Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors
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 direc...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2009-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/9/4/2719/ |
| _version_ | 1811278171768619008 |
|---|---|
| author | Guang Zheng L. Monika Moskal |
| author_facet | Guang Zheng L. Monika Moskal |
| author_sort | Guang Zheng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T00:30:59Z |
| format | Article |
| id | doaj.art-631adcc0ccd0462abb99b234f272d25a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:30:59Z |
| publishDate | 2009-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-631adcc0ccd0462abb99b234f272d25a2022-12-22T03:10:29ZengMDPI AGSensors1424-82202009-04-01942719274510.3390/s90402719Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and SensorsGuang ZhengL. Monika MoskalThe 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.http://www.mdpi.com/1424-8220/9/4/2719/Leaf area index (LAI)remote sensinglight detection and ranging (LiDAR)gap fractiongap sizeterrestrial LiDAR |
| spellingShingle | Guang Zheng L. Monika Moskal Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors Sensors Leaf area index (LAI) remote sensing light detection and ranging (LiDAR) gap fraction gap size terrestrial LiDAR |
| title | Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors |
| title_full | Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors |
| title_fullStr | Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors |
| title_full_unstemmed | Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors |
| title_short | Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors |
| title_sort | retrieving leaf area index lai using remote sensing theories methods and sensors |
| topic | Leaf area index (LAI) remote sensing light detection and ranging (LiDAR) gap fraction gap size terrestrial LiDAR |
| url | http://www.mdpi.com/1424-8220/9/4/2719/ |
| work_keys_str_mv | AT guangzheng retrievingleafareaindexlaiusingremotesensingtheoriesmethodsandsensors AT lmonikamoskal retrievingleafareaindexlaiusingremotesensingtheoriesmethodsandsensors |