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 2017 Mongolia

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_mongolia

98.19 KB
3877 rows
15 columns


CREATE TABLE modis_2017_mongolia (
  "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 2017 Montenegro

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_montenegro

45.73 KB
1345 rows
15 columns


CREATE TABLE modis_2017_montenegro (
  "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 2017 Montserrat

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_montserrat

10.37 KB
4 rows
15 columns


CREATE TABLE modis_2017_montserrat (
  "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 2017 Morocco

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_morocco

21.68 KB
294 rows
15 columns


CREATE TABLE modis_2017_morocco (
  "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 2017 Mozambique

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_mozambique

2.17 MB
132353 rows
15 columns


CREATE TABLE modis_2017_mozambique (
  "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 2017 Myanmar

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_myanmar

894.5 KB
47371 rows
15 columns


CREATE TABLE modis_2017_myanmar (
  "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 2017 Namibia

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_namibia

264.93 KB
12495 rows
15 columns


CREATE TABLE modis_2017_namibia (
  "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 2017 Nepal

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_nepal

63.24 KB
2218 rows
15 columns


CREATE TABLE modis_2017_nepal (
  "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 2017 Netherlands

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_netherlands

18.83 KB
232 rows
15 columns


CREATE TABLE modis_2017_netherlands (
  "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 2017 New Caledonia

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_new_caledonia

26.31 KB
467 rows
15 columns


CREATE TABLE modis_2017_new_caledonia (
  "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 2017 New Zealand

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_new_zealand

28.73 KB
534 rows
15 columns


CREATE TABLE modis_2017_new_zealand (
  "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 2017 Nicaragua

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_nicaragua

84.37 KB
3268 rows
15 columns


CREATE TABLE modis_2017_nicaragua (
  "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 2017 Niger

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_niger

40.38 KB
983 rows
15 columns


CREATE TABLE modis_2017_niger (
  "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 2017 Nigeria

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_nigeria

1.26 MB
71781 rows
15 columns


CREATE TABLE modis_2017_nigeria (
  "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 2017 Northern Mariana Islands

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_northern_mariana_islands

10.6 KB
8 rows
15 columns


CREATE TABLE modis_2017_northern_mariana_islands (
  "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 2017 Norway

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_norway

13.81 KB
85 rows
15 columns


CREATE TABLE modis_2017_norway (
  "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 2017 Oman

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_oman

42.32 KB
1165 rows
15 columns


CREATE TABLE modis_2017_oman (
  "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 2017 Pakistan

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_pakistan

261.57 KB
12234 rows
15 columns


CREATE TABLE modis_2017_pakistan (
  "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 2017 Palestine

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_palestine

11.59 KB
29 rows
15 columns


CREATE TABLE modis_2017_palestine (
  "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 2017 Panama

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_panama

48.36 KB
1407 rows
15 columns


CREATE TABLE modis_2017_panama (
  "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 2017 Papua New Guinea

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_papua_new_guinea

129.84 KB
5500 rows
15 columns


CREATE TABLE modis_2017_papua_new_guinea (
  "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 2017 Paraguay

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_paraguay

836.83 KB
46237 rows
15 columns


CREATE TABLE modis_2017_paraguay (
  "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 2017 Peru

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_peru

253.45 KB
11481 rows
15 columns


CREATE TABLE modis_2017_peru (
  "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 2017 Philippines

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_philippines

94.64 KB
3367 rows
15 columns


CREATE TABLE modis_2017_philippines (
  "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 2017 Poland

@kaggle.brsdincer_investigative_wildfire_data_for_turkey_nasa.modis_2017_poland

25.7 KB
466 rows
15 columns


CREATE TABLE modis_2017_poland (
  "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
);