Search Results (106)

Owing to the poor cold tolerance of Stenotaphrum Trin and the urgent need for shade-tolerant grass species in temperate regions of East China, this study evaluated the cold tolerance of 55 Stenotaphrum accessions, aiming to provide shade-tolerant materials for temperate regions. A fine cold-tolerant turfgrass should have both the advantages of delayed fall dormancy and early spring green-up. However, previous research on the cold resistance of turfgrass has mainly focused on the performance of the spring green-up, with less attention paid to the fall dormancy, which has affected the ornamental and application value of turfgrass. This study first dynamically investigated the leaf colour of each accession during the fall dormancy and the coverage during the spring green-up and evaluated the cold resistance of the accession through membership functions and cluster analysis. Significant differences in the cold resistance were found with the assignment of breeding lines to four categories. The weak correlation (R² = 0.1682) between leaf colour during the fall dormancy and coverage during the spring green-up indicates that using the performance of a single period to represent the cold resistance of accessions is not appropriate. To test whether using the laboratory-based LT50 and stolon regrowth rating analysis can replace the above-improved method, we conducted a related analysis and found that the fit between these two methods is very poor. This phenomenon is attributed to the poor correlation between the laboratory-based parameters and the pot-investigated data. Therefore, this study presents a cold resistance evaluation method for Stenotaphrum that integrates performance in both the fall dormancy and spring green-up periods. This improved evaluation method cannot be simplified by the growth performance of a single period or replaced by using laboratory-based LT50 and stolon regrowth tests. With the help of this improved method, several excellent cold tolerance accessions (ST003, S13, and S12) were identified for temperate regions of East China. Full article

Understanding the phenology of urban trees can help mitigate the heat island effect by strategically planting and managing trees to provide shade, reduce energy consumption, and improve urban microclimates. In this study, we carried out the first evaluation of high spatial resolution satellite images from Landsat-8, Sentinel-2, and PlanetScope images to quantify urban street tree phenology in downtown Beijing. The major research goals are to evaluate the consistency in pixel-level spring–summer growth period phenology and to investigate the capacity of high-resolution satellite observations to distinguish phenological transition dates of urban street trees. At the city scale, Landsat-8, Sentinel-2, and PlanetScope show similar temporal NDVI trends in general. The pixel-level analysis reveals that green-up date consistency is higher in areas with medium (NDVI > 0.5) to high (NDVI > 0.7) vegetation cover when the impacts of urban surfaces on vegetation reflectance are excluded. Similarly, maturity date consistency significantly increases in densely vegetated pixels with NDVI greater than 0.7. At the street scale, this study emphasizes the efficacy of NDVI time series derived from PlanetScope in quantifying the phenology of common street tree genera, including Poplars (Populus), Ginkgos (Ginkgo), Chinese Scholars (Styphnolobium), and Willows (Salix), in downtown Beijing to improve urban vegetation planning. Based on PlanetScope observations, we found that the four street tree genera have unique phenological patterns. Interestingly, we found that the trees along many major streets, where Chinese Scholars are the major tree genus, have later green-up dates than other areas in downtown Beijing. In conclusion, the three satellite observation datasets prove to be effective in monitoring street tree phenology during the spring–summer growth period in Beijing. PlanetScope is effective in monitoring tree phenology at the street scale; however, Landsat-8 may be affected by the mixture of land covers due to its relatively coarse spatial resolution. Full article

Today, rapid urbanization and increasing human activities have affected the climate at macro and micro scales in cities and caused unfavorable conditions in terms of human thermal comfort, especially in outdoor spaces. In this context, new solutions need to be researched, developed, tested, and updated to improve thermal comfort in cities. Using ENVI-met 5.1 software, this study investigated the effects of different NBS combinations on the urban microclimate and human bioclimatic comfort in Izmir (Turkey). The current situation, the scenarios where some nature-based solutions (NBS) are applied within the scope of the European Union’s HORIZON 2020 “URBAN GreenUP” project, and two other scenarios planned within the scope of the study were evaluated. The findings of the study showed that both the NBS scenarios created within the scope of the EU project and the NBS scenarios with large deciduous trees had the most positive impacts on improving thermal comfort conditions in all three study zones and achieved temperature reductions of up to 2.5 °C in urban temperatures. In terms of thermal comfort, the most significant differences were calculated between the minimum PMV values and were close to 1 °C. In addition, the simulation results showed positive changes in psychological stress levels. Full article

Carbon allocation in forest ecosystems is essential for the optimization of growth. However, remote-sensing-based research on the estimation of carbon allocation in forests is inadequate. This article considers forests in northeastern China as the research area and uses leaf area index (LAI) data combined with random forest and structural equation modelling methods to study the spatiotemporal distribution characteristics and driving factors of carbon allocation to leaves (ΔLAI) in deciduous broad-leaved forests (DBF), deciduous coniferous forests (DNF), and mixed forests (MF) during the green-up period (GUP) at a monthly scale during April, May, June, and July from 2001 to 2021, and clarifies the impact of leaf carbon allocation on gross primary productivity (GPP). The ΔLAI was the highest in DBF in April and in DNF and MF in May. The ΔLAI in April with an increasing trend year by year in DBF and MF, and the ΔLAI in May with an increasing trend in DNF. Among all the direct and indirect relationships that affect ΔLAI, temperature (TEM) has the highest path coefficient for DBF’s ΔLAI in April (−1.213) and the start of the season (SOS) has the highest path coefficient for DNF (−1.186) and MF (0.815). ΔLAI in the GUP has a significant positive impact on the GPP. In the MF, the higher ΔLAI in May was most conducive to an increase in GPP. During the critical period, that is April and May, carbon allocation to leaves effectively improves the carbon sequestration capacity of forestland. This information is of great value for the development and validation of terrestrial ecosystem models. Full article

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R² = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the best prediction result based on texture index was RFECV-RF at a flight height of 40 m (R² = 0.8894, RMSE = 0.4236, RRMSE = 0.1335, RPD = 3.0063); and the best prediction result combining texture and index was RFECV-RF at a flight height of 40 m (R² = 0.9210, RMSE = 0.3579, RRMSE = 0.1128, RPD = 3.5575). The results of this study demonstrate that combining vegetation indices and texture from multispectral drone imagery can improve the accuracy of LAI estimation during the vegetative growth stage of winter wheat. In addition, selecting a flight height of 40 m can improve efficiency in large-scale agricultural field monitoring, as this study showed that drone data at flight heights of 20 m and 40 m did not significantly affect model accuracy. Full article

Little is known regarding the effects of prescribed burning on soil water infiltration and soil physical properties in Western Gulf Coast forests dominated by longleaf pine (Pinus palustris), shortleaf pine (P. echinata), or loblolly pine (P. taeda). Soil water infiltration rates were measured pre-burn (before the fire), post-burn (one month after the fire), and at green-up (three months after the fire) in areas utilizing prescribed fire with different rotations and seasons. The National Forests and Grasslands of Texas predominantly perform dormant season burns every two to three years, while the Winston 8 Land and Cattle Ltd. Tree Farm often burned biannually during the dormant season, but occasionally during the growing season. Soil samples were also collected to determine the effects of prescribed burning on soil pH, bulk density, particle density, pore space, soil strength, O-horizon weight and depth (organic matter), and water-stable aggregates. There was a significant increase in soil water infiltration rates between pre-burn and post-burn and pre-burn and green-up, and between the two different burn intervals. Soil strength initially decreased slightly, but then increased over time. Soil-stable aggregates increased significantly over time, and soil physical properties that significantly changed included soil bulk density, pore space, water-stable soil aggregates, and soil strength. This study found there could be short-term (2–3 years) responses on soil physical properties and soil water infiltration rates from repeated burning treatments, regardless of overstory species. Full article

Climatic warming advances the start of the growing season (SOS) and sequentially enhances the vegetation productivity of temperate forests by extending the carbon uptake period and/or increasing the growth rate. Recent research indicates that the vegetation growth rate is a main driver for the interannual changes in vegetation carbon uptake; however, the specific effects of an earlier SOS on vegetation growth rate and the underlying mechanisms are still unclear. Using 268 year-site PhenoCam observations in temperate forests, we found that an earlier SOS reduced the vegetation growth rate and mean air temperature during the green-up period (i.e., from the SOS to the peak of the growing period), but increased the accumulation of shortwave radiation during the green-up period. Interestingly, an earlier-SOS-induced reduction in the growth rate was weakened in the highly humid areas (aridity index ≥ 1) when compared with that in the humid areas (aridity index < 1), suggesting that an earlier-SOS-induced reduction in the growth rate in temperate forests may intensify with the ongoing global warming and aridity in the future. The structural equation model analyses indicated that an earlier-SOS-induced decrease in the temperature and increase in shortwave radiation drove a low vegetation growth rate. Our findings highlight that the productivity of temperate forests may be overestimated if the negative effect of an earlier SOS on the vegetation growth rate is ignored. Full article

Little has been reported on the effects of repeated prescribed burning on southern United States’ forest soils, especially when site preparation is not the prescribed fire objective. This study was aimed at identifying any correlations between the soil chemical properties among differing burn intervals and the effects prescribed burning has on them. Sampling was performed in 36 plots at three sites with two different burn intervals (2–3 years and biannually) and measured properties: (1) pre-burn (before the fire), (2) post-burn (one month after the fire), and (3) at vegetation green-up (three months after the fire). Sites varied by overstory species with longleaf pine (Pinus palustris) and shortleaf pine (Pinus echinata Mill.) in the overstory on one site, a mix of loblolly pine (P taeda L.) and shortleaf pine on another, and longleaf and loblolly pines on the third. SAS was used to determine the effects of prescribed burning between different time spans (pre-burn to post-burn, post-burn to green-up, and pre-burn to green-up) and between the two different burn intervals. We found that there could be short-term responses in soil chemical properties from repeated burning treatments including nitrogen in the forms of ammonium and nitrate, the carbon-to-nitrogen ratio, and electrical conductivity, all of which decreased following fire regardless of overstory species. Full article

The relationship between time and energy is an important aspect related to energy savings in modern multicore architectures. In this paper, we investigated and analyzed the correlation between time and energy. We compared the execution time and energy consumption of the LU factorization algorithms (versions with and without pivoting) and Cholesky with the Math Kernel Library (MKL) on a multicore machine. To reduce the energy of these multithreaded factorizations, the Dynamic Voltage and Frequency Scaling (DVFS) technique was used. This technique allows the clock frequency to be scaled without changing the implementation. In particular, we studied the correlations between time and energy using two metrics: Energy Delay Product (EDP) and Greenup, Powerup, and Speedup (GPS-UP). An experimental evaluation was performed on an Intel Xeon Gold multicore machine as a function of the number of threads and the clock speed. Our test results showed that scalability in terms of execution time, expressed by the Speedup metric, had values close to a linear function as the number of threads increased. In contrast, the scalability in terms of energy consumption, expressed by the Greenup metric, had values close to a logarithmic function as the number of threads increased. The use of the EDP and GPS-UP metrics allowed us to evaluate the impact of the optimized code (DVFS and increase in the number of threads) on the time and energy consumption and to determine a better green category representing energy savings without losing performance. Full article

The rise in global average surface temperature has promoted the advancement of spring vegetation phenology. However, the response of spring vegetation phenology to different temperature parameters varies. The Mongolian Plateau, one of the largest grasslands in the world, has green-up dates (GUDs) with unclear sensitivity to different temperature parameters. To address this issue, we investigated the responses of GUDs to different temperature parameters in the Mongolian Plateau grasslands. The results show that GUDs responded significantly differently to changes in near-surface temperature (TMP), near-surface temperature maximum (TMX), near-surface temperature minimum (TMN), and diurnal temperature range (DTR). GUDs advanced as TMP, TMX, and TMN increased, with TMN having a more significant effect, whereas increases in DTR inhibited the advancement of GUDs. GUDs were more sensitive to TMX and TMN than to TMP. The sensitivity of GUDs to DTR showed an increasing trend from 1982 to 2015 and showed this parameter’s great importance to GUDs. Our results also show that the spatial and temporal distributions of temperature sensitivity are only related to temperature conditions in climatic zones instead of whether they are arid. Full article

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research has yet to consider the influence of different growth stages and UAV flight altitudes on the accuracy of SPAD estimation. This study aims to optimize UAV flight strategies and incorporate multiple feature selection techniques and machine learning algorithms to enhance the accuracy of the SPAD value estimation of different wheat varieties across growth stages. This study sets two flight altitudes (20 and 40 m). Multispectral images were collected for four winter wheat varieties during the green-up and jointing stages. Three feature selection methods (Pearson, recursive feature elimination (RFE), and correlation-based feature selection (CFS)) and four machine learning regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), and extreme gradient boosting (XGBoost)) were combined to construct SPAD value estimation models for individual growth stages as well as across growth stages. The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R² = 0.7270, RPD = 2.0672, RMSE = 1.1835, RRMSE = 0.0259; jointing stage: R² = 0.8092, RPD = 2.3698, RMSE = 2.3650, RRMSE = 0.0487). For cross-growth stage modeling, the optimal prediction results for SPAD values were achieved at a flight altitude of 40 m using the Pearson-XGBoost model (R² = 0.8069, RPD = 2.3135, RMSE = 2.0911, RRMSE = 0.0442). These demonstrate that the flight altitude of UAVs significantly impacts the estimation accuracy, and the flight altitude of 40 m (with a spatial resolution of 2.12 cm) achieves better SPAD value estimation than that of 20 m (with a spatial resolution of 1.06 cm). This study also showed that the optimal combination of feature selection methods and machine learning algorithms can more accurately estimate winter wheat SPAD values. In addition, this study includes multiple winter wheat varieties, enhancing the generalizability of the research results and facilitating future real-time and rapid monitoring of winter wheat growth. Full article

Nature-based solutions (NBS) have been central to the European Union’s drive to address climate change, ecological degradation, and promote urban prosperity. Via an examination of the Horizon 2020-funded URBAN GreenUP project in Liverpool, this paper explores mainstreaming NBS in city planning. It uses evidence from pre- and post-intervention surveys with Liverpool residents and interviews with local business, environmental, government, and community sector experts to illustrate how a complex interplay of scale, location, focus, and visibility of NBS influences perceptions of the added value of NBS. This paper highlights the requirement that NBS interventions be bespoke and responsive to the overarching needs of residents and other stakeholders. Moreover, we underscore the importance of expert input into the design, location, and maintenance of NBS and call for these key drivers of successful delivery to be better integrated into work programs. This paper also notes that the type and size of NBS interventions impact perceptions of their value, with smaller projects being viewed as less socially and ecologically valuable compared to larger investments. We conclude that while small-scale NBS can support climatic, health, or ecological improvements in specific instances, strategic, larger-scale, and more visible investments are required to accrue substantive benefits and gain acceptance of NBS as a legitimate and effective planning tool. Full article

Cities have always been places that generate problems, but also places where solutions are born. In recent years, cities have become a testing ground for finding and testing solutions to overcome the climate crisis. The subject of the paper is Nature-based Solutions (NbSs) that strengthen the resilience to climate change, influence urban spaces, and improve the quality of life of residents. The study focusses on the identification of NbSs, the diagnosis of its participation in local adaptation activities, and a comparative analysis of the activities planned and implemented in 44 large Polish cities in 2017–2023. The authors analyse data from 44 Polish cities, based on the types of NbSs implemented in European cities that carried out the URBAN GreenUP project. The results of the study show that Polish cities, while introducing sustainable urban lifestyles through NbSs, do not take advantage of all the opportunities inherent in these solutions. They focus on greening urban spaces and implement water interventions and singular green infrastructure measures to a lesser extent. There is negligible use of innovative and energy-using activities. The quantitative data obtained can be used to identify the gaps and potentials in the use of nature-based solutions in the adaptation of Polish cities to climate change and energy transformation. The aim of this work is also to identify recommendations to incorporate NbSs into urban adaptation policies in Poland. The authors conclude that the research conducted can serve to improve the knowledge on the links between NbS planning in adaptation activities and their implementation in urban spaces. An awareness of NbS gaps can influence the intensification of research on the implementation of innovative adaptation solutions, including energy-based solutions. The results obtained can also contribute to a better organisation of urban policies that aim to increase the resilience of cities to climate change. Full article

In response to the new concept of the impact of total climate production factors on plant phenology, this study verifies the feasibility of simulating plant phenology and triggering thresholds based on total climatic production factors by using the phenological and meteorological observation data of S. krylovii plants from 1985 to 2018 at the Xilinhot National Climate Observatory of China Meteorological Administration. The results indicate that the total climate production factors have a significant impact on plant phenological changes and can be effectively utilized for simulating phenology and determining triggering thresholds. The mutation of cumulative climate production potential based on total climate production factors can effectively indicate the green-up date and the wilting date of S. krylovii plants, and their triggering thresholds depend on the parameters of climate resource changes and the characteristics of plant biology, which are (0.085, −5.363) and (0.086, −27.620), respectively. The cumulative climate production potential based on total climate production factors can effectively indicate the heading date of S. krylovii plants, and its triggering thresholds also depend on the parameters of climate resource changes and the characteristics of plant biology, which is (394.632, −38,026.268). Furthermore, the results support the viewpoint that abrupt changes to the climate determine the beginning and ending of plant growth, while the accumulative climate resources determine the other phenological dates. This study provides new ideas for the study of plant phenology. Full article

Urban heat island (UHI) is a zone that is significantly warmer than its surrounding rural zones as a result of human activities and rapid and dense urbanization. Excessive air temperature due to the UHI phenomenon affects the energy performance of buildings and human health and contributes to global warming. Knowing that most of the building energy is consumed by residential buildings, therefore, developing a framework to mitigate the impact of the UHI on residential building energy performance is vital. This study develops an integrated framework that combines hybrid micro-climate and building energy performance simulations and multi-criteria decision-making techniques. As a case study, an urban area is analyzed under the Urban GreenUP project funded by the European Union’s Horizon 2020 Programme. Four different strategies to mitigate the UHI effect, including the current situation, changing the low-albedo materials with high-albedo ones, nature-based solutions, and changing building façade materials, are investigated with a micro-climatic simulation tool. Then, the output of the strategies, which is potential air temperature, is used in a dynamic building energy simulation software to obtain energy consumption and thermal comfort data of the residential buildings in the case area. Finally, a multi-criteria decision-making model, using real-life criteria, such as total energy consumption, thermal comfort, capital cost, lifetime and installation flexibility, is used to make a decision for decreasing the UHI effect on residential energy performance of buildings. The results showed that applying NBSs, such as green roofs and changing existing trees with high leaf area density ones, have the highest ranking among all mitigation strategies. The output of this study may help urban planners, architects, and engineers in the decision-making processes during the design phase of urban planning. Full article