This page has only limited features, please log in for full access.

Unclaimed
Li Zhang
Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 02 June 2021 in Journal of Advances in Modeling Earth Systems
Reads 0
Downloads 0

Terrestrial ecosystems provide multiple services interacting in complex ways. However, most ecosystem services (ESs) models (e.g., InVEST and ARIES) ignored the relationships among ESs. Process-based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes. Therefore, based on the Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA) models, we developed a process-based ES model (CEVSA-ES) integrating remotely sensed leaf area index to evaluate four important ESs (i.e., productivity provision, carbon sequestration, water retention, and soil retention) at annual timescale in China. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA-ES model was the consideration of soil erosion processes and its impact on carbon cycling. The new version also improved the carbon-water cycle algorithms. Then, the Sobol and DEMC methods that integrated the CEVSA-ES model with nine flux sites comprising 39 site-years were used to identify and optimize parameters. Finally, the model using the optimized parameters was validated at 26 field sites comprising 135 site-years. Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% interannual variabilities of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. The CEVSA-ES model performed well for productivity provision and carbon sequestration, which explained 96% and 81% of the spatial-temporal variations of the observed annual productivity provision and carbon sequestration, respectively. The model also captured the interannual trends of water retention and soil erosion for most sites or basins.

ACS Style

Zhongen Niu; Honglin He; Shushi Peng; Xiaoli Ren; Li Zhang; Fengxue Gu; Gaofeng Zhu; Changhui Peng; Pan Li; Junbang Wang; Rong Ge; Na Zeng; Xiaobo Zhu; Yan Lv; Qingqing Chang; Qian Xu; Mengyu Zhang; Weihua Liu. A Process‐Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services. Journal of Advances in Modeling Earth Systems 2021, 13, 1 .

AMA Style

Zhongen Niu, Honglin He, Shushi Peng, Xiaoli Ren, Li Zhang, Fengxue Gu, Gaofeng Zhu, Changhui Peng, Pan Li, Junbang Wang, Rong Ge, Na Zeng, Xiaobo Zhu, Yan Lv, Qingqing Chang, Qian Xu, Mengyu Zhang, Weihua Liu. A Process‐Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services. Journal of Advances in Modeling Earth Systems. 2021; 13 (6):1.

Chicago/Turabian Style

Zhongen Niu; Honglin He; Shushi Peng; Xiaoli Ren; Li Zhang; Fengxue Gu; Gaofeng Zhu; Changhui Peng; Pan Li; Junbang Wang; Rong Ge; Na Zeng; Xiaobo Zhu; Yan Lv; Qingqing Chang; Qian Xu; Mengyu Zhang; Weihua Liu. 2021. "A Process‐Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services." Journal of Advances in Modeling Earth Systems 13, no. 6: 1.

Journal article
Published: 27 October 2020 in Journal of Resources and Ecology
Reads 0
Downloads 0

Karst areas in southwest China have experienced significant land cover and land use change (LUCC) due to utilization for human activity and a comprehensive rocky desertification control project (RDCP) since 2008. It is important to quantify the effect of LUCC on ecosystem productivity in this region for assessing the overall benefit of this ecological restoration project. In this study, we used using MODIS land cover and NPP products to investigate the relative contribution of LUCC to the change in net primary productivity (NPP) during 2008–2013 in Huanjiang County, one of first one hundred pilot counties to implement RDCP. Our results show that NPP increased in 95.53% of the county, and the average growth of NPP in non-rocky desertification area was higher than in rocky desertification or potential rocky desertification areas. LUCC has an important contribution (25.23%) to the NPP increase in the county, especially in the LUCC area (70.97%), which increased the average NPP by 3.9% and 10.5%, respectively. Across the six RDCP regions in the county, the average increase in NPP for the vegetation restoration measure of governed karst area is significantly greater than in the ungoverned karst area, and the positive change in NPP increased with the increasing implementation area of the vegetation restoration measure.

ACS Style

Zhang Mengyu; Zhang Li; Ren Xiaoli; He Honglin; Lv Yan; Wang Junbang; Yan Huimin. Effect of Land Use and Land Cover Change on the Changes in Net Primary Productivity in Karst Areas of Southwest China: A Case Study of Huanjiang Maonan Autonomous County. Journal of Resources and Ecology 2020, 11, 606 -616.

AMA Style

Zhang Mengyu, Zhang Li, Ren Xiaoli, He Honglin, Lv Yan, Wang Junbang, Yan Huimin. Effect of Land Use and Land Cover Change on the Changes in Net Primary Productivity in Karst Areas of Southwest China: A Case Study of Huanjiang Maonan Autonomous County. Journal of Resources and Ecology. 2020; 11 (6):606-616.

Chicago/Turabian Style

Zhang Mengyu; Zhang Li; Ren Xiaoli; He Honglin; Lv Yan; Wang Junbang; Yan Huimin. 2020. "Effect of Land Use and Land Cover Change on the Changes in Net Primary Productivity in Karst Areas of Southwest China: A Case Study of Huanjiang Maonan Autonomous County." Journal of Resources and Ecology 11, no. 6: 606-616.

Journal article
Published: 09 March 2020 in Sustainability
Reads 0
Downloads 0

While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.

ACS Style

Xiaobo Zhu; Honglin He; Mingguo Ma; Xiaoli Ren; Li Zhang; Fawei Zhang; Yingnian Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Yu Zhang; Mingyuan Du; Rong Ge; Na Zeng; Pan Li; Zhongen Niu; Liyun Zhang; Yan Lv; Zengjing Song; Qing Gu. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison. Sustainability 2020, 12, 2099 .

AMA Style

Xiaobo Zhu, Honglin He, Mingguo Ma, Xiaoli Ren, Li Zhang, Fawei Zhang, Yingnian Li, Peili Shi, Shiping Chen, Yanfen Wang, Xiaoping Xin, Yaoming Ma, Yu Zhang, Mingyuan Du, Rong Ge, Na Zeng, Pan Li, Zhongen Niu, Liyun Zhang, Yan Lv, Zengjing Song, Qing Gu. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison. Sustainability. 2020; 12 (5):2099.

Chicago/Turabian Style

Xiaobo Zhu; Honglin He; Mingguo Ma; Xiaoli Ren; Li Zhang; Fawei Zhang; Yingnian Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Yu Zhang; Mingyuan Du; Rong Ge; Na Zeng; Pan Li; Zhongen Niu; Liyun Zhang; Yan Lv; Zengjing Song; Qing Gu. 2020. "Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison." Sustainability 12, no. 5: 2099.

Journal article
Published: 01 January 2020 in 资源科学
Reads 0
Downloads 0
ACS Style

Wanxin Sun; Li Zhang; Xiaoli Ren; Honglin He; Yan Lü; Zhongen Niu; Qingqing Chang. Trends and influencing factors of potential evapotranspiration in typical forest ecosystems of China during 1998-2017. 资源科学 2020, 42, 920 -932.

AMA Style

Wanxin Sun, Li Zhang, Xiaoli Ren, Honglin He, Yan Lü, Zhongen Niu, Qingqing Chang. Trends and influencing factors of potential evapotranspiration in typical forest ecosystems of China during 1998-2017. 资源科学. 2020; 42 (5):920-932.

Chicago/Turabian Style

Wanxin Sun; Li Zhang; Xiaoli Ren; Honglin He; Yan Lü; Zhongen Niu; Qingqing Chang. 2020. "Trends and influencing factors of potential evapotranspiration in typical forest ecosystems of China during 1998-2017." 资源科学 42, no. 5: 920-932.

Accepted manuscript
Published: 30 October 2018 in Environmental Research Letters
Reads 0
Downloads 0

China's terrestrial ecosystems play an important role in the global carbon cycle. Regional contributions to the interannual variation (IAV) of China's terrestrial carbon sink and the attributions to climate variations have not been well understood. Here we investigated how terrestrial ecosystems in the four climate zones with various climate variability contribute to the IAV in China's terrestrial net ecosystem productivity (NEP) using modeled carbon fluxes data from six ecosystems models. Model results show that the monsoonal region of China dominants national NEP IAV with a contribution of 86% (69%–96%) on average. Yearly national NEP changes are mostly driven by gross primary productivity IAV and a half of annual variation results from NEP changes in summer. Regional contributions to NEP IAV in China are consistent with their contributions to the magnitude of national NEP. Rainfall variability dominates the NEP annual variability in China. Precipitation in the temperate monsoon climate zone has a largest contribution (23%) to the IAV of NEP in China because of both high sensitivity of terrestrial ecosystem carbon uptake to rainfall and large fluctuation in the precipitation caused by the East Asian summer monsoon anomalies. Our results suggest that NEP IAV mainly attributes to ecosystems with larger productivity and response to precipitation, and highlight the importance of monsoon climate systems with high seasonal and interannual variability in driving internannual variation in the land carbon sink.

ACS Style

Li Zhang; Xiaoli Ren; Junbang Wang; Honglin He; Shaoqiang Wang; Miaomiao Wang; Shilong Piao; Hao Yan; Weimin Ju; Fengxue Gu; Lei Zhou; Zhongen Niu; Rong Ge; Yueyue Li; Yan Lv; Huimin Yan; Mei Huang; Guirui Yu. Interannual variability of terrestrial net ecosystem productivity over China: regional contributions and climate attribution. Environmental Research Letters 2018, 14, 014003 .

AMA Style

Li Zhang, Xiaoli Ren, Junbang Wang, Honglin He, Shaoqiang Wang, Miaomiao Wang, Shilong Piao, Hao Yan, Weimin Ju, Fengxue Gu, Lei Zhou, Zhongen Niu, Rong Ge, Yueyue Li, Yan Lv, Huimin Yan, Mei Huang, Guirui Yu. Interannual variability of terrestrial net ecosystem productivity over China: regional contributions and climate attribution. Environmental Research Letters. 2018; 14 (1):014003.

Chicago/Turabian Style

Li Zhang; Xiaoli Ren; Junbang Wang; Honglin He; Shaoqiang Wang; Miaomiao Wang; Shilong Piao; Hao Yan; Weimin Ju; Fengxue Gu; Lei Zhou; Zhongen Niu; Rong Ge; Yueyue Li; Yan Lv; Huimin Yan; Mei Huang; Guirui Yu. 2018. "Interannual variability of terrestrial net ecosystem productivity over China: regional contributions and climate attribution." Environmental Research Letters 14, no. 1: 014003.

Journal article
Published: 23 August 2018 in Agricultural and Forest Meteorology
Reads 0
Downloads 0

Subtropical forests in the East Asian monsoon region function as considerable carbon sinks in the Northern Hemisphere. Forest ecosystems in this region have experienced intensified seasonal drought that has limited their carbon sequestration capacity, but increasing atmospheric nitrogen deposition has contrarily enhanced their capacity to act as carbon sinks. Understanding and quantifying the interactive effects of seasonal drought and nitrogen deposition on the carbon sequestration of subtropical forests is of great significance for accurately predicting future changes to the terrestrial carbon cycle. In this study, we used the Community Land Model Version 4.5 (CLM4.5) to investigate how carbon fluxes, i.e. gross primary productivity (GPP), ecosystem respiration (Re), and net ecosystem productivity (NEP), respond to seasonal drought and nitrogen deposition in an evergreen coniferous forest in southern China. Our results showed that reduced GPP during the drought in the summers of 2003 and 2007 weakened the forest’s carbon sequestration capacity. The reduction in GPP mainly occurred at the sunlit canopy due to its higher sensitivity to soil water stress, and non-stomatal limitations played an important role in limiting leaf photosynthesis. The enhanced NEP by nitrogen deposition was attributed to increased plant growth, which could, in turn, be attributed to increases in leaf area. Interactions of seasonal drought and nitrogen deposition varied with drought severity. Interactive effects of the two drivers on GPP, Re, and NEP were additive under mild and moderate drought conditions but non-additive under severe drought. Their net effects on NEP shifted from +29% under mild and moderate drought conditions to -56% under severe drought. Our study highlights the importance of accounting for the interactive effects of seasonal drought and nitrogen deposition in assessing the carbon sequestration of subtropical forest ecosystems in the East Asian monsoon region.

ACS Style

Pan Li; Li Zhang; Guirui Yu; Congqiang Liu; Xiaoli Ren; Honglin He; Min Liu; Huimin Wang; Jianxing Zhu; Rong Ge; Na Zeng. Interactive effects of seasonal drought and nitrogen deposition on carbon fluxes in a subtropical evergreen coniferous forest in the East Asian monsoon region. Agricultural and Forest Meteorology 2018, 263, 90 -99.

AMA Style

Pan Li, Li Zhang, Guirui Yu, Congqiang Liu, Xiaoli Ren, Honglin He, Min Liu, Huimin Wang, Jianxing Zhu, Rong Ge, Na Zeng. Interactive effects of seasonal drought and nitrogen deposition on carbon fluxes in a subtropical evergreen coniferous forest in the East Asian monsoon region. Agricultural and Forest Meteorology. 2018; 263 ():90-99.

Chicago/Turabian Style

Pan Li; Li Zhang; Guirui Yu; Congqiang Liu; Xiaoli Ren; Honglin He; Min Liu; Huimin Wang; Jianxing Zhu; Rong Ge; Na Zeng. 2018. "Interactive effects of seasonal drought and nitrogen deposition on carbon fluxes in a subtropical evergreen coniferous forest in the East Asian monsoon region." Agricultural and Forest Meteorology 263, no. : 90-99.

Journal article
Published: 19 January 2018 in Remote Sensing
Reads 0
Downloads 0

It is important to accurately evaluate ecosystem respiration (RE) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST), enhanced vegetation index (EVI), and land surface water index (LSWI) to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China’s grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R2 = 0.79, RMSE = 0.77 g C m−2 day−1) and temperate grasslands (R2 = 0.75, RMSE = 0.60 g C m−2 day−1). The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided overestimating RE in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of RE for two typical grasslands and a new alternative up-scaling method for large-scale RE evaluation in grassland ecosystems.

ACS Style

Rong Ge; Honglin He; Xiaoli Ren; Li Zhang; Pan Li; Na Zeng; Guirui Yu; Liyun Zhang; Shi-Yong Yu; Fawei Zhang; Hongqin Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Mingguo Ma; Yu Zhang; Mingyuan Du. A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands. Remote Sensing 2018, 10, 149 .

AMA Style

Rong Ge, Honglin He, Xiaoli Ren, Li Zhang, Pan Li, Na Zeng, Guirui Yu, Liyun Zhang, Shi-Yong Yu, Fawei Zhang, Hongqin Li, Peili Shi, Shiping Chen, Yanfen Wang, Xiaoping Xin, Yaoming Ma, Mingguo Ma, Yu Zhang, Mingyuan Du. A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands. Remote Sensing. 2018; 10 (2):149.

Chicago/Turabian Style

Rong Ge; Honglin He; Xiaoli Ren; Li Zhang; Pan Li; Na Zeng; Guirui Yu; Liyun Zhang; Shi-Yong Yu; Fawei Zhang; Hongqin Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Mingguo Ma; Yu Zhang; Mingyuan Du. 2018. "A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands." Remote Sensing 10, no. 2: 149.

Journal article
Published: 31 August 2015 in Remote Sensing
Reads 0
Downloads 0

Water use efficiency (WUE) is a useful indicator to illustrate the interaction of carbon and water cycles in terrestrial ecosystems. MODIS gross primary production (GPP) and evapotranspiration (ET) products have been used to analyze the spatial and temporal patterns of WUE and their relationships with environmental factors at regional and global scales. Although MODIS GPP and ET products have been evaluated using eddy covariance flux measurements, the accuracy of WUE estimated from MODIS products has not been well quantified. In this paper, we evaluated WUE estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET during 2003–2008 at eight sites of the Chinese flux observation and research network (ChinaFLUX) and conducted sensitivity analysis to investigate the possible key contributors to the bias of MODIS products. Results show that MODIS products underestimate eight-day water use efficiency in four forest ecosystems and one cropland ecosystem with the bias from −0.36–−2.28 g·C·kg−1 H2O, while overestimating it in three grassland ecosystems with the bias from 0.26–1.11 g·C·kg−1 H2O. Mean annual WUE was underestimated by 14%–54% at four forest sites, 45% at one cropland site and 7% at an alpine grassland site, but overestimated by 66% and 9% at a temperate grassland site and an alpine meadow site, respectively. The underestimation of WUE by MODIS data results from underestimated GPP and overestimated ET at four forest sites, while MODIS WUE values are significantly overvalued mainly due to underestimated ET in the three grassland ecosystems. The maximum light use efficiency and fraction of photosynthetically-active radiation (FPAR) were the two most sensitive factors to the estimation of WUE derived from the MODIS GPP and ET algorithms. The error in meteorological data partly caused the overestimation of ET and accordingly underestimation in WUE in subtropical and tropical forests. The bias of MODIS-produced WUE was also derived from the uncertainties in eddy flux data due to gap-filling processes and unbalanced surface energy issue. Their contributions to the uncertainty in estimated WUE at both eight-day and annual scales still need to be further quantified.

ACS Style

Li Zhang; Jing Tian; Honglin He; Xiaoli Ren; Xiaomin Sun; Guirui Yu; Qianqian Lu; Linyu Lv. Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China. Remote Sensing 2015, 7, 11183 -11201.

AMA Style

Li Zhang, Jing Tian, Honglin He, Xiaoli Ren, Xiaomin Sun, Guirui Yu, Qianqian Lu, Linyu Lv. Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China. Remote Sensing. 2015; 7 (9):11183-11201.

Chicago/Turabian Style

Li Zhang; Jing Tian; Honglin He; Xiaoli Ren; Xiaomin Sun; Guirui Yu; Qianqian Lu; Linyu Lv. 2015. "Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China." Remote Sensing 7, no. 9: 11183-11201.

Journal article
Published: 13 May 2014 in Journal of Geographical Sciences
Reads 0
Downloads 0

Photosynthetically active radiation (PAR) is the energy source of plant photosynthesis, and the diffuse component can enhance canopy light use efficiency, thereby increasing the carbon uptake. Therefore, diffuse PAR is an important driving factor of ecosystem productivity models. In this study, we estimated the diffuse PAR of over 700 meteorological sites in China from 1981 to 2010 using an empirical model based on observational data from Chinese Ecosystem Research Network (CERN) and China Meteorology Administration. Then we derived the spatial data set of 10 km monthly diffuse PAR using ANUSPLIN software, and analyzed the spatiotemporal variation characteristics of diffuse PAR through GIS and trend analysis techniques. The results showed that: (1) The spatial patterns of annual average diffuse PAR during 1981–2010 are heterogeneous across China, lower in the northeast and higher in the west and south. The nationwide average value for 30 years ranges from 6.66 mol m−2 d−1 to 15.27 mol m−2 d−1, and the value in summer is the biggest while the value in winter is the smallest. (2) There is an evident increasing trend of annual diffuse PAR during recent 30 years, with the increasing amplitude at 0.03 mol m−2 d−1/10a. But a significant declining trend is shown in the first 10 years, and obvious anomalies can be seen in 1982, 1983, 1991 and 1992. And there is a downtrend in spring and an uptrend in all the other seasons. (3) The spatial distribution of temporal variation rates of diffuse PAR is inhomogeneous across the country, generally decreasing in the north and increasing in the south.

ACS Style

Xiaoli Ren; Honglin He; Li Zhang; Guirui Yu. Estimation of diffuse photosynthetically active radiation and the spatiotemporal variation analysis in China from 1981 to 2010. Journal of Geographical Sciences 2014, 24, 579 -592.

AMA Style

Xiaoli Ren, Honglin He, Li Zhang, Guirui Yu. Estimation of diffuse photosynthetically active radiation and the spatiotemporal variation analysis in China from 1981 to 2010. Journal of Geographical Sciences. 2014; 24 (4):579-592.

Chicago/Turabian Style

Xiaoli Ren; Honglin He; Li Zhang; Guirui Yu. 2014. "Estimation of diffuse photosynthetically active radiation and the spatiotemporal variation analysis in China from 1981 to 2010." Journal of Geographical Sciences 24, no. 4: 579-592.