Tropical Forest Canopy Gaps Analysis.
ForestGapR: An R Package for Airborne Laser Scanning-derived Tropical Forest Gaps Analysis
Authors: Carlos Alberto, Ekena Rangel, Midhun Mohan, Danilo Roberti Alves de Almeida, Eben North Broadbent, Wan Shafrina Wan Mohd Jaafar, Adrian Cardil, Ruben Valbuena, Toby Jackson, Carine Klauberg and Caio Hamamura
The GapForestR package provides functions to i) automate canopy gaps detection, ii) compute a series of forest canopy gap statistics, including gap-size frequency distributions and spatial distribution, iii) map gap dynamics (when multi-temporal ALS data are available), and iv) convert the data among spatial formats.
Installation
#The development version:
library(devtools)
devtools::install_github("carlos-alberto-silva/ForestGapR")
#The CRAN version:
install.packages("ForestGapR")
Getting Started
Forest Canopy Gap Detection
#Loading raster and viridis library
library(raster)
library(viridis)
# ALS-derived CHM over Adolpho Ducke Forest Reserve - Brazilian tropical forest
data(ALS_CHM_DUC)
# Plotting chm
plot(ALS_CHM_DUC, col=viridis(10))
# Setting height thresholds (e.g. 10 meters)
threshold<-10
size<-c(1,1000) # m2
# Detecting forest gaps
gaps_duc<-getForestGaps(chm_layer=ALS_CHM_DUC, threshold=threshold, size=size)
# Plotting gaps
plot(gaps_duc, col="red", add=TRUE, main="Forest Canopy Gap", legend=FALSE)
Forest Canopy Gaps Stats
This function computes a series of forest canopy gap statistics
List of forest gaps statistics: #gap_id: gap id; #gap_area - area of gap (m2); #chm_max - Maximum canopy height (m) within gap boundary; #chm_min - Minimum canopy height (m) within gap boundary; #chm_mean - Mean canopy height (m) within gap boundary; #chm_sd - Standard Deviation of canopy height (m) within gap boundary; #chm_range - Range of canopy height (m) within gap boundary
#Loading raster library
library(raster)
# ALS-derived CHM over Adolpho Ducke Forest Reserve - Brazilian tropical forest
data(ALS_CHM_DUC)
# Setting height thresholds (e.g. 10 meters)
threshold<-10
size<-c(5,1000) # m2
# Detecting forest gaps
gaps_duc<-getForestGaps(chm_layer=ALS_CHM_DUC, threshold=threshold, size=size)
# Computing basic statistis of forest gap
gaps_stats<-GapStats(gap_layer=gaps_duc, chm_layer=ALS_CHM_DUC)
## gap_id gap_area chm_max chm_min chm_mean chm_sd chm_gini chm_range
## 1 1 34 9.22 1.09 5.12 2.61 0.30 8.13
## 2 2 6 8.17 6.06 7.40 0.74 0.06 2.11
## 3 3 5 9.96 7.42 8.85 1.23 0.08 2.54
## 4 4 32 9.91 4.42 8.12 1.69 0.12 5.49
## 5 5 11 9.83 6.23 8.48 1.09 0.07 3.60
## 6 6 44 9.72 1.92 7.31 1.60 0.12 7.80
## 7 7 6 9.88 8.81 9.49 0.40 0.02 1.07
## 8 8 6 9.07 3.10 7.02 2.96 0.22 5.97
## 9 9 10 9.52 2.86 8.03 2.22 0.13 6.66
## 10 10 18 9.90 2.74 5.06 2.18 0.23 7.16
## 11 11 13 9.91 1.75 5.47 2.94 0.31 8.16
## 12 12 10 9.92 3.75 7.77 2.27 0.16 6.17
## 13 13 66 9.94 0.99 5.31 2.91 0.32 8.95
## 14 14 7 10.00 5.83 7.41 1.53 0.12 4.17
## 15 15 12 9.65 5.61 7.97 1.43 0.10 4.04
## 16 16 7 8.64 5.64 7.67 0.97 0.07 3.00
## 17 17 21 8.42 0.40 6.02 2.23 0.20 8.02
## 18 18 6 7.39 3.37 5.03 1.82 0.21 4.02
## 19 19 5 9.07 4.91 7.74 1.65 0.12 4.16
## 20 20 36 9.90 2.10 6.62 2.45 0.21 7.80
## 21 21 5 9.71 8.43 9.19 0.57 0.04 1.28
## 22 22 12 9.83 7.42 8.39 0.85 0.06 2.41
## 23 23 15 9.25 7.81 8.56 0.48 0.03 1.44
## 24 24 27 9.43 0.26 2.37 2.55 0.56 9.17
## 25 25 5 4.54 2.43 3.78 0.80 0.12 2.11
## 26 26 7 9.98 6.34 8.40 1.07 0.07 3.64
## 27 27 25 9.76 3.78 7.67 1.13 0.07 5.98
## 28 28 6 9.49 4.92 7.23 1.56 0.13 4.57
## 29 29 22 9.76 3.78 5.96 1.97 0.18 5.98
## 30 30 6 5.73 2.69 4.46 1.28 0.17 3.04
## 31 31 7 9.41 7.72 8.44 0.56 0.04 1.69
## 32 32 57 9.89 1.97 5.70 2.62 0.26 7.92
## 33 33 38 9.68 0.25 4.58 2.07 0.24 9.43
## 34 34 8 9.83 4.88 6.55 1.54 0.13 4.95
## 35 35 6 9.66 8.26 9.16 0.48 0.03 1.40
Forest Canopy Gap-size Frequency Distributions
#Loading raster library
library(raster)
# ALS-derived CHM over Adolpho Ducke Forest Reserve - Brazilian tropical forest
data(ALS_CHM_DUC)
# set height thresholds (e.g. 10 meters)
threshold<-10
size<-c(1,1000) # m2
# Detecting forest gaps
gaps_duc<-getForestGaps(chm_layer=ALS_CHM_DUC, threshold=threshold, size=size)
# Computing basic statistis of forest gap
gaps_stats<-GapStats(gap_layer=gaps_duc, chm_layer=ALS_CHM_DUC)
# Gap-size Frequency Distributions
GapSizeFDist(gaps_stats=gaps_stats, method="Hanel_2017", col="forestgreen", pch=16, cex=1,
axes=FALSE,ylab="Gap Frequency",xlab=as.expression(bquote("Gap Size" ~ (m^2) )))
axis(1);axis(2)
grid(4,4)
Forest Canopy Gaps as Spatial Polygons
#Loading raster and viridis libraries
library(raster)
library(viridis)
# ALS-derived CHM over Adolpho Ducke Forest Reserve - Brazilian tropical forest
data(ALS_CHM_DUC)
# set height thresholds (e.g. 10 meters)
threshold<-10
size<-c(4,1000) # m2
# Detecting forest gaps
gaps_duc<-getForestGaps(chm_layer=ALS_CHM_DUC, threshold=threshold, size=size)
# Converting raster layer to SpatialPolygonsDataFrame
gaps_spdf<-GapSPDF(gap_layer=gaps_duc)
# Plotting ALS-derived CHM and forest gaps
plot(ALS_CHM_DUC, col=viridis(10), xlim=c(173025,173125), ylim=c(9673100,96731200))
plot(gaps_spdf, add=TRUE, border="red", lwd=2)
# Populating the attribute table of Gaps_spdf with gaps statistics
gaps_stats<-GapStats(gap_layer=gaps_duc, chm_layer=ALS_CHM_DUC)
gaps_spdf<-merge(gaps_spdf,gaps_stats, by="gap_id")
head(gaps_spdf@data)
## gap_id x y gap_area chm_max chm_min chm_mean chm_sd chm_gini chm_range
## 1 1 173088.7 9673197 34 9.22 1.09 5.12 2.61 0.30 8.13
## 10 10 173044.2 9673143 18 9.90 2.74 5.06 2.18 0.23 7.16
## 11 11 173038.7 9673143 13 9.91 1.75 5.47 2.94 0.31 8.16
## 12 12 173182.0 9673138 10 9.92 3.75 7.77 2.27 0.16 6.17
## 13 13 173067.7 9673121 66 9.94 0.99 5.31 2.91 0.32 8.95
## 14 14 173179.9 9673132 7 10.00 5.83 7.41 1.53 0.12 4.17
Forest Gap Change Detection
#Loading raster and viridis libraries
library(raster)
library(viridis)
# ALS-derived CHM from Fazenda Cauxi - Brazilian tropical forest
data(ALS_CHM_CAU_2012)
data(ALS_CHM_CAU_2014)
# set height thresholds (e.g. 10 meters)
threshold<-10
size<-c(1,1000) # m2
# Detecting forest gaps
gaps_cau2012<-getForestGaps(chm_layer=ALS_CHM_CAU_2012, threshold=threshold, size=size)
gaps_cau2014<-getForestGaps(chm_layer=ALS_CHM_CAU_2014, threshold=threshold, size=size)
# Detecting forest gaps changes
Gap_changes<-GapChangeDec(gap_layer1=gaps_cau2012,gap_layer2=gaps_cau2014)
# Plotting ALS-derived CHM and forest gaps
par(mfrow=c(1,3))
plot(ALS_CHM_CAU_2012, main="Forest Canopy Gap - 2012", col=viridis(10))
plot(gaps_cau2012, add=TRUE, col="red", legend=FALSE)
plot(ALS_CHM_CAU_2014, main="Forest Canopy Gap - 2014", col=viridis(10))
plot(gaps_cau2014, add=TRUE,col="blue", legend=FALSE)
plot(ALS_CHM_CAU_2014,main="Forest Gaps Changes Detection",col=viridis(10))
plot(Gap_changes, add=TRUE, col="yellow", legend=FALSE)
Spatial Pattern of Forest Canopy Gaps
#Loading raster and viridis libraries
library(raster)
library(viridis)
# ALS-derived CHM from Fazenda Cauxi - Brazilian tropical forest
data(ALS_CHM_CAU_2012)
data(ALS_CHM_CAU_2014)
# set height thresholds (e.g. 10 meters)
threshold <- 10
size <- c(1,1000) # m2
# Detecting forest gaps
gaps_cau2012 <- getForestGaps(chm_layer = ALS_CHM_CAU_2012, threshold=threshold, size=size)
gaps_cau2014 <- getForestGaps(chm_layer = ALS_CHM_CAU_2014, threshold=threshold, size=size)
# Converting raster layers to SpatialPolygonsDataFrame
gaps_cau2012_spdf <- GapSPDF(gap_layer = gaps_cau2012)
gaps_cau2014_spdf <- GapSPDF(gap_layer = gaps_cau2014)
# Spatial pattern analysis of each year
gaps_cau2012_SpatPattern <- GapsSpatPattern(gaps_cau2012_spdf, ALS_CHM_CAU_2012)
gaps_cau2014_SpatPattern <- GapsSpatPattern(gaps_cau2014_spdf, ALS_CHM_CAU_2014)
Spatial Pattern in 2012
Clark-Evans test
No edge correction
Z-test
data: P
R = 0.89312, p-value = 0.001022
alternative hypothesis: two-sided
Spatial Pattern in 2014
Clark-Evans test
No edge correction
Z-test
data: P
R = 1.0596, p-value = 0.2688
alternative hypothesis: two-sided
References
Silva, C.A., Pinage,E., Mohan, M., Valbuena, R., Almeida, D., Broadbent,E., Jaafar, W., Papa, D., Cardil, A., Klauberg, C.2019. ForestGapR: An R Package for Airborne Laser Scanning-derived Tropical Forest Gaps Analysis. Methods Ecol Evolution. 10, 1347-1356 https://doi.org/10.1111/2041-210X.13211
Hanel,R., Corominas-Murtra, B., Liu, B., Thurner, S. Fitting power-laws in empirical data with estimators that work for all exponents,PloS one, vol. 12, no. 2, p. e0170920, 2017.https://doi.org/10.1371/journal.pone.0170920
Asner, G.P., Kellner, J.R., Kennedy-Bowdoin, T., Knapp, D.E., Anderson, C. & Martin, R.E. 2013. Forest canopy gap distributions in the Southern Peruvian Amazon. PLoS One, 8, e60875. https://doi.org/10.1371/journal.pone.0060875
White, E.P, Enquist, B.J, Green, J.L. (2008) On estimating the exponent of powerlaw frequency distributions. Ecology 89,905-912. https://doi.org/10.1890/07-1288.1
Sustainable Landscape Brazil. 2018. https://www.paisagenslidar.cnptia.embrapa.br/webgis/. (accessed in August 2018).
Acknowledgements
ALS data from Adolfo Ducke (ALS_CHM_DUC) Forest Reserve and Cauaxi Forest (ALS_CHM_CAU_2012 and ALS_CHM_CAU_2014) used as exemple datasets were acquired by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, USAID, and the US Department of State.