README.md
In Terrabrasilis/terrabrasilisAnalyticsAPI: The R client for TerraBrasilis analytics API of thematic maps
R Client Terrabrasilis Analytics API
terrabrasilisAnalyticsAPI is an R client package for Terrabrasilis Analytics API.
Getting started
Installing and loading terrabrasilisAnalyticsAPI package in R
devtools::install_github("terrabrasilis/terrabrasilisAnalyticsAPI") # github group name is terrabrasilis
library(terrabrasilisAnalyticsAPI) # R package name is terrabrasilisAnalyticsAPI
Initialize Terrabrasilis Analytics API path variable
apiPath <- "http://terrabrasilis.dpi.inpe.br/dashboard/api/v1/redis-cli/"
Define calls for application identifiers listing. From that information, it is possible then to make specific requests to other API end-points.
appIdentifier <- list_datasets(apiPath)
appIdentifier
[1] "prodes_cerrado" "prodes_amazon" "prodes_legal_amazon"
With that in mind, let's create a prodesCerrado variable.
prodesCerrado <- appIdentifier[1]
prodesCerrado
[1] "prodes_cerrado"
The first question that reminds us to ask to the API is: which periods do Prodes Cerrado contains?
periods <- list_periods(apiPath, prodesCerrado)
periods
periods
# A tibble: 13 x 6
startDate.year startDate.month startDate.day endDate.year endDate.month endDate.day
<int> <int> <int> <int> <int> <int>
1 1988 8 1 2000 7 31
2 2000 8 1 2002 7 31
3 2002 8 1 2004 7 31
4 2004 8 1 2006 7 31
5 2006 8 1 2008 7 31
6 2008 8 1 2010 7 31
7 2010 8 1 2012 7 31
8 2012 8 1 2013 7 31
9 2013 8 1 2014 7 31
10 2014 8 1 2015 7 31
11 2015 8 1 2016 7 31
12 2016 8 1 2017 7 31
13 2017 8 1 2018 7 31
Users can also ask to the API, for example, which classes do PRODES Cerrado contains?
classes <- list_classes(apiPath, prodesCerrado)
classes
In this case, it is just one class designating deforestation label. Other thematic mapping projects, however, would contain more than one class or even the same class name.
# A tibble: 1 x 3
id name description
<int> <chr> <chr>
1 1 deforestation It is the process of complete and permanent disappearance of forests
Besides responding which classes and periods, users might ask which local of interests (lois) as states, municipalities, conservation units, indigeneous areas, and Landsat Path/Row, the API provides.
locals <- list_locals(apiPath, prodesCerrado)
locals
# A tibble: 5 x 2
gid name
<int> <chr>
1 1 UF
2 2 MUN
3 3 ConsUnit
4 4 Indi
5 5 Pathrow
Nevertheless, locals are not considered the final granularity since each state, municipality, conservation unit, indigeneous areas, and Landsat Path/Row also contain small-scale local of interests.
localOfInterest <- list_local_of_interests(apiPath, prodesCerrado)
localOfInterest[14:23,]
# A tibble: 10 x 3
gid loiname loi
<int> <chr> <dbl>
1 1586 PARQUE NATURAL MUNICIPAL DO SETOR SANTA CRUZ 3
2 1410 RESERVA BIOLÓGICA DO GUARÁ 3
3 1431 MONUMENTO NATURAL ESTADUAL LAPA VERMELHA 3
4 1454 FLORESTA ESTADUAL SÃO JUDAS TADEU 3
5 1548 RESERVA PARTICULAR DO PATRIMÔNIO NATURAL INTEGRA O PARQUE 3
6 1496 ÁREA DE PROTEÇÃO AMBIENTAL MEANDROS DO ARAGUAIA 3
7 1481 PARQUE ESTADUAL SERRA VERDE 3
8 1566 RESERVA PARTICULAR DO PATRIMÔNIO NATURAL JOAQUIM THEODORO DE MORAES 3
9 1567 PARQUE ESTADUAL DE VASSUNUNGA 3
10 1404 ESTAÇÃO ECOLÓGICA ITABERÁ 3
Users are able to filter loinames by one specific loi such as UF.
loiUF = dplyr::filter(locals, grepl("UF", name))$gid
loinamesByLoi <- list_localOfInterestByLocal(apiPath, prodesCerrado, loiUF)
loinamesByLoi
# A tibble: 13 x 2
gid loiname
<int> <chr>
1 11 TOCANTINS
2 7 MINAS GERAIS
3 10 SÃO PAULO
4 9 PIAUÍ
5 12 PARANÁ
6 13 RONDÔNIA
7 4 MARANHÃO
8 5 MATO GROSSO
9 2 DISTRITO FEDERAL
10 1 BAHIA
11 3 GOIÁS
12 6 MATO GROSSO DO SUL
13 8 PARÁ
In order to fit data into governmental needs, we also considered filters for each data recognized that as type in the data API call.
filters <- list_filters(apiPath, prodesCerrado)
filters
# A tibble: 2 x 2
id type
<int> <chr>
1 1 fid_area >= 0.0625
2 2 fid_area >= 0.01
All this data is used to gather specific thematic map area values produced by government agencies such as the National Institute for Space Research. In this example, users are able to acquire data by loiname, that is, the function accepts as parameters, the class name and loiname gid as well.
data <- get_dataByLocalOfInterest(apiPath,
prodesCerrado,
classes$name,
loinamesByLoi[1,]$gid)
data
name clazz startDate.year startDate.month startDate.day endDate.year endDate.month endDate.day loi loiname type area
1 PRODES CERRADO deforestation 1988 8 1 2000 7 31 1 11 1 38003.720
2 PRODES CERRADO deforestation 1988 8 1 2000 7 31 1 11 2 38286.164
3 PRODES CERRADO deforestation 2000 8 1 2002 7 31 1 11 1 5424.295
4 PRODES CERRADO deforestation 2000 8 1 2002 7 31 1 11 2 5808.921
5 PRODES CERRADO deforestation 2002 8 1 2004 7 31 1 11 1 5723.244
6 PRODES CERRADO deforestation 2002 8 1 2004 7 31 1 11 2 6157.996
7 PRODES CERRADO deforestation 2004 8 1 2006 7 31 1 11 1 4880.821
8 PRODES CERRADO deforestation 2004 8 1 2006 7 31 1 11 2 5257.803
9 PRODES CERRADO deforestation 2006 8 1 2008 7 31 1 11 1 3228.224
10 PRODES CERRADO deforestation 2006 8 1 2008 7 31 1 11 2 3596.751
11 PRODES CERRADO deforestation 2008 8 1 2010 7 31 1 11 1 3245.410
12 PRODES CERRADO deforestation 2008 8 1 2010 7 31 1 11 2 3639.593
13 PRODES CERRADO deforestation 2010 8 1 2012 7 31 1 11 1 3263.893
14 PRODES CERRADO deforestation 2010 8 1 2012 7 31 1 11 2 3481.588
15 PRODES CERRADO deforestation 2012 8 1 2013 7 31 1 11 1 2543.343
16 PRODES CERRADO deforestation 2012 8 1 2013 7 31 1 11 2 2816.817
17 PRODES CERRADO deforestation 2013 8 1 2014 7 31 1 11 1 2006.936
18 PRODES CERRADO deforestation 2013 8 1 2014 7 31 1 11 2 2243.355
19 PRODES CERRADO deforestation 2014 8 1 2015 7 31 1 11 1 2753.371
20 PRODES CERRADO deforestation 2014 8 1 2015 7 31 1 11 2 3063.382
21 PRODES CERRADO deforestation 2015 8 1 2016 7 31 1 11 1 1421.360
22 PRODES CERRADO deforestation 2015 8 1 2016 7 31 1 11 2 1587.207
23 PRODES CERRADO deforestation 2016 8 1 2017 7 31 1 11 1 1498.426
24 PRODES CERRADO deforestation 2016 8 1 2017 7 31 1 11 2 1693.450
25 PRODES CERRADO deforestation 2017 8 1 2018 7 31 1 11 1 1391.557
26 PRODES CERRADO deforestation 2017 8 1 2018 7 31 1 11 2 1530.056
>
The same query can be performed using get data by parameters function. In this case, users pass also as parameters a start and end date. Unlikely the previous call, users will not receive all the available timeline as soon as they really desire.
data <- get_dataByParameters(apiPath,
prodesCerrado,
classes$name,
loinamesByLoi[1,]$gid,
"1988-01-01",
"2001-01-01")
data
name clazz startDate.year startDate.month startDate.day endDate.year endDate.month endDate.day loi loiname type area
1 PRODES CERRADO deforestation 1988 8 1 2000 7 31 1 11 1 38003.72
2 PRODES CERRADO deforestation 1988 8 1 2000 7 31 1 11 2 38286.16
>
References
Assis, L. F. F. G. A.; Ferreira, K. R.; Vinhas, L.; Maurano, L.; Almeida, C. A., Nascimento, J. R., Carvalho, A. F. A.; Camargo, C.; Maciel, A. M. TerraBrasilis: A Spatial Data Infrastructure for Disseminating Deforestation Data from Brazil. In Proceeding of the XIX Remote Sensing Brazilian Symposium, 2019.
Reporting Bugs
Any problem should be reported to prodes@dpi.inpe.br.
Terrabrasilis/terrabrasilisAnalyticsAPI documentation built on Nov. 11, 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.
R Client Terrabrasilis Analytics API
terrabrasilisAnalyticsAPI is an R client package for Terrabrasilis Analytics API.
Getting started
Installing and loading terrabrasilisAnalyticsAPI package in R
devtools::install_github("terrabrasilis/terrabrasilisAnalyticsAPI") # github group name is terrabrasilis
library(terrabrasilisAnalyticsAPI) # R package name is terrabrasilisAnalyticsAPI
Initialize Terrabrasilis Analytics API path variable
apiPath <- "http://terrabrasilis.dpi.inpe.br/dashboard/api/v1/redis-cli/"
Define calls for application identifiers listing. From that information, it is possible then to make specific requests to other API end-points.
appIdentifier <- list_datasets(apiPath)
appIdentifier
[1] "prodes_cerrado" "prodes_amazon" "prodes_legal_amazon"
With that in mind, let's create a prodesCerrado variable.
prodesCerrado <- appIdentifier[1]
prodesCerrado
[1] "prodes_cerrado"
The first question that reminds us to ask to the API is: which periods do Prodes Cerrado contains?
periods <- list_periods(apiPath, prodesCerrado)
periods
periods
# A tibble: 13 x 6
startDate.year startDate.month startDate.day endDate.year endDate.month endDate.day
<int> <int> <int> <int> <int> <int>
1 1988 8 1 2000 7 31
2 2000 8 1 2002 7 31
3 2002 8 1 2004 7 31
4 2004 8 1 2006 7 31
5 2006 8 1 2008 7 31
6 2008 8 1 2010 7 31
7 2010 8 1 2012 7 31
8 2012 8 1 2013 7 31
9 2013 8 1 2014 7 31
10 2014 8 1 2015 7 31
11 2015 8 1 2016 7 31
12 2016 8 1 2017 7 31
13 2017 8 1 2018 7 31
Users can also ask to the API, for example, which classes do PRODES Cerrado contains?
classes <- list_classes(apiPath, prodesCerrado)
classes
In this case, it is just one class designating deforestation label. Other thematic mapping projects, however, would contain more than one class or even the same class name.
# A tibble: 1 x 3
id name description
<int> <chr> <chr>
1 1 deforestation It is the process of complete and permanent disappearance of forests
Besides responding which classes and periods, users might ask which local of interests (lois) as states, municipalities, conservation units, indigeneous areas, and Landsat Path/Row, the API provides.
locals <- list_locals(apiPath, prodesCerrado)
locals
# A tibble: 5 x 2
gid name
<int> <chr>
1 1 UF
2 2 MUN
3 3 ConsUnit
4 4 Indi
5 5 Pathrow
Nevertheless, locals are not considered the final granularity since each state, municipality, conservation unit, indigeneous areas, and Landsat Path/Row also contain small-scale local of interests.
localOfInterest <- list_local_of_interests(apiPath, prodesCerrado)
localOfInterest[14:23,]
# A tibble: 10 x 3
gid loiname loi
<int> <chr> <dbl>
1 1586 PARQUE NATURAL MUNICIPAL DO SETOR SANTA CRUZ 3
2 1410 RESERVA BIOLÓGICA DO GUARÁ 3
3 1431 MONUMENTO NATURAL ESTADUAL LAPA VERMELHA 3
4 1454 FLORESTA ESTADUAL SÃO JUDAS TADEU 3
5 1548 RESERVA PARTICULAR DO PATRIMÔNIO NATURAL INTEGRA O PARQUE 3
6 1496 ÁREA DE PROTEÇÃO AMBIENTAL MEANDROS DO ARAGUAIA 3
7 1481 PARQUE ESTADUAL SERRA VERDE 3
8 1566 RESERVA PARTICULAR DO PATRIMÔNIO NATURAL JOAQUIM THEODORO DE MORAES 3
9 1567 PARQUE ESTADUAL DE VASSUNUNGA 3
10 1404 ESTAÇÃO ECOLÓGICA ITABERÁ 3
Users are able to filter loinames by one specific loi such as UF.
loiUF = dplyr::filter(locals, grepl("UF", name))$gid
loinamesByLoi <- list_localOfInterestByLocal(apiPath, prodesCerrado, loiUF)
loinamesByLoi
# A tibble: 13 x 2
gid loiname
<int> <chr>
1 11 TOCANTINS
2 7 MINAS GERAIS
3 10 SÃO PAULO
4 9 PIAUÍ
5 12 PARANÁ
6 13 RONDÔNIA
7 4 MARANHÃO
8 5 MATO GROSSO
9 2 DISTRITO FEDERAL
10 1 BAHIA
11 3 GOIÁS
12 6 MATO GROSSO DO SUL
13 8 PARÁ
In order to fit data into governmental needs, we also considered filters for each data recognized that as type in the data API call.
filters <- list_filters(apiPath, prodesCerrado)
filters
# A tibble: 2 x 2
id type
<int> <chr>
1 1 fid_area >= 0.0625
2 2 fid_area >= 0.01
All this data is used to gather specific thematic map area values produced by government agencies such as the National Institute for Space Research. In this example, users are able to acquire data by loiname, that is, the function accepts as parameters, the class name and loiname gid as well.
data <- get_dataByLocalOfInterest(apiPath,
prodesCerrado,
classes$name,
loinamesByLoi[1,]$gid)
data
name clazz startDate.year startDate.month startDate.day endDate.year endDate.month endDate.day loi loiname type area
1 PRODES CERRADO deforestation 1988 8 1 2000 7 31 1 11 1 38003.720
2 PRODES CERRADO deforestation 1988 8 1 2000 7 31 1 11 2 38286.164
3 PRODES CERRADO deforestation 2000 8 1 2002 7 31 1 11 1 5424.295
4 PRODES CERRADO deforestation 2000 8 1 2002 7 31 1 11 2 5808.921
5 PRODES CERRADO deforestation 2002 8 1 2004 7 31 1 11 1 5723.244
6 PRODES CERRADO deforestation 2002 8 1 2004 7 31 1 11 2 6157.996
7 PRODES CERRADO deforestation 2004 8 1 2006 7 31 1 11 1 4880.821
8 PRODES CERRADO deforestation 2004 8 1 2006 7 31 1 11 2 5257.803
9 PRODES CERRADO deforestation 2006 8 1 2008 7 31 1 11 1 3228.224
10 PRODES CERRADO deforestation 2006 8 1 2008 7 31 1 11 2 3596.751
11 PRODES CERRADO deforestation 2008 8 1 2010 7 31 1 11 1 3245.410
12 PRODES CERRADO deforestation 2008 8 1 2010 7 31 1 11 2 3639.593
13 PRODES CERRADO deforestation 2010 8 1 2012 7 31 1 11 1 3263.893
14 PRODES CERRADO deforestation 2010 8 1 2012 7 31 1 11 2 3481.588
15 PRODES CERRADO deforestation 2012 8 1 2013 7 31 1 11 1 2543.343
16 PRODES CERRADO deforestation 2012 8 1 2013 7 31 1 11 2 2816.817
17 PRODES CERRADO deforestation 2013 8 1 2014 7 31 1 11 1 2006.936
18 PRODES CERRADO deforestation 2013 8 1 2014 7 31 1 11 2 2243.355
19 PRODES CERRADO deforestation 2014 8 1 2015 7 31 1 11 1 2753.371
20 PRODES CERRADO deforestation 2014 8 1 2015 7 31 1 11 2 3063.382
21 PRODES CERRADO deforestation 2015 8 1 2016 7 31 1 11 1 1421.360
22 PRODES CERRADO deforestation 2015 8 1 2016 7 31 1 11 2 1587.207
23 PRODES CERRADO deforestation 2016 8 1 2017 7 31 1 11 1 1498.426
24 PRODES CERRADO deforestation 2016 8 1 2017 7 31 1 11 2 1693.450
25 PRODES CERRADO deforestation 2017 8 1 2018 7 31 1 11 1 1391.557
26 PRODES CERRADO deforestation 2017 8 1 2018 7 31 1 11 2 1530.056
>
The same query can be performed using get data by parameters function. In this case, users pass also as parameters a start and end date. Unlikely the previous call, users will not receive all the available timeline as soon as they really desire.
data <- get_dataByParameters(apiPath,
prodesCerrado,
classes$name,
loinamesByLoi[1,]$gid,
"1988-01-01",
"2001-01-01")
data
name clazz startDate.year startDate.month startDate.day endDate.year endDate.month endDate.day loi loiname type area
1 PRODES CERRADO deforestation 1988 8 1 2000 7 31 1 11 1 38003.72
2 PRODES CERRADO deforestation 1988 8 1 2000 7 31 1 11 2 38286.16
>
References
Assis, L. F. F. G. A.; Ferreira, K. R.; Vinhas, L.; Maurano, L.; Almeida, C. A., Nascimento, J. R., Carvalho, A. F. A.; Camargo, C.; Maciel, A. M. TerraBrasilis: A Spatial Data Infrastructure for Disseminating Deforestation Data from Brazil. In Proceeding of the XIX Remote Sensing Brazilian Symposium, 2019.
Reporting Bugs
Any problem should be reported to prodes@dpi.inpe.br.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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