Global hot spot monitoring with

Landsat 8 and Sentinel-2

Soushi Kato

Atsushi Oda

Ryosuke Nakamura (AIST)

Motivation for Detecting Hot Spots

Hotspot detection using satellite data

• To monitor wildfire and volcano

• 4 mm band of MODIS or VIIRS with ~1 km resolution

AIST had a ground station of Landsat 8 until March 2015

• Effective use of real-time and archival Landsat 8 data

• SWIR (2 mm) based method (Giglio et al., 2008)

• Limited to eastern Asian region

Landsat 8 archive on Amazon Web Service

• Expand our application to global scale

• Few days of delay

Landsat 8

• OLI (Operational Land Imager)

8 bands in visible – shortwave infrared (30 m)

+ Panchromatic (15 m)

• TIRS (Thermal Infrared Sensor)

2 bands in thermal infrared (100 m)

http://landsat.usgs.gov

Bands Sensitive to Hot Target

0

500

1000

1500

2000

2500

0 2 4 6 8 10 12

Ra

dia

nce

(W/m

2srm

m)

l (mm)

300K500K

700K

800K

900K

B5 B6 B7 B10 B11

NIR SWIR TIR

Planck curves

Detection Method by Giglio et al. (2008)

Fire detection by ASTER reflectance

NIR 0.81mm

SW

IR 2

.33m

m

Candidate fireObvious fire

Candidate fires are further

evaluated by comparing with

the surroundings to identify

fire.

Schroeder et al. (2016)

applied the same method to

Landsat 8 data.

Day Night

Lmax Lmax

Band 5: NIR Band 5: NIR

Band 7

: S

WIR

2

Band 7

: S

WIR

2

Reflectance thresholds

Min. radiance

Hotspot Hotspot

Min. radiance

LightReflected

radiance

Detection Algorithm Modified to Landsat 8

Cloud

Radiance thresholds

Detected Hot Spots

Example of Detection

Natural color

1 kmNshinoshima

Volcano

Mar. 30, 2014

(erupted during

Nov., 2013 – Nov. 2015)

B7 (SWIR) B10 (TIR)

WebGIS System to Visualize Results

http://landbrowser.geogrid.org/hotarea/

Detected Example: Wildfire

Hubei, China

Jan. 23, 2014

L8 detected

VIIRS

MODIS

May 3, 2015

0.5 km 2

October 26, 2015

2km2.2 km2

Detected Example: Open Burning

Jewish Autonomous

Region, Russia

Twice a year

L8 detected

VIIRS

MODIS

Detected Example: VolcanoSatsuma-Iojima, Japan in 2014 erupted in Jun. 2013

6 cloud free images in 46 scenes during 2014 (= 13%)

Not detected

Jul. 28

Apr. 23

Jul. 21Jul. 5

Jan. 1 Mar. 22

To Solve limited Observation Opportunity

Sentinel-2A

• Comparable waveband and spatial resolution with Landsat

series

• Revisit cycle of 10 days (16 days: Landsat 8)

• Sentinel-2B (the same design) will be launched in 2017.

NASA

Apply the Same Method to Sentinel-2 Data…

Sentinel-2A Jan. 4, 2016 Landsat 8 Mar. 17, 2016

Tokyo

Too many detected results in comparison with Landsat 8

30 km

Background: true color

Tokyo

False Positives

Sentinel-2 Jan. 4, 2016

Specular reflection resulted in increased SWIR radiance.

Greenhouse

Roof and

solar panel

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 0.5 1.0 1.5 2.0 2.5

Re

fle

cta

nce

Wavelength (mm)

Greenhouse

Roof

Steelplant

Openburning

Distinguish Hotspot from Specular Reflection

Examples of reflectance spectra

Additional empirical criteria

R2.2mm – R0.87mm

R1.6mm – R0.87mm> rmin

Result

Sentinel-2 Jan. 4, 2016

15% of the results satisfied the criteria.

Accepted

Discarded

Other Seasons

May 13, 2016

85 - 92 % of the results were discarded.

Accepted

DiscardedAug. 31, 2016

Inspection of Cause

Land use was identified using Google Map/street view

Land use Jan. 4 May 15 Aug. 31

Accepted result

Steel plant 2 2 2

Chimney stack 1 0 0

Farmland 3 0 0

Forest 1 0 0

Roof 0 2 3

Discarded result

Roof 22 34 20

Solar panel 18 9 5

Cloud 0 0 3

Chimney stack 0 1 0

Farmland 1 0 0

Prescribed fire

Jan. 24, 2015

Joso, Japan

Overpass time 21:30 JST

Thermocouple

Spectroradiometer

f 1.3m

Validation of Hotspot Detection

At the Overpass Time

Landsat 8 OLI

Overlayed on Open street map

NIR: B5

SWIR1: B6

SWIR2: B7

FOV of spectrometer

Spectrometer

radiance

1110 K

blackbody

0

2000

4000

6000

0 1 2 3

Wavelength (mm)

Sp

ec

tra

lra

dia

nt

inte

ns

ity

(W/s

rm

m) Landsat 8

1374 K

0.249 m2

f 20cm

Detected

Time Series Comparison

Difference in measurement positions produced overestimated

temperature from OLI data.

Hotspot Detection and Temperature Retrieval

Jan. 24, 2015 21:30 JST

Only 6% were obviously false detection due to light.

232 results in total

Further Study: Integration with the Other Data

Hotspot detection using TIR data

ASTER by GSJ https://gbank.gsj.jp/nyouga/all/

• Spatial resolution: 90m

• 2000~

• Criteria: TOA BT > 330K

CIRC by JAXA http://circgs.tksc.jaxa.jp/data/

• Spatial resolution: 200m (ALOS2)

130m (CALET on ISS)

• May 2014~ (ALOS2)

• Aug. 2015~ (CALET)

• Criteria: spatial anomaly

• Level 2 product compiles detected results in text file

Integrating these with our result will increase detection

opportunity.

Further Study: Hyperspectral Data

Hyperspectral sensors with spatial resolution of 30 – 60m will

be launched in few years.

• EnMap, DLR

• PRISMA, ASI

• HyspIRI, NASA

• HISUI, METI

HISUI will be onboard ISS.

Hyperspectral data will be useful to detect hotspots and

retrieve their temperature from spectra.

HISUI sensor ISS

Summary

Hotspot detection using SWIR data

• Finer spatial resolution than MODIS or VIIRS fire product

• Automated detection system was developed.

• Results are provided by web service.

• Integration of data from multiple satellite will potentially

increase data acquisition frequency.

Landsat 8, Sentinel-2A, ASTER, CIRC, …

• Temperature can be retrieved from nighttime NIR-SWIR

spectra.

• Hyperspectral data will provide suitable data to

temperature retrieval.

Thank you for your attention.

False Detection: Rooftop

Residential areaNo fire occurred

Specular reflection?

Apr. 27 Jun.30 Oct. 18

Industrial area Shangxi, China

Google map

Roof

26.5°

= roof pitch 6/12”

Roof slope 11.6° ?

0.5 1.0 1.5 2.0l (mm)

0.08

0.79TOA reflectance

Niigata,

Japan

Oct. 29