Name: INVESTIGATIVE WILDFIRE DATA FOR TURKEY/ NASA
Creator: Kaggle
License: http://opendatacommons.org/licenses/dbcl/1.0/

About this Dataset

INVESTIGATIVE WILDFIRE DATA FOR TURKEY/ NASA

BE CAREFUL OF THE FILE NAMES.

IT CONTAINS THE DATA NEEDED TO RESEARCH LATEST FOREST FIRES IN TURKEY.

PAY ATTENTION TO THE DATE INTERVALS. THESE ARE 7-11 DAILY DATA OF LAST TIMES.

fire _ nrt _ M _ C61 _ 212465 _ all _ countries.csv

This file is important for all countries becuase it contains fire data of last 11 days for all around the world

Content

Data on recent forest fires in Turkey, published with permission from NASA Portal.
The data was created based on the hotspots and obtained from the satellite.

3 SEPARATE SATELLITE DATA:

MODIS C6.1
SUOMI VIIRS C2
J1 VIIRS C1

GENERAL ATTRIBUTES

Latitude
Center of nominal 375 m fire pixel
Longitude
Center of nominal 375 m fire pixel
Bright_ti4
(Brightness temperature I-4)
VIIRS I-4: channel brightness temperature of the fire pixel measured in Kelvin.
Scan
(Along Scan pixel size)
The algorithm produces approximately 375 m pixels at nadir. Scan and track reflect actual pixel size.
Track
(Along Track pixel size)
The algorithm produces approximately 375 m pixels at nadir. Scan and track reflect actual pixel size.
Acq_Date
(Acquisition Date)
Date of VIIRS acquisition.
Acq_Time
(Acquisition Time)
Time of acquisition/overpass of the satellite (in UTC).
Satellite
N Suomi National Polar-orbiting Partnership (Suomi NPP)
Confidence
This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.

Please note:

Low confidence nighttime pixels occur only over the geographic area extending from 11° E to 110° W and 7° N to 55° S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.
Version
Version identifies the collection (e.g. VIIRS Collection 1) and source of data processing: Near Real-Time (NRT suffix added to collection) or Standard Processing (collection only).

"1.0NRT" - Collection 1 NRT processing.

"1.0" - Collection 1 Standard processing.

Bright_ti5
(Brightness temperature I-5)
I-5 Channel brightness temperature of the fire pixel measured in Kelvin.
FRP
(Fire Radiative Power)
FRP depicts the pixel-integrated fire radiative power in MW (megawatts). Given the unique spatial and spectral resolution of the data, the VIIRS 375 m fire detection algorithm was customized and tuned in order to optimize its response over small fires while balancing the occurrence of false alarms. Frequent saturation of the mid-infrared I4 channel (3.55-3.93 µm) driving the detection of active fires requires additional tests and procedures to avoid pixel classification errors. As a result, sub-pixel fire characterization (e.g., fire radiative power [FRP] retrieval) is only viable across small and/or low-intensity fires. Systematic FRP retrievals are based on a hybrid approach combining 375 and 750 m data. In fact, starting in 2015 the algorithm incorporated additional VIIRS channel M13 (3.973-4.128 µm) 750 m data in both aggregated and unaggregated format.

Satellite measurements of fire radiative power (FRP) are increasingly used to estimate the contribution of biomass burning to local and global carbon budgets. Without an associated uncertainty, however, FRP-based biomass burning estimates cannot be confidently compared across space and time, or against estimates derived from alternative methodologies. Differences in the per-pixel FRP measured near-simultaneously in consecutive MODIS scans are approximately normally distributed with a standard deviation (ση) of 26.6%. Simulations demonstrate that this uncertainty decreases to less than ~5% (at ±1 ση) for aggregations larger than ~50 MODIS active fire pixels. Although FRP uncertainties limit the confidence in flux estimates on a per-pixel basis, the sensitivity of biomass burning estimates to FRP uncertainties can be mitigated by conducting inventories at coarser spatiotemporal resolutions.

http://cedadocs.ceda.ac.uk/770/1/SEVIRI_FRP_documentdesc.pdf

Type
(Inferred hot spot type)
0 = presumed vegetation fire

1 = active volcano

2 = other static land source

3 = offshore detection (includes all detections over water)

DayNight
(Day or Night)

D= Daytime fire

N= Nighttime fire

Tables

Modis 2019 Ireland

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_ireland

12.7 KB
60 rows
15 columns


CREATE TABLE modis_2019_ireland (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Israel

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_israel

17.9 KB
188 rows
15 columns


CREATE TABLE modis_2019_israel (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Italy

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_italy

128.58 KB
5259 rows
15 columns


CREATE TABLE modis_2019_italy (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Jamaica

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_jamaica

18.36 KB
204 rows
15 columns


CREATE TABLE modis_2019_jamaica (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Japan

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_japan

85.46 KB
3280 rows
15 columns


CREATE TABLE modis_2019_japan (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Jersey

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_jersey

10.18 KB
1 row
15 columns


CREATE TABLE modis_2019_jersey (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" BIGINT,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Jordan

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_jordan

12.65 KB
53 rows
15 columns


CREATE TABLE modis_2019_jordan (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Kazakhstan

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_kazakhstan

1.04 MB
51114 rows
15 columns


CREATE TABLE modis_2019_kazakhstan (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Kenya

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_kenya

90.51 KB
3247 rows
15 columns


CREATE TABLE modis_2019_kenya (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Kosovo

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_kosovo

24.38 KB
445 rows
15 columns


CREATE TABLE modis_2019_kosovo (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Kuwait

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_kuwait

34.42 KB
792 rows
15 columns


CREATE TABLE modis_2019_kuwait (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Kyrgyzstan

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_kyrgyzstan

26.83 KB
496 rows
15 columns


CREATE TABLE modis_2019_kyrgyzstan (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Lao Pdr

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_lao_pdr

889.12 KB
49073 rows
15 columns


CREATE TABLE modis_2019_lao_pdr (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Latvia

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_latvia

14.43 KB
97 rows
15 columns


CREATE TABLE modis_2019_latvia (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Lebanon

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_lebanon

13.53 KB
74 rows
15 columns


CREATE TABLE modis_2019_lebanon (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Lesotho

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_lesotho

31.02 KB
635 rows
15 columns


CREATE TABLE modis_2019_lesotho (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Liberia

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_liberia

259.58 KB
13221 rows
15 columns


CREATE TABLE modis_2019_liberia (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Libya

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_libya

87.81 KB
4609 rows
15 columns


CREATE TABLE modis_2019_libya (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Lithuania

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_lithuania

15.04 KB
115 rows
15 columns


CREATE TABLE modis_2019_lithuania (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Luxembourg

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_luxembourg

13.34 KB
72 rows
15 columns


CREATE TABLE modis_2019_luxembourg (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Macedonia Former Yugoslav Republic Of

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_macedonia_former_yugoslav_republic_of

41.2 KB
1142 rows
15 columns


CREATE TABLE modis_2019_macedonia_former_yugoslav_republic_of (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Madagascar

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_madagascar

1.02 MB
56178 rows
15 columns


CREATE TABLE modis_2019_madagascar (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Malawi

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_malawi

181.88 KB
8612 rows
15 columns


CREATE TABLE modis_2019_malawi (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Malaysia

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_malaysia

116.42 KB
4735 rows
15 columns


CREATE TABLE modis_2019_malaysia (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);

Modis 2019 Maldives

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2019_maldives

11.43 KB
27 rows
15 columns


CREATE TABLE modis_2019_maldives (
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "brightness" DOUBLE,
  "scan" DOUBLE,
  "track" DOUBLE,
  "acq_date" TIMESTAMP,
  "acq_time" BIGINT,
  "satellite" VARCHAR,
  "instrument" VARCHAR,
  "confidence" BIGINT,
  "version" DOUBLE,
  "bright_t31" DOUBLE,
  "frp" DOUBLE,
  "daynight" VARCHAR,
  "type" BIGINT
);