSTg of dominant surface cover types were all statistically insignificant. These results suggest that statistical models may more reliably bridge the gap between remote sensing urban surface temperatures and reliable predictions of street-scale air temperatures if (1) analysis is at a sufficiently high resolution (e.g., <10 m) to avoid some of the known scale-dependence of urban thermal environments and enable simple weighted linear models, and (2) distinctions between thermal contributions of sunlit and shaded surfaces are included along with the influence of diurnal shading. Such models may provide effective and low-cost predictions of local UHIs and help inform effective street-level approaches to mitigating urban heat. Full article
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28 pages, 8474 KiB  
Article
Estimation of Tree Canopy Closure Based on U-Net Image Segmentation and Machine Learning Algorithms
by Yuefei Zhou, Jinghan Wang, Zengjing Song, Miaohang Zhou, Mengnan Lv and Xujun Han
Remote Sens. 2025, 17(11), 1828; https://doi.org/10.3390/rs17111828 - 23 May 2025
Viewed by 550
Abstract
Canopy closure is a critical indicator reflecting forest structure, biodiversity, and ecological balance. This study proposes an estimation method integrating U-Net segmentation with machine learning, significantly improving accuracy through multi-source remote sensing data and feature selection. Covering eight U.S. continental states, the study [...] Read more.
Canopy closure is a critical indicator reflecting forest structure, biodiversity, and ecological balance. This study proposes an estimation method integrating U-Net segmentation with machine learning, significantly improving accuracy through multi-source remote sensing data and feature selection. Covering eight U.S. continental states, the study utilized 13,000 stratified samples equally split for model training and validation. Four states were used to train models based on XGBoost, random forest (RF), LightGBM, and support vector machine (SVM), while the remaining four states served for validation. The results indicate that (1) U-Net effectively extracted tree crowns from aerial imagery to construct the sample dataset; (2) among the tested algorithms, XGBoost achieved the highest accuracy of 0.88 when incorporating Sentinel-1, Sentinel-2, vegetation indices, and land cover features, outperforming models using only Sentinel-2 data by 25.7%; and (3) XGBoost-estimated tree canopy cover (Model TCC) showed finer spatial details than the National Land Cover Database Tree Canopy Cover (NLCD TCC), with R2 against the true tree canopy closure from U-Net (True TCC) up to 49.1% higher. This approach offers a cost-effective solution for regional-scale canopy monitoring. Full article
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13 pages, 884 KiB  
Article
Tree Canopies Drive δ13C and δ15N Patterns in Mediterranean Wood Pastures of the Iberian Peninsula
by Mercedes Ibañez, Salvador Aljazairi, María José Leiva, Cristina Chocarro, Roland A. Werner, Jaleh Ghashghaie and Maria-Teresa Sebastià
Land 2025, 14(6), 1135; https://doi.org/10.3390/land14061135 - 22 May 2025
Viewed by 453
Abstract
Mediterranean wood pastures are the result of traditional silvo-pastoral uses that shaped these ecosystems into a mosaic of trees and open grassland. This ecosystem structure is generally associated with increased soil fertility under tree canopies. However, the response of herbaceous plant functional types [...] Read more.
Mediterranean wood pastures are the result of traditional silvo-pastoral uses that shaped these ecosystems into a mosaic of trees and open grassland. This ecosystem structure is generally associated with increased soil fertility under tree canopies. However, the response of herbaceous plant functional types (PFTs)—grasses, legumes, and non-legume forbs—to these heterogeneous microenvironments (under the canopy vs. open grassland) remains largely unknown, particularly regarding carbon (C) and nitrogen (N) acquisition and use. Even less is known about how different tree species and environmental conditions influence these responses. In this study, we aim to assess how tree canopies influence carbon and nitrogen cycling by comparing the effects of traditional oak stands and pine plantations on herbaceous PFTs and soil dynamics. For that we use C and N content and natural isotopic abundances (δ13C and δ15N) as proxies for biogeochemical cycling. Our results show that ecosystem C and N patterns depend not only on herbaceous PFTs and the presence or absence of tree canopies but also on tree species identity and environmental conditions, including climate. In particular, pine-dominated plantations exhibited lower nitrogen availability compared to those dominated by oak, suggesting that oak stands may contribute more effectively to enhance soil fertility in Mediterranean wood pastures. Furthermore, the canopy effect was more pronounced under harsher environmental conditions, highlighting the role of trees in buffering environmental stress, particularly in arid regions. This suggests that changes in tree cover and tree species may drive complex changes in ecosystem C and N storage and cycling. Full article
(This article belongs to the Special Issue Observation, Monitoring and Analysis of Savannah Ecosystems)
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21 pages, 5822 KiB  
Article
The Walkability Evaluation and Optimization Strategies of Metro Station Areas Taking Shanghai as an Example
by Xiaoyan Chen, Zhengyan Shi and Yanzhe Hu
Buildings 2025, 15(10), 1746; https://doi.org/10.3390/buildings15101746 - 21 May 2025
Viewed by 535
Abstract
Improving the pedestrian environment around metro stations and enhancing walkability are important for the daily travel and life quality of passengers. By reviewing existing studies, we summarized nine walkability elements and eventually refined them into 18 quantifiable research indicators. Walkability elements such as [...] Read more.
Improving the pedestrian environment around metro stations and enhancing walkability are important for the daily travel and life quality of passengers. By reviewing existing studies, we summarized nine walkability elements and eventually refined them into 18 quantifiable research indicators. Walkability elements such as street enclosure, number of lanes, and tree canopy coverage were quantified through field surveys and passenger perception data. A stepwise regression analysis identified key influencing factors for nine walkability dimensions. Based on the correlation coefficients, factor assignments, and constants, a composite walkability index formula was established to evaluate pedestrian routes near four Shanghai metro stations. The results show that the proportion of sidewalks covered by a tree canopy, the number of lanes, street enclosures, and the transparency of the ground-floor building facade are the most important factors affecting the walkability of the pedestrian environment. In this study, we calculated the scores of each road section, compared the walking facilities and walking distance of different stations, and finally proposed relevant strategies for improving the walking environment. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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23 pages, 4356 KiB  
Article
Understory Forage Quality for Grazing Animals in Chilean Patagonian Forests
by Thomas Brisard, Amelie Brisard, Mónica D. R. Toro-Manríquez, Soraya Villagrán Chacón, Pablo Jesús Marín-García, Lola Llobat, Guillermo Martínez Pastur, Sabina Miguel Maluenda and Alejandro Huertas Herrera
Land 2025, 14(5), 1081; https://doi.org/10.3390/land14051081 - 16 May 2025
Viewed by 579
Abstract
Native forests provide forage for grazing animals. We investigated whether native and exotic vegetation promotes the potential animal load (PAL, ind ha−1 yr−1) for cattle (Bos taurus, ~700 kg) and sheep (Ovis aries, ~60 kg) in [...] Read more.
Native forests provide forage for grazing animals. We investigated whether native and exotic vegetation promotes the potential animal load (PAL, ind ha−1 yr−1) for cattle (Bos taurus, ~700 kg) and sheep (Ovis aries, ~60 kg) in contrasting native forest types and canopy cover (closed, semi-open, open). This study was conducted in Chilean Patagonia (−44° to −49° SL). Vegetation cover (%) and growth habit data (trees, shrubs, forbs, graminoids, ferns, lianas, lichens, and bryophytes) were collected from 374 plots (>5 ha) in different environments: coihue (Nothofagus dombeyi, CO), lenga (N. pumilio, LE), mixed Nothofagus forests (MI), ñirre (N. antarctica, ÑI), evergreen forest (SV), and open land (OL). We combine this data with literature and laboratory analyses (e.g., crude protein, %) to develop PAL values for seasons. Data sampling was evaluated using descriptive analyses and uni- and multi-variate analyses (ANOVA, MCA, GLM). Results showed that closed forests had more native species (~56.6%) compared to open forests (~33.3%), while OL had higher cover of exotic species (~68.6%). LE presented the highest native species cover (~58.0%) and ÑI presented the highest exotic species cover (~53.0%). Closed forests had fewer exotic species than semi-open and open forests, which supported higher cover of native plants (p < 0.01). Forbs were the dominant growth habit in closed forests, while graminoids were dominant in OL (~45.8%). Multivariate analyses showed that LE and CO were associated with lower PAL values, explaining 91.2% variance. GLMs showed that the PAL increased in ÑI and the spring season, with forbs and graminoids having positive effects and shrubs and trees having negative effects (r2 = 0.57–0.67). Our analyses also showed that exotic species dominated environment types with a high PAL, particularly during spring and summer, when cover increased. This indicates a trade-off between forage production in forests with exotic plants. Full article
(This article belongs to the Section Land Use, Impact Assessment and Sustainability)
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24 pages, 9844 KiB  
Article
UFORE-D Modeling of Urban Tree Influence on Particulate Matter Concentrations in a High-Altitude Latin American Megacity
by Laura Ochoa-Alvarado, Juan Garzón-Gil, Sergio Castro-Alzate, Carlos Alfonso Zafra-Mejía and Hugo Alexander Rondón-Quintana
Earth 2025, 6(2), 36; https://doi.org/10.3390/earth6020036 - 9 May 2025
Viewed by 674
Abstract
Urban trees reduce particulate matter (PM) concentrations through dry deposition, interception, and modifying wind patterns, improving air quality and saving public health expenses in urban planning. The main objective of this article is to present an analysis of the influence of urban trees [...] Read more.
Urban trees reduce particulate matter (PM) concentrations through dry deposition, interception, and modifying wind patterns, improving air quality and saving public health expenses in urban planning. The main objective of this article is to present an analysis of the influence of urban trees on PM10 and PM2.5 concentrations in a high-altitude Latin American megacity (Bogotá, Colombia) using UFORE-D modeling. Six PM monitoring stations distributed throughout the megacity were used. Hourly climatic and PM data were collected for seven years, along with dendrometric and cartographic analyses within 200 m of the monitoring stations. Land cover was quantified using satellite imagery (Landsat 8) in order to perform a spatial analysis. The results showed that the UFORE-D model effectively quantified urban forest canopy area (CA) impact on PM10 and PM2.5 removal, showing strong correlations (R2 = 0.987 and 0.918). PM removal increased with both CA and ambient pollutant concentrations, with CA exhibiting greater influence. Sensitivity analysis highlighted enhanced air quality with increased leaf area index (LAI: 2–4 m2/m2), particularly at higher wind speeds. PM10 removal (1.05 ± 0.01%) per unit CA exceeded PM2.5 (0.71 ± 0.09%), potentially due to resuspension modeling. Model validation confirmed reliability across urban settings, emphasizing its utility in urban planning. Scenario analysis (E1–E4, CA: 8.30–95.4%) demonstrated a consistent positive correlation between CA and PM removal, with diminishing returns at extreme CA levels. Urban spatial constraints suggested integrated green infrastructure solutions. Although increased CA improved PM removal rates, the absolute reduction of pollutants remained limited, suggesting comprehensive emission monitoring. Full article
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16 pages, 1767 KiB  
Article
Microclimate Shifts and Leaf Miner Community Responses to Shelterwood Regeneration in Sessile Oak Forests
by Jovan Dobrosavljević, Branko Kanjevac and Čedomir Marković
Forests 2025, 16(5), 739; https://doi.org/10.3390/f16050739 - 25 Apr 2025
Viewed by 377
Abstract
For forests to provide ecosystem services and function optimally, they need to be managed. Forest management measures can cause significant environmental changes, which sometimes appear extreme. The most notable disturbance caused by forest regeneration is the change in canopy cover. Alteration of the [...] Read more.
For forests to provide ecosystem services and function optimally, they need to be managed. Forest management measures can cause significant environmental changes, which sometimes appear extreme. The most notable disturbance caused by forest regeneration is the change in canopy cover. Alteration of the canopy cover is followed by the modifications of many microclimatic factors. These changes subsequently affect all the living organisms in the forest. The present study was conducted to determine how the changes caused by modifications of canopy closure by shelterwood regeneration affect the leaf-mining insect community on sessile oak (Quercus petraea (Matt.) Liebl.). We identified that the removal of the canopy significantly affects the microclimate, vegetation, and the leaf miner community. The insolation and temperature increased in the more open areas, while relative air humidity decreased. This affects the characteristics of the young oak plants, which grow taller and produce more leaves in the open-canopy areas. All these changes consequentially affect the leaf miner community. While the species richness and abundance per tree increased with the decrease in canopy closure, the species richness and abundance per leaf decreased. The opening of the canopy positively affected the leaf miners in the end by increasing the diversity and evenness of their community. Full article
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