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• Building green view index (BGVI) was proposed to quantify 3D greenery visibility. • 76.10 % of the study area had a higher BGVI than the street view green view index. • Marginal effects of influencing factors on BGVI were explored using random forests. The vertical expansion of urbanization has increased the morphological heterogeneity of the urban landscape, affecting the physical and emotional wellbeing of urban dwellers by obstructing the view of greenery. In this study, multisource spatial data was used to calculate the Building Green View Index (BGVI). Baidu Street View (BSV) images were collected for comparison with the corresponding BGVI results. A random forest model was used to analyze the contributions and marginal effects of multiple influencing factors on BGVI. The results indicated that approximately 76.10 % of the sampled sites had a higher BGVI than the street view green view index, indicating buildings' superiority of visible greenery in height. The western edge of the research region frequently had the highest BGVI. Meanwhile, the hotspot regions were primarily located in the west, which was more consistent with the distribution of high value zones. The green area within the maximum visible distance, the maximum visible distance, and the average height were the most influential factors of BGVI according to marginal effects analysis with the highest IncNodePurity. As a quantitative measure of urban dwellers' visual accessibility to green space, the BGVI will contribute to urban green planning and the development of landscape architecture. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) of temperate forest plays an important role in global carbon cycles and needs to be estimated accurately. ALOS/PALSAR (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar) data has recently been used to estimate forest AGB. However, the relationships between AGB and PALSAR backscatter coefficients of different forest types in Northeastern China remain unknown. In this study, we analyzed PALSAR data in 2010 and observed AGB data from 104 forest plots in 2011 of needleleaf forest, mixed forest, and broadleaf forest in Heilongjiang province of Northeastern China. “ Poisson ” regression in generalized linear models (GLMs) and BRT (boosted regression tree) analysis in generalized boosted models (GBMs) were used to test whether the constructed PALSAR/AGB models based on individual forest types have better performance. We also investigated whether adding topographical and stand structure factors into the regression models can enhance the model performance. Results showed that GBM model had a better performance in fitting the relationships between AGB and PALSAR backscatter coefficients than did GLM model for needleleaf forest ( RMSE = 3.81 Mg ha −1 , R 2 = 0.98), mixed forest ( RMSE = 17.72 Mg ha −1 , R 2 = 0.96), and broadleaf forest ( RMSE = 19.94 Mg ha −1 , R 2 = 0.96), and performance of nonlinear regression models constructed on individual forest types were higher than that on all forest plots. Moreover, fitting results of GLM and GBM models were both enhanced when topographical and stand structure factors were incorporated into the predictor variables. Regression models constructed based on individual forest types outperform than that based on all forest plots, and the model performance will be enhanced when incorporating topographical and stand structure factors. With information of forest types, topography, and stand features, PALSAR data can express its full ability in accurate estimation of forest AGB. [ABSTRACT FROM AUTHOR]

With the continuous development of urbanization, two-dimensional (2D) landscape pattern is insufficient to explain the complex urban thermal phenomenon, while three-dimensional (3D) urban composition directly affects the process of surface energy exchange and becomes an important influencing factor in regard to land surface temperature (LST). There have been some studies on the relationship between 2D/3D urban patterns and LST, however, there are relatively few studies on the effects of 2D/3D urban landscape on LST in cities of different sizes. In this research, the relationship of 2D/3D urban patterns and LST in three cities (Beijing, Shijiazhuang and Cangzhou) of different sizes were analyzed by using the boosted regression tree model. The results were as follows: 1) With the increase in city size, LST gradually increased in summer and decreased in winter. 2) The larger the size of the city was, the greater the number of factors that significantly affected LST (p < 0.05). 3) In spring, summer and autumn, volume of tree (TV), mean tree height (MTH) and the largest patch index of impervious surface (LPI_IS) had a high relative influence, and the average total relative influences of these three metrics were 80.08%, 78.99% and 45.14%, respectively. 4)There was a significant negative correlation between TV and LST, a positive correlation between LPI_IS and LST and a combination of positive and negative correlations between MTH and LST. The study of the effects of 2D and 3D urban morphology on LST could help urban planners and managers create more scientific urban planning and development management measures to mitigate urban thermal environment problems. • Comprehensive 3D landscape metrics were designed by combining the height of buildings and vegetation. • The variations in the influencing factors of LST were explored among cities of different sizes. • With the increase in city size, LST gradually increased in summer and decreased in winter. • As the city size increased, the key influencing factors became more concentrated. [ABSTRACT FROM AUTHOR]

• Combining mobile monitoring and modeling can reduce costs and improve accuracy. • The first ring zone and residential-commercial land had highest exposure risks. • Exposure risks is higher in weekday than weekend. • First-class roads had higher relative contribution to PM 10 exposure risks. In this study, spatial-temporal characteristics of particular matter (PM) exposure risk in Shenyang were analyzed with landscape patterns using data from land use, cell phone signaling, and PM mobile monitoring. Pollution surfaces were established with geographically weighted regression models and impact factors analysis was implemented by boosted regression tree models. The results showed that weekdays and weekends had different spatial distributions of PM, and the exposure risk was lower on weekends. High exposure risks of PM 10 were concentrated in the first ring zone (76.53 people·m−2·μg·m−3) and residential-commercial land (292.34 people·m−2·μg·m−3). Exposure risks of PM 2.5 were most affected by residential-commercial land and fourth-class (relative contribution: 59.69 and 8.88, respectively). However, the exposure risks of PM 10 were more influenced by first-class roads (relative contribution: 2.01). The results indicated that independent modeling analysis of different types of PM and periods contribute to more detailed studies of spatial-temporal variation of PM. For human activity studies, cell phone signaling data can effectively distinguish spatial-temporal distribution characteristics of the population on weekdays and weekends. Multi-source big data combined with mobile monitoring and model simulations were used to make population exposure risk studies more accessible, real-time, and cost-effective for sustainable urban planning and development. [ABSTRACT FROM AUTHOR]

• Vertical distributions of urban pollution were investigated by instrumented UAVs. • A west-east sampling gradient belt of the built-up area was used for field monitoring. • The concentrations of particulate matter decreased from west to east on Days 2 and 3. • Ground cumulative effects of PM 1 , PM 2.5 and PM 10 were higher than those of SO 2 and NO 2. Understanding the vertical distribution patterns of air pollution is crucial to elucidate the formation mechanism of extreme air pollution events and explore the air pollution exposure risks of residents. The vertical air pollutant (SO 2 , NO 2 , PM 1 , PM 2.5 and PM 10) concentrations along a west-east sampling belt of 0–120 m height were investigated using a multirotor unmanned aerial vehicle (UAV) equipped with mobile sensors on 4 monitoring days. Vertical distribution patterns were explored by ordinary least-squares regression and Pearson correlation coefficient. The results indicated that the concentration of particulate matter decreased gradually from west to the east (S1 to S8) on monitoring Days 2 and 3. The ground cumulative effects of atmospheric particulate matter (PM 1 , PM 2.5 , PM 10) were significantly higher than those of gaseous pollutants (SO 2 , NO 2). The vertical variation ranges of pollutants from large to small were PM 10 (k = 0.18), PM 2.5 (k = 0.16), PM 1 (k = 0.07), NO 2 (k = 0.06) and SO 2 (k = 0.01). Atmospheric particulate matter tends to change significantly with vertical height, and the concentration decreases gradually with increasing height. The proposed UAV based gradient monitoring approach and vertical pollutant change trend analysis method could promote the urban air pollution researches and urban sustainable development. [ABSTRACT FROM AUTHOR]

Fire size and fire frequency are important indicators of fire characteristics. Characterizing fire size and fire frequency and understanding its environmental controls are indispensable to fire prediction and sustainable forest landscape management. In this paper, we determined the mean number of fires and the mean forest area burned by province to define five distinct patterns of fires across China by cluster analysis for the period 1987–2007. Then, we performed Redundancy Analysis (RDA) to explore factors influencing the spatial variation in the mean number of fires and the mean forest area burned, and to determine their relative contributions to this variation. Results showed that the north part of China generally had low mean number of fires. Percentage of the total number of fires in northeastern, northern and northwestern region to the total number of fires in whole country is 3.49%, 3.18% and 2.19% respectively. Neimenggu and Heilongjiang had larger mean burned forest area with 208,786 and 103,018 ha respectively. RDA analysis showed that the percentage of forest land and de Martonne aridity had significant impacts on the spatial variation in forest fire characteristics over China ( p < 0.05). These two variables and their interactions explained 44.9% of the total variance of the mean number of forest fires and mean forest area burned (63%), out of which 8.2% was explained by percentage of forest land, 11.7% by de Martonne aridity and 25.0% by their interactions. [ABSTRACT FROM AUTHOR]

Highlights: [•] We studied effects of burn severity and site environment on larch tree recruitment. [•] Burn severity negatively affected tree recruits density in Chinese boreal forest. [•] Topography exerted stronger influences on succession pathway than burn severity. [•] Upland larch forests were more likely to experience relay succession after fire. [ABSTRACT FROM AUTHOR]

Abstract: Successful management of forest fire risk in the Northeastern China boreal forest ecosystem often involves trade-offs between fire dynamics, fire hazard reduction, and fiscal input. We used the LANDIS model to study the effects of alternative fuel reduction strategies on fire dynamics and analyzed cost effectiveness for each fuel reduction strategy based on cost–benefit theory. Five levels of fuel treatment area (2, 4, 6, 8, and 10% for each decade) and two fuel treatment types (prescribed burning [PB] and mechanical treatments in combination with prescribed fire [PR]) under current fire suppression simulated by LANDIS were compared in a 5×2 factorial design over a 300-year period. The results showed that PR scenarios are more effective at reducing the occurrence and burn area of catastrophic fires than PB scenarios. In addition, area burned by high intensity fire can be tremendously reduced by increasing low intensity fires with a higher level of treatment area under the various PR scenarios. The cost effectiveness of alternative fuel reduction strategies is strongly dependent on treatment area. In general, PB scenarios will be more cost effective in larger treatment areas and PR scenarios in smaller. We recommend mechanical treatments in combination with prescribed fire, with 4% of landscape treated in each decade (PR04) to be the optimal fuel reduction strategy in the study area based on risk control and cost efficiency analysis. However, the most challenging work in China is to make local forest policy makers and land managers accept the ecological function of fire on forest ecosystems. [Copyright &y& Elsevier]

Abstract: The Small Khingan Mountains in northeastern China provide most of the timber and wood products in the country. Evaluating the long-term effects of harvesting and planting strategies is important especially as the climate changes. In this study, we evaluated the effects of the projected climate warming on potential changes in species’ coverage (percent cover), area harvested (percentage of the study area) and species harvested, using the LANDIS model. Our evaluation was based on the harvest and planting plans specified in Natural Forest Protection Project (NFPP). Our simulated results show that the coverage of southern species such as Korean pine (Pinus koraiensis) and ribbed birch (Betula costata) increases, whereas the coverage of northern species like larch (Larix gmelinii), Kingan fir (Abies nephrolepis), spruces (Picea koraiensis and P. jezoensis) and Dahur birch (Betula davurica) decreases under the warming climate in the region. The species harvested primarily consist of the southern species, especially deciduous species under the warming climate. The warming climate leads to 11.2% increase in area harvested compared to that under the current climate, when planting is not simulated. When planting is simulated, tradeoffs between planting and area harvested are complex. The area harvested only increases in places where moderate planting is implemented, and decreases in places with both low (≤5% area planted) and high (≥30%) planting percentage. This is because when the planting percentage is low, the rate of increase of harvestable species due to planting is lower than the rate of decrease of warming-declining species. When the planting percentage is high, the rate of increase of planted species is higher than the rate of colonization of warming-adapted deciduous species, and the planted species delay the establishment of the warming-adaptable species that have short harvest rotations (due to lower harvestable ages). Our results suggest that the management strategy with planting area of 20% is the best among all the scenarios simulated. Under this warming climate, moderate planting area (e.g. 20%) increases the area harvested to about 43%, which is still less than that (58%) designated in the NFPP. These results have important implications for forest managers designing sustainable forest harvest and reforestation strategies for the landscape under the warming climate. [Copyright &y& Elsevier]

Abstract: Understanding both historic and current fire regimes is indispensable to sustainable forest landscape management. In this paper, we use a spatially explicit landscape simulation model, LANDIS, to simulate historic and current fire regimes in the Great Xing’an Mountains, in northeastern China. We analyzed fire frequency, fire size, fire intensity, and spatial pattern of burnt patches. Our simulated results show that fire frequency under the current fire scenario is lower than under the historic fire scenario; total area burnt is larger with lower fire intensity under the historic fire scenario, and smaller with higher fire intensity under the current fire scenario. We also found most areas were burned by high intensity fires under the current fire scenario, but by low to moderate fires under the historic fire scenario. Burnt patches exhibit a different pattern between the two simulation scenarios. Large patches burnt by high intensity class fires dominate the landscape under the current fire scenario, and under historic fire scenario, patches burnt by low to moderate fire intensity fires have relatively larger size than those burnt by high intensity fires. Based on these simulated results, we suggest that prescribed burning or coarse woody debris reduction should be incorporated into forest management plans in this region, especially on north-facing slopes. Tree planting may be a better management option on these severely burned areas whereas prescribed burning after small area selective cutting, retaining dispersed seed trees, may be a sound forest management alternative in areas except for the severely burned patches. [Copyright &y& Elsevier]

Abstract: The larch (Larix) genus is the most important species group in the forest ecosystems in Northeastern China, occupying about 25% of the forest areas. The high tolerance to coldness and relatively fast growth rate make this genus the main species group for forestation. According to the predictions of the global circulation model CGCM3, temperature could rise by 2–4°C over the next 100 years. Few studies have been conducted on the response of larch species to climate warming in Northeastern China. Such studies are becoming increasingly needed due to the economic and ecological significance of this genus. This paper studies the potential distribution ranges of three larch species under the current and the warming climate conditions. A new classification and regression tree technique, Random Forest, was used to investigate the potential distributions of three larch species, based on 18 environmental variables which reflect the climate, topography and soil conditions of Northeastern China. The results showed that the biological coldness index (BCI) is the most important factor for Dahurian larch, annual precipitation (AP) is the most important factor for Korean larch and elevation (DEM) is the most important factor for Prince Rupprecht larch. Under the current climate regime, in general, the prediction accuracy for the training dataset is much higher than that of testing dataset. The prediction accuracy for Dahurian larch is much higher than that of other two larch species. Under three climate warming scenarios, the southeast boundary of suitable habitat of Dahurian Larch was modeled to retreat northwestward by 90km (CGCM3-B1) via 105km (CGCM3-A1B) to 140km (CGCM3-A2) scenario. The potential area would thus decrease from 25.5millionha currently to 13, 9.5 and 7.2millionha, correspondingly. The northwest boundary of suitable habitat for Korean larch was modeled move northwestward by 100km (CGCM3-B1) via 125km (CGCM3-A1B) to 340km (CGCM3-A2), while the southern boundary may move northeastward 125km via 170–200km, respectively. The modeled potential area thus decreased from 14.6millionha to 14.5, 12.6 and 9.7millionha, correspondingly. The suitable habitat of Prince Rupprecht Larch was modeled to disappear under each of the three scenarios. [Copyright &y& Elsevier]

Abstract: We use the LANDIS model to study the effects of planting intensity and spatial pattern of plantation on the abundance of three main species (larch (Larix gmelini), Mongolian Scotch pine (Pinus sylvestris var. Mongolica), and white birch (Betula platyphylla)) in the Tuqiang Forest Bureau on the northern slopes of Great Hing’an Mountains after a catastrophic fire in 1987. Four levels of planting intensity (covering 10%, 30%, 50%, and 70% of the severely burned area) and two spatial patterns of plantation (dispersed planting and aggregated planting) were compared in a 4×2 factorial design over a 300-year period. The results showed that increasing planting intensity positively influenced larch and Mongolian Scotch pine abundance, but negatively influenced white birch abundance. However, the increased degree of larch abundance with increasing planting intensity was significantly different between intensities. The difference in larch abundance between the 10% planting intensity scenario and the 30% planting intensity scenario was greater than that between the 50% planting intensity scenario and the 70% planting intensity scenario. However, the difference between 30% and 50% planting intensity scenarios was significantly low. Hence, given considerable labor input and economic costs, 30% planting intensity would be effective for forest recovery. In addition, dispersed planting showed more promising results on forest recovery than aggregated planting. However, the difference of larch abundance between dispersed planting and aggregated planting under intermediate planting intensity scenarios (30% and 50% planting intensity) was greater than that under a low planting intensity scenario and a high planting intensity scenario. Therefore, it is necessary to incorporate spatial pattern of plantation into planting practice, especially under an intermediate planting intensity scenario. These results have important implications for forest managers to design sound forest restoration projects for landscapes affected by large infrequent disturbances. In particular, the results suggest that the current planting strategy (50% planting intensity with aggregated planting) employed after the catastrophic fire in 1987 could not be optimum, and the dispersed planting strategy covering about 30% of the severely burned area would better stimulate forest recovery. [Copyright &y& Elsevier]

• The street trees reduced coarse particle matter rather than fine particle matter. • The purification effect of street trees on PM was strong in leaf expansion season. • The street trees increased PM pollution in defoliation season. • The street trees diminished the impact of wind direction on PM concentration. The net influence of trees on local air quality in urban street canyons, negative or positive, has rarely been evaluated by field investigations. To quantify the influence of street trees on particulate matter (PM) and total suspended particle (TSP) concentration near ground level, a seasonal field investigation was carried out in a street canyon with an aspect ratio of 0.52 in Shenyang, China. The greening tree species in this street were Sophora japonica and Ulmus pumila. We monitored PM 1 , PM 2.5 , PM 4 , PM 10 , TSP concentration and the microclimatic conditions at locations with or without trees from before the leaf expansion season until the end of the defoliation season. Concentration of PM 2.5 was higher at the tree-stand location, whereas concentration of PM 10 and TSP were higher at the tree-free location during the leaf-on season. Street trees were beneficial to coarse particle removal during the leaf-off and leaf-expansion season, but PM pollution increased during defoliation. Wind direction was more uniform at the tree-stand location. Street trees weakened the impact of the wind on PM dispersion near ground. This indicates that street trees might pose a health threat to pedestrians because fine PM can enter the human respiratory system more easily than coarse PM. The net influence of trees on local air quality in urban street canyons, negative or positive, has rarely been evaluated by field investigations. To quantify the influence of street trees on particulate matter (PM) and total suspended particle (TSP) concentration near ground level, a seasonal field investigation was carried out in a street canyon with an aspect ratio of 0.52 in Shenyang, China. The greening tree species in this street were Sophora japonica and Ulmus pumila. We monitored PM1, PM2.5, PM4, PM10, TSP concentration and the microclimatic conditions at locations with or without trees from before the leaf expansion season until the end of the defoliation season. Concentration of PM2.5 was higher at the tree-stand location, whereas concentration of PM10 and TSP were higher at the tree-free location during the leaf-on season. Street trees were beneficial to coarse particle removal during the leaf-off and leaf-expansion season, but PM pollution increased during defoliation. Wind direction was more uniform at the tree-stand location. Street trees weakened the impact of the wind on PM dispersion near ground. This indicates that street trees might pose a health threat to pedestrians because fine PM can enter the human respiratory system more easily than coarse PM. [ABSTRACT FROM AUTHOR]

• Quantified the effects on wetland distribution using structural equation model. • The infulence path of variables on wetland distribution is changing. • Human disturbance has become the major variable affecting wetland distribution. Wetlands are important to global ecosystems and are easily affected by changes in climate, soil properties, terrain, and human disturbances. To describe the effects quantitatively, structural equation modeling was applied to detect the impacts of these four latent variables on the distribution of wetlands in the Jing-Jin-Ji region in 1980, 1995, 2005, and 2015. The results of the models show that human disturbance and terrain conditions had negative effects on the distribution of wetlands, and their standard coefficients ranged from −0.084 to −0.011 and from −0.124 to −0.006, respectively. Climate and soil properties were beneficial for wetland distribution, and their standard coefficients ranged from 0.039 to 0.169 and from 0.015 to 0.290, respectively. The modeling results also indicate some differences in indirect influence among the four models, and 9 indirect pathways were detected in the four models. We also found that there was a significant negative correlation between the variation in the influencing factors and their impact on wetlands (p < 0.05), and the correlation coefficient was −0.71. These results indicate that the influence on the distribution of wetlands will change as in climatic conditions and human activities change over the long term, and the results of impact pathways could contribute to the restoration and management of wetlands. [ABSTRACT FROM AUTHOR]

As one of the principal components of haze, fine particulate matter (PM 2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM 2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52–61% of the variation in the PM 2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM 2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM 2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution. Unlabelled Image • The land use regression (LUR) model was built using data from fixed monitoring stations in seven urban areas. • The seasonal consistency of LUR models was studied. • Spatial modelling relied on building three-dimensional morphology variables. • PM 2.5 pollution surfaces of seven urban areas were predicted using regression kriging. [ABSTRACT FROM AUTHOR]

• A method for monitoring air pollutant by a vehicle-mounted monitor is proposed. • The hotspots of air pollution inside a city were found by spatial analysis. • SO 2 is negatively correlated with the other pollutants. • Temperature is the main influence factor of air pollution. The aim of this paper is to analyze the distribution characteristics of gaseous pollutants (SO 2 and NO 2) and particulate matter (PM 1.0 , PM 2.5 and PM 10) and to explore the main factors affecting the distribution pattern of air pollutants inside Shenyang urban areas in the heating season of Northeast China. To achieve this goal, the concentration of five air pollutants on two main roads in the Shenyang four-ring area was measured by a vehicle-borne air pollutant monitor. Through analysis of the spatial distribution pattern of these air pollutants, it can be found that the average concentrations of SO 2 for the three monitoring events are similar (43.1, 40.6 and 40.3 μg/m3), and the concentrations of NO 2 , PM 1.0 , PM 2.5 and PM 10 are the highest for the 20190304 event, followed by the 20190107 and 20181213 events. Pearson correlation was used to analyze the relationships of the gaseous pollutants and particulate matter based on the data from the three monitoring events. The results indicated that SO 2 is negatively correlated with the other pollutants, while there are positive correlations among the other four pollutants. In particular, there is a strong positive correlation among the three classes of particulate matter (correlation coefficients > 0.97, p < 0.01). The relative contributions of environmental factors and landscape pattern factors to the air pollutants were analyzed by the BRT model. It is revealed that the environmental factors have a significant influence (relative contributions> 10 %) on air pollution, and temperature was the main influencing factor (relative contributions > 20 %). The findings obtained through this study can increase the understanding of the distribution pattern of air pollutants and their main influencing factors in the heating season of Northeast China and provide decision makers and urban planners with a scientific basis for the amelioration of air pollution via urban planning and management. [ABSTRACT FROM AUTHOR]

• Urban street canyon morphology plays an important role in particle pollution. • Deep and narrow street canyons are more favourable than wide canyons. • Street orientations affect particle removal in street canyons. Exposure to near-ground air pollution in urban street canyons poses a serious threat to pedestrian health. To develop design guidelines for air quality improvement, correlations among PM 1 , PM 2.5 , PM 4 , PM 10 , TSP, street canyons morphology and climatic conditions were investigated by field experimental studies. The results showed that street canyons morphology and air humidity were two of the most important factors affecting suspended particulate matter concentration in urban street canyons. The concentrations of PM 1 (9.18 ± 0.17 μg/m3), PM 2.5 (18.12 ± 0.43 μg/m3), PM 4 (31.66 ± 0.82 μg/m3), PM 10 (77.57 ± 2.04 μg/m3) and TSP (104.06 ± 2.85 μg/m3) in deep canyons were significantly lower than those in medium and wide canyons. The pollutants distributed in E-W or N-S street directions were at a lower concentration compared with those in NE-SW or NW-SE street directions. Moreover, the pollutants showed significantly lower concentration in high-rise building areas than in multilayer building areas. This study provides a reference for future urban planning to reduce suspended particulate matter concentration in street environments. [ABSTRACT FROM AUTHOR]

Abstract As urbanization processes, the increasing direct runoff caused by land use change has become a major challenge for urban hydrological system. In this study, the impact of urbanization on direct runoff in the Shenyang urban area was investigated using a modified Soil Conservation Service Curve Number model combined with remote sensing. Urban functional zone (UFZ) was used as the basic unit for hydrological analysis. The hydrological changes in runoff were analyzed by calculating the runoff difference between the current condition and the pre-urbanization condition. Moran's I was used to estimate the spatial autocorrelation of the entire area. Then we assessed the relative influence and marginal effects of factors affecting direct runoff using boosted regression trees (BRT). Our results showed that direct runoff was significantly related to urbanization. Under current conditions, direct runoff increment depth affected by urbanization in the study area was 68.02 mm. For different UFZs, high-density residential, business and industrial zones tended to have large runoff volumes and high runoff coefficients. Through flooding hazard analysis, we found about 6.53% of the study area fell into a significant hazard category. The industrial zone had largest area of significant hazard land (40.97 km2) and the business zone had the largest significant hazard percentage (21.19%). Moran's I results illustrated that the high-high clusters in Shenyang were mainly concentrated in the urban center. BRT analysis indicated that runoff had the strongest correlation with rainfall (52.07%), followed by impervious ratio (27.28%), normalized difference vegetation index (14.31%), antecedent 5-day rainfall (3.02%), and UFZs (1.70%). The industrial zone, business zone and high-density residential zone tend to have greater influence on runoff. Our study could present method for recognizing hotspots of direct runoff in large city, and may provide potential implications for green infrastructure selection and urban planning. Graphical abstract Unlabelled Image Highlights • An improved method to evaluate direct runoff in urban areas is proposed. • Urbanization affected the direct runoff increments and flooding hazard distribution. • The direct runoff has significant positive spatial autocorrelation. • Urbanization factors have less impact than environmental and meteorological factors. [ABSTRACT FROM AUTHOR]

• An innovative method is proposed to estimate flooding and potential exposure risk. • The urban drainage capacity was assessed using the urban road network density. • Actual flood points were used to verify the urban flood evaluation results. • Flooding and potential exposure risk have spatial heterogeneity. Urban flooding is an important natural hazard that affects urban security in the context of global climate change and urbanization. The simulation of stormwater inundation and risk assessment in urban environments is critical for the sustainable development of urban agglomerations. However, few studies have focused on rainstorm flooding at the urban agglomeration scale. In this study, the Central and Southern Liaoning Urban Agglomeration (CSLN) was selected a case study. A method was proposed to assess the urban flooding and potential exposure risk. The urban flooding of CSLN was characterized by the average flooding depth (AFD) of each sub-catchment, and exposure risk was indicated by potential regional and population exposure risk. The results illustrate that the AFD increased with an increase in the rainfall return period, especially in the urban core. The AFD of each city in the CSLN ranged from 10 to 80 mm, and there were strong differences among cities. The spatial distribution of urban flooding in the nine cities presented aggregation characteristics, and hotspots were mainly concentrated in the old urban districts. The potential regional exposure risk tended to increase as the rainfall return period increased from 10 to 100 years; however, there were significant differences among cities. Among them, the area of potential regional exposure risk in Shenyang was the largest, increasing from 365 km2 under a 10-year rainfall return period to 463 km2 under a 100-year rainfall return period. The largest proportion of potential regional exposure risk was in Anshan, which increased from 3% under 10-year to 13.8% under 100-year rainfall return periods. Most cities had some potential population exposure risk. More residents in populous cities, such as Shenyang and Dalian, were subject to higher exposure risks. However, Benxi, Anshan, and Tieling had a high proportion of severely affected areas, although the size of their cities is much smaller than that of Shenyang and Dalian. Urban flooding and potential exposure risk were apparently higher in the business and government zones than in the industrial and greenspace zones. These methods and results may provide references for rainstorm flooding mitigation through urban planning in urban agglomeration areas. [ABSTRACT FROM AUTHOR]

The vertical distribution of air pollutants in urban street canyons is closely related to residents' health. However, the vertical air quality in urban street canyons has rarely been assessed using field observations obtained throughout the year. Therefore, this study investigated the seasonal and annual concentrations of particulate matter (PM 2.5 and PM 10), CO, NO 2 , SO 2 , O 3 , air quality index, and their responses to microclimatic factors at three height levels (1.5, 27, and 69 m above street level) in an urban street canyon. The PM concentration was higher at 27 m than at 1.5 m in winter, whereas the situation was reversed in other seasons. It was found that photochemical pollutants such as NO 2 and O 3 were the primary pollutants in the urban street canyon. The days on which O 3 was the primary pollutant at the height of 1.5 m accounted for 81.07% of the entire year. The days on which NO 2 was the primary pollutant at the height of 27 and 69 m accounted for 82.49% and 72.33% of the entire year, respectively. Substantially higher concentrations of NO 2 and O 3 were found at the height of 27 m than at 69 m. In-canyon concentrations of NO 2 and O 3 were strongly correlated with air temperature, wind speed, and wind direction, which played important roles in photochemical reactions and pollutant dispersion. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: The rationale behind this research concerns the need to better understand relationships between landscape pattern and soil loss processes. Landscape indicators are commonly used to delineate these relationships. However, most indicators were not developed on the basis of soil loss progresses, and therefore their specific relationships with soil loss are difficult to construct. We improved the Location-weighted landscape Contrast Index which was developed based on sediment source–sink theory. This indicator encompasses three factors of landscape pattern: contribution of land cover types to soil erosion; composition and configuration of land covers. To analyze correlations between the landscape indicator and soil loss processes, variables expressing soil loss status should be first quantified. Therefore we applied the sediment delivery distributed model which incorporates revised universal soil loss equation (RUSLE) and sediment delivery ratio. The methods were applied in the Heishui and Zhenjiangguan subwatershed in the Upper Min River. Modeling results showed that 90% of the study area experienced soil erosion larger than 10t/(hayr). While its sediment yield rate was smaller than most tributaries in the Upper Yangtze River. Results of Pearson correlation analysis indicated that the rainfall factor in RUSLE was the dominant control and explained 93% of variance in sediment yield rate. We suggested preserving and increasing percentage cover of forests to adapt to climate change for soil conservation. Besides precipitation, landscape pattern was a principle factor correlated with sediment yield rate. The landscape indicator was significantly correlated to sediment delivery ratio, and explained 98% variation in sediment yield rate not considering precipitation by dropping the rainfall factor. The landscape indicator indicated that the landscape pattern was generally favorable for soil conservation in the two subwatersheds. This advantage was mainly ascribed to superior sediment sink–source compositions. In the Heishui subwatershed, however, sediment source contributed more to soil erosion processes than sink. Spatial configuration of sediment source and sink related to flow length were the most unfavorable factor, mainly due to the crops located in river vallyes. We gave suggestion to further optimize the landscape pattern: reducing percentage cover of grasslands with high soil erosion rate; decreasing soil erosion rate of sediment source in the Heishui subwatershed; increasing lengths of flow path from crops to river channels. [Copyright &y& Elsevier]

Abstract: The relationship between landscape pattern and the function of nutrient reduction in the natural reed marsh of Liaohe Delta is studied with the help of some landscape metrics. The results discovered that not all the metrics selected are explanative in representing the function of nutrient reduction. Network connectivity, area size, and source to centre metrics are closely related to the simulation results from different pattern scenarios, while other metrics like area-weighted mean shape, fractal, contagion and aggregation are not related with the reduction process at all. Different metrics should be chosen according to the purpose of the study, based on the criteria of simplicity, generality and ecological meaning. [Copyright &y& Elsevier]

Abstract: Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great importance to generate a relatively small set of conditional realizations capturing most of the spatial variability. In this study, we introduced an effective sampling method (Latin hypercube sampling) into a stochastic simulation algorithm (LU decomposition simulation). Latin hypercube sampling is first compared with a common sampling procedure (simple random sampling) in LU decomposition simulation. Then it is applied to the investigation of uncertainty in the simulation results of a spatially explicit forest model, LANDIS. Results showed that Latin hypercube sampling can capture more variability in the sample space than simple random sampling especially when the number of simulations is small. Application results showed that LANDIS simulation results at the landscape level (species percent area and their spatial pattern measured by an aggregation index) is not sensitive to the uncertainty in species age cohort information at the cell level produced by geostatistical stochastic simulation algorithms. This suggests that LANDIS can be used to predict the forest landscape change at broad spatial and temporal scales even if exhaustive species age cohort information at each cell is not available. [Copyright &y& Elsevier]

LANDIS is a cell-based spatially explicit forest landscape model designed to explore successional dynamics under natural and anthropogenic disturbances. Species age cohort (10-year cohort of a given tree species) information is required for each cell in LANDIS. However, providing such information for a landscape comprising millions of cells is challenging. In this study, a stand-based assignation (SBA) approach was developed to stochastically assign species age cohorts to each cell based on forest inventory data. As a probability-based approach, SBA will introduce errors in LANDIS input. In order to assess the effect of errors produced by SBA on LANDIS results, 20 Monte Carlo simulations were produced. For each species simulated in LANDIS, the recurrence frequency (RF) of the majority species age cohort (MSAC, the most frequently occurring species age cohort) from 20 Monte Carlo simulations were used to quantify the uncertainty in species age cohorts for individual cell. Average recurrence frequency (ARF) of the MSAC was used to quantify the overall uncertainty in species age cohorts at the cell level. For each species, the coefficient of variation (CV) for the percent area and an aggregation index for the 20 Monte Carlo simulations was used to quantify the uncertainty at the landscape level. Results showed that at the cell level, uncertainty was relatively low at the beginning of the simulation (ARF was larger than 10). Seed dispersal, seedling establishment, mortality, and fire disturbance caused uncertainty to increase with simulation year. The uncertainty finally reached an equilibrium state, where input errors in original species age cohorts had little effect on the simulation outcomes. At the landscape level, species percent area and their spatial patterns were not substantially affected by the uncertainties in species age structure at the cell level. Since the typical use of LANDIS is to predict the long-term landscape pattern change, SBA can be used to parameterize species age cohorts for individual cells. [Copyright &y& Elsevier]

Rapid urbanization has resulted in an increase in impervious surfaces and a decrease in green infrastructure (GI), changing the process of the regional hydrological cycle, coupled with climate change, which is expected to increase the frequency and magnitude of urban floods resulting from extreme events in the city. GI can provide crucial stormwater regulation functions to help cities mitigate the increased stormwater runoff and related flood risks. This study presents a method to assess the runoff storage supply-demand structure of green infrastructure in the Shenyang urban area. The runoff storage supply of GI was calculated by rainfall and the maximum rainwater storage capacities of the canopy and soil. The runoff storage demand was estimated by the direct runoff and discharge capacity of the drainage pipe network. The runoff storage supply-demand structure was analyzed using a supply-demand index designed based on the supply-demand balance of runoff storage capacity. The results showed that large and contiguous areas of runoff storage supply are mainly concentrated in the north, southwest and southeast and are mainly distributed in the suburbs of Shenyang. The runoff storage supply in the center of Shenyang is limited and seriously fragmented. The runoff storage demand of the urban center is markedly higher than that of the peripheral suburban areas. With the increase in rainfall, the supply of runoff storage decreases, while the demand for runoff storage increases, resulting in the runoff storage supply gradually being unable to meet the demand. For different urban functional zones (UFZs), only the supply-demand balances of green space were positive under 78 mm rainfall (return period of 50 years) conditions, and those of other types of UFZs were negative, revealing that the runoff storage supplies of most UFZs were imbalanced. The method can help identify the runoff storage supply and demand of each catchment and further provide the possibility of interregional runoff storage capacity sharing or allocation. [ABSTRACT FROM AUTHOR]

Validation of the long-term biomass predictions of forest landscape models (FLMs) has always been a challenging task. Using the space-for-time substitution method, forest biomass curves over stand age were generated from a forest survey dataset (FSD) in the Lesser Khingan Mountains area ( LKM ), Northeastern China and compared with long-term biomass predictions of LANDIS-II model. The results showed that mean forest age and mean biomass of the LKM in 2000 were 51.6 years and 84.2 Mg ha −1 , respectively. Significant linear correlations were found between FSD derived biomass and simulated biomass in the aggradation phase for the entire LKM and most subregions. However, a considerable difference in the mean maximum biomass (53.45 Mg ha −1 ) existed between from FSD and simulation during the post-aggradation phase. The space-for-time substitution method has potential in validating time series biomass predictions of FLMs in aggradation phase when only limited forest inventory data is available. [ABSTRACT FROM AUTHOR]

• The importance of factors influencing ecosystem services at different stages was determined using random forests; • Significant spatial and temporal heterogeneity of ecosystem service influences identified using geographically and temporally weighted regression; • Dissecting the ecological implications of the spatial and temporal differentiation of factors influencing ecosystem services. Understanding the mechanisms that influence changes in ecosystem services (ESs) is critical to the sustainable management of ecosystems. However, existing studies ignore the different importance of influencing factors of ESs in different periods and do not consider the spatiotemporal heterogeneity of influence factors. In this study, we first quantified six ESs for the Yangtze River Delta (YRD) and Pearl River Delta (PRD) in 2000 and 2020 based on remote sensing data, including water yield, grain production, climate regulation, air purification, biodiversity, and recreation. Then, eight factors influencing ESs were selected from natural and human perspectives, and random forest was used to determine the importance level of factors influencing ESs. Finally, the GTWR model was used to explore the spatial and temporal differentiation of factors influencing ESs. The results showed that the spatial variation of the six ESs in the YRD and PRD was irregular from 2000 to 2020. In 2000, natural factors (forest, topography, climate) dominated the regional ESs, while in 2020 human factors (population, economy, human activities) gradually replaced the dominance of natural factors on ESs. The spatial and temporal heterogeneity of multiple influence factors on ESs in the YRD and PRD is significant, and we interpret the ecological implications in detail and propose a series of policy recommendations. The results of this study could provide an important reference for scientific guidance to enhance ecological sustainability in developed regions. [ABSTRACT FROM AUTHOR]

Plant and soil carbon pools are two important elements of forest ecosystems and both are affected by many environmental factors. Although forest types are typically characterized using species composition, the ways in which soil carbon storage (C S ) is distributed between plants and soil in different forest types and the factors influencing this distribution remain unclear. In this study, we calculated C S in plants and soil by sampling 108 tree plots and 324 soil profiles to determine whether plant and soil C S varied significantly among different forest types in this region. Boosted regression tree analysis was used to detect factors influencing forest C S allocation. The results showed that plant C S varied from 68.09 t ha −1 in aspen-white birch forests (AW) to 117.81 t ha −1 in mixed Korean pine-broadleaved hardwood forests (KB). Conversely, soil C S varied from 153.23 t ha −1 in KB to 261.58 t ha −1 in AW. Forest management was the main factor influencing plant C S while soil C S was most influenced by factors relevant to moisture. The influence on the ratio of plant C S to soil C S was quite complex because the dynamics of the relationship were affected by managerial, climatic, and topographical factors. In addition to species composition, ecosystem C S also varied in different forest types and was influenced by complex factors related to climate, topography, and forest management. [ABSTRACT FROM AUTHOR]

Urban green infrastructure (UGI) is considered to be an effective tool for mitigating PM 2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) pollution in urban areas. However, long and continuous time series analyses of the relationships between the UGI landscape and PM 2.5 pollution remain a challenge. In the present study, then we analyzed the PM 2.5 variations and their relationships with the UGI landscape patterns at the urban agglomeration (the urban area of CLUA) and neighborhood scales (6 neighborhood zones with radii ranging from 500 m to 3000 m around the sampling point.). The results illustrated PM 2.5 concentration increased slightly in the urban expansion areas and decreased in some old urban areas, but the changing trend was not significant. The PM 2.5 concentrations were different among the four seasons in this area, and the lowest and highest concentrations occurred in winter (66.79 μg/m³) and summer (24.32 μg/m³), respectively. At the urban agglomeration scale, just a very small proportion of the PM 2.5 variations in the CLUA during this study period could be attributed to the UGI landscape pattern, wind speed and relative humidity; PM 2.5 concentrations were affected more strongly by wind speed and relative humidity than by UGI landscape patterns; the LSI (landscape shape index) and bridges served as the main landscape indicators affecting the PM 2.5 concentrations among the UGI landscape patterns factors. At the neighborhood scale, the LPI (largest patch index), AWMSI (area-weighted mean shape index), AI (aggregation index), Core and Loop served as the main UGI landscape indicators influencing PM 2.5 and had different degrees of influence on the PM 2.5 concentrations; the seasonal and scale effects of UGI landscape patterns on PM 2.5 concentrations at neighborhood scales were observed. Comparatively, the effects of the UGI landscape patterns on PM 2.5 concentration seem more significant at the neighborhood scale than at the urban agglomeration scale. The findings of this multiscale analysis can provide new insights into understanding the relationships between UGI landscape patterns and urban air pollution at different scales, and provide a scientific reference for urban planning and air pollution control. [Display omitted] • Nonsignificant changes in PM 2.5 concentrations occurred in all urban areas of the CLUA. • PM 2.5 variations was less attributed to the UGI landscape at the urban agglomeration scale. • Effects of UGI landscape on PM 2.5 concentration was more significant at the neighborhood scale. • The seasonal and scale effects of UGI on the PM 2.5 concentrations existed at neighborhood scales. [ABSTRACT FROM AUTHOR]

Abstract: The behavior of several landscape pattern metrics were tested against various pattern scenarios generated by neutral landscape models, including number of classes, scale-map extent, scale-resolution, class proportion, aggregation level—RULE, and aggregation level—SimMap. The results demonstrate that most of the metrics are sensitive to certain pattern scenarios, yet are not sensitive to others; therefore, none of them is appropriate for all aspects of a landscape pattern. Despite these limitations, some of these metrics are recommended for future use, which include total number of patches, average patch size, total edge density, double-logged fractal, contagion, and aggregation index. Special attention should be paid to the relationships between metric values and ecological processes rather than the numbers themselves. [Copyright &y& Elsevier]

Changbai Natural Reserve in northeastern China provides an excellent opportunity to explore how temperate and boreal forest ecosystems in northeastern China will evolve and recover over large spatial and temporal scales. Such studies are increasingly needed to design scientifically sound forest management and restoration plans in this region. Long-term (300 years) successional trajectories of the dominant tree species are simulated on the heterogeneous, undisturbed area (within the reserve) using a spatially explicit landscape model. We also examine the spatial and temporal constrains of landscape recovery on the human disturbed areas (surrounding the reserve). Simulation results suggest that an equilibrium in landscape structure and composition is approached on the large landtypes dominated by shade tolerant species, but not on landtypes altered by humans. Such equilibrium can be observed in spruce-fir, mountain birch, and larch forests and not in aspen-birch forests. Our results suggest that direct and indirect human impact may produce long-term alterations to forest landscape patch structure that persist for decades to centuries. For example, even with complete natural succession over 300 years, Korean pine only recovers on 1/3 of the areas in the landtypes it can dominate. We estimate a full recovery would take another 200–300 years without human disturbance. Our results also indicate that landscape-scale recovery is often limited by the available seed sources and this is particularly true for Korean pines in this region. Comparison of simulation results for the entire study area with land types (two scales) reveals the greatest variations at the land type scale. This discrepancy indicates that the ‘space-for-time’ substitutions can be flawed as species composition and age class at a given site and time may represent only the specific successional history of that site. This is particularly true for human disturbed forest landscapes where higher variations are observed. [Copyright &y& Elsevier]

The Sanjiang Plain has the largest marsh wetland area in China. Since the 1950s its size has declined due to land development, between 1986 and 2016 nearly 6072 km2 (57.5% of the area) was lost due to farm land expansion. Since the "Wetland for Grain" project in 2003, efforts have been made to improve marsh area for animal habitat and ecological protection. A key management concern is prioritizing areas for wetland restoration in scientific planning and polices making. In this study, the natural wetland restoration potentials were evaluated based on land-use change trajectory, seed bank viability and watershed sustainability and restorability. The annual land use maps from 1986 to 2016 were reconstructed using CLUE-S model with land use maps in 1995, 2000, 2005, 2010 and 2016, which were interpreted from Landsat TM/ETM images. Seed bank viability was determined by field sampling in wetland and farm land with different reclamation years and germination in lab. Sub-catchment was chosen as sustainability analysis unit, which was quantified by the impacts of wetland on peak flow reduction. The watershed restorability was performed with the factors of wetland degradation degree, seed bank viability, and the percentage of wetland to watershed area (PWW) with different restoration years. The results indicated that reclaimed wetland with a time since last development (TLD) of <15 years had a higher recovery potential and accounted for 39.2% of the lost wetland. Seventeen sub-catchments with a total area of 2177 km2 of farmland could be planned for restoration, which could support more than half of the sub-catchments in the study area. Priority areas were identified for short-, mid- and long-term restoration planning. The results can support the scientific planning demands of various restoration goals in the study area, and provide a new method for wetland restoration. Unlabelled Image • Potential wetland restoration areas were identified by the seed bank viability and land use model. • The annual wetland degradation trajectory was determined for 1986–2016 using of CLUE-S model. • The restoration potential of farmland back to wetland decreases as the time since agricultural development increases. • The percent of wetland area to watershed area (PWW) could act as an indicator of watershed ecological sustainability. [ABSTRACT FROM AUTHOR]