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Yang G.,Chinese National Engineering Research Center for Information Technology in Agriculture | Zhao C.,Beijing Normal University | Liu Q.,Beijing Normal University | Huang W.,Beijing Normal University | Wang J.,Beijing Normal University
IEEE Transactions on Geoscience and Remote Sensing | Year: 2011

This paper presents a new forest leaf area index (LAI) inversion method from multisource and multiangle data combined with a radiative transfer model and the strategy of k-means clustering and artificial neural network (ANN). Four scenes of Landsat-5 Thematic Mapper (L5TM) and Beijing-1 small satellite multispectral sensors (BJ1) images, acquired at different times, were selected to construct multisource and multiangle image data in this study. Considering a vertical distribution of forest LAI from both overstory and understory, a hybrid model of the invertible forest reflectance model (INFORM) was used to support the retrieval of forest LAI to eliminate the dependence of understory vegetation. The simulated data from INFORM outputs, added with a random noise, were first clustered by k-means method, and were then trained by ANN to obtain the inversion model for each group (cluster). Next, the inversion model was applied to the different combinations of multiangle data to retrieve the forest LAI. Finally, a validation of inverted results with Moderate Resolution Imaging Spectroradiometer LAI product and field measurements was conducted. The experimental results indicate that the accuracy of the inverted forest LAI can be improved through the addition of observation angle data, if the quality of the image data is ensured. The inversion accuracy of LAI with the multiangle image data is improved by 30% compared to the average accuracy of the inverted LAI with the single angle data after considering the addition of random noise to the ANN training data. © 2006 IEEE.

Chen Z.,University of Cambridge | Devereux B.,University of Cambridge | Gao B.,Chinese National Engineering Research Center for Information Technology in Agriculture | Amable G.,University of Cambridge
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2012

Airborne Lidar (Light detection and ranging) is an efficient tool for the generation of Digital Terrain Models (DTMs). Although many studies have been conducted in generating DTMs using Lidar data, it is still a challenging research area. The difficulty in filtering large buildings as well as a diversity of urban features makes the design of urban DTM generating methods an ongoing priority.This research adopted an upward-fusion methodology to generate urban DTMs using airborne Lidar data. Firstly, several preliminary DTMs of different resolutions were obtained using a local minimum method. Next, upward fusion was conducted between these DTMs. This process began with the DTM of the largest grid size, which was treated as a trend surface. A finer DTM was compared with this large scale DTM. By setting appropriate thresholds, a new DTM was achieved by selecting qualified elevation values from the finer DTM and retaining the value of the trend surface when the value from the finer DTM was beyond the threshold. This process continued iteratively until all preliminary DTMs had been included in the upward fusion and a refined DTM of high resolution was achieved. To further reduce possible errors in the resulting DTM, an extended local minimum method was proposed for filtering outliers and generating preliminary DTMs.A case study was carried out in the city of Cambridge, which represents an urban landscape with a variety of building forms, street patterns and vegetation structures. The time efficiency, results of the accuracy assessments and comparison with leading software proved the success of the case study and indicated that upward-fusion was an effective method for the generation of urban DTMs with airborne Lidar data and could improve the accuracy of other DTM generating algorithms. This paper also proposed possible approaches for further improvements on this methodology. © 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

Ma R.,Beijing Institute of Technology | Xie G.Z.,Beijing Institute of Technology | Zhang C.,Chinese National Engineering Research Center for Information Technology in Agriculture
Advanced Materials Research | Year: 2014

Lack of effective and real-time information in agricultural logistics distribution, which will cause the vehicle cannot accurately obtain the current location and cannot select the best distribution route, results in serious vehicle load and resource waste. Aiming at the problems, this paper proposes an logistics distribution vehicle scheduling system based on LBS. Using three-tier system architecture including application layer, logic layer and data layer, the system provides services of location, navigation, route planning, surrounding location query and agricultural news. Mobile clients can not only receive the latest news about agriculture, but also receive real-time vehicle location and query distribution route planning, which will help the delivery staff complete distribution tasks efficiently, consequently reduce logistics distribution costs and the waste of resources. © (2013) Trans Tech Publications, Switzerland.

Yang G.,Chinese National Engineering Research Center for Information Technology in Agriculture | Pu R.,University of South Florida | Huang W.,Chinese National Engineering Research Center for Information Technology in Agriculture | Huang W.,CAS Institute of Remote Sensing Applications | And 2 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

Among the multisource data fusing methods, the potential advantages of remote sensing of solar-reflective visible and near-Infrared [(VNIR); 400-900 nm] data and thermalinfrared (TIR) data have not been fully mined. Usually, a linear unmixed method is used for the purpose, which results in low estimation accuracy of subpixel land-surface temperature (LST). In this paper, we propose a novel method to estimate subpixel LST. This approach uses the characteristics of high spatial-resolution advanced spaceborne thermal emission and reflection radiometer (ASTER) VNIR data and the low spatial-resolution TIR data simulated from ASTER temperature product to generate the high spatial-resolution temperature data at a subpixel scale. First, the land-surface parameters (e.g., leaf area index, normalized difference vegetation index (NDVI), soil water content index, and reflectance) were extracted from VNIR data and field measurements. Then, the extracted high resolution of land-surface parameters and the LST were simulated into coarse resolutions. Second, the genetic algorithm and self-organizing feature map artificial neural network (ANN) was utilized to create relationships between land-surface parameters and the corresponding LSTs separately for different land-cover types at coarse spatial-resolution scales. Finally, the ANN-trained relationships were applied in the estimation of subpixel temperatures (at high spatial resolution) from high spatial-resolution land-surface parameters. The two sets of data with different spatial resolutions were simulated using an aggregate resampling algorithm. Experimental results indicate that the accuracy with our method to estimate land-surface subpixel temperature is significantly higher than that with a traditional method that uses the NDVI as an input parameter, and the average error of subpixel temperature is decreased by 2-3 K with our method. This method is a simple and convenient approach to estimate subpixel LST from high spatial-temporal resolution data quickly and effectively. © 2009 IEEE.

Lu A.,CAS Research Center for Eco Environmental Sciences | Lu A.,Center for Monitoring Research | Wang J.,Center for Monitoring Research | Qin X.,Chinese National Engineering Research Center for Information Technology in Agriculture | And 3 more authors.
Science of the Total Environment | Year: 2012

An extensive survey was conducted in this study to determine the spatial distribution and possible sources of heavy metals in the agricultural soils in Shunyi, a representative agricultural suburb in Beijing, China. A total of 412 surface soil samples were collected at a density of one sample per km2, and concentrations of As, Cd, Cu, Hg, Pb and Zn were analyzed. The mean values of the heavy metals were 7.85±2.13, 0.136±0.061, 22.4±6.31, 0.073±0.049, 20.4±5.2, and 69.8±16.5mgkg-1 for As, Cd, Cu, Hg, Pb, and Zn, respectively, slightly higher than their background values of Beijing topsoil with the exception of Pb, but lower than the guideline values of Chinese Environmental Quality Standard for Soils. Multivariate and geostatistical analyses suggested that soil contamination of Cd, Cu and Zn was mainly derived from agricultural practices. Whereas, As and Pb were due mainly to soil parent materials, and Hg was caused by the atmospheric deposits from Beijing City. The identification of heavy metal sources in agricultural soils is a basis for undertaking appropriate action to protect soil quality. © 2012 Elsevier B.V.

Yin X.-C.,University of Science and Technology Beijing | Huang K.,Xi'an Jiaotong - Liverpool University | Hao H.-W.,CAS Institute of Automation | Iqbal K.,University of Science and Technology Beijing | Wang Z.-B.,Chinese National Engineering Research Center for Information Technology in Agriculture
Neurocomputing | Year: 2014

We consider the classifier ensemble problem in this paper. Due to its superior performance to individual classifiers, class ensemble has been intensively studied in the literature. Generally speaking, there are two prevalent research directions on this, i.e., to diversely generate classifier components, and to sparsely combine multiple classifiers. While most current approaches are emphasized on either sparsity or diversity only, we investigate the classifier ensemble by learning both sparsity and diversity simultaneously. We manage to formulate the classifier ensemble problem with the sparsity or/and diversity learning in a general framework. In particular, the classifier ensemble with sparsity and diversity can be represented as a mathematical optimization problem. We then propose a heuristic algorithm, capable of obtaining ensemble classifiers with consideration of both sparsity and diversity. We exploit the genetic algorithm, and optimize sparsity and diversity for classifier selection and combination heuristically and iteratively. As one major contribution, we introduce the concept of the diversity contribution ability so as to select proper classifier components and evolve classifier weights eventually. Finally, we compare our proposed novel method with other conventional classifier ensemble methods such as Bagging, least squares combination, sparsity learning, and AdaBoost, extensively on UCI benchmark data sets and the Pascal Large Scale Learning Challenge 2008 webspam data. The experimental results confirm that our approach leads to better performance in many aspects. © 2014 Elsevier B.V.

Jing X.,Xi'an University of Science and Technology | Yao W.-Q.,Xi'an University of Science and Technology | Wang J.-H.,Chinese National Engineering Research Center for Information Technology in Agriculture | Song X.-Y.,Chinese National Engineering Research Center for Information Technology in Agriculture
Mathematical and Computer Modelling | Year: 2011

Beijing's mountainous areas have an important ecological significance because they are ecological conservation and water source protection areas of Beijing. The impact of precipitation on the vegetation coverage of Beijing's mountainous areas is qualitatively analyzed using multi-temporal Landsat images obtained during the last 20 years. Firstly, the influence of external factors, such as phenology and relative radiometric correction, on the normalized difference vegetation index (NDVI) are removed by the normalization of remote sensing images for different periods. Then, the vegetation coverage is calculated using the method of the dimidiate pixel model. Finally, based on that, the relationship between precipitation in different seasons and change of vegetation coverage is discussed. The results indicate that the mean vegetation coverage has the same change trends with winter and summer precipitation, but an opposite change trend with spring precipitation. The paper provides a theoretical basis and data support for further research on driving models of natural vegetation restoration in Beijing's mountainous areas. © 2010 Elsevier Ltd.

Li B.,Chinese National Engineering Research Center for Information Technology in Agriculture | Wang M.,China Agricultural University
Intelligent Automation and Soft Computing | Year: 2013

Fruit recognition and navigation path extraction are important issues for developing fruit harvesting robots. This manuscript presents a recent study on developing an algorithm for recognizing "on-the-go" pineapple fruits and the cultivation rows for a harvesting robotic system. In-field pineapple recognition can be difficult due to many overlapping leaves from neighbouring plants. As pineapple fruits (Ananas comosus) are normally located at top of the plant with a crowned by a compact tuft of young leaves, image processing algorithms were developed to recognize the crown to locate the corresponding pineapple fruit in this study. RGB (Red, Green, and Blue) images were firstly collected from top-view of pineapple trees in the field and transformed into HSI (Hue, Saturation and Intensity) colour model. Then, Features of pineapple crowns were extracted and used for developing a classification algorithm. After the pineapple crowns were recognized, locations of the crowns grown in one row were determined and linearly fitted into a line, which could be used for navigating the harvesting robots to conduct the harvest. To validate the above algorithms, 100 images were taken in a pineapple field under different environments in Guangdong province as a validation set. The results showed that pineapple recognition rate can reach 94% on clear sky day, which was much better than that on overcast sky day and the navigation path was well fitted. © 2013 Copyright TSI® Press.

Shao Y.,Zhejiang University | Zhao C.,Chinese National Engineering Research Center for Information Technology in Agriculture | Bao Y.,Zhejiang University | He Y.,Zhejiang University
Food and Bioprocess Technology | Year: 2012

The estimation of nitrogen status non-destructively in rice was performed using canopy spectral reflectance with visible and near-infrared reflectance (Vis/NIR) spectroscopy. The canopy spectral reflectance of rice grown with different levels of nitrogen inputs was determined at several important growth stages. This study was conducted at the experiment farm of Zhejiang University, Hangzhou, China. The soil plant analysis development (SPAD) value was used as a reference data that indirectly reflects nitrogen status in rice. A total of 64 rice samples were used for Vis/NIR spectroscopy at 325-1075 nm using a field spectroradiometer, and chemometrics of partial least square (PLS) was used for regression. The correlation coefficient (r), root mean square error of prediction, and bias in prediction set by PLS were, respectively, 0. 8545, 0. 7628, and 0. 0521 for SPAD value prediction in tillering stage, 0. 9082, 0. 4452, and -0. 0109 in booting stage, and 0. 8632, 0. 7469, and 0. 0324 in heading stage. Least squares support vector machine (LS-SVM) model was compared with PLS and back propagation neural network methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SPAD values of rice. Independent component analysis was executed to select several sensitive wavelengths (SWs) based on loading weights; the optimal LS-SVM model was achieved with SWs of 560, 575-580, 700, 730, and 740 nm for SPAD value prediction in booting stage. It is concluded that Vis/NIR spectroscopy combined with LS-SVM regression method is a promising technique to monitor nitrogen status in rice. © 2009 Springer Science + Business Media, LLC.

Chen P.,China Agricultural University | Chen P.,Agriculture and Agri Food Canada | Chen P.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Haboudane D.,University of Quebec at Chicoutimi | And 5 more authors.
Remote Sensing of Environment | Year: 2010

To reduce environment pollution from cropping activities, a reliable indicator of crop N status is needed for site-specific N management in agricultural fields. Nitrogen Nutrition Index (NNI) can be a valuable candidate, but its measurement relies on tedious sampling and laboratory analysis. This study proposes a new spectral index to estimate plant nitrogen (N) concentration, which is a critical component of NNI calculation. Hyperspectral reflectance data, covering bands from 325 to 1075nm, were collected using a ground-based spectroradiometer on corn and wheat crops at different growth stages from 2005 to 2008. Data from 2006 to 2008 was used for new index development and the comparison of the new index with some existing indices. Data from 2005 was used to validate the best index for predicting plant N concentration. Additionally, a hyperspectral image of corn field in 2005 was acquired using an airborne Compact Airborne Spectrographic Imager (CASI), and the corresponding plant N concentration was obtained by conventional laboratory methods on selected area. These data were also used for validation. A new N index, named Double-peak Canopy Nitrogen Index (DCNI), was developed and compared to the existing indices that were used for N detection. In this study, DCNI was the best spectral index for predicting plant N concentration, with R2 values of 0.72 for corn, 0.44 for wheat, and 0.64 for both species combined, respectively. The validation using an independent ground-based spectral database of corn acquired in 2005, yielded an R2 value of 0.62 and a root-mean-square-error (RMSE) of 2.7mg Ng-1d.m. The validation using the CASI spectral information, DCNI calculation was related to actual corn N concentration with a R2 value of 0.51 and a RMSE value of 3.1mg N g-1d.m. It is concluded that DCNI, in association with indices related to biomass, has a good potential for remote assessment of NNI. © 2010.

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