These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: [The study of LAI estimation using a new vegetation index based on CHRIS data].
    Author: Wang LJ, Niu Z, Hou XH, Gao S.
    Journal: Guang Pu Xue Yu Guang Pu Fen Xi; 2013 Apr; 33(4):1082-6. PubMed ID: 23841433.
    Abstract:
    Leaf area index (LAI) is an important structural parameter of vegetation canopy, the correct estimation of which has been the focus in the remote sensing community. As a kind of hyperspectral and multi-angle remote sensing data with higher resolution (17 m), PROBA/CHRIS has significant application value in LAI inversion. In the present paper, the analytical two-layer canopy reflectance model (ACRM) was used to simulate a series of reflectances with different LAI values. Based on this, a new vegetation index was built and successfully applied to LAI inversion of PROBA/CHRIS image data. Our results indicated that: compared with the spectral index NDVI and multi-angle index HDS, the new index could make better use of spectrum and multi-angle messages and have a better correlation with LAI of the study area; moreover, the correlation coefficient R2 reached up to 0.734 7. And in order to obtain the figure of LAI distribution of the study area, we used the optimal fit equation between LAI and HDVI to estimate LAI, and the accuracy of the RMSE was 0.619 8.
    [Abstract] [Full Text] [Related] [New Search]