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Featured researches published by Shihao Tang.


Canadian Journal of Remote Sensing | 2004

Spatial distribution of net primary productivity and evapotranspiration in Changbaishan Natural Reserve, China, using Landsat ETM+ data

Rui Sun; Jing M. Chen; Qijiang Zhu; Yuyu Zhou; Jane Liu; Jiangtao Li; Suhong Liu; Guangjian Yan; Shihao Tang

Remote sensing has been a useful tool to monitor net primary productivity (NPP) and evapotranspiration (ET). In this paper, based on field measurements and Landsat enhanced thematic mapper plus (ETM+) data, NPP and ET are estimated in 2001 in the Changbaishan Natural Reserve, China. Maps of land cover, leaf area index, and biomass of this forested region are first derived from ETM+ data. With these maps and additional soil texture and daily meteorological data, NPP and ET maps are produced for 2001 using the boreal ecosystem productivity simulator (BEPS). The results show that the estimated and observed NPP values for forest agree fairly well, with a mean relative error of 8.6%. The NPP of mixed forests is the highest, with a mean of 500 g C m–2·a–1, and that of alpine tundra and shrub is the lowest, with a mean of 136 g C m–2·a–1. Unlike the spatial pattern of NPP, the annual ET changes distinctly with altitude from greater than 600 mm at the foot of the mountain to about 200 mm at the top of the mountain. ET is highest for broadleaf forests and lowest for urban and built-up areas.


international geoscience and remote sensing symposium | 2002

A conception of digital agriculture

Shihao Tang; Qijiang Zhu; Xiaodong Zhou; Shaomin Liu; Menxin Wu

On the analysis of characteristics of current agriculture, we put forward the conception of digital agriculture, and construct its framework. Relationships between precision agriculture, digital Earth, information agriculture, virtual agriculture and digital agriculture are analyzed. The necessities to put forward the concept of digital agriculture, the feasibility to realize digital agriculture and the measures should be taken are also discussed in our paper.


international geoscience and remote sensing symposium | 2002

Interception of PAR and relationship between FPAR and LAI in summer maize canopy

Xiaodong Zhou; Qijiang Zhu; Shihao Tang; Xue Chen; Menzin Wu

According to our observation under clear and cloudy days during growing season, we analyzed the diurnal changes of the incident PAR, the reflected PAR of canopy and soil, the transmitted PAR, the reflectance of soil, and the absorbed photosynthetically active radiation (APAR). FPAR at an interval of five minutes and the average value of a day are also calculated according to each PAR component in canopy. Then the relationship between daily variation of FPAR and crop growth periods as well as the variation of LAI was also investigated and a linear relationship between FPAR and LAI was presented.


international geoscience and remote sensing symposium | 2003

Quantitative remote sensing research on the vegetation 3-D visual simulation based on object oriented technique

Donghui Xie; Menxin Wu; Qijiang Zhu; Jindi Wang; Shihao Tang

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.


international geoscience and remote sensing symposium | 2003

New airborne multi-angle high resolution sensor AMTIS LAI inversion based on neural network

Yuyu Zhou; Guangjian Yan; Qijiang Zhou; Shihao Tang

Leaf area index (LAI) is an important biophysical parameter, and remote sensing provides the possibility for the LAI retrieval over large area. Model based inversion is one of the main LAI retrieval methods, and the multi-angle data are the important data sets. However, the general model-fitting algorithm is time consuming in LAI inversion. In this paper, we proposed a kernel-driven model and neural network based LAI inversion algorithm to speed the process. The data obtained by the new Airborne Multi-angle Thermal/Visible Imaging System (AMTIS) is synchronous and has higher resolution. Compared with the low-resolution multi-angle data such as MISR and MODIS, it has a resolution as high as 1.36 m. Using the kernel-driven model, the BRF was reconstructed from the AMTIS data. On the other hand, a 3-dimension radiative transfer model and the measured parameters were used to model the BRF. Then LAI was inversed based on the neural network. Synchronous ground-based measurements of LAI for wheat were taken in Shunyi to validate our method. Some conclusions from the study: (1) LAI can be retrieved successfully using the high-resolution multi-angle data based on neural network; (2) based on the neural network and the kernel-driven model, the inversion rate can be improved; (3) by adjusting the soil moisture classification, the inversion precision can be improved.


Third International Symposium on Multispectral Image Processing and Pattern Recognition | 2003

Study of BRDF changing feature of winter wheat in different season

Feng Zhao; Jindi Wang; Shihao Tang; Guangjian Yan; Ziti Jiao; Xiaowen Li

The anisotropic reflectance of vegetation canopy is mainly determined by its spectral and structural features, and can be described by Bidirectional Reflectance Distribution Function (BRDF). In this article, we select the winter wheat from the beginning of April to the beginning of May 2001 at Shunyi county, north of Beijing, as the research object, to study its BRDF changing rule with the changing time. In the process we compute the structural scattering index (SSI) by inverting the semiempirical linear kernel-driven BRDF model, and analyze its relation with the leaf area index (LAI) of winter wheat. The results show that there is a clear linear relationship between SSI and LAI of winter wheat. So SSI can well be used to reflect the seasonal BRDF changing rule of winter wheat.


international geoscience and remote sensing symposium | 2002

The BRDF model and analysis of hotspot effect of row crops

Menxin Wu; Qijiang Zhu; Jindi Wang; Yueqin Xiang; Yanmin Shuai; Shihao Tang

According to the Li-Strahler model, a BRDF model of row structure with gap, in which three components and six paths are taken into account, is established through analysis of the process of radiation transfer. The model is verified by the data obtained in the Satellite-Airborne-Ground Synchronous Quantitative Remote Sensing Experiment implemented from March 29 to May 10 in 2001, in Shunyi, Beijing, China. As is shown, the model can demonstrate effectively the reflection of crops with row structure.


international geoscience and remote sensing symposium | 2003

Practice of quantitative remote sensing model library based on COM technique

Xin Ding; Lihong Su; Shihao Tang; Jindi Wang; Menxin Wu

With the development of remote sensing, new models are available continuously. In order to extend the practicability of the model library, the authors introduce component object model (COM) technique. COM is a software architecture that allows the components made by different software vendors to be combined into a variety of applications. Remote sensing model library is composed of three parts, common objects, model objects and accessorial objects. Common objects include input/output procedure, solar angle calculating procedure in bi-directional reflectance, and metadata about all the models. Model objects include the models contributing to quantitative remote sensing applications, which comprise system models, simulant models and application models. Accessorial objects include prior knowledge, measurement data and image data. In the article, the executable project is validated with an instance in the end. The spectrum of typical land surface observed in nadir viewing direction is simulated in pixel scale. In this process, crop model, PROSPECT model and SAIL model are used to calculate the spectrum character of the pixel. Crop model is used to simulate leaf area index, and PROSPECT model is to simulate reflectance and transmission of leaves. The final result is calculated with SAIL model. We simulated the spectrum of winter wheat in given growing season. In the work, each model and each common object are designed to be components. The model library based on COM technique adapts to the progress and is propitious to be expanded and modified.


international geoscience and remote sensing symposium | 2003

Study on the quality of hyperspectral vegetation data observed in the field

Yanmin Shuai; Qijiang Zhu; Shihao Tang; Shuhong Liu; Jiacong Hu

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.


international geoscience and remote sensing symposium | 2002

Uncertainty of remote sensing model inversion and a synthetical inverse scenario

Shihao Tang; Qijiang Zhu; Xiaowen Li; Jindi Wang; Guangjian Yan

The sources of the inverse error of remote sensing physical models are analyzed and divided into two groups. From the point of view of controlling these errors, a synthetic inverse scenario is put forward. A case study using simulated data shows that this scenario is better than ordinary methods in robustness and global convergency.

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Qijiang Zhu

Beijing Normal University

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Guangjian Yan

Beijing Normal University

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Jindi Wang

Beijing Normal University

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Menxin Wu

Beijing Normal University

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Yuyu Zhou

Iowa State University

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Donghui Xie

Chinese Academy of Sciences

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Rui Sun

Beijing Normal University

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Xiaodong Zhou

Beijing Normal University

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Xiaowen Li

Beijing Normal University

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