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Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir.
Jiang Qun'Ou; Xu Lidan; Sun Siyang; Wang Meilin; Xiao Huijie. Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China. Ecological Indicators 2021, 124, 107356 .
AMA StyleJiang Qun'Ou, Xu Lidan, Sun Siyang, Wang Meilin, Xiao Huijie. Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China. Ecological Indicators. 2021; 124 ():107356.
Chicago/Turabian StyleJiang Qun'Ou; Xu Lidan; Sun Siyang; Wang Meilin; Xiao Huijie. 2021. "Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China." Ecological Indicators 124, no. : 107356.
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.
Chunli Wang; Qun’Ou Jiang; Xiangzheng Deng; Kexin Lv; Zhonghui Zhang. Spatio-Temporal Evolution, Future Trend and Phenology Regularity of Net Primary Productivity of Forests in Northeast China. Remote Sensing 2020, 12, 3670 .
AMA StyleChunli Wang, Qun’Ou Jiang, Xiangzheng Deng, Kexin Lv, Zhonghui Zhang. Spatio-Temporal Evolution, Future Trend and Phenology Regularity of Net Primary Productivity of Forests in Northeast China. Remote Sensing. 2020; 12 (21):3670.
Chicago/Turabian StyleChunli Wang; Qun’Ou Jiang; Xiangzheng Deng; Kexin Lv; Zhonghui Zhang. 2020. "Spatio-Temporal Evolution, Future Trend and Phenology Regularity of Net Primary Productivity of Forests in Northeast China." Remote Sensing 12, no. 21: 3670.
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.
Ling Xiao; Li Cui; Qun’Ou Jiang; Meilin Wang; Lidan Xu; Haiming Yan. Spatial Structure of a Potential Ecological Network in Nanping, China, Based on Ecosystem Service Functions. Land 2020, 9, 376 .
AMA StyleLing Xiao, Li Cui, Qun’Ou Jiang, Meilin Wang, Lidan Xu, Haiming Yan. Spatial Structure of a Potential Ecological Network in Nanping, China, Based on Ecosystem Service Functions. Land. 2020; 9 (10):376.
Chicago/Turabian StyleLing Xiao; Li Cui; Qun’Ou Jiang; Meilin Wang; Lidan Xu; Haiming Yan. 2020. "Spatial Structure of a Potential Ecological Network in Nanping, China, Based on Ecosystem Service Functions." Land 9, no. 10: 376.
Soil water moisture is one of the most important influencing factors in the fragile ecosystems in arid sandy regions, and it serves as a bridge connecting the rainfall and groundwater, two important water sources in arid sandy regions. The hydrological process of an arid sandy region occurs sporadically and is highly non-uniform temporally, making it difficult to monitor and predict. The deep soil recharge (DSR) at a sufficiently deep soil layer (usually greater than 200 cm below ground surface) is an important indicator for groundwater recharge in the arid sandy region, and thus the quantitative determination of DSR is of great significance to the evaluation of water resources and the study of water balance in the arid sandy region. Due to the large amount of evaporation, small amount of precipitation, and the long term of the frozen-soil period in the winter and spring, the monitoring of infiltration and determination of DSR in the arid sandy region become challenging. This study selects the Ulanbuh desert plots in northern China to monitor DSR, precipitation and seasonal frozen soil thickness change, and reaches the following conclusions: Even though the annual precipitation is only 48.2 mm in the arid sandy region, DSR will still occur and replenish groundwater. The daily threshold of precipitation for generating measurable DSR is lower than 4 mm, where the DSR value is defined as the downward flux over a unit area per day hereinafter. DSR continues during the frozen period of the winter and spring seasons, and it is generated from water vapor transport and condensation in the deep sandy layer. Summer rainstorms do no show an obvious correlation with DSR, which is unexpected. This study reveals the characteristics of the dynamic water resources movement and transformation in the arid sandy area in Ulanbuh Desert and can serve as an important guideline for the quantitative assessment of water resources in arid sandy regions.
YiBen Cheng; Wenbin Yang; HongBin Zhan; Qunou Jiang; Mingchang Shi; Yunqi Wang. On the Origin of Deep Soil Water Infiltration in the Arid Sandy Region of China. Water 2020, 12, 2409 .
AMA StyleYiBen Cheng, Wenbin Yang, HongBin Zhan, Qunou Jiang, Mingchang Shi, Yunqi Wang. On the Origin of Deep Soil Water Infiltration in the Arid Sandy Region of China. Water. 2020; 12 (9):2409.
Chicago/Turabian StyleYiBen Cheng; Wenbin Yang; HongBin Zhan; Qunou Jiang; Mingchang Shi; Yunqi Wang. 2020. "On the Origin of Deep Soil Water Infiltration in the Arid Sandy Region of China." Water 12, no. 9: 2409.
Guishui River Basin in northwestern Beijing has ecological significance and will be one of the venues of the upcoming Beijing Winter Olympic Games in 2022. However, accelerating climate change and human disturbance in recent decades has posed an increasing challenge to the sustainable use of water in the basin. This study simulated the runoff of the Guishui River Basin using the Soil and Water Assessment Tool (SWAT) model to reveal the spatio-temporal variations of runoff in the basin and the impacts of climate change and human activities on the runoff changes. The results showed that annual runoff from 2004 to 2018 was relatively small, with an uneven intra-annual runoff distribution. The seasonal trends in runoff showed a decreasing trend in spring and winter while an increasing trend in summer and autumn. There was a first increasing and then decreasing trend of average annual runoff depth from northwest to southeast in the study area. In addition, the contributions of climate change and human activities to changes in runoff of the Guishui River Basin were 60% and 40%, respectively, but with opposite effects. The results can contribute to the rational utilization of water resources in the Guishui River Basin.
Meilin Wang; Yaqi Shao; Qun’Ou Jiang; Ling Xiao; Haiming Yan; Xiaowei Gao; Lijun Wang; Peibin Liu. Impacts of Climate Change and Human Activity on the Runoff Changes in the Guishui River Basin. Land 2020, 9, 291 .
AMA StyleMeilin Wang, Yaqi Shao, Qun’Ou Jiang, Ling Xiao, Haiming Yan, Xiaowei Gao, Lijun Wang, Peibin Liu. Impacts of Climate Change and Human Activity on the Runoff Changes in the Guishui River Basin. Land. 2020; 9 (9):291.
Chicago/Turabian StyleMeilin Wang; Yaqi Shao; Qun’Ou Jiang; Ling Xiao; Haiming Yan; Xiaowei Gao; Lijun Wang; Peibin Liu. 2020. "Impacts of Climate Change and Human Activity on the Runoff Changes in the Guishui River Basin." Land 9, no. 9: 291.
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<0.01) on ecological environment at the watershed scale. Finally, the increase of the total investment in ecological engineering might directly promote the ecological recovery at the county scale. Those results could provide a reasonable scientific basis for the rational development and utilization of regional forest resources, and sustainable socio–economic development.
Chunli Wang; Qun'ou Jiang; Bernard Engel; Johann Alexander Vera Mercado; Zhonghui Zhang. Analysis on net primary productivity change of forests and its multi–level driving mechanism – A case study in Changbai Mountains in Northeast China. Technological Forecasting and Social Change 2020, 153, 119939 .
AMA StyleChunli Wang, Qun'ou Jiang, Bernard Engel, Johann Alexander Vera Mercado, Zhonghui Zhang. Analysis on net primary productivity change of forests and its multi–level driving mechanism – A case study in Changbai Mountains in Northeast China. Technological Forecasting and Social Change. 2020; 153 ():119939.
Chicago/Turabian StyleChunli Wang; Qun'ou Jiang; Bernard Engel; Johann Alexander Vera Mercado; Zhonghui Zhang. 2020. "Analysis on net primary productivity change of forests and its multi–level driving mechanism – A case study in Changbai Mountains in Northeast China." Technological Forecasting and Social Change 153, no. : 119939.
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.
Yaqi Shao; Qun'ou Jiang; Chunli Wang; Meilin Wang; Ling Xiao; Yuanjing Qi. Analysis of critical land degradation and development processes and their driving mechanism in the Heihe River Basin. Science of The Total Environment 2020, 716, 137082 .
AMA StyleYaqi Shao, Qun'ou Jiang, Chunli Wang, Meilin Wang, Ling Xiao, Yuanjing Qi. Analysis of critical land degradation and development processes and their driving mechanism in the Heihe River Basin. Science of The Total Environment. 2020; 716 ():137082.
Chicago/Turabian StyleYaqi Shao; Qun'ou Jiang; Chunli Wang; Meilin Wang; Ling Xiao; Yuanjing Qi. 2020. "Analysis of critical land degradation and development processes and their driving mechanism in the Heihe River Basin." Science of The Total Environment 716, no. : 137082.
This chapter focuses on the climate effect of ecological restoration programs in China. The fourth assessment report of Intergovernmental Panel on Climate Change (IPCC AR4) indicated that the human activities are a significant influencing factor of climate change. Deforestation, grassland degradation, and desertification, which are mainly induced by human activities, have been greatly intensified due to the irrational exploitation of the natural resources, rapid population growth, and the expansion of road network in the past decades. Some ecological restoration programs have been recently carried out, e.g., Green for Grain Project, which can affect the climate through not only the carbon sink but also the thermal properties of the land surface.
Qun’Ou Jiang; Enjun Ma; Yanfei Li; Anping Liu. Simulation of the Plausible Climate Effects of Ecological Restoration Programs in China. Digital Shutdowns and Social Media 2014, 135 -165.
AMA StyleQun’Ou Jiang, Enjun Ma, Yanfei Li, Anping Liu. Simulation of the Plausible Climate Effects of Ecological Restoration Programs in China. Digital Shutdowns and Social Media. 2014; ():135-165.
Chicago/Turabian StyleQun’Ou Jiang; Enjun Ma; Yanfei Li; Anping Liu. 2014. "Simulation of the Plausible Climate Effects of Ecological Restoration Programs in China." Digital Shutdowns and Social Media , no. : 135-165.