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Chen K.,Beijing Normal University | Chen K.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Yang S.,Beijing Normal University | Zhao C.,Beijing Normal University | And 14 more authors.
Water (Switzerland) | Year: 2016

Vegetation deterioration and soil loss are the main causes of more precipitation leakages and surface water shortages in degraded karst areas. In order to improve the utilization of water resources in such regions, water storage engineering has been considered; however, site selection and cost associated with the special karstic geological structure have made this difficult. According to the principle of the Soil Plant Atmosphere Continuum, increasing both vegetation cover and soil thickness would change water cycle process, resulting in a transformation from leaked blue water (liquid form) into green water (gas or saturated water form) for terrestrial plant ecosystems, thereby improving the utilization of water resources. Using the Soil Vegetation Atmosphere Transfer model and the geographical distributed approach, this study simulated the conversion from leaked blue water (leakage) into green water in the environs of Guiyang, a typical degraded karst area. The primary results were as follows: (1) Green water in the area accounted for < 50% of precipitation, well below the world average of 65%; (2) Vegetation growth played an important role in converting leakage into green water; however, once it increased to 56%, its contribution to reducing leakage decreased sharply; (3) Increasing soil thickness by 20 cm converted the leakage considerably. The order of leakage reduction under different precipitation scenarios was dry year > normal year > rainy year. Thus, increased soil thickness was shown effective in improving the utilization ratio of water resources and in raising the amount of plant ecological water use; (4) The transformation of blue water into green water, which avoids constructions of hydraulic engineering, could provide an alternative solution for the improvement of the utilization of water resources in degraded karst area. Although there are inevitable uncertainties in simulation process, it has important significance for overcoming similar problems. © 2016 by the authors.

Zhang T.-L.,Beijing Normal University | Zhang T.-L.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Sun R.,Beijing Normal University | Sun R.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | And 5 more authors.
Chinese Journal of Ecology | Year: 2011

Using Biome-BGC model can simulate vegetation productivity through the coupling of water and CO2 exchange processes between vegetation, soil and atmosphere, but the soil water balance module is not perfect enough, leading to a large deviation between simulated and observed values under condition of a long time no precipitation. Aiming at this problem, this paper improved and adjusted the equation of stomatal conductance stressed by soil water, the calculation formula of evapotranspiration, and the process of soil water loss in Biome-BGC model. Using this improved model, the evapotranspiration and vegetation productivity in Harvard Forest area were simulated, and compared with field observations. The accuracy of simulated results by the improved model enhanced obviously, with the evapotranspiration R2 between simulated and observed values increased from 0.483 to 0.617, NEE R2 increased from 0.658 to 0.813, root mean square error (RMSE) of annual evapotranspiration decreased averagely by 48.7%, and annual sum squared error (ASSE) of NEE decreased averagely by 39.8%, which suggested that the simulated results by using the improved model were more close to the observed results.

Zhang T.-L.,Beijing Normal University | Zhang T.-L.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Sun R.,Beijing Normal University | Sun R.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | And 3 more authors.
Chinese Journal of Ecology | Year: 2011

Ecological process model based on defined mechanism can well simulate the dynamic behaviors and features of terrestrial ecosystem, but could become a bottleneck in application because of numerous parameters needed to be confirmed. In this paper, simulated annealing algorithm was used to optimize the physiological and ecological parameters of Biome-BGC model. The first step was to choose some of these parameters to optimize, and then, gradually optimized these parameters. By using the optimized parameters, the model simulation results were much more close to the observed data, and the parameter optimization could effectively reduce the uncertainty of model simulation. The parameter optimization method used in this paper could provide a case and an idea for the parameter identification and optimization of ecological process models, and also, help to expand the application area of the models.

Liu X.,Beijing Normal University | Liu X.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Bo Y.,Beijing Normal University | Bo Y.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities
Remote Sensing | Year: 2015

Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR) data in an object-based image analysis (OBIA) paradigm. The aims of this work were the following: (i) to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii) to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii) to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species' separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI) with a 1 m spatial resolution and the Canopy Height Model (CHM) data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR), VHR/CHM, and minimum noise fractional transformed data (MNF)/CHM) were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that incorporating the crop height information into the hyperspectral extracted features provided a substantial increase in the classification accuracy. The combination of MNF and CHM produced higher classification accuracy than the combination of VHR and CHM, and the solely MNF-based classification results. The textural and geometric features in the object-based classification could significantly improve the accuracy of the crop species classification. By using the proposed object-based classification framework, a crop species classification result with an overall accuracy of 90.33% and a kappa of 0.89 was achieved in our study area. © 2015 by the authors.

Zhang L.,Beijing Normal University | Zhang L.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Sun R.,Beijing Normal University | Sun R.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | And 4 more authors.
PLoS ONE | Year: 2015

Quantifying carbon dioxide exchange and understanding the response of key environmental factors in various ecosystems are critical to understanding regional carbon budgets and ecosystem behaviors. For this study, CO2 fluxes were measured in a variety of ecosystems with an eddy covariance observation matrix between June 2012 and September 2012 in the Zhangye oasis area of Northwest China. The results show distinct diurnal variations in the CO2 fluxes in vegetable field, orchard, wetland, and maize cropland. Diurnal variations of CO2 fluxes were not obvious, and their values approached zero in the sandy desert, desert steppe, and Gobi ecosystems. Additionally, daily variations in the Gross Primary Production (GPP), Ecosystem Respiration (Reco) and Net Ecosystem Exchange (NEE) were not obvious in the sandy desert, desert steppe, and Gobi ecosystems. In contrast, the distributions of the GPP, Reco, and NEE show significant daily variations, that are closely related to the development of vegetation in the maize, wetland, orchard, and vegetable field ecosystems. All of the ecosystems are characterized by their carbon absorption during the observation period. The ability to absorb CO2 differed significantly among the tested ecosystems. We also used the Michaelis-Menten equation and exponential curve fitting methods to analyze the impact of Photosynthetically Active Radiation (PAR) on the daytime CO2 flux and impact of air temperature on Reco at night. The results show that PAR is the dominant factor in controlling photosynthesis with limited solar radiation, and daytime CO2 assimilation increases rapidly with PAR. Additionally, the carbon assimilation rate was found to increase slowly with high solar radiation. The light response parameters changed with each growth stage for all of the vegetation types, and higher light response values were observed during months or stages when the plants grew quickly. Light saturation points are different for different species. Nighttime Reco increases exponentially with air temperature. High Q10 values were observed when the vegetation coverage was relatively low, and low Q10 values occurred when the vegetables grew vigorously. © 2015 Zhang et al.

Liu X.,Beijing Normal University | Liu X.,State Key Laboratory of Remote Sensing Science | Liu X.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Sun R.,Beijing Normal University | And 7 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2010

The significance to agriculture, forestry production and environment monitoring is obvious that extraction of land cover information based on remote sensing data. So in this study, focusing on Henan Province, making use of MODIS 16-days composite EVI data at 2005, combining with crop phenology and other reference land cover data, the land-cover classification for Henan Province was performed. The raw EVI data was processed with cloud removing and smoothing, then the support vector machine (SVM) method was adopted for the classification. Refer to the classification result, compared with the statistics of crops acreage of Henan Province in 2005, the area accuracy of classification result was as following: for large-area planted crops, wheat got 81.47%, corn 94.87%, rice 82.43%; while for the economic crops, rape was 39.81%, soybean 93.65%, cotton 95.21%, peanut 74.27%. On the other hand, combining the classified land cover type into 5 types, farmland, woodland, grassland, water body, urban and built-up. The results were further compared with 1:100000 land cover map which was produced by using the Landsat ETM+ and TM data in 2000. The overall accuracy and Kappa coefficient were 78.07% and 0.66, respectively. It turns out that the feasibility of MODIS time-series VI data and classification strategies adopted to extract crops information.

Zhang Q.,Beijing Normal University | Zhang Q.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Sun R.,Beijing Normal University | Sun R.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | And 6 more authors.
Agricultural and Forest Meteorology | Year: 2016

Wetlands play an important role in the exchange of carbon and energy between the land and the atmosphere. Moreover, wetlands are sensitive to global changes because of their unique water-heat effects and greenhouse gas (GHG) metabolic processes. However, the temporal variations in carbon and energy fluxes in wetlands are not yet fully understood. As an artificial wetland in an arid area, the Zhangye wetland features complex meteorological conditions and human interventions, which have introduced uncertainties into the carbon and energy fluxes. In this study, eddy covariance technology was used to examine the characteristics of the carbon and energy fluxes over an artificial wetland in an arid area. The main objectives were to determine (1) the diurnal and seasonal variations in the carbon and energy fluxes; (2) the relationship between the carbon and energy fluxes and the controlling factors, including the meteorological conditions and human interventions; (3) the contribution of carbon emissions from the wetland ecosystem; (4) the evapotranspiration (ET) difference between an artificial wetland in an arid region and a natural wetland; and (5) a preliminary simulation of net ecosystem exchange (NEE) and ET using the Biome-BGC model (Wetland-BGC version). Significant diurnal variations were observed in the carbon dioxide (CO2) flux in different seasons, whereas variations in methane (CH4) were not significant. Both CO2 and CH4 fluxes peaked in summer, with the highest emission rates occurring at 12:00-16:00 and featuring values of -15.65μmolm-2 s-1 and 0.38μmolm-2 s-1, respectively. The CO2 and CH4 fluxes exhibited a strong relationship with soil temperature (R2 =0.7305 and 0.5949, respectively, for a soil depth of 0cm). CH4 emissions significantly influenced the total carbon budget, and the wetland was found to be a carbon sink with respect to the net exchange of carbon. The greatest ET in the Zhangye wetland during the study period was 12.33mmday-1, and the average annual ET was 1300.4mmyear-1. This study examined the main components of the energy flux, including variations in the net radiation (R n), latent heat flux (LE), sensible heat flux (H) and soil heat flux (G s), and the relationships between these variables and the environmental controls. The range of LE/R n was 0.32-0.74, and this ratio was 0.64 during the growing season. The ratio of H/LE ranged from -0.04 to 1.28, and the value was negative during June, July and August. Artificial wetlands had a large thermal capacity that tended to slow down the energy exchange. Human interventions, e.g., irrigation, policies, etc., significantly affected the CH4 flux and ET but did not affect the CO2 flux. © 2016 Elsevier B.V.

Liu X.,Beijing Normal University | Liu X.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Liu X.,Yunnan Normal University | Bo Y.,Beijing Normal University | And 6 more authors.
Remote Sensing | Year: 2015

The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area. © 2015 by the authors.

Cui T.,Beijing Normal University | Cui T.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Wang Y.,Northwest Regional Climate Center | Wang Y.,Nanjing University | And 11 more authors.
PLoS ONE | Year: 2016

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution. © 2016 Cui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Jiang G.,Beijing Normal University | Jiang G.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Sun R.,Beijing Normal University | Sun R.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | And 6 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

Light use efficiency (LUE) is a critical parameter for estimating carbon exchange in many ecosystem models, especially those models based on remote sensing algorithms. Estimation and monitoring of LUE and gross primary productivity (GPP) over wetland is very important for the global carbon cycle research and modelling, since the wetland plays a vital role in the ecosystem balance. In this paper, carbon flux data observed with an eddy covariance tower over a reeds-dominated wetland in Zhangye, northwest of China, was used to calculate LUE. Through the postprocessing of carbon flux data and estimation of ecosystem respiration, daily GPP was calculated firstly. Combining with fraction of absorbed photosynthetically active radiation (FPAR) inversed from HJ-1 satellite, LUE was determined. The maximum value of LUE was 1.03 g C•MJ-1 occurred in summer. Furthermore, a regional vegetation productivity model based on meteorological data and remote sensing data was used to estimate the wetland GPP. The results show that the modeled GPP results were consistent with in situ data. © 2014 SPIE.

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