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Forest Measurement and Inventory

by: Ms. Alene Howell

Forest Measurement and Inventory FOR 274

Ms. Alene Howell
GPA 3.99


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This 61 page Class Notes was uploaded by Ms. Alene Howell on Friday October 23, 2015. The Class Notes belongs to FOR 274 at University of Idaho taught by Staff in Fall. Since its upload, it has received 16 views. For similar materials see /class/227833/for-274-university-of-idaho in Natural Resource Ecology And Mgmt at University of Idaho.


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Date Created: 10/23/15
Lian A x w 39 How Would You Measure Vegetation Height 3 Lu ptors like good bette and higherquot only provide relative information erstand how good something isquot or whether it is best available approachquot we must compare and see ow our measure differs 39om a known standard Accuracy and Errors Signi cant Figures 39 Errors and Their Sources it i i r n Accuracy and Errors Signi cant Figures Ev ll 39 mu m swim he tree is exactiy 7 Erhtaii in 7 E Weuid be wrung e surerherit has Wu the 7 arid the E mu m swim 39 Aitheughthe same physieai vaqu this mpiiesthat uur measure is accurate to 3 sighitieahtt ures 1h e avmd Hi We must add in uur measuremehtermr The tree is 78D cm EcmtaH i 394 W mu m swim s r xx u w quot FOR 27 Forest Meamremenm and Invenmry r r 1 Accuracy and Errurs 5rgnmcanr Frgurcs Errurs and Thcrr Suurccs What Does it All Mean Errors Accuracy and Prec n Pvemse mpvemse Error B S and Random Errors Randum Efan l measmesm systemaucaHy snm m Randum Enuvs was 5 men caused by penny ahmated ms1mmems Randum Enuvs 71 many eases vepeanng a measuve Svs emauc Enuvs These vandum was set me vanammy m we measmemem TRUE VALUE m and smime orbance ofErrors Example 1 Afurester measures ne hewghts en grand hr at 2 ucatmnsA and a 3 ND 7 nuthh unmms dat We need m knuvv the measurement Errur m r V The Importance ofErrors Example 2 i a V Tvvu researchers measure me annua gruwm en a tree sperms m a p antatmn Researcher 1 4 feet 1 a menes Researcheri 3 feett menes Mis kes I Lhmk you should be more exphclt here m step two Fm The Types of Error Exh39aneous Influences 4 mwm These mdude 3 the unexpemed Fquot and unwanted effects that change 3 n surement a 1 an g yuurm a m an Wmd hangmg vames an e swag kw THE mFTE gap dmmm and eause yuu are mere Inshument Limimtions Stretched tapes The Types ofError S atlstlcal Flucmatlons These Errurs uncurvvhen we use a samp e m mer average prepemes abuut a pepmauen V quotI AR 11 The Types ofErrors Unrepresemative Samples TL T y m 39 EXamp E heTgm uftrees m a Siaer tvvumd be wrung m emy measure the pews uf a smg e tree s ee es Hu man Errors What 5 Systemau Samphng Why do We use 7 w Systemau samphng s a spamax case uf duster samphng Sysmma c Sampl Why do we use L A 47 m faresw there are three mam reasunsfur usmg a sys LEmatH samp e 1 Easytu app y and m ram 3912 Easy 2 check 3 Sam u be representatwe Sysmmatic Sampling What is it Exampies in furesiry ineiuue 1 urseedhng siarids 2 xampie every 5 h ur iu wee Line and sirip cruising Me was area Via dutgrids There are many mare I Sysmmatic Sampling Ship and LinerPlot Cruising Sysmma c Sampling LinerPlot Cruising I pin 5 iaid But an a grid pattern Sysmma c Sampl cuuiai and 15 a piuis iiwmm ummumy used imiimneiiaiiies i i ui Wuud inain uses mum 11EIEI an inn vegeneiaiiun uums Sysmmahc Sampllng ine Plot Crulslng Sysmmahc Sampllng Strlp and Llne Plot Crulslng Advantages ur Strip aver Pint Cruising Samphng is euniinuuus uwiravei time between pints Strips have fewer burderhne ireesinan pint cruising i in remute ur dangeruus regiuns aver Piui Cruising Tne cruiser unen rnisses buruemne trees Brush WmdfaHS fire damage ere get in ne Way iareiy reeurueu enser gndstu mcrease premsmn erwnen tne regmn ts sma new use average at severat randum urtentatmns Sys temau Randum Samphng 1 We have a Wanted sarnpte stze at n in and a peputatten at N 2B4 Cateutate Wm 43 andN sayB Sarnpte Every anme By acreage DMdE tne tetatArea mu aeres by t e number at sarnptes 2n 5 Tnts 5 tstne area re resented b eaen pumt Fur square p uts tne spamng rent at tne acreage 4B7 feet Asian 5 Systematic Random Sampling How to do it By a geographic map 1 Randomly select a sample point from awhole population this could be located anywhere a 2 Then overlay grid and follow as steps a before Notes Default method not easy to implement more convenient than in W Systema c Random Sampling How to do i r 1 List Sampling Findatrendin the data oranindicatorotthetrend 2 Reorderall data hythattrend 3 Selectarandomstarting pointand measure every Rh sample Sui113m lifeII i i39 Systematic Sampling Forest Regeneration Plots 77 a Fquot Systematic Sampling Forest Regeneration Plots 0 Mquot p c A 7 Answer Systematic grid wr h spacing equal to seedling r v spacing Then count presenceabsencewithin a sample 39 r m 39 1 j a 2 J j Answer Could use square of xed radius plots of diameter equal to seedling spacing V I This is called the stocked n gt Fix quot1 quot 7 Systematic Sampling Forest Regeneration Plots a quot u 1 A llernalrveAnswers Use the plotcount methodquot y yci 39 ethod synosis 1 Locate plots randomly or systematically 2 Count seedlings per plot and to i peracre 3 Extend count to per acre basis wan mvzn mm H mm mm m mm rmmw mm W Prob ems occurthh pe odwc vanatwonsw 5 w was Prob ems occurwnh pe odwc vananons m the popmahon l 39 c r 39 39 t A 5 Ques uun Whenvvuum n be vahd m use swmp e randum slaus39ucstu ana yze a systematu desgn i v a I 39 Slru lified mm was m mum Imus mm W mus i MATH ANV DRAW A sum a ch mm m EMquot mow umsumz Foe mm m mung 0 Au Mar M K smnrrzv n m 0F am wmw mu m on smruu Tu m swam a i mums lm Stratified Random Sampling What is it I PENNSYLVAMA I run FOREST msmms Examples of strata in forestry include The di sions of compartments into stands The divisions ofa stand by bd classes Tree species age e c slope aspect soils etc 2 3 122 Shati ed Random Sampling Why do we use 5 r th furesvy there are three mam reasehster rusthg a stratmeatteh Ehsuhhg thatthe sarhpte ts Epresentatwe acres the frame a 1 Wu r 2 Cuntrung HEVaHaUDn 3 peputattehs w a 39 4 41 Tu mErEaSE the preeaethty et Dbtammg a representatwe sampte acres the trarhe uf mtErESt a r V 397 Rerhheer er a Frame A eehstmctthathtghhghts the buundaHES et a pupu atmnr e g the Edge et the rhahagerhehteeeheary r z T A 1 Astrhpte raheerh sarhpte rhtght rhtss rhterrhatteh trerh stands We want te knuvv abuut r39 lt5 139 W 39 2 Tu cuntru vahatmh and thus reduce the stze ufthe standard errur ufthe mean 39 uvehappmg mserete subrgruup Where samphng Sthen dune per gruup H a Stmme random sampte th mtghtbe the reset 39 t 4 Samphng thntn eaen area makes t unhke y tnat untwdw ur ntgw vames Wm be prdddeed Repeated sarnptes Wm prdduee rneanstnat are mere strnttar reddeed vananee and standard Errur r l e V 39 t m Chear y fyuu sarnpte eaen stand separatety tnen 1 cunmbutetu tne standard errdrvn tnt n eaen gruup 539 L 4 If strati ed Random Sampling why do we use 39 s 7 it All 39 3 Tu aHthe dent dterentdestgns thtn quotquot s b updtattdns Tne managemenmmec we at eaen stand may vary and surne stands may SUH need prehmmary data r I 4quot maesaume USDAFS ata ess ufthe strata s and Parameters Shahfled Random Sampling How to do It 7 r 7 A A 1x Whatthe Letters Mean m stra rtrcatmrrs 1h Each strata m hZ Etc L Number or strata N number at samphng mm m vwme u utatrurr Nh Number or samphng mm m strata h I 1 Shati ed Random Sampling Strattfymg our trarhe W twe seektu mthtrhtze uur standard errur 1rhustdwrde the trarhe mm the must herhegeheus strata as pessrbte themegeheus strata shemd rhaxtrhtze the dtrterehees betvvaen the dtrtereht strata A I A Aehat phetes and ms settware We that Shawn here are eerhrhehty used te dehhe stands by vuturhe dasses 39 39 v 7 thnhnranh rtstepe aspect E Evatmn denstty ete 7 La VA Q5 C Ea y any stramcatmn deswgn mus L have a mmum El 2 sirata Huvvaver as the number uf strata mcreases The sz ufeach strata Wm get smaHEr ncrcascd chance uf Extreme vaMcs thhm Each strata mcrcascs vanancc mp hmmhc custtu cunductthe mvcm ry abuve B M nut pruvwde addmuna bene tsthat Pra mm bcr uf svata have shuvvn that mcreasmg the nu Stra ed Random Samp 9 How Many Strata Sa Heweyen fures t managers may requlre lnfurmatlurl abuutspecles lnal are subrdlvlded lnm further nndltlun classes such as Densltv SlZE pulEs sapllngs Etc Damage lnsEEL nre vvlrld ele Heweyen yuu can reduce sirata nmblnatluns by rEmDVlng wnal lsn t pusslble 39r vaina Allucatlun l the preeess lnal determlnes new ny samples sneuld be glVErl m eaen strata M I 4 L e en selemng an allucatlun melned we alse need m knuvvthe tutal sample 5le la fur snati ed Random Sampling Allocation r w 4 L 7 ln Equal allucatlun the sample 5le lslne ysarne lrl eaen enne sirata a v 39 t 3quot l7 7 ea Shati ed Random Sampling Allocation l p class a sample size or 2D regardless of its area Avewarid Bulkhan Chamev 3 Strati ed Random Sampling Allocation quotf quot L 7 r it ln proportional allocation tne sampling i number of plots per strata is proportional to the area ufthe strata 39 35 V Aierrailerriaimiea 39 39 Allocation sputum m mm incml i v5 ii A5 iii no i w v 73 Yum 3m its area is in to the total area lfvve had lEIEI samples Class i would get lSSEIW lEIEI 5 Avewarid Bulkhan Chamev 3 s v Strati ed Random Sampling Allocation 7 W7 1 c a T L c AA t in uptlmal cll Neymarl speclal case at Equal q dlvldEd by an equatlcln tnatensures tnattne standard enclr ls mlnlleEd Tu calculate tne area at eacn stratum ls multlplleu pytne standard ueylatlcln l J Tne numpelclt pints ls tne lcl unlcln ufthe area WEl HlEd siarldard dEVl UEIH El HE Dial T Strati ed Random Sampling Allocation slmlum ama so am Vuluma cuss Inlay leawm A x m m l5 2o 1 45 m s lso iii no 3 5850 lv so 5 2 7m 19 as I so I nlal m n quot750 Uslng Neymarl allclcatlcln class l only gets cut at lulu SUDimam inn 3 plats Avewarld Bulkhan cnapiel 3 Strati ed Random Sampling quotn Allocation 7 c E v A r 391 39There ls rarely any need furfures terstu ucl l Cuchrane lam slate Tne Slm lElL and selfrvvaluhtlnu feature at prupu lEIrZEI n lncrease ln yarlance ln furEs Uy uttne tlme soati ed Random Sampling quotn Allocation Advantages W D v s oes nut require stratum variances ample weignung dependentpurely on area Disadvantages Neyman alloeauon l more errieienl and has smaller s Landard errors and variances Will gel large sample SlZEsl WlH get small sample SlZEsl i r M Jahnsan Chamevl 5 soati ed Random Sampling quotn Allocation f1w 4 l as 39 r Tnenomperor sampling unltsln propomonal 7 dallucatlun lS calculated by Md Nh Number ofunlls in slralum N a nu oer ofunlls m populalron T n 339 I 72k 4 a Sha ed Random Samp 9 How to do 2L 4 The strattheatmh preeess has 5 dearsteps t Stramymg the trarhe 2 Determmmg huvv many strata 3 AHDEatmg ptets aeressthe strata 4 Evamatthg the Effemveness uf the strata 5 Cateutatthg stattsttes meters f ms and Para Strau catmn m at each strata a Strata rheahs mtterahu the Va anEE aeress the strat are herhegeheeusheteregeheeus aeruss the strata rs heterugeheuus r i1 Strata rheahs dtffer and the vaharree aerussthe strata MM re ugeneuus 39r 1 Shati ed Random Sampling Effectiveness 7 39 7 E39 7 V A Q Strata means dlffer and the varlance arms the Strata are netercigeriecius 7 Strati ed Random Sampling Effectiveness Strata means an ncit dlffer and tne varlance acrcisstne allcicaticin vvlll net increase tne g Hd m sampling WW Strati ed Random Sampling Effectiveness r w c n Summary We strata must be dlffererltlrl at least cine aspect varlance cir mean When petn tne mean and variance are Equal yciu essentially have tvvci areas tnat are tne same strata snati ed Random Sampling snara Size The re atwe 5sz er eaeh strata rs eareurared byte ratm enhe number er mm per sirata weed bythe tuta number Elf p uts NhN ufas uata i 7 gr 1 Strati ed Random Sampling Sham Size Error he ewatw Eras mtruduced frum assummg an sz Nh rs gwen by 7 IF A 1 L BYIIFZQF17NFI4FI 1 e1quot I39TI39E r e r c earyrhrs Wm un y be he pfu rryeu drseevera rsrahe a erthe fact strati ed Random Sampling Parameters amp sratistics 39 r 4 4mm HLW r A Thrs rs essermaHythe werghted average ufthe separate samp e vananees 39 1 39 r N am sample In all strata 39 w 39 a W A l Strati ed Random Sampling Parameters amp S atistics Q TE yanance amung ndwvwdua s mm a 39 mg e sirata by s cauated by 1 7 39 7 V 2 ZyiaEthm 4 n 71 The Standard Errur Elf the mean fur thhuut amazement s nah ated by 3 3 F 39 f u Strati ed Random Sampling Parameters amp S atistics J E k L The numberuf samphng mm m prupumuna JaHumtmn s caxcuxateu by if Akr 1 16 39The Management Objectives 39The Scale of Concern 39TheAmount cfAvailable Resources and if F 39 1 mm u AMIMum i Wham wear an ram 1 The Main Steps Define the Problem Explore Possible Approaches Explore Potential Sampling Methods Evaluate the Efficiency and Accuracy Check all Data and Results Present our Results 3 measurement activities whether g management or research TIME is nearly a 1 my 3 L4 The Measurement Process Use Your Time Wisely 7 7 3 T may k quot 6quot There are several steps you can follow to make the best use of your time gt Clearly specify each objective Know the appropriate tools for each task Practice the correct use of each tool prior Record measurements in field formsnotebook Check that your answer is sensible 57 T r g n The Measurement Process Don t Discard Discoveries w r a lfa correctly applied measurement roduces a value you do not expect on t ignore or remove it The apparently incorrect measurement may ctually be a new discovery 2 In general data points should never be v removed unless you have a really good contaminated sample n ar Ed 7 s uff Eva uatmn ur pan uf a r m FOR 27 Forest MeailremenE and Invenmry v Numbering Scales Its not just a matter of 09 50 W342 ENDSI 2 39 The modern world uses several tems number sys IMNOUTQ e smzmmm The Decimal system for Currency Le 10 cents in a dime The Duodecimal System in Natural Resources Le 12 inches in a foot The Sexagesimal System in Time Le 60 seconds in a minute Type ominal nurnberlng of oblec39ts forlD treezzln a plot ordinal rst second third grades etc product grades seventy classes Interval graduatlons at unlforrn lntervals yard stlclctnerrnorneter Ratio as lnterval but zero lncluded frequencyi l e numberper unlttlrne stand volume perunlt acre qrquot L We do this by assi nin units ESE number like 3 doesn39t mean anything means 3x Measurement Systen39s English and Metric The English system is used by landresource managers The Metric system is used in scienti c reports and proceedings A Metric World The 1 Systeme Internationan System 5 l39 Tnese are standard measures tnat have been repeated in muitipie ubseNatiuns e use tnese 4 in naturai reseidrees eitners are tne ampere mui and candeia kg Lengtn rneter rn Tne iengtn ot iighttraveied in a ta vacuum in i299792458 seconds 3 kiiovram K ne rn ss ot a certain cyiinder ot piatmumeiridium aHoy neid in a vauit in Sevres seconds s 9192631770 Vibrations ottne 2 radiation ernitted a s Specificwaveiength of cesiume i i hemare WEI EIEIEI sq m i sq miie 2 5899 sq km 1 sq km inn hemares i m r Uniis and Converslons nit Dimensions l FOR 27 Forest Meamremens and Invenmry i l v ve Meailr How Hot magme a thermumeterthhuut a scam and w ur u derthan the prevmus measure butyuu an nut knuvv by huvv much umtstms measure has nu untexl AQuan m ve Measur How Ho Quarmtauve me asure ments e nutashut asthe Sum ssi u 92516 am 22 Mrgm be s uvver and mere urmeume acqmre Pr39v39 s m desmbe a Em ugma urbmphysma phenumena Same Pamer Thuugms and skEDtmsm fur rt rs thmuuh these that new drseuverres are made 7 Dr Rmhard F vnman LeE Go OuEide Ewan and Acres n4 Stausucaw Study Samphng Data Samphng Umt F 1mm An mdwmua P o P A NEWS mvemuw mm Awuudpeckevsne Population minut owned WWW 503 him I m Elzmga et at W Wm ChapterS quotamttill elm ihu hum m it who mom uinpia oi 7quot 757 a ram Aconstruct that highlights the boundaries ofa population n i V 7 kc to or m WMSEQUIN iliiili i it Population or UniverseUniversal Set The collection of all the sampling units whal we want to learn about Simukinlninttion Populilionplnmtm w i I l i l y l l l C minTimtmwlmm Wu uupulu 3 A Mm plinthwtmt 0 lIi 3 Simwdt39dttm 2 0 inn quot 7 7 7 7 75 7 0 Simple monks n I it u if D 0 Minn I himninth J 7 757 z m quot h 0 union Mm 1 t nuns 3 Population Htlmntu Elmo Mulan 4 39 543V Winn Elzmga et at w mm Chapter 5 himl t ill plum lGUht Sui Pentium 014W whittntitwtd ln tutlm oiol to plain ills lune slum we rhutnm swan ton 1m x in warm Ml nth not mum out line papuhuan tumm 13 gt 12 and Precision What39s an Error Pvemse mmemse as and Random Errors Randum Eras Ev ms l l measuresm systemaucaHy smn m Bras 5 men caused by putu Randum ahmated msmmem E quotms Randum Enms m many eases vepeatmg a measure Svs emauc Enuvs TRUE VALUE These vandum enms set me vanammy m we measurement m m swear I Dwsmbutmns The Law of Large Numbers siatesthatasthe pupmauun parameters regan ess ufthe pupu atmn msmbuuun Tm Meansmat Assume a dws mbutmn s Nurma arm men can e 5 standard devwatmn very cus d 7 Can take fartuu much me e m same 2 e ean be mere accurate than that cuHemed mse v cuvered decreases mensny uf samphng mcreases as nsk uf makmg a bad management demsmn mereases Fur sttnventeny CuHectmg data untne ad quanuty quahty and cundmun uf a furest resuurce hea th Umberva ue Water quahty vnmwe habttats recreatmn VESEIUVEES SEIH ndmun at 1 v gt I TnnberEstnnatmn and Sate A 3 x veturne and vame uf merenantabte trees LanuAeqmstten SentSte mfurmatmn and Dtstanee te Mm quot Management Ptanntng Surveys destgneu te evamate tne gruwm I WW SunE s destgneu te evamate tne Eundmun and enange uf mum resuurces am as Wmhte na p e bttat V tun r 9 steps m a Forest Invenmr Over ew i Aeeesstbmty and Transpun Data mber Quanmy and Quahty uf Grevvtn V MH 7 9 Steps m a Forest Invenmr Over ew i surveys repuns maps phutus PersurmeL pudgep urume nns tramts Desmptmn m Lucatmn sag terram accesswbmty transpunatmn femurs n urmatmn reqmred frum mventury 7 Tame and graphs Maps 7 omth uf repun nventury deswgn Menmcaupn ufthe sampth umt Cunstrumun ufthe sampth frame Se emun ufthe sampth techmque Determmatmn ufthe samp e 5 ms v e surement prunedures 7 Lucatmn uf sampth mm 7 Estabhshmentufsamphng mm 7 Recurdmg mam data 7 SupEMsmn and quahty cpmrm cpmpuaupm and a m atmn prunedures 7 Data Ede a 7 Cunversmn facturs 7 Statwsucama w atmns YW Why Are Farsi Inventories not TreeBased Examp e Assume H V ma forest We Wantto quanufy the board feetofaH P PO gt15 DBH l rees Wm meet ma reqmrement rThese trees W New be Wwde y and rregu ar y dwspersed n W not be pracnca to deve op a sambhhg protoco where each P PO was a sambhhg um Twmberestwmanon 5 rare y done W the mdwwdua tree as the sambhhg uhn Thws 5 Why Pbts are used 3 Cluster Sampling What is it in many eases sampling unit WlH be i med lrl gruugsureluslers galrl me simple Example lS me use elf plats e example Elf elusler sampling lS V sys iematl sampling next lecture Y s 39r 4 Cluster Sampling What is it in a similar manner due in aeeess and VHS reslnmens We may wanna Salem plel gruups f M u 1 d Cluster Sampling Why do we use it quot A In mm quot4 Examp H en We Es ilmate all the heights uf trees in a planlallun when We have nu trEErlls i r all a a 39j v AS We know the numberof rows and cotumns We Set ms to be the FRAME rThen seteet random sampte ofrows ahd naHy Measure aH hetghtswtthth those sampte rows ththts case tree etements row usters l A E t We have a treerhst the vast stze ofa gwe ptahtatton ornurse may make etustersampte do we use it J f 39 Are these good reasohs7 l e What is the most trhoortaht thing when doing a rorest inventory I 7 397 Credibilityl r People have to trust yourestrrhates aho yourquoted errors regardless orthe terrain that the i ve lory has to be collected oh Avery and Burkhart clustersampling gives you more l foi mallo per unit oostthah Simple random sampling How do we do it Maximize the Cluster Variability Keep clusterelements oiose While retaining a Trees in lots Students Within classrooms Cluster Sampling Maximize the Cluster Variability 7 I The goal Ofai iy Samplli ig method l5 to produce ah eSlimale otah thvehtorywhere the SE otthe rheah l5 as low as possible dothls Co lali i as much variability as possible gt large 7 plots that cover large areas This is the opposite goal ofstratificationiii from which We then Select Samples v Cluster Sampling Maximize the Cluster Variability 7 39 I rll l the plot example Our goal ls to measure elem ehts trees across 7 the range of charactehstlcs such that there ls Cluster Sampling Keep Elements Close Agoal ofmost forest lrlvel ltory Sampllng deslgns l5 thatlhe Selected deslgrl Should be efflclel anl ld To do thls Keep clusterelemel lts close to reduce travel tlme 39 lrthe clusters are close together Won twe be more llkelyto 1 sample slmllar areas and lower the Variability l Cluster Sampling What Types Exist I In cluster sampling we divide the whole population into subsets These subgroups are often called primary H clusters DIVISION or subgroups produce secondary clusters and so on 39 39 I l population into the pri ry subgroups 39 Clusters can be any shape and in any configuration Selection of a cluster type is dependanton the management objective Th Cluster Samp 9 Two bage rs p antatmn Dr EquaHy nursery ease ean be made mm an EXamp E ufTWurStagE samphng ruvvs Take a s anqu samp e err Ehaments rmpre r u r unu w E 43 ean m muster Samphng number uf duster m bubmabun number or Ehaments m pupu atmn n number or usierse emed b am he ranqu samphn number or Ehaments m us terw tuta ur aH ubservatmns m us ter ge dus ter rze average us terswze 1 I F39 pu un an and StandardE ur


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