README.md
In FSantosCodes/SATAtools: Tools for SATA (Sistema de Alertas Tempranas del Sistema Nacional de Monitoreo de Bosques del Ecuador)
SATAtools
Tools for SATA (Sistema de Alertas Tempranas del Sistema Nacional de Monitoreo de Bosques del Ecuador)
Instalación
Para instalar el SATAtools, es necesario una version del R >=3.5.1 (se asume que esta usando el R desde RStudio), la librería 'devtools' de R (si no la tiene) y el OSGEO4W. Para hacerlo siga los pasos:
(1) Instalación del OSGEO4W (Descargue y ejecute el instalador. Mantenga la configuración de la instalación por defecto):
#install 32 bits version for windows:
http://download.osgeo.org/osgeo4w/osgeo4w-setup-x86.exe
#install 64 bits version for windows:
http://download.osgeo.org/osgeo4w/osgeo4w-setup-x86_64.exe
(2) Revisar que la version del R sea >=3.5.1 (caso contrario tiene que actualizarla):
#execute in Rstudio console:
> version
_
platform x86_64-w64-mingw32
arch x86_64
os mingw32
system x86_64, mingw32
status
major 3
minor 5.2
year 2018
month 12
day 20
svn rev 75870
language R
version.string R version 3.5.2 (2018-12-20) <--- ok
nickname Eggshell Igloo
(3) e instalar el 'devtools' y el 'SATAtools':
#install devtools
install.packages(devtools)
library(devtools)
#install SATAtools
install_github('FSantosCodes/SATAtools')
library(SATAtools)
Los ejemplos que se muestran a continuación se basan en los resultados de la descarga de los algoritmos corridos en el GEE. El pos-procesamiento de estos son una muestra de los potenciales usos de esta herramienta. Las rutinas de GEE se muestran a continuación:
- Landsat 4-8 compositing
https://code.earthengine.google.com/6a2c42cdbebe701dd8f3b31287da29c7
- Sentinel 1 compositing
https://code.earthengine.google.com/777ef0b8a4aaa638b4d923fc45f04efd
- Sentinel 2 compositing
https://code.earthengine.google.com/612a604f0baf41581d225f4e8b2b06c0
Filtrado de alertas
Las coberturas usadas en estos ejemplos usan las alertas de deforestacion del SATA:
##### FILTER DEFORESTATION #####
#Defining variables from spatial units shapefile
spatial_units_shp <- "C:/DATA/GAF/demo/shp/unidades_espaciales/provincias.shp"
column_units_name <- "dpa_despro" #case sensitive
#Defining variables from deforestation shapefile (output from SATA or 'detect_breakpoints' function)
deforestation_shp <- "C:/DATA/GAF/demo/shp/alertas/deforestation_2016_2017.shp"
column_date_name <- "inicio" #case sensitive
column_area_name <- "area" #case sensitive
min_area <- NA #minimun area to filter (same units as area)
#Defining variables of ouputs
size_jpg <- c(40,20) #first value defines width and second height
output_folder <- "C:/DATA/GAF/demo/estadisticas"
#running the function
filter_deforestation_alerts(spatial_units_shp = spatial_units_shp,
column_units_name = column_units_name,
deforestation_shp = deforestation_shp,
column_date_name = column_date_name,
column_area_name = column_area_name,
min_area = min_area,
size_jpg = size_jpg,
output_folder = output_folder)
aquí se lo hace con las alertas de incendios del MODIS:
#Defining variables from spatial units shapefile
spatial_units_shp <- "C:/DATA/GAF/demo/shp/unidades_espaciales/provincias.shp"
column_units_name <- "dpa_despro" #case sensitive
#Defining variables from MODIS shapefile
MODIS_shp <- "C:/DATA/GAF/demo/shp/alertas/pts_calor_MODIS_30d.shp"
column_date_name <- "acq_date" #case sensitive
column_frp_name <- "frp" #case sensitive
column_confidence_name <- "confidence" #case sensitive
min_frp <- 10 #same units as frp
min_confidence <- 50 #same units as confidence
#Defining variables of ouputs
size_jpg <- c(40,20) #first value defines width and second height
output_folder <- "C:/DATA/GAF/demo/estadisticas"
#running the function
filter_MODIS_alerts(spatial_units_shp = spatial_units_shp,
column_units_name = column_units_name,
MODIS_shp = MODIS_shp,
column_date_name = column_date_name,
column_frp_name = column_frp_name,
column_confidence_name = column_confidence_name,
min_frp = min_frp,
min_confidence = min_confidence,
size_jpg = size_jpg,
output_folder = output_folder)
Validación de alertas
Usando un shapefile de las alertas y sus fechas, esta rutina toma cada alerta (coodenadas, fecha) y la grafica usando la serie temporal generada con las rutinas del GEE. Estos graficos nos permiten evaluar el comportamiento antes/después de la alerta y observar la evidencia con que el algoritmo detectó el cambio.
#Defining variables for alerts shapefile
alerts_shp <- "C:/DATA/GAF/demo/estadisticas/MODIS_alerts.shp"
column_dates_name <- "date"
#Defining variables for GEE ts files (normally as a TIF). Take into account that should exist in the same folder its version as CSV (dates files)
red_channel_GEE_ts <- "C:/DATA/GAF/demo/sentinel1/gee_outputs/coca_S1_ts_VV_P50_I14.tif"
green_channel_GEE_ts <- "C:/DATA/GAF/demo/sentinel1/gee_outputs/coca_S1_ts_VH_P50_I14.tif"
blue_channel_GEE_ts <- "C:/DATA/GAF/demo/sentinel1/gee_outputs/coca_S1_ts_VV_P50_I14.tif"
#other variables required
observations_evaluate <- 5 #could be less if processing is slow
buffer_distance <- 500 #consider that is in meters and measured from the alert point
stretch_perc <- 5 #recommendable if plots do not have good contrast
size_jpg <- 1000 #plot is based in an square, so one dimension defines its size
ncores <- 3 #modify if your resources are less than this
#output folder to save plots
output_folder <- "C:/DATA/GAF/demo/test"
#running the function
validate_alerts(alerts_shp = alerts_shp,
column_dates_name = column_dates_name,
red_channel_GEE_ts = red_channel_GEE_ts,
green_channel_GEE_ts = green_channel_GEE_ts,
blue_channel_GEE_ts = blue_channel_GEE_ts,
observations_evaluate = observations_evaluate,
buffer_distance = buffer_distance,
stretch_perc = stretch_perc,
size_jpg = size_jpg,
ncores = ncores,
output_folder = output_folder)
Detección de cambios
Ejecución de rutinas de deteccion de cambios a partir de series temporales del GEE. La función calcula cambios en el promedio de la serie temporal estandarizada. El output que genera es un shapefile filtrado por área y fecha de cambio. Requiere que el OSGEO4W este previamente instalado.
DETECT BREAKPOINTS
##Defining variables for breakpoint detection
GEE_ts <- "C:/DATA/GAF/demo/sentinel1/gee_outputs/coca_S1_ts_VH_P50_I14.tif"
change_threshold <- 10
segment_size <- 3
min_area <- 4
#For calibration mode
samples_shp <- "C:/DATA/GAF/demo/sentinel1/samples/muestras.shp"
column_id_name <- "clase"
size_jpg <- 3000
#Variables for processing and outputs
ncores <- 3
output_folder <- "C:/DATA/GAF/demo/sentinel1/breakpoints"
#running the function in CALIBRATION mode
detect_breakpoints(GEE_ts = GEE_ts,
change_threshold = change_threshold,
segment_size = segment_size,
min_area = min_area,
calibration_mode = T,
samples_shp = samples_shp,
column_id_name = column_id_name,
size_jpg = size_jpg,
ncores = ncores,
output_folder = output_folder)
#running the function in PROCESSING mode
detect_breakpoints(GEE_ts = GEE_ts,
change_threshold = change_threshold,
segment_size = segment_size,
min_area = min_area,
calibration_mode = F,
column_id_name = column_id_name,
ncores = ncores,
output_folder = output_folder)
Contribuciones
Por favor revíse el DESCRIPTION para detalles sobre la implementación del SATAtools.
Autores
- Fabián Santos - Programación y concepto - (https://www.researchgate.net/profile/Fabian_Santos2)
Licencia
Este proyecto tiene licencia GNU-3 (General Public License v3.0) - mire el link (https://www.gnu.org/licenses/gpl-3.0.en.html) para más detalles.
Agradecimientos
- GAFAG, Ministerio del Ambiente, KFW, ZFL.
FSantosCodes/SATAtools documentation built on May 5, 2019, 11:06 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
SATAtools
Tools for SATA (Sistema de Alertas Tempranas del Sistema Nacional de Monitoreo de Bosques del Ecuador)
Instalación
Para instalar el SATAtools, es necesario una version del R >=3.5.1 (se asume que esta usando el R desde RStudio), la librería 'devtools' de R (si no la tiene) y el OSGEO4W. Para hacerlo siga los pasos:
(1) Instalación del OSGEO4W (Descargue y ejecute el instalador. Mantenga la configuración de la instalación por defecto):
#install 32 bits version for windows:
http://download.osgeo.org/osgeo4w/osgeo4w-setup-x86.exe
#install 64 bits version for windows:
http://download.osgeo.org/osgeo4w/osgeo4w-setup-x86_64.exe
(2) Revisar que la version del R sea >=3.5.1 (caso contrario tiene que actualizarla):
#execute in Rstudio console:
> version
_
platform x86_64-w64-mingw32
arch x86_64
os mingw32
system x86_64, mingw32
status
major 3
minor 5.2
year 2018
month 12
day 20
svn rev 75870
language R
version.string R version 3.5.2 (2018-12-20) <--- ok
nickname Eggshell Igloo
(3) e instalar el 'devtools' y el 'SATAtools':
#install devtools
install.packages(devtools)
library(devtools)
#install SATAtools
install_github('FSantosCodes/SATAtools')
library(SATAtools)
Los ejemplos que se muestran a continuación se basan en los resultados de la descarga de los algoritmos corridos en el GEE. El pos-procesamiento de estos son una muestra de los potenciales usos de esta herramienta. Las rutinas de GEE se muestran a continuación: - Landsat 4-8 compositing https://code.earthengine.google.com/6a2c42cdbebe701dd8f3b31287da29c7 - Sentinel 1 compositing https://code.earthengine.google.com/777ef0b8a4aaa638b4d923fc45f04efd - Sentinel 2 compositing https://code.earthengine.google.com/612a604f0baf41581d225f4e8b2b06c0
Filtrado de alertas
Las coberturas usadas en estos ejemplos usan las alertas de deforestacion del SATA:
##### FILTER DEFORESTATION #####
#Defining variables from spatial units shapefile
spatial_units_shp <- "C:/DATA/GAF/demo/shp/unidades_espaciales/provincias.shp"
column_units_name <- "dpa_despro" #case sensitive
#Defining variables from deforestation shapefile (output from SATA or 'detect_breakpoints' function)
deforestation_shp <- "C:/DATA/GAF/demo/shp/alertas/deforestation_2016_2017.shp"
column_date_name <- "inicio" #case sensitive
column_area_name <- "area" #case sensitive
min_area <- NA #minimun area to filter (same units as area)
#Defining variables of ouputs
size_jpg <- c(40,20) #first value defines width and second height
output_folder <- "C:/DATA/GAF/demo/estadisticas"
#running the function
filter_deforestation_alerts(spatial_units_shp = spatial_units_shp,
column_units_name = column_units_name,
deforestation_shp = deforestation_shp,
column_date_name = column_date_name,
column_area_name = column_area_name,
min_area = min_area,
size_jpg = size_jpg,
output_folder = output_folder)
aquí se lo hace con las alertas de incendios del MODIS:
#Defining variables from spatial units shapefile
spatial_units_shp <- "C:/DATA/GAF/demo/shp/unidades_espaciales/provincias.shp"
column_units_name <- "dpa_despro" #case sensitive
#Defining variables from MODIS shapefile
MODIS_shp <- "C:/DATA/GAF/demo/shp/alertas/pts_calor_MODIS_30d.shp"
column_date_name <- "acq_date" #case sensitive
column_frp_name <- "frp" #case sensitive
column_confidence_name <- "confidence" #case sensitive
min_frp <- 10 #same units as frp
min_confidence <- 50 #same units as confidence
#Defining variables of ouputs
size_jpg <- c(40,20) #first value defines width and second height
output_folder <- "C:/DATA/GAF/demo/estadisticas"
#running the function
filter_MODIS_alerts(spatial_units_shp = spatial_units_shp,
column_units_name = column_units_name,
MODIS_shp = MODIS_shp,
column_date_name = column_date_name,
column_frp_name = column_frp_name,
column_confidence_name = column_confidence_name,
min_frp = min_frp,
min_confidence = min_confidence,
size_jpg = size_jpg,
output_folder = output_folder)
Validación de alertas
Usando un shapefile de las alertas y sus fechas, esta rutina toma cada alerta (coodenadas, fecha) y la grafica usando la serie temporal generada con las rutinas del GEE. Estos graficos nos permiten evaluar el comportamiento antes/después de la alerta y observar la evidencia con que el algoritmo detectó el cambio.
#Defining variables for alerts shapefile
alerts_shp <- "C:/DATA/GAF/demo/estadisticas/MODIS_alerts.shp"
column_dates_name <- "date"
#Defining variables for GEE ts files (normally as a TIF). Take into account that should exist in the same folder its version as CSV (dates files)
red_channel_GEE_ts <- "C:/DATA/GAF/demo/sentinel1/gee_outputs/coca_S1_ts_VV_P50_I14.tif"
green_channel_GEE_ts <- "C:/DATA/GAF/demo/sentinel1/gee_outputs/coca_S1_ts_VH_P50_I14.tif"
blue_channel_GEE_ts <- "C:/DATA/GAF/demo/sentinel1/gee_outputs/coca_S1_ts_VV_P50_I14.tif"
#other variables required
observations_evaluate <- 5 #could be less if processing is slow
buffer_distance <- 500 #consider that is in meters and measured from the alert point
stretch_perc <- 5 #recommendable if plots do not have good contrast
size_jpg <- 1000 #plot is based in an square, so one dimension defines its size
ncores <- 3 #modify if your resources are less than this
#output folder to save plots
output_folder <- "C:/DATA/GAF/demo/test"
#running the function
validate_alerts(alerts_shp = alerts_shp,
column_dates_name = column_dates_name,
red_channel_GEE_ts = red_channel_GEE_ts,
green_channel_GEE_ts = green_channel_GEE_ts,
blue_channel_GEE_ts = blue_channel_GEE_ts,
observations_evaluate = observations_evaluate,
buffer_distance = buffer_distance,
stretch_perc = stretch_perc,
size_jpg = size_jpg,
ncores = ncores,
output_folder = output_folder)
Detección de cambios
Ejecución de rutinas de deteccion de cambios a partir de series temporales del GEE. La función calcula cambios en el promedio de la serie temporal estandarizada. El output que genera es un shapefile filtrado por área y fecha de cambio. Requiere que el OSGEO4W este previamente instalado.
DETECT BREAKPOINTS
##Defining variables for breakpoint detection
GEE_ts <- "C:/DATA/GAF/demo/sentinel1/gee_outputs/coca_S1_ts_VH_P50_I14.tif"
change_threshold <- 10
segment_size <- 3
min_area <- 4
#For calibration mode
samples_shp <- "C:/DATA/GAF/demo/sentinel1/samples/muestras.shp"
column_id_name <- "clase"
size_jpg <- 3000
#Variables for processing and outputs
ncores <- 3
output_folder <- "C:/DATA/GAF/demo/sentinel1/breakpoints"
#running the function in CALIBRATION mode
detect_breakpoints(GEE_ts = GEE_ts,
change_threshold = change_threshold,
segment_size = segment_size,
min_area = min_area,
calibration_mode = T,
samples_shp = samples_shp,
column_id_name = column_id_name,
size_jpg = size_jpg,
ncores = ncores,
output_folder = output_folder)
#running the function in PROCESSING mode
detect_breakpoints(GEE_ts = GEE_ts,
change_threshold = change_threshold,
segment_size = segment_size,
min_area = min_area,
calibration_mode = F,
column_id_name = column_id_name,
ncores = ncores,
output_folder = output_folder)
Contribuciones
Por favor revíse el DESCRIPTION para detalles sobre la implementación del SATAtools.
Autores
- Fabián Santos - Programación y concepto - (https://www.researchgate.net/profile/Fabian_Santos2)
Licencia
Este proyecto tiene licencia GNU-3 (General Public License v3.0) - mire el link (https://www.gnu.org/licenses/gpl-3.0.en.html) para más detalles.
Agradecimientos
- GAFAG, Ministerio del Ambiente, KFW, ZFL.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.