Fuels Inventory and Management
Fuels Inventory and Management FOR 451
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wag FOR 451 Estimating anow Biomass and Available canopy Fuel Load Chad Hoffman This week we will look at some of the techniques we have to estimate canopy fuel loadings We will cover a few of the techniques to estimate canopy fuel loadings biomass available canopy fuel loading for crown re crown bulk density and crown base height Crown fires are a concern to many mangers because they exhibit higher rates of spread then surface res produce more smoke per acre have longer lasting ecological effects have greater threats to fire fighter and public safety and have increased risks of property loss Scott and Reinhardt 2005 A variety of modeling systems have been developed to link surface fire behavior to crown re behavior such as NEXUS Scott 1999 FFEFVS Reinhardt and Crookston 2003 and Crown Fire Initiation and Spread CFIS Cruz et al 2005 Each of these models integrates separate equations to predict surface re behavior surface fire transition into a CrOWn re anu UIUVVII fIIU UUI aViOI The USU UI oUCII models Can hUIIJ managers develop silvicultural prescriptions to reduce the threat of crown res prioritize treatment areas assess current crown fire hazard and predict the effects of a crown fire under different scenarios It is outside the scope of this class to discuss the individual fire models available for us to use however all of these models have several common canopy characteristics which need to be measured or estimated 44 L Definitions Total Biomass or Canopy Fuel Loading The total amount of biomass in the canopy fuel layer Available Canopy Fuel Loading The art of the canopy that can burn in the flaming front typically the oliage and some of the l hr fuels Canopy Bulk Density The dry weight of the available canopy fuel per unit of canopy volume Canopy Base Height Distance from the ground surface to the base of the canopy fuel stratum Foliar Moisture Content Moisture content of the foliage Other Terminology Canopy is a stand level property I Crown is a tree level property Before we get into this lesson I want to review several definitions First the terms total canopy biomassquot or canopy fuel loadingquot will refer to the total amount of biomass or fuel in the canopy fuel stratum The available canopy fuels loading is the part of the canopy that can burn in the flaming front Typically this is all the foliage and some part of the 1hr fuels Canopy bulk density is the dry weight of the available canopy fuels per unit canopy volume and the canopy base height is the distance from the ground surface to the base of the canopy fuel stratum Another important terminology difference to recog c s that the term canopy refers to a standlevel property and the term crown refers to a tree property Current approaches to quantifying canopy fuels o Instrument based g nd based optical sensors estimate Leaf Area Index 0 LAI is used with specific leaf area and canopy depth to estimate CBD 0 Inventory based Use individual tree allometric equations which relate tree Size to crown biomass gilable fuel load is then divided by canopy depth to get 0 Heuristic approach 0 Rely on expert opinion to estimate CBD There are three broad methods used to estimate canopy fuel properties Instrument based approaches inventory based approaches and heuristic based approaches In this section we are mostly going to focus on inventory based approaches Calculating Canopy Biomass M an Where M ovendry weight D diameter at breast height a and b are parameters The most opular method for estimating crown biomass is the use of allometric equations Many studies have been conducte to predict crown biomass usmg allometric equations Ter Mikaelian and Korzukhin 1997 have summarized the existing equations for 65 tree species In their review they use the equation Man where M is the ovendry weight of the biomass D is the diameter at breast height and a and b are parameters They suggest that the use ofthis equation is a good balance between accurate predictions and low data reqwrements Since it uses only the diameter at breast height Although these equations provide an alternative to destructive sampling for the purpose of developing local equations the user has to rely on the estimates developed from other sites Although this problem is of concern several approaches have been suggested to over come it First you can simply find the closest geographic site or you could use several equations to estimate the range of biomass and last you could generate biomass equations using various equations and fit a new equation to the generated data In addition to the issue we just talked about crown biomass equations are not available for all tree species size classes and stand conditions so many times this method will not be practical Keane et al 2005 Although the equations we have been talking about only use diameter at breast height to predict canopy biomass other equations may include additional variables such as tree height Although other variables have been shown to be statistically signi cant their inclusion does not necessarily lead to a higher R2 or a decrease in SEE J M Example of Calculating Canopy Biomass m 1905 3277 3 Data was collected on a 110 1 acre plot in Idaho all 3885 trees are Doug fir 2454 3 3 Fun Diwgliu l Madame nuiuiuii Mier Fmol lA All 00008 25202 55 000072 0279 in I010 na BrilislIColumlia MlimlWangJWS 2057 SW 00150 10050 5 50 l00960 0493 In lil l good BIilishColumbia FeliciMl 31775 00101 20270 129 8000 0243 In 030 poor llril39ulalumim 000092 A 2337 H 4877 00131 20270 Ml 40090 0209 in i037 00000000100000 Micrl m j 133 0007 2001 535 42090 020 in I040 mm 30100000 00102 g 2032 172 5 l l quot lf 25 55 quot Wl iu fink393 0quot ll A Ef gy f 8 2921 39 g2 Table from TerMIkaelian and Korzukhin 1997 3302 2201 In this example we will calculate the total canopy biomass for a Dougfir stand in northern Idaho using the equations from TerMikaelian and Korzukhin 1997 and a data set of DBH from a 110th acre plot The first step we must do is find an appropriate equation to use The table above shows that only one equation exists to predict the total canopy biomass labeled as AB in the table We now will generate our equation The equation will have the form M 00808 x DBH raised to the 25282 Examolg of auditing Canopy Biomass Eatinm DBH gem Total Biomass kg 19 05 24 55 32 77 70 31 35 B1 83 55 11 43 9 11 38 BE 97 89 2 54 10 A4 34 34 7559 22 35 33 48 35 31 81 26 53 59 1B2 62 20 S7 28 51 3175 88 1d 23 37 3E 49 48 77 152 1 13 46 12 52 15 24 15 93 2032 27 83 25 55 43 74 2921 58 28 2083 29 1 35 81 83 55 33 02 71 37 22 I31 32 75 Total Biomass kg 135529 Total Biomass kqha 33547 77 In this table you see that we converted our DBH from inches to cm and calculated the total canopy biomass for each tree in the plot We then summed the total biomass for each tree in the plot We now want to calculate the total biomass per hectare To do this we can multiply the plots biomass by 10 since we conducted the sample on one tenth of an acre Then we convert this number from kgacre to kgha by multiplying it by 0404 We now have estimated the total canopy biomass for our stand Note that for this example we used an equation developed by Marshal and Wang Most fuels managmetn applicatiosn use equations develop dby Brown 1978 and by Brown et al 1977 t m Brown 1978 equations o Subset of equations from Brown 1978 Table 3 Brown39s equa ons used a anafySIS wilh reponed R2 Brown repelted the equations for live crown weight along wuh equations for prapoman oi foliage Here is a subset of the Brown 1978 equations Notice that the equations calculate total biomass as well as the proportion of foliage Brown 1978 equations o A quick alternative to the Brown Equations TAM 11 an weight per rc cy Wm I r LP H39LIIF DF 61 Ar Ml l 05 m aka M M mg Km M mc N Wm we A quick alternative to using the allometric equations presented in Brown 1978 is to use lookup tables that were published in the Brown et al paper you read this week Part of this table is shown here Notice that the table will lose some accuracy since it only has weights for whole diameters As an example let s look up the weight of a ponderosa pine with a diameter of 10 inches First find PP on the table then go down until you reach the 10 inch DBH line You will see that a 10 inch ponderosa pine has a crown weight of 171 pounds Brown 1978 equations Estimating fuel loading by size class BEE 13 Fracrwn of crown MU2 and dead bmndmoari and f1397r ompmerl of fofmgc and brmahwoi bi 519 Sass Specues D b h PP 111 IF CF ES L WP W11 inches FOUAQE 07 09 057 040 052 002 0 62 0 2 0 2 0 53 0 63 D 2 1 29 19 32 40 59 SS 55 46 51 58 31 3 49 44 29 43 54 50 gt31 47 SS 47 9 69 3959 27 4 Sn 1 17 37 43 51 13 710 33 33 37 44 37 12 32 38 46 38 11449 27 20 32 37 29 3 28 32 d1 33 1518 21 17 28 3930 33 J 34 37 35 38 1922 17 H 25 37 I9 25 20 23 31 25 23469 14 I2 23 25 1 11 16 20 26 20 730 11 10 20 23 11 18 13 17 22 17 3134 09 08 18 22 15 15 19 14 SSS359 07 07 16 0 12 12 16 12 We are now going to look at how the Brown equations can be used to estimate the fuel loading by size class To help us do this we will focus on a series of tables published in the Brown Shell and Brunnell paper you read this weekfrom 1977 The tables are shown in Appendix III Let s stick with our 10 inch ponderosa pine for this example Remember that the total crown weight was 171 pounds If we look at this table we see that the foliage makes up 33 of the total crown weight which is equal to 564 pounds m Brown 1978 equations REE 13Fration of crown Hun and dead bruntmood and faliagw Comprised of foliage and bmmulmooci by size class Specms 01711 PF 11quot F CF AF ES LP WP t 1111 Inches PsiLAKEWOOD D to 0 25 NH 9 36 25 26 27 27 26 25 9 on 37 24 2 2n 25 29 26 14 36 9 06 35 23 21 25 26 29 25 13 25 9 OS 33 21 20 24 25 18 24 12 23 9 m 30 20 1 22 23 26 23 11 211 9 03 27 17 1s 20 22 23 22 10 17 9 02 24 16 12 19 211 22 21 08 14 9 01 21 14 10 17 113 21 21 117 12 9 1 1a 13 09 15 17 20 20 as 10 9 01 16 12 03 13 15 19 19 05 OS 9 01 14 11 17 14 1s os 07 9 01 1 1o 07 13 17 04 05 con Now let s calculate the percentage of total biomass that is in the 1 hr canopy fuels We will continue with our ponderosa pine example In this case we see that 004 percent of total canopy biomass is in 1hr fuels Remember that our total canopy biomass was 171 pounds Thus we have a total of 68 pounds of 1hr fuels Brown 1978 equations Spacias Dbh PP KL DP GP AF is LP HP C M inches BRAXCEWOOD 023 to 1 INCH 0 09 029 018 021 012 012 012 021 0 21 012 0 12 1 29 36 31 30 19 19 19 23 23 28 23 3 49 37 32 31 20 24 22 29 26 29 23 5 619 38 33 32 23 27 23 32 28 30 25 7109 38 33 33 34 25 34 Z0 30 26 I1ld9 36 33 33 31 41 27 29 3O 29 27 15189 31 33 31 34 47 28 26 31 27 27 19229 31 33 28 32 49 29 2 31 25 26 2326 27 32 24 28 50 30 2 31 23 25 27309 23 31 20 25 51 20 2 31 21 24 31349 18 30 17 3 29 32 13 23 35 389 13 28 13 17 28 31 16 22 Now let s calculate the 10hr fuels for our ponderosa pine example In this case you see that about 38 of the total crown biomass is captured in 10hr fuels Thus our 10hrfuel loading is about 65 pounds V Brown 1978 equations Spacias Dbh PP KL DP GP AF ES LP HP K W inches BlKNCH A OOD l to 3 INCHES 9 D D 0 0 0 0 O 0 0 9 07 D 0 0 0 0 0 0 0 9 13 04 03 02 02 01 02 0 3 03 9 13 07 07 04 05 03 05 07 09 9 23 14 H 07 10 03 10 1395 16 9 27 l8 I7 10 15 20 16 l l 9 31 23 241 l2 2 28 21 30 31 9 35 28 31 l 8 34 25 38 3 1 9 39 33 38 I 32 39 29 45 4 09 12 33 H 22 3958 43 33 32 5 9 45 42 50 12 36 58 6 9 48 A16 56 d7 39 11 61 In this table you see the proportion of total biomass that is between 1 and 3 inches For our 10 inch ponderosa pine example you can see that it is equal to 22 or 376 pounds V Brown 1978 equations Spacias 01311 PP 11L DP GP AF ES LP HP JC W11 inches BP SCHWOOD 3 INCHES 1 29 0 0 O 0 O 0 0 O 0 0 3 49 0 0 J 0 0 0 O 0 0 0 5 69 0 0 0 0 O 0 0 0 0 O 7l19 03 0 O D 0 0 0 O 0 0 4119 07 02 0 0 0 0 0 0 0 O 15189 12 03 01 0 0 0 0 0 0 0 1922 16 04 01 0 O 0 0 0 0 0 23469 19 05 02 0 0 0 0 0 0 0 7309 23 05 02 0 0 0 0 1 0 0 WlSd9 27 0 03 0 0 0 0 0 05389 31 06 0 0 0 0 0 0 Lastly let s estimate the percentage of total biomass that is greater than 3 inches In this case we see that it is 003 percent or 5 pounds I Brown 1978 equations Crown component Weight in Pounds Foliage 564 lhr 68 10hr 65 100hr 376 1000hr 5 total 1708 or about 171 0171 lb per tree 150 trees 25650 Ibacre o25650 Ibacre ltonZOOOIb 128 tonsacre Here are our final numbers for our hypothetical ponderosa pine tree Now let s assume that we know there are 150 ponderosa pine trees with a diameter of 10 inches per acre we could simply multiply our final value by 10 to get a per acre estimate Now let s convert this estimate into tons per acre by dividing by 2000 When we do this calculation we get 128 tons per acre of canopy fuels Estimating Available Fuel Loading Crown component Weight in Pounds Foliage 564 lhr 68 10 hr 65 100hr 376 1000hr 5 total 1708 or about 171 128 tonsacre Remember that the available canopy fuels are the part of the canopy that can burn in the flaming front typically the foliage and some of the 1hr fuels Now that we have calculated the total canopy biomass using allometric equations let s calculate the total available canopy fuels In this example we will use our same ponderosa pine forest with 150 10inch trees on it 44 L39 Estimating Available Fuel Loading 0 Let s assume that available fuel loading is equal to the foliage 1hr fuels and 50 of the ten hour fuels in fuel Pounds 564 68 65 376 5 1708 or about 171 09 In this example you can see I added a row to our table called available fuel loading Since we decided that all foliage and 1hr fuels would be counted in the available fuels I simply moved these values over For the 10 hour fuels l divided our estimate in half and than added that to the column I then crossed out all the other fuel values and added the numbers up multiplied by 150 to get a stand average and than divided by 2000 to get tonsacre You can see that our available fuel loading estimate for this stand is 72 tons per acre I Estimating Available Fuel Loading Using Cruz el al 2003 TL in kg 111quotquot sland density Tl lll in trees per hectare bnsal 1ch G in in2 haquot Standard ilcvintion in parentheses All coclticicnls signi cant at 111C 00 a Icvcl Fuel typo 39octl39icicntx in InCFLflu ILuG mITPH n Adj R3 SEE n t 3 Douglastit 4059 10005 05 H026 ILI7S 01th 132 095 I I ll Pondcrom pm 4592 007 DEM 0028 ll 10 0013 IOU H12 USN Mixed conifer l 2l titllll 05m H047 335 0039 101 091 0332 LotlgcpoIc pinc Jl tih ill 5 00 I 0 0046 H130 103 52 Hit 0 I44 Table from Cruz el al 2003 Let s now go over calculating available canopy fuel loading using allometric equations developed by Cruz et al 2003 These equations are somewhat different in that they directly estimate available fuel loading Cruz calls this term Canopy Fuel loading CFL You can see that this equation is developed for 4 different ecosystems The equations itself predicts the natural log of canopy fuel loading so you will have take the inverse of the natural log to solve for the fuel loading This equation has two input parameters the basal area and trees per hectare It also has 3 coefficients w c aw Hz we dependent upw c ecosystem Estimating Available Fungi Loading Using Cruz at al 2003 f lairJed 0 Using ii m A Cruz et al 2003 J l u tn Northern Arizona Data Set Trees per Hectare 272 if 13 Basal Area 332 mZha mu m munmm V W4 1 ni39imiiw um i in In CFL Bo B1lnG B2 InTPH o In CFL 3592 0864 ln332 0110 In272 0 In CFL 3592 43 57 t In CFL 641 0 CFL e541 0 CFL 6079 kgha For this equation you can see we need to calculate the basal area in square meters per hectare and the trees per hectare We will use a data set from northern Arizona to do these calculations Note that the trees per hectare is equal to 272 and the basal area is 332 meters squared per hectare Let s go over the calculations We will begin by writing out the full equation Next let s add our numbers into this equation Now go ahead and calculate the right hand side of the equation At this point you should have natural log of CFL 641 To solve this we need to take the inverse of the natural log which is exp of both sides Yourfinal answer should be 6079 kg per ha megs Adjusted Allometric Equations Reinhardt et al 2006 found that o The Brown 1978 equations tended to overpredict compared to this data set 0 the average difference between the predicted and observed available canopy fuel values was 070 kgm2 0 The Cruz et al 2003 equations were also tested against this data set The average error was 056 kgm2 As an alternative Reinhardt et al 2006 suggests that there may be promise in using an adjustment factor along with the Brown 1978 equations Recently Reinhardt et al 2006 published a paper which compares several indirect methods for estimating canopy fuels to direct measurements of canopy fuels on 5 sites throughout the western US You will read this paper next week but until then I would like to cover several of their ndings regarding available canopy fuel loadings This study compared the allometric equations developed by Brown 1978 the regression equations developed by Cruz et al 2003 and a set of adjustments multiplication factors based on crown class The results of this study suggested that both the Brown 1978 and Cruz et al 2003 equations had poor correlations to the direct measurements with the average difference between the predicted and observed data begin 070 and 056 kgm2 respectively Due to the error associated with these estimates the authors suggest that there may be potential in using the adjusted allometric equations as an alternative Although they admit that much more work is needed in this field Adjusted Allometric Equations TthL39 3 Iiuslnlrnl luclurx quotml In rurrccl lnnmzm lii ulicllunx ul mun clzm Quill ilv 1 mm t39lim Dunununl ndwninzml lullrnmllulc Suppruwd lulu Iu lllmlgvll lv l umlcrwr Illl Flllgdllll 77 i39inumllc 13 lllmlgcll l luuvnxr mlur Blvdgull h Duuglzivlir himmile 1W zllmun 4h lelgcll l Luxl pulr Pllh39 39l39rmlcrlmvl v7 Sulmun l5 I115 ii 045 llm 0x lSI 1 130 016 on MS m 0J5 12h 03 1N mu MS mi LES Wt 05 lIZi Hull 053 Ill 075 iii U ui7l I Ll il ll ul515l 03 Ms LI ill ulc Numhrrx 1n parcnlhvsm arc numbcn nl39 Hm Wm Table 3 us from Reinhardt et al 2006 MAM MM WWW The multiplication factors that were developed by Reinhardt et al 2006 are shown here You can see that these multiplication factors are divided by both species and site Thus if you were to use one of the multiplication factors you would have to select the correct a location that best represents your data Reinhardt et al 2003 state that the error of the predictions was only 011 kgm2 which is why they suggest that this may be a good alternative to the other approaches At the very least these results suggest that crown position may be a important factor in estimating canopy fuel loadings 2O 42 Adjusted Allometric Equations 0 Let s return to our stand with 150 10 inch ponderosa pine trees 0 Let s assume that this stand is located near Flagstaff Arizona and that all our trees are co dominates Based on the table from Reinhardt et al we would adjust our predictions by 02 0 First adjust each individual tree 957 02 194 lb 150 tpa 2871ibacre 2000 144 tonsacre o In this case we could have also simply adjusted our final value 72 tonsacre 02 144 tons per acre Let s look at how to utilize the adjustment factors If we return to our stand of ponderosa pine in northern Arizona you will recall that there was an available fuel loading of 72 tons per acre and that each tree had an available fuel loading of 957 pounds Let s assume that all our trees are classified as codominate trees In this case we can look up a adjustment factor from the table just showed you Let s use 02 for ponderosa pine in flagstaffArizona Now we can apply that factor to each tree Thus instead of each tree having 957 lbs of fuel they will have 194 lbs Now multp s o c rees per acre and divide by 2000 to convert to tons per acre and we get 144 tons per acre of available fuel In this case since all our trees were classified as codominate we could have also simply applied the factor to our original estimate of 72 tons per acre 21 Estimating fuels from thinning operations 0 Example from Brown et al 1977 a Step 1 we need to summarize the tree inventory data as number of trees per acre by species and dbh Brown recommends summarizing by 1 inch DBH classes We need to include not only the trees that are to be cut but also the trees that we expect to be trampled by the logging equipment 0 Note in many cases trampling of trees can be ignored as it is a negligible amount of material Many times we are conducting fuels inventory to design and orto evaluate a treatments effects To help show you how you can use these equations and tables to help determine additions to the surface fuels during a thinning operation we will go over the example problem in the Brown et al paper you read The first step is for us to collect and summarize tree inventory data This summary should be done by listing trees per acre by 1 inch diameter classes by species 22 m Estimating fuels from thinning operations DBH Grand Fir Larch 1 300 O 2 100 0 10 38 O 11 30 O 12 20 9 13 16 10 14 15 0 16 10 0 17 O 5 Here is a list of the trees we expect to out Note that this is a partial out in a grand fir and larch stand in northern Idaho Estimating fuels from thinning operations 0 Step 2 select the correct weight table and multiply the number of trees per acre by the appropriate weight for each DBH species category 0 For pre commercial thinning and trampling from harvest activities Brown recommends using the tables for a 6 inch merchantable tip diameter when the tree will be left in place For trees over 6 inches only crown and bole material less than 6 inches will be counted The next step in this process is to select the correct weight tables for each tree and to multiply the number of trees per acre by the weight 24 g quot Estimating fuels from thinning operations 0 Step 3 estimate slash weight for defect and breakage WVfs2000 Where 0 W weight of slash from breakage and defect V merchantbale volume of trees to be cut F fraction of merchantable volume expected to be left S density of wood We than want to estimate slash weight defect using this equation We will not cover how to estimate defect in this class but you can nd methods for this in many forest measurement text boo ks 25 Estimating fuels from thinning operations SLASH WEIGHT SUMMARY StandgLL Locationtgmgz Unit 4L Date m Pagelofl species Here is a summary of our example slash weight 26 Estimating fuels from thinning operations SUMMARY OF DEBRIS WEIGHT DCutting l 2Tram in 3 Breakage Tonslacre Poundslacrel TonslacreIPoundslacrel Tonsacre m I a I Predicted weight 1 2 3 Tonslacre ALL 4 Existing downed debris Tonslacre LLD Total debris l2 3i4Tcnsacre 1681 Finally we can add up our expected slash weight from cutting trampling and breakage You can see from this calculation that we will expect to leave about 278 tons per acre of slash after our harvest If you have an estimate of the existing downed woody debris you can add this as well to estimate a total tonsacre after the harvest In this case it will be equal to about 458 tons acre 1 a g LA I am based approaches to estimating 8 o Instrument based approaches typically estimate the gap fraction Gap Fraction the proportion of the sky visible when viewed from below the canopy The gap fraction is used to estimate the leaf area index If we assume that biomass of the branches supporting the foliage is proportional to needle biomass then the leaf area index is linearly related to foliar crown biomass Brown 1978 0 Under this assumption the gap fraction should be related to the amount of crown biomass and canopy bulk density Keane et al 2005 Biomass can also be estimated using instruments Typically when we use instrument based approaches we are attempting to estimate the gap fraction of the site The gap fraction is the proportion of the sky visible when viewed from below the canopy We can use the gap fraction to estimate leaf area index then assume that the biomass of branches supporting the foliage is proportional to the foliage biomass Using this assumption we can estimate amount of canopy biomass as well as the canopy bulk density We will talk more about canopy bulk density in the next lecture 28 E Estimating gap fraction o Gap fraction is commonly estimated using two techniques Optically estimated from digital hemispherical imagery o Remote sensing data such as lidar we will cover this later in the course A common method in instrument based approaches is to use a gap fraction This can be estimated using several approaches Two of the most common approaches are digital imagery from the ground and from remote sensing 29 Using Heminie c r wtographv to Egtimatq C I 1 h 7 7 Here is a hemispherical photo for a Douglas r lodgepole pine stand When we analyze this stand we see that the gap fraction is equal to 053 the inverse of the gap fraction is the percent cover of the stand in this case it is equal to 47 Although we will not cover all the calculations for this stand from this image we can now estimate the foliage biomass to be 090 kgm2 and he total canopy fuel load to be 119 kgm2 30 m Conclusions 0 Estimating the canopy biomass and fuel loading are critical calculations in describing the canopy fuel complex We have covered several different methods to help us estimate these parameters Of these methods which ones do you think are most effective Think about the assumptions calculations and accuracy as you do this 31