Abstract
This paper presents new methods for the automatic mapping of vegetation from airborne lidar data. The methods are developed specifically for orienteering maps, which are detailed maps in scale 1:15,000 or 1:10,000 of forested areas. However, the methods may be modified to be used for automatic mapping of vegetation for national topographic map series in various scales, e.g., 1:25,000 or 1:50,000. We introduce the normalized difference vegetation density (NDVD) as an indicator of vegetation density in airborne lidar data. A modified version of NDVD is used for reduced runability mapping. By comparing pixel-by-pixel the automatic mapping with the manual survey in four different forest areas in Oslo, Norway, the correct classification rate varies from 71% to 75%. However, close investigation reveals that the automatic mapping is better than manual survey for open areas. On the other hand, the automatic mapping of reduced runability remains a difficult problem. In many cases, the automatic method is able to identify the major areas of reduced runability, while in other areas the correspondence is low between the automatic mapping and manual survey of reduced runability. Still, the automatic method may be used to quickly produce an initial mapping of reduced runability, or in the production of orienteering maps in remote areas where a full manual survey cannot be afforded.
Highlights
Airborne lidar data, called airborne laser scanning (ALS) data, are being collected and used for a number of purposes
Smith et al (2009) compared estimates of forest canopy cover from multispectral optical data, from lidar data, and field measurements, and found that the lidar estimates were reasonably close to the field measurements, and much more accurate than the multispectral optical estimates
In order to measure the density of the vegetation, we propose a quantity that we call the normalized difference vegetation density (NDVD): NDVD
Summary
Called airborne laser scanning (ALS) data, are being collected and used for a number of purposes. One major application area is forestry, and methods exist for estimating mean tree height and timber volume (e.g., Næsset 1997a, 1997b), and detection of individual trees (e.g., Kwak et al 2007; Ene et al 2012). The methods are based on the fact that one individual laser pulse may result in multiple returns, typically several returns from different parts of a tree canopy, and if the tree vegetation is not too dense, one return from the ground. For each vegetation point, the height above the ground may be estimated directly as the difference. Smreček and Michňová (2014) detected individual trees and groups of trees automatically in lidar data with > 20 points/m2 For each vegetation point, the height above the ground may be estimated directly as the difference. Smith et al (2009) compared estimates of forest canopy cover from multispectral optical data, from lidar data, and field measurements, and found that the lidar estimates were reasonably close to the field measurements, and much more accurate than the multispectral optical estimates. Smreček and Michňová (2014) detected individual trees and groups of trees automatically in lidar data with > 20 points/m2
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