| Title: | 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>. |
| Authors: | Zongzheng Chai [aut, cre]
|
| Maintainer: | Zongzheng Chai <[email protected]> |
| License: | GPL-2 |
| Version: | 2.0.4 |
| Built: | 2025-02-10 05:14:11 UTC |
| Source: | https://github.com/cran/forestSAS |
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>.
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.
Zongzheng Chai, [email protected]
None
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) IPVElibrary(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
Add marks for a point pattern
addmark.ppp(X, add.mark, add.name = "storey")addmark.ppp(X, add.mark, add.name = "storey")
X |
A point pattern (object of class "ppp"). |
add.mark |
Marks need added in the point pattern |
add.name |
Names of added marks (add.mark) |
A point pattern added marks
Zongzheng Chai, [email protected]
None
library(spatstat.data) data(finpines) ####Dividing the stories finpines.storey<-storeydvd(finpines$marks$height,storeynum=6) finpines.storey ####Computing the storey differation ##Add the storey mark for finepines finpines.addstorey<-addmark.ppp(finpines, add.mark=finpines.storey$heightdata[,2:3], add.name=c("interval","storey" )) finpines.addstoreylibrary(spatstat.data) data(finpines) ####Dividing the stories finpines.storey<-storeydvd(finpines$marks$height,storeynum=6) finpines.storey ####Computing the storey differation ##Add the storey mark for finepines finpines.addstorey<-addmark.ppp(finpines, add.mark=finpines.storey$heightdata[,2:3], add.name=c("interval","storey" )) finpines.addstorey
To eliminate edge effects and improve the accuracy of the Calculation,such as analyzing the forest spatial structure parameters, we always established a buffer zone around the plot. In the statistical analysis, only the trees in the reduced window are used as reference trees, and the individual trees in the buffer zone are only considered to be the nearest neighbors of the trees in the reduced window. This edge correction can individually evaluate each tree to determine whether all n nearest neighbors are truly located within the plot.
buffer(X, buf.xwid = 5, buf.ywid = 5)buffer(X, buf.xwid = 5, buf.ywid = 5)
X |
A point pattern (object of class "ppp"). |
buf.xwid |
The width of buffer zone in the x coordinates of data points |
buf.ywid |
The width of buffer zone in the y coordinates of data points |
An object of class "ppp", which add a marks "zone" in the orginal point pattern
Zongzheng Chai, [email protected]
Chai ZZ, Sun CL, Wang DX, Liu WZ, and Zhang CS.2016. Spatial structure and dynamics of predominant populations in a virgin old-growthoak forest in the Qinling Mountains, China. Scandinavian Journal of Forest Research. DOI: 10.1080/02827581.2016.1183703
library(spatstat.geom) ####Based on the simulated data#### # Creating a simulated point pattern x <- runif(20) y <- runif(20) X <- ppp(x, y, c(0,1), c(0,1)) X # Identifing the buffer zone and core zone in the point pattern # The width of buffer zone in the x coordinates of data points is 0.2 # The width of buffer zone in the y coordinates of data points is 0.3 X_buf<-buffer(X,buf.xwid =0.2,buf.ywid=0.3) X_buf as.data.frame(X_buf) ####Based on the example data#### library(spatstat.data) data(finpines) finpines # Marked planar point pattern: 126 points # Mark variables: diameter, height # window: rectangle = [-5, 5] x [-8, 2] metres # Identifing the buffer zone and core zone in the finpines # The width of buffer zone in the x coordinates of finpines is 2 # The width of buffer zone in the y coordinates of finpines is 3 finpines_buf<-buffer(finpines,buf.xwid =2,buf.ywid=3) finpines_buf as.data.frame(finpines_buf)library(spatstat.geom) ####Based on the simulated data#### # Creating a simulated point pattern x <- runif(20) y <- runif(20) X <- ppp(x, y, c(0,1), c(0,1)) X # Identifing the buffer zone and core zone in the point pattern # The width of buffer zone in the x coordinates of data points is 0.2 # The width of buffer zone in the y coordinates of data points is 0.3 X_buf<-buffer(X,buf.xwid =0.2,buf.ywid=0.3) X_buf as.data.frame(X_buf) ####Based on the example data#### library(spatstat.data) data(finpines) finpines # Marked planar point pattern: 126 points # Mark variables: diameter, height # window: rectangle = [-5, 5] x [-8, 2] metres # Identifing the buffer zone and core zone in the finpines # The width of buffer zone in the x coordinates of finpines is 2 # The width of buffer zone in the y coordinates of finpines is 3 finpines_buf<-buffer(finpines,buf.xwid =2,buf.ywid=3) finpines_buf as.data.frame(finpines_buf)
Analyze the crowding degree of a neighborhood unit according to the overlapping of the crown in spatial micro-environment which clearly define the crowding degree for a reference tree and its four nearest neighbors, and to some degree of the competition pressure on it as well.
crowding(x)crowding(x)
x |
A vector composed by 5 crowding degree value |
Result will retrun five values,0,0.25,0.5,0.75,1,which means none, one, two, three, or four overlap of nearest neighbors with reference tree,respectively.
Zongzheng Chai, [email protected]
Hu YB, Hui GY. 2015. How to describe the crowding degree of trees based on the relationship of neighboring trees. Journal of Beijing Forestry University. 37(9):1-8.
overlap1<-c(-0.5,-0.4,-0.3,-0.2) ##negative means no overlap overlap2<-c(-0.5,-0.4,-0.3,0.2) ##positive means overlap overlap3<-c(-0.5,-0.4,0.3,0.2) overlap4<-c(-0.5,0.4,0.3,0.2) overlap5<-c(0.5,0.4,0.3,0.2) crowding1<-crowding(overlap1) crowding1 crowding2<-crowding(overlap2) crowding2 crowding3<-crowding(overlap3) crowding3 crowding4<-crowding(overlap4) crowding4 crowding5<-crowding(overlap5) crowding5overlap1<-c(-0.5,-0.4,-0.3,-0.2) ##negative means no overlap overlap2<-c(-0.5,-0.4,-0.3,0.2) ##positive means overlap overlap3<-c(-0.5,-0.4,0.3,0.2) overlap4<-c(-0.5,0.4,0.3,0.2) overlap5<-c(0.5,0.4,0.3,0.2) crowding1<-crowding(overlap1) crowding1 crowding2<-crowding(overlap2) crowding2 crowding3<-crowding(overlap3) crowding3 crowding4<-crowding(overlap4) crowding4 crowding5<-crowding(overlap5) crowding5
Analyze the differation degree of tree attributies in a neighborhood unit according to the characteristics of the tree attributies,such as forest storey,speices,etc.in spatial micro-environment.which clearly define the differation degree of tree attributies for a reference tree and its four nearest neighbors.
differ(x)differ(x)
x |
A vector composed by 5 tree attributies. |
Result will retrun five values,0,0.25,0.5,0.75,1,which means one, two, three, four or five different attributies of nearest neighbors for reference tree,respectively.
Zongzheng Chai, [email protected]
None
##S1,S2,S3,S4,S5,represent 5 different forest stoeries storey1<-c("S1","S1","S1","S1","S1") storey2<-c("S1","S1","S1","S1","S2") storey3<-c("S1","S1","S1","S3","S2") storey4<-c("S1","S1","S4","S3","S2") storey5<-c("S1","S5","S4","S3","S2") differ1<-differ(storey1) differ1 differ2<-differ(storey2) differ2 differ3<-differ(storey3) differ3 differ4<-differ(storey4) differ4 differ5<-differ(storey5) differ5##S1,S2,S3,S4,S5,represent 5 different forest stoeries storey1<-c("S1","S1","S1","S1","S1") storey2<-c("S1","S1","S1","S1","S2") storey3<-c("S1","S1","S1","S3","S2") storey4<-c("S1","S1","S4","S3","S2") storey5<-c("S1","S5","S4","S3","S2") differ1<-differ(storey1) differ1 differ2<-differ(storey2) differ2 differ3<-differ(storey3) differ3 differ4<-differ(storey4) differ4 differ5<-differ(storey5) differ5
The tree attribute dominance was proposed by Hui et al. (1998) to relate the relative dominance of a given tree to its species or silvicultural significance. It is defined as the proportion of the n nearest neighbours of a given reference tree which are bigger than the reference tree.
dominance(x)dominance(x)
x |
A vector composed by 5 tree attributes, such as dbh,biomass. |
Result will retrun five values,0,0.25,0.5,0.75,1,which means the propertion of the larger tree attributes in the 4 nearest neighbours than the reference tree.
Zongzheng Chai, [email protected]
Gadow Kv, and Hui GY. 2002. Characterizing forest spatial structure and diversity. Proc of the SUFOR international workshop: Sustainable forestry in temperate regions. p 20-30.
dbh1<-c(5,4,4,4,4) dbh2<-c(5,4,4,4,6) dbh3<-c(5,4,4,7,6) dbh4<-c(5,4,8,7,6) dbh5<-c(5,9,8,7,6) dominance1<-dominance(dbh1) dominance1 dominance2<-dominance(dbh2) dominance2 dominance3<-dominance(dbh3) dominance3 dominance4<-dominance(dbh4) dominance4 dominance5<-dominance(dbh5) dominance5dbh1<-c(5,4,4,4,4) dbh2<-c(5,4,4,4,6) dbh3<-c(5,4,4,7,6) dbh4<-c(5,4,8,7,6) dbh5<-c(5,9,8,7,6) dominance1<-dominance(dbh1) dominance1 dominance2<-dominance(dbh2) dominance2 dominance3<-dominance(dbh3) dominance3 dominance4<-dominance(dbh4) dominance4 dominance5<-dominance(dbh5) dominance5
When computing forest spatial structure indices, The trees near the edges of the study region are distorted because the outside is empty. Common solutions to this problem are not to use indices computed for trees near the edges, or (with rectangular regions) to attach translated copies, thus changing the topology into a torus.
expandedge(X, xwidth, ywidth,id = 1:X$n, marks=X$marks,type="com")expandedge(X, xwidth, ywidth,id = 1:X$n, marks=X$marks,type="com")
X |
A point pattern (object of class "ppp"). |
xwidth |
Distance from the edges x coordinates,note xwidth is the half of your width wanted, because the width will expand both edges in the x coordinates. |
ywidth |
Distance from the edges y coordinates,note xwidth is the half of your width wanted, because the width will expand both edges in the y coordinates. |
id |
Specific identification number of the points in the point pattern. |
marks |
Marks of nearest neighbour in the point pattern. |
type |
Data format,com is data.frame and ppp is the ppp format in the spapstat package. |
When expanding for the full pattern, and using expandedge() with a positive xwidth and ywidth; conversely, shrinking for the full pattern, and using shrinkedge () with a negative xwidth and ywidth.
Result returns original data (not a a point pattern with the same structure as trees), id is new id of points after edges expanded and old.id is orginal id of points. The pattern is first expanded by surrounding it with 8 shifted copies (the window must be rectangular). Then, the parts of the pattern that are at a distance less than xwidth or ywidth from an edge of the enlarged pattern are discarded. If xwidth or ywidth = 0, trees are returned unchanged.
Zongzheng Chai, [email protected]
library(spatstat) library(spatstat.data) data(finpines) finpines$window # window: rectangle = [-5, 5] x [-8, 2] metres # Expand the rectangle [-5,5]x[-8,2] to [-6,6]x[-9,3] Expand.trees<- expandedge(finpines,xwidth=2,ywidth=1,id=1:126) Expand.trees # Show the changes by figures opar<-par(mfrow=c(1,2)) plot(finpines$x,finpines$y) text(finpines$x,finpines$y,1:126) plot(Expand.trees$x,Expand.trees$y) text(Expand.trees$x,Expand.trees$y,Expand.trees$old.id) rect(-5,-8,5,2,border="red") par(opar)library(spatstat) library(spatstat.data) data(finpines) finpines$window # window: rectangle = [-5, 5] x [-8, 2] metres # Expand the rectangle [-5,5]x[-8,2] to [-6,6]x[-9,3] Expand.trees<- expandedge(finpines,xwidth=2,ywidth=1,id=1:126) Expand.trees # Show the changes by figures opar<-par(mfrow=c(1,2)) plot(finpines$x,finpines$y) text(finpines$x,finpines$y,1:126) plot(Expand.trees$x,Expand.trees$y) text(Expand.trees$x,Expand.trees$y,Expand.trees$old.id) rect(-5,-8,5,2,border="red") par(opar)
The function provided a easy flexible funciton body to analyze the forest saptial structure.
fsasN4(nnattri, match.fun, para = NULL)fsasN4(nnattri, match.fun, para = NULL)
nnattri |
attributies of nearest neighbour |
match.fun |
funcitons,such as "differ","dominance","ideal","uniform.angle","crowding","mingling",etc. |
para |
the parameter in the above match.fun |
result is the "nnmark" and,colum I is the sptial structre indices value Icount is the count distribution of I Ifreq is the frequency distribution of I meanI is the average value of I data is the final point pattern
Zongzheng Chai, [email protected]
Gadow Kv, and Hui G. 2002. Characterizing forest spatial structure and diversity. Proc of the SUFOR international workshop: Sustainable forestry in temperate regions. p 20-30.
data(tree.ppp) ##Get the tree attributies of nearest neighbour for calculation 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) Bdata(tree.ppp) ##Get the tree attributies of nearest neighbour for calculation 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
Analyze the distribution of ideal state for tree attributies in a neighborhood unit according to the characteristics of the tree attributies,such as tree health,climax species distribution,etc.in spatial micro-environment.which clearly define the ideal degree of tree attributies for a reference tree and its four nearest neighbors.
ideal(x, para)ideal(x, para)
x |
A vector composed by 5 tree attributies. |
para |
Ideal state of tree attributies. |
Result will retrun five values,0,0.25,0.5,0.75,1,which means none,one, two, three or four ideal tree attributies of nearest neighbors for reference tree,respectively.
Zongzheng Chai, [email protected]
None
health1<-c("poor","poor","poor","poor") health2<-c("poor","poor","poor","excellent") health3<-c("poor","poor","good","excellent") health4<-c("poor","excellent","good","excellent") health5<-c("good","excellent","good","excellent") ideal1<-ideal(health1,para=c("good","excellent")) ideal1 ideal2<-ideal(health2,para=c("good","excellent")) ideal2 ideal3<-ideal(health3,para=c("good","excellent")) ideal3 ideal4<-ideal(health4,para=c("good","excellent")) ideal4 ideal5<-ideal(health5,para=c("good","excellent")) ideal5health1<-c("poor","poor","poor","poor") health2<-c("poor","poor","poor","excellent") health3<-c("poor","poor","good","excellent") health4<-c("poor","excellent","good","excellent") health5<-c("good","excellent","good","excellent") ideal1<-ideal(health1,para=c("good","excellent")) ideal1 ideal2<-ideal(health2,para=c("good","excellent")) ideal2 ideal3<-ideal(health3,para=c("good","excellent")) ideal3 ideal4<-ideal(health4,para=c("good","excellent")) ideal4 ideal5<-ideal(health5,para=c("good","excellent")) ideal5
Converting the data with class list to the data with class matrix
list_to_matrix(x, item = NA)list_to_matrix(x, item = NA)
x |
Data with class list |
item |
After converting list to matrix, using NA to pad out the blanks within matrix |
Data with class matrix
Zongzheng Chai,[email protected]
# Creating a list data "datalist", and covert "datalist" to a matrix data "datamat" datalist<-list(dataA=1:5,dataB=1:10,dataC=1:15,dataD=1:20) datamat<-list_to_matrix(datalist) datamat# Creating a list data "datalist", and covert "datalist" to a matrix data "datamat" datalist<-list(dataA=1:5,dataB=1:10,dataC=1:15,dataD=1:20) datamat<-list_to_matrix(datalist) datamat
The tree attribute mingling describes the species variety in the vicinity of a given reference tree and has been defined as the proportion of the n nearest neighbours that do not belong to the same species
mingling(x)mingling(x)
x |
A vector composed by 5 tree species |
Result will retrun five values,0,0.25,0.5,0.75,1,which means the propertion of the same tree species with refercence tree in the 4 nearest neighbours
Zongzheng Chai, [email protected]
Hui GY, Zhao X, Zhao Z, Gadow Kv. 2011. Evaluating tree species spatial diversity based on neighborhood relationships. Forest Sci 57:292-300
spname1<-c("sp1","sp1","sp1","sp1","sp1") spname2<-c("sp1","sp1","sp1","sp1","sp2") spname3<-c("sp1","sp1","sp1","sp3","sp2") spname4<-c("sp1","sp1","sp4","sp3","sp2") spname5<-c("sp1","sp5","sp4","sp3","sp2") mingling1<-mingling(spname1) mingling1 mingling2<-mingling(spname2) mingling2 mingling3<-mingling(spname3) mingling3 mingling4<-mingling(spname4) mingling4 mingling5<-mingling(spname5) mingling5spname1<-c("sp1","sp1","sp1","sp1","sp1") spname2<-c("sp1","sp1","sp1","sp1","sp2") spname3<-c("sp1","sp1","sp1","sp3","sp2") spname4<-c("sp1","sp1","sp4","sp3","sp2") spname5<-c("sp1","sp5","sp4","sp3","sp2") mingling1<-mingling(spname1) mingling1 mingling2<-mingling(spname2) mingling2 mingling3<-mingling(spname3) mingling3 mingling4<-mingling(spname4) mingling4 mingling5<-mingling(spname5) mingling5
Describes the degree of regularity in the spatial distribution of n trees that are nearest to a reference tree. Moving clockwise around reference tree,the angle was obtained between two adjacent neighbours.
nnangle(nndist, nnx, nny)nnangle(nndist, nnx, nny)
nndist |
Distance of nearest neighbour |
nnx |
x coordinate of nearest neighbour |
nny |
y coordinate of nearest neighbour |
The angles obtained between two adjacent neighbours.
Zongzheng Chai, [email protected]
None
data(tree.ppp) NNcoord<-nnIndex(tree.ppp,N=4,smark=c("x","y"),buffer=FALSE) NNangle<-nnangle(NNcoord$nndist,NNcoord$nnx,NNcoord$nny) NNangledata(tree.ppp) NNcoord<-nnIndex(tree.ppp,N=4,smark=c("x","y"),buffer=FALSE) NNangle<-nnangle(NNcoord$nndist,NNcoord$nnx,NNcoord$nny) NNangle
Find the id of nearest neighbour for each point in a point pattern by specific numbers or circular neighborhood.
nnid(X, N = NULL, R = NULL, id, exclude = TRUE)nnid(X, N = NULL, R = NULL, id, exclude = TRUE)
X |
A marked point pattern (object of class "ppp"). |
N |
Specific number of points in the neighborhood, the value is always assinged 4. |
R |
Specific circular neighborhood, the value is always assinged 5. |
id |
Specific identification number of each row in the point pattern. |
exclude |
Wether including id of core points, if exclude is TRUE the result return the data removed the id of core points, if exclude is FALSE the result return the data with the id of core points. |
id of nearest neighbour for each point
Zongzheng Chai, [email protected]
library(spatstat) library(spatstat.data) data(finpines) #Find the id of the nearest neighbour in a certain numbers and without id finpines.rmidN4<-nnid(finpines,id=paste("T",1:finpines$n),N=4) finpines.rmidN4 #Find the id of the nearest neighbour in a certain numbers with id finpines.idN4<-nnid(finpines,id=paste("T",1:finpines$n),N=4,exclude=FALSE) finpines.idN4 #Find the id of the nearest neighbour within a certain radius without id data(tree.ppp) finpines.rmidR0.5<-nnid(tree.ppp,id=paste("T",1:41),R=0.5) finpines.rmidR0.5 #Find the id of the nearest neighbour within a certain radius with id finpines.idR0.5<-nnid(tree.ppp,id=paste("T",1:41),R=0.5,exclude=FALSE) finpines.idR0.5library(spatstat) library(spatstat.data) data(finpines) #Find the id of the nearest neighbour in a certain numbers and without id finpines.rmidN4<-nnid(finpines,id=paste("T",1:finpines$n),N=4) finpines.rmidN4 #Find the id of the nearest neighbour in a certain numbers with id finpines.idN4<-nnid(finpines,id=paste("T",1:finpines$n),N=4,exclude=FALSE) finpines.idN4 #Find the id of the nearest neighbour within a certain radius without id data(tree.ppp) finpines.rmidR0.5<-nnid(tree.ppp,id=paste("T",1:41),R=0.5) finpines.rmidR0.5 #Find the id of the nearest neighbour within a certain radius with id finpines.idR0.5<-nnid(tree.ppp,id=paste("T",1:41),R=0.5,exclude=FALSE) finpines.idR0.5
Get the marks of nearest neighbour in the point pattern
nnIndex(X, id = 1:(X$n), smark = NULL, N = NULL, R = NULL, rm.id = NULL, add.X = NULL, add.id = paste("add", 1:(add.X$n), sep = ""), buffer = FALSE, buf.xwid = 5, buf.ywid = 5, exclusion = FALSE)nnIndex(X, id = 1:(X$n), smark = NULL, N = NULL, R = NULL, rm.id = NULL, add.X = NULL, add.id = paste("add", 1:(add.X$n), sep = ""), buffer = FALSE, buf.xwid = 5, buf.ywid = 5, exclusion = FALSE)
X |
A marked point pattern (object of class "ppp"). |
id |
Specific identification number of each row in the point pattern. |
smark |
Selected marks to find the marks of nearest neighbour. |
N |
Specific number of points in the neighborhood, the value is always assinged 4. |
R |
Specific circular neighborhood, the value is always assinged 5. |
rm.id |
Needed removed the specific rows in the point pattern, refer to the id. |
add.X |
Another point pattern need added in the point pattern. |
add.id |
Specific identification number of each row in the added point pattern. |
buffer |
if buffer is TRUE,show the all of data with buffer and core zone. if buffer is FALSE,only show the data in the core zone. |
buf.xwid |
The width of buffer zone in the x coordinates of data points. |
buf.ywid |
The width of buffer zone in the y coordinates of data points. |
exclusion |
Wether including the data of the buffer zone, if exclude is TRUE the result return the data remove the data of the buffer zone, and only the data of the core zone;if exclude is FALSE the result return the data with buffer and core zone. |
Given a marked point pattern dataset X this function computes, for each desired location y, the mark attached to the point of X that is nearest to y. The desired locations y can be either a pixel grid or the point pattern X itself.see also the function "nnmark" in the package spatstat
Multiple "nnmarks" are the selected marks (smark) of nearest neighbour nnid is the id of nearest neighbour nndist is the distance of nearest neighbour data is the final point pattern
Zongzheng Chai, [email protected]
The function "nnmark" in the package spatstat
library(spatstat) library(spatstat.data) library(spatstat.geom) data(finpines) #### Based on specific number (N=4) of nearest neighbour#### ## Basic usage nndN<-nnIndex(finpines,id=paste("T",1:126,sep=""),N=1, smark="diameter",buffer=TRUE,buf.xwid =2,buf.ywid=3) nndN nndhN<-nnIndex(finpines,id=paste("T",1:126,sep=""),N=4, smark=c("diameter","height"),buffer=TRUE,buf.xwid =2 ,buf.ywid=3) nndhN # Only the points in the core zone (Removed the points in the buffer zone) nndN_core<-nnIndex(finpines,id=paste("T",1:126,sep=""),N=4, smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndN_core nndhN_core<-nnIndex(finpines,id=paste("T",1:126,sep=""),N=4, smark=c("diameter","height"),buffer=FALSE,buf.xwid =2, buf.ywid=3) nndhN_core ## Remove some points, and then identify the marks of Nearest Neighbour nndN.rm_core<-nnIndex(finpines,id=paste("T",1:126,sep=""), rm.id=c("T1","T3","T8","T9","T59","T60","T120"), N=4,smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndN.rm_core ## add some points, and then identify the marks of Nearest Neighbour add.x=c(-2,-1,0,2,4) add.y=c(-4,-3,-6,0,1) add.marks=data.frame(diameter=c(2.0,3.0,4.0,5.0,6.0), height=c(2.5,3.5,4.5,5.5,6.5)) add.Xdata=ppp(x=add.x,y=add.y,marks=add.marks,c(-5,5),c(-8,2)) nndN.add_core<-nnIndex(finpines,id=paste("T",1:126,sep=""), add.X=add.Xdata, add.id=paste("NT",1:5,sep=""), N=4,smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndN.add_core #### Based on specific circular (R=5) of nearest neighbour#### ## Basic usage nndR<-nnIndex(finpines,id=paste("T",1:126,sep=""),R=0.5, smark="diameter",buffer=TRUE,buf.xwid =2,buf.ywid=3) nndR nndhR<-nnIndex(finpines,id=paste("T",1:126,sep=""),R=0.5, smark=c("diameter","height"),buffer=TRUE, buf.xwid =2,buf.ywid=3) nndhR # Only the points in the core zone (Removed the points in the buffer zone) nndR_core<-nnIndex(finpines,id=paste("T",1:126,sep=""),R=0.5, smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndR_core nndhR_core<-nnIndex(finpines,id=paste("T",1:126,sep=""),R=0.5, smark=c("diameter","height"),buffer=FALSE, buf.xwid =2,buf.ywid=3) nndhR_core ## Remove some points, and then identify the marks of Nearest Neighbour nndR.rm_core<-nnIndex(finpines,id=paste("T",1:126,sep=""), rm.id=c("T1","T3","T8","T9","T59","T60","T120"), R=0.5,smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndR.rm_core ## add some points, and then identify the marks of Nearest Neighbour nndR.add_core<-nnIndex(finpines,id=paste("T",1:126,sep=""), add.X=add.Xdata, add.id=paste("NT",1:5,sep=""), R=0.5,smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndR.add_corelibrary(spatstat) library(spatstat.data) library(spatstat.geom) data(finpines) #### Based on specific number (N=4) of nearest neighbour#### ## Basic usage nndN<-nnIndex(finpines,id=paste("T",1:126,sep=""),N=1, smark="diameter",buffer=TRUE,buf.xwid =2,buf.ywid=3) nndN nndhN<-nnIndex(finpines,id=paste("T",1:126,sep=""),N=4, smark=c("diameter","height"),buffer=TRUE,buf.xwid =2 ,buf.ywid=3) nndhN # Only the points in the core zone (Removed the points in the buffer zone) nndN_core<-nnIndex(finpines,id=paste("T",1:126,sep=""),N=4, smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndN_core nndhN_core<-nnIndex(finpines,id=paste("T",1:126,sep=""),N=4, smark=c("diameter","height"),buffer=FALSE,buf.xwid =2, buf.ywid=3) nndhN_core ## Remove some points, and then identify the marks of Nearest Neighbour nndN.rm_core<-nnIndex(finpines,id=paste("T",1:126,sep=""), rm.id=c("T1","T3","T8","T9","T59","T60","T120"), N=4,smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndN.rm_core ## add some points, and then identify the marks of Nearest Neighbour add.x=c(-2,-1,0,2,4) add.y=c(-4,-3,-6,0,1) add.marks=data.frame(diameter=c(2.0,3.0,4.0,5.0,6.0), height=c(2.5,3.5,4.5,5.5,6.5)) add.Xdata=ppp(x=add.x,y=add.y,marks=add.marks,c(-5,5),c(-8,2)) nndN.add_core<-nnIndex(finpines,id=paste("T",1:126,sep=""), add.X=add.Xdata, add.id=paste("NT",1:5,sep=""), N=4,smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndN.add_core #### Based on specific circular (R=5) of nearest neighbour#### ## Basic usage nndR<-nnIndex(finpines,id=paste("T",1:126,sep=""),R=0.5, smark="diameter",buffer=TRUE,buf.xwid =2,buf.ywid=3) nndR nndhR<-nnIndex(finpines,id=paste("T",1:126,sep=""),R=0.5, smark=c("diameter","height"),buffer=TRUE, buf.xwid =2,buf.ywid=3) nndhR # Only the points in the core zone (Removed the points in the buffer zone) nndR_core<-nnIndex(finpines,id=paste("T",1:126,sep=""),R=0.5, smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndR_core nndhR_core<-nnIndex(finpines,id=paste("T",1:126,sep=""),R=0.5, smark=c("diameter","height"),buffer=FALSE, buf.xwid =2,buf.ywid=3) nndhR_core ## Remove some points, and then identify the marks of Nearest Neighbour nndR.rm_core<-nnIndex(finpines,id=paste("T",1:126,sep=""), rm.id=c("T1","T3","T8","T9","T59","T60","T120"), R=0.5,smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndR.rm_core ## add some points, and then identify the marks of Nearest Neighbour nndR.add_core<-nnIndex(finpines,id=paste("T",1:126,sep=""), add.X=add.Xdata, add.id=paste("NT",1:5,sep=""), R=0.5,smark="diameter",buffer=FALSE,buf.xwid =2,buf.ywid=3) nndR.add_core
Identify wheter crown overlap among nearest neighbour.
nnoverlap(nncrown, nndist)nnoverlap(nncrown, nndist)
nncrown |
Crown width of nearest neighbour. |
nndist |
Distance of nearest neighbour. |
Crown overlap among nearest neighbour,the value is positive means overlap, value is negative means no overlap.
Zongzheng Chai, [email protected]
data(tree.ppp) NNcrown<-nnIndex(tree.ppp,N=4,smark="crownwid.m",buffer=FALSE) NNoverlap<-nnoverlap(NNcrown$nncrownwid.m,NNcrown$nndist) NNoverlapdata(tree.ppp) NNcrown<-nnIndex(tree.ppp,N=4,smark="crownwid.m",buffer=FALSE) NNoverlap<-nnoverlap(NNcrown$nncrownwid.m,NNcrown$nndist) NNoverlap
Optimal forest spatial structure indices.
opt_spastr(X_df,inter=100,pop=0.2, smark=c("spe","storey","dbh","cw","x","y"), xrange=c(0,100),yrange=c(0,100), xwidth=5,ywidth=5, buf.xwid = 5, buf.ywid = 5)opt_spastr(X_df,inter=100,pop=0.2, smark=c("spe","storey","dbh","cw","x","y"), xrange=c(0,100),yrange=c(0,100), xwidth=5,ywidth=5, buf.xwid = 5, buf.ywid = 5)
X_df |
Forest community data (object of class "data.frame") |
inter |
Inter quanitity. |
pop |
Cutting intensity. |
smark |
Selected marks to compute forest spatial structure indices. |
buf.xwid |
The width of buffer zone in the x coordinates of data points. |
buf.ywid |
The width of buffer zone in the y coordinates of data points. |
xrange |
Range of X axes |
yrange |
Range of Y axes |
xwidth |
Length of buffer zone of X axes |
ywidth |
Length of buffer zone of Y axes |
NULL
NULL
Zongzheng Chai, [email protected]
library(spatstat) data(treecom_example) head(treecom_example) treecom_opt<-opt_spastr(X_df=treecom_example,inter=5,pop=0.1, xrange=c(0,100),yrange=c(0,100), xwidth=10,ywidth=10, buf.xwid =10, buf.ywid = 10, smark=c("spe","storey","dbh","cw","x","y")) treecom_optlibrary(spatstat) data(treecom_example) head(treecom_example) treecom_opt<-opt_spastr(X_df=treecom_example,inter=5,pop=0.1, xrange=c(0,100),yrange=c(0,100), xwidth=10,ywidth=10, buf.xwid =10, buf.ywid = 10, smark=c("spe","storey","dbh","cw","x","y")) treecom_opt
A descriptive index to analyze the forest saptial structure indices
pv(index, optm)pv(index, optm)
index |
Actual value of forest spatial structure indices |
optm |
Optimal value of forest spatial structure indices |
Result of forest spatial structure heterogeneity assessment
Zongzheng Chai, [email protected]
None
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) IPVEdata(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
Reestablishing an object of class "ppp" by adding or removing some data from orginal point pattern (object of class "ppp")
rebuild.ppp(X, id = 1:(X$n), rm.id = NULL, add.X = NULL, add.id = paste("add", 1:(add.X$n), sep = ""))rebuild.ppp(X, id = 1:(X$n), rm.id = NULL, add.X = NULL, add.id = paste("add", 1:(add.X$n), sep = ""))
X |
A point pattern (object of class "ppp"). |
id |
Specific identification number of each row in the point pattern. |
rm.id |
Needed removed the specific rows in the point pattern, refer to the id. |
add.X |
Another point pattern need added in the point pattern. |
add.id |
Specific identification number of each row in the added point pattern. |
An object of class "ppp" describing a point pattern in the two-dimensional plane (see ppp.object), which was Reestablished by adding or removing some data from orginal point pattern
A new point pattern after reestablished by adding or removing some data from orginal point pattern
Zongzheng Chai, [email protected]
R function ppp in the package "spatstat"
library(spatstat.geom) ####Based on the simulated data#### # Creating a simulated point pattern x <- runif(20) y <- runif(20) X <- ppp(x, y, c(0,1), c(0,1)) X # Adding a identification number (ID) for each data point in the point pattern # the following are equivalent X1<-rebuild.ppp(X) X1<-rebuild.ppp(X,id=1:X$n) X1<-rebuild.ppp(X,id=1:20) X1 as.data.frame(X1) # Adding a identification number (ID) for each data point in the point pattern, and # Removing the assinged points according to the ID. X2<-rebuild.ppp(X,id=1:20,rm.id=1:5) X2 as.data.frame(X2) # Adding some points into orginal point pattern newx <- runif(10) newy <- runif(10) newX <- ppp(newx, newy, c(0,1), c(0,1)) XY<-rebuild.ppp(X,add.X=newX,add.id=paste("new",1:10,sep="")) XY as.data.frame(XY) ####Based on the example data#### library(spatstat.data) data(finpines) finpines # Marked planar point pattern: 126 points # Mark variables: diameter, height # window: rectangle = [-5, 5] x [-8, 2] metres # Adding a identification number (ID) for each tree in the finpines # finpines$n # [1] 126 finpines_id<-rebuild.ppp(finpines,id=paste("T",1:126,sep="")) finpines_id head(as.data.frame(finpines_id)) # Removing the trees with id "T1","T3","T8","T9","T59","T60","T120" finpines_rm<-rebuild.ppp(X=finpines,id=paste("T",1:126,sep=""), rm.id=c("T1","T3","T8","T9","T59","T60","T120")) finpines_rm as.data.frame(finpines_rm) # Adding some trees data into finpines add.x=c(-2,-1,0,2,4) add.y=c(-4,-3,-6,0,1) add.marks=data.frame(diameter=c(2.0,3.0,4.0,5.0,6.0), height=c(2.5,3.5,4.5,5.5,6.5)) add.Xdata=ppp(x=add.x,y=add.y,marks=add.marks,c(-5,5),c(-8,2)) finpines_add<-rebuild.ppp(X=finpines,id=paste("T",1:126,sep=""), add.X=add.Xdata,add.id=paste("NT",1:5,sep="")) finpines_add as.data.frame(finpines_add)library(spatstat.geom) ####Based on the simulated data#### # Creating a simulated point pattern x <- runif(20) y <- runif(20) X <- ppp(x, y, c(0,1), c(0,1)) X # Adding a identification number (ID) for each data point in the point pattern # the following are equivalent X1<-rebuild.ppp(X) X1<-rebuild.ppp(X,id=1:X$n) X1<-rebuild.ppp(X,id=1:20) X1 as.data.frame(X1) # Adding a identification number (ID) for each data point in the point pattern, and # Removing the assinged points according to the ID. X2<-rebuild.ppp(X,id=1:20,rm.id=1:5) X2 as.data.frame(X2) # Adding some points into orginal point pattern newx <- runif(10) newy <- runif(10) newX <- ppp(newx, newy, c(0,1), c(0,1)) XY<-rebuild.ppp(X,add.X=newX,add.id=paste("new",1:10,sep="")) XY as.data.frame(XY) ####Based on the example data#### library(spatstat.data) data(finpines) finpines # Marked planar point pattern: 126 points # Mark variables: diameter, height # window: rectangle = [-5, 5] x [-8, 2] metres # Adding a identification number (ID) for each tree in the finpines # finpines$n # [1] 126 finpines_id<-rebuild.ppp(finpines,id=paste("T",1:126,sep="")) finpines_id head(as.data.frame(finpines_id)) # Removing the trees with id "T1","T3","T8","T9","T59","T60","T120" finpines_rm<-rebuild.ppp(X=finpines,id=paste("T",1:126,sep=""), rm.id=c("T1","T3","T8","T9","T59","T60","T120")) finpines_rm as.data.frame(finpines_rm) # Adding some trees data into finpines add.x=c(-2,-1,0,2,4) add.y=c(-4,-3,-6,0,1) add.marks=data.frame(diameter=c(2.0,3.0,4.0,5.0,6.0), height=c(2.5,3.5,4.5,5.5,6.5)) add.Xdata=ppp(x=add.x,y=add.y,marks=add.marks,c(-5,5),c(-8,2)) finpines_add<-rebuild.ppp(X=finpines,id=paste("T",1:126,sep=""), add.X=add.Xdata,add.id=paste("NT",1:5,sep="")) finpines_add as.data.frame(finpines_add)
Sometimes, the window of point pattern is too large, we can shrink its edges to the wanted window of point pattern
shrinkedge(X, xwidth, ywidth, id)shrinkedge(X, xwidth, ywidth, id)
X |
A point pattern (object of class "ppp"). |
xwidth |
Distance from the edges x coordinates,note xwidth is the half of your width wanted, because the width will shrink both edges in the x coordinates. |
ywidth |
Distance from the edges y coordinates,note xwidth is the half of your width wanted, because the width will shrink both edges in the y coordinates. |
id |
Specific identification number of the points in the point pattern. |
When expanding for the full pattern, and using expandedge() with a positive xwidth and ywidth; conversely, shrinking for the full pattern, and using shrinkedge () with a negative xwidth and ywidth,the parts of the pattern that are at a distance less than -width from an edge are discarded.
Result returns original data (not a point pattern with the same structure as trees), id is new id of points after edges expanded and old.id is orginal id of points. If xwidth or ywidth = 0, trees are returned unchanged.
Zongzheng Chai, [email protected]
library(spatstat.data) data(finpines) finpines$window # window: rectangle = [-5, 5] x [-8, 2] metres #Shrink the rectangle [-5,5]x[-8,2] to [-3,3]x[-5,-1] shrink.trees<- shrinkedge(finpines,xwidth=2,ywidth=3,id=1:126) shrink.trees # Show the changes by figures opar<-par(mfrow=c(1,2)) plot(finpines$x,finpines$y) text(finpines$x,finpines$y,1:126) rect(-3,-5,3,-1,border="red") plot(shrink.trees$x,shrink.trees$y) text(shrink.trees$x,shrink.trees$y,shrink.trees$old.id) par(opar)library(spatstat.data) data(finpines) finpines$window # window: rectangle = [-5, 5] x [-8, 2] metres #Shrink the rectangle [-5,5]x[-8,2] to [-3,3]x[-5,-1] shrink.trees<- shrinkedge(finpines,xwidth=2,ywidth=3,id=1:126) shrink.trees # Show the changes by figures opar<-par(mfrow=c(1,2)) plot(finpines$x,finpines$y) text(finpines$x,finpines$y,1:126) rect(-3,-5,3,-1,border="red") plot(shrink.trees$x,shrink.trees$y) text(shrink.trees$x,shrink.trees$y,shrink.trees$old.id) par(opar)
Simulated a forest community data.
simtreecom(size,nspe,nspe_max,xrange, yrange,dbhrange,type="com",lambda=1)simtreecom(size,nspe,nspe_max,xrange, yrange,dbhrange,type="com",lambda=1)
size |
Pool of species |
nspe |
Number of species |
nspe_max |
Maximum number of species |
xrange |
Range of X axes |
yrange |
Range of Y axes |
dbhrange |
Range of DBH |
type |
Data type, "ppp" or "com" |
lambda |
Degree of coordinate aggregation |
Data frame of forest community data
Zongzheng Chai, [email protected]
NULL
treecom<-simtreecom(size=10,nspe=10,nspe_max=36,xrange=c(0,100), yrange=c(0,100),dbhrange=c(5,50),type="com",lambda=1) treecomtreecom<-simtreecom(size=10,nspe=10,nspe_max=36,xrange=c(0,100), yrange=c(0,100),dbhrange=c(5,50),type="com",lambda=1) treecom
Computing forest spatial structure indices.
spastr(X_df,smark=c("spe","storey","dbh","cw","x","y"), buffer = FALSE, xrange=c(0,100),yrange=c(0,100), xwidth=5,ywidth=5, buf.xwid = 5, buf.ywid = 5, exclusion = FALSE)spastr(X_df,smark=c("spe","storey","dbh","cw","x","y"), buffer = FALSE, xrange=c(0,100),yrange=c(0,100), xwidth=5,ywidth=5, buf.xwid = 5, buf.ywid = 5, exclusion = FALSE)
X_df |
Forest community data (object of class "data.frame") |
smark |
Selected marks to compute forest spatial structure indices. |
buffer |
if buffer is TRUE,show the all of data with buffer and core zone. if buffer is FALSE,only show the data in the core zone. |
buf.xwid |
The width of buffer zone in the x coordinates of data points. |
buf.ywid |
The width of buffer zone in the y coordinates of data points. |
xrange |
Range of X axes |
yrange |
Range of Y axes |
xwidth |
Length of buffer zone of X axes |
ywidth |
Length of buffer zone of Y axes |
exclusion |
Wether including the data of the buffer zone, if exclude is TRUE the result return the data remove the data of the buffer zone, and only the data of the core zone;if exclude is FALSE the result return the data with buffer and core zone. |
NULL
NULL
Zongzheng Chai, [email protected]
library(spatstat) data(treecom_example) head(treecom_example) treecom_spastr<-spastr(X_df=treecom_example, xrange=c(0,100),yrange=c(0,100), xwidth=10,ywidth=10, buf.xwid =10, buf.ywid = 10, smark=c("spe","storey","dbh","cw","x","y")) treecom_spastrlibrary(spatstat) data(treecom_example) head(treecom_example) treecom_spastr<-spastr(X_df=treecom_example, xrange=c(0,100),yrange=c(0,100), xwidth=10,ywidth=10, buf.xwid =10, buf.ywid = 10, smark=c("spe","storey","dbh","cw","x","y")) treecom_spastr
Forest overstory can be divided the many stories by dividing the range of tree height.
storeydvd(height, minH = min(height), maxH = max(height), storeynum = 6, include.lowest = TRUE, right = TRUE)storeydvd(height, minH = min(height), maxH = max(height), storeynum = 6, include.lowest = TRUE, right = TRUE)
height |
Data of tree height |
minH |
Minimum value of height |
maxH |
Maximum value of height |
storeynum |
Number of storey |
include.lowest |
Logical, indicating if an "[i]" equal to the lowest (or highest, for right=FALSE) "breaks" value should be included. See the function "cut". |
right |
Logical, indicating if the intervals should be closed on the right (and open on the left) or vice versa.See the function "cut". |
heightfreq is the frequency of tree height heightdata is the result after divided the storey
Zongzheng Chai, [email protected]
None
library(spatstat) data(finpines) finpinesdata<-as.data.frame(finpines) ####Dividing the stories finpines.storey<-storeydvd(finpinesdata$height,storeynum=6) finpines.storey ####Computing the storey differation ##Add the storey mark for finepines finpines.addstorey<-addmark.ppp(finpines, add.mark=finpines.storey$heightdata[,2:3], add.name=c("interval","storey" )) finpines.addstorey ##Compute the storeies of nearest neighbour in the point pattern finpines.nnstorey<-nnIndex(finpines.addstorey,smark="storey",N=4, buf.xwid =2,buf.ywid=3) ##Compute the stoery differation finpines.H<-fsasN4(finpines.nnstorey$nnstorey,match.fun=differ) finpines.Hlibrary(spatstat) data(finpines) finpinesdata<-as.data.frame(finpines) ####Dividing the stories finpines.storey<-storeydvd(finpinesdata$height,storeynum=6) finpines.storey ####Computing the storey differation ##Add the storey mark for finepines finpines.addstorey<-addmark.ppp(finpines, add.mark=finpines.storey$heightdata[,2:3], add.name=c("interval","storey" )) finpines.addstorey ##Compute the storeies of nearest neighbour in the point pattern finpines.nnstorey<-nnIndex(finpines.addstorey,smark="storey",N=4, buf.xwid =2,buf.ywid=3) ##Compute the stoery differation finpines.H<-fsasN4(finpines.nnstorey$nnstorey,match.fun=differ) finpines.H
A sample data of field survey, to help us understanding the method of forest spatial structure analysis.class is "ppp".
data("tree.ppp")data("tree.ppp")
The function "ppp" in the package spatstat.geom
None
data(tree.ppp)data(tree.ppp)
A example data of field survey, to help us understanding the method of forest spatial structure analysis.
data("treecom_example")data("treecom_example")
A data frame
xx coordinates of trees
yy coordinates of trees
idSpecific identification number of trees
speSpecific code of tree species in the point pattern
dbhDiameter at breast height (DBH),1.3 m. unit:cm
htTree height,unit:m
cwCrown width,unit:m
hcbheight of crown base,unit:m
volumevolume of individual trees,unit:m3
qualityQuality of individual trees
storeyforest storey
None
data(treecom_example) treecom_exampledata(treecom_example) treecom_example
A sample data of field survey, to help us understanding the method of forest spatial structure analysis.
data("treedata")data("treedata")
A data frame with 41 observations on the following 11 variables.
tree.idSpecific identification number of trees in the point pattern
xx coordinates of trees
yy coordinates of trees
sp.codeSpecific code of tree species in the point pattern
dbh.cmDiameter at breast height (DBH),1.3 m. unit:cm
h.mTree height,unit:m
storeyforest storey
crownwid.mCrown width,unit:m
groupTree group, which can be divided exotic,pioneer,transtional,climax,5 types
biomass.kgTree biomass,unit:kg
qualityQuality of individual trees
None
data(treedata)data(treedata)
The uniform angle index (UAI) is used to characterize the spatial distribution of a forest community or of individual tree species within that community.By gradually comparing the included 4 angles with the standard angle (72).
uniform.angle(x, para)uniform.angle(x, para)
x |
Moving clockwise around reference tree,4 angle were obtained among adjacent neighbours |
para |
standard angle (72) |
Result will retrun five values,0,0.25,0.5,0.75,1,which means none, one, two, three, or four angles are smaller than 72, respectively.
Zongzheng Chai, [email protected]
Zhao ZH, Hui GY, Hu YB, Wang HX, Zhang GQ, and von Gadow K. 2014. Testing the significance of different tree spatial distribution patterns based on the uniform angle index. Canadian Journal of Forest Research 44:1419-1425.
angle1<-c(80,80,80,80) angle2<-c(80,80,80,70) angle3<-c(80,80,60,70) angle4<-c(80,50,60,70) angle5<-c(40,50,60,70) uai1<-uniform.angle(angle1,para=72) uai1 uai2<-uniform.angle(angle2,para=72) uai2 uai3<-uniform.angle(angle3,para=72) uai3 uai4<-uniform.angle(angle4,para=72) uai4 uai5<-uniform.angle(angle5,para=72) uai5angle1<-c(80,80,80,80) angle2<-c(80,80,80,70) angle3<-c(80,80,60,70) angle4<-c(80,50,60,70) angle5<-c(40,50,60,70) uai1<-uniform.angle(angle1,para=72) uai1 uai2<-uniform.angle(angle2,para=72) uai2 uai3<-uniform.angle(angle3,para=72) uai3 uai4<-uniform.angle(angle4,para=72) uai4 uai5<-uniform.angle(angle5,para=72) uai5