inst/extdata/examples/lucC_example_Sinop.R
In ammaciel/sits.LUC.Calculus: Land Use Change Calculus2
library(lucCalculus)
#----------------------------
# 1- Open idividual images and create a RasterBrick with each one and metadata with SITS
#----------------------------
# create a RasterBrick from individual raster saved previously
lucC_create_RasterBrick(path_open_GeoTIFFs = "inst/extdata/raster/rasterSinop", path_save_RasterBrick = "inst/extdata/raster")
# ------------- define variables to use in sits -------------
# open files
file <- c("inst/extdata/raster/rasterSinop.tif")
file
# create timeline with classified data from SVM method
timeline <- lubridate::as_date(c("2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
timeline
#library(sits)
# create a RasterBrick metadata file based on the information about the files
raster.tb <- sits::sits_coverage(files = file, name = "Sinop", timeline = timeline, bands = "ndvi")
raster.tb
# new variable
rb_sits <- raster.tb$r_objs[[1]][[1]]
rb_sits
# ------------- define variables to plot raster -------------
# original label - see QML file, same order
label <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture1", "Pasture2", "Pasture3", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask"))
label
# colors
colors_1 <- c("#b3cc33", "#d1f0f7", "#8ddbec", "#228b22", "#afe3c8", "#7ecfa4", "#64b376", "#e1cdb6", "#b6a896", "#b69872", "#b68549", "#9c6f38", "#e5c6a0", "#e5a352", "#0000ff", "#3a3aff")
colors_1
# plot raster brick
lucC_plot_raster(raster_obj = rb_sits,
timeline = timeline, label = label,
custom_palette = TRUE, RGB_color = colors_1, plot_ncol = 6)
#----------------------------
# 2- Discover Secondary Vegetation - LUC Calculus
#----------------------------
# 1. Verify if forest RECUR ins econd interval
system.time(
forest_recur <- lucC_pred_recur(raster_obj = rb_sits, raster_class = "Forest",
time_interval1 = c("2001-09-01","2001-09-01"),
time_interval2 = c("2002-09-01","2016-09-01"),
label = label, timeline = timeline)
)
head(forest_recur)
# 2. Verify if occur forest EVOLVE from a different class in 2001
forest_evolve <- NULL
# classes without Forest
classes <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Pasture1", "Pasture2", "Pasture3", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask"))
# percor all classes
system.time(
for(i in seq_along(classes)){
print(classes[i])
temp <- lucC_pred_evolve(raster_obj = rb_sits, raster_class1 = classes[i],
time_interval1 = c("2001-09-01","2001-09-01"), relation_interval1 = "equals",
raster_class2 = "Forest",
time_interval2 = c("2002-09-01","2016-09-01"), relation_interval2 = "contains",
label = label, timeline = timeline)
forest_evolve <- lucC_merge(forest_evolve, temp)
}
)
head(forest_evolve)
# 3. Merge both forest_recur and forest_evolve datas
forest_secondary <- lucC_merge(forest_evolve, forest_recur)
head(forest_secondary)
lucC_plot_bar_events(forest_secondary, custom_palette = FALSE, pixel_resolution = 232, legend_text = "Legend:")
# 4. Remove column 2001 because it' is not used to replace pixels's only support column
forest_sec <- lucC_remove_columns(data_mtx = forest_secondary, name_columns = c("2001-09-01"))
head(forest_sec)
lucC_plot_bar_events(forest_sec, custom_palette = FALSE, pixel_resolution = 232, legend_text = "Legend:")
# 5. Plot secondary vegetation over raster without column 2001 because it' is not used to replace pixels's only support column
lucC_plot_raster_result(raster_obj = rb_sits,
data_mtx = forest_sec,
timeline = timeline,
label = label, custom_palette = TRUE,
RGB_color = colors_1, relabel = FALSE, shape_point = ".")
# create images output
rb_sits_VS <- lucC_raster_result(raster_obj = rb_sits,
data_mtx = forest_sec, # without 2001
timeline = timeline, label = label) # new pixel value
lucC_save_GeoTIFF(raster_obj = rb_sits,
data_mtx = rb_sits_VS, path_raster_folder = "~/Desktop/N_Sinop", as_RasterBrick = FALSE)
#----------------------------
# 3- Update original raster to add new pixel value
#----------------------------
rm(forest_evolve, forest_recur, forest_secondary, raster.tb)
gc()
# 1. update original RasterBrick with new class
rb_sits_new <- lucC_update_raster(raster_obj = rb_sits,
data_mtx = forest_sec, # without 2001
timeline = timeline,
class_to_replace = "Forest", # only class Forest
new_pixel_value = 17) # new pixel value
head(rb_sits_new)
lucC_plot_bar_events(data_mtx = rb_sits_new, pixel_resolution = 232, custom_palette = FALSE)
# 2. save the update matrix as GeoTIFF images
lucC_save_GeoTIFF(raster_obj = rb_sits,
data_mtx = rb_sits_new,
path_raster_folder = "inst/extdata/raster/rasterSinopSecVeg", as_RasterBrick = FALSE)
#===================================================================================================
#----------------------------
# 4- Open idividual images reclassified and create a RasterBrick with each one and metadata ith SITS
#----------------------------
# create a RasterBrick from individual raster saved previously
lucC_create_RasterBrick(path_open_GeoTIFFs = "inst/extdata/raster/rasterSinopSecVeg", path_save_RasterBrick = "inst/extdata/raster")
# ------------- define variables to use in sits -------------
# open files with new pixel secondary vegetation
file <- c("inst/extdata/raster/rasterSinopSecVeg.tif")
file
# create timeline with classified data from SVM method
timeline <- lubridate::as_date(c("2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
timeline
#library(sits)
# create a RasterBrick metadata file based on the information about the files
raster.tb <- sits::sits_coverage(files = file, name = "SinopVegSec", timeline = timeline, bands = "ndvi")
raster.tb
# new variable
rb_sits2 <- raster.tb$r_objs[[1]][[1]]
rb_sits2
# new class Seconary vegetation
label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture1", "Pasture2", "Pasture3", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask", "Secondary_vegetation"))
label2
# colors
colors_2 <- c("#b3cc33", "#d1f0f7", "#8ddbec", "#228b22", "#afe3c8", "#7ecfa4", "#64b376", "#e1cdb6", "#b6a896", "#b69872", "#b68549", "#9c6f38", "#e5c6a0", "#e5a352", "#0000ff", "#3a3aff", "red")
# plot raster brick
lucC_plot_raster(raster_obj = rb_sits2,
timeline = timeline, label = label2,
custom_palette = TRUE, RGB_color = colors_2, plot_ncol = 6)
#----------------------------
# 5- Discover Forest and Secondary vegetation - LUC Calculus
#----------------------------
secondary.mtx <- lucC_pred_holds(raster_obj = rb_sits2, raster_class = "Secondary_vegetation",
time_interval = c("2001-09-01","2016-09-01"),
relation_interval = "contains", label = label2, timeline = timeline)
head(secondary.mtx)
forest.mtx <- lucC_pred_holds(raster_obj = rb_sits2, raster_class = "Forest",
time_interval = c("2001-09-01","2016-09-01"),
relation_interval = "contains", label = label2, timeline = timeline)
head(forest.mtx)
Forest_secondary.mtx <- lucC_merge(secondary.mtx, forest.mtx)
head(Forest_secondary.mtx)
# plot results
lucC_plot_bar_events(data_mtx = Forest_secondary.mtx,
pixel_resolution = 232, custom_palette = FALSE, side_by_side = TRUE)
# Compute values
measuresFor_Sec <- lucC_result_measures(data_mtx = Forest_secondary.mtx, pixel_resolution = 232)
measuresFor_Sec
# save raster
new_raster <- lucC_update_raster_result(raster_obj = rb_sits2, data_mtx = Forest_secondary.mtx, timeline = timeline, label = label2)
# save the update matrix as GeoTIFF images
lucC_save_GeoTIFF(raster_obj = rb_sits2,
data_mtx = new_raster,
path_raster_folder = "inst/extdata/raster/rasterSinopResultForestVegSec", as_RasterBrick = FALSE)
# save the update matrix as GeoTIFF images
lucC_plot_raster(raster_obj = rb_sits2, timeline = timeline, label = label2,
plot_ncol = 6, custom_palette = TRUE, RGB_color = colors_2)
#----------------------------
# 6- Discover Land use transitions - LUC Calculus
#----------------------------
label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture", "Pasture", "Pasture", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask", "Secondary_vegetation"))
label2
# create timeline with classified data from SVM method
timeline <- lubridate::as_date(c("2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
timeline
class1 <- c("Forest")
classes <- c("Pasture", "Secondary_vegetation") #
direct_transi.df <- NULL
# along of all classes
system.time(
for(x in 2:length(timeline)){
t_1 <- timeline[x-1]
t_2 <- timeline[x]
cat(paste0(t_1, ", ", t_2, sep = ""), "\n")
# moves across all classes
for(i in seq_along(classes)){
cat(classes[i], collapse = " ")
temp <- lucC_pred_convert(raster_obj = rb_sits2, raster_class1 = class1,
time_interval1 = c(t_1,t_1), relation_interval1 = "equals",
raster_class2 = classes[i],
time_interval2 = c(t_2,t_2), relation_interval2 = "equals",
label = label2, timeline = timeline)
if (!is.null(temp)) {
temp <- lucC_remove_columns(data_mtx = temp, name_columns = as.character(t_1))
} else{
temp <- temp
}
direct_transi.df <- lucC_merge(direct_transi.df, temp)
}
cat("\n")
}
)
Forest_Pasture <- direct_transi.df
head(Forest_Pasture)
Forest_Pasture[ Forest_Pasture == "Pasture" ] <- "Forest_Pasture"
head(Forest_Pasture)
# plot results
lucC_plot_bar_events(data_mtx = Forest_Pasture,
pixel_resolution = 232, custom_palette = FALSE, side_by_side = FALSE)
# Compute values
measures_Forest_Pasture <- lucC_result_measures(data_mtx = Forest_Pasture, pixel_resolution = 232)
measures_Forest_Pasture
#---------------------------------
# 7- Soybean Moratotium - LUC Calculus
# - Pasture to soybean (deforested before 2006)
#---------------------------------
# 1. All locations (pixels) that are soybean in a year?
# 2. In the past this location (pixel) was pasture in any time?
# 3. This location (pixel) was deforested before 2006? Soy Moratorium.
#
# o = geo-objects, the own df_input data.frame
#---------------------------------
#label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture1", "Pasture2", "Pasture3", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask", "Secondary_vegetation"))
label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture", "Pasture", "Pasture", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Water", "Water", "Secondary_vegetation"))
label2
# create timeline with classified data from SVM method
timeline2 <- lubridate::as_date(c("2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
# soy moratorium
timeline1 <- lubridate::as_date(c("2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01"))
# intereting classes
soybean_before.df <- NULL
raster.data <- rb_sits2
# along of all classes
system.time(
for(x in 2:length(timeline2)){
#x = 7
t_1 <- timeline1[x-1]
t_2 <- timeline2[x]
cat(paste0(t_1, ", ", t_2, sep = ""), "\n")
soybean.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Soybean",
time_interval = c(t_2,t_2),
relation_interval = "equals", label = label2, timeline = timeline)
pasture.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Pasture",
time_interval = c(timeline1[1],t_1),
relation_interval = "contains", label = label2, timeline = timeline)
forest.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Forest",
time_interval = c(timeline1[1],t_1),
relation_interval = "contains", label = label2, timeline = timeline)
fores_past.temp <- lucC_relation_occurs(pasture.df, forest.df)
temp <- lucC_relation_precedes(soybean.df, fores_past.temp)
if (!is.null(temp)) {
tempF <- lucC_select_columns(data_mtx = temp, name_columns = t_2)
} else {
tempF <- NULL
}
soybean_before.df <- lucC_merge(soybean_before.df, tempF)
}
)
#Soybean_Before_2006 <- soybean_before.df
#Soybean_Before_2006[ Soybean_Before_2006 == "Soybean" ] <- "Soybean_Before_2006"
#head(Soybean_Before_2006)
# remove(temp, soybean_before.df, forest.df, pasture.df, soybean.df, fores_past.temp, tempF, t_1, t_2, x)
# plot results
lucC_plot_bar_events(data_mtx = soybean_before.df, pixel_resolution = 231.656, custom_palette = FALSE, side_by_side = TRUE)
## Compute values
# Soybean_Before_2006.tb <- lucC_result_measures(data_mtx = Soybean_Before_2006, pixel_resolution = 231.656)
# Soybean_Before_2006.tb
#
# # plot
# colors_3 <- c("#b3cc33", "#d1f0f7", "#8ddbec", "#228b22", "#7ecfa4", "#b6a896", "#3a3aff", "red", "#b6a896", "#b69872", "#b68549", "#9c6f38", "#e5c6a0", "#e5a352", "#0000ff", "#3a3aff", "red")
#
# lucC_plot_raster(raster_obj = raster.data, timeline = timeline,
# label = label2, custom_palette = TRUE,
# RGB_color = colors_3, relabel = FALSE, plot_ncol = 6)
#
# lucC_plot_raster_result(raster_obj = raster.data, data_mtx = Soybean_Before_2006, timeline = timeline,
# label = label2, custom_palette = TRUE,
# RGB_color = colors_3, relabel = FALSE, plot_ncol = 6, shape_point = ".")
#
#---------------------------------
# 8 - Soybean Moratotium - LUC Calculus
# - Pasture to soybean (deforested after 2006)
#---------------------------------
# 1. All locations (pixels) that are soybean in a year?
# 2. In the past this location (pixel) was pasture in any time?
# 3. This location (pixel) was deforested after 2006? Soy Moratorium.
#
# o = geo-objects, the own df_input data.frame
#---------------------------------
#label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture1", "Pasture2", "Pasture3", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask", "Secondary_vegetation"))
label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture", "Pasture", "Pasture", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Water", "Water", "Secondary_vegetation"))
label2
# create timeline with classified data from SVM method
timeline2 <- lubridate::as_date(c("2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
# soy moratorium
timeline1 <- lubridate::as_date(c("2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
# intereting classes
soybean_after.df <- NULL
raster.data <- rb_sits2
# along of all classes
system.time(
for(x in 2:length(timeline2)){
# x = 3
t_1 <- timeline1[x-1]
t_2 <- timeline2[x]
cat(paste0(t_1, ", ", t_2, sep = ""), "\n")
soybean.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Soybean",
time_interval = c(t_2,t_2),
relation_interval = "equals", label = label2, timeline = timeline)
pasture.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Pasture",
time_interval = c(timeline1[1],t_1),
relation_interval = "contains", label = label2, timeline = timeline)
forest.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Forest",
time_interval = c(timeline1[1],t_1),
relation_interval = "contains", label = label2, timeline = timeline)
fores_past.temp <- lucC_relation_occurs(pasture.df, forest.df)
temp <- lucC_relation_precedes(soybean.df, fores_past.temp)
if (!is.null(temp)) {
tempF <- lucC_select_columns(data_mtx = temp, name_columns = t_2)
} else {
tempF <- NULL
}
soybean_after.df <- lucC_merge(soybean_after.df, tempF)
}
)
#Soybean_After_2006 <- soybean_after.df
#Soybean_After_2006[ Soybean_After_2006 == "Soybean" ] <- "Soybean_After_2006"
#head(Soybean_After_2006)
# remove(temp, soybean_before.df, forest.df, pasture.df, soybean.df, fores_past.temp, tempF, t_1, t_2, x)
# plot results
lucC_plot_bar_events(data_mtx = soybean_after.df, pixel_resolution = 231.656, custom_palette = FALSE, side_by_side = TRUE)
# # Compute values
# Soybean_After_2006.tb <- lucC_result_measures(data_mtx = Soybean_After_2006, pixel_resolution = 231.656)
# Soybean_After_2006.tb
#
# # plot
# colors_3 <- c("#b3cc33", "#d1f0f7", "#8ddbec", "#228b22", "#7ecfa4", "#b6a896", "#3a3aff", "red", "#b6a896", "#b69872", "#b68549", "#9c6f38", "#e5c6a0", "#e5a352", "#0000ff", "#3a3aff", "red")
#
# lucC_plot_raster(raster_obj = raster.data, timeline = timeline,
# label = label2, custom_palette = TRUE,
# RGB_color = colors_3, relabel = FALSE, plot_ncol = 6)
#
# lucC_plot_raster_result(raster_obj = raster.data, data_mtx = Soybean_After_2006, timeline = timeline,
# label = label2, custom_palette = TRUE,
# RGB_color = colors_3, relabel = FALSE, plot_ncol = 6, shape_point = ".")
#--------------------------------------------------------------
Soy <- lucC_merge(Soybean_Before_2006, Soybean_After_2006)
head(Soy)
lucC_plot_bar_events(data_mtx = Soy, pixel_resolution = 231.656, custom_palette = FALSE, side_by_side = TRUE)
.
.
#------------------------------------
# explit a raster by blocks
#------------------------------------
blocks <- lucC_create_blocks(rb_sits2, number_cells = 400)
blocks
lucC_plot_raster(raster_obj = blocks[[1]], timeline = timeline,
label = label2, custom_palette = TRUE,
RGB_color = colors_3, relabel = FALSE, plot_ncol = 6)
lucC_plot_raster(raster_obj = blocks[[2]], timeline = timeline,
label = label2, custom_palette = TRUE,
RGB_color = colors_3, relabel = FALSE, plot_ncol = 6)
ammaciel/sits.LUC.Calculus documentation built on May 3, 2019, 9:36 p.m.
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library(lucCalculus)
#----------------------------
# 1- Open idividual images and create a RasterBrick with each one and metadata with SITS
#----------------------------
# create a RasterBrick from individual raster saved previously
lucC_create_RasterBrick(path_open_GeoTIFFs = "inst/extdata/raster/rasterSinop", path_save_RasterBrick = "inst/extdata/raster")
# ------------- define variables to use in sits -------------
# open files
file <- c("inst/extdata/raster/rasterSinop.tif")
file
# create timeline with classified data from SVM method
timeline <- lubridate::as_date(c("2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
timeline
#library(sits)
# create a RasterBrick metadata file based on the information about the files
raster.tb <- sits::sits_coverage(files = file, name = "Sinop", timeline = timeline, bands = "ndvi")
raster.tb
# new variable
rb_sits <- raster.tb$r_objs[[1]][[1]]
rb_sits
# ------------- define variables to plot raster -------------
# original label - see QML file, same order
label <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture1", "Pasture2", "Pasture3", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask"))
label
# colors
colors_1 <- c("#b3cc33", "#d1f0f7", "#8ddbec", "#228b22", "#afe3c8", "#7ecfa4", "#64b376", "#e1cdb6", "#b6a896", "#b69872", "#b68549", "#9c6f38", "#e5c6a0", "#e5a352", "#0000ff", "#3a3aff")
colors_1
# plot raster brick
lucC_plot_raster(raster_obj = rb_sits,
timeline = timeline, label = label,
custom_palette = TRUE, RGB_color = colors_1, plot_ncol = 6)
#----------------------------
# 2- Discover Secondary Vegetation - LUC Calculus
#----------------------------
# 1. Verify if forest RECUR ins econd interval
system.time(
forest_recur <- lucC_pred_recur(raster_obj = rb_sits, raster_class = "Forest",
time_interval1 = c("2001-09-01","2001-09-01"),
time_interval2 = c("2002-09-01","2016-09-01"),
label = label, timeline = timeline)
)
head(forest_recur)
# 2. Verify if occur forest EVOLVE from a different class in 2001
forest_evolve <- NULL
# classes without Forest
classes <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Pasture1", "Pasture2", "Pasture3", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask"))
# percor all classes
system.time(
for(i in seq_along(classes)){
print(classes[i])
temp <- lucC_pred_evolve(raster_obj = rb_sits, raster_class1 = classes[i],
time_interval1 = c("2001-09-01","2001-09-01"), relation_interval1 = "equals",
raster_class2 = "Forest",
time_interval2 = c("2002-09-01","2016-09-01"), relation_interval2 = "contains",
label = label, timeline = timeline)
forest_evolve <- lucC_merge(forest_evolve, temp)
}
)
head(forest_evolve)
# 3. Merge both forest_recur and forest_evolve datas
forest_secondary <- lucC_merge(forest_evolve, forest_recur)
head(forest_secondary)
lucC_plot_bar_events(forest_secondary, custom_palette = FALSE, pixel_resolution = 232, legend_text = "Legend:")
# 4. Remove column 2001 because it' is not used to replace pixels's only support column
forest_sec <- lucC_remove_columns(data_mtx = forest_secondary, name_columns = c("2001-09-01"))
head(forest_sec)
lucC_plot_bar_events(forest_sec, custom_palette = FALSE, pixel_resolution = 232, legend_text = "Legend:")
# 5. Plot secondary vegetation over raster without column 2001 because it' is not used to replace pixels's only support column
lucC_plot_raster_result(raster_obj = rb_sits,
data_mtx = forest_sec,
timeline = timeline,
label = label, custom_palette = TRUE,
RGB_color = colors_1, relabel = FALSE, shape_point = ".")
# create images output
rb_sits_VS <- lucC_raster_result(raster_obj = rb_sits,
data_mtx = forest_sec, # without 2001
timeline = timeline, label = label) # new pixel value
lucC_save_GeoTIFF(raster_obj = rb_sits,
data_mtx = rb_sits_VS, path_raster_folder = "~/Desktop/N_Sinop", as_RasterBrick = FALSE)
#----------------------------
# 3- Update original raster to add new pixel value
#----------------------------
rm(forest_evolve, forest_recur, forest_secondary, raster.tb)
gc()
# 1. update original RasterBrick with new class
rb_sits_new <- lucC_update_raster(raster_obj = rb_sits,
data_mtx = forest_sec, # without 2001
timeline = timeline,
class_to_replace = "Forest", # only class Forest
new_pixel_value = 17) # new pixel value
head(rb_sits_new)
lucC_plot_bar_events(data_mtx = rb_sits_new, pixel_resolution = 232, custom_palette = FALSE)
# 2. save the update matrix as GeoTIFF images
lucC_save_GeoTIFF(raster_obj = rb_sits,
data_mtx = rb_sits_new,
path_raster_folder = "inst/extdata/raster/rasterSinopSecVeg", as_RasterBrick = FALSE)
#===================================================================================================
#----------------------------
# 4- Open idividual images reclassified and create a RasterBrick with each one and metadata ith SITS
#----------------------------
# create a RasterBrick from individual raster saved previously
lucC_create_RasterBrick(path_open_GeoTIFFs = "inst/extdata/raster/rasterSinopSecVeg", path_save_RasterBrick = "inst/extdata/raster")
# ------------- define variables to use in sits -------------
# open files with new pixel secondary vegetation
file <- c("inst/extdata/raster/rasterSinopSecVeg.tif")
file
# create timeline with classified data from SVM method
timeline <- lubridate::as_date(c("2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
timeline
#library(sits)
# create a RasterBrick metadata file based on the information about the files
raster.tb <- sits::sits_coverage(files = file, name = "SinopVegSec", timeline = timeline, bands = "ndvi")
raster.tb
# new variable
rb_sits2 <- raster.tb$r_objs[[1]][[1]]
rb_sits2
# new class Seconary vegetation
label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture1", "Pasture2", "Pasture3", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask", "Secondary_vegetation"))
label2
# colors
colors_2 <- c("#b3cc33", "#d1f0f7", "#8ddbec", "#228b22", "#afe3c8", "#7ecfa4", "#64b376", "#e1cdb6", "#b6a896", "#b69872", "#b68549", "#9c6f38", "#e5c6a0", "#e5a352", "#0000ff", "#3a3aff", "red")
# plot raster brick
lucC_plot_raster(raster_obj = rb_sits2,
timeline = timeline, label = label2,
custom_palette = TRUE, RGB_color = colors_2, plot_ncol = 6)
#----------------------------
# 5- Discover Forest and Secondary vegetation - LUC Calculus
#----------------------------
secondary.mtx <- lucC_pred_holds(raster_obj = rb_sits2, raster_class = "Secondary_vegetation",
time_interval = c("2001-09-01","2016-09-01"),
relation_interval = "contains", label = label2, timeline = timeline)
head(secondary.mtx)
forest.mtx <- lucC_pred_holds(raster_obj = rb_sits2, raster_class = "Forest",
time_interval = c("2001-09-01","2016-09-01"),
relation_interval = "contains", label = label2, timeline = timeline)
head(forest.mtx)
Forest_secondary.mtx <- lucC_merge(secondary.mtx, forest.mtx)
head(Forest_secondary.mtx)
# plot results
lucC_plot_bar_events(data_mtx = Forest_secondary.mtx,
pixel_resolution = 232, custom_palette = FALSE, side_by_side = TRUE)
# Compute values
measuresFor_Sec <- lucC_result_measures(data_mtx = Forest_secondary.mtx, pixel_resolution = 232)
measuresFor_Sec
# save raster
new_raster <- lucC_update_raster_result(raster_obj = rb_sits2, data_mtx = Forest_secondary.mtx, timeline = timeline, label = label2)
# save the update matrix as GeoTIFF images
lucC_save_GeoTIFF(raster_obj = rb_sits2,
data_mtx = new_raster,
path_raster_folder = "inst/extdata/raster/rasterSinopResultForestVegSec", as_RasterBrick = FALSE)
# save the update matrix as GeoTIFF images
lucC_plot_raster(raster_obj = rb_sits2, timeline = timeline, label = label2,
plot_ncol = 6, custom_palette = TRUE, RGB_color = colors_2)
#----------------------------
# 6- Discover Land use transitions - LUC Calculus
#----------------------------
label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture", "Pasture", "Pasture", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask", "Secondary_vegetation"))
label2
# create timeline with classified data from SVM method
timeline <- lubridate::as_date(c("2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
timeline
class1 <- c("Forest")
classes <- c("Pasture", "Secondary_vegetation") #
direct_transi.df <- NULL
# along of all classes
system.time(
for(x in 2:length(timeline)){
t_1 <- timeline[x-1]
t_2 <- timeline[x]
cat(paste0(t_1, ", ", t_2, sep = ""), "\n")
# moves across all classes
for(i in seq_along(classes)){
cat(classes[i], collapse = " ")
temp <- lucC_pred_convert(raster_obj = rb_sits2, raster_class1 = class1,
time_interval1 = c(t_1,t_1), relation_interval1 = "equals",
raster_class2 = classes[i],
time_interval2 = c(t_2,t_2), relation_interval2 = "equals",
label = label2, timeline = timeline)
if (!is.null(temp)) {
temp <- lucC_remove_columns(data_mtx = temp, name_columns = as.character(t_1))
} else{
temp <- temp
}
direct_transi.df <- lucC_merge(direct_transi.df, temp)
}
cat("\n")
}
)
Forest_Pasture <- direct_transi.df
head(Forest_Pasture)
Forest_Pasture[ Forest_Pasture == "Pasture" ] <- "Forest_Pasture"
head(Forest_Pasture)
# plot results
lucC_plot_bar_events(data_mtx = Forest_Pasture,
pixel_resolution = 232, custom_palette = FALSE, side_by_side = FALSE)
# Compute values
measures_Forest_Pasture <- lucC_result_measures(data_mtx = Forest_Pasture, pixel_resolution = 232)
measures_Forest_Pasture
#---------------------------------
# 7- Soybean Moratotium - LUC Calculus
# - Pasture to soybean (deforested before 2006)
#---------------------------------
# 1. All locations (pixels) that are soybean in a year?
# 2. In the past this location (pixel) was pasture in any time?
# 3. This location (pixel) was deforested before 2006? Soy Moratorium.
#
# o = geo-objects, the own df_input data.frame
#---------------------------------
#label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture1", "Pasture2", "Pasture3", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask", "Secondary_vegetation"))
label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture", "Pasture", "Pasture", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Water", "Water", "Secondary_vegetation"))
label2
# create timeline with classified data from SVM method
timeline2 <- lubridate::as_date(c("2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
# soy moratorium
timeline1 <- lubridate::as_date(c("2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01", "2006-09-01"))
# intereting classes
soybean_before.df <- NULL
raster.data <- rb_sits2
# along of all classes
system.time(
for(x in 2:length(timeline2)){
#x = 7
t_1 <- timeline1[x-1]
t_2 <- timeline2[x]
cat(paste0(t_1, ", ", t_2, sep = ""), "\n")
soybean.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Soybean",
time_interval = c(t_2,t_2),
relation_interval = "equals", label = label2, timeline = timeline)
pasture.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Pasture",
time_interval = c(timeline1[1],t_1),
relation_interval = "contains", label = label2, timeline = timeline)
forest.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Forest",
time_interval = c(timeline1[1],t_1),
relation_interval = "contains", label = label2, timeline = timeline)
fores_past.temp <- lucC_relation_occurs(pasture.df, forest.df)
temp <- lucC_relation_precedes(soybean.df, fores_past.temp)
if (!is.null(temp)) {
tempF <- lucC_select_columns(data_mtx = temp, name_columns = t_2)
} else {
tempF <- NULL
}
soybean_before.df <- lucC_merge(soybean_before.df, tempF)
}
)
#Soybean_Before_2006 <- soybean_before.df
#Soybean_Before_2006[ Soybean_Before_2006 == "Soybean" ] <- "Soybean_Before_2006"
#head(Soybean_Before_2006)
# remove(temp, soybean_before.df, forest.df, pasture.df, soybean.df, fores_past.temp, tempF, t_1, t_2, x)
# plot results
lucC_plot_bar_events(data_mtx = soybean_before.df, pixel_resolution = 231.656, custom_palette = FALSE, side_by_side = TRUE)
## Compute values
# Soybean_Before_2006.tb <- lucC_result_measures(data_mtx = Soybean_Before_2006, pixel_resolution = 231.656)
# Soybean_Before_2006.tb
#
# # plot
# colors_3 <- c("#b3cc33", "#d1f0f7", "#8ddbec", "#228b22", "#7ecfa4", "#b6a896", "#3a3aff", "red", "#b6a896", "#b69872", "#b68549", "#9c6f38", "#e5c6a0", "#e5a352", "#0000ff", "#3a3aff", "red")
#
# lucC_plot_raster(raster_obj = raster.data, timeline = timeline,
# label = label2, custom_palette = TRUE,
# RGB_color = colors_3, relabel = FALSE, plot_ncol = 6)
#
# lucC_plot_raster_result(raster_obj = raster.data, data_mtx = Soybean_Before_2006, timeline = timeline,
# label = label2, custom_palette = TRUE,
# RGB_color = colors_3, relabel = FALSE, plot_ncol = 6, shape_point = ".")
#
#---------------------------------
# 8 - Soybean Moratotium - LUC Calculus
# - Pasture to soybean (deforested after 2006)
#---------------------------------
# 1. All locations (pixels) that are soybean in a year?
# 2. In the past this location (pixel) was pasture in any time?
# 3. This location (pixel) was deforested after 2006? Soy Moratorium.
#
# o = geo-objects, the own df_input data.frame
#---------------------------------
#label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture1", "Pasture2", "Pasture3", "Soybean_Cotton", "Soybean_Crop1", "Soybean_Crop2", "Soybean_Crop3", "Soybean_Crop4", "Soybean_Fallow1", "Soybean_Fallow2", "Water", "Water_mask", "Secondary_vegetation"))
label2 <- as.character(c("Cerrado", "Crop_Cotton", "Fallow_Cotton", "Forest", "Pasture", "Pasture", "Pasture", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Soybean", "Water", "Water", "Secondary_vegetation"))
label2
# create timeline with classified data from SVM method
timeline2 <- lubridate::as_date(c("2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
# soy moratorium
timeline1 <- lubridate::as_date(c("2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01", "2011-09-01", "2012-09-01", "2013-09-01", "2014-09-01", "2015-09-01", "2016-09-01"))
# intereting classes
soybean_after.df <- NULL
raster.data <- rb_sits2
# along of all classes
system.time(
for(x in 2:length(timeline2)){
# x = 3
t_1 <- timeline1[x-1]
t_2 <- timeline2[x]
cat(paste0(t_1, ", ", t_2, sep = ""), "\n")
soybean.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Soybean",
time_interval = c(t_2,t_2),
relation_interval = "equals", label = label2, timeline = timeline)
pasture.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Pasture",
time_interval = c(timeline1[1],t_1),
relation_interval = "contains", label = label2, timeline = timeline)
forest.df <- lucC_pred_holds(raster_obj = raster.data, raster_class = "Forest",
time_interval = c(timeline1[1],t_1),
relation_interval = "contains", label = label2, timeline = timeline)
fores_past.temp <- lucC_relation_occurs(pasture.df, forest.df)
temp <- lucC_relation_precedes(soybean.df, fores_past.temp)
if (!is.null(temp)) {
tempF <- lucC_select_columns(data_mtx = temp, name_columns = t_2)
} else {
tempF <- NULL
}
soybean_after.df <- lucC_merge(soybean_after.df, tempF)
}
)
#Soybean_After_2006 <- soybean_after.df
#Soybean_After_2006[ Soybean_After_2006 == "Soybean" ] <- "Soybean_After_2006"
#head(Soybean_After_2006)
# remove(temp, soybean_before.df, forest.df, pasture.df, soybean.df, fores_past.temp, tempF, t_1, t_2, x)
# plot results
lucC_plot_bar_events(data_mtx = soybean_after.df, pixel_resolution = 231.656, custom_palette = FALSE, side_by_side = TRUE)
# # Compute values
# Soybean_After_2006.tb <- lucC_result_measures(data_mtx = Soybean_After_2006, pixel_resolution = 231.656)
# Soybean_After_2006.tb
#
# # plot
# colors_3 <- c("#b3cc33", "#d1f0f7", "#8ddbec", "#228b22", "#7ecfa4", "#b6a896", "#3a3aff", "red", "#b6a896", "#b69872", "#b68549", "#9c6f38", "#e5c6a0", "#e5a352", "#0000ff", "#3a3aff", "red")
#
# lucC_plot_raster(raster_obj = raster.data, timeline = timeline,
# label = label2, custom_palette = TRUE,
# RGB_color = colors_3, relabel = FALSE, plot_ncol = 6)
#
# lucC_plot_raster_result(raster_obj = raster.data, data_mtx = Soybean_After_2006, timeline = timeline,
# label = label2, custom_palette = TRUE,
# RGB_color = colors_3, relabel = FALSE, plot_ncol = 6, shape_point = ".")
#--------------------------------------------------------------
Soy <- lucC_merge(Soybean_Before_2006, Soybean_After_2006)
head(Soy)
lucC_plot_bar_events(data_mtx = Soy, pixel_resolution = 231.656, custom_palette = FALSE, side_by_side = TRUE)
.
.
#------------------------------------
# explit a raster by blocks
#------------------------------------
blocks <- lucC_create_blocks(rb_sits2, number_cells = 400)
blocks
lucC_plot_raster(raster_obj = blocks[[1]], timeline = timeline,
label = label2, custom_palette = TRUE,
RGB_color = colors_3, relabel = FALSE, plot_ncol = 6)
lucC_plot_raster(raster_obj = blocks[[2]], timeline = timeline,
label = label2, custom_palette = TRUE,
RGB_color = colors_3, relabel = FALSE, plot_ncol = 6)
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