The novel method for LAI inversion using Lidar and hyperspectral data

For inversion of Leaf area index (LAI) in large scale, it is of great significance to integrate space-borne Lidar and optical remote sensing data effectively. In order to improve the estimation precision of leaf area index, an analyzing method based on Lidar and hyperspectral data was proposed. Thro...

Full description

Saved in:
Bibliographic Details
Published in:Functional Materials
Date:2017
Main Authors: Zuowei Huang, Feng Liu, Guangwei Hu
Format: Article
Language:English
Published: НТК «Інститут монокристалів» НАН України 2017
Subjects:
Online Access:https://nasplib.isofts.kiev.ua/handle/123456789/136804
Tags: Add Tag
No Tags, Be the first to tag this record!
Journal Title:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Cite this:The novel method for LAI inversion using Lidar and hyperspectral data / Zuowei Huang, Feng Liu, Guangwei Hu // Functional Materials. — 2017. — Т. 24, № 3. — С. 442-450. — Бібліогр.: 23 назв. — англ.

Institution

Digital Library of Periodicals of National Academy of Sciences of Ukraine
Description
Summary:For inversion of Leaf area index (LAI) in large scale, it is of great significance to integrate space-borne Lidar and optical remote sensing data effectively. In order to improve the estimation precision of leaf area index, an analyzing method based on Lidar and hyperspectral data was proposed. Through the processing of Lidar (Light Identification Detection and Ranging) and hyperspectral data, the LAI estimation model was established based on statistic analysis method in the study area. The results showed that the Lidar and hyperspectral data joint inversion model which considers the optical remote sensing of biophysical parameters can provide good estimates of LAI inversion, shows high accuracy (R2=0.8948, RMSE=0.2120),which reveals the great potential to enhance the accuracy of LAI estimation by using Lidar and hyperspectral data.
ISSN:1027-5495