analysis/scripts/6b_iSEG05_mtpi_WS_ta.R
In keltoskytoi/iSEGMound: iSEGMound - A Reproducible Workflow for Mound Detection in LiDAR-derived DTMs
################################################################################
#######################THE WORKFLOW APPLIED ON THE TEST AREA####################
################################################################################
####--------------------------------SHORTCUTS-------------------------------####
lstrainarea <- list.files(file.path(path_analysis_results_train_area),
full.names = TRUE, pattern = glob2rx("*.tif"))
lstrainarea
#[1] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//3dm_32482_5616_1_he_xyzirnc_ground_05.tif"
#[2] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//3dm_32482_5617_1_he_xyzirnc_ground_05.tif"
#[3] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//3dm_32482_5618_1_he_xyzirnc_ground_05.tif"
#[4] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//3dm_32482_5619_1_he_xyzirnc_ground_05.tif"
#[5] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//3dm_32483_5616_1_he_xyzirnc_ground_05.tif"
#[6] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//test_area_xyzirnc_ground_05.tif"
################################################################################
####-----------------------------#1. READ DTM#------------------------------####
trainarea05 <- raster(lstrainarea[[6]])
crs(trainarea05)
#CRS arguments: +proj=utm +zone=32 +datum=WGS84 +units=m +no_defs
mapview(trainarea05)
################################################################################
####--------------------------#2. CREATE A DERIVATIVE#----------------------####
#-----------------write testdtm as a SAGA grid into the tmp folder-------------#
#We understood from creating derivatives, that the multiscale topographic position
#index is very useful to enhance the flatly preserved burial mounds; so we will use
#MTPI before a filter
####------------------MULTISCALE TOPOGRAPHIC POSITION INDEX-----------------####
generate_mtpi_SAGA(dtm=trainarea05,
output= paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta),
tmp= paste0(path_tmp),
scale_min=1,
scale_max=30,
scale_num=20,
crs=crs(trainarea05))
#read mtpi
TAdDTM05 <- raster::raster(paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta, "dDTM05.tif"))
################################################################################
####--------------------------#3. FILTER DERIVATIVE#------------------------####
#let's use a mean/low-pass filter with 3x3 moving window because we are looking
#for relatively delicate features
TAfdDTM05<- filtR(TAdDTM05, filtRs="mean", sizes=3, NArm=TRUE)
names(TAfdDTM05)
#"dDTM05_mean3"
#export filter
raster::writeRaster(TAfdDTM05, paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta, "/TAfdDTM05.tif"),
format= "GTiff", overwrite = TRUE, NAflag = 0)
#because we are writing continuously in the folder let's use the exact path
TAfdDTM05 <- raster::raster(paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta, "TAfdDTM05.tif"))
################################################################################
####------------------------#4.INVERT THE FILTERED DTMS#--------------------####
#spatialEco::raster.invert Inverts raster values using the formula: (((x - max(x)) * -1) + min(x)
TAifdDTM05 <- spatialEco::raster.invert(TAfdDTM05)
#write inverse filtered DTM05
raster::writeRaster(TAifdDTM05, paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta,
"/TAifdDTM05.tif"), format= "GTiff", overwrite = TRUE, NAflag = 0)
#read inverse filtered DTM05
TAifdDTM05 <- raster::raster(paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta, "/TAifdDTM05.tif"))
################################################################################
####---------------------#5.PIT FILLING OF INVERSE FILTERED DTM#------------####
#Freeland et al. 2016 & Rom et al. use Wang and Liu 2006, which is implemented in Whitebox GAT & SAGA
#to make it simple we are going to use SAGA
#----------------------------------------SAGA----------------------------------#
#with a value of zero sinks are filled up to the spill elevation (which results in flat areas
#(from the module description)
filled_DTM_SAGA_WL(dtm= TAifdDTM05,
output= paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta),
tmp= paste0(path_tmp),
minslope= 0,
crs= crs(trainarea05))
#read pit filled raster
TApf_ifdDTM_WL05 <- raster::raster(paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta, "pf_ifDTM_W&L.tif"))
################################################################################
####-----------#6.SUBRATCTION OF INVERSE DTM FROM PIT-FILLED DTM#-----------####
#subtract inverse DTM from pit-filled DTM
TAdiff_ifdDTM05 <- TAifdDTM05 - TApf_ifdDTM_WL05
raster::writeRaster(TAdiff_ifdDTM05, paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta,
"/TAdiff_ifdDTM05.tif"), format= "GTiff", overwrite = TRUE, NAflag = 0)
################################################################################
####--------------------------#7.FILTER diff_ifDTM05#-----------------------####
#When checking in QGIS and making profiles along the barrows can see that there
#are a lot of small outliers which disturb when processing further
#let's use a 3x3 moving window with different filters: sum, min, max, mean,
#median, modal, sd, sobel and check in QGIS how well the barrows still are visible
TAfiltered_diff_ifdDTM05<- filtR(TAdiff_ifdDTM05, filtRs= c("all"), sizes=3, NArm=TRUE)
names(TAfiltered_diff_ifdDTM05)
#[1] "layer_sum3" "layer_min3" "layer_max3" "layer_mean3"
#[5] "layer_median3" "layer_modal3" "layer_sd3" "layer_sobel3"
names(TAfiltered_diff_ifdDTM05) <- c("TAfdiff_ifdDTM05_sum3", "TAfdiff_ifdDTM05_min3",
"TAfdiff_ifdDTM05_max3", "TAfdiff_ifdDTM05_mean3",
"TAfdiff_ifdDTM05_median3", "TAfdiff_ifdDTM05_modal3",
"TAfdiff_ifdDTM05_sd3", "TAfdiff_ifdDTM05_sobel3")
#export filter one by one
writeRaster(TAfiltered_diff_ifdDTM05, paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta,
filename = names(TAfiltered_diff_ifdDTM05), ".tif"),
format= "GTiff", bylayer = TRUE, overwrite=TRUE, NAflag = 0)
#after checking in QGIS it can be said that any filter which seems to be useful
#to enhance you OoI can be used in the next step.
################################################################################
####------------------------#8.WATERSHED SEGMENTATION#----------------------####
TAfdiff_ifdDTM05_modal3 <- raster::raster(paste0(path_analysis_results_iSEG05_6b_mtpi_WS_ta, "TAfdiff_ifdDTM05_modal3.tif"))
watershed_SAGA(dtm=TAfdiff_ifdDTM05_modal3,
output= paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta),
tmp= paste0(path_tmp),
output_choice=0,
down=1,
join=1,
threshold=0.5,
edge=0,
borders=0,
crs=crs(trainarea05))
#----------------------------POLYGONIZE RG SEGMENTS----------------------------#
#class_id & all_vertices are default value
polygonize_segments_SAGA(segments=paste0(path_tmp, "segments.sgrd"),
tmp=paste0(path_tmp),
class_all=1,
class_id=1,
split=1,
all_vertices=0)
################################################################################
####-------------------------#9.JOIN NEIGHBORING POLYGONS#------------------####
#read segments and plot them together with the burial mounds
segments <- readOGR(paste0(path_tmp, "segments.shp"))
burial_mounds_5 <- readOGR(paste0(path_analysis_data_burial_mounds_Dobiat_1994, "5_Dobiat_1994.shp"))
crs(segments) <- crs(burial_mounds_5)
#NOTE: there are MANY segments that it can break R; thus it is easier to check
#the segments in QGIS
mapview::mapview(segments) + burial_mounds_5
#It is already clear that there are many small sized segments which might have
#the form and area of the burial mound segments AND the burial mounds are divided
#into multiple neighboring segments, thus these segments have to be joined together
#the following code was adapted:
#https://gis.stackexchange.com/questions/79114/joining-nearest-neighbor-small-polygons-using-r
#create neighbourhood list of adjacent segments
neighb <- poly2nb(segments)
region <- create_regions(neighb)
pol_rgn <- spCbind(segments, region)
segments <- unionSpatialPolygons(pol_rgn, region)
#SpP is invalid because there are self-intersections; but it still works
##NOTE: there are MANY segments that it can break R; thus it is easier to check
#the segments in QGIS
mapview::mapview(segments)
#---------------------#transform to a spatial polygons dataframe#--------------#
#check the class of the segments
class(segments)
#"SpatialPolygons"
#creata a data frame with the IDs of the SpatialPolygons
df<- data.frame(id = getSpPPolygonsIDSlots(segments))
row.names(df) <- getSpPPolygonsIDSlots(segments)
segments <- SpatialPolygonsDataFrame(segments, data =df)
class(segments)
#"SpatialPolygonsDataFrame"
#test projection and assign projection of original data set
crs(segments)
#------------------------#export the spatial polygons dataframe#---------------#
rgdal::writeOGR(obj=segments, dsn = "/home/keltoskytoi/repRCHrs/tmp",
layer ="segments", driver = "ESRI Shapefile",
verbose = TRUE, overwrite_layer = TRUE)
################################################################################
####----------------------#10.COMPUTE SHAPE INDEX SEGMENTS#-----------------####
compute_shape_index_SAGA(segments=paste0(path_tmp, "segments.shp"),
tmp=paste0(path_tmp),
index=paste0(path_tmp, "segments.shp"),
gyros=1,
feret=1,
feret_dirs=18)
################################################################################
#####------------#11.CALCULATE ADDITIONAL DECRIPTIVE VARIAIBLES#------------####
#----------------------------#LOAD SHAPE FILES & PLOT THEM#--------------------#
segments_iSEG_mtpi_WS_ta <- readOGR(paste0(path_tmp, "segments.shp"))
burial_mounds_5 <- readOGR(paste0(path_analysis_data_burial_mounds_Dobiat_1994, "5_Dobiat_1994.shp"))
burial_mounds_7 <- readOGR(paste0(path_analysis_data_burial_mounds_Dobiat_1994, "7_Dobiat_1994.shp"))
burial_mounds_14 <- readOGR(paste0(path_analysis_data_burial_mounds_Dobiat_1994, "14_Dobiat_1994.shp"))
burial_mounds_35 <- readOGR(paste0(path_analysis_data_burial_mounds_Dobiat_1994, "35_Dobiat_1994.shp"))
crs(segments_iSEG_mtpi_WS_ta)
#do not run, might bring R to crash
mapview::mapview(segments_iSEG_mtpi_WS_ta) + burial_mounds_5 + burial_mounds_7 + burial_mounds_14 + burial_mounds_35
#------------------#CALCULATE ADDITIONAL DECRIPTIVE VARIAIBLES#----------------#
#---------------------------#after Niculitca 2020a#----------------------------#
segments_iSEG_mtpi_WS_ta@data$compactness <- (sqrt(4*segments_iSEG_mtpi_WS_ta@data$A/pi))/segments_iSEG_mtpi_WS_ta@data$P
segments_iSEG_mtpi_WS_ta@data$roundness <- (4*segments_iSEG_mtpi_WS_ta@data$A)/(pi*segments_iSEG_mtpi_WS_ta@data$Fmax)
segments_iSEG_mtpi_WS_ta@data$elongation <- segments_iSEG_mtpi_WS_ta@data$Fmax/segments_iSEG_mtpi_WS_ta@data$Fmin
names(segments_iSEG_mtpi_WS_ta)
# [1] "ID" "A" "P" "P.A" "P.sqrt.A."
#[6] "Depqc" "Sphericity" "Shape.Index" "Dmax" "DmaxDir"
#[11] "Dmax.A" "Dmax.sqrt.A" "Dgyros" "Fmax" "FmaxDir"
#[16] "Fmin" "FminDir" "Fmean" "Fmax90" "Fmin90"
#[21] "Fvol" "compactness" "roundness" "elongation"
#------------------------------------------------------------------------------#
#export the segments with the additional descriptive variables
rgdal::writeOGR(obj=segments_iSEG_mtpi_WS_ta, dsn = "/home/keltoskytoi/repRCHrs/analysis/results/6b_iSEG05_mtpi_WS_ta",
layer ="segments_shape_descriptive_joined_mtpi_WS_ta", driver = "ESRI Shapefile",
verbose = TRUE, overwrite_layer = TRUE)
################################################################################
#in case that you want to read the segments at a later point, you will have to
#change the column names:
segments_iSEG_mtpi_WS_ta <- readOGR(paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta,
"segments_shape_descriptive_joined_mtpi_WS_ta.shp"))
#names(segments_iSEG_mtpi_WS_ta)
#[1] "ID" "A" "P" "P_A" "P_sq_A_" "Depqc" "Sphrcty" "Shp_Ind"
#[9] "Dmax" "DmaxDir" "Dmax_A" "Dmx_s_A" "Dgyros" "Fmax" "FmaxDir" "Fmin"
#[17] "FminDir" "Fmean" "Fmax90" "Fmin90" "Fvol" "cmpctns" "rondnss" "elongtn"
####----------------#12. THRESHOLD BASED FILTERING OF SEGMENTS--------------####
##the segment IDs were checked in QGIS if not R would have crashed
#NB: the Shape Index characterizes the deviation from an optimal circle. A Shape
#Index of 1.4 - 1.7 can be seen as relatively compact but of course it depends
#on the context
#------------------basic descriptive values of the burial mounds---------------#
#keep in mind, that you need to call ID+1!
#### denominates burial mound segments with amorph stucture
#------------------------#Dobiat 1994, Grave group 5#--------------------------#
Mound_5_1 <- segments_iSEG_mtpi_WS_ta[10409,] #10408+1
Mound_5_2 <- segments_iSEG_mtpi_WS_ta[9660,] #9659+1
Mound_5_3 <- segments_iSEG_mtpi_WS_ta[8590,] #8589+1
Mound_5_4 <- segments_iSEG_mtpi_WS_ta[9041,] #9040+1
Mound_5_5 <- segments_iSEG_mtpi_WS_ta[8533,] #8532+1
Mound_5_6 <- segments_iSEG_mtpi_WS_ta[9306,] #9305+1
Mound_5_7 <- segments_iSEG_mtpi_WS_ta[10419,] #10418+1
Mound_5_8 <- segments_iSEG_mtpi_WS_ta[11778,] #11777+1
Mound_5_9 <- segments_iSEG_mtpi_WS_ta[6702,] #6701+1
#------------------------#Dobiat 1994, Grave group 7#--------------------------#
#Mound_7_1 <- segments_iSEG_mtpi_WS_ta[20489,] #20488+1 #### extremely elongated
Mound_7_2 <- segments_iSEG_mtpi_WS_ta[20460,] #20459+1
Mound_7_3 <- segments_iSEG_mtpi_WS_ta[21711,] #21710+1
Mound_7_4 <- segments_iSEG_mtpi_WS_ta[23108,] #23107+1
Mound_7_5 <- segments_iSEG_mtpi_WS_ta[24409,] #24408+1
Mound_7_6 <- segments_iSEG_mtpi_WS_ta[24082,] #24081+1
Mound_7_7 <- segments_iSEG_mtpi_WS_ta[25727,] #25726+1
Mound_7_8 <- segments_iSEG_mtpi_WS_ta[26120,] #26119+1
Mound_7_9 <- segments_iSEG_mtpi_WS_ta[26204,] #26203+1
#------------------------#Dobiat 1994, Grave group 14#-------------------------#
#Mound_14_1 <- segments_iSEG_mtpi_WS_ta[60160,] #60159+1 #### has an elongated tail
Mound_14_2 <- segments_iSEG_mtpi_WS_ta[65456,] #65455+1
Mound_14_3 <- segments_iSEG_mtpi_WS_ta[58359,] #58358+1
Mound_14_4 <- segments_iSEG_mtpi_WS_ta[81481,] #81480+1
Mound_14_5 <- segments_iSEG_mtpi_WS_ta[84210,] #84209+1
#Mound_14_6 <- segments_iSEG_mtpi_WS_ta[84594,] #84593+1 ####
Mound_14_7 <- segments_iSEG_mtpi_WS_ta[87096,] #87095+1
Mound_14_8 <- segments_iSEG_mtpi_WS_ta[83486,] #83485+1 ####
Mound_14_9 <- segments_iSEG_mtpi_WS_ta[90015,] #90014+1
Mound_14_10 <- segments_iSEG_mtpi_WS_ta[90945,] #90944+1
#Mound_14_11 <- segments_iSEG_mtpi_WS_ta[88236,] #88235+1 ####
Mound_14_12 <- segments_iSEG_mtpi_WS_ta[91751,] #91750+1
#Mound_14_13 <- segments_iSEG_mtpi_WS_ta[93759,] #93758+1 #### too elongated
#Mound_14_14 <- segments_iSEG_mtpi_WS_ta[95099,] #95098+1 #### too elongated
Mound_14_15 <- segments_iSEG_mtpi_WS_ta[96873,] #96872+1
#Mound_14_16 <- segments_iSEG_mtpi_WS_ta[103663,] #103662+1 #### has an elongated tail
Mound_14_17 <- segments_iSEG_mtpi_WS_ta[105097,] #105096+1
#Mound_14_18 <- segments_iSEG_mtpi_WS_ta[106078,] #106077+1 #### has an elongated tail
Mound_14_19 <- segments_iSEG_mtpi_WS_ta[89930,] #89929+1
#------------------------#Dobiat 1994, Grave group 35#-------------------------#
Mound_35 <- segments_iSEG_mtpi_WS_ta[13882,] #13881+1
#bind the reference segments together:
#minus Mound_7_1, Mound_7_9, Mound_14_6, Mound_14_8, Mound_14_11, because their amorphity
#is small
reference_descriptors_mtpi_WS_ta <- bind(Mound_5_1, Mound_5_2, Mound_5_3, Mound_5_4,
Mound_5_5, Mound_5_6, Mound_5_7, Mound_5_8,
Mound_5_9, Mound_7_2, Mound_7_3, Mound_7_4,
Mound_7_5, Mound_7_6, Mound_7_7, Mound_7_8,
Mound_14_2, Mound_14_3, Mound_14_4,
Mound_14_5, Mound_14_7, Mound_14_9, Mound_14_10,
Mound_14_12, Mound_14_15, Mound_14_17,
Mound_14_19, Mound_35)
#we want to filter the segments even more, thus we also use the variable roundness
min(reference_descriptors_mtpi_WS_ta$A) #45.25
max(reference_descriptors_mtpi_WS_ta$A) #576
min(reference_descriptors_mtpi_WS_ta$Sphrcty) #0.2951892
max(reference_descriptors_mtpi_WS_ta$Sphrcty) #0.6417926
min(reference_descriptors_mtpi_WS_ta$Shp_Ind) #1.558136
max(reference_descriptors_mtpi_WS_ta$Shp_Ind) #3.387658
min(reference_descriptors_mtpi_WS_ta$elongtn) #1.122967
max(reference_descriptors_mtpi_WS_ta$elongtn) #1.913615
min(reference_descriptors_mtpi_WS_ta$cmpctns) #0.09396165
max(reference_descriptors_mtpi_WS_ta$cmpctns) #0.2042889
min(reference_descriptors_mtpi_WS_ta$rondnss) #5.00867
max(reference_descriptors_mtpi_WS_ta$rondnss) #22.2683
####---------------------------#MOUND THRESHOLDS#---------------------------####
#let's filter for segments of the area, sphericity, shape index, elongation
#and compactness
#based on the experiences it is best to aim below and above the respective thresholds to get all mounds
segments_iSEG_mtpi_WS_ta_filt <- segments_iSEG_mtpi_WS_ta[segments_iSEG_mtpi_WS_ta$A <= 576.1 & segments_iSEG_mtpi_WS_ta$A >= 45.20 &
segments_iSEG_mtpi_WS_ta$Sphrcty >= 0.2951891 & segments_iSEG_mtpi_WS_ta$Sphrcty <= 0.6417927 &
segments_iSEG_mtpi_WS_ta$Shp_Ind >= 1.558135 & segments_iSEG_mtpi_WS_ta$Shp_Ind <= 3.387660 &
segments_iSEG_mtpi_WS_ta$elongtn >= 1.122960 & segments_iSEG_mtpi_WS_ta$elongtn <=1.913620 &
segments_iSEG_mtpi_WS_ta$cmpctns >= 0.09396160 & segments_iSEG_mtpi_WS_ta$cmpctns <= 0.2042890 &
segments_iSEG_mtpi_WS_ta$rondnss >= 5.00866 & segments_iSEG_mtpi_WS_ta$rondnss <= 22.2684,]
mapview::mapview(segments_iSEG_mtpi_WS_ta_filt) + burial_mounds_5 + burial_mounds_7 + burial_mounds_14 + burial_mounds_35
#------------------------------------------------------------------------------#
rgdal::writeOGR(obj=segments_iSEG_mtpi_WS_ta_filt, dsn = "/home/keltoskytoi/repRCHrs/analysis/results/6b_iSEG05_mtpi_WS_ta",
layer ="iSEG_mtpi_WS_ta", driver = "ESRI Shapefile", verbose = TRUE, overwrite_layer = TRUE)
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################################################################################
#######################THE WORKFLOW APPLIED ON THE TEST AREA####################
################################################################################
####--------------------------------SHORTCUTS-------------------------------####
lstrainarea <- list.files(file.path(path_analysis_results_train_area),
full.names = TRUE, pattern = glob2rx("*.tif"))
lstrainarea
#[1] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//3dm_32482_5616_1_he_xyzirnc_ground_05.tif"
#[2] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//3dm_32482_5617_1_he_xyzirnc_ground_05.tif"
#[3] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//3dm_32482_5618_1_he_xyzirnc_ground_05.tif"
#[4] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//3dm_32482_5619_1_he_xyzirnc_ground_05.tif"
#[5] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//3dm_32483_5616_1_he_xyzirnc_ground_05.tif"
#[6] "/home/keltoskytoi/repRCHrs/analysis/results/train_area//test_area_xyzirnc_ground_05.tif"
################################################################################
####-----------------------------#1. READ DTM#------------------------------####
trainarea05 <- raster(lstrainarea[[6]])
crs(trainarea05)
#CRS arguments: +proj=utm +zone=32 +datum=WGS84 +units=m +no_defs
mapview(trainarea05)
################################################################################
####--------------------------#2. CREATE A DERIVATIVE#----------------------####
#-----------------write testdtm as a SAGA grid into the tmp folder-------------#
#We understood from creating derivatives, that the multiscale topographic position
#index is very useful to enhance the flatly preserved burial mounds; so we will use
#MTPI before a filter
####------------------MULTISCALE TOPOGRAPHIC POSITION INDEX-----------------####
generate_mtpi_SAGA(dtm=trainarea05,
output= paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta),
tmp= paste0(path_tmp),
scale_min=1,
scale_max=30,
scale_num=20,
crs=crs(trainarea05))
#read mtpi
TAdDTM05 <- raster::raster(paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta, "dDTM05.tif"))
################################################################################
####--------------------------#3. FILTER DERIVATIVE#------------------------####
#let's use a mean/low-pass filter with 3x3 moving window because we are looking
#for relatively delicate features
TAfdDTM05<- filtR(TAdDTM05, filtRs="mean", sizes=3, NArm=TRUE)
names(TAfdDTM05)
#"dDTM05_mean3"
#export filter
raster::writeRaster(TAfdDTM05, paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta, "/TAfdDTM05.tif"),
format= "GTiff", overwrite = TRUE, NAflag = 0)
#because we are writing continuously in the folder let's use the exact path
TAfdDTM05 <- raster::raster(paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta, "TAfdDTM05.tif"))
################################################################################
####------------------------#4.INVERT THE FILTERED DTMS#--------------------####
#spatialEco::raster.invert Inverts raster values using the formula: (((x - max(x)) * -1) + min(x)
TAifdDTM05 <- spatialEco::raster.invert(TAfdDTM05)
#write inverse filtered DTM05
raster::writeRaster(TAifdDTM05, paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta,
"/TAifdDTM05.tif"), format= "GTiff", overwrite = TRUE, NAflag = 0)
#read inverse filtered DTM05
TAifdDTM05 <- raster::raster(paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta, "/TAifdDTM05.tif"))
################################################################################
####---------------------#5.PIT FILLING OF INVERSE FILTERED DTM#------------####
#Freeland et al. 2016 & Rom et al. use Wang and Liu 2006, which is implemented in Whitebox GAT & SAGA
#to make it simple we are going to use SAGA
#----------------------------------------SAGA----------------------------------#
#with a value of zero sinks are filled up to the spill elevation (which results in flat areas
#(from the module description)
filled_DTM_SAGA_WL(dtm= TAifdDTM05,
output= paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta),
tmp= paste0(path_tmp),
minslope= 0,
crs= crs(trainarea05))
#read pit filled raster
TApf_ifdDTM_WL05 <- raster::raster(paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta, "pf_ifDTM_W&L.tif"))
################################################################################
####-----------#6.SUBRATCTION OF INVERSE DTM FROM PIT-FILLED DTM#-----------####
#subtract inverse DTM from pit-filled DTM
TAdiff_ifdDTM05 <- TAifdDTM05 - TApf_ifdDTM_WL05
raster::writeRaster(TAdiff_ifdDTM05, paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta,
"/TAdiff_ifdDTM05.tif"), format= "GTiff", overwrite = TRUE, NAflag = 0)
################################################################################
####--------------------------#7.FILTER diff_ifDTM05#-----------------------####
#When checking in QGIS and making profiles along the barrows can see that there
#are a lot of small outliers which disturb when processing further
#let's use a 3x3 moving window with different filters: sum, min, max, mean,
#median, modal, sd, sobel and check in QGIS how well the barrows still are visible
TAfiltered_diff_ifdDTM05<- filtR(TAdiff_ifdDTM05, filtRs= c("all"), sizes=3, NArm=TRUE)
names(TAfiltered_diff_ifdDTM05)
#[1] "layer_sum3" "layer_min3" "layer_max3" "layer_mean3"
#[5] "layer_median3" "layer_modal3" "layer_sd3" "layer_sobel3"
names(TAfiltered_diff_ifdDTM05) <- c("TAfdiff_ifdDTM05_sum3", "TAfdiff_ifdDTM05_min3",
"TAfdiff_ifdDTM05_max3", "TAfdiff_ifdDTM05_mean3",
"TAfdiff_ifdDTM05_median3", "TAfdiff_ifdDTM05_modal3",
"TAfdiff_ifdDTM05_sd3", "TAfdiff_ifdDTM05_sobel3")
#export filter one by one
writeRaster(TAfiltered_diff_ifdDTM05, paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta,
filename = names(TAfiltered_diff_ifdDTM05), ".tif"),
format= "GTiff", bylayer = TRUE, overwrite=TRUE, NAflag = 0)
#after checking in QGIS it can be said that any filter which seems to be useful
#to enhance you OoI can be used in the next step.
################################################################################
####------------------------#8.WATERSHED SEGMENTATION#----------------------####
TAfdiff_ifdDTM05_modal3 <- raster::raster(paste0(path_analysis_results_iSEG05_6b_mtpi_WS_ta, "TAfdiff_ifdDTM05_modal3.tif"))
watershed_SAGA(dtm=TAfdiff_ifdDTM05_modal3,
output= paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta),
tmp= paste0(path_tmp),
output_choice=0,
down=1,
join=1,
threshold=0.5,
edge=0,
borders=0,
crs=crs(trainarea05))
#----------------------------POLYGONIZE RG SEGMENTS----------------------------#
#class_id & all_vertices are default value
polygonize_segments_SAGA(segments=paste0(path_tmp, "segments.sgrd"),
tmp=paste0(path_tmp),
class_all=1,
class_id=1,
split=1,
all_vertices=0)
################################################################################
####-------------------------#9.JOIN NEIGHBORING POLYGONS#------------------####
#read segments and plot them together with the burial mounds
segments <- readOGR(paste0(path_tmp, "segments.shp"))
burial_mounds_5 <- readOGR(paste0(path_analysis_data_burial_mounds_Dobiat_1994, "5_Dobiat_1994.shp"))
crs(segments) <- crs(burial_mounds_5)
#NOTE: there are MANY segments that it can break R; thus it is easier to check
#the segments in QGIS
mapview::mapview(segments) + burial_mounds_5
#It is already clear that there are many small sized segments which might have
#the form and area of the burial mound segments AND the burial mounds are divided
#into multiple neighboring segments, thus these segments have to be joined together
#the following code was adapted:
#https://gis.stackexchange.com/questions/79114/joining-nearest-neighbor-small-polygons-using-r
#create neighbourhood list of adjacent segments
neighb <- poly2nb(segments)
region <- create_regions(neighb)
pol_rgn <- spCbind(segments, region)
segments <- unionSpatialPolygons(pol_rgn, region)
#SpP is invalid because there are self-intersections; but it still works
##NOTE: there are MANY segments that it can break R; thus it is easier to check
#the segments in QGIS
mapview::mapview(segments)
#---------------------#transform to a spatial polygons dataframe#--------------#
#check the class of the segments
class(segments)
#"SpatialPolygons"
#creata a data frame with the IDs of the SpatialPolygons
df<- data.frame(id = getSpPPolygonsIDSlots(segments))
row.names(df) <- getSpPPolygonsIDSlots(segments)
segments <- SpatialPolygonsDataFrame(segments, data =df)
class(segments)
#"SpatialPolygonsDataFrame"
#test projection and assign projection of original data set
crs(segments)
#------------------------#export the spatial polygons dataframe#---------------#
rgdal::writeOGR(obj=segments, dsn = "/home/keltoskytoi/repRCHrs/tmp",
layer ="segments", driver = "ESRI Shapefile",
verbose = TRUE, overwrite_layer = TRUE)
################################################################################
####----------------------#10.COMPUTE SHAPE INDEX SEGMENTS#-----------------####
compute_shape_index_SAGA(segments=paste0(path_tmp, "segments.shp"),
tmp=paste0(path_tmp),
index=paste0(path_tmp, "segments.shp"),
gyros=1,
feret=1,
feret_dirs=18)
################################################################################
#####------------#11.CALCULATE ADDITIONAL DECRIPTIVE VARIAIBLES#------------####
#----------------------------#LOAD SHAPE FILES & PLOT THEM#--------------------#
segments_iSEG_mtpi_WS_ta <- readOGR(paste0(path_tmp, "segments.shp"))
burial_mounds_5 <- readOGR(paste0(path_analysis_data_burial_mounds_Dobiat_1994, "5_Dobiat_1994.shp"))
burial_mounds_7 <- readOGR(paste0(path_analysis_data_burial_mounds_Dobiat_1994, "7_Dobiat_1994.shp"))
burial_mounds_14 <- readOGR(paste0(path_analysis_data_burial_mounds_Dobiat_1994, "14_Dobiat_1994.shp"))
burial_mounds_35 <- readOGR(paste0(path_analysis_data_burial_mounds_Dobiat_1994, "35_Dobiat_1994.shp"))
crs(segments_iSEG_mtpi_WS_ta)
#do not run, might bring R to crash
mapview::mapview(segments_iSEG_mtpi_WS_ta) + burial_mounds_5 + burial_mounds_7 + burial_mounds_14 + burial_mounds_35
#------------------#CALCULATE ADDITIONAL DECRIPTIVE VARIAIBLES#----------------#
#---------------------------#after Niculitca 2020a#----------------------------#
segments_iSEG_mtpi_WS_ta@data$compactness <- (sqrt(4*segments_iSEG_mtpi_WS_ta@data$A/pi))/segments_iSEG_mtpi_WS_ta@data$P
segments_iSEG_mtpi_WS_ta@data$roundness <- (4*segments_iSEG_mtpi_WS_ta@data$A)/(pi*segments_iSEG_mtpi_WS_ta@data$Fmax)
segments_iSEG_mtpi_WS_ta@data$elongation <- segments_iSEG_mtpi_WS_ta@data$Fmax/segments_iSEG_mtpi_WS_ta@data$Fmin
names(segments_iSEG_mtpi_WS_ta)
# [1] "ID" "A" "P" "P.A" "P.sqrt.A."
#[6] "Depqc" "Sphericity" "Shape.Index" "Dmax" "DmaxDir"
#[11] "Dmax.A" "Dmax.sqrt.A" "Dgyros" "Fmax" "FmaxDir"
#[16] "Fmin" "FminDir" "Fmean" "Fmax90" "Fmin90"
#[21] "Fvol" "compactness" "roundness" "elongation"
#------------------------------------------------------------------------------#
#export the segments with the additional descriptive variables
rgdal::writeOGR(obj=segments_iSEG_mtpi_WS_ta, dsn = "/home/keltoskytoi/repRCHrs/analysis/results/6b_iSEG05_mtpi_WS_ta",
layer ="segments_shape_descriptive_joined_mtpi_WS_ta", driver = "ESRI Shapefile",
verbose = TRUE, overwrite_layer = TRUE)
################################################################################
#in case that you want to read the segments at a later point, you will have to
#change the column names:
segments_iSEG_mtpi_WS_ta <- readOGR(paste0(path_analysis_results_6b_iSEG05_mtpi_WS_ta,
"segments_shape_descriptive_joined_mtpi_WS_ta.shp"))
#names(segments_iSEG_mtpi_WS_ta)
#[1] "ID" "A" "P" "P_A" "P_sq_A_" "Depqc" "Sphrcty" "Shp_Ind"
#[9] "Dmax" "DmaxDir" "Dmax_A" "Dmx_s_A" "Dgyros" "Fmax" "FmaxDir" "Fmin"
#[17] "FminDir" "Fmean" "Fmax90" "Fmin90" "Fvol" "cmpctns" "rondnss" "elongtn"
####----------------#12. THRESHOLD BASED FILTERING OF SEGMENTS--------------####
##the segment IDs were checked in QGIS if not R would have crashed
#NB: the Shape Index characterizes the deviation from an optimal circle. A Shape
#Index of 1.4 - 1.7 can be seen as relatively compact but of course it depends
#on the context
#------------------basic descriptive values of the burial mounds---------------#
#keep in mind, that you need to call ID+1!
#### denominates burial mound segments with amorph stucture
#------------------------#Dobiat 1994, Grave group 5#--------------------------#
Mound_5_1 <- segments_iSEG_mtpi_WS_ta[10409,] #10408+1
Mound_5_2 <- segments_iSEG_mtpi_WS_ta[9660,] #9659+1
Mound_5_3 <- segments_iSEG_mtpi_WS_ta[8590,] #8589+1
Mound_5_4 <- segments_iSEG_mtpi_WS_ta[9041,] #9040+1
Mound_5_5 <- segments_iSEG_mtpi_WS_ta[8533,] #8532+1
Mound_5_6 <- segments_iSEG_mtpi_WS_ta[9306,] #9305+1
Mound_5_7 <- segments_iSEG_mtpi_WS_ta[10419,] #10418+1
Mound_5_8 <- segments_iSEG_mtpi_WS_ta[11778,] #11777+1
Mound_5_9 <- segments_iSEG_mtpi_WS_ta[6702,] #6701+1
#------------------------#Dobiat 1994, Grave group 7#--------------------------#
#Mound_7_1 <- segments_iSEG_mtpi_WS_ta[20489,] #20488+1 #### extremely elongated
Mound_7_2 <- segments_iSEG_mtpi_WS_ta[20460,] #20459+1
Mound_7_3 <- segments_iSEG_mtpi_WS_ta[21711,] #21710+1
Mound_7_4 <- segments_iSEG_mtpi_WS_ta[23108,] #23107+1
Mound_7_5 <- segments_iSEG_mtpi_WS_ta[24409,] #24408+1
Mound_7_6 <- segments_iSEG_mtpi_WS_ta[24082,] #24081+1
Mound_7_7 <- segments_iSEG_mtpi_WS_ta[25727,] #25726+1
Mound_7_8 <- segments_iSEG_mtpi_WS_ta[26120,] #26119+1
Mound_7_9 <- segments_iSEG_mtpi_WS_ta[26204,] #26203+1
#------------------------#Dobiat 1994, Grave group 14#-------------------------#
#Mound_14_1 <- segments_iSEG_mtpi_WS_ta[60160,] #60159+1 #### has an elongated tail
Mound_14_2 <- segments_iSEG_mtpi_WS_ta[65456,] #65455+1
Mound_14_3 <- segments_iSEG_mtpi_WS_ta[58359,] #58358+1
Mound_14_4 <- segments_iSEG_mtpi_WS_ta[81481,] #81480+1
Mound_14_5 <- segments_iSEG_mtpi_WS_ta[84210,] #84209+1
#Mound_14_6 <- segments_iSEG_mtpi_WS_ta[84594,] #84593+1 ####
Mound_14_7 <- segments_iSEG_mtpi_WS_ta[87096,] #87095+1
Mound_14_8 <- segments_iSEG_mtpi_WS_ta[83486,] #83485+1 ####
Mound_14_9 <- segments_iSEG_mtpi_WS_ta[90015,] #90014+1
Mound_14_10 <- segments_iSEG_mtpi_WS_ta[90945,] #90944+1
#Mound_14_11 <- segments_iSEG_mtpi_WS_ta[88236,] #88235+1 ####
Mound_14_12 <- segments_iSEG_mtpi_WS_ta[91751,] #91750+1
#Mound_14_13 <- segments_iSEG_mtpi_WS_ta[93759,] #93758+1 #### too elongated
#Mound_14_14 <- segments_iSEG_mtpi_WS_ta[95099,] #95098+1 #### too elongated
Mound_14_15 <- segments_iSEG_mtpi_WS_ta[96873,] #96872+1
#Mound_14_16 <- segments_iSEG_mtpi_WS_ta[103663,] #103662+1 #### has an elongated tail
Mound_14_17 <- segments_iSEG_mtpi_WS_ta[105097,] #105096+1
#Mound_14_18 <- segments_iSEG_mtpi_WS_ta[106078,] #106077+1 #### has an elongated tail
Mound_14_19 <- segments_iSEG_mtpi_WS_ta[89930,] #89929+1
#------------------------#Dobiat 1994, Grave group 35#-------------------------#
Mound_35 <- segments_iSEG_mtpi_WS_ta[13882,] #13881+1
#bind the reference segments together:
#minus Mound_7_1, Mound_7_9, Mound_14_6, Mound_14_8, Mound_14_11, because their amorphity
#is small
reference_descriptors_mtpi_WS_ta <- bind(Mound_5_1, Mound_5_2, Mound_5_3, Mound_5_4,
Mound_5_5, Mound_5_6, Mound_5_7, Mound_5_8,
Mound_5_9, Mound_7_2, Mound_7_3, Mound_7_4,
Mound_7_5, Mound_7_6, Mound_7_7, Mound_7_8,
Mound_14_2, Mound_14_3, Mound_14_4,
Mound_14_5, Mound_14_7, Mound_14_9, Mound_14_10,
Mound_14_12, Mound_14_15, Mound_14_17,
Mound_14_19, Mound_35)
#we want to filter the segments even more, thus we also use the variable roundness
min(reference_descriptors_mtpi_WS_ta$A) #45.25
max(reference_descriptors_mtpi_WS_ta$A) #576
min(reference_descriptors_mtpi_WS_ta$Sphrcty) #0.2951892
max(reference_descriptors_mtpi_WS_ta$Sphrcty) #0.6417926
min(reference_descriptors_mtpi_WS_ta$Shp_Ind) #1.558136
max(reference_descriptors_mtpi_WS_ta$Shp_Ind) #3.387658
min(reference_descriptors_mtpi_WS_ta$elongtn) #1.122967
max(reference_descriptors_mtpi_WS_ta$elongtn) #1.913615
min(reference_descriptors_mtpi_WS_ta$cmpctns) #0.09396165
max(reference_descriptors_mtpi_WS_ta$cmpctns) #0.2042889
min(reference_descriptors_mtpi_WS_ta$rondnss) #5.00867
max(reference_descriptors_mtpi_WS_ta$rondnss) #22.2683
####---------------------------#MOUND THRESHOLDS#---------------------------####
#let's filter for segments of the area, sphericity, shape index, elongation
#and compactness
#based on the experiences it is best to aim below and above the respective thresholds to get all mounds
segments_iSEG_mtpi_WS_ta_filt <- segments_iSEG_mtpi_WS_ta[segments_iSEG_mtpi_WS_ta$A <= 576.1 & segments_iSEG_mtpi_WS_ta$A >= 45.20 &
segments_iSEG_mtpi_WS_ta$Sphrcty >= 0.2951891 & segments_iSEG_mtpi_WS_ta$Sphrcty <= 0.6417927 &
segments_iSEG_mtpi_WS_ta$Shp_Ind >= 1.558135 & segments_iSEG_mtpi_WS_ta$Shp_Ind <= 3.387660 &
segments_iSEG_mtpi_WS_ta$elongtn >= 1.122960 & segments_iSEG_mtpi_WS_ta$elongtn <=1.913620 &
segments_iSEG_mtpi_WS_ta$cmpctns >= 0.09396160 & segments_iSEG_mtpi_WS_ta$cmpctns <= 0.2042890 &
segments_iSEG_mtpi_WS_ta$rondnss >= 5.00866 & segments_iSEG_mtpi_WS_ta$rondnss <= 22.2684,]
mapview::mapview(segments_iSEG_mtpi_WS_ta_filt) + burial_mounds_5 + burial_mounds_7 + burial_mounds_14 + burial_mounds_35
#------------------------------------------------------------------------------#
rgdal::writeOGR(obj=segments_iSEG_mtpi_WS_ta_filt, dsn = "/home/keltoskytoi/repRCHrs/analysis/results/6b_iSEG05_mtpi_WS_ta",
layer ="iSEG_mtpi_WS_ta", driver = "ESRI Shapefile", verbose = TRUE, overwrite_layer = TRUE)
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