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United States Department of Agriculture

Agricultural Research Service


item Farid, A.
item Rautenkranz, D.
item Goodrich, David - Dave
item Marsh, S.
item Sorooshian, S.

Submitted to: Canadian Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/19/2006
Publication Date: 3/10/2006
Citation: Farid, A., Rautenkranz, D., Goodrich, D.C., Marsh, S.E., Sorooshian, S. 2006. Riparian vegetation classification from airborne laser scanning data with an emphasis on cottonwood trees. Canadian J. of Remote Sensing. 32(1): 15-19.

Interpretive Summary: Arid and semi-arid regions account for approximately one-third of the land mass of earth. These regions are experiencing continued pressure from population growth in many parts of the world. Water is a critical resource in these regions and is often in short supply. To maintain the economic, social, and ecological viability of these areas it is essential that decision makers and resource managers have a solid scientific basis on which to make watershed based decisions including management of riparian vegetation. Riparian trees use water in proportion to their size, and are especially large users of water in flood plains along rivers in semi-arid environments. It's difficult to measure tree size using traditional ground-based techniques to determine their water use. For this reason new techniques that are more accurate and efficient need to be developed. This study demonstrated that a lidar (light detecting and ranging) system mounted in an airplane can accurately measure features of forest canopies that are related to water use. This includes not only the size, shape and density of leaves on the trees but also the intensity of the light returned from the lidar pulse bouncing back from the tree canopy. Such information is not readily available from other remote sensing methods and was used in this study to classify riparian cottonwood trees of different sizes and ages. The results illustrate the potential of airborne lidar data to differentiate different age classes of cottonwood trees for riparian areas quickly and quantitatively. This information can be used in many forestry, ecological and hydrologic applications that will improve management of hydrologic resources and ecological models.

Technical Abstract: The high point density of airborne laser mapping systems enables achieving a detailed description of geographic objects and of the terrain. Growing experience indicates, however, that extracting useful information directly from the data can be difficult. In this study, small-footprint lidar data were used to differentiate between young, mature, and old cottonwood trees in the San Pedro River Basin near Benson, Arizona, USA. The lidar data were acquired in June 2003, using Optech’s 1233 ALTM (Optech Incorporated, Toronto, Canada), during flyovers conducted at an altitude of 750 m. The lidar data was pre-processed to create a two-band image of the study site: a high accuracy canopy altitude model band and a near-infrared intensity band. These lidar-derived images provided the basis for supervised classification of cottonwood age categories, using a maximum likelihood algorithm. The results of classification illustrate the potential of airborne lidar data to differentiate age classes of cottonwood trees for riparian areas quickly and accurately.

Last Modified: 06/21/2017
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