Forest Fires in Brazil 2019 - 2020

Numbers and locations of forest fires and heat spots detected by screening satellite images in Brazil

FAQS

  1. In what context are INPE's fires data useful?

    Monitoring of fires and forest fires in satellite images is particularly useful for remote regions without intensive means and monitoring sites, a condition that represents the general situation in the country. For an area with observation towers continuously manned and maintaining direct communication with brigades fire fighting equipment, satellite data is of marginal interest.

    The detection of vegetation burning spots in the images uses the same fire identification method, in all regions, every day and for years in a row, which allows temporal and spatial analyzes of the occurrence of the fire that would otherwise be impossible, given precariousness, discontinuity and difference in methods in local information sources. In particular, the data from the "reference satellite" allows comparison between any country on the planet.

  2. What products does this INPE fire / fire system offer?

    There are hundreds of products generated and distributed daily, such as: geographic coordinates of the outbreaks, e-mail alerts of occurrences in areas of special interest, meteorological fire risk, smoke concentration estimates, mapping of burnt areas, etc. . Explore the INPE Queimadas Program Portal and see the list of the most recommended on the general presentation page.

  3. Do the products from Queimadas do INPE have any cost?

    No, all data and products are released on the internet by INPE at no cost to the user, about three hours after their generation; it is clear that for the user there is the cost of the internet provider or the use of the telephone line.

  4. Can INPE work with those who burn?

    No. INPE has no powers to inspect, control and combat the use of fire in the country, nor to punish violators. Within its attributions, INPE, through its Queimadas Program, seeks to generate the largest possible number of data related to the use of fire in vegetation so that the government and society benefit from the information generated. See our links page for more information.

  5. What does INPE do with fire data?

    The data generated is distributed in two ways: for the general public, all data and products are available for free access on the Internet about three hours after their generation; for public agents with special operational needs, distribution is immediate to their generation.

  6. Which satellites are used and where are the images received and processed?

    All nine satellites that have optical sensors operating in the thermal-average range of 4um and that INPE can receive are used. (Thermal Infrared Imaging) Currently (September / 2019), images from the polar satellites, the AVHRR / 3 of the NOAA-18, NOAA-19 and METOP are processed operationally in the Imaging Division - DGI and in the Satellite and Environmental Systems Division - DSA -B, the MODIS of NASA TERRA and AQUA and the VIIRS of NPP-Suomi and NOAA-20 and, the images of the geostationary satellites, GOES-16 and MSG-3.

    Each polar orbit satellite produces at least two sets of images per day, and the geostationary generates four images per hour, with a total of INPE automatically processing over 200 images per day specifically to detect vegetation fires. It is also expected to include the reception of images from the Chinese Fenyun polar satellites. The receptions are made at the stations of Cachoeira Paulista, SP (near the border with RJ) and Cuiabá, MT.

Wild fires detected in 2018, numbers divided per state.

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1806600 entries, 2018-03-10 20:05:35 to 2018-03-11 21:05:40
Data columns (total 11 columns):
 #   Column        Dtype  
---  ------        -----  
 0   satelite      object 
 1   pais          object 
 2   estado        object 
 3   municipio     object 
 4   bioma         object 
 5   diasemchuva   int64  
 6   precipitacao  float64
 7   riscofogo     float64
 8   latitude      float64
 9   longitude     float64
 10  frp           float64
dtypes: float64(5), int64(1), object(5)
memory usage: 165.4+ MB
satelite pais estado municipio bioma diasemchuva precipitacao riscofogo latitude longitude frp
datahora
2018-03-10 20:05:35 GOES-16 Brasil RORAIMA CAROEBE Amazonia 0 0.00 0.06 0.81 -59.72 NaN
2018-03-20 13:35:39 GOES-16 Brasil RORAIMA CARACARAI Amazonia 0 0.00 0.42 1.55 -61.48 NaN
2018-03-24 14:20:36 GOES-16 Brasil RORAIMA CARACARAI Amazonia 0 0.33 0.47 1.15 -60.78 NaN
2018-03-01 16:05:53 GOES-16 Brasil RORAIMA NORMANDIA Amazonia 0 0.00 0.63 3.65 -60.37 NaN
2018-03-22 22:05:47 GOES-16 Brasil RORAIMA MUCAJAI Amazonia 0 0.00 0.62 2.63 -60.99 NaN
latitude longitude
datahora
2018-03-10 20:05:35 0.81000 -59.72000
2018-03-20 13:35:39 1.55000 -61.48000
2018-03-24 14:20:36 1.15000 -60.78000
2018-03-01 16:05:53 3.65000 -60.37000
2018-03-22 22:05:47 2.63000 -60.99000
... ... ...
2018-03-31 17:12:00 -15.63578 -45.77114
2018-03-09 04:48:00 3.19729 -60.01252
2018-02-21 20:20:35 0.95000 -60.08000
2018-03-12 05:30:00 2.61829 -60.60953
2018-03-11 21:05:40 1.75000 -60.38000

1806600 rows × 2 columns

This plot gives you an idea of the incredible variation in the amount of fires over different places.

bioma ... riscofogo
estado ACRE ... TOCANTINS All
municipio ACRELANDIA ASSIS BRASIL BRASILEIA BUJARI CAPIXABA CRUZEIRO DO SUL EPITACIOLANDIA FEIJO JORDAO MANCIO LIMA ... TAGUATINGA TAIPAS DO TOCANTINS TALISMA TOCANTINIA TOCANTINOPOLIS TUPIRAMA TUPIRATINS WANDERLANDIA XAMBIOA
satelite
AQUA_M-M 1.0 NaN 1.0 1.0 1.0 2.0 NaN NaN NaN 2.0 ... 4.0 6.0 4.0 5.0 2.0 NaN 1.0 3.0 1.0 11346.0
AQUA_M-T 160.0 190.0 505.0 218.0 204.0 463.0 149.0 839.0 142.0 183.0 ... 37.0 40.0 12.0 121.0 35.0 28.0 22.0 38.0 11.0 126181.0
GOES-13 NaN NaN NaN NaN NaN 1.0 NaN 1.0 NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
GOES-16 215.0 174.0 389.0 299.0 270.0 552.0 181.0 465.0 78.0 239.0 ... 103.0 125.0 185.0 201.0 135.0 77.0 79.0 126.0 11.0 NaN
METOP-B 4.0 NaN NaN 1.0 NaN 9.0 NaN 2.0 NaN 2.0 ... 17.0 24.0 29.0 26.0 10.0 7.0 8.0 5.0 1.0 NaN
MSG-03 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN 2.0 NaN NaN
NOAA-15 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 6.0 8.0 21.0 9.0 NaN NaN 9.0 1.0 NaN NaN
NOAA-15D NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
NOAA-18 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 10.0 2.0 34.0 5.0 6.0 4.0 28.0 11.0 2.0 NaN
NOAA-18D 3.0 2.0 5.0 6.0 7.0 3.0 12.0 5.0 1.0 1.0 ... NaN 4.0 NaN NaN NaN 1.0 NaN 1.0 NaN NaN
NOAA-19 53.0 16.0 41.0 52.0 41.0 40.0 34.0 79.0 7.0 16.0 ... 25.0 68.0 26.0 71.0 29.0 30.0 30.0 66.0 10.0 NaN
NOAA-19D NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 5.0 6.0 3.0 4.0 7.0 NaN NaN NaN NaN NaN
NPP-375 710.0 659.0 1809.0 910.0 832.0 1538.0 717.0 3907.0 571.0 658.0 ... 352.0 598.0 160.0 852.0 318.0 145.0 210.0 321.0 89.0 812293.0
TERRA_M-M 7.0 NaN 6.0 8.0 7.0 7.0 3.0 6.0 NaN 2.0 ... 11.0 14.0 21.0 14.0 6.0 5.0 3.0 14.0 1.0 28211.0
TERRA_M-T 29.0 2.0 19.0 27.0 21.0 20.0 18.0 59.0 1.0 7.0 ... 17.0 44.0 9.0 82.0 11.0 19.0 21.0 22.0 5.0 54777.0
All 904.0 851.0 2336.0 1164.0 1065.0 2010.0 886.0 4809.0 714.0 838.0 ... 418.0 678.0 205.0 1070.0 369.0 196.0 250.0 392.0 101.0 1032808.0

16 rows × 43144 columns

The most prolific satellites are: AQUA_M-T, GOES-16, NOAA-19, NPP-375, TERRA_M-M, TERRA_M-T.

list
satelite pais estado municipio bioma diasemchuva precipitacao riscofogo latitude longitude frp
datahora
2018-03-10 20:05:35 GOES-16 Brasil RORAIMA CAROEBE Amazonia 0 0.00 0.06 0.81000 -59.72000 NaN
2018-03-20 13:35:39 GOES-16 Brasil RORAIMA CARACARAI Amazonia 0 0.00 0.42 1.55000 -61.48000 NaN
2018-03-24 14:20:36 GOES-16 Brasil RORAIMA CARACARAI Amazonia 0 0.33 0.47 1.15000 -60.78000 NaN
2018-03-01 16:05:53 GOES-16 Brasil RORAIMA NORMANDIA Amazonia 0 0.00 0.63 3.65000 -60.37000 NaN
2018-03-22 22:05:47 GOES-16 Brasil RORAIMA MUCAJAI Amazonia 0 0.00 0.62 2.63000 -60.99000 NaN
... ... ... ... ... ... ... ... ... ... ... ...
2018-03-31 17:12:00 NPP-375 Brasil MINAS GERAIS ARINOS Cerrado 0 0.00 0.51 -15.63578 -45.77114 NaN
2018-03-09 04:48:00 NPP-375 Brasil RORAIMA BONFIM Amazonia 0 0.00 0.34 3.19729 -60.01252 NaN
2018-02-21 20:20:35 GOES-16 Brasil RORAIMA SAO LUIZ Amazonia 0 0.00 0.67 0.95000 -60.08000 NaN
2018-03-12 05:30:00 NPP-375 Brasil RORAIMA CANTA Amazonia 0 0.00 0.24 2.61829 -60.60953 NaN
2018-03-11 21:05:40 GOES-16 Brasil RORAIMA CANTA Amazonia 0 0.00 0.28 1.75000 -60.38000 NaN

1698578 rows × 11 columns

count2018 =FocosState2018.groupby("municipio").count()

satelite pais estado bioma diasemchuva precipitacao riscofogo latitude longitude frp
municipio
ABADIA DE GOIAS 30 30 30 30 30 30 27 30 30 27
ABADIA DOS DOURADOS 45 45 45 45 45 45 45 45 45 37
ABADIANIA 56 56 56 56 56 56 53 56 56 44
ABAETE 62 62 62 62 62 62 61 62 62 60
ABAETETUBA 525 525 525 525 525 525 391 525 525 452
... ... ... ... ... ... ... ... ... ... ...
XIQUE-XIQUE 610 610 610 610 610 610 602 610 610 440
ZABELE 1 1 1 1 1 1 1 1 1 1
ZACARIAS 52 52 52 52 52 52 52 52 52 36
ZE DOCA 419 419 419 419 419 419 419 419 419 169
ZORTEA 13 13 13 13 13 13 13 13 13 13

5125 rows × 10 columns

pais estado bioma diasemchuva precipitacao riscofogo latitude longitude frp
satelite municipio
AQUA_M-T ABADIA DE GOIAS 5 5 5 5 5 5 5 5 5
ABADIA DOS DOURADOS 3 3 3 3 3 3 3 3 3
ABADIANIA 5 5 5 5 5 5 5 5 5
ABAETE 6 6 6 6 6 6 6 6 6
ABAETETUBA 75 75 75 75 75 41 75 75 75
... ... ... ... ... ... ... ... ... ... ...
TERRA_M-T XINGUARA 20 20 20 20 20 13 20 20 20
XIQUE-XIQUE 29 29 29 29 29 29 29 29 29
ZACARIAS 1 1 1 1 1 1 1 1 1
ZE DOCA 6 6 6 6 6 6 6 6 6
ZORTEA 1 1 1 1 1 1 1 1 1

19708 rows × 9 columns

We select here only detections done by the NPP-375 algoritme

satelite pais estado municipio bioma diasemchuva precipitacao riscofogo latitude longitude frp
datahora
2018-02-01 17:06:00 NPP-375 Brasil RORAIMA UIRAMUTA Amazonia 0 0.00 0.35 4.35564 -60.20242 NaN
2018-03-12 05:30:00 NPP-375 Brasil RORAIMA CANTA Amazonia 0 0.00 0.41 2.18465 -60.81395 NaN
2018-03-24 17:36:00 NPP-375 Brasil SANTA CATARINA SANTA CECILIA Mata Atlantica 0 27.85 0.00 -27.06369 -50.48926 NaN
2018-03-13 05:12:00 NPP-375 Brasil RORAIMA CARACARAI Amazonia 0 0.00 0.34 1.71623 -60.99483 NaN
2018-02-27 17:18:00 NPP-375 Brasil RORAIMA CARACARAI Amazonia 0 1.15 0.85 1.96740 -60.78246 NaN
... ... ... ... ... ... ... ... ... ... ... ...
2018-03-19 15:54:00 NPP-375 Brasil MINAS GERAIS ALMENARA Mata Atlantica 0 0.00 0.13 -15.96370 -40.59505 NaN
2018-02-18 04:06:00 NPP-375 Brasil BAHIA RIACHAO DAS NEVES Cerrado 0 0.55 0.23 -11.49848 -44.87274 NaN
2018-03-31 17:12:00 NPP-375 Brasil MINAS GERAIS ARINOS Cerrado 0 0.00 0.51 -15.63578 -45.77114 NaN
2018-03-09 04:48:00 NPP-375 Brasil RORAIMA BONFIM Amazonia 0 0.00 0.34 3.19729 -60.01252 NaN
2018-03-12 05:30:00 NPP-375 Brasil RORAIMA CANTA Amazonia 0 0.00 0.24 2.61829 -60.60953 NaN

894841 rows × 11 columns

plt.figure( figsize= (16, 6)) sns.violinplot(x="riscofogo", y="frp", data= COUNT2018, markersize=2);# scatter #.satelitex="estado", y="satelite", data= count2018

is there a connection between riscofogo at day-1 and frp at day?

Number of heatspots detected by satellites in 2018 (daily)

satelite
datahora
2018-01-01 353
2018-01-02 2025
2018-01-03 1592
2018-01-04 1514
2018-01-05 1386
... ...
2018-12-27 3992
2018-12-28 5348
2018-12-29 2168
2018-12-30 1577
2018-12-31 1072

365 rows × 1 columns

FocosState2018LL_gdf = geopandas.read_file(FocosState2018LL) #, layer='countries'

latitude longitude geometry
datahora
2018-03-10 20:05:35 0.81 -59.72 POINT (-59.72000 0.81000)
2018-03-20 13:35:39 1.55 -61.48 POINT (-61.48000 1.55000)
2018-03-24 14:20:36 1.15 -60.78 POINT (-60.78000 1.15000)
2018-03-01 16:05:53 3.65 -60.37 POINT (-60.37000 3.65000)
2018-03-22 22:05:47 2.63 -60.99 POINT (-60.99000 2.63000)
UsageError: %%capture is a cell magic, but the cell body is empty.