Aboveground biomass prediction using height and crown diameter

Description

Prediction of aboveground biomass (AGB) using height and crown diameter and the equations of Jucker et al. (2017).

Usage

agb(H = NULL, CA = NULL, species = 1)

Arguments

Value

The AGB value in kilograms.

Author(s)

Michele Dalponte

References

T. Jucker, J. Caspersen, J. Chave, C. Antin, N. Barbier, F. Bongers, M. Dalponte, K. Y. van Ewijk, D. I. Forrester, M. Haeni, S. I. Higgins, R. J. Holdaway, Y. Iida, C. Lorimer, P. L. Marshall, S. Momo, G. R. Moncrieff, P. Ploton, L. Poorter, K. A. Rahman, M. Schlund, B. Sonke, F. J. Sterck, A. T. Trugman, V. A. Usoltsev, M. C. Vanderwel, P. Waldner, B. M. M. Wedeux, C. Wirth, H. Woell, M. Woods, W. Xiang, N. E. Zimmermann, and D. A. Coomes, "Allometric equations for integrating remote sensing imagery into forest monitoring programs," Global Change Biology, 23 (1), pp. 177-190, January 2017.

Examples

## Not run: 
data(lasData)

## function takes a while to run

#Extraction of the ITCs
se<-itcLiDAR(lasData$X,lasData$Y,lasData$Z,epsg=32632)

summary(se)

#Computation of the crown diameter from the crown area
se$CD_m<-2*sqrt(se$CA_m2/pi)

#AGB prediction
se$agb<-NA
se$agb<-agb(se$Height_m,se$CD_m,species=1)

summary(se)


## End(Not run)

Diameter at breast height prediction using height and crown diameter

Description

Prediction of diameter at breast height (DBH) using height and crown diameter and the equations of Jucker et al. (2017).

Usage

dbh(H = NULL, CA = NULL, biome = 0)

Arguments

Value

The DBH value in centimeters.

Author(s)

Michele Dalponte

References

T. Jucker, J. Caspersen, J. Chave, C. Antin, N. Barbier, F. Bongers, M. Dalponte, K. Y. van Ewijk, D. I. Forrester, M. Haeni, S. I. Higgins, R. J. Holdaway, Y. Iida, C. Lorimer, P. L. Marshall, S. Momo, G. R. Moncrieff, P. Ploton, L. Poorter, K. A. Rahman, M. Schlund, B. Sonke, F. J. Sterck, A. T. Trugman, V. A. Usoltsev, M. C. Vanderwel, P. Waldner, B. M. M. Wedeux, C. Wirth, H. Woell, M. Woods, W. Xiang, N. E. Zimmermann, and D. A. Coomes, "Allometric equations for integrating remote sensing imagery into forest monitoring programs," Global Change Biology, 23 (1), pp. 177-190, January 2017.

Examples

## Not run: 
data(lasData)

## function takes a while to run

#Extraction of the ITCs
se<-itcLiDAR(lasData$X,lasData$Y,lasData$Z,epsg=32632)

summary(se)

#Computation of the crown diameter from the crown area
se$CD_m<-2*sqrt(se$CA_m2/pi)

#DBH prediction
se$dbh<-NA
se$dbh<-dbh(se$Height_m,se$CD_m,biome=0)

summary(se)


## End(Not run)

Individual Tree Crowns segmentation with imagery data

Description

The ITC delineation approach finds local maxima within an imagery, designates these as tree tops, then uses a decision tree method to grow individual crowns around the local maxima.

Usage

itcIMG(
  imagery = NULL,
  epsg = NULL,
  searchWinSize = 3,
  TRESHSeed = 0.45,
  TRESHCrown = 0.55,
  DIST = 10,
  th = 0,
  ischm = FALSE
)

Arguments

Value

An object of the class SpatVector containing the delineated ITCs. The informaion for each ITC contained in the data frame are the X and Y coordinates position of the tree, the tree height in meters (Height_m; only if ischm=TRUE) and its crown area in square meters (CA_m2).

Author(s)

Michele Dalponte

References

M. Dalponte, F. Reyes, K. Kandare, and D. Gianelle, "Delineation of Individual Tree Crowns from ALS and Hyperspectral data: a comparison among four methods," European Journal of Remote Sensing, Vol. 48, pp. 365-382, 2015.

Examples

## Not run: 
library(terra)
library(itcSegment)

imgData<-rast("./inst/extdata/imgData.tif")

se<-itcIMG(imgData,epsg=32632)
summary(se)
plot(se,axes=T)

## to save the data use rgdal function called writeOGR. For more help see rgdal package.


## End(Not run)

Individual Tree Crowns segmentation with LiDAR data

Description

The ITC delineation approach finds local maxima within a rasterized canopy height model (CHM), designates these as tree tops, then uses a decision tree method to grow individual crowns around the local maxima. The approach goes through the following steps: (1) a low-pass filter is applied to the rasterized CHM to smooth the surface and reduce the number of local maxima; (2) local maxima are located using a moving window with size that adapts inside a user defined range (minimum and maximum size) according the pixel height; a pixel of the CHM is labelled as local maxima if its z value is greater than all other z values in the window, and with z greater than some minimum height above-ground; (3) each local maximum is labelled as an 'initial region' around which a tree crown can grow; the heights of the four neighboring pixels are extracted from the CHM and these pixels are added to the region if their vertical distance from the local maximum is less than some user-defined percentage of the local-maximum height, and less than some user-defined maximum difference; this procedure is repeated for all the neighbors of cells now included in the region, and so on iteratively until no further pixels are added to the region; (4) from each region that had been identified the first-return ALS points are extracted (having first removed low elevation points), (5) a 2D convex hull is applied to these points, and the resulting polygons becomes the final ITCs.

Usage

itcLiDAR(
  X = NULL,
  Y = NULL,
  Z = NULL,
  epsg = NULL,
  resolution = 0.5,
  MinSearchFilSize = 3,
  MaxSearchFilSize = 7,
  TRESHSeed = 0.55,
  TRESHCrown = 0.6,
  minDIST = 5,
  maxDIST = 40,
  HeightThreshold = 2,
  cw = 1
)

Arguments

Value

An object of the class SpatVector containing the delineated ITCs. The informaion for each ITC contained in the data frame are the X and Y coordinates position of the tree, the tree height in meters (Height_m) and its crown area in square meters (CA_m2).

Author(s)

Michele Dalponte

References

M. Dalponte, and D. A. Coomes, "Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data," Methods in Ecology and Evolution, Vol. 7, No. 10, pp. 1236-1245, 2016.

Examples

## Not run: 
data(lasData)

## function takes a while to run
se<-itcLiDAR(lasData$X,lasData$Y,lasData$Z,epsg=32632)
summary(se)
plot(se,axes=T)

## If we want to seperate the height of the trees by grayscales:

plot(se,col=gray((max(se$Height_m)-se$Height_m)/(max(se$Height_m)-min(se$Height_m))),axes=T)

## to save the data use rgdal function called writeOGR. For more help see rgdal package.


## End(Not run)

Individual Tree Crowns segmentation with LiDAR data and crown diameter-height relationship

Description

The ITC delineation approach finds local maxima within a rasterized canopy height model (CHM), designates these as tree tops, then uses a decision tree method to grow individual crowns around the local maxima. The approach goes through the following steps: (1) a low-pass filter is applied to the rasterized CHM to smooth the surface and reduce the number of local maxima; (2) local maxima are located using a moving window with size that adapts inside a user defined range (minimum and maximum size) according the pixel height; a pixel of the CHM is labelled as local maxima if its z value is greater than all other z values in the window, and with z greater than some minimum height above-ground; (3) each local maximum is labelled as an 'initial region' around which a tree crown can grow; the heights of the four neighboring pixels are extracted from the CHM and these pixels are added to the region if their vertical distance from the local maximum is less than some user-defined percentage of the local-maximum height, and less than some user-defined maximum difference; this procedure is repeated for all the neighbors of cells now included in the region, and so on iteratively until no further pixels are added to the region; (4) from each region that had been identified the first-return ALS points are extracted (having first removed low elevation points), (5) a 2D convex hull is applied to these points, and the resulting polygons becomes the final ITCs.

Usage

itcLiDARallo(
  X = NULL,
  Y = NULL,
  Z = NULL,
  epsg = NULL,
  resolution = 0.5,
  TRESHSeed = 0.55,
  TRESHCrown = 0.6,
  HeightThreshold = 2,
  lut = NULL,
  cw = 1
)

Arguments

Value

An object of the class SpatVector containing the delineated ITCs. The informaion for each ITC contained in the data frame are the X and Y coordinates position of the tree, the tree height in meters (Height_m) and its crown area in square meters (CA_m2).

Author(s)

Michele Dalponte

References

D. A. Coomes, M. Dalponte, T. Jucker, G. P. Asner, L. F. Banin, D. F.R.P. Burslem, S. L. Lewis, R. Nilus, O. L. Phillips, M.-H. Phua, L. Qie, "Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data," Remote Sensing of Environment, Vol. 194, Issue 1, pp. 77-88, June 2017.

Examples

## Not run: 
data(lasData)

##Creation of the look-up-table

lut<-matrix(6,2,data=NA)
lut<-data.frame(lut)
names(lut)<-c("H","CD")

lut$H<-c(2,10,15,20,25,30)
lut$CD<-c(0.5,1,2,3,4,5)

## function takes a while to run
se<-itcLiDARallo(lasData$X,lasData$Y,lasData$Z,epsg=32632,lut=lut)
summary(se)
plot(se,axes=T)

## If we want to seperate the height of the trees by grayscales:

plot(se,col=gray((max(se$Height_m)-se$Height_m)/(max(se$Height_m)-min(se$Height_m))),axes=T)

## to save the data use rgdal function called writeOGR. For more help see rgdal package.


## End(Not run)

LiDAR data point cloud acquired over a forest area

Description

A dataset containing the X Y Z coordinates of LiDAR points acquired over a forest area. The EPSG code of the coordinates is 32632.

Usage

data(lasData)

Format

A data frame with 16907 rows and 3 variables

Details

• X. X coordinate in UTM WGS84 32 N (EPSG code 32632).

• Y. Y coordinate in UTM WGS84 32 N (EPSG code 32632).

• Z. Z coordinate. The Z coordinate is normalized respect to the ground.