Forest understory characterization at regional levels with satellite data: a conceptual approach

Mario R. Caetano; Alfredo R. Huete; Jose M. Cardoso Pereira; Wenge Ni

doi:10.1117/12.332757

11 December 1998 Forest understory characterization at regional levels with satellite data: a conceptual approach

Mario R. Caetano, Alfredo R. Huete, Jose M. Cardoso Pereira, Wenge Ni

Proceedings Volume 3499, Remote Sensing for Agriculture, Ecosystems, and Hydrology; (1998) https://doi.org/10.1117/12.332757
Event: Remote Sensing, 1998, Barcelona, Spain

Abstract

In this study we propose a methodology for understory characterization based on the isolation of the forest reflectance that is exclusively due to the background. This is achieved by extracting the contribution of the forest canopy from the overall forest reflectance using forward modeling based on forest reflectance models and existing data on forest structure. Then, the understory can be characterized by applying any methodology developed for open-sky shrub lands, such as vegetation indices or linear mixture models. The developed methodology was successfully applied to simulated data of maritime pine forests with different biophysical stand characteristics and with different understory characteristics.

Citation Download Citation

Mario R. Caetano, Alfredo R. Huete, Jose M. Cardoso Pereira, and Wenge Ni "Forest understory characterization at regional levels with satellite data: a conceptual approach", Proc. SPIE 3499, Remote Sensing for Agriculture, Ecosystems, and Hydrology, (11 December 1998); https://doi.org/10.1117/12.332757

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