Open AccessThis article is
- freely available
Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors
Remote Sensing and Geospatial Analysis Laboratory and Precision Forestry Cooperative, College of Forest Resources, University of Washington, Box 352100, Seattle, Washington, 98195-2100, USA
* Author to whom correspondence should be addressed.
Received: 25 November 2008; in revised form: 3 April 2009 / Accepted: 17 April 2009 / Published: 17 April 2009
Abstract: The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.
Keywords: Leaf area index (LAI); remote sensing; light detection and ranging (LiDAR); gap fraction; gap size; terrestrial LiDAR
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
MDPI and ACS Style
Zheng, G.; Moskal, L.M. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors 2009, 9, 2719-2745.
Zheng G, Moskal LM. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors. 2009; 9(4):2719-2745.
Zheng, Guang; Moskal, L. Monika. 2009. "Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors." Sensors 9, no. 4: 2719-2745.