Insights into the BRT (Boosted Regression Trees) Method in the Study of the Climate-Growth Relationship of Masson Pine in Subtropical China

Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario

Surface urban heat islands (SUHIs) are mostly an urban ecological issue. There is a growing demand for the quantification of the SUHI effect, and for its optimization to mitigate the increasing possible hazards caused by SUHI. Satellite-derived land surface temperature (LST) is an important indicator for quantifying SUHIs with frequent coverage. Current LST data with high spatiotemporal resolution is still lacking due to no single satellite sensor that can resolve the trade-off between spatial and temporal resolutions and this greatly limits its applications. To address this issue, we propose a multiscale geographically weighted regression (MGWR) coupling the comprehensive, flexible, spatiotemporal data fusion (CFSDAF) method to generate a high-spatiotemporal-resolution LST dataset. We then analyzed the SUHI intensity (SUHII) in Chengdu City, a typical cloudy and rainy city in China, from 2002 to 2022. Finally, we selected thirteen potential driving factors of SUHIs and analyzed the relation between these thirteen influential drivers and SUHIIs. Results show that: (1) an MGWR outperforms classic methods for downscaling LST, namely geographically weighted regression (GWR) and thermal image sharpening (TsHARP); (2) compared to classic spatiotemporal fusion methods, our method produces more accurate predicted LST images (R2, RMSE, AAD values were in the range of 0.8103 to 0.9476, 1.0601 to 1.4974, 0.8455 to 1.3380); (3) the average summer daytime SUHII increased form 2.08 °C (suburban area as 50% of the urban area) and 2.32 °C (suburban area as 100% of the urban area) in 2002 to 4.93 °C and 5.07 °C, respectively, in 2022 over Chengdu City; and (4) the anthropogenic activity drivers have a higher relative influence on SUHII than other drivers. Therefore, anthropogenic activity driving factors should be considered with CO2 emissions and land use changes for urban planning to mitigate the SUHI effect.

Changes in wheat traits under future climate change and their contributions to yield changes in conventional vs. conservational tillage systems

Exploring the changes in wheat traits under future climate change and their contributions to yield changes is essential to improve the understanding of climate impact mechanisms and develop climate-resilient cultivars, which however has been seldom conducted. In this study, using a process-based crop model (APSIM-Wheat), meta-regression analyses, and machine learning approaches, we assessed the impacts of different warming levels on soil environments and wheat traits; investigated the impacts of future climate change on wheat traits, growth and development; and identified the favorable wheat traits for breeding under future climate change conditions. Meta-analyses showed that climate warming could significantly advance anthesis date by 3.50% and shorten the entire growth duration by 1.18%, although the duration from anthesis to maturity could be elongated by 7.72%. It could also increase grain yield slightly by 2.72% in the North China Plain, mainly due to the increase in biomass by 6.66%, grain weight by 3.86% and the elongating grain-filling period. However, high temperatures could significantly reduce aboveground biomass. The APSIM-Wheat model was validated based on three years' high-quality environment-controlled experimental data in the long-term warming and conservation tillage fields at Yucheng comprehensive experiment station in the North China Plain. The results showed that the mean yield would decrease under RCP4.5 for both tillage managements (conservational tillage: 0.55%, no-tillage: 6.88%), but increase conservational tillage yield (7.7%) under RCP8.5, relative to 1980-2010, owing to the interactive impacts of climate, CO₂ and tillage on wheat traits. Soil moisture would play a more important role in biomass, yield, height, LAI, and grain number for conventional tillage than for no-tillage system, and in the future than in the historical period. Our findings gained insights into the impacts of climate change on wheat traits and yield under different tillage managements, which are essential to understand climate change impact mechanisms and develop climate-resilient cultivars.

Driving Forces of Forest Expansion Dynamics across The Iberian Peninsula (1987–2017): A Spatio-Temporal Transect

This study analyzes the spatio-temporal dynamics of the drivers of forest expansion in the Iberian Peninsula for the periods 1987–2002–2017 through a 185 km-wide north–south Landsat scene transect. The analysis has considered a variety of biogeographical regions [0–3500 m.a.s.l, annual rainfalls 150–2200 mm] and 30 explanatory variables. A rigorous map production at 30 m resolution, including detailed filtering methods and uncertainty management at pixel scale, provided high-quality land cover maps. The main forest expansion trajectories were related to explanatory variables using boosted regression trees. Proximity to previous forests was a key common factor for forest encroachment in all forest types, with other factors being distance to the hydrographic network, temperature and precipitation for broadleaf deciduous forests (BDF), precipitation, temperature and solar radiation for broadleaf evergreen forests (BEF) and precipitation, distance to province capitals, and solar radiation for needleleaf evergreen forests (NEFs). Results also showed contrasting forest expansion trajectories and drivers per biogeographic region, with a high dynamism of grasslands towards new forest in the Eurosiberian and the mountainous Mediterranean regions, a high importance of croplands as land cover origin of new forest in the Mesomediterranean, and increasing importance over time of socioeconomic drivers (such as those employed in the industry sector and the utilized agricultural area) in the Supramediterranean region but the opposite pattern in the Southern Mesomediterranean. Lower precipitation rates favored new NEFs from shrublands in the Thermomediterraean region which, together with the Northern Mesomediterranean, exhibited the highest relative rates of new forests. These findings provide reliable insights to develop policies considering the ecological and social impacts of land abandonment and subsequent forest expansion.

Separate and combined effects of 3D building features and urban green space on land surface temperature

Deduction of urban green space (UGS) and the multidimensional growth of building have exacerbated the urban heat island (UHI). Yet thorough investigations into how 3D building features and UGS combinedly influence urban land surface temperature (LST) are limited, especially at the road-based blocks scale. Therefore, the study uses the boosted regression tree (BRT) model to explore the relative contribution and marginal effects of the influential factors on LST, and quantify the warming/cooling effects of buildings and UGS. Results show that, (1) building coverage ratio (BCR) is the most influential factor among seven building metrics with a relative contribution of 44.6%. Besides, high-rise buildings tend to alleviate LST while low- and mid-rise buildings heat the surroundings. (2) Green coverage ratio (GCR), edge density (ED), and patch density (PD) are the most influential factors among six UGS metrics, with the relative contribution of 21.0%, 20.9%, and 20.4%, respectively. (3) Comprehensively considering 13 metrics, we find that the dominant influential factor is BCR with a relative contribution of 28.3%, while the regulation amplitudes to LST of aggregation index (AI) and GCR dramatically reduced. These findings indicate that the cooling effect of UGS will be obscured when the buildings coverage is large. Hence, only relying on UGS to alleviate the heat island effect seems inadequate, the keys are the reasonable planning and optimization of 3D built environment.

Influence of the Environment on the Distribution and Quality of Gentiana dahurica Fisch

Gentiana dahurica Fisch. is a characteristic medicinal plant found in Inner Mongolia, China. To meet the increase in market demand and promote the development of medicinal plant science, we explored the influence of the environment on its distribution and the quantity of its active compounds (loganic acid and 6'-O-β-D-glucosylgentiopicroside) to find suitable cultivation areas for G. dahurica. Based on the geographical distribution of G. dahurica in Inner Mongolia and the ecological factors that affect its growth, identified from the literature and field visits, a boosted regression tree (BRT) was used to model ecologically suitable areas in the region. The relationship between the content of each of active compound in the plant and ecological factors was also established for Inner Mongolia using linear regression. The results showed that elevation and soil type had the most significant influence on the distribution of G. dahurica-their relative contribution was 30.188% and 28.947%, respectively. The factors that had the greatest impact on the distribution of high-quality G. dahurica were annual precipitation, annual mean temperature, and temperature seasonality. The results of BRT and linear regression modeling showed that suitable areas for high-quality G. dahurica included eastern Ordos, southern Baotou, Hohhot, southern Wulanchabu, southern Xilin Gol, and central Chifeng. However, there were no significant correlations between the contents of loganic acid and 6'-O-β-D-glucosylgentiopicroside and the ecological factors. This study explored the influence of the environment on the growth and quantity of active compounds in G. dahurica to provide guidance for coordinating the development of medicinal plant science.

Identifying Key Watershed Characteristics That Affect the Biological Integrity of Streams in the Han River Watershed, Korea

Understanding the complex human and natural processes that occur in watersheds and stream ecosystems is critical for decision makers and planners to ensure healthy stream ecosystems. This study aims to characterize the Han River watershed in Korea and extract key relationships among watershed attributes and biological indicators of streams using principal component analysis (PCA) and self-organizing maps (SOM). This study integrated watershed attributes and biological indicators of streams to delineate the watershed and stream biological status. Results from PCA strongly suggested that the proportions of watershed and riparian land use are key factors that explain the total variance in the datasets. Forest land in the watershed appeared to be the most significant factor. Furthermore, SOM planes showed that the biological indicators of streams have strong positive relationships with forest land, well-drained soil, and slope, whereas they have inverse relationships with urban areas, agricultural areas, and poorly drained soil. Hierarchical clustering classified the watersheds into three clusters, exclusively located in the study areas depending on the degree of forest, urban, and agricultural areas. The findings of this study suggest that different management strategies should be established depending on the characteristics of a cluster to improve the biological condition of streams.

Forward Modeling Reveals Multidecadal Trends in Cambial Kinetics and Phenology at Treeline

Significant alterations of cambial activity might be expected due to climate warming, leading to growing season extension and higher growth rates especially in cold-limited forests. However, assessment of climate-change-driven trends in intra-annual wood formation suffers from the lack of direct observations with a timespan exceeding a few years. We used the Vaganov-Shashkin process-based model to: (i) simulate daily resolved numbers of cambial and differentiating cells; and (ii) develop chronologies of the onset and termination of specific phases of cambial phenology during 1961-2017. We also determined the dominant climatic factor limiting cambial activity for each day. To asses intra-annual model validity, we used 8 years of direct xylogenesis monitoring from the treeline region of the Krkonoše Mts. (Czechia). The model exhibits high validity in case of spring phenological phases and a seasonal dynamics of tracheid production, but its precision declines for estimates of autumn phenological phases and growing season duration. The simulations reveal an increasing trend in the number of tracheids produced by cambium each year by 0.42 cells/year. Spring phenological phases (onset of cambial cell growth and tracheid enlargement) show significant shifts toward earlier occurrence in the year (for 0.28-0.34 days/year). In addition, there is a significant increase in simulated growth rates during entire growing season associated with the intra-annual redistribution of the dominant climatic controls over cambial activity. Results suggest that higher growth rates at treeline are driven by (i) temperature-stimulated intensification of spring cambial kinetics, and (ii) decoupling of summer growth rates from the limiting effect of low summer temperature due to higher frequency of climatically optimal days. Our results highlight that the cambial kinetics stimulation by increasing spring and summer temperatures and shifting spring phenology determine the recent growth trends of treeline ecosystems. Redistribution of individual climatic factors controlling cambial activity during the growing season questions the temporal stability of climatic signal of cold forest chronologies under ongoing climate change.

Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

As air pollution becomes highly focused in China, the accurate identification of its influencing factors is critical for achieving effective control and targeted environmental governance. Land-use distribution is one of the key factors affecting air quality, and research on the impact of land-use distribution on air pollution has drawn wide attention. However, considerable studies have mostly used linear regression models, which fail to capture the nonlinear effects of land-use distribution on PM_2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) and to show how impacts on PM_2.5 vary with land-use magnitudes. In addition, related studies have generally focused on annual analyses, ignoring the seasonal variability of the impact of land-use distribution on PM_2.5, thus leading to possible estimation biases for PM_2.5. This study was designed to address these issues and assess the impacts of land-use distribution on PM_2.5 in Weifang, China. A machine learning statistical model, the boosted regression tree (BRT), was applied to measure nonlinear effects of land-use distribution on PM_2.5, capture how land-use magnitude impacts PM_2.5 across different seasons, and explore the policy implications for urban planning. The main conclusions are that the air quality will significantly improve with an increase in grassland and forest area, especially below 8% and 20%, respectively. When the distribution of construction land is greater than around 10%, the PM_2.5 pollution can be seriously substantially increased with the increment of their areas. The impact of gardens and farmland presents seasonal characteristics. It is noted that as the weather becomes colder, the inhibitory effect of vegetation distribution on the PM_2.5 concentration gradually decreases, while the positive impacts of artificial surface distributions, such as construction land and roads, are aggravated because leaves drop off in autumn (September-November) and winter (December-February). According to the findings of this study, it is recommended that Weifang should strengthen pollution control in winter, for instance, expand the coverage areas of evergreen vegetation like Pinus bungeana Zucc. and Euonymus japonicus Thunb, and increase the width and numbers of branches connecting different main roads. The findings also provide quantitative and optimal land-use planning and strategies to minimize PM_2.5 pollution, referring to the status of regional urbanization and greening construction.

Modeling the impact of 2D/3D urban indicators on the urban heat island over different seasons: A boosted regression tree approach

Understanding how complex urban factors affect the Urban Heat Island (UHI) is crucial for assessing the impacts of urban planning and environmental management on the thermal environment. This paper investigates the relationships between two-dimensional (2D) and three-dimensional (3D) factors and land surface temperatures (LST) within the Olympic Area of Beijing in different seasons, using the boosted regression tree (BRT) model. The BRT model captures the specific contributions of each urban factor to LST in each season and across a continuum of magnitudes for this factor. The results show that these relationships are complex and highly nonlinear. The four most common dominant factors are the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), a gravity index for parks (GPI), and average building height (BH). The most important factor in spring is NDBI, with a 45.5% contribution rate. In the other seasons, NDVI is the dominant factor, with contributions of 40% in summer, 21% in autumn, and 19% in winter. NDVI has an overall negative impact on LST in spring and summer, with a quadratic nonlinear decreasing curve, but a positive one in autumn and winter. The 2D land-use variables are most strongly related to LST in summer and spring, but 3D building-related variables have stronger impacts in colder weather. The Sky View Factor (SVF), a 3D measure of urban morphology, has also strong impacts in summer and winter. Both a building-based and a DSM-based SVFs are computed. The latter accounts for buildings, bridges, and trees. In contrast to a building-based SVF, the DSM-based SVF reduces LST when it varies between 0 and 0.75, reflecting the effects of high-density tree canopies that increase shades and evapotranspiration while blocking sky view. The marginal effect curves produced by the BRT are often characterized by thresholds. For instance, the maximal NDVI effect in summer takes place when NDVI = 0.7, suggesting that a very intense green coverage is not necessary to achieve maximal thermal results. Implications for urban planning and environmental management are outlined, including the increased use of evergreen trees that provide thermal benefits in both summer and winter.

Multi-Scale Evaluation of the Effect of Phenol Formaldehyde Resin Impregnation on the Dimensional Stability and Mechanical Properties of Pinus Massoniana Lamb

The local chemistry and mechanics of the control and phenol formaldehyde (PF) resin modified wood cell walls were analyzed to illustrate the modification mechanism of wood. Masson pine (Pinus massoniana Lamb.) is most widely distributed in the subtropical regions of China. However, the dimensional instability and low strength of the wood limits its use. Thus, the wood was modified by PF resin at concentrations of 15%, 20%, 25%, and 30%, respectively. The density, surface morphology, chemical structure, cell wall mechanics, shrinking and swelling properties, and macro-mechanical properties of Masson pine wood were analyzed to evaluate the modification effectiveness. The morphology and Raman spectra changes indicated that PF resin not only filled in the cell lumens, but also penetrated into cell walls and interacted with cell wall polymers. The filling and diffusing of resin in wood resulted in improved dimensional stability, such as lower swelling and shrinking coefficients, an increase in the elastic modulus (Er) and hardness (H) of wood cell walls, the hardness of the transverse section and compressive strength of the wood. Both the dimensional stability and mechanical properties improved as the PF concentration increased to 20%; that is, a PF concentration of 20% may be preferred to modify Masson pine wood.