ANALYSIS OF REPEATED ICESAT FULL WAVEFORM DATA: METHODOLOGY AND LEAF-ON / LEAF-OFF COMPARISON
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1 ANALYSIS OF REPEATED ICESAT FULL WAVEFORM DATA: METHODOLOGY AND LEAF-ON / LEAF-OFF COMPARISON Hieu Duong 1 Norbert Pfeifer 2 Roderik Lindenbergh 1 1 1: DEOS, MGP-FRS, 2: University of Innsbruck, Institute of Geography
2 Outline Introduction ICESat/GLAS. Waveform groundtracks in study area Waveform processings Results and Discussion. Conclusion 2
3 ICESat Facts Main goals: measuring ice sheet mass balance, cloud and aerosol heights, as well as land topography and vegetation characteristics. Altitude: 6km NASA mission Launched 23 Life time 5 years Carries GLAS instrument 3
4 GLAS instrument Footprint: 7m diameter Spot space: 175m 3 sensors: L1 (failed), L2 and L3 Geolocation: star tracker and GPS Full waveform laser altimetry! 4
5 ICESat repeated tracks February: 184 waveforms September: 2942 waveforms Forest pairs: 358 5
6 ICESat repeated tracks Green: February 23 Red: September Histogram of footprint shift mean =73.86[m] Select forest footprints manually by comparing to Landsat images. 6
7 Target: detect waveform changes GLA1 GLA14 Extraction of ELevation Points by IDL Conversion -Bin-ASCII -Counts-Voltage Waveform Normalization -Smoothing -Initial parameters estimation Fitting algorithm Shifting Computation Waveform Distances -> DI, Intensity Dist -> RP, Peak Ratio 25% smallest RP & DI -> C_DI threshold -> C_RP threshold 7
8 Target: detect waveform changes DI & RP small Class: same DI & RP not small Class: not comparable DI small RP not small Class: strong change (deciduous) DI not small RP small Class: slight change (pine) 8
9 Waveform units Y axis: Original waveform in counts (-255) is converted to voltage units X axis: 1ns ~ 15cm 1 Original waveform: :1.6 Volt-waveform: : Volts (V) Relative Time (ns) Relative Time (ns) 9
10 Normalization Total intensity: 544 V i i= 1 Mean total intensity February: September: 39.9 => Normalization step: V nori i = 544 i= 1 Area under waveform equals to 1. V After that we smooth the waveform. V i 1
11 Fitting: Gaussian decomposition Transmitted pulse is in Gaussian model Return waveform is a sum of Gaussian components Fitting algorithm: least square estimation (Vienna Univ. of Tech.) 11
12 .4 Results of Gaussian decomposition Red: raw waveform Green: Gaussian components Myfit-date: :1.4 Myfit-date: : Relative Time (ns) First mode: left most Gaussian component Last mode: right most Relative Time (ns) 12
13 Shift Blue: winter waveform Red: summer waveform Green: shifted winter waveform X: Y: 5 Nor Volts (V) Waveform: <-> : in winter : in summer :shifted in winter Relative Time (ns) The average shift of 4.26[m] maybe caused by changes in the GLAS configuration 13
14 Waveform distances Along vertical axis Intensity distance: DI( W, W ) F S 544 ( VF( i) VS( i)) = 544 i = 1 2 Along horizontal axis Peak ratio: RP( W, W ) F S LMF FMF 1, LM FM > LM FM LMS FMS = LMS FMS 1, LM FM LM FM LMF FMF F F S S F F S S 14
15 Waveform parameters Peak locations last mode ~ ground surface first mode ~ canopy top Widths of modes: ~ feature roughness or slope surface, or both, canopy thickness y Amplitudes of modes first mode ~ canopy density Distance first-last mode ~ tree height Relative Time (ns) 15
16 Mean parameter values First mode width February: 4.28m September: 3.74m First mode amplitude February:.418 September:.56 Last mode amplitude February:.83 September:.88 First-Last peak distance February: 2.41m September: 19.18m Histogram of amplitude of first mode in February mean = Histogram of amplitude of first mode in September 8 mean =
17 Results: Waveform classes same: <> winter summer 2 pairs 2 x 1-3 pine forest: <> winter 15 summer 1 69 pairs small-small not small-small.3.2 deciduous f orest <> winter summer x 1-3 not comparable: <> winter summer pairs pairs small-not small: deciduous? not small-not small 17
18 Conclusion Possible to compare repeated track ICESat waveform data, but, standardization steps are needed. Standardization steps, waveform parametrizations and waveform distances can be applied to non-icesat waveform data as well. Waveform processing procedure should be extended, and tested more extensively on a more controlled data set. 18
19 Upcoming Article on land cover classification via analyzing ICESat s full waveform data (ISPRS Symposium, Enschede, May 26) 19
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