remote sensing Article Multiple UAV Flights across the Growing Season Can Characterize Fine Scale Phenological Heterogeneity within and among Vegetation Functional Groups David J. A. Wood 1,2,* , Todd M. Preston 1, Scott Powell 2 and Paul C. Stoy 3 1 U.S. Geological Survey Northern Rocky Mountain Science Center, Bozeman, MT 59715, USA; tmpreston@usgs.gov 2 Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717, USA; spowell@montana.edu 3 Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; pcstoy@wisc.edu * Correspondence: dwood@usgs.gov Abstract: Grasslands and shrublands exhibit pronounced spatial and temporal variability in structure and function with differences in phenology that can be difficult to observe. Unpiloted aerial vehicles (UAVs) can measure vegetation spectral patterns relatively cheaply and repeatably at fine spatial resolution. We tested the ability of UAVs to measure phenological variability within vegetation functional groups and to improve classification accuracy at two sites in Montana, U.S.A. We tested four flight frequencies during the growing season. Classification accuracy based on reference data increased by 5–10% between a single flight and scenarios including all conducted flights. Accuracy increased from 50.6% to 61.4% at the drier site, while at the more mesic/densely vegetated site,   we found an increase of 59.0% to 64.4% between a single and multiple flights over the growing Citation: Wood, D.J.A.; Preston, T.M.; season. Peak green-up varied by 2–4 weeks within the scenes, and sparse vegetation classes had Powell, S.; Stoy, P.C. Multiple UAV only a short detectable window of active phtosynthesis; therefore, a single flight could not capture Flights across the Growing Season all vegetation that was active across the growing season. The multi-temporal analyses identified Can Characterize Fine Scale differences in the seasonal timing of green-up and senescence within herbaceous and sagebrush Phenological Heterogeneity within classes. Multiple UAV measurements can identify the fine-scale phenological variability in complex and among Vegetation Functional mixed grass/shrub vegetation. Groups. Remote Sens. 2022, 14, 1290. https://doi.org/10.3390/ Keywords: accuracy; classification; four-dimensional (4D) structure-from-motion (SfM); grassland; rs14051290 sagebrush; semi-arid; UAV; remote sensing Academic Editor: Martin Claverie Received: 25 January 2022 Accepted: 2 March 2022 1. Introduction Published: 6 March 2022 Rangelands are widely distributed globally and contribute significant ecosystem ser- Publisher’s Note: MDPI stays neutral vices [1]. The grasslands, shrublands, and other ecosystems that make up rangelands with regard to jurisdictional claims in are comprised of diverse plant species with divergent life-history strategies (e.g., annual published maps and institutional affil- vs. perennial), structural differences (e.g., grass vs. shrub), and photosynthetic pathways iations. (e.g., C3 vs. C4), which creates pronounced spatial variability in plant function and high biodiversity [2–4]. Interannual variability in climate, coupled with community and abiotic differences, can lead to large interannual variability in rangeland vegetation function [5–8]. Copyright: © 2022 by the authors. Due to the collective variability and complexity of vegetation communities, understanding Licensee MDPI, Basel, Switzerland. of the drivers of rangeland vegetation processes (e.g., carbon accumulation, reproduction, This article is an open access article productivity, nutrient availability, etc.) across scales is needed to develop effective manage- distributed under the terms and ment strategies and predictive models of changes in a dynamic world [9,10]. Ground-based conditions of the Creative Commons measurements provide crucial information for understanding these processes but are lim- Attribution (CC BY) license (https:// ited in scope. Linking in situ measurements with remotely sensed data can expand the creativecommons.org/licenses/by/ geographic extent of observation, furthering the ability to prioritize management efforts 4.0/). and aid in answering key ecological questions [11–13]. Remote Sens. 2022, 14, 1290. https://doi.org/10.3390/rs14051290 https://www.mdpi.com/journal/remotesensing Remote Sens. 2022, 14, 1290 2 of 28 Data collected at high temporal frequencies and fine spatial extents may be necessary to detect complex patterns and ecologically relevant processes [14,15]. Multi-temporal analyses based on remote sensing are increasingly used to monitor ecosystems and their dynamics over multiple time steps and improve the thematic resolution of remote sens- ing products. For example, analyses of multiple remotely sensed images from within the same year are used to improve classification accuracy [16–18], measure species com- position and coverage in shrublands [19], classify crop types [20], examine yields and performance [21,22], differentiate tree species in an urban environment [23], identify plants with C3 versus C4 photosynthetic pathways [24], detect invasive species [25], and examine the number of annual green-up cycles across ecosystems [26]. Importantly, examining continuous patterns can reveal new dynamics of a system across seasons and over multiple years [26–28]. Understanding the timing, magnitude, and duration of life-history events, phenol- ogy, forms a key aspect of the assessment of the function of vegetation communities [29]. Changes in phenology can lead to ecological disruptions, plant-herbivore mismatch, alter- ations to competitive interactions, and disrupted nutrient fluxes, e.g., [30–33]. Phenology and production of rangelands assessed at the community level has identified important spatial and temporal variability and trends [34–37]. However, differences in growing season length and timing exist among and within vegetation functional groups: aggregations of species with similar characteristics and ecosystem roles. Measuring heterogeneity at smaller spatial extents provides the ability to observe differences in species and functional groups such as temporal patterns in green-up, peak production, and senescence [38]. Measurement at the functional group level is needed for quantifying phenological mismatches, timing management and restoration actions, understanding the species-specific impacts of climate change, and connecting phenological processes between scales [32,38–40]. However, the spatial and temporal resolution needed to identify phenological events at smaller spatial scales, such as within species and functional groups, precludes many satellite-based remote sensing approaches [41,42]. In situ approaches can be time-consuming and logistically difficult across large spatial areas, which compromises our ability to observe ecosystem dynamics at all relevant scales in space and time [29]. The use of an unpiloted aerial vehicle (UAV) can examine such questions at appro- priate spatial resolutions, link to aerial- and satellite-based systems [23,43–45], and build upon ground-based surveys [46–49]. Multi-flight UAV approaches can measure seasonal changes, e.g., [22,50,51], variability between individual plants and/or species [52,53], and can increase accuracy of classifications [54]. However, UAV approaches are not a panacea; there are multiple challenges and questions about approaches to be resolved. Phenology can be difficult to measure in dryland systems [55]. The sensors on UAVs, normally uti- lizing visible color and near-infrared (NIR) bands, work best for classifying dominant species, whereas rare species and some herbaceous species can be hard to identify [44], requiring additional specific data collection [51]. While using multi-temporal images can improve classification, matching scenes between UAV flights can be challenging, e.g., [42]. Furthermore, flight times and sensor payloads are limited on lightweight UAVs [43,56], constraining the size of sampling areas. Aerial systems (planes and helicopters) are used to map large extents of vegetation at fine spatial scales [45,57,58], measure canopy struc- ture [59–61], and survey for wildlife [62]. While aerial platforms can carry larger payloads for longer time periods, they are more limited in takeoff and landing locations and come with increased costs [56]. Appropriate application of UAVs should consider survey area, the need for repeat flights, and research questions [43]. For example, UAVs are demon- strated to be particularly well suited to sampling ecosystem indicators in rangeland settings, e.g., [44,46,51]. Understanding phenology implies measuring vegetation at multiple intervals, which of course, incurs a cost. It is important to understand what inference can be gained (or lost) by measuring more (or fewer) times within a growing season. Due to onboard GPS accu- racy limitations, approaches such as manual co-registration to known surface models [52], Remote Sens. 2022, 14, 1290 3 of 28 ground control points (GCPs) [22,51,63,64], photogrammetric image orientation [65,66], and identifying objects post-flight [42] are used to successfully align imagery from multiple UAV flights. Object-based and/or supervised image classification techniques demonstrate utility for high-resolution imagery, e.g., [54,67,68]. However, the lack of prior knowledge of classes within a scene can impact accuracy [69] and complicate classification decisions. Furthermore, some rangeland species (e.g., sagebrush, Artemisia tridentata) show intra- canopy variability from different growing season lengths for new versus prior season perennial leaves and semi-deciduous phenology [70,71]. However, while supervised clas- sification techniques are often used for increased accuracy and a better understanding of classes, e.g., [72], unsupervised approaches identify spectral variation within a dataset [73] and may be better suited for detecting the phenological heterogeneity that exists among and within vegetation functional groups. Therefore, a deeper investigation into the ability to use UAVs to identify within func- tional group phenological variability is a needed step in linking information across scales, approaches, and components of rangeland systems. Specifically, identification within- group phenological variability at fine spatial scales is a key component for monitoring and downscaling vegetation relationships to climate, providing scale-appropriate data on management, and studying ecological consequences of phenological change, e.g., [29,38,52]. The goal of this study is to examine tradeoffs in classification depth (e.g., identification of phenological differences) and accuracy between collecting data from individual versus multiple UAV flights during the growing season in rangelands. Multiple UAV flights were used to: (1) compare vegetation classification based on a single flight versus multiple flights, (2) examine the timing variations of green-up, peak productivity, and senescence within vegetation functional groups, (3) determine the increased information gained from various, multiple flight scenarios over a growing season, and (4) quantify the increased logistical and analytical effort of various flight scenarios. The prediction was that multiple flights over the growing season would increase classification accuracy due to detection of differences in phenology among vegetation functional groups and by accounting for spatial variability within functional groups, thus enabling better spectral differentiation of classes. In addition, accuracy improvement could be achieved with flights over the first half of the growing season, which would capture green-up variation and initial differences in senescence that would improve the differentiation of functional groups. 2. Materials and Methods 2.1. Study Areas We selected two rangeland sites in southwestern Montana (Mont.), U.S.A., to capture two levels of precipitation of shrub-grassland sites in the region (Figure 1). Argenta, a drier and lower-elevation site, is located 20 km west of Dillon, Mont., in upland sagebrush steppe with primarily alluvial alfisols, sandy-loam soils, at approximately 1900 m elevation [74]. Mean monthly temperatures range from −7.3 to 16.4 ◦C with annual average precipitation of 283 mm (PRISM Climate Group, Oregon State University, http://prism.oregonstate.edu, created July 2012, accessed on 12 December 2019) [75]. The wetter, cooler, and higher elevation Virginia City site is located 5 km east of Virginia City, Mont., at an elevation of approximately 2240 m. Mean monthly temperatures range from −6.9 to 14.6 ◦C with annual average precipitation of 588 mm (PRISM Climate Group, Oregon State University, http://prism.oregonstate.edu, created July 2012, accessed on 12 December 2019) [75]. Soils are primarily mollisols with a mix of gravelly loam and stony loam on the steeper slopes [74]. Both sites are composed of sagebrush steppe; however, Virginia City generally has taller sagebrush, denser grasses, and less bare ground than Argenta. Remote Sens. 2022, 14, 1290 4 of 30 Remote Sens. 2022, 14, 1290 Virginia City generally has taller sagebrush, denser grasses, and less bare ground 4tohfa2n8 Argenta. FFiiggurree 11.. Looccaattiioonnss ooff tthhee Arrggeennttaa aannd Viirrggiinniiaa Ciittyy,, Moonnttaannaa,, ssttudyy ssiitteess whheerree unnpiillootteed aaeerriiaall vehicle (UAV) flflights were conducted over the growing season of 2018. Land cover data from the 2016 National Land Cover Dataset (MLRC.gov)a. ccessed on 1 December 2021). 22..22.. DDaattaa CCoolllleeccttiioonn aanndd PPrroocceessssiinngg WWee ccoonndduucctteedd nnuummeerroouuss UUAAVV flfliigghhttss ((TTaabbllee 11)),, eeaacchh ccoonnssiissttiinngg ooff mmuullttiippllee mmiissssiioonnss ppeerr ssiittee ((iinnddiivviidduuaall ttaakkeeooffff aanndd llaannddiinngg ttoo rreeppllaaccee bbaatttteerriieess,, cchhaannggee ccaammeerraa sseettttiinnggss,, eettcc..)),, oovveerr tthhee 22001188 ggrroowwiningg sesaesaosonn frformom eaeralyrl yMMaya uynutinl tOilcOtocbteorb aetr eaatceha ocfh thoef tshtuedsytu sdityess. iWtese. 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We recorded 300 300 m scene at each site. 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Virginia City resulted in missing data for portions of these scenes. Therefore, all multi-flight UAV classifications described hereafter are based on up to eight flights at Argenta and six at Virginia City (Table 1). 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Table 1. Information for the unpiloted aerial vehicle (UAV) flight dates and for which flights were TTTTaaabaTbbTblTTalelulallebaae esb 1 lbb le l11.le1 .lld.e .I e.e. 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dseseedssdape ae ee at a d udtat tddattdadr a aaw ptt taa twatatwlttaae e aa e erwe wrderrreee o eru r ee teu wweerrreeusurrsee sees s u u–eed ueusdsuusdd.sesepe .ss.. ee..Ldd eer LddLi.Lid.d.. ing.. i .. i ii ..gL Lhgg LihLLiLihtihgtigitg iitg tt hggh r hghgtorthhgtt r te tr wr r tt g gee egregrgrgenieernenrerre nnee egd ee ndneeddonns nn o ot o ed dts tdtdattsdd oss so –t too t–os–ast–tt saootsts nal ssa l l–l –l l l––llaca ––llf a flllfaafllffailll ig li is llfii gfgfh glslflihffhilithillgifit tiitg ttc hgg c hclcthhcatlatl l tll at ttacs cai sclsolscclsailssnllifasiisaafiifsfs;isffcisiisciadcissfcfiafaitfiaaitcffitcitittoriiciaciciokoantotaatninit;nittg; oraal l flfilgighht t cclalasssifiifcicaatitoioi ;ii;o o;;nr noo nneor;o;nn;; ;; ae;;r rr ;; ooan aoornrroongngrraadrrgaeagnnaaoene neg nngd t g gedsegegddooee— ee ot o d dts ttdttpsdd oss o– doot tetoo t–ss–tlst–alttis sl li smsll i ki –iim m–l i ––ll––ligililitmllimiitiiertmtmtteodeieiiidttwditid teii etet tt ee dideednd dd g cclcclallllaacscca sclslscclsclalailsslliafas i sisaafii fsisfi sefssfcasisi sciiacissfcfi a fifaitiffiaitcff t ciicitittoi i ccaiacici ooantotaatnatiitnit;nittoioi; ;ii;o op;; p n noo nnpup;;nn;; ;; u ;;u ru p;; p r pprrruu pplpuurlruuer llllerrppe re p pl ldeldle oe o dt tdtsdos o to t–st–ts s p– p–s–rrsipis son, single flirpdgl lp eedlhole eott ds dttUod so–t to As–stts psV –pr –ss–ircsrpnsi iipnnrgppnrigr rowing sea on classification; dark green dots—peak growing lprari ig igrrnini gin ngg g r g oggrroriowoiwniigni in ngsgg e sa seseaeoasanso so noclnn l ca clsclalsliasafisisfsiiscfififaitfciitcaciaotatitnitoi;i o;no ndn;;ssin gnirgfi ogrr gcow grragwroitroniwioionwgniig nis;in gnesgg ea gr s sa eseeosyeaoenassanb sosco a nolcrna l n—l acs c cllscalflislafssiaiifssgfcsisiiacshffiaitiftcfciitstoicaicioantctaiitno;toi io;nd;n o ndd;a;n ;; ;; ar a;u d; dk rddrrdcda ak kaa trrg raa rrkkgrrgkk e kkr rr g ge eggrrgred bgen e uer reenrre nee ed ee nneeddonn nn o t o d dt t pos dt dttdood— ossro ot—ttootsststptsdts— —sspep——aea ep tpaakp paeeppek keea aeqge a a kkaugar gkk ok kr rr a g gow ogglrr gr igwtr rooirroioynwi wooiinwwgniiig ig; bni ni i lin n nggg agngc g k ssesseeaesasaaeseosseosseeaaeeonoaasss naan,n,ssoo ,sso, os,, n noo sinsns,i,nn, i, in ,, ,ii, n s s,,g ns is gle f siiglissgninileiinlnllgnnge e ggfl lglgf lleeflelf leil ie ig ffg fll bar—no fligfiel hg lei fi fgth llfsi hffilihltihglligit ti cigt g ttU hghg U hhUtthhttA t t A ttA U U VUU V UVAA AA c A VV clcVVcla lV l lonductel a acs c ca s ccdls s llscclalailsslliafas i ssiaafii fsfssisffc isiisciacsisfcfiafaitifaitcffictsifiifciitittoi i ccaiacici ooaantottaat niitnit;nit. t oi ;;ii; o;;g noo goionnngr;g;nn;; r ;;e ;r; rr ;; g eye egry bar—flight ggrrggy yrere ebrr e eyybeeabyy y yar a b b—r bbrraabb—aarrraafrfr——lfrrl—ffili—gllifiiffgfhgllfflliffilihltihglligitsiigtgtt s hghg ss hc ht thht t coscststctt o so no ss cnducted s c cn dnccooccododunnoonnucudnndddtccdudtuetuuttcedcuuceccttdtd ccteetet btt ee b ddeebubdd d u d u tu b bt t btb ttpuubb p uuptoptuutt o t to o tt p o p poprppo r r dat quooorr odo oordrroodar r a trr a d dta tddattdada aaqtt taa tataqtuqttaa au au q q a qaqlauquqli lluiutll iiauuatiiy ttyaattlllayal;iy ; ilil ;ittlliti; tb;; iiyt yt bttbylb;y;yla; l;; l; ll a;; abca bcy; ; bblkc k llbbclalalk kll aac ccaa c ckkcckk kk bbbabaarabr—rbrr—abb—arraanr—nnrron—oo onf fnl flibbaarr——nnofnnolffioloigll ooi fii g f f g fhgllf flli hffilihltih ts glligitsiigtgtthgh gss c hht thht cotcsosctstttos sn o ssc n c c ndnc coocc ducted. ododunnoonnucudnndddtccdudtuetuuttcdecuuceccttdtd.ccteetet. tt. ee..dd ee dd.d.d.. .. .. .. FFFFllilllliFgiiiiFglgFlghlillihihllhiitgiitg ttggtt h hhttt t tt 111 1 111 11 222 2 22 22 333 3 3 4 5 6 7 8 9FFlliigghhtt 11 22 3333 3 3 44 4 4444 44 555 5 5555 55 666 6666 6 6 777 7 7777 77 888 8 888 88 999 9 9999 9 SSSiSitiitiiSetStttSeSei/iSeSii/tDt/iit///iittDeeDtteea///ee/a/D/Data//DtDettttaeae eaa t tata t tteettee ee MMMaMMaayaMMyyy-aa-a2-a-ay--yaa22 2yy- -yy- - -2 -2-2--22 22 MMMaMMaayaMMyyy-aa-3-aa---yaya330y3y-yy-0-0 -0-3- 3 -- 3 033000 00 JJuJJJJuuunJJJnJnJuunJeJJuuee-nuuen-1-nn-e--nne1e121-ee--2-ee12- 2-1-1 --2 11 221122 22 JJuJJJJuuunJJJJnnJuJueJJuuee-uuenn-2nn---2nnee272ee7---ee7- 7-2-2 -- 22 772277 77 JJuJJJuuulJlJJylJlyJulJluyyJJyu-u--uull1-l1-l--ylyl1199ll1yy-yy-9-9- 9-1-1 -- 11 991199 99 AAAAuuAAuugAAgggu-u-uu4--uu--gg44 4gg -gg- - - -4-4--44 44 A AAuuAuAugAAggug-u-uu1--uu--gg1191gg--gg-9-9- 19-1-1 -- 191 991199 99 S SSeSeepeSSpSpSp-eSSe-e1-e-p--ee1p10p1pp--0-0 0-1-1 -- 11 001100 00 O OOccOOctcOt-ttOtt-1-cc---1cc1tt tc1ct -t- - t-t -11---11 11 AAArrAgArrggAgerrreenrrerggrrngnggntggeetateeetttnneeaan nan tnnt t tt tataattaa aa VVViiriViViirgVrVrgVigiigiirririni irriigngnrirrgniggi ggiaiiiniini aa niiann Ci nn ii i CaiCaiiait y iiaaia i at CiC t iiCyt CttCyiiy iiit tittit iy tyty ttyy yy FFFFoooorFFrFF rrFFo o oo rororrr rr eeeaeaacaeeccheecaaeehhaah caca c c hhcchh hh fflfffflillillgiiffiifgfglfghlfllffiilhilhihlltgigitiig,tgttgt,gh h,, h,,h thh ttt,t,t,, tt ,, , , wwwewewe eww eeee ee uuuussesusueueudeuusssddsds eess ee d deeddd MMMiMiMciiiiccaMMcaiiaiSaiciciSSiicecSaacceaeaneSaSannSSnseSSeseessnneeenn esnn ss s seessee ee PPPPyyyPytPtPPhttPPttyyhhhyoytyyttototonhthtthnhnnhh oo o o ononnn nn ttottttoootttotltotolttsloolllosos soo lolo l lsslslllss ss ((h((((hhht(t((t(t(tth(hptt((htthppthhttpsttttst:stpstp:/:ttp:p/:://pps/ss//g//ss://::gss:g//g/i:F://i::/t/ii/tii/go/htgt//ttgghhiggirhiuittitiutuiituhtbhetthhbbhhuab.u.cuu..uu..cbbcocbbho.obb..om.cc..mm..ccoflocc/oo/omomi////mmgm///hi/i/m/cmii//iimtmccac,aiiaisaiciciwsesiiccsaacceenaeassaasnnnsseesseuessnneeenn/essnn/ssi//es/i/smieeissiimeedm///ee/i/i/aii//mimiaaMgiiammgggeaaeiaeapeggaacpgpgpraggeereoerSprpeeooppcoepprrrccercronorreeosoecoocsscsceescciseseissniseeisiinPssnsngssiiigssygig,nini, iin,nt,a ,n,ng g ha gagca,gg,,c,o c ,,c aa,, ccena a ccacaeescceccsstcsceesccosesessesedosesssddsdls eesso ee d o deeoondd(d n n n h oo 1o o 1 onotn1 1ntnD nn p D 1D1 11 e s 1 1 eDDe c e:D Dc/ceceeme/ecceemmccgeeccbeemmeebibbemmteehrebbr brbur2 bebe 2 ee2r0r2breer0r 0 0rr .22 c2 210202o1100)12200) )m)22)t) 1 122 to t 1t1t)t)1)1o/o)o ) ) c t)t)t tm c totcocttoo ono cic nnc cnvooccoavovvoneonsnenernenvvrtvrvrtvvene ttt tee rr ree srrtttrrte t t tt / iimiiiimmiiaiimimiaagiiammigggmeaaeaaesegagasag sgs ggee ge etssseetotetss tt oss o po t tt t tarotottoaoal a oo lil clillg iaaiieigaaglgnlalasliilnilninllsegigiiigegiedeggnndnndd,nneeg, ,ee, d,,d ee , cddd,,ca,co, c ,, oc,, o or ccrrccrooccereroorreorosrecerrrrsccrtcrretetrrtettecceddeccttdtdcctd teteo tt ee d dfeenfddiffd ffi vi i ii1 vvf fveffififieDei-ffevivi-b-iiv-v--vvebebbaee-c--eea-ana-b-be--nbnbndbabmadaaddnan a nnb nnd dsdedstsd s t ratt ttas sasca2ssttctcskstc0tatakttkakas2cacas.sccs1kk. cc..k k.. )s kkss Tss..tT.sTs. T .ho. .. hh ThiTciTsTiiTiihhso sshh ihhi ni ipssisipiivpss p r ss reor prpooprpcopprrrtccercroorreieosoemcoocsscsceescc sesea ss see s isgssiniiss ii ness nn c i siiccilcniniluliintlnllunncuocudccllldccldudlulaelluueeluudediddgd ee ee nd dueedduued u s sd s usu u u, uusssucuss u s s o sis usuinriuiuiinuusrsnsngsseiiigssigg ninic iin n ntntggteghtgt tgtg h hd h et ttete tehthfi tt hh hheevee ee - cccacaalaclcillcicllbiaaicciibaablbrlalaliilrilaarirllbibiaaiinbtbartbbrritttrtidroaiairriiaoaottonatatsititninittn oioi iio ao p ononpcnpnpa nn k a anpap snppnneppa.aeaaelenanaTl lnnl lln nee hi eiemillileeiilm lm ls ll iiai imipmiaagiiammgrggeaaoeaaesegagacsg sgsg gee te estssoeettttss o oss o ttt i tc totoncttcooc o oc o on cc lnnccunvooccovovdvoneonnenernenvvrtvrvrdtvvee ttt tee rr ree urrttatrtr t t asa dtta rrr ddudrriaarriaiaisdiidaandaiddiniinincaiaiiigcaacecnaanenen etnn cc vhc ceeccvvevea ee a a lavv clulvvlllvuvaauueaallllaaelesiuelulbllsuu ssuu ee r t e estssoeetatttss o osst o ttit tr totortteroor n oo ee fe rfrrlf fpfflrrelelrrlleefcafeefflcflflctnlcffetlelattlletteaccaeenacctltntccntnctataittcaacemcnanaenen ennc c a vc ceeccvgvevea ee ae a lavv lulsvvlllvvuaauueaatllalaeloesuelulllsuu,ssuu,ec e,, ,e,e so cssee scs,c,o,ncss, ,,o o,, ro vcc rrccroeoccrerororeroorecertrrrcrctcrrete trrtet te acc eef cfctoftdtfcctff to t o ortti fffr a frrfof o ff no o rororcr r rr e cccacaamaccmmccaacce values t aamamaeeremmrarreeaa aeer lrree le rlralalrlreaeane a a lnll n lnse lellsl es o esnne e nn esennsfse rfsfs f essff efe fff fleeffcfeeffefcfctfcfeftestftfcettesc,cseest, cc,tt,ta cac,,t s nce values,tsts aattnsas,,,ss,n n ,,n da,a, d aaddna na ann nnd daaladdlidl l ill giaa i iig aaglgnlalaliilnilninll gigi eiig g g gneneaenna nna ca co ee cch eecaaeehhaah rcaca i c rcin ect for cahihcciiihnhnn hhd iii d didn i niiinnviinndiidvvdvidiidiiiividviddiivuvivviiuiudiudiaiiddaadlmuaul luul blluu eaa babablrlalal a l lan abllb a n lebnbndbbaadaaddnan a tnn tnnd odttttddood gos ttt g gtge efftototteooetegoogthtgtgttggehehheetcttetertehthttrh hrrs hhiee in ie,eirririeennarnr t rr itiio t tittn i niooiino ndt nnatt t taotoa attaoo s o o saisa lsi anai i aaiin sgsns ng s siiignssigglniniliienlnlllnngege egg ila ggll ilm ielieliicmlleme eeh iai i miaagiiamiggni mimgeaaedaea.eggaa. .g.g W..i gge e WvWee...ee. i ..e WW. .d ee eWW tu t httetthaeeeh h e e tltt eten tehthttbnhhnn hhee a ee nee dnnn nn iiniiiinnnpiiippipniuniiinuu t nnnpupt ot gppttpputtuettuttueeuutdttettdtdtdtt tete tth ee d deetedtdhttd r tt h h he tttetiesethnttseshshhet eso see sia sin le image. We t en in utted these images into M tashape (v1.5.2, Agis ft,thheesseessimieei ii eem m iiaiimimiaagiiammgggeaaeaeaseggaasg sgs ggee e eissseeinisis ii nss n nt itiiottittniniooiin ont nntt t toMtottooMM oo e MMeetetMMatttteaeasaeettsteehststatahhttahasssaaasspahhssphhppehhaaeaae epapa pp p(pee(v(ee( (( eev v v1 (((1(1.1(v(v.5((.vv...5vv151.51.12...1.1..2525..,2..5,5 ,..,5.5 ,,. 2 2.. A..22,,2,2AA, ,, g, , gAAggiAAisiiiigsgossggoiggioifoissisfitfifissfftoo,tssttt,o o,,f foof,, f tf tfSttff,t,t,SS, tt S ,, t,, .t tt t. SS.. .. SS t tSSPtt.t.t.PP. tt P .e. . . ee tPePtetPPtttePPeereettrtseertrtetesbsttseerrbreebburrsssrruussubrbssbbrgrbburugguug,uurr,r ,, rr,,g g rr Rgg,gg,,RR, ,,u ,, u uRRusRRsRsususuisusiuusasisisiiassas)a)ssi )ii)ssi)) ai ai iifaa))fa)aof)f ) )ff o ))o or fffr frrfof o ff oo rororrr rr pppphhhhoppooppothStothttthhotgo.tootggtgrtottroarProogaamgargegrmmratrraeamamresmeetembtrttttrupphhoottooggrraammmmeeirrieecittiteeiitrcrct rtrc tgtr prii i rripcip,priic ccr orR rpo opcopprurccecrorresesoeoocssscescciisseaisniseeisiinsn)sngsiigssgigf.ni. iio.n. ..nn Ag r p t rammetric processing. Alth ugh t e flights at eachcic pprroocceesssiningggAA.gg... l .. lt.. l AAltll htAAttthhllhlolttltlotolltouhthtthuhuuhhogoooggouoghuuuhhuuh gg g tg g gthhtthtth h he t t tete tehth tt fhh fhhelfefffleiel l ieellg iif f iifgfg lfghlfllffiilhilhihlltgigitiigstgttgtghhs shsh a thht t tsatstastatsts t sts tt a ea aaetetaatet t at a ctat e ec cheecaaeehhaah caca sc c hhccsishshit ihh i ti iets ts stt esesi ieissi tt fiti t fiiteofteftftfeeo o eelo fl f lflfl llfoflolfflloolowolllllwlwlololellooowoewedewwddd ee tee d tdeehttddtthd h he t t tete tehth tt shh hheseassee a aeem as s sm mssaasseaamamaee emm f felfefffleiel l ieellgi if f iifgfg lfghlfllffiilhilhihlltgigitiig tgt tgtghh h hthhttt t t tt pplllpaapplnllnsal,l,ia , antpplalplanppln,al la,,na m e nnm,nn,m,, ,, , ifi ,mnm ioiiimmnnnloiiioilooroniniiinrn rrnno o w do orroordddrr r ei rifi ifdiid ffdffdeffdiiffieiftheefrifiefiiffrferfrfefefnfeerrrenennrcrseerrceeccnneenenasencsmnc scsc ee cc i e esissneeieisiis n nss n i ii ifle niniiiennexe nin xgx xhtee ttteet ttxexenexxtntxxtntntetetpl , min rt teett tnnee an ntnntatatna t t nttn ndaa daaddnan a nn rnnd drdderrd e ese rrr sossrreeorroeloesssleeullssllououssutooltdloolitltuit iltuloiilluiiuoouuttontftititnifenittnsoioiiioso sson rno nne esnnces in extent and resolution existed nsseexses ss xx ixee isiieeiixsxeetssxtxetiitxxittiessiesideiissttdtdsstd tete tt ee bd deebddbbed e e tebb twtbtbttbwbeeweettetettwtewettewweeneeenneen ee ee t nneethtnnttt nn h h he ttt ete tehth tt h ho hheeooeeor ee rt r roto httootthrorhrhorrtttorrtotomhthtthmhmhhoooommooosmmsassooaaoioasosoisciisiiscaacsscaasii;saiasi; cici;; ii;;c cs ss ccss;;;ss; ;; ; ; ttthhttetethtrhrbheerfereffroetroewfferfrfoefoeer,or, , ree rwe,n the orthothttheterththerheferoeffreoreferfofe,o ro,,rwe rew,,ee, ,e, ,, wewe eww e eexee x eex px ee ppeepoxxeexoxoporxxpprptrprpotoettootteroroerderrtdttdrtd mte t e deor tetea d eaadldadl l ll l s l la l a al alollloil lcr lr tots othro;rhrttorttoththmhhoeomoromefore,al loa laolr ltrl oh totrhortrotmhthmhoomosmsassooaaoioasosoisciisiiscaacsscaasii saiasi cici f ii cfcsofssfccffsos w o orss f ffr e expo fr rfof o eff o o eroroeraer r a arr ca eec cheecaaeehhaah caca sc c hhccs r ishshi hi te th it iiet s tsstt eses iieisi dt tit it all ortho osaisti iteoettteeo o ee o t tt t totohtottoh h oo he t ttet e tehth tt shh hhesemssee m eem s ssa ssmmassalammllllllelaallleaaeslelalallslltslstelel ttllett e ss see ses ctetxtesst s t tx x ttx eet tteet f txex o enexxtntx r xtntntetett teett ea tnnee a n natnatntat t t nn ttn c d aa daa h ddnan a n n dll sdlaitenl nd aldlda r a l rlglrl ralalggllgaearrare to the aesrreggrrsgtsgstggee tttet e ss see sstttsst t t tt rreerrssreoroessslelsmluosuoltoltlat llest extent and largest resolution identified across all flights to facilitate additionalreresrosreroelseulrs loustoloitutastl iuloiilluiiuoouuttonttititninittn oioi iio o i ononidinniii nnd d de ii ieiener calcd eidiiinddnntdeetiteettitnnfeeiifninifftnntffiteitititieiettfdfiefifiidfifididff eiei ii ee ad deedadacad c cr ca aroraarcoacaosoccrrrccssrsroorrs soos ssoo s as sssaalssa lss ll l ll la al ll aa lf lalalfllflflff il ll illg i iff i ifgfglfghlfllffiilhilhihlltgigitiigstgttgtghhs shsh thht tt tststsottstst o sso o t t t tf totofttafoofff oo aa ca ff cfcifcfafialiffialaiilclaacillticictiiatcctilttlilialaitilailittlltietittiitateae tteaa t tat ta teteattaeeda ee d d da aadadadaididtiitdidittdttidoidiiioiottiontitiititninittnaoioiiioaoalaononl lnnl llnn aa r aallralarlr l la alls a rr rstssrrataetrrtattaesseasaressttrt rsstr tete ttc eerrreeccarcr rar a l acl cllclclaacucaaullualaullclclalclcluluaccatauutuluitlltlittoalialiiillaoaottonaattititninittnsoioiiioso.ssonon. ..n n.. s nnssW ss.W..Wss. .. .. eW Wee eWW eeee ee ttrttttrarrattatnatrtrtrnnttnrsraarrsaafssnanafeffnnffesnnsesressffrfrssfrfeferrffreerrrederrrrdrddr reerr M ee d deeM ula Mddd e M Mee tetMMatttteaeasaeettstet t ehsstatah i htha osasantassaasphhssphh s ppehhaaeaaeap .--p-pepeWe trae-pap--epre---eer-or-rp-po--oppodprprrddrrdouorroouuodoudcddcceducue n ueudeuuccddc sec ef deperreddc ecpe d epdpdrd r or pr po oppdopprrrddrdrouorroouudooudcddc M ctducutstututtuuscc ss e cc( tt tc( ta c te(sts(ts((ttesse . e ss . s g( (. (.(.. g(e(geg.( h (ee.,...ee.,. ., a gg.,. ,,o ..g g .go p g..o.,ro,.,. r, e r,t ot -tohorphroduced products (e.g., orthomosaics,,. .,tr, oht otrrhortrtorrtoDigital tt omhthtthmhmhhoooommooosmmsassooaaoioasosoisciisiiscaacsscaasii,saiasi, cici,, ii,,Dc cs ss ccDDss,,,ss,i ,, igi,, D Diii gDDggiiitiiiitgiatgittitiggaailggiaiilt tiltilt lliiEt ata ttEaaEllEalal l le l Ell Eeev EeEElvllvlvaelelalleaetavveetitvvtttiovvaiaiiiaoaottonatatititninittn oioi iiMo o ononMMnn nn o M Mo o odMMdddoeoooeelodoedlslddllldsee ss ee ll lee lsslslllss ss ((D((((DD(E((((ED(DEE((MDDMMEEEEsEMMs)ss)MM)))) )ss)si)) ss) i)n) i ss)iii))n)n)nt ) ) t)it i Elevation Models (DEMs)) into Ar ii)o tttni n iooiino nAt nntt tA totAottoor o o rAcAr rAA ccGcrrGrGrrccIrrccSIGGIccIISGGS S1 III I1 ISI1S01IISS0 0 SS .0 11. 6 ..1 1 ..606101 60( 0 .. (0.0E( . (66..((E..6E6E S6 6( (( S (S(RE (S E((ERERESSISS,IIRSSRII, ,,R R,, I R IIRI,Ie,I,, II ,e, eRdRe,, R dRddRleelaleeldlldeeaanadddlnllnlndalallldaaddnansanns,snndsd, ,d,dC ,, ds ss C Css,,A,ss, ,,A ACC,, C C)) cA) AfG ff)o)oIS 10.6 (ESRI, Redlands, CA) for additional) C A)f)A o f)f)or ) ) f)f)fr frrfoa f o ff o ao ardroaordrr d drr aa da adadaiiddtiitdidittdttidoidiiioiottiontitiititninittnaoioiiioaoalaononl lnnlp llnn aa p paaplrlalal lr lor prllp oop pcopprrrccercroorreeosoecoocsscsceesccisseeissniseeisiinssnsngssiiigssgig ninis iin n s nnggtsstggett gg tt e ssesp es sttptpstpstetettsee ssppeea p pa apspsasss ss da d adsdasaes e esst ed tat d tttdaeaiaeitlprocessing steps as detailed below. a ass d deet iteeitliietlatallltteaaedieiaiaildlilidld iielel ll ee d deeddd bbbbeeelebblolblbllobbeeooweellwleewlolol.lloo. owo.w. .. ww .... .. . . WWeeW We eem e ee emepepmllmlpooppylplyloyed a four-dimensional structure-from-motion approach (4D SfM) for ourWWeWe We lmulti em-ed mee p eamepmltoelployppilyemol ollleooedeoyoydydyd yyee a ee d daeea add fd f ofa af ff oaao u oa a ff fu fuu forfofforo-ruorou-d--uu--duurrdrdirr--i-mirr-i-d-iidmm--dddiieiimimieeniiemmnnnseeseisesnneeioiinniiosnsosonssiinisnsinaoioiiioaoalaononl lnnl llsnn aa satsalslalatrlt t l ltt rslulsr s r susttutusstcrtrtrctctrrucutrrtututtuccuurcctttcrtrtuetueur-r--frferfre--er-of-fof-rfrmfroro-om--m---agery [65,66] and used grouncertuteud-urf--refrtfeor-ae-fom-rfrfmgror-oemo--mtms-o-m -moo--tommtittittooiiiioottonottititninittn oioi aiio o onoanapann pnn p p a a t paapaparpprorprpooppaopprrraacraroorrcochocaooahhaah caca ( c c( h4(hcc(((hh44 Dhh4 ((D ( D( (4(4 ((44 S D44D SDSDfSf Mf f ffSS M M SSffSSf)ffMf)M )ff))) f MM f off)f)f)o)o )ro ) f))f fr frr fof o ff o o rrrouorr u ur uor o oor rouoru uu uurrrr r rr mmmummuuummlltlltulluitttutiu-iiuul-liild-l-ttl--tldtdllititdiaitt--i-ia-aii-td-adt--edtdtttdaeae eaa tit ata ti mtieteiiittmeem ee i ia ii mimiaagiiammgggeaaeaaeregagarygrgrggeeyyy eerr ree[ rr [ yy6[rr[[y6 y 6 [ 5[[ 5[6,,[6,5656,5,,6],],6] 6 a6]6]a] n] ]n a dadan n n duud s s ue ueussddses e deggd r r go gorgruurornoonuududn n nt dttda a tr tt rtagtgarareregtrtgtgses e tte tststos o s t tt toatao os s o as saeasseasssssesse s sest estssths s h s t t set eth t ht hpehpe e e e p rprpfef pfeoerrreorffrfrfomfo forormaramrnnmacacaneanen c n coecoecf fe f eoi oiofimfof f ifi miaiamigmgaeaeag ag gege e er f i aannadadan an fnf dnflldili difg fgf llfhiliflhlgititgitg h ga hahtlthlt il ita itga gallanilinlgilimgigngnnmenemnnmettet.ne. e [y6y 5n [5, [6[,6,565,65]6, ]6,]6a 6a]n6] n]d a adna nu nd udsd es u uedusds esge d egdrd or g ogugruronronuodudun nt nd atdtad r tgr tagtaeraetrgrsgttge set e otstso s ta toasto os aes asaessse seste hsttsh es te thtph hepe er p pfrpeofferoerrfmrfofmoroarmranmncaacenane nco c eocfe eff oi omifoimf f ia miagimgeaae gag gee e aanndaadna fn ndl ffidldl g iffi glfhl fiilhltgiig tatgh h althti tl lgati ia glnlaiilnlmgiigmgnnenmenmnte.ettn e.Tn..n tnnT ttTtT t h.t.t.. tt h ..h ThT. . Teeh T h4 he4heD e 4 e4 4DS D4Sff Df SM SSfffS M ftt fMteMe ct tc tehthecetncenhcihichinqqhnnuiiunqiiqeqeiu quw ueue e ow eo wrrwokokorsorsk r kb rkbsksy sy b s bii byinbyny icy icni llinlunicuncldcldulcliuiulidnundigidgni i ns nigsngugu sg r srusvuvsureurevryvyrve vep yepyeyh hpy popohtpththohootgtgototrortogaogagrpgraphs from all flights (i.e., datese. )T e Th4ih nh4eDe D e4 t 4S hD4 SDfeM ffS MaS ffS lfMtif e tgMtec nttc hte mhtencecnhicehqihinnquninituqie,iqe qucwu a uweeom ew orw ekrokrosrroa srkbr k cbsksysa ys b ilb ni byiibync y iir clni auinllncutdclilcdouliluninudindg,diig niasin ngnusgu g drss vsr usvugeurreeyrvrvyo vepe r ypehey y hfpo ep opthrhottheogonttogrtoctoargoiagpnrgrp prrhaargr haahhpsapappso s ppshhf ffh hrf fshhsffsrorgs sr ssof fof om frrfr f rmffmrroorr oo omom mmoun d aaalallllll laal llf aa fl lalfafllfflilll i lg li ifllfii f gfg lfglhfllffiilihlhilhltgigitiigstgttgtghhs shsh ( thht t( ti(s(tsts((i.ittsis .ei i .ss .((.. (e (e.e i (i(i.,i(.(..i.i, ..,eii,e. .d,, ..ee .d..dee.d,a,,.., .,.a, atda,, d t edtdtttdaeaeseaattsata)stst)ete ))tt)e) ei ss seei nisis))ii)nss)n n) ) ))iit i it nihntitititnnhh nnh e tt t ete tehth att h h ahheaelaelei l leei llgia a iii g aaglgnlalaliilnilninllmgigiiigmgmggnnnennnmmeenemmnnnteet,teett,tn nee,, cn,,n tnn tctcatc,t,t,a, tta ,m,a c,, c mm ccaacceaamamaeeremmrarreeaa aeer rcree rrcaacrrcaa a la cl c ill cicllbiaaicciibaablbrllaaliilrilarirllbibiaaiitbbartbbrritttitrroaiairriiaoaottonatatititninittn,oioi, iio,o, a,,ono n annaa,nn,,, n ,n, nda,, a d aaddnana ngn nnd dgggeddd e eo eg g o gogroggeerererooeefoeorfrooreffffrreerreeffrfeerfrfefefnfeerrrenennrcreerrceecicnneeiininiicncngcciiigccgig nini iion n n ngogofoggf gg f f ffog o ogogffogorfffr control steps f or ffrg g oo ggourggrruurruonorroonnouonduuudduudnn nn nnd dddd cccocooonccnnccntoocctortotttonnrornrnotnntotlotrtrtlr lttlr lrlo os rr oosltsloolslt e tl lt ttll es sespe ssttptpstpstetettsee ssppee op p pspossoofss f fss f ff oo of the ph t oothtfofotfttfh fh f he ff t ttet e tehth tt phh hheepppehe ee hh phop ppooppothhthoth othoototgtogtotrogrammetry process. The process located objects (e.g., plants, rocks, litter, etc.) that remaintthogoettogdrtotoar rogogaaigm garggrrmmrraaraamnrafimam memmeetemmtrtxet t dte reyrreeyttyteey trtrt r ptt rr yyprrpypyr yy ror rpp ooppocprprrcrcoreoeocssclocaticerooreosncosce esccs.se se.s s.see. s.. Tts s s TssT..T.hss. .. hh.. TheT TTee Tehh ph h hrou hheepppere ege r hor prp ooppcopprrrccercroorreeosoecoocsscescse sethescec seessel s loslssllloss o ocs clnts llica l cololallaootaocotcettccttaeaccedeaatdtatre tiad td mte te tt eoe d deeooddobd b b bj oo jejoojjjeobobecebbcjbbjctjcjetjejsjttejjttescc sees cc t(t tcc t(stes(ts(((ttses e .ess . g( (.(.( ..g(e(geg.((ee.,...e,.,g. , .g e series. Effe. .g.e,,p.g . g.g.p.p,,p,..l, . .,l,a ll,, pllpa anapppplnllnlntalaltlctivels taatttnanas,snsn, ,tnnt,t ,,t s tsts ttss,,,ss, ,, ,, rrorrooo ly, 4D crrrcckrrcoorrkkooksococs,sccskk, cc,,k k,, s kkss lssl,i,l,ssl,il i, ti,SfM lt,iii , tt lttlletltiilttileideet llrtitietiittrttr,n,rt e te,, tt,,e e rt rr eerr,,e,err,t e ,, tct, , ttt ecec.ce.e)t.tt.ee)t.. )t)ctc)) t tc c. .t.cc.))t)..ht)t ..) t)t h )h) h a tttatatahthtt thhttt hha a raattatarter t rt ette m e rrrmmrreremeaamiiinanaaieienidindne e deidii n n i i niifnfnfi i i x xffefiifexidixdxe e deldll o o cllcolaloaotctctiiacioaoatttniitnoiisoson n nsttst sh h ified thousandseraemeoaimnifianeagiiaednirindo nee ud iednind in d ii nifinicf n fxi oix ffenifiedfxitidx rexeo d ledolld loc p llcaololaotcoiitnctoaciiaotttnasitintsoiito snoh n tsnsahst ttsh r tt trotrtrhthottohhourhhrruurruogorrogogoare cu gohuou uhhuuh gg ng g g ghththhtt h tt h h h e tttete tehth tt hh hee e n n ettetineisiste hneen et nt etiehttnrein iinrertnntrtteieti tieittr riri ii rtreerrtiteet tti emei i ii mm ttttititeiittmimiee iiemm seesees s e e e re sssrirsrsieeissiiieerrsreerirsiris.ei. i.e se s.s. . . Effff rough the temp i.srei .ioe.s se sr..s a.. l EEEffEfEefEEffeEfefcfefffcfctfcfefteitftfeitteviccieeiivcvcttvetcctititieeilttevivilyliivlvllyvveeyy,ee,l l,lee,l ,,y lyl4 llyy4,4yy,D,4, ,,D D,, 44 44 D4D4 S DDSSfS f Mf f ffSSM MSSffSSf ffMf M ffi idMMiiiid d d e iiie iendiediiinddnntdeetiteetttinnfeeiifninifftnntffiteitititieiettfdfiefifiifdifididff eiei ii ee td deethtdtdttd h h h o ttto totouhthtthuhuuhhosoosoasousouauaunauusssnnsnsdaassdaaddnasnanns snndsd d od dsssooofss fss ff ffoo g oofgofofgfgr f f rffor rgg oogguorggrruururonorrononuonoduuudduudn n nn cnnd dcdcodcd o oon cc nnccntoocttotonrrnontottlrlt lr or opolpl l ol op iippinonotoititnisisn nt ttstsh sh ta tathttht h a aperiod of the imagery to reference all photographs priorcortttnoorntntolrtrb tlrlo pru ollpoi lll opid npipiointoniiosttnig sintnt ostshtts rhsa ttt athht htthoa atta trat t t r ertt r aae ea ar recrce oe o c ncnocsosoniinsinsttsitsiesieisntstntettet ne nnttt t through t e t mporal period of the imagery to ref r nc mo e arras c reracoe eoin cc ncsoscoisnoasinisntsnseiittdssienisttnsotet etttne nh tnt et t r tthttthhrtttotrtrhthttohhourhhrrurruogorroogouoghuuuhuuh ggt gg t g ghhttthh hhe ttt te te h tht thh t hhteetteem ee ttt m tt e teptteemmeeppmomoproppparprpooaloaorlroor lp lrlraa rr apaplelalal l le rpellp p i prrppieoeiiieerorreeodririidrirdoi oi iioo d doofddfd f fftoo t h ootfttofoffh houtput products [65,66]. Followfe f tfftt te te h thi tith h im hhieeiimee ee iiai imi miagiiammggeaaaaeregagaygrgrggeeyy eerrt ree trr o yytrrttyyoo yy rttt t tortong the 4D Sf toerMtoe f ooe rfrre fffr reerreefffeerfrfefenffeerealigrren nrcreerreenc cnneenen ennca c c acaeelcacleell l eell laal pll aa l lpalaplplhlll lh lh phllop p pooppothhthothttthhoogootototgtgment, we segrto tottrooarrogapgoaagp garggrrprp prrhaarrhaahphsapapps spshhp hh ppshhssprss r irss prpi oiip piiopprorroririiated ima r iroit oi tiioo g ttrrottroorore rr bttt t btotobbuttoo uo o u ubib ibl biilbbuiidullluludduuidiiillilnilidliidlnlnds by datnldgedii iggig nini iin n n ngggg gang d oooorrtrrotohttootthrorhrhosr rtttorrtotomhthtthmhmhitho ooommooosmmsassooaaoioasosoisciisiiscaacsscaasii saiasi cici a ii ccsassaccnass nss nn d aa daaddnana nn o nnd dooddotd t ht t tt oohh hoeotototetertehthttrh hrr hhee o eerorroeeuorr urr u u oto toptotuototuppupuutttututputpttpp ttpputt u p u upuutpttptr t t rttor prp ooppdopprrrddrdrouorroouudooudcddcctducutstututtuuscc ss cc t[t tcc t[e to complete the remaining photogrsts6 [ts[[t[ts6s 6 5s6s [ [5[5 [,5[6[6,,[[,66,,56565565,,6amm,,65]e6, ],.]],,66]]. 6.6. .. 6F6 ] ]6]6]FF.].]F..tryo ]] ..o o.. l oFF lllFFlllFFolollloolwolollllwlwlololillooinowoiwiinwnigigni i n ntgtgthg h te teth tsteps (iwnggiig neiint n nghtgte gh e ttre that 4hh hhee44D4ee eeD D 44 44 DS4D4 DDSSfS f Mf f ff SSMM SSffSSf ffMf M aff aMMalal l iltion of po i ilnl g iaai iigaaglgnlalaliilnilninllmgigiiigmgmggnnnnmeemnnt clonenmuenmndte et,teett,t ,n nee,, n,,n w tnn ttwtw,t,t,,su tt ,e, ,, wewe ew rwf eeeeac ee e ssesseepesssppspsaeessaeearpapeerpparrppaataartraraettttrraeaerrdeaattdatadtd tete tt ee id deeimididiimd m iiai imimiaagiiammgggeaaeaaesegagasg sgs ggee b e esssbeebbyss ss yy y bb bb d bbyyddyyda y y a a tdad tetddtttdaeae eaa t tat at teteaattneae een n ndaa daaddnan a nn snnd dsdidssdi t ii tii e tstss ttesesi ieissi tt iti tt iiteteottette o oee o ttt tc totocttcooc o oom cc mmccooccpooomompppmlmlellpllpeetepptppletlltlttelel llee tt tee tt tetehttette h he he ttt e te tehth tt h hr hheerrere e e me rrrm mrreerraeemmeeaaiamminiiiinaanniaaiinaiaiininiiingninniiggig nini iin n p n nggppggph gg h h hpop ppooppothhthothttthhoogootototggtgrtotottrooarrogogaagmgarggrrmmrraraammmeemtttrereyteytt r tr yrsysty t t es esptstptetsespe p pssmodels, orthomosaics, etc.). 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We created a digital surface model (DSM) from all points in the dense cloud and a digital Remote Sens. 2022, 14, 1290 6 of 28 terrain model (DTM), which uses only ground points and interpolates the elevation beneath vegetation. We calculated vegetation height using the raster calculator in ArcGIS by using the difference in DEMs technique, effectively subtracting the DTM from the DSM. To provide additional information for classification, we created a texture raster layer based on the peak growing season flight for each site. We first created a single band image using the grayscale function from Image Analyst in ArcGIS. We equally weighted the red, green, and NIR bands from the peak growing season flight, effectively creating a grayscale version of a color infrared image. Then, using the focal statistics tool from Spatial Analyst, we calculated the standard deviation of a circular five-pixel region of the color infrared grayscale (single band) image. Window size and shape were chosen after testing other buffer distances for edge and scale effects [79,80]. Vegetation height and texture were normalized to match the range of NDVI values. 2.3. Image Classification We classified vegetation for four sets of UAV flights (Table 1) to compare phenological information and classification accuracy differences between four scenarios of flight timing and frequency. These scenarios include (1) a single flight at the presumed peak of the growing season (hereafter “single”), (2) three flights over the growing season consisting of an early, peak, and end of season flight (hereafter “limited”), (3) flights during the early and core part of the growing season to cover initial green-up and the beginning of senescence (hereafter “spring”), and (4) using all available data covering the full growing season (hereafter “all flights”). We performed an iterative self-organized (ISO) unsupervised data analysis classification using NDVI from the selected flight(s) and vegetation height and texture data derived from the flight at the peak of the growing season. We allowed for a maximum of 30 classes. We repeated this approach for the multiple flight scenarios but included additional NDVI values with the same normalized vegetation height and texture. For Argenta, the all flights classification was based on 10 inputs (8 flights), the spring classification on 7 inputs (5 flights), and the limited classification on 5 inputs (3 flights). For Virginia City, the all flights classification was based on eight inputs (six flights), the spring classification on six inputs (four flights), and the limited classification on five inputs (three flights). For each scenario, we examined the mean height and NDVI of the resulting classes along with visual interpretation to assign classes and collapsed classes together that could not be categorized into separate vegetation types, densities, or phenological patterns. For the single flight scenario, we used a single mean NDVI value; however, we used the mean NDVI values across included flight dates (i.e., a partial phenological curve) for each included flight for multiple flight scenarios. We first classified the image to identify a base set of six functional groups: bare ground, litter, sparse, medium, and dense herbaceous, and sagebrush. Differentiation between sparse, medium, and dense herbaceous categories was based on mean NDVI of the class (NDVI or peak NDVI for sparse between 0.2 and 0.3, medium between 0.3 and 0.4, and dense > 0.4), supplemented by visual inspection of pixels and areas of the class. Sagebrush classes also had an NDVI or peak NDVI of about 0.45, but vegetation height was higher (mean about 0.3 m versus < 0.1 m for herbaceous). Soil and litter classes were identified based on NDVI patterns that showed only minor deviations over the growing season (due to changes in moisture content and possible pixel mixing), height (for standing litter), and visual inspection of color and/or location in the scene. In addition, if possible for a given scenario, we identified subcategories of each of these classes to reflect differences in green-up and senescence timing and height. We chose to use NDVI as our primary input into our classification algorithms as it is a surrogate for plant photosynthetic activity and was an indicator that matched our objective of examining phenological differences. In addition, even after post-processing, we had illumination differences due to clouds and shadows among flights which caused discontinuities on the five original bands produced by the MicaSense RedEdge-M camera that can impact the classification algorithms. Illumination differences were greatly reduced Remote Sens. 2022, 14, 1290 7 of 28 or eliminated by employing a ratio of bands such as NDVI, which removed brightness issues within and across flights. 2.4. Accuracy Assessment To assess the accuracy of our classification scenarios, we performed analyst-based classification to derive reference data following recommendations for using the same orthomosaics as used for our classification, see [81,82]. At each study site, we created a stratified random sample of 1000 points, stratified on the classes derived from the all flights scenario. We used zonal statistics to calculate the mean NDVI for all flight classes for each flight date to create NDVI curves representing the phenological pattern for each class. Then, for each sample point, we extracted NDVI values at each flight date and vegetation height as derived from the peak growing season flight. Then, at each sample point, we deduced the reference classification for the point by comparing NDVI curves, height ranges, and RGB images from available flights. These reference data were then used to create a confusion matrix for each classification scenario at each site. Although reference data were based on all flight classifications (with subcategories), we collapsed subcategories into the main class to allow comparison across the full set of scenarios. In addition, we used our base set of six functional groups (bare ground, litter, sparse, medium, and dense herbaceous, and sagebrush) as a standard comparison of scenarios, as the single flight classification approach contains no subcategories. For all confusion matrices, we also calculated the kappa statistic (the classification agreement relative to random allocation) as a measure of accuracy [[73], but see [81] for limitations]. 3. Results 3.1. Flight Classifications The single flight classifications based on the 12 June 2018, flight at Argenta and the 27 June 2018, flight at Virginia City resulted in our base set of six functional groups. Most multi-flight scenarios could identify different phenological patterns within functional groups, i.e., subcategories (Figure 2, see also Appendix A). Subcategories were defined in several ways because we found that areas within the same functional group were actively growing (or peaking) at different time periods. Subcategories were defined by differences in the length of the growing season (i.e., how long pixels had NDVI values above baseline), the timing of green-up (NDVI increase), peak values (day of peak NDVI), and senescence (when NDVI begins to quickly decline). Furthermore, at the Virginia City site, sagebrush heights covered a larger range, and we could differentiate sagebrush into two height classes that also coincided with the magnitude of NDVI values. Including subcategories, we differentiated 9 vegetation classes at Argenta (Figures 2, 3 and A1) and 10 classes at Virginia City (Figures 2, 4 and A2), using the all flights scenario for each site. In the sparse flights scenario, we were unable to identify subcategories at Argenta, which resulted in the 6 main classes (Figure A3); however, we identified the same 10 classes as in the all flight scenario at Virginia City (Figure A4). For the spring scenario, we could partially differentiate the 6 classes at Argenta, with differences observed in the herbaceous class but not in the sagebrush class (Figure A5), and fully differentiate all 10 classes at Virginia City (Figure A6). Remote Sens. 2022, 14, 1290 8 of 30 at flight 3, and then declined, with differences within herbaceous functional groups related to NDVI decline. The subclass ‘short season’ quickly declined after the peak at flight 3, whereas the moderate season remained close to peak values at flight 4, then declined quickly after flight 5. Flights 3 and 4 were 15 days apart, so these phenological differences represent at least two additional weeks of increased photosynthesis. Sagebrush short and moderate seasons were similar to herbaceous classes but shifted one additional week into the growing season (short season declines after flight 4, moderate after flight 5). At Virginia City, senescence dates across classes were similar, and herbaceous subcategories were differentiated by early or late green-up dates. Early season herbaceous had a peak value at flight 3 (the first available at Virginia City) and slowly declined through flight 7, whereas late-season continued to increase through flights 4 and 5 before declining. Flights 3 and 5 were 37 days apart, representing a peak green-up value about a month later for the late-season phenological subcategories. Differences in sagebrush subcategories were related to height, with a short class (mean height about 0.1 m, primarily in the upslope portions of the scene) and a tall subcategory (mean height about 0.25 m). In addition, there was one class where pixels were mixed between sagebrush and herbaceous vegetation with an early peak green-up at flight 3 (from herbaceous Remote Sens. 2022, 14, 1290 vegetation) and a long season with plateaued NDVI values through flight 5 and a drop 8 of 28 after, similar to sagebrush, and with height values (mean height of 0.24 m), similar to the tall class. Remote Sens. 2022, 14, 1290 9 of 30 Figure 2. Comparisons at Argenta and Virginia City of Iterative Self-Organized (ISO) unsupervised Figurcela2s.siCficoamtiopnasr uistoilnizsinagt aA srigneglnet aunapnildotVedir gaeinriiaal vCeihtyicloe f(UItAerVa)t ifvlieghSte alft -tOher gpaenakiz oefd th(IeS gOr)owuninsgu pervised classifisecaastoino n(ssinugtliel)i zainndg faligshintsg alecruosnsp thileo tgerdowaienrgi aslevaseohni c(lliem(iUteAd,V s)pflriinggh, tanadt tahlle, speeea Tkabolfet 1h)e ing r2o0w18i. ng season (singlUe)ppaenrd leflfti ganhdt sriagchrt oimssagthese agrreo owrthinogmosseaaiscso nfro(mlim thiet epde,aks pgrrionwgin, ga nsedasaolnl, fsliegehtT aatb Alerg1e)nitna a2n0d1 8. Upper Virginia City, respectively. Input variables are normalized difference vegetation index (NDVI; one left anodr mriuglhtitpliem daagteess, raersepeocrttihveolmy),o vseagicetsaftiroonm hetihgehtp, eanadk tgerxotuwrein. Mg assekaesdo nareflaisg, hint awthAiter,g ceonvtear athned Virginia City, rUeAspVe cgtriovuenlyd. cIonnpturotl vsatartiiaobnl easnadr/oern voerhmicalelsi.z eSd—dshifofretr ednucraetivoeng gertoawtioinng isnedaseoxn(, NMD—VmI;oodnereatoer multiple datesd, ruerastpioenc tgivroewlyin),gv seeagseotna.t Eio—nehareliyg ghrte,eann-udp,t eLx—tulartee.r Mgreaesnk-eudp, aUr—eausp, silnopwe/hshitoer,tecro, vMeirx—thme iUxeAdV ground herbaceous and sagebrush, T—tall. control station and/or vehicles. S—short duration growing season, M—moderate duration growing season. E—early green-up, L—later green-up, U—upslope/shorter, Mix—mixed herbaceous and sagebrush, T—tall. RemoteR Semenoste. 2S0en2s2.,2 1042,2 1, 1249,01 290 9 of 2180 of 30 FigFuigreu r3e. 3M. eMaena nnonromrmalaizliezded ddififfeferreennccee vveeggeettaattiioonn iinnddeexx( (NNDDVVI)I)v valauleusebs ybyd adyayof otfh tehyee yarea(Dr (ODYO) Y) for thef onr itnhee nviengeevtaegtieotnat ioclnascslaesss epsrpordoudcuecded tthhrroouugghh aanni teitreartaivteivsee lfs-eolrfg-oanrgizaendiz(eISdO )(IuSnOs)u puenrvsiuspedervised clacslsaisfsicifiactaiotino nofo ifmimaaggeerryy ffrroomm nniinnee uunnppiliolotetdeda eareiarilavle vheichleic(lUe A(UVA) flVig) hfltisgahtttsh aetA thrgee Antragseintetain s2it0e1 8in. 2018. DoDtso tasnadn dararrorwows saat tfflliigghhtt ddaattee 227744 rerepprerseesnetntth tehcel acslsasms emaneavneg veteagtieotnathioenig hhteiagnhdt oanneds toanned asrtdandard devdieavtiiaotnio. nC. oCloolrosr saraere aassssiiggnneedd ttoo mmaatctchhF iFgiugruer2e. 2H. eHrbe—rbh—erhbearcbeoauces,oSu—s, sSh—ortshdourrta tdiounragtrioowni nggrowing seasseoanso, na,nadn dMM——mmooddeerraattee dduurraattiioonng grorowwinigngse saesaosno. n. Reemoottee SSeennss.. 22002222,, 1144,, 11229900 110 of 2380 FFiigguurree 44.. MMeeaann nnoorrmmaalilzizeedd ddififfefererennccee vvegegeteattaitoinon inidnedxe x(N(NDVDIV) Iv)avlualeus ebsyb dyady aoyf tohfet hyeeayre (aDrO(DYO) fYor) the 10 classes produced through an iterative self-organized (ISO) unsupervised classification of for the 10 classes produced through an iterative self-organized (ISO) unsupervised classification of imagery from 7 unpiloted aerial vehicle (UAV) flights at the Virginia City site in 2018. Dots and imagery from 7 unpiloted aerial vehicle (UAV) flights at the Virginia City site in 2018. Dots and arrows at flight date 274 represent the class mean vegetation height and one standard deviation. aCrorolowrss aartefl aigsshitgndeadte t2o7 m4 raetcphr eFsiegnutrteh 2e. cHlaesrsbm—ehaenrbvaecgeeotuasti,o En—heeaigrlhyt garnedeno-nuep,s tLa—ndlaatredr dgerveeiant-iuopn,. CUo—losrhsoarrte (gaesnsiegrnaelldy tuopmsloaptceh), FTi—gutraell,2 M. Hixe—rbm—ixheedrb haecreboaucse,oEu—s aenadrl syaggreebernu-suhp. , L—later green-up, U—short (generally upslope), T—tall, Mix—mixed herbaceous and sagebrush. 3.2. Accuracy, Class Differentiations, and Comparisons between Scenarios Subcategories were identified within the herbaceous and sagebrush classes and repreTseon ctodmifpfearreen tthpe hfoenuor lsocgeincaarliroess, pwoen isneistiwaliltyh ainsstehsesseedf tuhnec aticocnuarlacgyro ouf pesac(Fhi sgcuerneasr2io– 4in). AidteAnrtigfeynintag, tNhDe VsiIx sbloawsel ylainndcr ecoasveedr bcleatswseese n(Tflaibglhetss 21 aanndd 2A,1r–aApi8d)l.y Tihnec raecacsuerdactoy afrpoemak thaet flsiingghlte3 f,liagnhdt cthlaesnsidfieccaltiinoends ,awt tihthe dAirfgfeernetnac seistew (5it0h.i6n%h) ewrbaasc meoaurgsifnuanllcyt iiomnpalrogvroeudp usnrdeelar ttehde tloimNitDedV Ifldigehcltisn es.ceTnhaerisou b(5c1la.9s%s ‘)s haonrdt sseparsionng’ sqcueincakrliyo d(e5c2l.i2n%ed). aHftoewr tehveerp, etahkea at lfll igflhigth3t, wscheenraeraios tihmepmroovdeder abtye aslemasoosnt 1r0e%m a(6in1e.4d%c)l. oTsheet oacpceuarkacvya oluf ethsea talfll ifglihgth4t ,stcheennardioe,c wlinheedn qinuciclukdlyinagft seur bflciagthetg5o.rFielsig (hthtse 3fualnl dni4nwe iedreen1t5ifdieady sinacpluardti,nsgo pthheesneoplohgeincoallo dgiifcfaelrednifcfeesr)e, nwceass rseimprielaser ntot atthlee asisntgtwleo flaigdhdti tainonda oltwheere kmsuolftiipnlcer eflaisgehdt spcheontaorsiyons t(hTeasbisle. SAa9g,e obvruersahlls ahcocrut raancdy m= 5o1d.e8r%at)e. Wseea swonersew uenraebsliem tiol airdetonthifeyr baancye opuhsecnloalsosgeiscbaul dt isfhfeifrteendcoesn e(i.aed.,d nitoi osnuablcwateeegkorinietso) tuhnedgerro twhein lgimseitaesdo nfli(gshhtosr stcseenaasorino.d Hecolwineevsearf,t werefl wigehrte 4a,bmleo tdoe irdaetne taiffyte sreflaisgohnta5l )d. ifferences within the herbaceous categories with the spring scenario but with limited accuracy (45.6%). Remote Sens. 2022, 14, 1290 11 of 28 At Virginia City, senescence dates across classes were similar, and herbaceous subcate- gories were differentiated by early or late green-up dates. Early season herbaceous had a peak value at flight 3 (the first available at Virginia City) and slowly declined through flight 7, whereas late-season continued to increase through flights 4 and 5 before declining. Flights 3 and 5 were 37 days apart, representing a peak green-up value about a month later for the late-season phenological subcategories. Differences in sagebrush subcategories were related to height, with a short class (mean height about 0.1 m, primarily in the upslope portions of the scene) and a tall subcategory (mean height about 0.25 m). In addition, there was one class where pixels were mixed between sagebrush and herbaceous vegetation with an early peak green-up at flight 3 (from herbaceous vegetation) and a long season with plateaued NDVI values through flight 5 and a drop after, similar to sagebrush, and with height values (mean height of 0.24 m), similar to the tall class. 3.2. Accuracy, Class Differentiations, and Comparisons between Scenarios To compare the four scenarios, we initially assessed the accuracy of each scenario in identifying the six base land cover classes (Tables 2 and A1–A8). The accuracy from the single flight classifications at the Argenta site (50.6%) was marginally improved under the limited flights scenario (51.9%) and spring scenario (52.2%). However, the all flight scenario improved by almost 10% (61.4%). The accuracy of the all flight scenario, when including subcategories (the full nine identified including phenological differences), was similar to the single flight and other multiple flight scenarios (Table A9, overall accuracy = 51.8%). We were unable to identify any phenological differences (i.e., no subcategories) under the limited flights scenario. However, we were able to identify seasonal differences within the herbaceous categories with the spring scenario but with limited accuracy (45.6%). Table 2. Summary of accuracy results from confusion matrices derived from vegetation classification scenarios at sites near Virginia City and Argenta, Montana. Base categories include bare ground, litter, sparse, medium, and dense herbaceous, and sagebrush. Subcategories include differences between green-up and senescence timing and/or height within a base class. Scenario Single Limited Spring All Site Overall Kappa Overall Kappa Overall Kappa Overall Kappa Argenta Base Categories 50.6% 0.50 51.6% 0.51 52.5% 0.52 61.4% 0.61 Subcategories — — — — 45.6% 0.46 51.8% 0.52 Virginia City Base Categories 59.0% 0.59 60.2% 0.60 61.6% 0.62 64.4% 0.64 Subcategories — — 44.9% 0.39 46.6% 0.40 53.2% 0.53 At the Virginia City site, a similar class comparison (with six classes) resulted in an overall accuracy of 59.0% for the single flight scenario, 60.2% for the limited scenario, 61.6% for the spring scenario, and 64.4% for the all flight scenario (Table 2). When considering phenological differences, we were able to identify the same 10 classes in each of the multiple flight scenarios, but the overall accuracy of the limited (38.5%) and spring (40.0%) were poor compared to the all flight scenario (53.2%, Table A10). Typically, the lowest class-specific accuracy rates at both sites were for the litter class across all scenarios (Tables A1–A10), with improvement in most situations between the sin- gle and multi-flight approaches (Table 2). The misclassification of litter was primarily with bare ground and sparse herbaceous classes. Herbaceous misclassification errors tended to be between densities (sparse, medium, and dense), in addition to some errors between dense herbaceous and sagebrush misclassifications at Argenta and to a lesser extent at Remote Sens. 2022, 14, 1290 12 of 28 Virginia City. The identification of phenological subcategories resulted in increased classifi- cation errors within the multi-flight classifications. These errors tended to be between the phenological classes (e.g., short versus moderate season length herbaceous); however, at Ar- genta, we also found classification errors between herbaceous and sagebrush classes. More flights (i.e., moving from single, to limited, to spring, to the all flights scenario) improved the classification of subcategories within functional groups (Tables 2, A9 and A10). 4. Discussion This study examined the ability of UAVs to identify fine-scale phenological het- erogeneity, can be used to inform logistical decisions of future UAV studies (timing, number of flights, etc.), and illustrated options to effectively process data from multi- ple UAV flights. This approach successfully identified plant level phenological differences (i.e., differences in growing season length and timing within vegetation functional groups) and demonstrated improved classification accuracy by utilizing multi-flight UAV classi- fication approaches. At both study locations, multi-flight scenarios improved vegetation classification accuracy over a single flight, mainly due to asynchronies in the timing and duration of herbaceous cover. Two main findings emerge from this study: (1) phenological heterogeneity at fine spatial scales can be identified by UAV flights, and (2) heterogeneity in phenological timing causes accuracy issues between class designations. The implications of these results include the ability to use these data to make finer scale ecological comparisons than satellite-based land surface phenology measures. In addition, while land surface phenology measures from satellite remote sensing can be influenced by the proportion of vegetation functional group, which may exhibit different phenological patterns, within a pixel [83], this study’s results suggest that within functional group heterogeneity needs to be considered as well. Furthermore, UAV-based studies provide phenological information at the plant or sub-plant level to compliment the broader spatial coverage available from satellite-based systems [22,42,52]. 4.1. Phenological Heterogeneity within Functional Groups Remote sensing is increasingly used to measure land surface phenology, which incor- porates the mixed response of vegetation and background materials (e.g., soil and litter) within a sensor’s spatial resolution [83–85]. This study demonstrated the ability to examine patterns within functional groups at a fine resolution. Many of our multi-flight classification scenarios based on NDVI at multiple points during the growing season identified spatial variation within functional groups. These differences highlighted the role of topographic variations in phenology. For example, small valleys at Argenta have a longer growing season than upland locations, and there are seasonal asynchronies in herbaceous species between south-facing slopes and other areas at Virginia City. A study in marsh vegetation also found phenological differences in timing and duration within close spatial proximity, with implications for restoration and management [86]. Additional asynchronies in plant re- sponses identified from remote sensing include differences in photosynthetic pathways [24], water use efficiency [28], growing conditions within fields [22], and variable intra- and inter-specific phenology [52]. Changing or variable vegetation phenology has consequences to other species and processes within the ecosystem [30–33,87]. Fine-scale measurement of variable vegetation functional group phenology is needed to inform restoration plan- ning [86] and to better quantify the degree of phenological mismatch between members of an ecosystem under a changing climate. Understanding plant-level phenological differences as demonstrated here helps advance understanding of these processes. At the drier Argenta site, there were functional group differences when plants were senescing and differences in the timing of green-up at the more mesic Virginia City site. These differences in limiting factors between sites may be difficult to identify a priori. In other sagebrush systems, communities in meadow (more mesic) locations had longer growing seasons compared to upland communities, with the difference due to earlier start of season dates [70]. In addition, new growth on sagebrush has a longer growing Remote Sens. 2022, 14, 1290 13 of 28 season [70], matching some of the sub-canopy variation identified herein, possibly also tied to the semi-deciduous pattern of sagebrush [71]. Phenology differences between and within species can be highly variable [23,52], asynchronies can be in the spring [70] or fall [42], and can be driven by variable growing conditions [22,70]. Building on phenology, temporal rangeland assessments can aid in ecological and land use studies. For example, high spatial resolution imagery can be used for within-season forage utilization [51], monitoring vegetation trends over time [44], and identifying invasive species [25]. 4.2. Accuracy and Tradeoffs of Single versus Multi-Flight Approaches Utilizing multiple flights over the growing season increased the accuracy of our classifications by up to 5–10% when comparing the different multi-flight scenarios to the single-flight scenarios (Table 2). In a single-flight classification, shadows are difficult to classify accurately [88], and spectral differences through time cannot be utilized. Multi- temporal UAV imagery improved accuracy in other cases, resolving issues with flowering and shadows, as well as utilizing phenology to identify different spectral signatures over time [25,54]. While our overall accuracy was higher at the more densely vegetated site (mesic), the greatest increase in accuracy occurred at the sparser, drier site. The improved accuracy at both sites was from increasing the correct classifications of short duration herbaceous, bare ground, and litter, with these classes covering more of the land surface at Argenta. Specifically, the spectral patterns of soil and litter pixels separate out over the growing season. Sparse, short-duration herbaceous material may be missed with one flight, even at the presumed peak of the growing season, as it is spectrally similar to litter or soil except for a short time period, which may differ temporally across a scene. This study only included two sites; therefore, to further test the hypothesis of increased accuracy at drier sites but better overall accuracy at wetter sites, a study examining accuracy across a precipitation gradient would be advantageous. Even with multiple flights, classification challenges remain between spectrally similar classes, such as bare ground and sparse vegetation [17]. Likewise, the phenological cycle is very similar between dense herbaceous and sagebrush groups (Figures 3 and 4), which also had overlapping height distributions, leading to some misclassification errors between these categories. Overall, classification errors were more likely between similar classes or subcategories (e.g., bare ground vs. litter, the density of herbaceous classes, and length of the growing season) than distinct classes (e.g., herbaceous vs. bare ground or sagebrush vs. litter). However, due to scene heterogeneity, fuzzy class boundaries, and possible mixed pixels, these errors exist between distinct classes in the classification. Classification of our rangeland sites, even with high spatial resolution, still had challenges (Figure 5), and we note several specific issues for our study areas. First, some of the hardest pixels to identify are edges (e.g., sagebrush exterior pixels), as these were often mixed vegetation types, despite our small pixel size (~3 cm). An example of mixed classes within a pixel is grass growing up through sagebrush or through sagebrush skeletons. The mixing of desired classes within a pixel results in classification challenges and complicates the creation of reference data. Second, shadows move through time-series data and complicate the classification even when using NDVI to limit these effects. Third, the shapes of the phenology curves are highly variable; very few actually match up with the calculated class mean curves (Figures 3 and 4). Fourth, relatively small plants (or short sagebrush) can be hard to visually classify or separate between classes. The advantage of the multiple flight approach is while accuracy drops when classifying based on within functional group heterogeneity, this accuracy is the same as the single flight but provides more information about the timing and duration of when herbaceous and sagebrush classes are photosynthesizing. Remote Sens. 2022, 14, 1290 14 of 30 accuracy at drier sites but better overall accuracy at wetter sites, a study examining accuracy across a precipitation gradient would be advantageous. Even with multiple flights, classification challenges remain between spectrally similar classes, such as bare ground and sparse vegetation [17]. Likewise, the phenological cycle is very similar between dense herbaceous and sagebrush groups (Figures 3 and 4), which also had overlapping height distributions, leading to some misclassification errors between these categories. Overall, classification errors were more likely between similar classes or subcategories (e.g., bare ground vs. litter, the density of herbaceous classes, and length of the growing season) than distinct classes (e.g., herbaceous vs. bare ground or sagebrush vs. litter). However, due to scene heterogeneity, fuzzy class boundaries, and possible mixed pixels, these errors exist between distinct classes in the classification. Classification of our rangeland sites, even with high spatial resolution, still had challenges (Figure 5), and we note several specific issues for our study areas. First, some of the hardest pixels to identify are edges (e.g., sagebrush exterior pixels), as these were often mixed vegetation types, despite our small pixel size (~3 cm). An example of mixed classes within a pixel is grass growing up through sagebrush or through sagebrush skeletons. The mixing of desired classes within a pixel results in classification challenges and complicates the creation of reference data. Second, shadows move through time-series data and complicate the classification even when using NDVI to limit these effects. Third, the shapes of the phenology curves are highly variable; very few actually match up with the calculated class mean curves (Figures 3 and 4). Fourth, relatively small plants (or short sagebrush) can be hard to visually classify or separate between classes. The advantage of the multiple flight approach is while accuracy drops when classifying based on within functional group heterogeneity, this accuracy is the same as the single Remote Sens. 2022, 14, 1290 flight but provides more information about the timing and duration of when herba1c4eoofu2s8 and sagebrush classes are photosynthesizing. A B C D Figure 5. Ground photos showing examples of mixed and/or hard to classify areas that likely created between-class errors in our single- and multiple-flight vegetation classifications from unpiloted aerial vehicle (UAV) imagery collected during 2018 in Montana, U.S. (A) Mixed litter, sparse herbaceous, and bare ground, (B) mixed litter and herbaceous, (C) mixed sparse herbaceous and variable substrate bare ground, and (D) mixed sagebrush and herbaceous. (A,C) are from the Argenta site, and (B,D) are from Virginia City (Photos by co-author David Wood). We tested the limited and spring flight scenarios to assess whether reducing the frequency of flights or concentrating flights during the primary growing season would have similar accuracy and ability to identify within-group phenological heterogeneity as compared to the all flights scenario. While we predicted that we could achieve similar accuracy for the classification of both vegetation categories and subcategories containing phenological differences, we instead only found increased land-cover (at the class level) accuracy with the spring and limited scenarios. We found limited accuracy and/or ability to identify subcategory phenological differences, as the timing of the flights was key for identifying these within functional groups phenological differences. In our approach, late- season flights helped separate sagebrush (higher NDVI in the early and late season as most leaves stay on) from herbaceous classes (fully brown/senesced). Sagebrush and herbaceous (medium and dense classes) had similar green-up timing and midseason patterns, as well as overlapping heights in some cases, so differences between these classes occurred in late summer. The limited scenario worked better at the higher elevation Virginia City site indicating the timing of flights should be concentrated during the green-up period (near the peak rather than the early season when there is little activity). For a study in the nearby U.S. Great Basin, accurately separating invasive species and other classes from fine spatial resolution multi-flight UAV imagery was found to be tied to having the correct timing of flights that captured phenological differences, more than increased spectral information Remote Sens. 2022, 14, 1290 15 of 28 from any given point in time [25]. In addition, the growing season at Virginia City (with the exception of the NW part of the scene) was longer than at Argenta, increasing the window to capture flights that are diagnostic in separate classes. In differentiating between herbaceous, especially sparse, limited duration classes, and bare ground/litter classes, there was often only a short window (i.e., short green/growing season) that was asynchronous across the scene. Classification accuracy can be improved with multiple flights, and these should be focused on a combination of spring and late season flights. However, accurately differentiating subcategories required flights across the whole growing season in our study and was more successful in areas of the scene without herbaceous species mixed within the shrub canopy. Tradeoffs between the single and multiple flight approaches beyond the type of information gained are primarily logistical and processing-time based. We reused flight plans and marked ground target locations with rebar for easy relocation. However, despite increased efficiency in operations through the growing season, multiple flights take up a significant amount of time and should be weighed against specific objectives. Specifically, in our case, we conducted 16 total flights, and with ideal flight conditions, theoretically could have mapped vegetation distribution at 16 sites instead of two with a similar effort but with lower accuracy. It took us twofour missions to cover each site based on battery life of 9–10 min, wind speed, cloud cover, and other operational factors (e.g., software issues, clear air space, etc.). Including setup/takedown, the time to swap batteries, and reacquire GPS position, our total operation took between 40 and 60 min per site, per round. Processing time also increased with multiple flights, although most of this effort was in increased background processing. After initial loading and the creation of project workspaces in Python and Metashape, we utilized batch processing. Therefore, manual input time was marginally increased, while total processing time was increased by approximately a factor of six–eight (representing the number of flights at each site). However, in many situations, these increased data collection and processing times can greatly increase the accuracy of classification approaches [17,23,54]. Specific applications and study questions will dictate required information, accuracy, precision, and sample size. Individual applications must weigh these factors and make a decision that balances economy with accuracy needs. Multi-temporal UAV analyses can complement and build on techniques from multi- temporal satellite-based remote sensing, e.g., [14,89]. Furthermore, high-resolution imagery was used to create training data for satellite-based image classification, e.g., invasive species in [90], relatively pure signals for spectral unmixing, e.g., [91], and to validate continuous cover models, e.g., [92]. However, additional research is needed to improve accuracy and application, as well as to capture spatial and temporal variability to answer questions such as measuring the success of management actions, identifying consequences of a changing climate, and quantifying impacts to ecosystem functions and services. Other approaches such as object-based classification [54], are used in multi-temporal classification; however, the variety of scales of vegetation sizes, and desire to identify possible within canopy pheno- logical differences in sagebrush [70], precluded use in this study. Additional improvements to classification approaches such as classification based on vegetation functional groups with continuous values for phenology (e.g., start of spring vs. early green-up), as well as object-based approaches that can capture fine scale phenological differences are needed. UAVs are a tool to sample the high spatial variability of rangeland ecosystems, and emerg- ing areas of research and application of multi-flight UAV studies show great promise for improving monitoring and assessment of these systems. Author Contributions: This is a multi-author work with substantial contributions from all authors. Specific contributions include: conceptualization, D.J.A.W., T.M.P., S.P. and P.C.S.; methodology, D.J.A.W., T.M.P. and S.P.; formal analysis, D.J.A.W. and T.M.P.; investigation, D.J.A.W. and T.M.P.; resources, D.J.A.W. and T.M.P.; data curation, D.J.A.W. and T.M.P.; writing—original draft prepara- tion, D.J.A.W.; writing—review and editing, D.J.A.W., T.M.P., S.P. and P.C.S.; supervision, S.P. and P.C.S.; funding acquisition, D.J.A.W. All authors have read and agreed to the published version of the manuscript. Remote Sens. 2022, 14, 1290 16 of 28 Funding: This research was funded by the Bureau of Land Management Montana-Dakotas State Office, and authors were supported by Montana State University (S.P.), University of Wisconsin- Madison (P.C.), and the U.S. National Science Foundation (P.S. from grant numbers DEB-1552976 and OIA-1632810). D.W. and T.P. were funded by the Bureau of Land Management interagency agreements L15PG00230 and L20PG00168. Data Availability Statement: The data presented in this study are openly available in the USGS ScienceBase repository at https://doi.org/10.5066/P96848FL, accessed on 1 December 2021 [93]. Acknowledgments: We thank the BLM Dillon Field Office for access and advice on study locations and the Dillon Interagency Dispatch Center for flight coordination. Lisa Rew, Lance McNew, and Kathryn Irvine reviewed a prior version of this manuscript. We also thank four peer reviewers for comments that improved this manuscript. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Remote Sens. 2022, 14, 1290 17 of 30 Conflicts of Interest: The authors declare no conflict of interest. Appendix A Appendix A FFiigguurree AA11.. PPhheennoollooggiiccaall ppaatttteerrnnss eexxpprreesssseedd aass mmeeaann nnoorrmmaalliizzeedd ddiiffffeerreennccee vveeggeettaattiioonn iinnddeexx ((NNDDVVII)) by day of the year (DOY), for the 27 classes produced from iterative self-organized (ISO) by day of the year (DOY), for the 27 classes produced from iterative self-organized (ISO) unsupervised unsupervised classification of data from 8 unpiloted aerial vehicle (UAV) flights (all flights scenario) cilna s2s0i1fi8c aatti othneo Af drgaetantfaro smite8, Munopnitlaontead. Caelarsiaslesv eahreic rleeo(UrdAeVre)dfl tiog hptlsa(cael lfiflniaglh ctlsasscseens anreioxt) tino 2ea0c1h8 aotththere; Athreg eonritgaisniatel ,cMlasosniftiacnatai.oCn lcaasns ebsea froeurnedo ridne trheed utpoppelra creigfihnt-ahlacnlads csoesrnneerx otft oeaecahc hpaontehle. rA; t dhaesohreidg ilninale calta s0s.i2fi cias tipornovciadnedb efofor urnedferienntchee bueptwpeerenr igphatn-ehlasn. dS—cosrhnoerrt odfueractihonp agnreolw. iAngd saesahseodn,l ianneda tM0—.2 ims podroevraidte duforartiroenfe grernowceinbge tsweaeseonnp. anels. S—short duration growing season, and M—moderate duration growing season. RemoteR eSmenotse. S2e0n2s2. ,2 10242, ,11249,01 290 17 of 2188 of 30 FigFuigruer Ae 2A.2 P. hPehneonolologgicicaall ppaatttteerrnnss eexxpprreesssesdeda sams meaenanno nrmoramlizaeldizdediff derieffnecreevnecgee vtaetgioentaintidoenx i(nNdDeVx I()NDVI) byb yddaayy ooff thtehyee ayre(aDrO Y(D),OfoYr )t,h ef2o7r cltahsese s2p7r ocdluascesdesf ropmroidteuracteivde sferlof-mor giatneirzaetdiv(eIS Ose) lufn-osurgpaernviizseedd (ISO) uncsluaspseifirvcaisteiodn colafsdsaiftiacafrtoiomn 6ouf dnpatilao ftreodmae 6r iualnvpeihloicteled( aUeArVia)l flvieghhitcsle(a (lUl flAigVh)t fslisgchentsa (raioll) filnig2h0t1s8 sactenario) in t2h0e1V8 iargt itnhiae CViitrygsinitiea. CCiltayss seistea.r eCrleaossrdese raerde troepolradceerfiedna tloc lpalsascees nfienxatlt oclaeascsheso ntheexrt; ttoh eeoarcihg iontahler; the oricglainssailfi cclaatisosnificcaantiboen fcoaunn dbein fothuendup ipne trhreig uhpt-phearn drigcohrtn-heranodf e caocrhnpear noefl .eaAchd apsahnedel.l inAe daat s0h.3edis line at 0.3p irso pvirdoevdidfoerdr feofer rreenfceerebnectwe ebeentwpaenenel sp.aHneerlsb.— Hheerrbb—acheoeurbs,aEc—eoeuasr,l yE—greeeanr-lyu pg,rLe—enl-autepr, gLr—eelna-tuepr, green- upU, U——shsohrto(rgte (ngeernaellryaullpys luoppsel)o, pT—e)t, aTll—, Mtaixll—, Mmiixxe—dmheirxbeadc ehoeursbaancdeosuags eabnruds sha. gebrush. RemotRe eSmeontse. S2e0n2s2. 2, 01242, ,11249, 01 290 18 of 2189 of 30 FigFuigruer Ae A3.3 P. hPehnenoolologgiiccaall ppaatttteerrnnsse exxpprersessesdedas ams meaenanno nrmoramlizaeldizdeidff edriefnfecreevnecgee tvaetigoentaintidoenx i(nNdDeVx I()NDVI) byb yddaayy oofft htehyee ayre(DarO Y(D),OfoYr t)h, ef2o8r cltahsese s2p8r ocdluacsesdesf ropmroitdeuracteivde sferlof-mor giatneirzaetdiv(IeS Os)eulfn-osurpgearnviizseedd (ISO) uncsluaspseifirvcaistieodn colfasdsaitfaicfarotimont horfe deautnap firloomted thareeriea luvnephiilcoltee(dU aAeVr)iafll ivgehhtsic(lsep a(UrsAe flVi)g hfltisghsctesn (asrpioa)rsine flights sce2n01a8riaot) tihne 2A0r1g8e natta tshiete A. Crglaesnsetas asrieter.e Corldaessreeds taorep lraecoerfidnearledcl atsos epslanceex tfitnoaela cchlaosstheesr n; tehxet otori geiancahl other; thecl oasrsigifiicnaatli ocnlacsasnifibceatfioounn cdainn bthe efouupnpder inri gthhte- huapnpdecro rringehrt-ohfaenadch cpoarnneelr. oAf edaacshh epdanlinele. aAt d0.a2sihsed line at p0r.o2v iisd pedrofvoirdreefde rfeonrc reebfeetrweneecne bpaentweles.en panels. Remote SenRse.m 2o0 te 22S,e n1s4. ,2 012229,01 4, 1290 19 of 28 20 of 30 Figure A4. Phenological patterns expressed as mean normalized difference vegetation index (NDVI) by day of the year (DOY), for the 30 classes produced from iterative self-organized (ISO) unsupervised Figure A4. Phenological patterns expressed as mean normalized difference vegetation index (NDVI) classification of data from three unpiloted aerial vehicle (UAV) flights (sparse flights scenario) in 2018 by day of the year (DOY), for the 30 classes produced from iterative self-organized (ISO) at the Virginia City site. Classes are reordered to place final classes next to each other; the original unsupervised classification of data from three unpiloted aerial vehicle (UAV) flights (sparse flights classification can be found in the upper right-hand corner of each panel. A dashed line at 0.3 is scenparroiovi)d iend 2fo0r1r8e faetr etnhcee Vbeitrwgeineniap Canietyls .sHiteer. bC—lahsesrebsa caeroeu sr,eEo—rdeearrleydg rteoe np-luapc,eL f—inlaatle crlgarseseens- unpe,xt to each otheUr;— thseh oorrti(ggiennaelr aclllaysuspifsilcoaptei)o,nT —catnal lb, eM fiox—unmdi xiend thheer buapcpeoeurs raignhdts-ahgaenbrdu csho.rner of each panel. A dashed line at 0.3 is provided for reference between panels. Herb—herbaceous, E—early green-up, L—later green-up, U—short (generally upslope), T—tall, Mix—mixed herbaceous and sagebrush. RemoteR eSmenotse. S2e0n2s2. ,2 10422, ,11249,01 290 20 of 2281 of 30 FigFuigruer Ae 5A. 5P.hPehneonloologgicicaall ppaatttteerrnnss eexxpprreesssesdeda sasm meaenanno nrmoramlizaeldizdediff derieffnecreevnecgee vtaetgioentaintidoenx i(nNdDeVx I()NDVI) byb yddaayy ooff thtehye eayre(aDrO (YD),OfoYr )t,h ef2o8r cltahses es2p8r ocdluascsedesf ropmroidteuracteivde sferlof-mor giatneirzaetdiv(eIS Ose) lufn-osurgpaenrviizseedd (ISO) uncsluaspseifirvcaistieodn colfadsastiaficfraotmionfi voef udnaptialo ftreodmae friivalev uehnipclielo(tUeAdV a)eflriigahl tvse(shpicrilneg (UfliAghVts) sfcliegnhatrsio ()sipnr2i0n1g8 flights sceantatrhieo)A irng e2n0t1a8 saitte t.hCe lAasrsgeesnatrae srieteo.r dCelraesdsetso aprlea creeofirndaelrceldas tsoes pnlaecxet tfoineaalc chlaostsheesr ;ntehxet otori geiancahl other; thec loarsisgifiincaatli ocnlascsainficbaetifoonu ncdanin beth feouupnpde irnr tighhet u-hpapnedr croigrnhetr-hoafneda cchorpnaenre ol.f Aeacdha sphaendelli.n Ae adta0s.h2ed line at i0s .p2 roisv idperdovfiodrerdef efroern creefbeertewneceen bpeatnwelese. nS —pashnoerlts.d uS—ratsiohnorgtr odwuirnagtisoena sgorno, wanindgM s—eamsoodne, raatned M— modduerraatitoen dgurorawtiinogn sgeraosowni.ng season. Remote Sens. 2022, 14, 1290 22 of 30 Remote Sens. 2022, 14, 1290 21 of 28 FigFuigreu rAe6A. 6P.hPehneonloologgicicaall ppatternss eexxpprersesesdedas ams meaneanno rnmoarlmizaedlizdeifdfe dreifnfceerevnegcet avteiognetiantdieoxn( iNnDdVexI) (NDVI) by bdyadya yof ththeey eayre(aDrO (YD),OfoYr )th, ef3o0r cltahsese s3p0r ocdluacsesdesfr opmroitderuactievde sefrlfo-omrg ainteizreadti(vISeO s)eulnf-sourpgearvniiszeedd (ISO) unsculapsseirfivciasteiodn colfadsastiaficfraotmionfo uorf udnaptialo ftreodmae froiaulrv euhnicpleilo(UteAdV )aflerigiahlt sv(eshpricinleg fl(UigAhtVs )s cfelingahritos) (isnp2r0i1n8g flights sceantatrhieo)V irng i2n0ia18C iatty tshitee .VCilragsisneisaa Creitryeo sridter.e Cdltaospselasc aerfien raeloclradseseresdn etxot tpolaecaech fionthale rc;ltahseseosr ignienxatl to each othcelar;s sthifiec aotriiognincaln cblaesfsoiufincdatinonth ceanu pbpee froruignhdt -ihna tnhde cuoprnpeerro rfigehact-hhpaannde lc.oArndera sohfe edaclihn epatn0e.l3. Ais dashed linep raotv 0id.3e dis fpororvefiedrednc feobr ertewfereennpcaen beelst.wHeeernb —pahneerblsa.c Heoeursb,—E—hearbrlaycgeroeuens,- uEp—, Le—arlayt egrrgereene-nu-pup, L, —later greUen—-ushpo,r Ut (—gensheroarltly (guepnsleorpael)l,yT u—ptsallol,pMei)x, —T—mitxaeldl, hMerixba—cemouixseadn dhseargbeabcreuosuh.s and sagebrush. Remote Sens. 2022, 14, 1290 22 of 28 Table A1. Confusion matrix of vegetation classification accuracy from a single unpiloted aerial vehicle (UAV) flight on June 12th, 2018, at the Argenta site. Kappa = 0.50. Reference Bare Ground 105 39 10 5 1 2 162 64.8% Litter 3 14 3 0 0 7 27 51.9% Sparse Herb 29 51 92 14 4 12 202 45.5% Medium Herb 1 2 52 100 20 50 225 44.4% Dense Herb 0 2 10 62 18 76 168 10.7% Sagebrush 0 1 9 26 3 176 215 81.9% Total 138 109 176 207 46 323 999 Producers Accuracy 76.1% 12.8% 52.3% 48.3% 39.1% 54.5% 50.6% Table A2. Confusion matrix of vegetation classification accuracy from three unpiloted aerial vehicle (UAV) flights (the limited flights scenario) in 2018 at the Argenta site. Kappa = 0.52. Reference Bare Ground 71 21 7 3 0 1 103 68.9% Litter 29 37 30 12 2 8 118 31.4% Sparse Herb 36 46 97 44 8 45 276 35.1% Medium Herb 1 1 32 99 24 43 200 49.5% Dense Herb 0 1 1 24 8 19 53 15.1% Sagebrush 1 3 9 25 4 207 249 83.3% Total 138 109 176 207 46 323 999 Producers Accuracy 51.5% 33.9% 55.1% 47.8% 17.4% 64.1% 51.9% Table A3. Confusion matrix of vegetation classification accuracy from 4 unpiloted aerial vehicle (UAV) flights (spring flights scenario) in 2018 at the Argenta site. Kappa = 0.52. Reference Bare Ground 106 28 33 6 1 2 176 60.2% Litter 19 38 25 13 0 6 101 37.6% Sparse Herb 10 33 90 65 2 9 209 43.1% Medium Herb 2 8 25 113 27 132 307 36.8% Dense Herb 0 0 0 0 6 6 12 50.0% Sagebrush 1 2 3 10 10 168 194 86.6% Total 138 109 176 207 46 323 999 Producers Accuracy 76.8% 34.9% 51.1% 54.6% 13.0% 52.0% 52.2% User’s Accuracy User’s Accuracy User’s Accuracy Total Total Total Sagebrush Sagebrush Sagebrush Dense Herb Dense Herb Dense Herb Medium Herb Medium Herb Medium Herb Sparse Herb Sparse Herb Sparse Herb Litter Litter Litter Bare Ground Bare Ground Bare Ground Class Names Class Names Class Names Classification Classification Classification Remote Sens. 2022, 14, 1290 23 of 28 Table A4. Confusion matrix of vegetation classification accuracy from 8 unpiloted aerial vehicle (UAV) flights in 2018 (all flights scenario) at the Argenta site. Kappa = 0.61. Reference Bare Ground 83 15 8 3 0 2 111 74.8% Litter 24 41 21 10 1 14 111 36.9% Sparse Herb 29 47 110 28 1 7 222 49.6% Medium Herb 0 4 34 135 11 38 222 60.8% Dense Herb 1 1 2 29 30 48 111 27.0% Sagebrush 1 1 1 2 3 214 222 96.4% Total 138 109 176 207 46 323 999 Producers Accuracy 60.1% 37.6% 62.5% 65.2% 65.2% 66.3% 61.4% Table A5. Confusion matrix of vegetation classification accuracy from a single unpiloted aerial vehicle (UAV) flight on June 27th, 2018, at the Virginia City site. Kappa = 0.59. Reference Bare Ground 66 22 37 2 0 0 127 51.9% Litter 21 45 51 25 0 24 166 27.1% Sparse Herb 0 2 14 25 5 15 61 22.9% Medium Herb 0 0 0 43 36 6 85 50.6% Dense Herb 1 0 0 3 141 38 183 77.1% Sagebrush 2 16 6 52 21 281 378 74.3% Total 90 85 108 150 203 364 1000 Producers Accuracy 73.3% 52.9% 12.9% 28.7% 69.5% 77.2% 59.0% Table A6. Confusion matrix of vegetation classification accuracy from three unpiloted aerial vehicle (UAV) flights in 2018 (limited flights scenario) at the Virginia City site. Kappa = 0.60. Reference Bare Ground 78 45 25 2 0 7 157 49.7% Litter 4 9 7 11 0 26 57 15.8% Sparse Herb 5 19 74 26 0 9 133 55.6% Medium Herb 0 3 1 65 58 37 164 39.6% Dense Herb 0 0 0 2 104 13 119 87.4% Sagebrush 3 9 1 44 41 272 370 73.5% Total 90 85 108 150 203 364 1000 Producers Accuracy 86.7% 10.6% 68.5% 43.3% 51.2% 74.7% 60.2% User’s Accuracy User’s Accuracy User’s Accuracy Total Total Total Sagebrush Sagebrush Sagebrush Dense Herb Dense Herb Dense Herb Medium Herb Medium Herb Medium Herb Sparse Herb Sparse Herb Sparse Herb Litter Litter Litter Bare Ground Bare Ground Bare Ground Class Names Class Names Class Names Classification Classification Classification Remote Sens. 2022, 14, 1290 24 of 28 Table A7. Confusion matrix of vegetation classification accuracy from five unpiloted aerial vehicle (UAV) flights in 2018 (spring flights scenario) at the Virginia City site. Kappa = 0.615. Reference Bare Ground 78 34 33 2 0 2 149 52.4% Litter 7 25 23 8 0 19 82 30.5% Sparse Herb 1 9 45 21 0 11 87 51.7% Medium Herb 0 2 0 54 26 43 125 43.2% Dense Herb 0 0 0 7 139 14 160 86.9% Sagebrush 4 15 7 58 38 275 397 69.3% Total 90 85 108 150 203 364 1000 Producers Accuracy 86.7% 29.4% 41.7% 36.0% 68.5% 75.6% 61.6% Table A8. Confusion matrix of vegetation classification accuracy from six unpiloted aerial vehicle (UAV) flights in 2018 (all flights scenario) at the Virginia City site. Kappa = 0.64. Reference Bare Ground 73 19 6 1 0 1 100 73.0% Litter 7 38 33 6 0 16 100 38.0% Sparse Herb 6 16 57 18 0 3 100 57.0% Medium Herb 2 5 12 93 17 71 200 46.5% Dense Herb 0 0 0 16 147 37 200 73.5% Sagebrush 2 7 0 16 39 236 300 78.7% Total 90 85 108 150 203 364 1000 Producers Accuracy 81.1% 44.7% 52.8% 62.0% 72.4% 64.8% 64.40% Table A9. Confusion matrix of vegetation classification accuracy from eight unpiloted aerial vehicle (UAV) flights (all flights scenario) in 2018 at the Argenta site. Kappa = 0.52. S—short duration growing season, and M—moderate duration growing season. Reference Bare Ground 83 15 3 5 0 3 0 2 0 111 74.8% Litter 24 41 8 13 2 8 1 7 7 111 36.9% Sparse Herb (S) 11 15 50 14 12 6 0 3 0 111 45.0% Sparse Herb (M) 18 32 18 28 3 7 1 1 3 111 25.2% Medium Herb (S) 0 2 7 3 72 7 4 13 3 111 64.9% Medium Herb (M) 0 2 7 17 9 47 7 9 13 111 42.3% Dense Herb 1 1 1 1 11 18 30 26 22 111 27.0% Sagebrush (S) 0 1 0 0 1 1 2 85 21 111 76.6% Sagebrush (M) 1 0 0 1 0 0 1 27 81 111 73.0% Total 138 109 94 82 110 97 46 173 150 999 Producers Accuracy 60.1% 37.6% 53.2% 34.1% 65.5% 48.5% 65.2% 49.1% 54.0% 51.8% User’s Accuracy User’s Accuracy User’s Accuracy Total Total Total Sagebrush (M) Sagebrush Sagebrush Sagebrush (S) Dense Herb Dense Herb Dense Herb Medium Herb Medium Herb Medium Herb (M) Medium Herb (S) Sparse Herb Sparse Herb Sparse Herb (M) Sparse Herb (S) Litter Litter Litter Bare Ground Bare Ground Bare Ground Class Names Class Names Class Value Classification Classification Classification Remote Sens. 2022, 14, 1290 25 of 28 Table A10. Confusion matrix of vegetation classification accuracy from six unpiloted aerial vehicle (UAV) flights (all flights scenario) in 2018 at the Virginia City site. Kappa = 0.53. Herb—herbaceous, E—early green-up, L—later green-up, U—short (generally upslope), T—tall, Mix—mixed herbaceous and sagebrush. Reference Bare Ground 73 19 6 0 1 0 0 1 0 0 100 73.0 Litter 7 38 33 4 2 0 0 12 3 1 100 38.0 Sparse Herb 6 16 57 16 2 0 0 1 2 0 100 57.0 Medium Herb (E) 1 1 9 43 11 8 1 13 10 3 100 43.0 Medium Herb (L) 1 4 3 6 33 3 5 34 0 11 100 33.0 Dense Herb (E) 0 0 0 7 0 75 16 1 0 1 100 75.0 Dense Herb (L) 0 0 0 0 9 4 52 9 2 24 100 52.0 Sagebrush (U) 0 1 0 1 10 4 2 42 4 36 100 42.0 Sagebrush (Mix) 0 1 0 0 1 11 8 3 57 19 100 57.0 Sagebrush (T) 2 5 0 0 4 5 9 9 4 62 100 62.0 Total 90 85 108 77 73 110 93 125 82 157 1000 Producers Accuracy (%) 81.1 45.7 52.8 55.8 45.2 68.2 55.9 33.6 69.5 39.5 53.2 References 1. Lund, H.G. Accounting for the World’s Rangelands. Rangelands 2007, 29, 3–10. [CrossRef] 2. 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