Abstract
Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that described heterogeneity of the outermost layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicated that aerial LiDAR could be of use in quantifying broad-scale variation in structural diversity across macroscales.
Highlights
Forest structural diversity is the physical arrangement and variability of the living and non-living biotic elements within forest stands that support many essential ecosystem functions [1]
The only exception was in the category of external heterogeneity, where there was a weak significant correlation between aerial laser scanning (ALS) and terrestrial laser scanning (TLS) metrics of top rugosity (r = 0.44), but not rumple (r = 0.09) (Table 4, Figure 3)
There was significant and frequent intercorrelation among metrics of structural diversity from different categories (Figure 3). This analysis suggested that ALS-derived metrics of structural diversity could provide statistically similar estimates of structural diversity compared to TLS, though the accuracy of these estimates varied depending on the specific structural metric
Summary
Forest structural diversity is the physical arrangement and variability of the living and non-living biotic elements within forest stands that support many essential ecosystem functions [1]. As a critical driver of forest function, estimates of structural diversity are a useful proxy for predicting forest ecosystem functions. Forest structural diversity arises from the complex interactions of a range of abiotic and biotic factors that influence the growth and the quantity of vegetation [6,7,8]. A wide variety of structural diversity metrics can be estimated using methods that range from traditional forest inventory approaches (e.g., basal area [9]) to next-generation remote sensing techniques (e.g., canopy traits or multivariate structural types [7]). 2s02t0h,a1t2, r1a40n7ge from traditional forest inventory approaches (e.g., basal area [9]) to ne2xotf-14 generation remote sensing techniques (e.g., canopy traits or multivariate structural types [7]). AenLtisDwAiRthiisnafourseesftusltatonodlsf,obrut eacthhLeiDmAulRtip-dlaimtfoenrmsiohnaasltrcahdaera-octffesriznarteiosnoluotfiofonr[e1s3t].stAruLcitDurAeRthisaat uhsaesfuvletrosoatlifloerttehreremsturilatil-dainmdeanesriioanl al chadreapctloeryimzaetniot nploaftffoorrmest ssptrauncntiunrge athamtuhltaips lveeorsfatsipleatitaelrrexsterinatls aanndd areersioaludtieopnlsoy[1m4,e1n5t,1p6,l1a7tf,1o8r]m. s spaTnenrirnesgtraiaml luaslteirpslceaonfnsinpgat(iTaLl Se)xatenndtasearniadl lraesseorlsuctainonisn[g1(4A–1L8S]).hTaevrerebsottrhiableleansesrhoscwannntoinbge (eTffLecSt)ivaend aeraiatlqluaasenrtisfycainngncinogm(pAoLnSen) thsaovfefobroetsht sbtereunctsuhraolwdnivteorsbiteye[ff1e4c,1t5iv,1e6a,1t7q,1u8a,1n9t,i2fy0]in; hgocwoemvpero,neeanchtsLoifDfAorRest strupclatutfroarlmdhivaesrtsriatdye[-1o4ff–s2f0o]r; dhaotawreevseorlu, teiaocnhanLdiDsApaRtiapllcaotfvoerrmageh.aSstattriaodnear-oyffTsLfSoirnsdtarutamreenstoslauntidoAnLaSnd spastcianl ctoheveforaregset.frSotmatioopnpaorsyiteTLanSgilnes,traunmd eoncctsluasniodn AbyLtShesccaannothpye cfornesstrtafirnosmtheopcappoascititeyaonfgelaecsh, atond occolubstaioinndbaytathfreocmanpooprtyiocnosnostfrtahienscathnoepcyapdaisctiatyl tooftehaecihnsttoruombteanint [d21a]ta(Ffirgoumrep1o).rtTioLnSsmoefatshuerecsanthoepy distfaolretosttfhreominswtriuthmine,nptr[o2v1i]d(iFnigguhirgeh1-)r.eTsoLlSutmioenadsuatraesotnhceofmorpelsetxf,rfoinme-wsciathleinin, tperronvaildfienagtuhriegsho-frecsaonlouptyion datsatrounctcuormalpdlievxe,rfisintye-[s2c2a]l.eHionwteervnearl,fTeLatSudreastaoafrcealneosspyreslitarbulcetuforralthdeivueprpsietryc[a2n2o]p. yHdouweetvoeor,cTclLuSsidonata areblyesisntreerlviaebnliengfofrotlihaegeup[2p3e].rCcoannvoeprysedlyu,eAtLoSomcceluassuiorensbtyheinfoterervstefnrionmg afobloiavge,ep[r2o3v].idCinognvtheersheilgyh, eAstLS melaesvuerleosf tdheetafiol orenstthferoomutearbcoavneo,ppyrsouvrifdacine gwitthhethheigdheecslitnlienvgeclaopfacdietytatiol roensotlhvee coauntoeprycafenaotupryesswuriftahce wdeiltivoihnnreceitgerhaeentetaetaasdtvitineioeocgnrnltiicn[co2aaif4nnlt]oghs,pterbcyaoautpvtidfieaetrcchpsaiettttohyioarytc[no2c[3ua2r]nr5e.ad]sAcoayulnLvndSoedftecchmaraesnnetmooadrspeeyutylrriliniafcneeysgaaebttretueshirnoevegfessrvuuwteisbcgie-atedchlata[sni2tntoir6ocap,r2ntyei7fa[]iw2.cs4aFiint]tui,hgorbtncAhuaetLanrtnSmhodepcoayraunecnd,cdidueteperhraptsahcetsyon[rbd2oye3feu]lmn.apyAesoehanLrsosuSwthrocniefnang thetshuabt -AcaLnSoinpsytrwumithenAtaLtSiocnasnpdeceipfiecnadtiounpso, nsutchheaosrpieonintattdioennsoitfyt,hceanovinefrlsutoenryce[2th5e] aabnidlittyhetomacectreiscsssbuebi-ng usecdan[2o6p,y27e]l.emFuerntthse[r2m8,2o9re]., it has been shown that ALS instrumentation specifications, such as point density, can influence the ability to access sub-canopy elements [28,29]
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