Lidar Remote Sensing for Ecosystem Studies

Abstract

Articlesface is repeatedly measured along a transect, the result is an outline of both the ground surface and any vegetation obscuring it. Even in areas with high vegetation cover, where most measurements will be returned from plant canopies, some measurements will be returned from the underlying ground surface, resulting in a highly accurate map of canopy height.Key differences among lidar sensors are related to the laser's wavelength, power, pulse duration and repetition rate, beam size and divergence angle, the specifics of the scanning mechanism (if any), and the information recorded for each reflected pulse. Lasers for terrestrial applications generally have wavelengths in the range of 900-1064 nanometers, where vegetation reflectance is high. In the visible wavelengths, vegetation absorbance is high and only a small amount of energy would be returned to the sensor. One drawback of working in this range of wavelengths is absorption by clouds, which impedes the use of these devices during overcast conditions. Bathymetric lidar systems (used to measure elevations under shallow water bodies) make use of wavelengths near 532 nm for better penetration of water. Early lidar sensors were profiling systems, recording observations along a single narrow transect. Later systems operate in a scanning mode, in which the orientation of the laser illumination and receiver field of view is directed from side to side by a rotating mirror, or mirrors, so that as the plane (or other platform) moves forward, the sampled points fall across a wide band or swath, which can be gridded into an image.The power of the laser and size of the receiver aperture determine the maximum flying height, which limits the width of the swath that can be collected in one pass (Wehr and Lohr 1999). The intensity or power of the return signal depends on several factors: the total power of the transmitted pulse, the fraction of the laser pulse that is intercepted by a surface, the reflectance of the intercepted surface at the laser's wavelength, and the fraction of reflected illumination that travels in the direction of the sensor. The laser pulse returned after intercepting a morphologically complex surface, such as a vegetation canopy, will be a complex combination of energy returned from surfaces at numerous distances, the distant surfaces represented later in the reflected signal. The type of information collected from this return signal distinguishes two broad categories of sensors. Discrete-return lidar devices measure either one (single-return systems) or a small number (multiple-return systems) of heights by identifying, in the return signal, major peaks that represent discrete objects in the path of the laser illumination. The distance corresponding to the time elapsed before the leading edge of the peak(s), and some-

Abrupt Change in Forest Height along a Tropical Elevation Gradient Detected Using Airborne Lidar

“…Airborne light detection and ranging (LiDAR) is the state-of-the-art method for observing three-dimensional structural properties of forests at landscape spatial scales [8]. Of all vegetation metrics that can be derived from LiDAR datasets, forest height can be derived most consistently across sensor systems [9].…”

Characterizing 3D vegetation structure from space: Mission requirements

“…Numerous studies 886 (Dubayah et al 2000, Lefsky et al 2002, Drake et al 2003, Hyde et al 2005) have 887 validated this approach. Additionally it is the foundation of the Tandem-L concept.…”

“…In addition to 908 canopy height, it has been shown that for lidar, other metrics are required for optimal 909 biomass estimation, such as HOME; these internal height quantiles should also be known 910 to about 10% relative to canopy height (Dubayah et al 2000, Lefsky et al 2002, Drake et 911 al. 2003, Hyde et al 2005 In certain regions of the world, especially in the tropics, forest biomass is known 916 to exceed 100 -200 Mg ha -1 .…”

Energy, water, and carbon fluxes in a loblolly pine stand: Results from uniform and gappy canopy models with comparisons to eddy flux data

“…However, we may not need to estimate all five parameters independently as crown radius is related to canopy height through allometry, and crown radius and stem density are constrained by canopy cover. Moreover, recent advances in remote sensing both with high spatial resolution optical sensors to extract LAI and tree crown size [Song and Dickinson, 2008;Song, 2007;Clark et al, 2004;Leckie et al, 2003] and with Lidar to extract canopy height [Lefsky et al, 2002;Sun et al, 2008] are making it possible to extract these canopy structural parameters as input to GCR.…”