This article appeared in a journal published by Elsevier. The attached copy is furnished to the ...

This article appeared in a journal published by Elsevier. The attached copy is furnished to the ...

15 Pages · 2008 · 1.77 MB · English

a Radar Systems, MS 300-319D, Jet Propulsion Laboratory, Pasadena, CA 91109, USA The method is based on SRTM (Shuttle Radar Topography Mission) elevation data, ICEsat/GLAS waveforms (Ice, Cloud, and Land Elevation .. such as agricultural, urban, sand and bare soils, plantations etc.

This article appeared in a journal published by Elsevier. The attached copy is furnished to the ... free download

This ar tic le appeared in ajournal pub lished byElse vier The attac hed cop yis furnished to the author for internal noncommer cial resear ch and education use ,inc luding for instruction at the authors institution and sharing with collea gues Other uses, inc luding repr oduction and distrib ution, or selling or licensing copies, or posting to per sonal, institutional or thir dpar ty websites are pr ohibited In most cases author sare permitted to post their ver sion of the ar tic le (eg in Wor dor Texform) to their per sonal website or institutional repositor yAuthor srequiring fur ther inf ormation regar ding Else vier? sar chiving and man uscript policies are encoura ged to visit: http://www else vier com/cop yright Author's personal copy A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Missio n elevation data, ICEsat/GLAS waveforms and field data: Applic ation to Ci?naga Grande de Santa Marta, Colombia Marc Simard a,!,Victor H RiveraMonroy b,Jos? Ernesto ManceraPineda c, Edward Casta?edaMoya b,Robert R Twilley b aRadar Systems, MS 300319D, Jet Propulsion Laborat ory ,Pasadena, CA 91109, USA bWetland Biogeoch emistry Institute, Louisiana State University ,Baton Rouge, LA, 70803, USA cUniversidad Nacional de Colombia Sede Caribe, San Andr es, Colombia Received 2Novembe r2006; received inrevised form 2October 2007; accepted 28 October 2007 Abstract Mangrove forests are found within the intertropical zone and are one ofthe most biodiverse and productive wetlands on Earth Wefocus on the Ci?naga Grande de Santa Marta (CGSM) in Colombia, the largest coastal lagoon ?delta ecosystem in the Caribbean area with an extension of 1280 km 2,where one of the largest mangrove rehabilitation projects inLatin America iscurrently underway Extensive manmade hydrological modifications in the region caused hypersaline soil (N90 gkg!1)conditions since the 1960s triggering alarge dieback of mangrove wetlands (~247 km 2)Inthis paper ,wedescribe anew systematic methodology tomeasure mangrove height and aboveground biomass by remote sensing The method isbased on SR TM (Shuttle Radar Topography Mission) elevation data, ICEsat/GLAS waveforms (Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System) and field data Since the locations of the ICEsat and field datasets do not coincide, they are used independently tocalibrate SR TM elevation and produce amap of mangrove canopy height Wecompared height estimation methods based on waveform centroids and the canopy height profile (CHP) Linear relationships between ICEsat height estimates and SR TM elevation were derived Wefound the centroid ofthe canopy waveform contribution (CWC) tobe the best height estimator The field data was used toestimate aSRTM canopy height bias (!13 m) and estimation error (rms =19 m) The relationship was applied tothe SR TM elevation data toproduce amangrove canopy height map Finally ,weused field data and published allometric equations toderive an empirical relationship between canopy height and biomass This relationship was used toscale the mangrove height map and estimate aboveground biomass distribution for the entire CGSM The mean mangrove canopy height inCGSM is77 mand most ofthe biomass isconcentrated inforests around 9minheight Our biomass maps will enable estimation ofregeneration rates ofmangrove forests under hydrological rehabilitation atlarge spatial scales over the next decades They will also be used toassess how highly disturbed mangrove forests respond toincreasing sea level rise under current global climate change scenarios ?2008 Elsevier Inc All rights reserved Keywor ds: Radar; SRTM; Lidar; ICEsat; GLAS; Mangroves; Mangrove; Wetlands; Forest; Height; Waveforms 1 Introduction Mangr oves are found betw een latitud es 31? north and 38? south ,particu larly along the tropical and subtro pical coasts of Austral ia, Asia, Afr ica and the Ameri cas The mangr ove fores t isone of the most product ive eco systems on Earth with amean production of 25 gC m!2perday (Jenner jahn & Ittekkot, 2002 ) The combi nation of shallow waters, high levels of nutrients ,and high primary product ivity makes these areas ideal forsupporting intricate food webs in severaltypes of environm entalsettin gs (Twilley & RiveraMonroy,2005) Mangr ove wetland sgenerate ampl e goods and services to society such as providing critical habitat for bird, fish andother wildlife, playi ng key roles inbiogeo chemi cal hydrologic cyc les, regulatin gwater quality ,reduci ng shoreline erosio n, off ering Availab le online at www sciencedirectcom Remote Sensing ofEnvironme nt112(2008) 2131 ?2144 www elsevier com/locate /rse !Correspondin gauthor Tel: +1 818 354 6972; fax: +1 818 393 5184 Email addr esses: marcsima [email protected] ov (M Simard), [email protected] edu (VH RiveraMonroy) ,[email protected] naleduco (JE ManceraPine da) 00344257/$ see front matter ?2008 Elsevier Inc All rights reserved doi: 101016/jrse 200710012 Author's personal copy flood protectio n(as resul toftropical stor ms, hu rricanes, and tsuna mis (Kathir esan & Rajendr an, 2006 )), moderati ng clim ate, and suppor ting numer ous econom ic acti vities such as hunting, fishing, and recreation (Ewel et al, 1998 ) Because mangroves couple biogeochemicalprocesses between land and sea, lands cape degradation in these coast al zones magnifie sregio nal imp acts A recent United Nat ions Environ ment Program me report (UNE P,2006 )estimates that their economical value varies geograp hical ly between $200 k and $900 kper km 2per year The prim ary drive rsof mangrove convers ion are relat ed to hu man impacts: urban expansion, shrimp farming, water management practices, charcoal cut as well as natural hazards such as sea level rise, hurri canes, severe storms and tsunami sAm ong the major impac ts of mangr ove loss are decline in biodi versity ,degrada tion of clean water suppl ies, siltation of coral reefs and acidi fication of coast al soils, erosion, loss of shoreline stabili ty,relea se of more carbon into the atmo sph ere, and red ucti on (or disap pea ran ce) of importan tcommerci al fish stocks (Sanchez Ramirez & Rueda, 1999; Rueda & Defeo, 2001 )Itis estimat ed that the loss of original mangrove fores tsisas high as 35 % and may reach 60% by 2030 (Valiela et al, 2001; UNE P,2006; Alongi, 2002 ) These are, however ,gross estimates and donot rely on accurate lands cape analys es, whi ch can only be improved through remo te sensing lands cape scale asses sment Both radar and opti cal remo te sensi ng have bee n used extens ively to map mangr oves with varying degrees of success (eg Kova cs etal, 2005; Lab aetal,1997, Ram sey etal, 1996; Rasolof oharinoro et al, 1998; Wang et al, 2004; Hel detal, 2003; Sima rd et al, 2000; Mougi netal, 1999 ) Rec ently , structura l(tre eheight )and funct ional (biomass )attribut es of mangr oves have been esti mated using radar interfero metry (Simard et al, 2006 ) In February of 2000, Space Shutt le Endeavou rcollected nearly global coverag eofEar th's topo graphy using radarinterferometry (SRTM,Shuttle Radar Topograp hy Mission )And because of limited penetr ation of microw aves withi nvegeta tion, the SR TM topogr aphic maps contain infor mation related to vegeta tion height (Kellndo rfer etal, 2004 )Mangrove forests are located within the interti dal zone (ie atsea level ),whi ch particula rly sim plifies the canopy height esti mation technique since the ground topogr aphy isas flat as the tida lrange SR TM data are distrib uted with a90m spatial resol ution around the Earth, reduced from the origi nal 30 m through averaging and subsampl ing In apreviou spaper , Sima rd et al(2006) used an airborne lidar (ie ligh tdetect ion and rangi ng) to cali brate SR TM elevation Lid ar measures the time of return of alight pulse refl ected off atarget and thus meas ures the relative dist ance Rec ent results using space borne lidar show ed that these data could also be used to estimate vegeta tion height and correlate itwith biomass (Lefsky et al, 2005; Dra ke et al, 2002a,b )GLA S(ICEsat Geos cience Laser Altimete rSystem )isthe first spaceborne lidar inst rument for global observ ations of Earth (Schutz et al, 2005 )which has been collecti ng data since early 2003 and is the benchmark Earth Observi ng System mission for meas uring ice sheet mass balanc e,cloud and aeroso lheight s,as wel lasland topography and vegeta tion charact eristics Carabaja land Harding (2006) showed that the GLAS wave form (laser return as afunction of time) centr oid is highl ycorrel ated to the SR TM phase center elevation over densel yvegeta ted regio ns In this paper ,wepresen tamet hodolo gy based on SR TM elevation, ICEsat/ GLAS, and field data to map mangr ove forest height and abovegr ound biom ass Wefocus on the Cienaga Grande de Santa Marta (CGSM ),Colom bia, alarge wetland complex where one of the largest mangr ove rehabilit ation projects in Latin Am erica is curren tly underw ay (Botero & Salzwedel ,1999; Rivera Monro yetal, 2004; Rivera Monro y etal, 2006 )Large man made hydrol ogical modi fications inthe region caused hypers aline soil condition s(N90 gkg!1)since the 1960s triggeri ng alarge diebac kofmangr ove wetland s (~247 km 2)Thus, remo tesensi ng tool sare needed to evaluate ifcurren tfreshwate rdiver sions initiat ed in 1995 will be suc cessful in rest oring mangr ove wet lands at the lands cape scale Our object ive is to build abaseline map to quantitat ively estimate the extent ,height and biomass of the mangr ove forests in CGSM Wedescri be how to use ICEsat/ GLAS data to systematically calibrate SRTM elevation data,potentially providing arobust met hod to extend 3D mapping of mangr ove forests toother parts of the World In addition, we collected field data on structura lattr ibutes along four mangr ove trans ects in CGSM to calibrate SRTM and to derive a sitespecific relations hip between mean canopy height and abovegr ound biomass The GLAS and fiel ddata do no toverlap since we were unable to obtai naccurate geolocation for our samplin gpoints because of weak GPS signal under the dense can opy Werelied on distance and orien tation using ameas uring tape and a compass to locat ethe samp ling point sonthe SR TM maps The height ?biomass relations hip enable smapping of biomass in CGSM by extra polating with the calibrate d SRTM canopy height estimates Bioma ss estimat es in this ecoregi on are badly needed toevalua tethe impact of mangr ove mortali tyon nutrient cycling (ie carbon, nitr ogen, phospho rus) andtounders tand how the loss of above and belowgro und biom ass affect the role of mangr oves as carbon sinks 2 Dat aandmethod s 21 Site descri ption Loc ated on the Caribbe an coast (10? 37"to11?07"Nand 74? 15"to 74? 51"W), the Ci?naga Grande de Santa Mart a(CGSM ) forms the exterior delt aofthe Magdalena River ,the fifth lar gest river in South Ameri ca with an annual average water disch arge of 7000 m3s!1(Restre po & Kjerfve, 2000; Rivera Monro y etal, 2004 )(Fig 1)The wetland complex was designated as a Wetland of Inter national Impo rtanc eunder the Ramsar Con vention by the Gove rnme nt of Colo mbia on the 18th of June 1998 (Ramsar site no 951) It was also desig nated as a UNESC OBiosphe reRes erve in2000 The CGSM isthe lar gest lagoon ?delta complex in Colom bia (1280 km 2)Tothe north , the ecosys tem is separa ted from the Caribbe an Sea by the barrier island Isla de Salamanca, which has an inle t(Boca de la Barra) approxi mat ely 100 m wideand 10 m deep on its eastern end that connect sthe lar gest lagoon directly to the sea Tothe 2132 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy west and south wes tthe lagoon ?delta complex islimited by the flood plai nofthe Magdalena River ,throu gh which five main tributar ies histori cally brought freshwate rfrom the river to the compl ex unti lthe 1970s Tothe east and south east, CGSM is border ed bythe footh ills of the Sierr aNeva da de Santa Marta, the highest coastal mountain in the wor ld (5800 m above sea level) Four mai n river sdrain this watershe d, cross ing an extens ive agric ultural zone and drain ing into the maj or lagoon with an average annual flow of about 20 m3s!1(Botero & Salzw edel, 1999; Rivera Monro yetal, 2004 )Acco rding to its geomo rphol ogical, geophys ical and biol ogical charact eristics, CGSM can be classified as Type Isetting (Thom, 1982 ),that is riverdominated with mic ro tida lregime (?30 cm), and arid climate (Twilley et al, 1998 )The mic ro tida lregime implie s ground topogr aphy in the mangr ove fores ttovary only within 30 cm The estua rine regio ns wer esurrounded unti laround 1960 by approxi mately 52,00 0 ha ofmangrove wetland s dominated by Rhizoph ora mang le (L), Avicenn ia germinan s (L), and Laguncul aria racem osa (Gaertn) (Card ona & Botero, 1998 ) The CGSM was stro ngly impacted by human activities that disrupted major hydrol ogical linkage satboth fresh water and marine boundar ies The anthropogeni calterati ons star ted with a highway constructe d in 1956 that inte rrupted most of the connect ions betw een the Car ibbean Sea and the system After 1970, fresh water from Magdalena River was diverted from CGSM by aroad buil talong the river without culverts This resulted in soil hypers alinization that caused the death of Fig 1Map ofthe Cienaga Grande deSanta Marta (CGSM) lagoon ?delta complex with location ofthe field transects used tocharacterize structural attributes of mangroves Transects were selected oneach side ofBoca Fundacion River (ie one onthe east and one onthe west ofFundacion River shown onthe map), atCienaga ElTorno and Cienaga La Atascosa The dotted area represen tsthe boundary ofthe Salamanca National Park 2133 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy approxi mately 70% of the original mangr ove fores tThe rate of mangr ove loss has gradually incre ased from 175 km 2yr!1 from 1956 to 1968, to 98 km 2yr!1from 1968 to 1987, to 1332 km 2yr!1from 1987 to1993 The peak rate occu rred from 1993 to 1995 at 1843 km 2yr!1(Cardona & Botero, 19 98; G?nima et al, 1998 ) Hydrologic alterations have also caused waterquality changes in the ecosys tem ,mainly in the Pajarales Compl ex For the past four decades, the system has undergone severe envir onmental stre ss associ ated with fresh water diver sion, causi ng largescale mort ality of mangr oves (mai nly in the Pajarales Compl ex, Fig 1),fish kill s,wat er contamination ,and loss of biodiversi ty (SantosMart ?nez & Acero, 1991; Mancer a & Vidal ,1994; Botero & Mancer a, 1996; Cardona & Botero, 1998 )Since 1996, alar gescale rehabi litati on proje cthas been implem ented in the system to rest ore the hy drologic regim eand induce the natur alregenerati on of mangrove fores tsto improve water quality and local fisheries product ion (Twille yetal, 1998; Bot ero & Salzwedel ,1999; Polani aetal, 2001 ) The area serves as habitat and winter breeding ground for severa lbird speci es, and spaw ning ground for many fish speci es CGS M has been the mai nsource of fish and shellfish for the north coa st of Colom bia (Santos Martine z& Acero, 1991; Mancer a& Mendo 1996; Rueda & SantosMartine z, 1999; Rue da, 2001 )The ecolog ical values of the system are well know n, as itisalrea dy declared as two national protected areas: the V?a Parque Isla de Salamanca andthe Santuario de Fauna yFlora de laCi?naga Grande de Santa Mart aPartof the site is stateow ned, while alarge area is priva telyown ed and comm ercial and artisanalfish ing is imp ortantforhuman communi ties living around the lagoon Shell fish and crayfish are also harves ted in the area, while higher elevat ion zones are used for agric ulture Ecotour ism isalso being develo ped in the protected area 22 Field data Wecollectedfielddatain Au gust2005 alongfourmangrove transectsin CGSM (16 plots):eastand we st side of Bo ca Fundaci?n River and two transects(Ci?nagaElTorno and Ci?nagaLaAtascosa)locatedinV?aPar queIsladeSalam anca (Fig1)Weused thevariablecircularplotmethod(Gr osenbaugh, 1952;Dilworth&Bell,1975)toselecttreesThismethodallows efficientsamplingoflarge treesthatgenerallyhave lowe rspatial densitythansmallertreesIn variableplot samp ling,plotsizeis dependentontreediameter,andtreesaretalliedas?in?or?out?of theplotdependingon wh ethertheirdiam eter at breast height (D BH )islarge enough to subtendafixedcriticalanglevisible usingananglegaugefromtheplotcenterEach?in?tree accounts forafixedbasalareaSincetheangleis know n, we canalso estima tethespatialdensityoftreesas afunctionofDBHUsinga BasalAreaFactorof5(00232radatarmlength),wetalliedatotal of 166trees,anaverageof 10 treesperplotForeach of the selected trees,we identified thespecies(Table1),me asured height Table 1 Proportion (percentage) oftree species tallied during the field campaign bysites and overall CGSM West of Fundaci?n(5plots) East of Fundaci?n(5plots) Torno(4plots) Atascosa(2plots) CGSM(16 plots) Rmangle 8 10 42 89 28 Agerminans 92 88 40 0 66 Lracemos a 0 2 18 11 6 The overall CGSM percentages are computed using every tree from all plots The number ofplots isinparenthes is Fig 2Measured diameter atbreast height (DBH) and tree height for each specie atall sites inCGSM The fits are given inTable 2 Table 2 Sample fits for each mangrove specie asshown inFig 2 Mangrove specie Regressio nfit Error inrms percent Rmangle DBH =12 !H 03 Agerminans DBH =11 !H+005 !H2 052 Lracemosa DBH =11 !H 046 2134 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy usingalaserranger,andmeasuredDBH with aDBH tapeThe datacollectedareshow ninFig2andregressionfitsaregivenin Table2Inthefield, atreeheightmeasurementby different operatorsvariedsignificantlyduethedifficultyin identifyinga speci fic tree top in a dense canopy We fou nd, by field experime nts, that itcaused atreeheightmeasurementrandom errorofapproximately10% Ge ographicallocation wa sobtained wh ere possible with a hand held GP S (GlobalPo sitioning System)Th eGPScoverage underthema ngrove canopy wa s poor andinsufficienttofindtheexactlocationoftheICEsat footprintTocomp leme nt theGPSdata,wefollowedastraight line with aconstantcompassreadingandme asured thedistance betweenplotsin each transect with ametrictapeBa sedonthe data,weestimatedamaximum location errorof43m 23 La nd cover map Weproduce daland cover map speci fic to the mangr ove forests of CGSM using acloud free Landsat 5TM scene from March 1999 (Fig 3a) The specie scompo sition of CGSM is generally mix ed with avariety of propor tion s(Table 1)and the dominant speci es may change rapid ly over tens of meter Therefor e,we use the Lan dsat scene toderiv egeneri cmangr ove classes with astand ard nonsu pervi sed IsoData classifi cation algorithm (Ite rative SelfOrganizing Data Analysi s; Jensen , 1996 ) This met hod is used instead of superv ised algor ithm because it does not require the interpreter to selec ttraining samples for every potent ial land cover in the imag ethat would otherwi se resul tin multi modal class distrib utions Isodata simply requi res sett ing an arbitrary numbe rofclass es that are mer gedsubseq uently by the user First ,all areas with SR TM topography great er than 30 m wer emask ed since there are no tallermangrove forests in CGSM and there cannotbe mangroves outside the intertidal regio n at0 m elevation Isodata with 30 classes resul ted in spati al patterns corres pond ing to those observ ed by visua linterpreta tion The 30 classes were then merged into afinal four land cover class es by visual interpret ation using field data and expert know ledge of the area The four land cover class es are: water ,playon ,live mang roves , dead mang roves and other The playon areusually mud flats that may have bee npartiall ycovered with mangr oves in the past The Dead Mangr oves class is characterize d by forest remnants with dead trunks and branche sThe Live Mangr oves class isthe generic class of mangr ove forest with any mix ture of the three specie sfound in CGSM Finally ,all other land covers such as agric ultural, urban, sand and bare soils, plantati ons etc are incl uded in class Other Land Covers The final land cover map isshow ninFig 3b The land cover classifi cation had a100% accuracy within the field transects locat ions and the perimete rofthe Ci?naga lagoon (Fig 1)which isbordered by live and dead mangroves areas Due tothe limit ed field samplin g,we obtai ned an estimat eofthe classificati on accuracy by selec ting vali dation samp les again by visual inte rpretati on of the Landsat scene, field data, Google Fig 3a)Landsat 5image of March 4th, 1999 inRed ?Green ?Blue color composite with bands 7,4and 1respectively The dark blue areas are water and the mangroves are inshades ofgreen while the dead mangro ves are seen in bright blue The surrounding region iscovered mainly by agriculture (orange and green colors), plantatio ns(green color) and urban areas (pink color) The city ofBarranquilla ison the Western side along the Magdalena River b)Land cover classifica tion showing live and dead mangrove forests produced from the 1999 Landsat 5scene using Isodata algorithm The confusi onmatrix isgiven in Table 3 Table 3 Confusion matrix inpercent for land cover map ofFig 3b Class Water Dead Other LC Playon Mangrove s Water 9995 0 0 738 004 Dead mangroves 0 9854 0 1166 0 Other classes 0 0 9905 0 1485 Playon 005 12 008 6973 002 Live mangroves 0 027 087 1122 8508 Total 100 100 100 100 100 2135 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy Earth ima ges, and know ledge of the regio n Weobtai ned a confusion matrix (Table 3)with an overall classification accuracy of 80% The live mang rove class covers 29,042 ha with an estimat ed 4and 15% commis sion and omi ssion error respec tively The dead mang roves class covers 74 31 ha and the playon class 19,683 ha with error sshow ninTable 2 24 Shuttle Rad ar Topograp hy Mis sion (SRT M) data In February of2000, Sp ace Sh uttle Endeavour colle cted nearl y global coverag e ofEarth's topogr aphy using radar interfero metry (SR TM ,Shuttle Radar Topograp hy Mis sion) Because of limit ed penetr ation of microw aves within vegeta tion, the SR TM topogr aphic maps contai ninformat ion relat ed to vegeta tion height (Kellndo rfer etal, 2004 )The SR TM Cband data are dist ributed freel yataspatial resol ution of 30 mover the US and 90 m for the rest of the World (Slate retal, 2006 ) The refore ,the SR TM elevat ion data has a 90 m spati al resoluti on in CGSM Weused SR TM's Versi on 2data (by naming convent ion SR TM3 for 3arc sec data) from the Jet Propul sion Labo ratory These data wer ebuil tbybox averaging with awindow of 3by3elevation pixels 25 GLAS wav eforms Laser pulses from ICEs at/GLA Silluminate spots (footprin ts) about 70 m indiam eter and spaced at abou t172 m inte rvals along Earth's surface GLAS record sthe energy return as a funct ion of time (ie lida rwave forms) GLA Swave forms are charact erized by asingle Gaussia npeak over ocea ns, sea ice, and ice sheet s,however multip lepeak smay occur over irre gular surfa ce such as land covered by vegeta tion As descri bed by Brenner et al (2003) ,the retur nsigna lcan be represented as a sum of Gaussia n peaks plus abias Fo rland surfaces, the ICEsat/ GLAS standard algorithm characteri zes the return pulse by using mul tiple Gaus sian dist ribution sto fit every mode (peak) in the wave form (Harding & Car abajal, 20 05) The stand ard ICEsat elevation product over land (ie GLA 14) is deriv ed from the centr oid of the retur n(Bre nner et al, 2003 ) Since the CGSM isrelatively small, we wer eable to collect all wave forms (ie product GLA 01) located in this area and perfor m data processing to estimat etree height The use ofICEsat/GLAS waveforms to characterize mangr ove fores tsisanew and syste matic approach to improve mapping accuracy of mangrove canopy height by calibrating SR TM elevat ion data Weused GLAS wave forms to estimate mangr ove forest height wher edata over mangr ove forests were available As ofOctober 2006, we found atotal of 326 ICEsat/ GLAS footprint slocated inmangr ove fores tsof CGSM after we elimin ated the waveform swith low signa lvoltage and high thermal noise (ie maxi mum less than 03Vor mean therm al noise great er then 01 V asesti mated from the beginn ing of wave form) Most footprints are locat ed on the Eastern side of Boca Fun daci?n and Parque Isla de Salamanca In the case of mangr ove forests, the GLA Swave forms are generally bim odal distrib utions resul ting from scatteri ng withi nthe canopy and the ground The top ofthe canopy in amature mangr ove forest is homogen eous and is general ly void of understory vegeta tion with roots and detritus on the ground, while the scrub mangr ove canopies are short and dense verticall yThus aclean bimodal waveform can be expect ed for tall mangrove forests but not for scrub mangrovesGenerally,the waveforms from Boca Fundaci ?n GLA S trans ects hav e a signi ficant contr ibution from aseconda ry lower canopy (ie Gaussia npeaks between the ground andthe mai ncanopy) sugges ting canopi es with irregular surfa ces while the Salama nca trans ects are dominated by the tall canopy signature 3 Anal ysis and results 31 Mangr ove extent in CGSM The tota lesti mated mangr ove fores tsextent using the 1999 land cover map was 29,042 ha (Fig 3b) with an estimated 4% and 15% commis sion and omission error respec tively (Table 3) This isless than half the value report ed by Cardona and Botero (1998) before 1960 and higher than values reported for 1995 (16,631 48 ha) before the diver sion of fresh water as part of the rehabilitati on project (Twilley etal, 1998; G?ni ma etal, 1998 ) In the land cover map, the dead mangr ove class account sfor an extr a 743 1 ha loc ated in the hyp ers alin e soils (Pa jara les complex, Fig 1)clear ly dist inguishabl einthe remo te sensi ng im agery (Fig3a)This class sets a min imum value on mangrove area loss since the early 1960s In add ition, the pla yon class may have been parti ally covered with mangr oves several dec ades ago; ifthe playon isaccounted for ,the area loss is 27 ,114 ha The tota larea covered by these three classes (56,156 ha) issimil ar to the 1960s 52,000 ha of mangr ove area (Car dona & Botero, 1998 ) 32 Estimation ofmangr ove for ests height from field data In this sec tion ,we descr ibe the me thod olo gy use d to compute the mangrove canopy height from the fiel d data Fig 2 show sthe large natur al varia bility found withi n the mangrove forest Since our samp lesets are relat ively small (an average of 10 trees per plot ),itis importan ttoconsi der the impactofthis variability on extrapolations necessary to calibrate a90m SR TM pixel A germinan sis the dominant specie in CGSM with aheight varia bility for agiven DBH of 52% (Table 2)Historical ly,A germ inans was the dominant species in this semiarid coastal region due to its physi ological adaptation stowithstand high soil salinit ies (Rivera Monro y etal, 2001 )For A germinan s,our samp ling set has an average DBH and height of 334 cm and 161 m respec tive lyGiven the angle gauge of 00232 rad, this DBH represents an ?in?tree with amaximum plot radiu sof144 m Toesti mate tree spatial density (tre es/ha )ofthis DBH popula tion (ie 334 cm), we multiply by 1535, obtained from the ratio of plot sizes 1ha/ (!144 2),and the canopy height means are computed on the extrapolat ed samp les, thus preser ving the sample error The plot sizes are signi ficantly smaller than SR TM spatial resoluti on and itis reason able to assum ethat the natur al height variability observed in Fig 2was not fully repres ented within our plot 2136 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy Therefor e, assum ing that the impact of this varia bility (ie 52% with DBH) is random,the mean canopy height estimat ion has an error of 26 m (16 :1d0:52= !!!!!10p )Inclu ding the normalized heightmeasurementerror in the field (!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!! !!!!!!!!!!! ! 16:1d0:1 ! "2#16:1d0:52 ! "2 " #=10 r ), we obtain a field canop y height estimat ion error of approxi mately 27 m We inves tigated severa lmet hods to compu te the mean canopy height from the field data: the arit hmetic mean ,the coverag eareaweight ed mean andacrown weight ed mean The arithm etic mean does not take into account crown size of trees alth ough itvaries signifi cantly with tree height and size and thus consi ders small trees occupy as much horiz ontal space as lar ge trees Onthe other hand, we used two canopy height estimat es based on a wei ghted mean, adjustin g the aeria l coverag eofasingle tree to its height, ie the taller the tree the wider the crow n: ?The coverage weight ed mean uses asimple linear relat ion between tree height and crow ncoverag earea assum ing that the canopy isclose dThu s,the sum of the weig hts of all trees withi nthe plot is equal to the area A of the plot and the canopy iscompletel yclose d(100% cover) For examp le, if there are ntrees in aplot of size A,the mean area occ upied by atree isx=A/nwith aradius r$ !!!!!!! ! x=k p Ifthe mean tree height of the plot isH,then r/Hcan be used as the slope to compu tethe area aoccupied by each tree of height hin this plot such that a=!(r/H!h)2; ?The crown weight ed mean isbased on an empi rical relat ion between tree DBH andcrown diam eter r=0222 !DBH 0654 (Cintron &Shaeffer Novell i,1984 )The weight isdefin ed as the area of the crown (ie circular disk) and isno trelat ed to the plot area Thus this height estimat eismore realistic when the canop yisnot comple tely closed The regres sion fits shown on Fig 4had poor correl ation (below 02) and cannot be used for cali bration Inst ead, we used the fiel ddata means to estimat ethe random error (rms) and SR TM bias whi ch resul ts from microw ave penetrati on within the canopy and residual mis calibration of SRTM3 The rms variation and biase sbetween SR TM elevation and field data means aregiven in Table 4Asexpect ed, the arithmet icmean is generally lower than the weighte d means since large trees (DBH) have lar ger crow ns and wei gh more in the compu tation of the mean height The crown weighte dmean is the most consistent with SR TM elevat ion with an overal lrms noise of 29 m but generally less than 18 m, except for the transect East of Boca Fundaci ?n The lar ge varia tion within this site ismainly due to asingle plot clear ly visible above 20m inFig 4Itis apparen tly an outlier that when remo ved, reduces the overall bias and rms varia tion to !13 ?19 m The plot may have been part of an isolated patch (~30 m inwidth) of taller trees whose height signature is smoo thed or remo ved in the 90 m SRTM Fig 4Field estimates offorest height compared toSRTM elevation within the four sampling sites shown inFig 1The variability ofthe height estimator with respect toSRTM elevation isgiven inTable 4The fits were uncorrelated and therefore are not used inthe study Table 4 This table compares the mean canopy height estimations from field data and SRTM elevation Field height method East of Fundaci?n West of Fundaci?n Torno Atascosa Overall Arithm mean (m) !28 ?35 30 ?31 03 ?06 02 ?06 00 ?39 Crown weight (m) !38 ?27 !05 ?18 !06 ?12 !05 ?05 !17 ?29 Area weight (m) !49 ?25 !14 ?18 !14 ?12 !08 ?06 !26 ?30 The first value isamean canopy height bias (Hbias =HSRTM!Hfield )and the second isthe SRTM rms height error (ie ?rms) Overall, the crown weighted mean isthe field height estimation method most similar tothe SRTM elevation with anrms of29 m 2137 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy elevation data product Neglecting this plot ,weobtain the follow ing correct ion for SR TM bias: HfieldF1:9m ! "$ 1:3#HSRTM : !1" 33 Estimation of mangr ove for est height with ICEs at/GLAS wavefor ms The ICEsat/GLA Swave form scan be used to quantitat ively charact erize canopy structure and model the Can opy Hei ght Profi le(CHP) as the relative dist ribution of canopy surfa ce area (Harding et al, 2001 ) To derive the CHP from the lidar wave form, one must consider ligh tocclus ion resultin gfrom intercept ion ofligh tfrom upper layer sLight occlusion reduces the signa lcontribut ion from lower layer softhe canopy The technique is based on severa lassum ptions such as arandom distributio nofhorizontal canopy compo nents indepe ndent of lay ers above and below ,a consta nt leaf orien tation ,and reflectivit yasafunct ion of height also implyi ng the ratio of wood and leave mat erial isconst ant as afunction of height First the wave form ground compo nent is scale d to account for dif ference inreflectiv ity (50%) within the can opy Then, canopy closure ismodel ed by compu ting the cumulative distrib ution of the waveform from the top to the botto m ofthe canopy and normaliz ed by the tota lreturn of the canopy and ground This height distrib ution of canopy closure is then weighte dbyan Fig 5Relation between a)canopy height estimatio nfrom ICEsat/GLAS Canopy Waveform Contribution (CWC) and SRTM elevation and; b)canopy height estimation from ICEsat/GLAS Canopy Height Profile (CHP) and SRTM elevation As areference the linear fitofthe CWC estimates isalso shown in(b) 2138 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy occlus ion transform ation of the form [!ln(1 !closure)] It is finally norm alized to obtain acumulati ve distributio nofplant area In summ ary ,the CHP trans form shoul dincre ase the signa l level of the lower layer softhe canopy in the wave form as ifall layer sreceived an equiva lent amoun toflight Although, we do not have suff icie nt data to realistical ly validate the CHP ,we perfor m our analys is on both raw wave forms andCHP's as describ ed by Har ding et al (2001) Th eoverallanalysisincludes characterizing thecanopyand ground contributionswithinthewa veform Webeganby fittinga single Ga ussian distribution to theGLASrecordedtransmitted pulse(acopyofthelaserpulsetransm ittedbythelidarinstrument isrecorded by theGLASsystem)Thisfitisthen ma tchedtothe wa veform ground peak to accurately determ ine the ground location within thewaveformThenwe estima tedthetherma l noiselevel(system and me asurem entnoise)using thedata betweenthebeginningofthewa veform andthetopofthecanopy In ordertolocatethetopofthecanopy (iemaximum height) within thewaveform,we foundthatusingthemaximum noise valuewasmo re robust than thermsnoiselevelforbatch processingWedefinedthetopofthecanopyasthefirstwaveform samp le with avoltagevaluelargerthantwicethemaximum therma lnoisevalueFinally,Ga ussian distributionswerefittedto theremainingof thewaveform untilthermsdeviationofthefit converged below thethermalnoiselevelAtthispointthe rema iningvariationsareduetonoiseandnotto forestcanopy signal The waveform centroid is computed on the multiple Gaussia nfit in two ways :using the full waveform incl uding the ground and canopy peaks and the wave form without the ground peak The former close ly corresp onds to the standa rd GLAS algor ithm (Harding & Carabaja l, 2005 )to estimate ground elevation while the latter is desig ned to extra ct the Canop yWavefor m Con tribution (CWC) The CWC isobtained by subtr acting the record ed trans mitted pulse from the wave form at the detected ground peak posit ion and compu ting the centr oid of the rema ining signa lThus CW Cisanestimate of the canopy height The combi ned geo referencing error of ICEsat (24 m error ) and SR TM (9 m error for South Ameri ca) is 95 m The geolocation errorand the diffe rentspatialresolution and samp ling lead to different portions of the fores tbeing samp led Fig 5show sthe compariso nbetween height estimates from ICEsa tCWC and CHP model swith SR TM elevation data (ie SRTM 3)The resul ting linear fits are the follow ing: HCWC F2:6m ! "$ 0#0:94 HSRTM !2" HCHP F2:3m ! "$ %0:1#0:67 HSRTM : !3" In both cases the intercept and slope error sare 02 and 003 with correlati ons of fits 085 and 08 respec tively Asexpected, the imp act of the CHP transform isto low er the canopy height estimat e; the smaller slope of Eq (3) indicates the higher the SR TM elevation, the more itisreduced as compared to CWC The estimat ion of height using CHP does not agree with the field data; using the average fiel dplot height of 136 m asa refere nce, Eq (2) imp lies SR TM elevation shoul dbe204 m The mangr ove fores ts general ly have spars eunders tory ,and light occlusion occurs within atree crow nnot obstr ucting light penetratio n to neighbo ring smallertrees (egsmalltrees popula ting gaps) Thus, we assum ethat most of the energy is reflected from the top layer softhe canopy and the ground, especiall ywhen there are canopy gaps In summary ,neglec ting light occlus ion and using CWC means that we are in fact describing the height distributio nofthe canopy surface This assumpti on is also suppor ted by the simil arity between the CWC estimates and SR TM elevat ion (Fig 5a) The simil arity between the meas urem ents indic ates that radar scatteri ng in mangrove forests occurs mai nly in the upper canopy These results are simil ar to findings in Everglades Nationa lPark (Simard etal, 2006 )and other types ofvegeta tion (Car abajal & Harding, 2006 ) The rms varia tion between SR TM elevation and GLA S CWC is26 m, whi ch issignifican tly lower than observ ed for other types of fores ts(Carabaja l& Harding, 2006 )and close to the SR TM elevat ion random error report ed for flat areas by Rodrigu ez et al (2006) The difference betw een SR TM and ICEsat/ GLAS isdue to different parts of fores tsamp led, natural variability ,differences in elect roma gnetic scatteri ng mecha n isms, syst em noise and apotent ial SRTM 3calibrati on bias We found apositive bias value of 1?1m in bare ground areas near mangrove areas of CGSM Finally ,wealso searched for apotential correlati on between height estimat ion error and canopy closure using the ratios of the wave form compo nents for canopy and total return (Harding etal, 2001 )Wefound that most wave form sinCGSM (ie 69% of the wave form s) were dominated by the canopy compo nent (ratio N05) and found no correlati on between canopy closure and height estimation error Fig 6Map ofheight and biomass oflive mangrove forest built from SRTM elevation data 2139 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy 34 Calibrati ng SRTM mang rove height estima tion ICEsat provides asystemat icmet hod tocali brate SR TM data wherever fiel ddata is not available with accurat egeoloc ation and a large (~70 m) footp rin tclose to SRTM 3's spati al resoluti on (~90 m) Weremove dthe residual SR TM bias by combinin gthe field data bias andICEsat calibration (Eq (2)) The average fiel dplot height was 136 mwithanoveral lSRTM height bias of !13 m The relat ion between ICEsat wave form centr oid height and SR TM elevation (Eq (2)) indica tes that SR TM elevation shoul dbe145 m toobtai nthe cali brated 136 m Thus the impact of cali brating SR TM with ICEsa tisto reduce SR TM elevat ion values and thus the resultin gbias to !21 m, which isstill withi nthe error margin of the fiel ddata (ie !13 ?19 m) The final calibration equatio nusing both field and ICEsat height estimates becom es: HCWM F1:9m ! "$ 2:1#0:94 HSRTM ; !4" where HCWM is the crown weight ed mean height of the mangrove fores tcanopy and HSRTM is the SR TM elevation This equation is the mean height of the canopy surface since radar andlidar penetr ation throu gh the canopy were correct ed Fig 7a)Comparison ofthe three sets ofallometric equations used inthis study These equations are for single trees ofthe three species found inCGS M: Agerminans (Av),Lracemosa (L) and Rmangle (red) While the equations ofDay etal(1987) may not beapplicable totrees larg erthan 15cm, there isalso alarg ediscrepancy between the equations ofSmith and Whelan (2006) and Fromard etal(1998) reaching about 200 kg for aRhizophora mangle tree of25 cm DBH b)Estimation of biomass asafunction ofplot mean tree height weighted by crown size The slopes and error obtained for each allometric equations are given inTable 5 2140 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy by using the field data bias Applica tion of Eq (4) tothe SRTM3 produce sthe cali brated mangrove forest height for the entire CGSM as show ninFig 6 4 Estimatio nofbioma ss distr ibuti on an dloss in CGSM 41 Allometr ic equations Weestima tedabovegroundbiom ass(B)ofmangroveforestsof CG SM as afunctionofmeancanopyheightusingfielddataand publishedallometricequations(Fig 7a)Wechosethecrow n we ighted me an toderive thebiomass?heightrelationshipsinceit isthecanopyheightestimatorwiththelowestrmsdifferencewith respecttoSRTM canopyheightmeasurementWecompared threedifferent setsof allome tricequationsderivedin La guna de Terminos,Mexico(Da yetal,1987)FrenchGuyana(From ard etal,1998)andtheEverglades,Florida(Sm ith&Wh elan,2006) Th e first two sites have simi lar geo mor pho log ica lset tings (deltaic)andhave largeextensionof ma ngrove forestsasinthe case of theCGS M Th ethirdsetwasderivedusingtreesfrom threelocationswithin theEvergladesNationalPark, akarstic domi nated ecosystemWeonly used theDa y etal(1987) equationsforcomp arison purposes sincetheywerederivedfrom treessmallerthan 15 cm DB H (Fig 7a)Weappliedalinear regression throughthefielddata(Fig7b) such thataboveground biom ass(Mg/ha)=b0+m!HCW M(m eters)Theinterceptsb0and slopes mforeachallometricequationaregiveninTable5These slopes arelowerthan thevalueof m=10estimatedin theENP (Sima rd et al,2006)indicatingthataforestof thesameheight contains less biom assinCGSM thaninENP Tocomp utethespatialdistributionofbiomassatthelandscape scale, we used thecalibratedSRTM canopyheightestimate HCW M comp uted with Eq (4)Fig 8 show sthehistogram distribution of canopy heightinCG SM wit hame an of 77mTo assess potentialvariationin biom assestimatesduetoallometric equations,we used themodelsofSmithandWh elan (2006) and Fromardetal(1998)Sinceeach mo delrepresentsdifferent geom orphological settings,thecalculated biom assrangesre presentdifferentenvironm entalconditionsinfluencingma ximu m asym ptotic tree heightsforeach species(sensu)Theresulting abovegroundbiomassdistribution ma pisshowninFig6andthe totalbiomassestima tesvarybetween12and17(?01)!106Mg usingSm ithandWh elan (2006) andFrom ardetal(1998) Ce rtainly,ma ngrove totalbiomassvalues arearelatively mi nor contribution to theregionalcarboncycleHowever,estimated Table 5 Height ?biomass relation: B(Mg/ha) =m!Height (m) Allometricequations Regressio nfit b0+m!HCWM Intercepterror Slopeerror Correlationoffit ResidualRMS Fromard etal B=11+ 62HCWM 9 07 09 186 Day etal B=13+ 68HCWM 12 10 084 255 Smith and Whelan B=21+ 27HCWM 82 07 067 173 The correlation and the rms residual error indicate the goodness ofthe linear fit and the residual variability ofthe data respective ly Fig 8Mangrove height and biomass distribution inCGSM The mean forest canopy height weighted bycrown size is77 mThe biomass concentration peak sin9m forests 2141 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy values show thatallome tricequationsareacriticalcomponentof robust biom assestimationsandmoreeffortsarenecessaryin deriving sitespecificrelationsforcarbonbudgetsandlongterm mo nitoring ofwe tlands intheCGSMInthecontextoftheCGSM mangrov e rehab ilitati on project ,resea rch efforts should be directed to estima ting in situ allome tric equationstoimprove presentbiomassestima tes Thesebiomassvalues arefirstrate estima testhatillustratetheecological applicationoftreeheight obtained throughSRTM elevationandGLASwaveforms 42 Biomass loss Since we have esti mates of area loss of mangr ove forests as well as curren tbiom ass regres sion curves (Fig 7b), itispossi ble to evalua te mangr ove fores tloss in terms of biomass We assume that the dead forest had the same mean canopy height as the curren tforest with amean abovegr ound biom ass of 59 Mg ha!1 using the equa tion s ofFro mard etal (1998 )The estimat ed biom ass loss isaround 16 !106Mg considering an area loss of 27,1 14 ha The se biomass estimates will be used to evalua te the impact of mangrove mortality on carbon and nutrient cycli ng in the ecoregi on Itisnot clear how mangrove mortali ty has affected carbon cyc ling and how net export of dissolved organic and inorganic carbon and nutri ents (nitrogen and phospho rus) from dead mangroves have modi fied water column productivi ty in open estua rine and coasta lwat ers Although there is a welldocumented directrelationship between mangrove mortality and collapse of comm ercial and artisanal fish eries in the CGSM (RiveraMonr oy etal, 2006 ),it is not clear if mangrove imp acted areas are contribut ing to incre asing eutrophicat ion as result of excess nutrient leaching from dead mangr ove fores ts 5 Conclusio n Wepresen ted amethod using ICEsat/GLA S, SR TM and field data to estimat eheight and biomass distrib utio ninthe Ci?naga Grande de Santa Mart a(CGSM ),Colom bia ICEsat/ GLAS data does notprovide a complete and spatially continuou spict ure of the CGS M but is apowe rful tool to estimat ecanopy height profi les (CHP) and canopy height In this study ,the extra polation to lar ge scale is achiev ed with SR TM elevation data, which contains avegeta tion height sig natur edue to limit ed mic rowave penetrati on in the canopy Since SR TM data was acquired in February of 2000, the final maps repres ent the threedim ensional status of mangr oves of that perio dTh emeanmangrovecanopyheightinCGSM is77m andmostofthebiom assisconcentratedinforestaround9m in height(Fig8)Wealso foundthatCGSM mayhave lostapprox im ately27,114haofmangroveforestsduring thelastdecades Althou gh the man grov e cov erag e arou nd the wor ld is relativel ysmall compa red to other ecosys tem s, the area may not reflect the ecolog ical product ivity and economical impor tance atregional levels, parti cularly in develo ping nations The interest in deriv ing biomass estimat es isdue to the lack of data on this imp ortant ecosys tem component at regio nal levels Althoug hglobal biomass estimates are no tnew for mangrove forests (eg Saenger & Snedaker ,1993 ), our resul ts offer valuable informat ion that can be readi ly used to produce regional estimat es of carbon seques tration at tropical latitud es And by providing abiom ass budget ,wedemon strate the direct utility of our method to evalua te eco system healt hasitisof great concern in the ongoin grehabilitati on projec tinCGSM Weused different mangrove speciessp ecific allo metric equa tions to account for meas urement and natural variability The biom ass maps produce dinthis study are first order estimates that will allow manag ers and scien tists to eva luate regeneration rates ofmangrove forestunderhydrological rehabilitati on at large spatial scale sover the next decades ,as well as to assess how highly disturbed mangr ove forests respond to incre asing sea level rise under curren tglobal climate change scenariosThe methodology could potentially be applied to any mangr ove forests as long as ICEs at data are available and forest extent is sufficiently large to be mappe d with the 90 m resoluti on SR TM data The newest compo nent from this study is the esti mation of local values, with their respective accuracy ,toproduce data that canbeused in other ecological studies (eg nu trient cycli ng) andtoasses smangr ove resto ration /rehabil itatio n projects using ?performa nce mea sures ?,such as biomass and tree height at landscape levels Acknowl edgments Thi sstudy was con ducted by the Jet Propulsion Laborato ry, California Institu te of Technol ogy ,under contra ct with the Nationa lAer onautics and Space Admini stration (NAS A) and funded by the NASA Inter disciplinary Science (IDS) progra m This wor kwas partially funded by the Institu to Colom biano para el Desarrol lo de la Cienci ayla Tecnologia (COLCIE N CIAS: code#: 210513 0809 7) and the Instituto de Investiga ciones Marinas (IN VEMAR) Wealso ackn owledge the suppor t of the Instituto de Investigac iones Tropicales (INT ROPIC) de la Univers idad del Magdal ena Wewant to thank Paola Reyes Forero from INVE MAR and Alina Gam ez Castro ,Juan M Carvajalino Fernandez ,Mari aPaz Consuegr a,Fredy Guar diola, Yesenia R Guerrero Bellos o, Fatima C Botto Lub o, Ivan Medina, Linda J Ortiz Prada, Johan Rod riguez and Cesar Valverde from the Univers idad del Magdal ena and Eduardo Montero for fiel dsuppor tand assi stanc e References Alongi, DM (2002) Present state and future ofthe world's mangrove forests Envir onmental Conservat ion ,29(3), 331 !349 Botero,L,&Mancera,JE(1996)S?ntesisdeloscambiosdeor?genantr?pico ocurridosenlos ?ltimos40a?osenlaCi?nagaGrande deSantaMarta (Colombia)RevistadelaAcademiaColombianadeCiencias,20,465!474 Botero, L, &Salzwede l,H (1999) Rehabilitation ofthe Cienaga Grande de Santa Marta, amangrove ?estuarine system in the Caribbean coast of Colombia Ocean and Coastal Managemen t,42,243 !256 Brenner ,AC, Bentley ,CR, Csatho, BM, Harding, D J,Hofton, M A, Minster ,J,Roberts, L, Saba, JL, Schutz, R, Thomas, RH, Yi,D, & Zwally ,HJ(2003) Derivatio nofrange and range distribution sfromlaser pulse waveform analysis for surface elevations, roughness, slope, and vegetation heights Algorithm theoretical basis document Version 30 Greenbelt, MD: Goddard Space Flight Center 2142 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy Carabajal, C C, &Harding, D J(2006) SRTM Cband and ICEsat laser altimetry elevation comparison sasafunction of tree cover and relief Photogramme tric Engineering and Remote Sensing ,72(3), 287 !298 Cardona, P,&Botero, L(1998) Soil characterist ics and vegetation structure in aheavily deteriora ted mangrove forest inthe Caribbean Coast ofColombia Biotr opica ,30,24!34 Cintron, G, &Schaef ferNovelli, Y(1984) Methods for studying mangrove structure In S C Sneadaker & JG Sneadaker (Eds), The mangr ove ecosystem: Resear chmethods (pp 3!17) Paris: UNESCO Day ,JW,Jr,Conner ,WH, LeyLou, F,Day ,RH, &Machado Navarro, A (1987) The product ivity and composi tion ofmangrove forests, Laguna de Terminos, Mexico Aquatic Botany ,27,267 !284 Dilworth,JR,&Bell,JF(1975)VariableplotcruisingInJRDilworth(Ed), LogscalingandtimbercruisingCorvallis,OR :OregonStateUniversityBook Store,Inc Drake, JB, Dubayah, RO, Knox, RG, Clark, DB, &Blair ,JB(2002) Sensitivity of large footprint lidar tocanopy structure and biomass ina neotropica lrainforest Remote Sensing ofEnvir onment ,8V1(23), 378 !392 Drake,JB,Dubayah,RO,Clark,DB,Knox,RG,Blair,JB,Hofton,MA, Chazdon,R L,Weishampel,JF,&Prince,SD(2002)Estimationof tropicalforeststructuralcharacteristicsusinglargefootprintlidarRemote SensingofEnvironment,79,305!319 Ew el,KC,Twilley,RR,&Ong,JE(1998)Differentkindsofmangrove forestsprovidedifferentgoodsandservicesGlobalEcologyand Biogeo graphyLetters,7,83!94 Fromard, F,Puig, H, Mougin, E, Marty ,G, Betoulle, JL, &Cadamuro, L (1998) Structure, aboveground biomass and dynamics of mangrove ecosystems :New data from French Guyana Oecolog ia,115,39!53 G?nima,L,Mancera,JE,&Botero,L(1998)Aplicaci?ndeim?genesdesat?lite aldiagn?sticoambientaldeuncomplejolagunarestuarinotropical:Ci?naga GrandedeSantaMarta,CaribecolombianoSeriepublicacionesespecialesdel InstitutodeInvestigacionesMarinasyCosteras,INVEMAR4,SantaMarta56p Grosenbaug h,L (1952) Plotless timber estimates ? New ,fast, and easy Journal ofFor estry ,50,32!37 Harding, D J,&Carabajal, CC(2005) ICESat waveform measurement sof withinfoo tprint topogra phic relief and vegetation vertical structure Geo physical Resear chLetters ,32,L21S10 doi:101029/2005 GL023471 Harding, D J, Lefsky ,MA, Parker ,GG, &Blair ,JB(2001) Laser altimeter canopy height profiles: Methods and validation for deciduous, broadleaf forests Remote Sensing ofEnvir onment ,76(3), 283 !297 Held,A,Ticehurst,C,Lymburner,L,&Williams,N(2003)Highresolution mappingoftropicalmangroveecosystemsusinghyperspectralandradar remotesensingInternationalJournalofRemoteSensing,24(13),2739!2759 Jennerjahn, TC, & Ittekkot, V(2002) Relevance of mangroves for the production and deposition of organic matter along tropical continental mar gins Naturwissensc haften ,89,23!30 Jensen, JR(1996) Intr oductory digital image processing ?Aremote sensing perspectiv e:PrenticeHall Kathiresan, K, &Rajendran, N (2006) Coastal mangrove forests mitigated tsunami Estuari ne, Coastal and Shelf Science ,65(3), 601 !606 Kellndorfer ,J,Walker ,W,Perce, L, Dobson, C, Fites, JA, Hunsaker ,C, Vona, J,&Clutter ,M(2004) Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets Remote Sensing ofEnvir onment ,93,339 !358 Kovacs, JM, Wang, JF,&FloresV erdugo, F(2005) Mapping mangrove leaf area index atthe species level using IKONOS and LAI2000 sensors for the Agua Brava Lagoon, Mexican Pacific Estuarine Coastal and Shelf Science ,62,377 !384 Laba, M, Smith, SD, &Degloria, SD (1997) Landsatbased land cover mapping inthe lower Yuna River watershed inthe Domin ican Republic Internationa lJournal ofRemote Sensing ,18(14), 301 1!3025 Lefsky ,MA, Harding, DJ,Keller ,M, Cohen, WB, Carabajal, CC, Del Bom Espirit oSanto, F,Hunter ,MO, & deOliveira, R, Jr(2005) Estimates offorest canopy height and abovegrou nd biomass using ICESat Geophysical Reasear chLetters ,32 Ma ncera,JE,&Me ndo,J(1996)PopulationdynamicsoftheoyterCrassostrea rhizophoraefromtheCi?nagaGrandedeSantaMarta,ColombiaFisheries Research,26,139!148 Mancera, JE, &Vidal, A(1994) Florecimiento demicroalga srelacionado con muerte masiva de peces en elcomple jolagunar Ci?naga Grande de Santa Marta, Caribe colombiano Anales del Instituto deInvestigac iones Marinas dePunta deBet?n ,23,103 !117 Mougin, E, Proisy ,C, Marty ,G, Fromard, F,Puig, H, Betoull e,JL, & Rudant, JP(1999) Multifreque ncy and multipolarisa tion radar back scattering from mangrove forests IEEE Transactions on Geoscience and Remote Sensing ,37(1), 94!102 Polania,J,SantosMa rtinez,A,Ma ncera,JE,&Botero,L(2001)Thecoastal lagoonCienagaGrandedeSantaMarta,ColombiaInUSeeliger&B Kjerfve(Eds),(Org)CoastalMarineEcosystemsofLatinAmericaEcolo gicalStudies,vol144(pp33!45)Berlin:SpringerISBN 3540672281 Rueda,M (2001)Spatialdistributionoffishspeciesinatropicalestuarinelagoon: AgeostatisticalappraisalMa rineEcologyProgressSeries,222,217!226 Rueda, M, &SantosMa rtinez, A (1999) Populati on dynamics ofthe striped mojarra Eugerr es plumieri from the Cienaga Grande de Santa Marta, Colomb ia Fisheries Resear ch,42,155 !166 Ramsey ,EW,III, &Jensen, JR (1996) Remote sensing of mangrove wetlands: Relating canopy spectra tositespecif icdata Photogramm etric Engineer ing and Remote Sensing ,62(8), 939 !948 Rasolofoharinoro,M,Blasco,F,Bellan,M,Azipuru,M,Gauquelin,T,&Denis,J (1998)A remotesensingbasedmethodologyformangrovestudiesin MadagascarInternationalJournalofSemoteSensing,19(10),1873!1886 Restrepo, JD, &Kjerfve, B(2000) Magdalena River: Interannu alvariability (1975 ?1995) and revidsed water dischar ge and sediment load estimates Journal ofHydr ology ,235 (1), 137 !149 RiveraMonr oy,VH, Twilley ,RR, Bone, D, Childers, D L, Coronado Molina, C, Feller ,IC, Herrera Silveira, J, Jaffe, R, Mancera ,E, Rejmankova, E, Salisbury ,JE, & Weil, E (2004) A conceptual framework to develop longterm ecological research and management objectiv esinthe wider Caribbean region Bioscience ,54,843 !856 RiveraMonr oy,VH, Twilley ,RR, Mancera, E, Alcanta raEguren, A, Casta?ed aMoya, E, Casas, O, Reyes, P,Restrepo, J, Perdomo, L, Campos, E, Cotes, G, &Viloria, E(2006) Aventuras yDesventu ras en Macondo: Rehabilita ci?n delaCi?naga Grande deSanta Marta, Colombia Ecotr opicos ,19,72!93 RiveraMonr oy,VH, Twilley ,RR, Mancera, E, Castaneda, E, Casas, O, Daza, F,Restrepo, J,Perdomo, L, Reyes, P,Villamil, M, and Pinto, F (2001) Estructura yfunci?n deun ecosistema demanglar alolarg odeuna trayector iade restaurac i?n en diferente sniveles de perturbaci?n Informe T?cnico MMA, INVEMAR, COLCIENC IAS 331 p Rodriguez ,E, Morris, CS, &Beltz, JE(2006) Aglobal assessment ofthe SRTM performa nce Photogramme tric Engineer ing and Remote Sensing , 72(3), 249 !260 Rueda, M, &Defeo, O(2001) Survey abundance indices inatropical estuarine lagoon and their manageme ntimplications: Aspatially explicit approach Journal ofMarine Science ,58,1219 !1231 Saenger ,P,&Snedaker ,SC(1993) Pantropic altrends inmangrove above ground biomass and annual litterfall Oecoloia ,69,263 !299 SantosMa rt?nez, A, &Acero, A (1991) Fish community of the Ci?naga Gra nde de San ta Ma rta (Col ombia );compo sition and zoogeog raphy Ichthyolog yExplor erFreswaters ,2,247 !263 SanchezR amirez, C, & Rueda, M (1999) Variacion de ladiversidad de especies icticas dominantes en elDelta del rio Magdalena Colombia Rev Biol Trop,vol 47,1067 !1079 Schutz, B E, Zwally ,HJ,Shuman, C A, Hancock, D, &DiMarzio, JP (2005) Overview ofthe ICEsat Mission Geophysical Reasear ch Letters , 32 Simard, M, DeGrand i,G, Saatchi, S, &Mayaux, P(2000) Mapping tropical coastal vegetation using JERS1 and ERS1 radar data with adecision tree classifi erInternationa lJournal ofRemote Sensing ,23(7), 1461 !1474 Simard, M, Zhang, K, RiveraMonroy ,VH, Ross, M, Ruiz, P,Castaned a Moya, E, Rodriguez, E, &Twilley ,R(2006) Mapping mangrove height and estimate biomass inthe Ever glades using SRTM elevation data Pho togramm etric Engineering and Remote Sensing ,72(3), 299 !311 Slater ,JA, Garvey ,G, Johnston, C, Haase, J,Heady ,B, Kroenung, G, & Little, J(2006) The SRTM data fishing process and products Photo grammetric Engineering and Remote Sensing ,72(3), 237 !247 2143 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144 Author's personal copy Smith, JT,III, &Whelan, KRT(2006) Develop ment ofallometric relations for three mangrove species inSouth Florida for use inthe Greater Ever glades Ecosystem restoration Wetlands Ecology and Managemen t,14,409 !419 Thom, BG(1982) Mangrove ecology geomorpho logical perspective InBF Clough (Ed), Mangr ove ecosystems in Australia Canberr a: Australia n National University Press Twilley ,RR, & RiveraM onroy ,VH(2005) Developing performance measures of mangro ve wetlands using simulation models of hydrology , nutrient biogeochemistr yand commun ity dynamics Journal of Coastal Resear ch,40,79!93 Twilley ,RR, RiveraMonroy ,VH, Chen, R, &Botero, L(1998) Adapting anecologi cal mangrove model tosimulate trajectories inrestoration ecology Marine Pollutio nBulletin ,37,404 !419 UNEPWCMC(2006)Inthefrontline:Shorelineprotectionandotherecosystem servicesfrommangrovesandcoralreefsCambridge,UK:UNEPWCMC33pp Valiela,I,Bowen,JL,&York,JK(2001)Mangroveforests:Oneoftheworld's threatenedmajortropicalenvironmentsBioscience,51(10),807!815 Wang, L, Sousa, WP,Gong, P,&Bibging, G S(2004) Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast ofPanama Remote Sensing ofEnvir onment ,91,432 !440 2144 M Simar detal /Remote Sensing ofEnvir onment 112(2008) 2131 ?2144

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