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Huang J.,Zhejiang University | Huang J.,Zhejiang University of Science and Technology | Chen L.,Zhejiang University | Chen L.,Key Laboratory of Polluted Environment Remediation and Ecological Health | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2012

Uncertainty in output of ORYZA2000 (a rice growth model) and sensitivities of model inputs were analyzed through global sensitivity analysis. The actual measured data was as the reference values, used for describing uncertainty in the model inputs. Results revealed high uncertainties in model output such as total above-ground dry matter (WAGT), leaf area index (LAI), leaf N content (NFLV) and weight of seed (WSO). The degree of variation in model outputs of LAI and WSO were more than 20% and 10% respectively. Among 17 analyzed inputs of ORYZA2000, the model variable of sowing time (EMD) had the highest index of sensitivity on model output. Errors in daily minimum temperature (TMIN), daily maximum temperature (TMAX) and daily sunshine hour (DHOUR), i. e. the driving variables of ORYZA200, had much influence on rice yield at mature. The fraction dry matter partitioned to leaves (FLVTB) had much effect on model outputs related to leaf and grain weight, so precision of FLVTB data should be put more attention for reducing uncertainty of yield estimation. The effects of integrating variables with 20% stochastic errors estimated from remote sensed on model outputs (WAGT and WSO) of ORYZA2000 were studied via global sensitivity analysis. Three scenarios of integrating variables (i. e. only LAI, only NFLV, or both of them) were simulated. Among three integrating scenarios, both LAI and NFLV simultaneously integrating with ORYZA2000 showed the highest adjusting effect on simulated WAGT and WSO, LAI alone showed the second highest, and NFLV alone showed the lowest. When WSO and WAGT are estimated integrating ORYZA2000 with variables inverted from remote sensing data, for all integrating scenarios remote sensing data on 70-80th day around after transplanting are more significant that need to be attained, and the remote sensing data before and after this time are also important and should be attained as well. Remote sensing data used for integrate with ORYZA2000 on the period of recovering after transplanting and the later mature of rice have no significance for WSO and WAGT estimation. Source


Huang J.,Zhejiang University | Huang J.,Zhejiang University of Science and Technology | Chen L.,Key Laboratory of Polluted Environment Remediation and Ecological Health | Chen L.,Zhejiang University of Science and Technology | And 4 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

Rice is the staple food for over half of the world's population and two-thirds of the population of China. One of the main methods to implement an estimate of the planting area is to classify an image of the study area. Systematic quality assessment and some quantitative researches have been made on uncertainties in rice area estimation using remote sensing data. In this paper, sub-meter GPS data from a field campaign and TM image of study area were combined to obtain 1m resolution sub-pixels of simulated images. Maximum Likelihood Classifier (MLC), K-Nearest Neighbors (KNN), BP neural network (BPN) and Fuzzy ARTMAP neural network (FUZZY ARTMAP) were used as hard classification approaches to classify the TM image of the study area. Classification results showed that the classification precision of all non-parametric approaches (KNN, BPN and FUZZY ARTMAP) were higher than that of parametric approach (MLC). The differences of overall accuracy between these three non-parameters classifications were small. As for rice area, it's better to choose MLC to get higher User's Accuracy, and choose KNN to get higher Producer's Accuracy. Full fuzzy BPN, partial fuzzy BPN and KNN classifiers were used to estimate area of classes in sub-pixels of simulated and TM images. The accuracies of area estimation by full fuzzy BPN classifier were significantly higher than these by partial fuzzy BPN and KNN classifiers. The correlation coefficient between the predicted area and true area of sub-pixels was not suitable in accuracy assessment for fuzzy classification, but a paired t-test could be used to assess well accuracy of area estimation. Full fuzzy classifiers have advantages of selecting eligible and enough training samples over partial fuzzy classifiers and enhance classification precision. But classification results failed to offer different categories of each pixel in space in the location information. The combined multiple classifiers either in voting mode or in measuring mode showed capacities to enhance the overall classification uncertainty in this study. It can help to improve the precision of the rice area extraction to some extent. An approach to analyzing the mixing degree of pixels was proposed in this study. The mixing degree of pixels of 30 m resolution TM image was calculated by up scaling thematic map on majority rule in Matlab. As far as the condition of rice growing regions in southern China is concerned, the problem of mixed pixel is much more severe for commonly used images like TM images. And the classification results demonstrated that the classification precision decreased with the pureness of pixels and four classifiers showed no difference in capacity to classify mixing pixels. Based on Probability Vector which was available to BPN and KNN classifiers, the maps of maximum probability, entropy of all pixels and probability of pixels with rice label were made to represent uncertainties of classification for the TM image of the study area. These maps with the traditional classification map can transfer not only results of classification but also information of spatial variation of classification uncertainty to users. Source


Fu X.T.,Zhejiang University | Fu X.T.,Zhejiang Provincial Key Laboratory of Subtropic Soil and Plant Nutrition | Fu X.T.,Key Laboratory of Polluted Environment Remediation and Ecological Health | Zhang L.P.,Zhejiang University | And 11 more authors.
Water Resources | Year: 2012

The economic forest management is one of the main land use models on low hill gentle slope. In order to investigate the soil erosion properties of bare slope under economic forest, dynamic simulation on hydraulic characteristic values of overland flow was carried out under 0. 5 mm min-1, 1. 2 mm min-1 and 1. 8 mm min-1 rainfall intensities. Results indicated that runoff shear stress increased with increasing of slope length and their relationship can be described by quadratic equation. There were abnormal points at the length of 4 m and 5. 5 m under rainfall intensity of 1. 8 mm min-1. The shallow flow was pseudo-laminar flow under 0. 5 mm min-1, 1. 2 mm min-1 and 1. 8 mm min-1 rainfall intensities, and the runoff at upslope was sluggish flow then changed to torrential flow at downslope with increasing of slope length. Critical Reynolds number varied from sluggish flow to torrential flow with 1. 8 mm min-1 rainfall intensity and was more than that under 0. 5 mm min-1. Reynolds number can be estimated by power function of slope length. And there was a positive correlation between runoff shear stress and both Froude number Fr and Reynolds number Re. We hope this study can provide scientific gist for soil erosion control under economic forest. © 2012 Pleiades Publishing, Ltd. Source

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