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ARS Home » Pacific West Area » Tucson, Arizona » SWRC » Research » Publications at this Location » Publication #131041
Title: LEAF AREA INDEX ESTIMATES USING REMOTELY SENSED DATA AND BRDF MODELS IN A SEMIARID REGION 1415

Author
item QI, J. - UNIV. OF MICH.
item KERR, Y. - CESBIO
item Moran, Mary
item Weltz, Mark
item HUETE, A. - UNIV. OF ARIZONA
item SOROOSHIAN, S. - UNIV. OF ARIZ.
item BRYANT, R. - UNIV. OF ARIZ.

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/17/2000
Publication Date: 2/17/2000
Citation: Qi, J., Kerr, Y., Moran, M.S., Weltz, M.A., Huete, A.R., Sorooshian, S., Bryant, R. 2000. Leaf area index estimates using remotely sensed data and brdf models in a semiarid region. Rem. Sens. Environ., 73:18-30.

Interpretive Summary: Resource managers often make decisions based largely on the amount and distribution of vegetation within their management region, and the changes in this vegetation over time. Accurate estimates of vegetation status and dynamics are rarely available. In this study, a new approach was proposed to use satellite images of the region combined with vegetation growth simulation models to map regional vegetation amount (termed leaf area index) and change in vegetation over time. The approach was designed to be operational so that users could acquire the maps with little expertise and minimal investment. The approach was tested over a large area in Africa and results compared well with conventional ground-based measurements. This provides a vegetation mapping tool for resource managers that is simple, accurate and requires little knowledge of the study area and few ground measurements.

Technical Abstract: The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. Using remotely sensed imagery, leaf area index (LAI) can be derived from spectral vegetation indices (SVI) from radiometric measurements. The major limitation of this empirical approach is that there is no single LAI-SVI equation that can be applied to remote-sensing images of different surface types. In this study, we present a strategy that combines bi-directional reflectance distribution (BRDF) models and conventional LAI-SVI approaches to circumvent these limitations. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements.