forestSAS-package: Forest Spatial Structure Analysis Systems
In forestSAS: Forest Spatial Structure Analysis Systems
Description
Details
Author(s)
References
Examples
Description
In recent years, there has been considerable interest in a group of neighborhood-based structural parameters that properly express the spatial structure characteristics of tree populations and forest communities and have strong operability for guiding forestry practices.the 'forestSAS' package provide more important information and allow us to better understand and analyze the fine-scale spatial structure of tree populations and stand structure.
Details
Forest structure commonly refers to a distribution pattern of tree attributes within a forest ecosystem. Similarly, tree population structure describes the distribution characteristics of individuals of conspecifics within a community, and the spatial structure of a tree population is largely determined by the relationships among neighboring groups of trees.
Author(s)
Zongzheng Chai, chaizz@126.com
References
None
Examples
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data(tree.ppp)
##Get the tree attributies of nearest neighbour
nnindices<-nnIndex(tree.ppp,N=4,
smark=c("sp.code","dbh.cm","storey",
"crownwid.m","group","biomass.kg",
"quality","x","y"),buffer=FALSE)
#Species mingling
M<-fsasN4(nnindices$nnsp.code,match.fun=mingling)
M
#Stand storey differation degree
H<-fsasN4(nnindices$nnstorey,match.fun=differ)
H
#Tree successional degree
S<-fsasN4(nnindices$nngroup,match.fun=ideal,para="Climax")
S
#Tree quality ideal state
Q<-fsasN4(nnindices$nnquality,match.fun=ideal,
para=c("Excellent","Good"))
Q
#Tree corwding degree
C<-fsasN4(nnoverlap(nnindices$nncrownwid.m,
nnindices$nndist),match.fun=crowding)
C
#Tree uniform angle index
W<-fsasN4(nnangle(nnindices$nndist,nnindices$nnx,
nnindices$nny)$nnangle,
match.fun=uniform.angle,para=72)
W
#Tree dominance
U<-fsasN4(nnindices$nndbh.cm,match.fun=dominance)
U
#Tree biomass dominance
B<-fsasN4(nnindices$nnbiomass.kg,match.fun=dominance)
B
##Compute the structrue heterogeneity of index
M.pv<-pv(M$result$index,optm=1)
M.pv
H.pv<-pv(H$result$index,optm=1)
H.pv
S.pv<-pv(S$result$index,optm=1)
S.pv
Q.pv<-pv(Q$result$index,optm=1)
Q.pv
C.pv<-pv(C$result$index,optm=0.5)
C.pv
W.pv<-pv(W$result$index,optm=0.5)
W.pv
U.pv<-pv(U$result$index,optm=0)
U.pv
B.pv<-pv(B$result$index,optm=0)
B.pv
##Compute total forest saptial structrue heterogeneity
#based on the average of indices preference value
IAVE<-pv(index=c(M$meanI,H$meanI,S$meanI,Q$meanI,
C$meanI,W$meanI,U$meanI,B$meanI),
optm=c(1,1,1,1,0.5,0.5,0,0))
IAVE
##Compute total forest saptial structrue heterogeneity
#based on the preference value of indices average
IPVE=mean(M.pv,H.pv,S.pv,Q.pv,C.pv,W.pv,U.pv,B.pv)
IPVE
Example output
$result
sp.code sp.code1 sp.code2 sp.code3 sp.code4 index
12 sp1 sp9 sp6 sp2 sp3 1.00
13 sp2 sp3 sp9 sp2 sp3 0.75
14 sp3 sp2 sp9 sp6 sp2 1.00
15 sp9 sp6 sp2 sp2 sp3 1.00
20 sp2 sp3 sp1 sp1 sp3 1.00
21 sp1 sp3 sp2 sp8 sp3 1.00
22 sp6 sp5 sp2 sp6 sp2 0.75
23 sp5 sp7 sp5 sp9 sp3 0.75
24 sp5 sp7 sp5 sp3 sp9 0.75
25 sp7 sp5 sp5 sp3 sp9 1.00
26 sp2 sp9 sp3 sp6 sp2 0.75
28 sp3 sp6 sp2 sp9 sp2 1.00
29 sp3 sp2 sp1 sp3 sp1 0.75
32 sp6 sp3 sp2 sp6 sp9 0.75
33 sp8 sp3 sp1 sp3 sp3 1.00
34 sp9 sp5 sp1 sp7 sp5 1.00
35 sp9 sp2 sp3 sp2 sp3 1.00
38 sp3 sp8 sp1 sp3 sp2 0.75
39 sp6 sp3 sp2 sp9 sp1 1.00
$Icount
Ind
0 0.25 0.5 0.75 1
0 0 0 8 11
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.0000000 0.0000000 0.0000000 0.4210526 0.5789474
$meanI
[1] 0.8947368
$result
storey storey1 storey2 storey3 storey4 index
12 S5 S6 S3 S4 S4 0.75
13 S6 S6 S5 S4 S4 0.50
14 S6 S6 S5 S3 S4 0.75
15 S5 S3 S3 S6 S6 0.50
20 S6 S1 S6 S4 S6 0.50
21 S4 S4 S6 S6 S1 0.50
22 S6 S6 S5 S3 S6 0.50
23 S5 S3 S6 S6 S1 0.75
24 S6 S3 S5 S1 S6 0.75
25 S3 S6 S5 S1 S6 0.75
26 S4 S5 S4 S3 S6 0.75
28 S4 S3 S4 S5 S6 0.75
29 S1 S6 S6 S6 S4 0.50
32 S3 S6 S6 S6 S5 0.50
33 S6 S4 S4 S1 S6 0.50
34 S6 S5 S5 S3 S6 0.50
35 S5 S4 S6 S6 S4 0.50
38 S4 S6 S4 S1 S6 0.50
39 S3 S4 S4 S5 S5 0.50
$Icount
Ind
0 0.25 0.5 0.75 1
0 0 12 7 0
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.0000000 0.0000000 0.6315789 0.3684211 0.0000000
$meanI
[1] 0.5921053
$result
group group1 group2 group3 group4 index
12 Pioneer Transitional Climax Transitional Pioneer 0.25
13 Transitional Pioneer Transitional Transitional Pioneer 0.00
14 Pioneer Transitional Transitional Climax Transitional 0.25
15 Transitional Climax Transitional Transitional Pioneer 0.25
20 Transitional Pioneer Pioneer Pioneer Pioneer 0.00
21 Pioneer Pioneer Transitional Transitional Pioneer 0.00
22 Climax Climax Transitional Climax Transitional 0.50
23 Climax Exotic Climax Transitional Pioneer 0.25
24 Climax Exotic Climax Pioneer Transitional 0.25
25 Exotic Climax Climax Pioneer Transitional 0.50
26 Transitional Transitional Pioneer Climax Transitional 0.25
28 Pioneer Climax Transitional Transitional Transitional 0.25
29 Pioneer Transitional Pioneer Pioneer Pioneer 0.00
32 Climax Pioneer Transitional Climax Transitional 0.25
33 Transitional Pioneer Pioneer Pioneer Pioneer 0.00
34 Transitional Climax Pioneer Exotic Climax 0.50
35 Transitional Transitional Pioneer Transitional Pioneer 0.00
38 Pioneer Transitional Pioneer Pioneer Transitional 0.00
39 Climax Pioneer Transitional Transitional Pioneer 0.00
$Icount
Ind
0 0.25 0.5 0.75 1
8 8 3 0 0
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.4210526 0.4210526 0.1578947 0.0000000 0.0000000
$meanI
[1] 0.1842105
$result
quality quality1 quality2 quality3 quality4 index
12 Poor Average Good Average Excellent 0.50
13 Excellent Average Good Average Excellent 0.50
14 Average Excellent Good Poor Average 0.50
15 Good Poor Good Excellent Average 0.50
20 Excellent Excellent Good Excellent Average 0.75
21 Excellent Good Excellent Excellent Excellent 1.00
22 Average Low Excellent Poor Excellent 0.50
23 Average Excellent Poor Average Average 0.25
24 Poor Excellent Average Average Average 0.25
25 Excellent Poor Average Average Average 0.00
26 Average Good Excellent Good Excellent 1.00
28 Excellent Good Average Good Excellent 0.75
29 Excellent Excellent Good Average Excellent 0.75
32 Poor Average Excellent Average Good 0.50
33 Excellent Good Excellent Excellent Average 0.75
34 Average Average Poor Excellent Poor 0.25
35 Good Average Average Excellent Excellent 0.50
38 Good Excellent Excellent Excellent Excellent 1.00
39 Good Excellent Average Good Low 0.50
$Icount
Ind
0 0.25 0.5 0.75 1
1 3 8 4 3
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.05263158 0.15789474 0.42105263 0.21052632 0.15789474
$meanI
[1] 0.5657895
$result
overlap1 overlap2 overlap3 overlap4 index
12 0.8939449 1.3136917 -0.004759208 -0.44110231 0.50
13 3.9400980 0.2707640 0.219415640 0.02590013 1.00
14 3.9400980 1.6557075 2.348333796 1.54355509 1.00
15 -1.4509090 -1.6771284 -2.803990259 -1.62943717 0.00
20 1.7357864 2.5500000 -0.101219331 -0.19499706 0.50
21 0.1050039 -0.1012193 -1.555948169 -1.74221549 0.25
22 3.3615224 2.9845241 0.817409819 -0.15399026 0.75
23 1.8596876 2.1719890 0.187722340 -1.10105618 0.75
24 1.5675445 2.1719890 -0.896309242 -0.73549865 0.50
25 1.5675445 1.8596876 -1.926955262 -1.11961536 0.50
26 2.9257359 0.5841367 1.542866230 0.21941564 1.00
28 3.3937742 0.5841367 0.185037314 0.02590013 1.00
29 1.7357864 1.5682179 -0.046049894 -1.74221549 0.50
32 2.3483338 0.6399405 0.817409819 -1.44181209 0.75
33 -0.2076810 -1.5559482 -4.416912417 -3.80000000 0.00
34 0.1877223 0.8939449 -1.119615365 -0.73549865 0.50
35 2.9257359 1.6557075 0.270763990 0.18503731 1.00
38 -0.2076810 0.1050039 -4.517834424 -3.31045324 0.25
39 3.3937742 1.5428662 1.059936306 3.07584597 1.00
$Icount
Ind
0 0.25 0.5 0.75 1
2 2 6 3 6
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.1052632 0.1052632 0.3157895 0.1578947 0.3157895
$meanI
[1] 0.6184211
$result
angle1 angle2 angle3 angle4 index
12 3.963980 108.992340 68.122053 36.90631 0.75
13 52.939593 7.189115 80.986796 141.11550 0.50
14 82.381454 6.433286 139.893689 131.29157 0.25
15 53.923212 4.541996 29.279072 87.74428 0.75
20 96.732921 6.888258 46.909152 150.53033 0.50
21 147.707114 38.305771 91.182752 18.21859 0.50
22 148.018503 108.353530 75.341691 28.28628 0.25
23 103.912495 41.707161 54.605204 159.77514 0.50
24 55.619655 28.145865 100.998122 175.23636 0.50
25 16.858399 69.775141 3.945186 90.57873 0.75
26 64.337170 16.614424 139.085617 58.13402 0.75
28 84.596209 6.818215 55.905022 147.31945 0.50
29 73.354962 65.048488 125.256359 96.34019 0.25
32 6.547216 19.490162 164.416121 169.54650 0.50
33 46.255130 50.475354 122.471192 25.74071 0.75
34 164.331302 6.234480 3.366461 154.73036 0.50
35 10.198465 63.966270 50.710593 124.87533 0.75
38 11.525372 18.079079 91.218875 61.61442 0.75
39 162.924357 5.076730 73.712637 118.28628 0.25
$Icount
Ind
0 0.25 0.5 0.75 1
0 4 8 7 0
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.0000000 0.2105263 0.4210526 0.3684211 0.0000000
$meanI
[1] 0.5394737
$result
dbh.cm dbh.cm1 dbh.cm2 dbh.cm3 dbh.cm4 index
12 24.3 17.8 26.7 17.6 14.0 0.25
13 11.7 28.1 13.1 17.6 14.0 1.00
14 28.1 11.7 13.1 22.1 17.6 0.00
15 14.3 22.1 23.8 11.7 28.1 0.75
20 20.5 8.8 20.4 18.2 19.8 0.00
21 18.2 12.7 20.5 7.1 8.8 0.25
22 28.9 13.4 20.7 22.1 20.5 0.00
23 16.2 7.9 9.4 17.8 7.4 0.25
24 9.4 7.9 16.2 7.4 17.8 0.50
25 7.9 9.4 16.2 7.4 17.8 0.75
26 17.6 13.1 14.0 26.7 11.7 0.25
28 14.0 26.7 17.6 13.1 11.7 0.50
29 8.8 20.5 20.4 19.8 18.2 1.00
32 22.1 28.1 11.7 28.9 13.1 0.50
33 7.1 12.7 18.2 8.8 19.8 1.00
34 17.8 16.2 24.3 7.9 9.4 0.25
35 13.1 17.6 28.1 11.7 14.0 0.75
38 12.7 7.1 18.2 8.8 20.5 0.50
39 26.7 14.0 17.6 13.1 29.2 0.25
$Icount
Ind
0 0.25 0.5 0.75 1
3 6 4 3 3
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.1578947 0.3157895 0.2105263 0.1578947 0.1578947
$meanI
[1] 0.4605263
$result
biomass.kg biomass.kg1 biomass.kg2 biomass.kg3 biomass.kg4 index
12 311.95 124.58 648.48 209.53 171.19 0.25
13 42.40 203.87 81.71 209.53 171.19 1.00
14 203.87 42.40 81.71 464.12 209.53 0.50
15 117.96 464.12 517.70 42.40 203.87 0.75
20 82.79 120.80 101.01 239.05 113.10 1.00
21 239.05 115.78 82.79 19.01 120.80 0.00
22 193.86 55.54 268.49 464.12 82.79 0.50
23 152.90 58.10 38.16 124.58 87.72 0.00
24 38.16 58.10 152.90 87.72 124.58 1.00
25 58.10 38.16 152.90 87.72 124.58 0.75
26 209.53 81.71 171.19 648.48 42.40 0.25
28 171.19 648.48 209.53 81.71 42.40 0.50
29 120.80 82.79 101.01 113.10 239.05 0.25
32 464.12 203.87 42.40 193.86 81.71 0.00
33 19.01 115.78 239.05 120.80 113.10 1.00
34 124.58 152.90 311.95 58.10 38.16 0.50
35 81.71 209.53 203.87 42.40 171.19 0.75
38 115.78 19.01 239.05 120.80 82.79 0.50
39 648.48 171.19 209.53 81.71 397.11 0.00
$Icount
Ind
0 0.25 0.5 0.75 1
4 3 5 3 4
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.2105263 0.1578947 0.2631579 0.1578947 0.2105263
$meanI
[1] 0.5
[1] 0.8377786
[1] 0.5746518
[1] 0.1649141
[1] 0.4902271
[1] 0.6511296
[1] 0.8097785
[1] 0.4351293
[1] 0.3876276
[1] 0.5656649
[1] 0.8377786
forestSAS documentation built on May 1, 2019, 10:15 p.m.
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Description Details Author(s) References Examples
Description
In recent years, there has been considerable interest in a group of neighborhood-based structural parameters that properly express the spatial structure characteristics of tree populations and forest communities and have strong operability for guiding forestry practices.the 'forestSAS' package provide more important information and allow us to better understand and analyze the fine-scale spatial structure of tree populations and stand structure.
Details
Forest structure commonly refers to a distribution pattern of tree attributes within a forest ecosystem. Similarly, tree population structure describes the distribution characteristics of individuals of conspecifics within a community, and the spatial structure of a tree population is largely determined by the relationships among neighboring groups of trees.
Author(s)
Zongzheng Chai, chaizz@126.com
References
None
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | data(tree.ppp)
##Get the tree attributies of nearest neighbour
nnindices<-nnIndex(tree.ppp,N=4,
smark=c("sp.code","dbh.cm","storey",
"crownwid.m","group","biomass.kg",
"quality","x","y"),buffer=FALSE)
#Species mingling
M<-fsasN4(nnindices$nnsp.code,match.fun=mingling)
M
#Stand storey differation degree
H<-fsasN4(nnindices$nnstorey,match.fun=differ)
H
#Tree successional degree
S<-fsasN4(nnindices$nngroup,match.fun=ideal,para="Climax")
S
#Tree quality ideal state
Q<-fsasN4(nnindices$nnquality,match.fun=ideal,
para=c("Excellent","Good"))
Q
#Tree corwding degree
C<-fsasN4(nnoverlap(nnindices$nncrownwid.m,
nnindices$nndist),match.fun=crowding)
C
#Tree uniform angle index
W<-fsasN4(nnangle(nnindices$nndist,nnindices$nnx,
nnindices$nny)$nnangle,
match.fun=uniform.angle,para=72)
W
#Tree dominance
U<-fsasN4(nnindices$nndbh.cm,match.fun=dominance)
U
#Tree biomass dominance
B<-fsasN4(nnindices$nnbiomass.kg,match.fun=dominance)
B
##Compute the structrue heterogeneity of index
M.pv<-pv(M$result$index,optm=1)
M.pv
H.pv<-pv(H$result$index,optm=1)
H.pv
S.pv<-pv(S$result$index,optm=1)
S.pv
Q.pv<-pv(Q$result$index,optm=1)
Q.pv
C.pv<-pv(C$result$index,optm=0.5)
C.pv
W.pv<-pv(W$result$index,optm=0.5)
W.pv
U.pv<-pv(U$result$index,optm=0)
U.pv
B.pv<-pv(B$result$index,optm=0)
B.pv
##Compute total forest saptial structrue heterogeneity
#based on the average of indices preference value
IAVE<-pv(index=c(M$meanI,H$meanI,S$meanI,Q$meanI,
C$meanI,W$meanI,U$meanI,B$meanI),
optm=c(1,1,1,1,0.5,0.5,0,0))
IAVE
##Compute total forest saptial structrue heterogeneity
#based on the preference value of indices average
IPVE=mean(M.pv,H.pv,S.pv,Q.pv,C.pv,W.pv,U.pv,B.pv)
IPVE
|
Example output
$result
sp.code sp.code1 sp.code2 sp.code3 sp.code4 index
12 sp1 sp9 sp6 sp2 sp3 1.00
13 sp2 sp3 sp9 sp2 sp3 0.75
14 sp3 sp2 sp9 sp6 sp2 1.00
15 sp9 sp6 sp2 sp2 sp3 1.00
20 sp2 sp3 sp1 sp1 sp3 1.00
21 sp1 sp3 sp2 sp8 sp3 1.00
22 sp6 sp5 sp2 sp6 sp2 0.75
23 sp5 sp7 sp5 sp9 sp3 0.75
24 sp5 sp7 sp5 sp3 sp9 0.75
25 sp7 sp5 sp5 sp3 sp9 1.00
26 sp2 sp9 sp3 sp6 sp2 0.75
28 sp3 sp6 sp2 sp9 sp2 1.00
29 sp3 sp2 sp1 sp3 sp1 0.75
32 sp6 sp3 sp2 sp6 sp9 0.75
33 sp8 sp3 sp1 sp3 sp3 1.00
34 sp9 sp5 sp1 sp7 sp5 1.00
35 sp9 sp2 sp3 sp2 sp3 1.00
38 sp3 sp8 sp1 sp3 sp2 0.75
39 sp6 sp3 sp2 sp9 sp1 1.00
$Icount
Ind
0 0.25 0.5 0.75 1
0 0 0 8 11
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.0000000 0.0000000 0.0000000 0.4210526 0.5789474
$meanI
[1] 0.8947368
$result
storey storey1 storey2 storey3 storey4 index
12 S5 S6 S3 S4 S4 0.75
13 S6 S6 S5 S4 S4 0.50
14 S6 S6 S5 S3 S4 0.75
15 S5 S3 S3 S6 S6 0.50
20 S6 S1 S6 S4 S6 0.50
21 S4 S4 S6 S6 S1 0.50
22 S6 S6 S5 S3 S6 0.50
23 S5 S3 S6 S6 S1 0.75
24 S6 S3 S5 S1 S6 0.75
25 S3 S6 S5 S1 S6 0.75
26 S4 S5 S4 S3 S6 0.75
28 S4 S3 S4 S5 S6 0.75
29 S1 S6 S6 S6 S4 0.50
32 S3 S6 S6 S6 S5 0.50
33 S6 S4 S4 S1 S6 0.50
34 S6 S5 S5 S3 S6 0.50
35 S5 S4 S6 S6 S4 0.50
38 S4 S6 S4 S1 S6 0.50
39 S3 S4 S4 S5 S5 0.50
$Icount
Ind
0 0.25 0.5 0.75 1
0 0 12 7 0
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.0000000 0.0000000 0.6315789 0.3684211 0.0000000
$meanI
[1] 0.5921053
$result
group group1 group2 group3 group4 index
12 Pioneer Transitional Climax Transitional Pioneer 0.25
13 Transitional Pioneer Transitional Transitional Pioneer 0.00
14 Pioneer Transitional Transitional Climax Transitional 0.25
15 Transitional Climax Transitional Transitional Pioneer 0.25
20 Transitional Pioneer Pioneer Pioneer Pioneer 0.00
21 Pioneer Pioneer Transitional Transitional Pioneer 0.00
22 Climax Climax Transitional Climax Transitional 0.50
23 Climax Exotic Climax Transitional Pioneer 0.25
24 Climax Exotic Climax Pioneer Transitional 0.25
25 Exotic Climax Climax Pioneer Transitional 0.50
26 Transitional Transitional Pioneer Climax Transitional 0.25
28 Pioneer Climax Transitional Transitional Transitional 0.25
29 Pioneer Transitional Pioneer Pioneer Pioneer 0.00
32 Climax Pioneer Transitional Climax Transitional 0.25
33 Transitional Pioneer Pioneer Pioneer Pioneer 0.00
34 Transitional Climax Pioneer Exotic Climax 0.50
35 Transitional Transitional Pioneer Transitional Pioneer 0.00
38 Pioneer Transitional Pioneer Pioneer Transitional 0.00
39 Climax Pioneer Transitional Transitional Pioneer 0.00
$Icount
Ind
0 0.25 0.5 0.75 1
8 8 3 0 0
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.4210526 0.4210526 0.1578947 0.0000000 0.0000000
$meanI
[1] 0.1842105
$result
quality quality1 quality2 quality3 quality4 index
12 Poor Average Good Average Excellent 0.50
13 Excellent Average Good Average Excellent 0.50
14 Average Excellent Good Poor Average 0.50
15 Good Poor Good Excellent Average 0.50
20 Excellent Excellent Good Excellent Average 0.75
21 Excellent Good Excellent Excellent Excellent 1.00
22 Average Low Excellent Poor Excellent 0.50
23 Average Excellent Poor Average Average 0.25
24 Poor Excellent Average Average Average 0.25
25 Excellent Poor Average Average Average 0.00
26 Average Good Excellent Good Excellent 1.00
28 Excellent Good Average Good Excellent 0.75
29 Excellent Excellent Good Average Excellent 0.75
32 Poor Average Excellent Average Good 0.50
33 Excellent Good Excellent Excellent Average 0.75
34 Average Average Poor Excellent Poor 0.25
35 Good Average Average Excellent Excellent 0.50
38 Good Excellent Excellent Excellent Excellent 1.00
39 Good Excellent Average Good Low 0.50
$Icount
Ind
0 0.25 0.5 0.75 1
1 3 8 4 3
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.05263158 0.15789474 0.42105263 0.21052632 0.15789474
$meanI
[1] 0.5657895
$result
overlap1 overlap2 overlap3 overlap4 index
12 0.8939449 1.3136917 -0.004759208 -0.44110231 0.50
13 3.9400980 0.2707640 0.219415640 0.02590013 1.00
14 3.9400980 1.6557075 2.348333796 1.54355509 1.00
15 -1.4509090 -1.6771284 -2.803990259 -1.62943717 0.00
20 1.7357864 2.5500000 -0.101219331 -0.19499706 0.50
21 0.1050039 -0.1012193 -1.555948169 -1.74221549 0.25
22 3.3615224 2.9845241 0.817409819 -0.15399026 0.75
23 1.8596876 2.1719890 0.187722340 -1.10105618 0.75
24 1.5675445 2.1719890 -0.896309242 -0.73549865 0.50
25 1.5675445 1.8596876 -1.926955262 -1.11961536 0.50
26 2.9257359 0.5841367 1.542866230 0.21941564 1.00
28 3.3937742 0.5841367 0.185037314 0.02590013 1.00
29 1.7357864 1.5682179 -0.046049894 -1.74221549 0.50
32 2.3483338 0.6399405 0.817409819 -1.44181209 0.75
33 -0.2076810 -1.5559482 -4.416912417 -3.80000000 0.00
34 0.1877223 0.8939449 -1.119615365 -0.73549865 0.50
35 2.9257359 1.6557075 0.270763990 0.18503731 1.00
38 -0.2076810 0.1050039 -4.517834424 -3.31045324 0.25
39 3.3937742 1.5428662 1.059936306 3.07584597 1.00
$Icount
Ind
0 0.25 0.5 0.75 1
2 2 6 3 6
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.1052632 0.1052632 0.3157895 0.1578947 0.3157895
$meanI
[1] 0.6184211
$result
angle1 angle2 angle3 angle4 index
12 3.963980 108.992340 68.122053 36.90631 0.75
13 52.939593 7.189115 80.986796 141.11550 0.50
14 82.381454 6.433286 139.893689 131.29157 0.25
15 53.923212 4.541996 29.279072 87.74428 0.75
20 96.732921 6.888258 46.909152 150.53033 0.50
21 147.707114 38.305771 91.182752 18.21859 0.50
22 148.018503 108.353530 75.341691 28.28628 0.25
23 103.912495 41.707161 54.605204 159.77514 0.50
24 55.619655 28.145865 100.998122 175.23636 0.50
25 16.858399 69.775141 3.945186 90.57873 0.75
26 64.337170 16.614424 139.085617 58.13402 0.75
28 84.596209 6.818215 55.905022 147.31945 0.50
29 73.354962 65.048488 125.256359 96.34019 0.25
32 6.547216 19.490162 164.416121 169.54650 0.50
33 46.255130 50.475354 122.471192 25.74071 0.75
34 164.331302 6.234480 3.366461 154.73036 0.50
35 10.198465 63.966270 50.710593 124.87533 0.75
38 11.525372 18.079079 91.218875 61.61442 0.75
39 162.924357 5.076730 73.712637 118.28628 0.25
$Icount
Ind
0 0.25 0.5 0.75 1
0 4 8 7 0
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.0000000 0.2105263 0.4210526 0.3684211 0.0000000
$meanI
[1] 0.5394737
$result
dbh.cm dbh.cm1 dbh.cm2 dbh.cm3 dbh.cm4 index
12 24.3 17.8 26.7 17.6 14.0 0.25
13 11.7 28.1 13.1 17.6 14.0 1.00
14 28.1 11.7 13.1 22.1 17.6 0.00
15 14.3 22.1 23.8 11.7 28.1 0.75
20 20.5 8.8 20.4 18.2 19.8 0.00
21 18.2 12.7 20.5 7.1 8.8 0.25
22 28.9 13.4 20.7 22.1 20.5 0.00
23 16.2 7.9 9.4 17.8 7.4 0.25
24 9.4 7.9 16.2 7.4 17.8 0.50
25 7.9 9.4 16.2 7.4 17.8 0.75
26 17.6 13.1 14.0 26.7 11.7 0.25
28 14.0 26.7 17.6 13.1 11.7 0.50
29 8.8 20.5 20.4 19.8 18.2 1.00
32 22.1 28.1 11.7 28.9 13.1 0.50
33 7.1 12.7 18.2 8.8 19.8 1.00
34 17.8 16.2 24.3 7.9 9.4 0.25
35 13.1 17.6 28.1 11.7 14.0 0.75
38 12.7 7.1 18.2 8.8 20.5 0.50
39 26.7 14.0 17.6 13.1 29.2 0.25
$Icount
Ind
0 0.25 0.5 0.75 1
3 6 4 3 3
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.1578947 0.3157895 0.2105263 0.1578947 0.1578947
$meanI
[1] 0.4605263
$result
biomass.kg biomass.kg1 biomass.kg2 biomass.kg3 biomass.kg4 index
12 311.95 124.58 648.48 209.53 171.19 0.25
13 42.40 203.87 81.71 209.53 171.19 1.00
14 203.87 42.40 81.71 464.12 209.53 0.50
15 117.96 464.12 517.70 42.40 203.87 0.75
20 82.79 120.80 101.01 239.05 113.10 1.00
21 239.05 115.78 82.79 19.01 120.80 0.00
22 193.86 55.54 268.49 464.12 82.79 0.50
23 152.90 58.10 38.16 124.58 87.72 0.00
24 38.16 58.10 152.90 87.72 124.58 1.00
25 58.10 38.16 152.90 87.72 124.58 0.75
26 209.53 81.71 171.19 648.48 42.40 0.25
28 171.19 648.48 209.53 81.71 42.40 0.50
29 120.80 82.79 101.01 113.10 239.05 0.25
32 464.12 203.87 42.40 193.86 81.71 0.00
33 19.01 115.78 239.05 120.80 113.10 1.00
34 124.58 152.90 311.95 58.10 38.16 0.50
35 81.71 209.53 203.87 42.40 171.19 0.75
38 115.78 19.01 239.05 120.80 82.79 0.50
39 648.48 171.19 209.53 81.71 397.11 0.00
$Icount
Ind
0 0.25 0.5 0.75 1
4 3 5 3 4
$Ifreq
Ind
0 0.25 0.5 0.75 1
0.2105263 0.1578947 0.2631579 0.1578947 0.2105263
$meanI
[1] 0.5
[1] 0.8377786
[1] 0.5746518
[1] 0.1649141
[1] 0.4902271
[1] 0.6511296
[1] 0.8097785
[1] 0.4351293
[1] 0.3876276
[1] 0.5656649
[1] 0.8377786
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