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To quantitatively reflect the relationship between dust and plant spectral reflectance. Dust from different sources in the city were selected to simulate the spectral characteristics of leaf dust. Taking Euonymus japonicus as the research object. Prediction model of leaf dust deposition was established based on spectral parameters. Results showed that among the three different dust pollutants, the reflection spectrum has 6 main reflection peaks and 7 main absorption valleys in 350–2500 nm. A steep reflection platform appears in the 692–763 nm band. In 760–1400 nm, the spectral reflectance gradually decreases with the increase of leaf dust coverage, and the variation range was coal dust > cement dust > pure soil dust. The spectral reflectance in 680–740 nm gradually decreases with the increase of leaf dust coverage. In the near infrared band, the fluctuation amplitude and slope of its first derivative spectrum gradually decrease with the increase of leaf dust. The biggest amplitude of variation was cement dust. With the increase of dust retention, the red edge position generally moves towards short wave direction, and the red edge slope generally decreases. The blue edge position moved to the short wave direction first and then to the long side direction, while the blue edge slope generally shows a decreasing trend. The yellow edge position moved to the long wave direction first and then to the short wave direction (coal dust, cement dust), and generally moved to the long side direction (pure soil dust). The yellow edge slope increases first and then decreases. The R2 values of the determination coefficients of the dust deposition prediction model have reached significant levels, which indicated that there was a relatively stable correlation between the spectral reflectance and dust deposition. The best prediction model of leaf dust deposition was leaf water content index model (y = 1.5019x − 1.4791, R2 = 0.7091, RMSE = 0.9725).

This study analyzed the process of vegetation restoration in the eastern margin ecotone of Qinghai-Tibet Plateau (EMEQTP) from 2001 to 2019 under different restoration modes based on the vegetation net primary productivity (NPP) which was estimated by the Carnegie-Ames-Stanford approach (CASA) model. Then the potential NPP was calculated and the durations of NPP restoration were predicted based on the Autoregressive Integrated Moving Average (ARIMA) model. Finally, the relative importance of main parameters in the CASA model as well as global sensitivity and uncertainty were discussed. The results showed that the NPP under a natural restoration mode had the highest increase with 20.1%, and its spatial change showed an overall trend of restoration. The NPP under the comprehensive restoration mode achieved an increase of 11.5%, with the southwestern area experiencing an insignificant trend of degradation, whereas the northeastern area experiencing a significant restoration trend. As for the NPP under the artificial restoration mode, it experienced the lowest increase with 10.5%, and the northern part experiencing a significant restoration trend, whereas the southeastern part experiencing an insignificant degradation trend. In addition, the highest potential NPP of vegetation restoration would be appeared in the area under a comprehensive restoration mode. The area under an artificial restoration mode would have the lowest potential NPP of vegetation restoration, but the duration of restoration would be longest under this mode, reaching up to 64 years. Although the natural restoration mode achieved the best effect of vegetation restoration, considering the limited scope of the promotion of this mode, further attention should be placed on restoration measures and the effects of the artificial and comprehensive restoration modes. Finally, APAR, GST and PET were relatively important for the CASA model, and the parameters including Topt, GST, and PET were sensitive. Those results can offer scientific references for the management of ecological environment in the EMEQTP.

Some essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the remote sensing data and ground monitoring data, this study firstly constructed a more accurate retrieval model for total phosphorus (TP) concentration by comparing 12 machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), Bayesian ridge regression (BRR), lasso regression (Lasso), elastic net (EN), linear regression (LR), decision tree regressor (DTR), K neighbor regressor (KNR), random forest regressor (RFR), extra trees regressor (ETR), AdaBoost regressor (ABR) and gradient boosting regressor (GBR). Then, this study applied the constructed retrieval model to explore the spatial-temporal evolution of the Miyun Reservoir and finally assessed the water quality. The results showed that the model of TP concentration built by the ETR algorithm had the best accuracy, with the coefficient R2 reaching over 85% and the mean absolute error lower than 0.000433. The TP concentration in Miyun Reservoir was between 0.0380 and 0.1298 mg/L, and there was relatively significant spatial and temporal heterogeneity. It changed remarkably during the periods of the flood season, winter tillage, planting, and regreening, and it was lower in summer than in other seasons. Moreover, the TP in the southwest part of the reservoir was generally lower than in the northeast, as there was less human activities interference. According to the Environmental Quality Standard for the surface water environment, the water quality of Miyun Reservoir was overall safe, except only for an over-standard case occurrence in the spring and September. These conclusions can provide a significant scientific reference for water quality monitoring and management in Miyun Reservoir.

Net Primary Productivity (NPP) is one of the significant indicators to measure environmental changes; thus, the relevant study of NPP in Northeast China, Asia, is essential to climate changes and ecological sustainable development. Based on the Global Production Efficiency (GLO-PEM) model, this study firstly estimated the NPP in Northeast China, from 2001 to 2019, and then analyzed its spatio-temporal evolution, future changing trend and phenology regularity. Over the years, the NPP of different forests type in Northeast China showed a gradual increasing trend. Compared with other different time stages, the high-value NPP (700–1300 gC·m−2·a−1) in Changbai Mountain, from 2017 to 2019, is more widely distributed. For instance, the NPP has an increasing rate of 6.92% compared to the stage of 2011–2015. Additionally, there was a significant advance at the start of the vegetation growth season (SOS), and a lag at the end of the vegetation growth season (EOS), from 2001 to 2019. Thus, the whole growth period of forests in Northeast China became prolonged with the change of phenology. Moreover, analysis on the sustainability of NPP in the future indicates that the reverse direction feature of NPP change will be slightly stronger than the co-directional feature, meaning that about 30.68% of the study area will switch from improvement to degradation. To conclude, these above studies could provide an important reference for the sustainable development of forests in Northeast China.

The increasing scale of urbanization and human activities has resulted in the fragmentation of natural habitats, leading to the reduction of ecological landscape connectivity and biodiversity. Taking Nanping as the study area, the core areas with good connectivity were extracted as ecological sources using a morphological spatial pattern analysis (MSPA) and landscape connectivity index. Then the ecosystem service functions of the ecological sources were evaluated based on the InVEST model. Finally, we extracted the potential ecological corridor based on the land type, elevation and ecosystem service functions. The results showed that the ecological source with higher landscape connectivity is distributed in the north and there are clear landscape connectivity faults in the northern and southern regions. Moreover, the areas with high habitat quality, soil retention and water production are mainly distributed in the northern ecological source areas. The 15 potential ecological corridors extracted were distributed unevenly. Among them, the important ecological corridors formed a triangle network, while the general ecological corridors were concentrated in the northwest. Therefore, it is suggested that the important core patches in the north be protected, and the effective connection between the north and south be improved. These results can provide a scientific basis for ecological construction and hierarchical management of the ecological networks.

This study proposed the measuring models for the energy efficiency and carbon efficiency of the tourism industry in destination by the detailed bottom-up analysis method, the theory of life cycle assessment, and material flow. The Wulingyuan Scenic and Historic Interest Area (WSHIA) in China was chosen as the research area; the energy efficiency and carbon efficiency of tourism industry in the WSHIA were measured and analyzed from 1979 to 2015. The research results showed that energy efficiency and carbon efficiency of tourism sectors and the whole tourism industry were improved with the evolution of the stage of tourism life cycle. There was a big discrepancy in energy efficiency and carbon efficiency among different sectors of tourism industry. Tourism activity had the highest of direct and total energy efficiencies and carbon efficiencies. Compared with the industries, the tourism industry in the WSHIA belonged to the low energy consumption and green low-carbon industry. The energy and environmental policies and the different strategies on the improvement of energy efficiency and carbon efficiency of tourism industry should be formulated and implemented by the government sector of tourism management and the tourism enterprises for green and low-carbon development of tourism destination.

Ecological environment assessment can not only provide significant references for effective solutions to regional ecological problems, but also promote the benign interaction of socio-economic-ecological development. This study selected the Heihe River Basin (HRB) as the typical area to comprehensively evaluate the ecological environment from 2010 to 2030 based on the dynamics of land system (DLS) model, which is coupled with the biological abundance index (BAI), vegetation coverage index (VCI), water density index (WDI), land degradation index (LDI), and eco-environment quality index (EQI). The results indicate that the BAI and VCI will be high in the south and east, low in the north and west in general. Under the future baseline scenario, the changes of biological abundance, vegetation coverage, and deteriorating land in the midstream and downstream regions will may seriously hinder restoration of the ecological environment. In particular, the BAI in the midstream and downstream regions will decline more rapidly, with a rate of 10.30% and 18.59%, respectively. The water area will be scattered and less abundant overall, but the WDI in the midstream, that is up to 3.86 in 2030, will be higher than that in other regions. Results also show that the regions with high EQI are mainly located in the northeast and south region. It is predicted that the ecological fragile zones in the future will mainly distribute in the midstream and downstream regions, especially in Jiayuguan City and Jinta County. The EQI will drop by 44.28% and 11.40% during 2010-2030 without external conditions intervened. In addition, Qilian Mountain, Jiuquan City, Gaotai County, Linze County, and Shandan County will also have relatively strong recovery capacities under the influence of ecological policies. All above could provide the basis for the development of future watershed ecological environment management and protection planning.

Forestland dynamics can affect the ecological security of a country and even the global environment, and therefore it is of great practical significance to understand the characteristics of temporal and spatial variations of forestland. Taking Jiangxi Province as the study area, this study first explored the driving mechanism of the natural environment and social economy on deforestation and afforestation using a simultaneous equation model. The results indicate that population size, topographic and geomorphologic factors, climate, and location play leading roles in influencing forestland density fluctuations. Specifically, the population size, economic development level, gross value of forestry production, climate conditions, and government policies are key influencing factors of afforestation. Deforestation is mainly influenced by agricultural population, non-agricultural economy, forestry production, forestry density, location, transportation, and climate. In addition, this study simulated the spatial distribution of land use and analyzed the spatial characteristics and variation trends of forestland area and quality under the Representative Concentration Pathways (RCPs) climate scenarios from 2010 to 2030 using the Conversion of Land Use and its Effects (CLUE) model. The results indicate that forestland declines under the Asia-Pacific integrated model (AIM) climate scenario. The environment tends to be heavily damaged under this kind of scenarios, and measures should be taken in order to protect the environment. Although the model for energy supply strategy alternatives and their general environmental impact (MESSAGE) scenario is to some extent better than the AIM scenario, destruction of the environment will still occur, and it is necessary to restrain deforestation and convert shrub land into forestland or garden land. These results can provide significant information for environmental protection, forest resource exploitation, and utilization in the areas experiencing deforestation and afforestation.

In arid regions, land development and degradation (LDD) is sustained by the undesirable land development, human production and living, and climate change. Therefore, the understanding of LDD processes and their driving mechanism in the arid or semi-arid regions is significant to guarantee the sustainable development of ecological environment. This study explored the critical LDD processes in the Heihe River Basin (HRB) during 1990-2010 with the spatio-temporal evaluation of critical land use dynamics and its land quality changing trends. Then, the driving mechanism of cultivated land development process, grassland degradation process and water resource change process were analyzed by a simultaneous equations model which took the interaction of three processes into account. The results showed that the mutual transfers of cultivated land were primarily gathered in the middle reaches from 1990 to 2010. Its area grew by 13.5% and the average dynamic degree remained at 0.61%. The transfers between grassland and cultivated land, unused land were more remarkable, which led to the decline of grassland quality and even grassland degradation. Water area maintained a dynamic balance with almost unchanged area, but its dynamic trend was initially increasing and then decreasing. However, the average degradation of land quality in the whole study area is continuously alleviated. These changes were mainly due to the interaction of the LDD processes above, as well as socio-economic and climate change. Among them，agricultural research investments could restrain the unordered expansion of cultivated land resource for a relatively short period of time. Meanwhile, the variable of whether it is the main grain producing county is the main driver of grassland and water resource degradation in this region. These conclusions will provide scientific references for ecological land restoration and land quality improvement in the HRB.

It is significant to clarify the driving mechanism of the forest ecosystem changes at different scales in Northeast China with serious forest degradation. With Changbai Mountains in Northeast China as the study area, this study integrated multi–source data to explore the spatio–temporal changes of Net Primary Productivity (NPP) and its spatial agglomeration patterns, and probed its multi–level driving mechanism based on the Hierarchical Linear Model (HLM). The results showed the overall NPP in the study area had a gradual declining trend from southeast to northwest from 2001 to 2015. Besides, the ecological risk regions, including Low-Low (L–L) and High–Low (H–L) cluster types, expanded from 27.56% during 2001–2008 to 28.21% during 2008–2015, suggesting the local departments should focus on optimizing these regions and strengthen the construction of complex forests with large age differences to make the ecological environment healthier. In addition, results from the HLM suggested that key driving factors, e.g., the precipitation and vegetation coverage rate, had significant promoting effects on NPP at the grid scale. Whereas the soil organic matter content, distance to the highway, irrigation rate, percentage of the disaster area had significant inhibitory effects (p

Net primary productivity (NPP) lays the foundation for provision of various ecosystem services, and understanding the impacts of potential influencing factors on NPP is of great significance to formulating appropriate management measures to guarantee the sustainable provision of essential ecosystem services. This study analyzed the impacts of potential influencing factors on NPP in the lower Heihe River Basin, a typical arid and semi-arid region in China. First, NPP was estimated with the C-FIX model, and then the multilevel model was used to analyze the impacts of potential influencing factors on NPP during 2000-2008. Finally decomposition analysis was used to further analyze the contribution of influencing factors to NPP change during 2000-2008. The average NPP increased by approximately 9.07% during 2000-2008, and results of the multilevel model indicate that both the socioeconomic variables and demographic variables are useful in explaining NPP change. In particular, coefficients of rainfall and evapotranspiration which represent the water availability reached 0.0456 and 0.2956, respectively. Results of decomposition analysis suggested that the water availability played an important role in increasing NPP, with a contribution rate of 44.17%, and it is necessary to carry out some policies that can promote the water use efficiency to increase NPP under the background of climate change and intensified human activities. There are some uncertainties in the results of this study, but these results still can provide valuable reference information for the water resource management to increase the ecosystem service supply in the lower Heihe River Basin. (C) 2015 Elsevier Ltd. All rights reserved.

It is of great practical significance to construct a water-based ecological network in arid and semi-arid areas. The spatial distribution of water resources is one of the most important factors in determining the ecological stability of such areas. In this study, groundwater level trends were analyzed with a model called Empirical Mode Decomposition (EMD). The temporal and spatial evolution of groundwater depth data from 1990 to 2016 were analyzed. The surface water bodies were analyzed using a point pattern analysis method. Based on this, a water-based ecological network was constructed with a minimum cumulative resistance surface model. The study indicated that the trend lines for the groundwater tables of 17 wells could be divided into five types in Denko County. The landscape types that changed from a desert landscape to an oasis landscape had a positive impact on groundwater. Precipitation trend was related to the spatial distribution of the groundwater depth, and the spatial pattern of the water nodes was characterized by a small-scale highly aggregated distribution and a large-scale uniform distribution in Denko County. These results suggest that for the stability of arid and semi-arid ecological environments, the appropriate human intervention (such as construction of an artificial oasis) is of great significance. Based on the analysis of groundwater and surface water bodies, a water-based ecological network in Denko County, which consisted of 391 ecological sources and 7360 ecological corridors, was constructed in 2016. The water-based ecological network constructed in this study was more sustainable and stable, and also suitable for arid and semi-arid areas, which is of great practical significance and application value.

Near surface energy budget changes have been proved to be induced by the land cover conversion through changing the surface physical properties, which can further impact the regional climate change. This study applies the DLS model to simulate the land cover under the business as usual (BAU) scenario and then analyses the seasonal and interannual variation of energy balance in the semiarid grassland area of China based on the simulated land cover with the Weather Research and Forecasting (WRF) model. The results indicate that the grassland will show a growing trend under the BAU scenario. Downward long wave radiation and downward short wave radiation will all have small-scale increase with time going by, while the surface net radiation will decrease from 2030 to 2050. However, there is obvious seasonal variation. Summer has the highest downward long wave radiation and downward short wave radiation, followed by spring and autumn. The lowest are in winter. As for the net surface radiation, there is obvious decrease in southeast of study area due to returning cropland to grassland. Those research conclusions can offer valuable information for the land use planning and relieving the effects of land cover change on climate change at the semiarid grassland area.

Water and heat fluxes of terrestrial ecosystem play an important role in the sustainable development of ecosystem services. In this chapter, we first investigated the spatial variation of heat fluxes and surface temperature in an inland irrigation area of the northern China. Irrigated agriculture has the potential to alter regional to global climate significantly. We investigate how irrigation will affect regional climate in the future in an inland irrigation area of northern China, focusing on its effects on heat fluxes and near-surface temperature. Using the Weather Research and Forecasting (WRF) model, we compare simulations among three land cover scenarios: the control scenario (CON), the irrigation scenario (IRR), and the irrigated cropland expansion scenario (ICE). Our results show that the surface energy budgets and temperature are sensitive to changes in the extent and spatial pattern of irrigated land. Conversion to irrigated agriculture at the contemporary scale leads to an increase in annual mean latent heat fluxes of 12.10 W m−2, a decrease in annual mean sensible heat fluxes of 8.85 W m−2, and a decrease in annual mean temperature of 1.3 °C across the study region. Further expansion of irrigated land increases annual mean latent heat fluxes by 18.08 W m−2, decreases annual mean sensible heat fluxes by 12.31 W m−2, and decreases annual mean temperature by 1.7 °C. Our simulated effects of irrigation show that changes in land use management such as irrigation can be an important component of climate change and need to be considered together with greenhouse forcing in climate change assessments. Then, the spatial variation of surface temperature and precipitation due to grassland conversion to forestry area in southeast China was examined. The land use/land cover change (LUCC) is the synthetic result of natural processes and human activities; it largely depends on the surface vegetation conditions, and the mutual conversion among land cover types can accelerate or alleviate the regional and global climate change. Aiming at analyzing the regional climatic effects of the conversion from grassland to forestland, especially in the long-term perspective, we carried out the comparison simulation using the WRF model in Fujian Province, and results indicated that this conversion had a significant influence on the regional climate; the annual average temperature decreased by 0.11 °C, and the annual average precipitation increased by 46 mm after 11.2 % of the grassland was converted into the forestland in the study area from 2000 to 2008. In the future (from 2010 to 2050), the conversion from grassland to forestland is significant under two representative concentration pathways (RCPs) (RCP6 and RCP8.5); the spatial pattern of this conversion under the two scenarios is simulated by dynamics of land system (DLS); then, the regional climate effects of the conversion are simulated using WRF model. Further, the spatial variation of surface heat fluxes due to land use change across China was estimated. We estimate the heat flux changes caused by the projected land transformation over the next 40 years across China to improve the understanding of the impacts of land dynamics on regional climate. We use the WRF model to investigate these impacts in four representative land transformation zones, where reclamation, overgrazing, afforestation, and urbanization dominate the LUCC in each zone, respectively. As indicated by the significant variance of albedo due to different LUCCs, different surface properties cause great spatial variance of the surface flux. From the simulation results, latent heat flux increases by 2 and 21 W/m2 in the reclamation and afforestation regions, respectively. On the contrary, overgrazing and urban expansion result in decrease of latent heat flux by 5 and 36 W/m2, correspondingly. Urban expansion leads to an average increase of 40 W/m2 of sensible heat flux in the future 40 years, while reclamation, afforestation, and overgrazing result in the decrease of sensible heat flux. Results also show that reclamation and overgrazing lead to net radiation decrease by approximately 4 and 7 W/m2, respectively; however, afforestation and urbanization lead to net radiation increase by 6 and 3 W/m2, respectively. The simulated impacts of projected HLCCs on surface energy fluxes will inform sustainable land management and climate change mitigation. Finally, we summarized the predicted impacts of land use change on surface temperature in the typical areas around the world. This study focuses on the potential impacts of large-scale LUCC on surface temperature from a global perspective. As important types of LUCC, urbanization, deforestation, cultivated land reclamation, and grassland degradation have effects on the climate, the potential changes of the surface temperature caused by these four types of large-scale LUCC from 2010 to 2050 are downscaled, and this issue is analyzed worldwide along with RCPs of the Intergovernmental Panel on Climate Change (IPCC). The first case study presents some evidence of the effects of future urbanization on surface temperature in the Northeast megalopolis of the USA. In order to understand the potential climatological variability caused by future forest deforestation and vulnerability, we chose Brazilian Amazon region as the second case study. The third selected region in India is a typical region of cultivated land reclamation, where the possible climatic impacts are explored. In the fourth case study, we simulate the surface temperature changes caused by future grassland degradation in Mongolia. Results show that the temperature in built-up area would increase obviously throughout the four land types. In addition, the effects of all four large-scale LUCCs on monthly average temperature change would vary from month to month with obviously spatial heterogeneity.