Characterization of ICESat/GLAS waveforms over terrestrial ecosystems: Implications for vegetation mapping

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi: /2007jg000557, 2008 Characterization of ICESat/GLAS waveforms over terrestrial ecosystems: Implications for vegetation mapping Amy L. Neuenschwander, 1 Timothy J. Urban, 1 Roberto Gutierrez, 1 and Bob E. Schutz 1 Received 20 July 2007; revised 17 November 2007; accepted 31 December 2007; published 23 April [1] ICESat/GLAS laser altimetry data have become increasingly utilized for vegetation mapping and canopy characterization. Waveform shapes are dependent upon complex relationships between several factors on the illuminated surface including topography, brightness, clouds, satellite pointing, laser energy, footprint size, shape and orientation, and vegetation height and position within the footprint. However, the understanding of these factors is presently unclear. We first examine the simple case introduced into the GLAS waveform by laser retro-reflectors placed at White Sands, New Mexico, as a proxy for vegetation height detection. We observed that the 1/e 2 energy distribution was only an approximation and that strong reflectors contribute to the returned energy beyond the estimated parameters. The precise position of the corner cube reflector within each footprint coupled with laser pointing angle is an important factor on the estimated vegetation height. Next we examine the implications of vegetation structure and surface topography on the waveform shape and derived elevations, compared with data from an airborne lidar system at Freeman Ranch, Texas, which has been targeted by ICESat since Small-footprint waveforms were combined to synthesize the energy distribution within a GLAS footprint, and they compared well (>90% correlation) to the GLAS waveforms. The GLAS-estimated canopy heights compared well (m = 0.21 m) to the airborne lidar estimates during leaf-on conditions. However, GLAS-estimated ground elevations are biased by 1 m compared to airborne lidar in vegetated regions. In this landscape, the GLAS-energy ratio (canopy-to-ground energy) was a good indicator (R 2 = 0.74) of the amount of woody cover within the footprint. Citation: Neuenschwander, A. L., T. J. Urban, R. Gutierrez, and B. E. Schutz (2008), Characterization of ICESat/GLAS waveforms over terrestrial ecosystems: Implications for vegetation mapping, J. Geophys. Res., 113,, doi: /2007jg Introduction [2] NASA s Ice, Cloud, and land Elevation Satellite (ICESat) was launched 13 January 2003, carrying the Geoscience Laser Altimeter System (GLAS) payload. Its primary mission objective is to measure long-term polar ice changes. Additionally, ICESat obtains global measurements over all surface types including sea ice, land/vegetation, oceans, and the distribution of clouds and aerosols in the atmosphere. Laser degradation occurred more rapidly than predicted, and thus a modified mission scenario was developed whereby the satellite operates in two or three laser-on operation periods per year (called campaigns ) to satisfy the primary mission goals [Schutz et al., 2005]. [3] The utilization of ICESat/GLAS laser altimetry for mapping terrestrial properties and for the validation of Digital Elevation Models (DEM) is becoming more common. Elevations derived from ICESat are used for calibrating DEMs from the Shuttle Radar Topography 1 Center for Space Research, University of Texas at Austin, Austin, Texas, USA. Copyright 2008 by the American Geophysical Union /08/2007JG Mission (STRM) [Zwally et al., 2002; Carabajal and Harding, 2005; Carabajal and Harding, 2006]. Large footprint airborne lidar systems such as SLICER and LVIS have been used to estimate canopy height and biomass [Harding et al., 2001] and most recently canopy height estimation methods have been applied to the space-based lidar system ICESat [Lefsky et al., 2005, 2007]. One advantage of ICESat over airborne systems is near global coverage of waveform lidar data. For flat, non-vegetated surfaces the vertical accuracy of ICESat has been measured at better than 10 cm and with a vertical precision of 2 3 cm [cf. Fricker et al., 2005; Martin et al., 2005; Magruder et al., 2007; Urban et al., 2008]. However, uncertainties arise in the accuracy of elevations computed for vegetated surfaces. [4] Magruder et al. [2007] examined an area of the White Sands Missile Range (WSMR), where ICESat/GLAS calibration and validation experiments are conducted. We revisit this site, which was surveyed by conventional airborne first-and-last-return lidar, and examine simple proxy trees (laser retro-reflectors mounted on an array of poles, see next section) in this otherwise flat, unvegetated area. With no discernable vertical or horizontal structure, our proxy trees introduce additional peaks to the waveform solely according to their height. The resulting eleva- 1of18

2 Figure 1. Corner cube reflector array at White Sands. The elevations above the TOPEX reference ellipsoid (in meters) of the surface as mapped by an airborne lidar survey conducted in 2003 are shown as the backdrop. ICESat routinely targets the center of the array 4 times per laser campaign. tion variations are chiefly due to laser incident angle (offnadir pointing angle plus surface slope) (here essentially zero slope) and proxy tree location within the footprint relative to the laser spot centroid or peak of the GLAS energy illumination. Note that the surface slope at White Sands is essentially zero and therefore the laser incident angle is the spacecraft off-nadir pointing angle. [5] Real vegetation structure introduces more complexity into the investigation of waveforms and derived elevations. Relatively unknown contributions arise from real vegetation structure (height and leaf density) and distribution within the footprint. In addition, the ambiguity of laser incident angle on the returned waveform has not been fully investigated. Progress in identifying these uncertainties has been slow, primarily due to insufficient field data available. One biome where very little research has been completed is in non-homogeneous woodland savanna ecosystems, which have a lack of both field data and waveform lidar collection. For this study, we target a non-homogeneous woodland savanna near San Marcos, Texas. The test site was selected from special targeting by ICESat. Small-footprint airborne waveform lidar has been acquired along the ICESat ground tracks within the test area and will serve as the ground truth for comparison. 2. Study Site 2.1. White Sands Missile Range [6] The White Sands Missile Range area in New Mexico is used as a precision calibration and validation site for ICESat, with experiments operated and maintained by the University of Texas at Austin Center for Space Research (UTCSR). See Magruder et al. [2007] for a detailed description of several experiments. A target site (experiment array) for ICESat within the Space Harbor area was selected ( N, W), and ICESat routinely points to this site 4 or more times per campaign, and more than 50 times since launch, with necessary off-nadir (laser incident) angles from 0.3 (nominal) to just over 5. The topography of the ICESat calibration array is flat (less than 40 cm vertical relief) and there is sparse vegetation in the immediate vicinity of the array. Within the array, twenty-five corner cube reflectors (laser retro-reflectors) were placed on top of poles of various heights. The poles are grouped in columns of 1.5, 3, 4.5, and 6 m heights to aid in the determination of the cross-track (longitudinal) footprint position. Each corner cube has a diameter of 13 mm, and is affixed to the top of a 1-inch diameter PVC cap placed on top of each pole. The array, shown in Figure 1, is arranged such that 45 m exists between each approximate east-west (cross-track) oriented row and 58 m exists between each north-south (along-track) oriented column. This size and arrangement ensures that at least one ICESat footprint falls within the corner cube reflector array during each pass (assuming accurate pointing of the spacecraft and no cloud obscuration of the laser). A discrete return lidar survey covering an addition 30 km 2 beyond the corner cube array was flown in 2003 by UTCSR Table 1. ICESat/GLAS Campaign Parameters Over Freeman Ranch, Texas a GLAS Parameters L3b L3d L3e L3g DOY Date Mar 14 Oct 26 Feb 26 Oct 29 Footprint Major Axis (m) 68 m 54 m 53 m 53 m Eccentricity Off-nadir Pointing ( ) Look Direction Left Right Right Right Track Direction Descending Ascending Ascending Ascending a Pass averages are shown for the footprint and off-nadir parameters. 2of18

3 Figure 2. GLAS laser far field patterns (LPA images) observed during two ICESat laser campaigns over the corner cube array at White Sands Space Harbor. L2b major axis 95 m, eccentricity, 349 orientation angle. L3d major axis 53 m, eccentricity, and 328 orientation angle. These images have been smoothed, but calculations are computed on unsmoothed data. Each pixel in the LPA image represents an IFOV of 3.38 arcseconds. to provide a high resolution DEM of the White Sands area. The lidar data were projected into geographic coordinates with respect to the TOPEX ellipsoid and have vertical precision RMS of 7.4 cm Freeman Ranch, Texas [7] The Freeman Ranch (17 km 2 in size), a research site located near San Marcos, Texas, is operated by Texas State University and contains a mixture of rangeland and woodlands. Freeman Ranch ( N, 98 W) lies within the Balcones Canyonland subregion of the Edwards Plateau and this region is undergoing successional change from grassland to Oak (Quercus virginiana)-juniper (Juniperus ashei) dominated woodlands. The topography at Freeman Ranch has low hills dissected by small, typically dry, creeks. However, steep slopes do occur along the drainage channels. While the property does function as a working ranch, Freeman Ranch has been utilized as a field site for rangeland and ecosystem studies conducted by Texas State University, Texas A&M University, and the University of Texas at Austin. Three eddy covariance flux towers were installed at Freeman Ranch by the University of Texas and Texas A&M University to measure CO 2 and water vapor exchange of vegetation in areas experiencing woody encroachment due to decades of fire suppression policies and grazing practices. Airborne lidar was collected by the University of Texas in August 2005 in an effort to derive vegetation structure information for the characterization of carbon stocks over a local area, which will subsequently be used to understand ecosystem processes in non-homogeneous land cover. Several on-site field campaigns have also been conducted to measure the height distribution of vegetation structure. However, the primary focus of the research presented here is to investigate the ambiguities due to vegetation canopy structure, vegetation distribution, and laser incident angle on elevation data derived from spaceborne waveform lidar. Freeman Ranch was selected as a special Target of Opportunity (TOO) for ICESat in 2005, and the spacecraft has targeted the test area twice during each operational campaign since March The selection and analysis of a well-established research area such as Freeman Ranch also highlights the site s potential use as a calibration/validation site for future spaceborne lidar missions. 3. Data and Methods 3.1. ICESat/GLAS [8] The GLAS surface altimetry laser wavelength is 1064 nm (near-infrared) and GLAS operates continuously at 40 Hz during each campaign, yielding footprints with a centroid separation of 165 m from ICESat s 600 km, 94 inclination frozen orbit. The geolocation of the ICESat footprint is determined through the combination of precise orbit and attitude determination of the satellite [Schutz, 2002; Bae and Schutz, 2002; Rim and Schutz, 2002]. ICESat orbit precision is 2 cm radially (5 cm mission requirement) and fully calibrated data (data Release 428) have an attitude precision of 1.5 arcseconds (meets mission requirement) [Schutz et al., 2003]. The ICESat satellite is operated in a near-repeat ground track orbit to provide repeatable measurements throughout the mission, using 33 d of a 91-d repeat orbit used for each campaign [Schutz et al., 2005]. The spacecraft targets reference ground tracks in the polar regions and does not typically use active pointing elsewhere. However, the instrument can be pointed to targets of opportunity (TOO), such as our test area to examine selected features on the Earth s surface, up to 5 (or 50 km) from nadir. The pointing angle, depending on direction, either adds or subtracts to the local surface slope which determines the total laser incident angle; it is this total incident angle which affect the return waveforms. [9] The nominal GLAS footprint size was designed to be 65 m, whereas the computed sizes determined from instrumentation onboard the spacecraft are closer to about 110, 90 and 55 m for lasers 1, 2 and 3, respectively [Schutz et al., 2005]. Footprint size may vary significantly during 3of18

4 Figure 3. Representation of GLA14 Gaussian fitting (four pdfs shown in red) of raw GLAS waveform (shown in blue). the span of each campaign, over the course of one orbit, and even shot by shot, and so the footprint parameters reported on the data product are examined. Each laser campaign is given a designation based on the laser number and a sequential letter: the data examined in this paper span L2a (the first campaign from laser 2, Oct-Nov 2003) through L3g (the seventh campaign from laser 3, Oct-Nov 2006). [10] ICESat has pointed to Freeman Ranch as a TOO since March 2005 during one ascending and one descending pass each campaign. Acquisitions have been made from the second through eighth operational periods of laser 3: campaigns L3b (Feb Mar 2005), L3c (May Jun 2005), L3d (Oct Nov 2005), L3e (Feb Mar 2006), L3f (May Jun 2006), L3g (Oct Nov 2006), and L3h (Mar Apr 2007). At the time of writing, the fully calibrated solutions for the L3c, L3f, and L3h campaigns have yet to be released publicly; therefore data from those campaigns will not be included in this analysis. From the measurements collected to date from each of the two opportunities during each campaign, one pass has been clear and the other cloudy (obscuring surface returns). The orbit, target angle, and footprint parameters for each of the four clear passes over Freeman Ranch are listed in Table 1. The fully calibrated ICESat products with nominal pointing (0.3 pitched forward, to avoid specular reflections) have a mission specified accuracy of 1.5 arcseconds (1-sigma), equivalent to 4 m potential horizontal error and 2.25 cm potential vertical error for flat, non-vegetated surfaces [Urban et al., 2008]. For off-nadir targeted acquisitions, the estimated horizontal error is not affected by incident angle, as the potential error is only a function of the precision of satellite attitude knowledge. The potential vertical error, however, is a function of both satellite attitude precision and laser incident angles, thereby increasing the potential elevation errors at larger incident angles. [11] The GLAS far-field footprint parameters are estimated from ICESat s Laser Profile Array (LPA) image for each laser shot. The LPA images for two laser shots over the corner cube array at White Sands are shown in Figure 2. The LPA measures the far-field spatial pattern of the laser energy for each transmitted pulse using an pixel array imager, with a portion selected onboard for transmission to the ground. Each pixel in the LPA image represents an instantaneous field of view (IFOV) of 3.38 arcseconds: equivalent to 10 m on the ground. The major axis, eccentricity, and footprint azimuth angle are estimated for each LPA image assuming an elliptical distribution under a 1/e 2 Table 2. Laser Specifications for UT Small-Footprint System and ICESat/GLAS System Characteristics UT Small-Footprint ICESat/GLAS Laser Wavelength 1064 nm 1064 nm Nominal Footprint 10 20cm 70m Diameter Laser Frequency 25 khz 40 Hz Laser Pulse Width 10 ns 6 ns (nominal) Laser Field of View 1 mrad or mrad 0.2 mrad Beam Divergence (1/e) (1/e 2 ) Waveform Pulse Sampling Rate 1 ns 5/1 ns (600 records/ 400 records) Return Pulse Record Length of18

5 Figure 4. Elevation differences between GLA06 and GLA14 elevations and a 2003 airborne lidar survey as a function of look angle at White Sands. Negative pointing angles indicate the spacecraft is looking to the left, positive pointing angles indicate the spacecraft is looking to the right. energy distribution assuming an altitude of 600 km and no ground slope in the calculations [Bae and Schutz, 2002]. These values are averaged at 1 Hz and reported in the ICESat elevation products; however, the 1 Hz reported laser footprint size does not take into account the variability of the 40 Hz LPA images. The main cause of the variations in the footprint sizes of the different lasers is due to the initial GLAS hardware fabrication (laser divergence). Beyond this, foot- Figure 5. Depiction of waveform separability dependent upon the position of a corner cube reflector ( proxy tree ) within the ICESat/GLAS footprint with off-nadir pointing: (a) the corner cube placed at the leading edge of the footprint results in an increase of Dt and therefore the apparent height of the reflector, (b) the corner cube placed in the center of the footprint results in a correct Dt between the reflector elevation and ground elevation, and (c) the corner cube placed at the trailing edge of the footprint decreases the Dt and results in the reflector waveform convolved with the ground return waveform. 5of18

6 Figure 6. (a) Location of ICESat energy distribution (1/e 2 ) of shot L2b-075 on the calibration array at White Sands, NM. (b) Return waveform of L2b-075 from calibration array. Estimated height from waveforms is 5.99m. The ground track for this descending pass is located to the west of the array and the laser is pointing 0.89 to the east (looking left). Laser centroid is m from the closest 6 m corner cube pole. Arrow indicates a narrow ground pulse. 6of18

7 Figure 7. (a) Location of ICESat energy distribution (1/e 2 ) of shot L3d-296 on the calibration array at White Sands, NM. (b) Return waveform of L3d-296 from calibration array. Estimated height from waveforms is 5.24 m. The ground track for this descending pass is located to the east of the array and the laser is pointing 4.4 to the west (looking right). Laser centroid is m from the closest 6 m corner cube pole. Arrow indicates a broadening of the ground pulse due to off-nadir pointing. 7of18

8 Figure 8. Differences in estimated ground height from ICESat/GLAS and airborne lidar as a function of woody cover within each ICESat footprint. print characteristics may change due to thermal conditions, laser energy output, spacecraft altitude and other effects. [12] ICESat/GLAS surface elevations are reported with respect to the TOPEX reference ellipsoid, and small-footprint airborne LIDAR elevations are converted to the TOPEX ellipsoid for comparison. The TOPEX ellipsoid is similar to the WGS-84 ellipsoid with the primary exception being a 60 cm difference in the semi-major axis and a small change Figure 9. Differences in estimated vegetation height from ICESat/GLAS and airborne lidar as a function of woody cover within each ICESat footprint. 8of18

9 Table 3. Vegetation Height Differences at Freeman Ranch for Each ICESat Campaign ICESat Campaign Date AVG DH, m STD DH, m Sample Size, n L3b Mar L3d Oct L3e Feb L3g Oct in the flattening. The GLAS products used in this analysis include the GLA14 (land surface altimetry), GLA06 (maximum peak global elevation) and GLA01 (waveforms). The GLA14 land surface altimetry product is used to extract the vegetation height and ground height from multiple return waveforms. The last 392 records (392 ns) of each GLAS waveform (GLA01) were geolocated using the latitude and longitude provided in the GLA06 product by matching the time-tags reported in both products. Since the last 392 records of GLAS waveforms are sampled at a 1 ns rate, the GLAS energy can be equated to height above the TOPEX ellipsoid in 15 cm increments based on the speed of light. [13] The GLA14 land surface elevation products detail several parameters which describe the return waveform. The GLA14 product includes latitude, longitude, and height above the reference ellipsoid based upon the centroid of the returned energy pulse, sometimes referred to as a center of gravity. However, it is easy to see from Figure 3 that in the presence of vegetation, the reported elevation derived from the centroid will neither represent the ground nor the height of the vegetation. GLA14 contains up to six Gaussian distributions (mode, amplitude, and sigma) which are fit to characterize the shape of each total waveform by combining up to 6 distinct peaks after accounting for waveform compression schemes. GLA14 parameters are derived by implementing a first derivative on the return waveform, to determine the number of Gaussians fit to represent the raw waveform (e.g., four in Figure 3) and a second derivative on the return waveform to locate inflection points which are related to each Gaussian s width. If more than six reflections are detected, the six modes with the highest amplitude values are chosen. It has been observed in this research, however, that receiver noise within the trailing edge of the GLAS waveforms are often fit with a Gaussian distribution. To ensure that the correct Gaussian reflection is being used to represent the ground surface, we implement an independent analysis on the waveforms examined in this research. Here, the mode locations from an independent first derivative of the return waveform (GLA01) having an amplitude above a signal threshold are matched to the GLA14 first derivative modes, to ensure a true ground measurement for the last peak. [14] ICESat elevations derived from this quality-controlled GLA14 product will be compared against elevation data collected by small-footprint laser mapping mission at two locations: White Sands, New Mexico and Freeman Ranch, Texas. This study examines the implications of vegetation structure, vegetation position within the footprint, and surface topography on the ICESat waveform shape and derived elevations, compared with the smallfootprint data. [15] One issue associated with laser mapping over terrestrial surfaces is surface reflectance. The GLAS instrument is designed such that the detector gain will be actively adjusted to the intensity of the backscatter with an effective lag time of two to three shots. Over heterogeneous terrestrial surfaces where the reflectance and topography vary from shot to shot, the result is many saturated waveforms. Since Figure 10. Percent woody cover within each ICESat footprint as a function of the ratio of ICESat/ GLAS canopy/ground energy. A log trend was revealed for this savanna environment. 9of18

10 Figure 11. (a) Synthesized (blue dotted) waveform and GLAS (red) waveform and (b) vegetation heights (m) within GLAS footprint for L3d (October 2005) shot 178. Estimated 98.45% woody cover within the footprint. Correlation between two waveforms is the surface reflectivity and topography at White Sands is relatively homogenous over the target array, many of the waveforms are not saturated. However, a reflection from the nearest corner cube (usually the second main peak) is often high enough to saturate the waveform over the calibration array. An empirical saturation correction is provided on the GLAS data products to correct the elevation, but was designed for single peaks over polar ice [Abshire et al., 2005], and no such correction can restore the waveform profile. Hence, GLAS waveforms at White Sands that were saturated were excluded from this analysis. At Freeman Ranch, the presence of rolling topography, varying landcover and residential areas along the ground track also results in many saturated waveforms due to adjustment of the automatic gain controls. Of the potential 120 GLAS shots which lie within the ALTM survey area over Freeman Ranch from the L3b, L3d, L3e, and L3g campaigns, only 37 waveforms were not saturated and used in subsequent analysis Small-Footprint Waveform Lidar [16] The University of Texas at Austin (UT) owns and operates an Optech ALTM 1225 small-footprint lidar system with a full waveform digitizer. The integration of the waveform digitizer into a commercial lidar system greatly increases the amount of information that can be derived from each pulse. Commercial, topographic lidar systems typically record a limited number of discrete reflections (2 to 6 returns are common) which limits their ability to characterize complex reflecting surfaces such as vegetation. The small-footprint mode of the UT system provides detailed characterization of the Earth s surface in both the horizontal and vertical dimensions. The UT lidar system operates at 25 khz and the waveform sampling rate is at 1 ns or 15 cm in the vertical dimension. The waveform digitizer is integrated into the Optech system so that both full waveform and the conventional first and last returns are recorded for each transmitted laser pulse making direct comparison between the two systems possible [Gutierrez et al., 2005]. The specifications of the UT small-footprint laser system and the ICESat/GLAS laser system are provided in Table 2. [17] The small-footprint waveform lidar system was flown over Freeman Ranch on 12 August 2005 along the ascending and descending tracks of ICESat. Five passes were flown at an altitude of m above ground level (AGL) resulting in a footprint diameter of approximately cm. Geolocation of an individual smallfootprint waveform is determined by computing the range from the timing information and having precise knowledge of the aircraft position, attitude and laser scanner angle. Small-footprint lidar data were projected to UTM Zone 14 North coordinates with respect to the TOPEX ellipsoid. The ICESat/GLAS centroid locations were reprojected into 10 of 18

11 Figure 11. (continued) UTM coordinates such that the locations were consistent with the small-footprint lidar. The accuracy of the smallfootprint lidar data was determined via comparison to a kinematic GPS survey and had a vertical precision of 6.9 cm Synthesizing Small-Footprint Waveform Lidar to GLAS [18] Small-footprint waveforms were combined to synthesize the energy distribution within a GLAS footprint. The last return of each small-footprint waveform was geolocated to determine a X,Y,Z location in absolute space. The waveforms that fell within a GLAS footprint were averaged weighted based on their distance to the GLAS centroid following equations: 9 d 1 ¼ x sin a þ y cos a >= d 2 ¼ ysin a x cos a pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi w ¼ e 2 ðd 1=aÞ 2 2 þðd 2=bÞ >; where w is the raw weight, x is the distance of the smallfootprint waveform along the major axis (a) from the GLAS centroid, y is the distance of the small-footprint waveform along the minor axis (b) from the GLAS centroid, and a is footprint azimuth orientation angle. For analysis, each individual waveform was evaluated to determine if the ð1þ signal-to-noise ratio (SNR) was above a threshold of three. The weights of all waveforms meeting the SNR criteria were normalized such that their sum totaled to one. The first and last returns of each waveform were computed and the maximum of the first return elevations was recorded as the maximum canopy height within the GLAS footprint. To determine an estimate of the ground elevation within the GLAS footprint, the last return elevations less than the mean of all the last return elevations were averaged together based upon their distance to the GLAS centroid. Again, weights based upon the distance of each waveform to the GLAS centroid (equation (1)) were normalized such that their sum equals one. By averaging over only the points that fell below the mean of all the initial last return points, outliers due to trees and shrubs were removed and did not bias the ground elevation. With an initial height and standard deviation of the ground elevations, this process was repeated by selecting ground returns less than 1 sigma from the initial ground elevation estimate. Next a canopy height vector was constructed by computing the difference between the maximum canopy height and the weighted ground average. Both GLAS and the small footprint lidar have a 1 ns sampling rate, approximately 15 cm of vertical relief. To integrate each individual waveform into the canopy height vector, the first return was again geolocated 11 of 18

12 Figure 12. (a) Synthesized (blue dotted) waveform and GLAS (red) waveform and (b) vegetation heights (m) within GLAS footprint for L3g (October 2006) shot 172. Estimated 72.96% woody cover within the footprint. Correlation between two waveforms is with the Z value representing the height at which each waveform is pinned and added into the canopy height vector. The amplitude of each individual waveform was multiplied by the normalized weight and then added into the canopy height vector at the appropriate location. For the analysis conducted over Freeman Ranch, approximately ,000 small-footprint waveforms were integrated to represent each single GLAS waveform. Both the GLAS and synthesized GLAS waveforms were normalized based upon their maximum energy. [19] In addition to creating the synthesized GLAS waveform, simple statistics derived from the individual smallfootprint waveforms were generated to examine the withinfootprint variability of the GLAS footprint. These statistics include the weighted average of the canopy height, the variance of the canopy heights, the amount of woody cover, and the variance of the terrain elevations within the footprint. The canopy height was determined by computing the time difference between the first pulse and the last detected pulse of each airborne waveform. The range computed between the first and last returns was adjusted to compensate for laser scan angle to reflect the canopy height. The amount of woody cover within each GLAS footprint was determined by dividing the number of waveforms with a canopy height range greater than 1 m by the total number of waveforms within the footprint. The variance of the underlying topography was computed similar to the way that the weighted ground elevation was computed where only the points that fell below the mean minus 1-sigma of all the initial last return points were used. 4. Results 4.1. White Sands Missile Range, Calibration Array [20] The White Sands calibration array is targeted for at least two ascending and two descending passes during each ICESat campaign. To date, over 50 passes have been acquired over White Sands since the launch of ICESat. After removing elevations influenced by the corner cube reflections, an elevation offset for each ICESat pass over the calibration site is computed and compared to the airborne lidar survey flown in The biases between GLA06 and GLA14 elevations to the airborne lidar data for all fully calibrated (Release 428) campaigns are plotted against laser pointing angle and shown in Figure 4. As laser pointing angle increases, the skewness of the returned waveform is altered. The GLA06 elevations are determined by ranging to the maximum peak whereas the GLA14 elevations are determined by ranging to the centroid of the returned waveform. There appears to be a trend or spread to the elevation difference between GLA06 and GLA14 as a function of laser pointing angle, most likely related to skewness. Most elevation differences, however, fall within a 1.5-arcsecond 1-sigma pointing error which fulfills the 12 of 18

13 Figure 12. mission requirement for fully calibrated campaigns. The cause of the apparently systematic differences cannot be explained definitively at this time. [21] For mult-ipeak waveforms, in addition to increasing waveform skewness, off-nadir pointing affects the return time of elements within the laser footprint. The relative times (and apparent relative heights) depend upon their location with respect to the laser centroid. We call this effect waveform pulse compression and broadening. As illustrated in Figure 5, an apparent timing difference between the corner cube reflector and ground return decreases as the corner cube reflector moves closer to the leading edge of the laser footprint. At the nominal pointing angle of ICESat (0.3 ) and using a footprint size of 50 m, the pulse compression and broadening is ±1 ns (equivalent to ±15 cm in relative height) at the edges of the footprint. At a pointing angle of 4.4, the effect of the pulse compression and broadening is ±13 ns (±1.95 m). Two non-saturated corner cube hits over the calibration array were identified from campaigns L2b (day 075) and L3d (day 296) and are shown in Figures 6 and 7, respectively. The LPA images for these two hits are those shown in Figure 2. [22] The geometry of the descending pass of L2b is such that the ICESat nadir ground track is situated west of the array and so the laser is pointing slightly to the east at an angle of 0.89 to hit the target. The reported centroid of this (continued) GLAS footprint is 11.4 m from the closest corner cube pole. The laser footprint major axis is 95 m, minor axis is 60.6 m, and the footprint azimuth is 348. In this case, the centroid of the laser footprint energy falls close to the 6 m corner cube and the height between the two detected Gaussians is 5.99 m. Because the corner cube is close to the centroid and the off-nadir pointing is relatively small (<1 ), the height of the corner cube is accurately determined. Figure 6a shows that two additional 6 m corner cube reflectors fall at the edge of the 1/e 2 energy distribution, however they are not detected in the return waveform. The standard deviation of the last (ground) pulse is 3.47 ns. [23] For the descending pass of L3d, the ICESat nadir ground track is east of the array and so laser must point to the west at an angle of 4.4 to illuminate the target. Figure 7a shows the 1/e 2 energy distribution based upon the reported footprint major axis of 53 m, minor axis of 44 m, and a footprint azimuth of 328. The centroid of the GLAS shot is m from the closest corner cube reflector, yet the waveform indicates multiple scatterers. A corner cube on a 6 m pole is located just outside the estimated footprint size, yet its influence is apparent in the waveform, indicating that the 1/e 2 distribution is only an approximate representation to the true returned energy, even assuming a 4 m (1-sigma) geolocation imprecision. The height differential of the first detected Gaussian and the presumed ground 13 of 18

14 Figure 13. (a) Synthesized (blue dotted) waveform and GLAS (red) waveform and (b) vegetation heights (m) within GLAS footprint for L3g (October 2006) shot 173. Estimated 74.34% woody cover within the footprint. Correlation between two waveforms is Gaussian is 5.24 m. The 0.76 m vertical difference between the first Gaussian and the 6 m corner cube reflector is likely an artifact of the ambiguity associated with the geolocation error and pulse compression due to large-angle off-nadir pointing. In addition, the waveform shows two intermediate Gaussians having elevations of 2.84 and 4.04 m above the surface. Although a 3 m pole is located outside the 1/e 2 distribution, the strong reflectivity of the corner cube reflector could be contributing to the 2.84 m detected Gaussian. Additionally, the LPA image for this shot indicates energy beyond edge of the nominal footprint characterization. The 4.04 m elevation appears to be an inflection on the 6 m pulse, rather than a distinct target. It is plausible that this Gaussian is a result of a minor reflection from the pole or guide ropes supporting the 6 m reflector. The standard deviation of the ground pulse is 6.9 ns and the broad pulse width is expected due to the large off-nadir pointing angle. In both L2b and L3d cases, the 1/e 2 distribution appears to be an approximation of the laser energy, which further complicates the characterization of the waveforms over terrestrial surfaces Freeman Ranch, Texas [24] To examine the role of vegetation in the detected ground elevation from GLAS, the ground elevations for 37 cloud-free, saturation-free GLAS shots over Freeman Ranch were computed using the small-footprint synthesized GLAS waveforms. Since the GLAS footprint is rather large, the variance of the ground topography (VT) from the smallfootprint lidar that fell within each GLAS footprint was computed. In addition, the variance of the canopy height (VC) and the mean canopy height (CH) were computed from the airborne lidar along with the percentage of woody cover (WC). Five GLAS shots over Freeman Ranch were found to occur over significantly variable topographic relief within the footprint and were eliminated from analysis. [25] To evaluate each GLAS waveform and corresponding GLA14 elevation data, the ground elevations were determined and compared to the airborne lidar. The GLA14 ground elevation was determined by ranging to the mode of the last return in the waveform rather than using the reported elevation corresponding to the waveform centroid. As a reminder, the off-nadir pointing angle for the ICESat campaigns over Freeman Ranch is approximately 2.2. For comparison purposes, the ICESat passes having a 2.4 offnadir pointing at White Sands were found to have an average elevation offset of 18.5 cm (closely matching 18.0 cm potential (1-sigma) error due to knowledge in pointing precision) with a standard deviation of 8 cm in the determined ground elevation compared to airborne lidar. The average ground elevation difference (GLA14 ground Airborne Lidar ground) for all four campaigns over Freeman Ranch is 1.01 m with a standard deviation of 0.31 m. The ground elevation differences between ICESat GLA14 ground elevations and the small-footprint system are plotted against the percentage of woody cover in Figure 8. The 14 of 18

15 Figure 13. observed ground elevation differences do not show any trend as a function of the amount of vegetation present. Similarly, there was no trend observed between ground elevation differences and mean canopy height or variance of canopy heights and those plots are not shown. Thus, the presence of vegetation does not appear to systematically impact the ability of ICESat/GLAS to determine the ground elevation, however a 1 m bias is observed in this study. To determine the effect of geolocation error on the robustness of results, the analysis was repeated with a 4 m offset to the centroid position. This shift in geolocation position did not impact the estimated ground or canopy height derived from the airborne lidar. [26] Vegetation height is determined by subtracting the ground elevation from the computed top of canopy elevation. In savanna regions where woody cover is intermittent, the beginning of the GLAS waveform signal above the background noise was considered to be the top of the canopy [Harding and Carabajal, 2005; Lefsky et al., 2005]. A comparable location in the synthesized waveform was used to estimate the canopy height for comparison with the airborne data. Figure 9 depicts the relative vegetation heights differences between GLAS waveforms and the synthesized waveforms over Freeman Ranch for all four laser campaigns. In general, the vegetation height differences between the airborne lidar and ICESat/GLAS are less (continued) than a meter (see Table 3), despite the fact that two of the four ICESat/GLAS campaigns were collected in a different season than the airborne lidar (i.e., March compared to August). Comparing the four campaigns, the two February/ March campaigns (L3b and L3e) had the worst ability to represent the vegetation heights when compared to airborne lidar, having larger means and standard deviations. In central Texas, many of the trees begin to leaf-out in the month of March which could be the primary reason for the poor comparison with the August (full leaf-on) airborne data. In contrast, the two ICESat campaigns (L3d and L3g) that are acquired in October have an average relative canopy height difference of 21 cm and a standard deviation less than one meter. The average differences between the GLAS estimated canopy height and lidar estimated canopy height for each ICESat campaign are reported in Table 3. [27] The ability to estimate the amount of woody cover is of great utility for vegetation mapping. For each GLAS waveform over Freeman Ranch, the returned energy was partitioned into ground energy and canopy energy. Figure 10 depicts the ratio of GLAS canopy energy to GLAS ground energy plotted against the percent woody cover as estimated by airborne lidar. Here, the ratio of GLAS energy against woody cover followed a log trend with 73.6% of the variance explained. Freeman Ranch is a semi-arid savanna 15 of 18

16 Figure 14. (a) Synthesized (blue dotted) waveform and GLAS (red) waveform and (b) vegetation heights (m) within GLAS footprint for L3g (October 2006) shot 165. Estimated 42.92% woody cover within the footprint. Reflections from a power line can be seen crossing the upper portion of the footprint. Correlation between two waveforms is and thus similar analysis should be conducted in other areas to develop local parameters for other types of ecosystems. 5. Discussion 5.1. Comparison of Waveform Shapes [28] A fundamental question regarding the extraction of vegetation structure is how well the ICESat/GLAS waveforms capture vertical structure information despite the coarse footprint size. Figures depict several examples of waveforms (left) and the vegetation distribution (right) within each footprint for a savanna environment. On the left side of each figure, the GLAS waveforms (in red) are plotted as a function of height above the reference ellipsoid and are matched to the synthesized (in blue) waveforms at the computed ground elevation. On the right, tree heights derived from the airborne waveform lidar survey of August 2005 are mapped within each footprint. The examples in Figures were selected based upon the relatively flat topography within each GLAS footprint. In all three waveform examples, the ground return energy (signal end) from GLAS extends beyond the synthesized waveform which is attributed to the response of the GLAS system receiver. Despite some additional noise in the waveform tails, the GLAS returned waveforms match the synthesized waveforms well (r = 0.96, 0.94, and 0.92, respectively). [29] The laser incident (pointing) angle can cause either waveform compression or broadening depending upon the geometry and the location of each individual element within the footprint. For the corner cube reflectors at White Sands, the high laser pointing angle resulted in a compression of the waveform. In the presence of multiple reflectors, such as in Figures 11 13, the impacts of pulse compression and broadening appear to be spread equally across the footprint Topography [30] The effect of topography coupled with off-nadir pointing angle on the returned GLAS waveforms can be seen in Figure 14. The reference ground track for ICESat is located west of Freeman Ranch and the spacecraft is looking to the right. In this example, the local topography within the footprint (3 surface slope) combined with the off-nadir pointing of 2.25 results in a return GLAS waveform where the vegetation signal is convolved with the signal from the underlying topography (>5 apparent relief). When the amount of topographic relief within each footprint (here 4 m) or laser incident angle is equivalent or greater than the height of the vegetation, it will not be possible to resolve the ground elevation or to directly 16 of 18

17 Figure 14. (continued) determine the relative vegetation height using GLAS alone. However, the elevation at the top of the canopy is still capable of being retrieved from ICESat/GLAS and thus vegetation height can be determined when utilizing an alternate source of ground elevation data [Lefsky et al., 2005] or by using alternate waveform shape indices [Lefsky et al., 2007]. 6. Conclusions [31] Spaceborne laser altimeter measurements from the GLAS instrument are being used for the estimation of terrestrial properties; however, the effect of vegetation on elevation accuracy is not known. Waveforms over the array of corner cube reflectors ( proxy trees ) at White Sands were used to examine the effect of laser pointing angle and footprint energy distribution on the return waveform. The GLAS data product parameters describing the GLAS footprint (major axis, eccentricity, and footprint azimuth angle) are estimated using a 1/e 2 energy distribution and averaged to 1 Hz. At White Sands, it was apparent that the 1/e 2 energy distribution was only an approximation and strong reflectors (such as corner cube reflectors) can contribute to the returned energy beyond the estimated parameters. The precise position of the corner cube reflector within each footprint is an important factor, as significant as laser pointing angle, and can affect the amplitude of the return waveform in as-yet uncharacterized ways. The combination of laser pointing angle, surface topography, reflectivity, and reflector position within the footprint on the return waveform energy, however, may be lessened or diluted in the presence of multisurface scatters (i.e., vegetation with vertical and horizontal structure). Nevertheless, these factors remain important considerations when examining vegetation structure from large-footprint satellite-based lidar. [32] Comparisons between the airborne and space-based lidar systems at Freeman Ranch, Texas show agreement in the characterization of canopy height and structure. However, the GLAS estimated ground elevations appear to be biased by an average of 1.01 m in the presence of vegetation. This bias, though, has only been examined in this one biome and at this time cannot be regarded as a standard for all vegetated areas. It remains uncertain whether this offset is primarily a function of the laser pointing angle or other factors. Certainly the vegetation location within the footprint is important (as observed in White Sands), but it is still unclear what relative importance may be attributed to vegetation position, true footprint size (compared to the 1/e 2 estimate), a 4 m (1-sigma) horizontal precision, or other unknown factors. Continuing investigation requires more data at Freeman Ranch and in different biomes, and 17 of 18

18 at different off-nadir angles, in order to better characterize the cause of the apparent 1.01 m bias. [33] The shapes of the GLAS waveforms compared well with GLAS waveforms synthesized from airborne lidar waveforms (>90% correlation for flat terrain). However, the shapes of the returned waveforms from GLAS are dependent upon complex relationships between several factors on the illuminated surface including surface topography (roughness and slope), surface reflectance at the laser wavelength (1064 nm), cloud cover, satellite pointing, laser energy, footprint size, shape and orientation, vegetation height, canopy thickness, and position within the footprint. In this analysis we have excluded data affected by cloud cover (forward scattering) and reflectance (saturation) effects. We have also excluded steep topography, thereby concentrating this study on the primary effects of ICESat off-nadir pointing. A smaller footprint reduces the topographic effects and uncertainties associated with steep topography. In flat terrain, the vegetation height (that is the relative height between the top of the canopy and the ground) was accurately determined by ICESat/GLAS even within a heterogeneous savanna landscape. In this savanna landscape, the GLAS energy ratio (canopy energy to ground energy) was a good indicator (R 2 = 0.74) of the amount of woody cover within the footprint yielding the empirical equation P wc ¼ 20:043 lnðþþ60:09 x where P wc is the percent woody cover within the GLAS footprint and x is the GLAS canopy to ground energy ratio. While these results are from a limited study area, they do provide insight into the role of vegetation on the returned energy and thus the implication and potential of using ICESat/GLAS over terrestrial areas. As the use of laser altimeters increases for vegetation characterization, it is recommended that future laser altimetry missions target a variety of biomes including savanna/woodlands for vegetation calibration and validation. [34] Acknowledgments. This research was performed at the University of Texas at Austin Center for Space Research (UTCSR), USA, in support of ICESat. We are grateful to NASA for this funding provided through contract/grant NAS and NNG06GA99. ICESat data are distributed to the ICESat Science Team by the NASA Science Computing Facility; identical ICESat data products from fully calibrated laser campaigns and analysis software are publicly available through NSIDC at We would like to thank the anonymous reviewers for their comments and feedback on this manuscript. References Abshire, J. B., X. Sun, H. Riris, J. M. Sirota, J. F. McGarry, S. Palm, D. Yi, and P. Liiva (2005), Geoscience Laser Altimeter System (GLAS) on the ICESat Mission: On-orbit measurement performance, Geophys. Res. Lett., 32, L21S02, doi: /2005gl ð2þ Bae, S., and B. E. Schutz (2002), Precision attitude determination (PAD), Geoscience Laser Altimeter System (GLAS) Algorithm Theoretical Basis Document Version 2.2, pad_10_02.pdf, last accessed 16 July Carabajal, C. C., and D. J. Harding (2005), ICESat validation of SRTM C-band digital elevation models, Geophys. Res. Lett., 32, L22S01, doi: /2005gl Carabajal, C. C., and D. J. Harding (2006), SRTM C-band and ICESat Laser Altimetry Elevation Comparisons as a Function of Tree Cover and Relief, Photogrammetric Engineering and Remote Sensing, 72(3), Fricker, H. A., A. Borsa, B. Minster, C. Carabajal, K. Quinn, and B. Bills (2005), Assessment of ICESat performance at the salar de Uyuni, Boliva, Geophys. Res. Lett., 32, L21S06, doi: /2005gl Gutierrez, R., A. L. Neuenschwander, and M. M. 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Schutz, B., H. J. Zwally, C. A. Shuman, D. Hancock, and J. P. DiMarzio (2005), Overview of the ICESat Mission, Geophys. Res. Lett., 32, L21S01, doi: /2005gl Urban, T., B. Schutz, and A. Neuenschwander (2008), ICESat coastal altimetry, Terr. Atmos. Oceanic Sci., in press. Zwally, H. J., et al. (2002), ICESat s laser measurements of polar ice, atmosphere, ocean, and land, J. Geodyn., 34, R. Gutierrez, A. L. Neuenschwander, B. E. Schutz, and T. J. Urban, Center for Space Research, University of Texas at Austin, 3925 W. Braker Lane, Suite 200, Austin, TX , USA. (amy@csr.utexas.edu) 18 of 18