Study on land cover classification for China with NDVI/T<sub>s<sub> space

Cheng-Feng Luo; Chang-Yao Wang; Zheng Niu

doi:10.1117/12.565075

26 October 2004 Study on land cover classification for China with NDVI/T_s space

Cheng-Feng Luo, Chang-Yao Wang, Zheng Niu

Proceedings Volume 5568, Remote Sensing for Agriculture, Ecosystems, and Hydrology VI; (2004) https://doi.org/10.1117/12.565075
Event: Remote Sensing, 2004, Maspalomas, Canary Islands, Spain

Abstract

In this paper Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (Ts) were combined to indicate different land-cover types based on the fact that the biome has a similar seasonal trajectory in the NDVI/Ts space. Normalized Temperature-Vegetation Angel and Norm (NTVA &TVN) based on NDVI/Ts space, were put forward as input parameters for regional-scale land-cover classification. Remote sensing data used in this study are MODIS data products: MOD13 and MOD09, firstly the monthly Ts and NDVI were produced by the maximum value composite; secondly the monthly NDVI/Ts spaces were created; then NTVA &TVN were calculated for each of the 12 months. The monthly NTVA, TVN, NDVI, Ts were dealt with Principal Component Analysis (PCA) method, and their first three principal components were assembled to four groups as input parameters for classification. Remotely sensed land-cover system for China Based on land-ecosystem and maximum likelihood classifier were adopted to classify with four different input parameters. The classification accuracy for different inputs were compared and analyzed, and the results showed that combination of NDVI and Ts can indicate different land-cover types well; as input parameters, NTVA and TVN are applicable to macro land-cover classification, and can work well to improve classification accuracy at coarse spatial scales without other accessorial data.

Citation Download Citation

Cheng-Feng Luo, Chang-Yao Wang, and Zheng Niu "Study on land cover classification for China with NDVI/T_s space", Proc. SPIE 5568, Remote Sensing for Agriculture, Ecosystems, and Hydrology VI, (26 October 2004); https://doi.org/10.1117/12.565075

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Vegetation

Remote sensing

MODIS

Principal component analysis

Soil science

Ecosystems

Composites

Show All Keywords

Keywords/Phrases

Search In:

Publication Years