Qijiang Zhu

Beijing Normal University, Peping, Beijing, China

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Publications (29)7.99 Total impact

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    ABSTRACT: The ecological structure in the arid and semi-arid region of Northwest China with forest, grassland, agriculture, Gobi, and desert, is complex, vulnerable, and unstable. It is a challenging and sustaining job to keep the ecological structure and improve its ecological function. Net primary productivity (NPP) modeling can help to improve the understanding of the ecosystem, and therefore, improve ecological efficiency. The boreal ecosystem productivity simulator (BEPS) model provides the possibility of NPP modeling in terrestrial ecosystem, but it has some limitations for application in arid and semi-arid regions. In this paper, we improve the BEPS model, in terms of its water cycle by adding the processes of infiltration and surface runoff, to be applicable in arid and semi-arid regions. We model the NPP of forest, grass, and crop in Gansu Province as an experimental area in Northwest China in 2003 using the improved BEPS model, parameterized with moderate resolution remote sensing imageries and meteorological data. The modeled NPP using improved BEPS agrees better with the ground measurements in Qilian Mountain than that with original BEPS, with a higher R 2 of 0.746 and lower root mean square error (RMSE) of 46.53 gC m−2 compared to R 2 of 0.662 and RMSE of 60.19 gC m−2 from original BEPS. The modeled NPP of three vegetation types using improved BEPS shows evident differences compared to that using original BEPS, with the highest difference ratio of 9.21 % in forest and the lowest value of 4.29 % in crop. The difference ratios between different vegetation types lie on the dependence on natural water sources. The modeled NPP in five geographic zones using improved BEPS is higher than those with original BEPS, with higher difference ratio in dry zones and lower value in wet zones.
    Environmental earth sciences 01/2013; · 1.45 Impact Factor
  • Qijiang Zhu, Taizong Rong, Rui Sun
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    ABSTRACT: This paper relates to the semi-empirical model based on fire field energy balance and the physical model based on land temperature, aiming to provide a practical way of describing fire spread. Fire spread is determined by the characteristics of combustible materials and the agency of meteorological factors and terrains. Combustible materials, such as surface area, have no featured scale, yet the process of forest fire spread contains the self-replicating feature, both of which contribute to the self-similarity of fire spread. Consequently, fire behavior can be described by fractal geometry. In this research, we select Wuchagou forest in Da Hinggan Mountains as the experimental site where a forest fire took place three years ago. The forest fire was detected on low-resolution NOAA-AVHRR images, and fire spread was simulated on high-resolution TM images as another attempt to merge information. Based on remote sensing and GIS, we adopted the method of limited spreading lumping (DLA) to describe growing phenomenon to simulate the dynamic process of fire spread and adjusting shape of the result of fire simulation by the scale rule. As a result, the simulated fire and the actual fire manifest the self-similarity in their spreading shapes as well as the quantitative similarity in their areas.
    Science in China Series E Technological Sciences 04/2012; 43:104-112. · 1.02 Impact Factor
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    ABSTRACT: As one of the computer simulation models, RGM (Radiosity-Graphics combined Model) can take the processes of reflectance, transmittance and multiple scattering among and between canopies into account. It is appropriate for simulating the directional reflectance from some small canopies and the simulating results are validated well, but the model is difficult to simulate the reflectance of complex scenes, such as mountainous region because the algorithm of RGM is complicated and time-consuming. In order to research the characteristics of the radiation and the reflectance from complex mountainous region, RGM is extended to simulate the directional reflectance from all kinds of topography in this article. To conquer the two disadvantages in RGM, the model is modified in two steps. Firstly, complex mountainous scenes are simplified to only keep DEM. Secondly the algorithm of Radiosity is modified to couple other BRDF (Bidirectional Reflectance Distribution Function) models of vegetation, such as NADIM model, to consider directional radiation of the surfaces of DEM (Digital Elevation Model). A DEM scene produced by a normal distribution random number and a real DEM scene from Tibet Plateau are used to test the modified RGM and the results are analyzed simply.
    Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International; 01/2012
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    ABSTRACT: The accurate prediction of crop yield is of great help for grain policy making. By assuming a horizontally homogeneous, vertically laminar structure and introducing a multilayer-two-big-leaf model, we develop a radiative-transfer equation for winter-wheat canopy and a model, named the remote-sensing–photosynthesis–yield estimation for crops (RS–P–YEC) model, for winter-wheat yield estimation. The yield is calculated by multiplying the net primary productivity (NPP) by the harvest index (HI). In this study, the yield of winter wheat in the North China Plain in 2006 is estimated using the RS–P–YEC model. The simulated yield is consistent with observations from 17 agro-meteorological stations, and the mean relative error is 4.6%. The results demonstrate that the RS–P–YEC model is a useful tool for winter-wheat yield estimation in the North China Plain with widely available remotely sensed imageries.
    International Journal of Remote Sensing 11/2011; 32(21):6335-6348. · 1.36 Impact Factor
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    ABSTRACT: Qinghai-Tibetan Plateau plays an important role in estimating net primary productivity of grassland ecosystem for global carbon cycling research. In this paper, boreal ecosystem productivity simulator (BEPS) model was modified according to the characteristics of grassland canopy. A hypothesis of horizontal homogeneity and vertical layer was put forward for grassland canopy and BEPS was modified to GEPS (grassland ecosystem productivity simulator) to simulate the NPP of grassland ecosystem. With MODIS products (MOD15A2 and MOD12Q1) and routine meteorological data, net primary productivity of grassland ecosystem was simulated in Qinghai-Tibetan Plateau in 2006 based on GEPS. The result shows that NPP of grassland ecosystem in Qinghai-Tibetan Plateau is between 20 and 500 gC/m2·a, which is close to the other studies. The spatial distribution of NPP of grassland ecosystem in Qinghai-Tibetan Plateau has the trend of decreasing from east to west. Finally the seasonal change of NPP was investigated based on monthly NPP, which has good coherence with the seasonal changes of temperature. This study suggests that the process model - GEPS - is suitable to simulate NPP of grassland ecosystem in Qinghai-Tibetan Plateau.
    IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2010, July 25-30, 2010, Honolulu, Hawaii, USA, Proceedings; 01/2010
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    ABSTRACT: PAR and FPAR are two important variables in agricultural field. Some researches show that many factors, such as LAI (leaf area index), LAD (leaf ange distribution) and the heterogeneity of vegetation will affect the distribution of PAR and FPAR. In order to understanding the exchange process of material and energy, Radiosity-Graphics combined Model (RGM) (Qin et al., 2000) is used to simulate the distribution of PAR and FPAR in canopy and some effect factors, such as the structure of canopy and sun zenith angle, can be analyze carefully. PAR and FPAR of a typical winter wheat canopy is simulated and the results are validated with the measured data. They agreed well. Next work is to simulate and analyze several factors of the distribution of PAR and FPAR, including sun incident angle, LAD, LAI, special for the heterogeneous canopies such as that crop with width and narrow ridges which can direct cropping patterns and remote sensing inversion.
    IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2010, July 25-30, 2010, Honolulu, Hawaii, USA, Proceedings; 01/2010
  • Donghui Xie, Peijuan Wang, Qijiang Zhu
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    ABSTRACT: The distributions of polarized reflectance from several leaves surfaces are measured by the multi-direction instrument, including corn tender leaf, corn mature leaf, and lilac leaf. The degrees of polarization corresponding to different incident zenith angle and view zenith angle are calculated. Some results can be concluded by comparing the degree of polarization: the degree of polarization will increase with incident zenith angle and view zenith angle. These indicate that non-Lambertian of leaf surface will be distinct with the increasing of incident zenith angle.
    IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, July 12-17, 2009, University of Cape Town, Cape Town, South Africa, Proceedings; 01/2009
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    ABSTRACT: Land surface vegetation phenology is an important process for the real-time monitoring and detecting inter-annual variability in terrestrial ecosystem carbon exchange and climate-biosphere interactions. Crop phenology is an important factor that influences crop growth and yield estimation models. Since the mid-1980s, coarse-resolution, temporally-composited satellite data have been used to study vegetation phenology. View-angle corrected nadir reflectances from the 16-day, 1km operational MODIS BRDF/Albedo product are currently used to monitor global land cover dynamics. In this paper, we developed an improved methodology for using the new 500-m MODIS BRDF/Albedo Version 005 product to monitor global vegetation phenology by utilizing time series of the Normalized Difference Vegetation Index (NDVI). The method adopts a rolling strategy for the continuous updating of the underlying anisotropy (or BRDF shape), so that the latest land surface BRDF information can be used as prior-knowledge for next retrieval. Using this approach, transition dates for vegetation phenology in time series of NDVI can be determined from MODIS data at finer temporal and spatial resolution. Preliminary results based on monitoring crops in northern China demonstrate the effectiveness of our rolling retrievals coupled with the improved spatial resolution of the new MODIS product.
    Proc SPIE 03/2008;
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    ABSTRACT: Different from visible signals, thermal infrared radiances depend on both temperature and emissivity. It is a key problem for us to separate temperature and emissivity in thermal infrared remote sensing research. Another difficulty encountered in the retrieval of surface temperature is the correction of downwelling sky irradiance, because it is closely related to surface emissivity. When emissivity is unknown, the downwelling sky irradiance is difficult to be removed. In this paper, we introduce a correction term of downwelling sky irradiance developed by Li and Becker into Wien’s approximation, to derive an improved ALPHA difference spectrum which is independent of temperature, and furthermore develop a correction term to remove the error of Wien’s approximation. Under the support of the above work, attractive features of Alpha derived emissivity method and ASTER TES algorithm are combined together to acquire a new Improved TES algorithm based on Corrected ALPHA Difference Spectrum (ICADS TES). Because a multi-band inversion technique is applied, and the operations of band ratios and differences are included in the algorithm, it can partly remove the influence of atmosphere and noises. Numerical simulation experiments show that for various combinations of atmosphere, land covers and surface temperatures, the algorithm is applicable and stable. Its accuracy for temperature is 0–1.5 K, and that for emissivity is 0–0.015. Compared with current TES algorithms, our method has clear physical meaning, is easy to be implemented, and is applicable for a wide temperature range and surface types. The results are not influenced by the directional characteristic of emissivity. Because ICADS TES does not need the support of a priori information of surface types, it is also not influenced by the accuracy of classification and the problem of mixture pixels. Compared with our former TES algorithm based on corrected Alpha difference spectra (CADS TES), the new algorithm takes the effect of downwelling atmospheric radiation into account. When the quantity of atmosphere radiation can be estimated precisely, the performance of ICADS TES is much better.
    Science in China Series D Earth Sciences 01/2007; 50(2):274-282. · 1.59 Impact Factor
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    ABSTRACT: Vegetation index is a simple, effective and experiential measurement of terrestrial vegetation activity, and plays a very important role in qualitative and quantitative remote sensing. Aiming at shortages of current vegetation indices, and starting from the analysis of vegetation spectral characteristics, we put forward a new vegetation index, the three-band gradient difference vegetation index (TGDVI), and established algorithms to inverse crown cover fraction and leaf area index (LAI) from it. Theoretical analysis and model simulation show that TGDVI has high saturation point and the ability to remove the influence of background to some degree, and the explicit functional relation with crown cover fraction and LAI can be established. Moreover, study shows that TGDVI also has the ability to partly remove the influence of thin cloud. Experiment in the Shunyi District, Beijing, China shows that reasonable result can be reached using the vegetation index to retrieve LAI. We also theoretically analyzed the reason why the normalized difference vegetation index (NDVI) owns the low saturation point, and show that it is determined by the definition of NDVI and the characteristic of vegetation spectra, and is unavoidable to some degree. Meanwhile, through model simulation, we also indicate that the relationship between simple ratio vegetation index (SR) and LAI closes to a piecewise linear one instead of a linear one, which is mainly caused by the influence of background and different change rates of reflectance in red and infrared bands with LAI increasing.
    Science in China Series D Earth Sciences 04/2005; 48(2):241-249. · 1.59 Impact Factor
  • Jiangtao Lee, Yanmin Shuai, Qijiang Zhu
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    ABSTRACT: More and more different resolution images are used in the process of forests vegetations classifying and recognizing. Most classifying methods, including supervising and non-supervising methods, are based on spectral information and work well in forest vegetations recognizing. But some forest vegetations such as conifer, broadleaf and mixed forest are not distinguished clearly by using these methods just because these vegetations have similar spectral characteristic. In this paper, TM image of Changbai Mountains was obtained and spectral information of the image is analyzed. At the same time, the vegetation types covering the Changbai Mountains are acquired by investigating the region in person. On the other hand, DEM (digital elevation model) is employed as an important criterion in classifying forest vegetations basing on spectral character in this article. The classified results are better than those calculated by original classifying methods. At last, the results are verified by data surveyed on the spot. The results illustrate that DEM is an important factor in classifying forest vegetations distributed by the elevation. Spectrum combined with elevation information is very useful for object recognizing and classifying. Images and DEM have widely practical application and should be used in more research fields.
    Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International; 10/2004
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    ABSTRACT: doi: 10.5589/m04-040
    Canadian journal of remote sensing 10/2004; 30:731-742. · 0.99 Impact Factor
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    ABSTRACT: In the field of remote sensing, it is important to understand interaction between light and vegetation. The interrelation of them has been addressed in many works, and many different radiant models of vegetation have been proposed, such as: geometrical optical models, turbid medium models, hybrid models and computer simulation models. With developing of quantitative remote sensing research, computer simulation models, for example, Monte Carlo simulation model and Radiosity show their importance in analyzing the experimental data. In order to continue calculating the reflectivity from the vegetation by using a computer simulation model, it is essential to build the 3D structure of the vegetation. Therefore, many 3D structure data and optical parameters about the real winter wheat were measured firstly, i.e. height of stem, positions and sizes of the leaves, distributions on the field of wheat. Because these data are numerous and discrete, it is very difficult to simulate the virtual scene with them directly. To cope with it, we arranged all data and parameters in several layers based on the object oriented technique. Moreover, in order to simplify and deduce the structural variables that will be applied to build the 3D visual winter wheat model, we analyzed experimental data statistically in the process of realistic structural model. Several geometric and logical relations about structural variables were developed subsequently, and some variables varying with season were summarized to get the simple regulation with the purpose of simulating growing process of the winter wheat. The extended Lindenmayer system (L-system) method is then used to simulate the virtual scene of winter wheat by giving a few structural variables simplified before. Once the simulation is correct, scattering and reflectance from the 3D structural scene can be calculated using the Monte Carlo simulation model or Radiosity and so on. Our results show that (a) our lighting simulation system efficiently provides the required information at the desired level of accuracy, and (b) the plant growth model is extremely well calibrated against real plants. Furthermore, the method and the relations developed in this paper can be used in other subjects, such as computer graphics.
    Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International; 08/2003
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    ABSTRACT: The mineral environment of the DeXing Copper, in JiangXi Province in China, is monitored and analyzed by making use of the field spectral data and remote sensing images, TM data as well as ETM data, in different mineral developmental period. The location of the mine tailings is identified and its change in area and volume versus the time is calculated as well. A method to use DEM (Digital Elevation Model) for analyzing the change of the volume for the pollution source and its impact to the local environment is proposed in this paper. It provides the quantitative description for the mineral environmental pollution. This method can be used to monitor the open mineral environment, which has the same environmental problem like DeXing Copper Mine. It's beneficial for the local government to supervise the mineral environmental changes and be aware of the pollution status dynamically and lively.
    Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International; 08/2003
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    ABSTRACT: How does one effectively manage spectral data to find information oriented subjects? How does one mine the spectral data in observation intervals from data in existence? In this paper, a tree data structure, which includes dimension tables and fact tables, is presented to establish the top architecture and tie together the various data tables, for instance, spectrum data and its interrelated matching parameters. A new method on mining canopy spectra in observation intervals is discussed, which is based on certain crop models and the tree structure data warehouse. At last, a case on vegetation spectral simulation is given to test the structure of a data warehouse and to verify the data mining method using SE590 data obtaining during the field experiment conducted from March to May 2001 in Shunyi, Beijing, China (40°00'-40°18'N, 116°28'-116°58'E).
    Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International; 08/2003
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    ABSTRACT: A measurement model on spectra quality is presented through a bigram composed of a spectra quality grade and a metadata integrality grade. Quantitative describing datasets and qualitative describing datasets of spectra quality are extracted with spectra enveloping line analysis, spectra line profile analysis, principles of relative parameters matching and spectra prior knowledge. The quality grade is converted from subordinative degree of eigenpoints and eigenvalues from quantitative datasets. The metadata integrality grade is obtained by visiting each node in a multicross tree by which metadata about vegetation spectra is organized. The two grades make up a bigram by which one can evaluate vegetation spectra quality.
    Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International; 08/2003
  • Jiangtao Lee, Suhong Liu, Qijiang Zhu
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    ABSTRACT: More and more high resolution images are used in the process of land use dynamic monitoring and object recognizing. Different resolution images have different capabilities in land recognition and classification. The high resolution images play an important role in this area. This paper uses a model to analyze the spatial correlation among the images of different resolutions. The spatial correlations of different types are calculated and compared within this model. As the analysis illustrates, the spatial correlation decreases with the spatial extension for the city land, whereas it is the reverse case for the plantation under the same condition. Furthermore, the research study on the different resolution images for the same area shows that the images dimensions are varied with the spatial resolution and the spatial correlations are changed with different dimensions. The spatial correlation of large-dimension images is greater than that of small-dimension images. It means the spatial correlation of high resolution images is a little bit higher than that of low resolution images. This study reveals that the spatial correlation can be used in evaluating the information entropy, and is very helpful in object recognizing and classifying.
    Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International; 08/2003
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    ABSTRACT: This paper presents the results of an intercomparison study of data fusion methods. Three data fusion techniques, based respectively on the Daubechies wavelet basis method, the IHS transform, and the Principle Component Analysis (PCA), are compared with each other. According to the data set we used in this study, the Daubechies Wavelet Basis method is far more efficient than the PCA and the IHS transform, It thus establishes the advantages for data fusion, formally called multiple resolution analysis. This method is the best among the three for image sharpening and for maintaining the information of the original data. We conclude with the result from this study that the Daubechies Wavelet Basis method has the largest application potential for merging the spatial and spectral characteristics of multiple resolution remote sensing data with high efficiency.
    Proc SPIE 07/2003;
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    ABSTRACT: Large Aperture Scintillometers (LAS), Bowen ratio and eddy covariance measurements were employed to study sensible heat flux over homogeneous bare soil surface from March 20<sup>th</sup> to April 20<sup>th</sup>, 2002, at XiaoTangshan area, Beijing. The diurnal variation of sensible heat flux from LAS is analyzed in this paper, and the relation between sensible heat flux and weather conditions is discussed. Further, test comparisons of the scintillometer flux measurements with the measurements of Bowen ratio and eddy correlation methods show good agreement; The correlation coefficients are over 0.8.
    Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International; 02/2003
  • Yuyu Zhou, Hong Chen, Qijiang Zhu
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    ABSTRACT: Algorithms for remote sensing image classification can be cataloged into classic classification, fuzzy classification, and neural network classification. For classic classification algorithm, the space distribution of data features should be assumed, and it is difficult to put in expert knowledge to remote sensing information. In the fuzzy classification algorithm, the meaning of the subordinate degree is not definite. In the neural network classification algorithm, network framework parameters are difficult to decide, training time is long, and the neural network tends to fall into a local optimization situation. A modified Fuzzy-ISODATA algorithm and a BP neural network algorithm have been developed. The integration of the two algorithms was applied to remote sensing classification. A comparison of classification accuracy, speed and practicability for each algorithm was made based on the same training sampling area. The experiment was conducted in Shunyi, Beijing, China (40°00'-40°18'N, 116°28'-1161°58'E, which covers a total area of 1021 km<sup>2</sup>) with a TM image. The result indicates that the accuracy of integration classification algorithm increases compared with the simple fuzzy clustering algorithm and the simple neural network algorithm in the Shunyi area, but the speed should be improved.
    Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International; 07/2002