Exploring the use of ICESat’s Exploring the use of ICESat’s Geoscience Laser Altimeter Geoscience Laser Altimeter

System to assess large scale System to assess large scale forest disturbance caused by forest disturbance caused by

Hurricane KatrinaHurricane KatrinaKatelyn Dolan

Natural Resources UNH ’09

kdolan@unh.edu

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CommitteeJeff Chambers- Tulane University

Ralph Dubayah- University of MarylandSteve Frolking- University of New Hampshire

Jeff Masek- NASA Goddard Flight CenterGeorge Hurtt, chair- University of New Hampshire

BackgroundBackground

“Disturbance impacts are important to consider when estimating the amount of carbon sequestered by U.S. forests. There has been little research in this area…” ~EFETAC Southern Global Change Program, 2008

“Hurricanes are a significant factor in reducing long-term carbon storage in US forests.” ~McNulty 2001

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On August 29th 2005 Hurricane On August 29th 2005 Hurricane Katrina made Landfall Katrina made Landfall

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popsci.typepad.com

Chambers 2007

sciencemode.com

Key Research QuestionKey Research QuestionTo what extent can GLAS data be used to detect and help

quantify forest disturbance from Hurricane Katrina?

Objectives1. Assess change in vegetation structure pre

and post Katrina using GLAS– Analyze sampling– Explore height equations– Produce estimates of change that account for

uncertainty 2. Assess carbon consequences of change in

vegetation structure– Calculate carbon loss with ED (Ecosystem

Demography Model)

3. Compare to Other Estimates of Damage

ICESat’s Geoscience Laser ICESat’s Geoscience Laser Altimeter System (GLAS)Altimeter System (GLAS)

• Launched onboard ICESat January 12 2003 as part of NASA's Earth Observing System (EOS).

• The main objective of the GLAS instrument is to measure ice sheet elevations and changes in elevation through time. Secondary objectives include measurement of cloud and aerosol height profiles, land elevation, vegetation cover, and sea ice thickness.

~NSIDC

Adapted from the Attributes for ICESat Laser Operations Periods last access 7-7-08

5lvis.gsfc.nasa.gov

Light Waveform to Forest Structure Light Waveform to Forest Structure

Mean Canopy Height = Mean height of the dominant and co-dominant trees or the mean height of lidar returns.

– Considers heterogeneity of forest structure in a 50m or more lot

Current Mean Canopy Equation-Mean Canopy Height = 3.65728 + ( 0.599102 * ( [WIDTH] + (-0.346713 *( [TRAIL] + [LEAD] ))))

(Lefsky 2007)

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Research Research MethodsMethods

Proposed Data Sets and Products

H*WIND NOAA (Powel,1998/ last access Feb. 08)

IVPU (ICESat Vegetation Product Utility)

National Land Cover Data Set 2001 (NLCD ‘01)

MODIS and LANDSAT ΔNPV Maps(Chambers et al 2007)

National Elevation Data Set

Forest Inventory Analysis

ED (Ecosystems Demography Model)

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Pearl River Basin

DeSeto National Park

Mobile, AL

Land CoverLand Cover

Safire-Simpson

Scale

Total Area (km²)

Total Forested

Area (km²) NLCD 01

% forest area

Number of Glass Shots Pre Katrina

Total (L3ABC)

Number of Glass Shots Post Katrina

Total(L3DEF)

Number of Forested shots Pre

Number of Forested

shots Post

% forested

GLAS Pre

% forested

GLAS PostTropical

Storm (40-74 mph) 120,585 70,819 59 61,764 86,639 16,849 22,618 27 26

Category 1 (74-96mph) 26,065 13,401 51 8,467 9,942 2,798 3,073 33 31

Category 2 (96-

111mph) 5,843 1,586 27* 3,581 3,581 934 767 26 21

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Preliminary Results Using Preliminary Results Using New Mean Canopy Height New Mean Canopy Height

Equation Equation

Fall

Winter

Spring

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Summary of Mean Canopy Heights derived from GLAS using Summary of Mean Canopy Heights derived from GLAS using Lefsky (2008) equation.Lefsky (2008) equation.

Hurricane Wind

Category period

Avg Height New

Equation Count

Hurricane Wind

Category period

Avg Height New

Equation CountClass 2 L3A 15.1944 404 TS L3A 16.9731 4355

L3B 13.1199 256 L3B 11.513 2940 L3C 15.0585 154 L3C 15.8852 5610 Pre 14.4576 814 Pre 14.79043 12905 L3D 10.6832 290 L3D 15.09 7556 L3E 9.2998 268 L3E 11.7532 5299 L3F 12.061 172 L3F 15.405 7118

Post 10.68133 730 Post 14.08273 19973Class 1 L3A 15.4113 1349 L3A 17.3185 4427

L3B 11.7516 507 L3B 12.7656 4432 L3C 14.968 424 L3C 16.5729 4961 Pre 14.04363 2280 Non Pre 15.55233 13820 L3D 12.5758 1458 tropical L3D 15.724 5617 L3E 10.15 597 Storm L3E 11.6494 4429 L3F 12.8015 673 L3F 15.818 6686 Post 11.84243 2728 Post 14.39713 16732

Strong patterns of seasonalityStrong pattern of shorter mean canopy Heights in Hurricane ZonesPotential for biased sampling or overweighting certain campaigns

Need for non parametric statistical comparisonsNeed to further explore mean canopy height equation i.e. 4m interceptWould like to further stratify into damage intensityPotential to break down into forest class and disturbance history

Preliminary Preliminary ObservationsObservations

Next stepsNext steps13

Back of the Envelope Carbon Calculation…Back of the Envelope Carbon Calculation…

Standing Carbon Loss in Category Two Hurricane Areas• Assuming 1 kgC/m2 loss (calculated looking at a linear regression of biomass

compared to Height created from Ed)• 1 kgC/m2 * 1585 km2 * 106m2/1km2 *1ton/1000kg = 1,586,000 Tons C• Assuming pre storm biomass=7kg/m2 pre-storm Biomass in tons would be

11,102,000. Therefore biomass loss would be 14% which is equivalent to Oswalt’s(2007) estimates of windthrow and bole breakage.

Category One Hurricane Areas• Assuming .5kg C/m2 loss • 13400 km2 *106m2/1km2 *1ton/1000kg = 6,700,000 Tons C.• Assuming 7kg/m2, storm loss would be 7% which is again consistent with Oswalt’s

windthrow and bole breakage estimates.

Total Category 1&2 would be equal to 8.3 million tons C

(does not include damage in tropical storm zone)

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ReferencesAbshire J et al (2005) Geoscience Laser Altrimeter System (GLAS) on the ICESat Mission: ON-orbit measurement performance, Geophysical Research Letters 32 L21S02,

doi:10.1029/2005GL024028,

Chambers et al.(2007) Hurricane Katrina's carbon footprint on U. S. Gulf Coast forests. Science 318 1107 Lefsky MA, Keller M, Pang Y, Camargo PB & Hunter M. (2007) Revised method for forest height estimation form Geoscience Laser Altimeter System waveforms. Journal of Applied Remote Sensing. 1 1-17

Doung et al. (2008) Single and two epoch analysis of ICESat full waveform data over forested areas. International Journal of Remote Sensing, 29: 5 pp 1453–1473

Drake, J.B (2002), Estimation of tropical forest structural characteristics using large-footprint Lidar, Remote Sensing of

Environment, 79, pp. 305–319.

Harding, D. J., and C. C. Carabajal (2005) ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure, Geophys. Res. Lett., 32 (Art. No.L21S10.

Hurricane Katrina H*WIND Model (Powel, 1998) last updated

I VP (Icesat Vegetation Products) Created by Colorado States Universitiy’s Center for Ecological Applications of Lidar

Kumfer et al. (2007) Patterns of Forest Damage in a Southern Mississippi Landscape Caused by Hurricane Katrina. Ecosystems.

Lefsky MA et al (2007) Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms, Journal of Applied Remote Sensing, Vol 1, 013537

Lefsky MA et al. (2005) Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity, Remote Sensing of Environment 95 549-558.

Lefsky MA, et al (2005) Estimates of Forest Canopy Height and Aboveground Biomass using ICEsat, Geophysical Research Letters 32

Lefsky MA (2008) personal com (New Height Equation)

Masek J G and Collatz J. (2006) Estimating carbon fluxes in a disturbed southeastern landscape:Integration of remote sensing, forest inventory, and biogeochemical modeling, Journal of Geophysical Research. 111

McNulty SG (2002) Hurricane impacts on US forest carbon sequestration, Environmental Pollution 116 S17-S24

Neuenschwander AL et al (2008) Charecterization of ICESat/ GLAS waveforms over terrestrial ecosystems: Implications for vegetation mapping. Journal of Geophysical Research 113 G02S03

NLCD 01

NSIDC- http://nsidc.org/data/icesat/data.html

Oswalt SN, Oswalt CM (2008) Relationships between common forest metrics and realized impacts of Hurricane Katrina on forest resources in Mississippi, Forest Ecology and Management 255 pp 1692-1700   

Pacala et al (2001) Science 292

Powell et al. 1998: The HRD real-time hurricane wind analysis system. J. Wind Engineer. and Indust. Aerodyn. 77&78, 53-64 (Katrina Revised access Jan 2008 http://www.aoml.noaa.gov/hrd/Storm_pages/katrina2005/wind.html)

Rosette JAB, et al (2008) Vegetation height estimates for a mixed temperate forest using satellite laser altimetry. International Journal of Remote Sensing 29:5 pp1475-2493

Stanturf JA, et al (2007) Disturbance and coastal forests: A strategic approach to forest management in hurricane impact zones, Forest Ecology and Management 250 pp119-135

Sun G et al (2008) Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data. Remote Sensing of Environment. 112 107-117

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Special Thanks to:Special Thanks to:The Research and Discover Program The Research and Discover Program

(NASA&UNH)(NASA&UNH)My Advisor and Committee MembersMy Advisor and Committee MembersRob Braswell, Justin Fisk & Michael Rob Braswell, Justin Fisk & Michael

LefskyLefskyThe GIS Lab GroupThe GIS Lab GroupFriends and FamilyFriends and Family

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QuestionsQuestions

18Pearl River

Basin

DeSeto National Park

Mobile, AL

NLCD01 Overlaid by GLAS Campaign Paths NLCD01 Overlaid by GLAS Campaign Paths

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Mobile County, AL

Jackson County, MS

Harrison County, MS

Subset of Landsat Derived Disturbance History Subset of Landsat Derived Disturbance History product ‘84-04product ‘84-04

Category 2 winds

Category 1 winds

Tropical Storm

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Previous ResearchPrevious Research

In 2002 McNulty published a paper on hurricane impacts on US carbon sequestration. His findings showed that a single hurricane can convert 10% of the total annual carbon storage for the US into dead and downed forest biomass 20Tg.

A recent study by Chambers et al, 2007, estimated the carbon footprint of hurricane Katrina to be five times higher than McNulty’s estimate, approximating a total biomass loss of 105 TG or between 50-140% of the net annual US carbon sink. This study used a mixture of image processing, field work, and FIA data to make its damage estimates.

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• Interests in forest disturbance and recovery following a hurricane or major disturbance range from carbon accounting, forest management & wildlife management. Depending on interest, scale and methods will vary.

• Kupfer (2007) looked at patterns of forest damage based on well sampled field data in DeSeto National Park, Al.

• Stanturf (2007) approximated 2 million ha of timberland damage in Louisiana, Mississippi and Alabama. Study estimated potential stem breakage for different damage categories created by the US forest service following the methodology of the GALES model

• Oswalt (2008) looked at impacts of Katrina on Mississippi forests. The study compared USFS-SRS-FIA field recorded hurricane damage across Mississippi to initial modeled estimates of damage by the USFS.

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Estimates of forest damage and carbon loss vary. This is further complicated by the fact that estimates of US and global carbon storage and sequestration vary among experts.

Scale of studies and methods vary and results of damage are reported differently (i.e board feet versus tons of carbon)

Previous Research ContinuedPrevious Research Continued

First Preliminary Analysis Yielded Confounding First Preliminary Analysis Yielded Confounding ResultsResults

• Equation Issues:– Very complex (See Lefsky 2007)– Very small waveform extents led to

unrealistically large heights

• Sampling Issues:– Not stratified by forest

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Category 1 L3 A-B-C   L3 D-E-F  And 2  total shots % total shots %(0-39m) 6888 53 7189 5339m+ * 2871 22 5180 38

value (-1) 3197 25 1214 9total 12956 100 13583 100

Tentative Research ObjectivesTentative Research Objectives

• Explore ability of GLAS to detect change in forest structure caused by Hurricane Katrina– Look into height equations, sampling, and other waveform

characteristics related to structure

– Compare to FIA, Satellite Imagery, field data and other auxiliary data

• Explore ability of GLAS to aid in or improve carbon loss/ forest damage estimates.– Use Ed model

• Assess how results may help inform needed data acquisition characteristics (e.g. accuracy, sampling) of future missions with goals of studying vegetation structure from space.

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This study aims to use GLAS, in combination with other data, to improve the detection and modeling of the impacts and recovery of ecosystems from

hurricanes.

OutlineOutline• Background

• Research Question and Objectives

• An overview of GLAS and how it may aid in forest impact assessments

• Methods

• Preliminary data explorations

• A back of the envelope carbon estimate

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Chambers et al 2007