Entity

Time filter

Source Type


Zhang Z.,Chinese Academy of Sciences | He G.,Chinese Academy of Sciences | Jiang H.,Spatial Information Research Center
Journal of Theoretical and Applied Information Technology | Year: 2013

Leaf area index (LAI) is an important surface biophysical parameter as an input to many process-oriented ecosystem models. Remote sensing technology provides a practical way to estimate LAI at a large spatial scale, and hence, considerable effort has been expended in developing LAI estimation models from remotely sensed imagery. LAI estimation models were usually formulated using multi-spectral satellite imagery, and hyper-spectral satellite data was scarcely used because it is very difficult to acquire the needed hyper-spectral satellite imagery. Compared to multi-spectral imagery, hyper-spectral imagery has its advantage in LAI retrieving because hyper-spectral data can be used to extract red edge optical parameters, which provides a new way to estimate LAI. In this paper, EO-1 hyperion hyper-spectral imagery was used to estimate LAI in the forested area of Yongan county, Fujian province, located in southeast of China. Two primary red edge optical parameters, red edge position (REP) and red well position (RWP), were extracted from hyperion data; and LAI estimation models for broad-leaf forest in Fujian province were formulated. © 2005 - 2013 JATIT & LLS. All rights reserved. Source


Zhang Z.,Chinese Academy of Sciences | He G.,Chinese Academy of Sciences | Wang X.,Spatial Information Research Center | Jiang H.,Spatial Information Research Center
34th International Symposium on Remote Sensing of Environment - The GEOSS Era: Towards Operational Environmental Monitoring | Year: 2011

Leaf area index (LAI) is a key parameter in carbon cycling models of forest ecosystem and acquiring LAI with a high spatial and temporal accuracy is of great importance to improve the performance of carbon cycling models. Remote sensing technology provides a promising and practical way to estimate LAI at a large area with high temporal coverage, and hence, considerable effort has been expended in developing LAI retrieval models from remotely sensed imagery. In the past two decades, much work has been done for LAI estimation in boreal forest based on remote sensing imagery. However, such studies performed in Asian subtropical monsoon climate region are relatively less. Therefore, this study has been conducted to retrieve LAI in the forested area of Yongan county, Fujian province, located in southeast of China, which has a typical subtropical monsoon climate. IPS P6 LISS 3 imagery acquired on 24 March 2008 in Yongan county was employed in this study. Firstly, a practical atmospheric correction algorithm combining MODIS imagery with conventional Dark Object Subtraction (DOS) technique was used in the atmospheric correction procedure. Then various vegetation indices (NDVI, SR, RSR, etc.) were formulated with the atmospherically corrected reflectance. Finally, LAI retrieval models for three major forest types (pinus, China fir, and broad-leaf forest) in Fujian province were determined through a comparative analysis. Source


Zhang Z.,Chinese Academy of Sciences | He G.,Chinese Academy of Sciences | Wang X.,Spatial Information Research Center | Jiang H.,Spatial Information Research Center
International Journal of Remote Sensing | Year: 2011

Leaf area index (LAI) is an important surface biophysical parameter as an input to many process-oriented ecosystem models. Much work has been reported in the literature on LAI estimation in boreal forests using remotely sensed imagery. However, few if any explicit LAI retrieval studies on bamboo forests in Asian subtropical monsoon-climate regions based on remote sensing technology have been performed. Our goal is to carry out a comparative study on the LAI estimation methods of bamboo forest in Fujian province, China, based on IRS P6 LISS 3 imagery. Both the traditional empirical-statistical approach and the newly proposed normalized distance (ND) method were employed in this study, and a total of 18 modelling parameters were regressed against ground-based LAI measurements. The results show that simple ratio (SR) is the best predictor for LAI estimation in this study area, with the highest R2 (coefficient of determination) value of 0.68; modified simple ratio (MSR) and normalized difference vegetation index (NDVI) ranked second and third, respectively. The good performance of these three vegetation indices (VIs) can be explained by the ratioing principle. The overall good modelling performance of the ND method in our study area also indicates it is a promising method. © 2011 Taylor & Francis. Source


Zhang Z.,Chinese Academy of Sciences | Zhang Z.,CAS Institute of Remote Sensing Applications | Zhang Z.,University of Chinese Academy of Sciences | He G.,Chinese Academy of Sciences | Wang X.,Spatial Information Research Center
International Journal of Remote Sensing | Year: 2010

Atmospheric correction is of great importance in quantitative remote sensing studies. However, many of the atmospheric correction algorithms proposed in the literature are not easily applicable in real cases. In order to develop a practical atmospheric correction algorithm, Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is employed to obtain aerosol optical depth and the total atmospheric water vapour content, which are used to compute the transmittances in a dark object subtraction (DOS) model. An improved DOS atmospheric correction method combining MODIS imagery with the conventional DOS technique is proposed. A Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image acquired on 21 October 2001 in Wuyi mountain, south-eastern China, and a CBERS 02 CCD image acquired on 24 August 2005 in Dunhuang, north-western China, were atmospherically corrected with this new approach. Various tests are performed, from spectral signature analysis, to vegetation index spatial profile and image information content comparisons, and by direct comparison with ground-measured reflectances, to evaluate the performance of the improved DOS model. The evaluation shows it can generally achieve a good atmospheric correction result. © 2010 Taylor & Francis. Source


Wu Q.,Spatial Information Research Center | Wu Q.,Fuzhou University | Zheng X.,Spatial Information Research Center | Zheng X.,Fuzhou University | And 2 more authors.
2nd International Conference on Information Science and Engineering, ICISE2010 - Proceedings | Year: 2010

The service matching algorithm is key to service discovery. In this paper, according to the four-level matching algorithm, the geospatial services discovery algorithm is also designed, which uses hierarchy matching, and uses ontology classification tree in I/O matching to converse the ontology similarities into the distance between nodes. It is proved that the new algorithm not only differentiates the geospatial services matching level, but also differentiates the similarities between the identical matching level, and can meet the geospatial services discovery well. © 2010 IEEE. Source

Discover hidden collaborations