forestSAS-package {forestSAS}R Documentation

Forest Spatial Structure Analysis Systems

Description

Recent years have seen significant interest in neighborhood-based structural parameters that effectively represent the spatial characteristics of tree populations and forest communities, and possess strong applicability for guiding forestry practices. This package provides valuable information that enhances our understanding and analysis of the fine-scale spatial structure of tree populations and forest stands. Reference: Yan L, Tan W, Chai Z, et al (2019) <doi:10.13323/j.cnki.j.fafu(nat.sci.).2019.03.007>.

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

library(spatstat)
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

[Package forestSAS version 2.0.4 Index]