Study on applying MODIS image into drought indicator analysis in TaiwanStudy on applying MODIS image into drought indicator analysis in Taiwan

Yuh-Lurng Chung, Chaur-Tzuhn Chen Chen-Ni Hsi , Shih-Ming Liu

2004.11.04

Yuh-Lurng Chung, Chaur-Tzuhn Chen Chen-Ni Hsi , Shih-Ming Liu

2004.11.04

Introduction Introduction

When drought occurs, due to insufficient water supply, the variation/change of leaves in aridity can be sensed by spectral reflection of multi-temporal satellites.

When drought occurs, due to insufficient water supply, the variation/change of leaves in aridity can be sensed by spectral reflection of multi-temporal satellites.

Overseas researches of employing satellite images to efficiently forecast and manage drought has achieved great outcomes.

Overseas researches of employing satellite images to efficiently forecast and manage drought has achieved great outcomes.

Introduction Introduction

This research employ MODIS images to select sensitive thermal bands suitable for monitoring drought. And by using bands for the calculation of all kinds of vegetation indices, it is expected to find proper and practical indices for drought monitoring, which could be used for future management and determination of drought disaster.

This research employ MODIS images to select sensitive thermal bands suitable for monitoring drought. And by using bands for the calculation of all kinds of vegetation indices, it is expected to find proper and practical indices for drought monitoring, which could be used for future management and determination of drought disaster.

Using bands for the calculation of all kinds of vegetation indices, it is expected to find proper and practical indices for drought monitoring, which could be used for future management and determination of drought disaster.

Using bands for the calculation of all kinds of vegetation indices, it is expected to find proper and practical indices for drought monitoring, which could be used for future management and determination of drought disaster.

Research Data/Information and Methodology

Research Data/Information and Methodology

1. Study Area 1. Study Area

Include whole Taiwan area of 19 districts. Include whole Taiwan area of 19 districts.

2. Required Data/Information 2. Required Data/Information

Precipitation Data Precipitation Data Information concerned includes the records of all rainfall stations from the January of 1991 to the March of 2004 in the entire Taiwan area.

Information concerned includes the records of all rainfall stations from the January of 1991 to the March of 2004 in the entire Taiwan area.

Legend

Boundary

Rainfall station

LocationsLocations of All of All Taiwan Taiwan Rainfall Stations Rainfall Stations

MODIS Images MODIS Images

BandBand Width

(μm)Central Wavelen

gth (μm)Required NeΔT (K)

Primary use*

20 3.660－ 3.841 3.7882 0.05 O,L

21 3.929－ 3.989 3.9921 2.00 Fire,volcano

22 3.929－ 3.989 3.9719 0.07 A,L

23 4.020－ 4.080 4.0567 0.07 A,L

29 8.400－ 8.700 8.5288 0.05 L

31 10.78－ 11.28 11.0186 0.05 A,L

32 11.77－ 12.27 12.0325 0.05 A,L* A:atmospheric studies, L:land studies, O:oceanstudies * A:atmospheric studies, L:land studies, O:oceanstudies

Thermal Bands of MODIS Image Thermal Bands of MODIS Image Seven MODIS Bands for Monitoring Earth’s Surface Seven MODIS Bands for Monitoring Earth’s Surface

By utilizing the information of surface regression through Kriging Model, our approach then can get drought indicator and drought amount of this area.

Two images of the dry season (January 25, 2004) and wet season (June 30, 2004) are accordingly chosen for further analysis.

By utilizing the information of surface regression through Kriging Model, our approach then can get drought indicator and drought amount of this area.

Two images of the dry season (January 25, 2004) and wet season (June 30, 2004) are accordingly chosen for further analysis.

Research MethodologyResearch Methodology

Select clear MODIS Select clear MODIS images without cloudimages without cloud

Select clear MODIS Select clear MODIS images without cloudimages without cloud

Rainfall of Rainfall of continuous 30 continuous 30

daysdays

Rainfall of Rainfall of continuous 30 continuous 30

daysdays

Data from Data from rainfall stationsrainfall stations

Data from Data from rainfall stationsrainfall stations

Cumulative Cumulative rainfall of allrainfall of all Cumulative Cumulative

rainfall of allrainfall of all

Drought Drought AmountAmount>130m>130m

mm

Drought Drought AmountAmount>130m>130m

mm

Th

resho

ld

Th

resho

ld

of E

ach

of E

ach

Co

un

ty and

C

ou

nty an

d

City

City

Th

resho

ld

Th

resho

ld

of E

ach

of E

ach

Co

un

ty and

C

ou

nty an

d

City

City

Climate Drought IndicesClimate Drought Indices Climate Drought IndicesClimate Drought Indices

NONO

YESYES

PrePreprocessingprocessing of MO of MODIS imagesDIS images

PrePreprocessingprocessing of MO of MODIS imagesDIS images

Use MODIS images of dry and wet Use MODIS images of dry and wet seasons to select thermal bands fseasons to select thermal bands f

or drought or drought monitormonitor

Use MODIS images of dry and wet Use MODIS images of dry and wet seasons to select thermal bands fseasons to select thermal bands f

or drought or drought monitormonitor

Locate sample grasslLocate sample grasslandand areas areas

Locate sample grasslLocate sample grasslandand areas areas

Choose index for Choose index for droughtdrought

Choose index for Choose index for droughtdrought

Specify Ranges of Dry Specify Ranges of Dry and Wet Seasons Using and Wet Seasons Using Climate Drought IndicesClimate Drought Indices

Specify Ranges of Dry Specify Ranges of Dry and Wet Seasons Using and Wet Seasons Using Climate Drought IndicesClimate Drought Indices

Calculate all indices& select Calculate all indices& select some for preliminary analysissome for preliminary analysis Calculate all indices& select Calculate all indices& select

some for preliminary analysissome for preliminary analysis

Discussions on Applying Drought Indices to Drought Monitor Discussions on Applying Drought Indices to Drought Monitor

Normalized Thermal Index (NTI) Normalized Thermal Index (NTI)

Normalized Difference Vegetation Index (NDVI) Normalized Difference Vegetation Index (NDVI)

bands) hermal()

bands) (thermal)(

t22or (Band21

22or Band21NTI

Band1Band2Band1and2

B

NDVI

Normalized Difference Water Index (NDWI) Normalized Difference Water Index (NDWI)

5

5B

BandBand2Bandand2

NDWI

Shortwave Infrared Water Stress Index (SIWSI) Shortwave Infrared Water Stress Index (SIWSI)

26

26B

BandBandBandand

SIWSI

Results and Discussion Results and Discussion

Characteristics of Taiwan Rainfall Data Characteristics of Taiwan Rainfall Data

Based on the rainfall data of 355 rainfall stations from 1992 to 2003, clearly shows different standards (levels) of different places in different periods. It also indicates the relativity of drought definition due to spatial and temporal factors.

Based on the rainfall data of 355 rainfall stations from 1992 to 2003, clearly shows different standards (levels) of different places in different periods. It also indicates the relativity of drought definition due to spatial and temporal factors.

Historical Curves of the first decile values of Cumulative

0

50

100

150

200

250

300

350

400

一月 三月 五月 七月 九月 十一月 月份

30(m

m)

連續日累積降雨量第ㄧ個十分位數

台北縣桃園縣新竹縣苗栗縣台中縣彰化縣南投縣雲林縣宜蘭縣花蓮縣台東縣嘉義縣台南縣高雄縣

Taipei

Taoyuan

Hsinchu

Miaoli

Taichung

Janghua

Nantou

Yunlin

Yilan

Hualien

Taitung

Chiayi

Tainan

Kaohsiung

January March May July September November Month

Precip

itation w

ithin 30 consecutive days

recorded by rainfall stations in

each county

in wh

ole Taiw

an Province

Application of MODIS Images to Select Thermal Bands for Drought Monitor

Application of MODIS Images to Select Thermal Bands for Drought Monitor

Original MODIS Image Original MODIS Image MODIS Images After Geometric CorrectionMODIS Images After

Geometric Correction

Extraction of Sample Sites of Grasslands Extraction of Sample Sites of Grasslands

SampleBoundary

Legend

From land-use maps we query all natural grasslands from the database of ArcGIS. And after removing those sample cloud hovering, we mark those sample sites on the extracted images of natural grasslands without cloud covered.

From land-use maps we query all natural grasslands from the database of ArcGIS. And after removing those sample cloud hovering, we mark those sample sites on the extracted images of natural grasslands without cloud covered.

Select Thermal Bands of MODIS images for Drought MonitorSelect Thermal Bands of MODIS images for Drought Monitor

Extracts data for the seven MODIS

bands, and compares the seven bands o

f dry season and wet season to find out

what are the real differences.

Extracts data for the seven MODIS

bands, and compares the seven bands o

f dry season and wet season to find out

what are the real differences.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

B20(3.660-3.840)

B22(3.929-3.989)

B23(4.020-4.080)

B29(8.400-8.700)

B31(10.780-11.280)

B32(11.770-12.270)

B33(13.185-13.485)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

B20(3.660-3.840) B22(3.929-3.989) B23(4.020-4.080) B29(8.400-8.700) B31(10.780-11.280) B32(11.770-12.270) B33(13.185-13.485)

Differences of Differences of Thermal Infrared Thermal Infrared Band Values of Band Values of MODIS Images of MODIS Images of Grasslands in Dry Grasslands in Dry and Wet Seasonsand Wet Seasons

Differences of Differences of Thermal Infrared Thermal Infrared Band Values of Band Values of MODIS Images of MODIS Images of Grasslands in Dry Grasslands in Dry and Wet Seasonsand Wet Seasons

Differences of Mean Differences of Mean Thermal Infrared Thermal Infrared Bands of Taiwan Bands of Taiwan Grasslands MODIS Grasslands MODIS Images in Dry and Images in Dry and Wet SeasonsWet Seasons

Differences of Mean Differences of Mean Thermal Infrared Thermal Infrared Bands of Taiwan Bands of Taiwan Grasslands MODIS Grasslands MODIS Images in Dry and Images in Dry and Wet SeasonsWet Seasons

The Calculation of Normalized TherThe Calculation of Normalized Thermal Indexmal Index (NTI) (NTI)

The Calculation of Normalized TherThe Calculation of Normalized Thermal Indexmal Index (NTI) (NTI)

NTINTI

Nu

mb

er of P

ixelsN

um

ber o

f Pixels

NTINTI

Nu

mb

er of P

ixelsN

um

ber o

f Pixels

STD

Mean

STD =

Mean=

NTINTI Image and Histogram In Image and Histogram In Dry SeasonDry Season

NTINTI Image and Histogram In Image and Histogram In Wet SeasonWet Season

The research done by Robert et al. (2002) about monitoring volcano indicates that NTI value are ranged between -0.850 ~ -0.950 due to the high surface temperature of the volcanic region.

Statistics of NTI Values of MODIS Images in Dry and Wet Season, and T-test

Statistics of NTI Values of MODIS Images in Dry and Wet Season, and T-test

MODIS NTI Images

Sample Numbers

NTI Average

P(T<=t) Difference

In Dry Season 36600 -0.1990 Distinct

In Wet Season 36600 0.058

Note: T-test with confidence interval 5% Note: T-test with confidence interval 5%

The Calculation of Normalized DifferThe Calculation of Normalized Difference ence VegetationVegetation Index (NDVI Index (NDVI))

The Calculation of Normalized DifferThe Calculation of Normalized Difference ence VegetationVegetation Index (NDVI Index (NDVI))

MODIS NDVI Images in Dry Season

MODIS NDVI Images in Wet Season

NDVINDVI

Nu

mb

er of P

ixelsN

um

ber o

f Pixels

NDVINDVI

Nu

mb

er of P

ixelsN

um

ber o

f PixelsSTD=

Mean=STD=

Mean=

MODIS NDVI Images

SamplesNDVI

AverageP(T<=t) Difference

In Dry Season 78 0.882.84E-12 significant

In Wet Season 78 0.94

Statistics of NDVI Values of MODIS Images in Dry and Wet Season, and T-test

Statistics of NDVI Values of MODIS Images in Dry and Wet Season, and T-test

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77

草生地樣本數

NDVI

乾季NDVI

濕季NDVI

Dry NDVI

Wet NDVI

Sample

NDVINDVI Differences Differences of of MODISMODIS Grassland Images Grassland Images in Dry and Wet in Dry and Wet SeasonsSeasons

NDVINDVI Differences Differences of of MODISMODIS Grassland Images Grassland Images in Dry and Wet in Dry and Wet SeasonsSeasons

The Calculation of Normalized The Calculation of Normalized Difference Water Index (NDWI)Difference Water Index (NDWI)The Calculation of Normalized The Calculation of Normalized Difference Water Index (NDWI)Difference Water Index (NDWI)

MODIS NDWI Images in Dry Season

MODIS NDWI Images in Wet Season

NDWINDWI

Nu

mb

er of P

ixelsN

um

ber o

f Pixels

NDWINDWI

Nu

mb

er of P

ixelsN

um

ber o

f Pixels STD

Mean

STD

Mean

Statistics of NDWI Values of MODIS Images in Dry and Wet Seasons, and T-test

Statistics of NDWI Values of MODIS Images in Dry and Wet Seasons, and T-test

MODIS NDWI Images

Samples NDWI

AverageP(T<=t) Difference

In Dry Season 78 0.527.67E-08 significant

In Wet Season 78 0.63

Difference of Difference of MODIS NDWI MODIS NDWI Images of Images of Grasslands in Dry Grasslands in Dry and Wet Seasonsand Wet Seasons

Difference of Difference of MODIS NDWI MODIS NDWI Images of Images of Grasslands in Dry Grasslands in Dry and Wet Seasonsand Wet Seasons

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77

草生地樣點數

ND

WI

NDWI乾季

NDWI濕季

Dry NDWI

Wet NDWI

Sample

Difference Between NDVI and NDWI of MODIS Images of Grasslands in Dry and Wet Seasons Difference Between NDVI and NDWI of MODIS Images of Grasslands in Dry and Wet Seasons

NDVI in Dry Season

NDVI in Wet Season

NDWI in Dry Season

NDWI in Wet Season

NDVI in DS 1.000

NDVI in WS 0.079 1.000

NDWI in DS 0.461** 0.045 1.000

NDWI in WS 0.197 0.799** 0.023 1.000

**: Distinct Correlation as distinction level if 0.01(**). 　

DS: Dry Season WS: Wet Season

**: Distinct Correlation as distinction level if 0.01(**). 　

DS: Dry Season WS: Wet Season

Correlation Matrix Between NDVI and NDWI in DS and WS

Correlation Matrix Between NDVI and NDWI in DS and WS

Calculation of Shortwave Infrared Calculation of Shortwave Infrared Water Index (SIWSIWater Index (SIWSI))

Calculation of Shortwave Infrared Calculation of Shortwave Infrared Water Index (SIWSIWater Index (SIWSI))

SIWSISIWSI

Nu

mb

er of P

ixelsN

um

ber o

f Pixels

MODIS SIWSI Images of Taiwan in Dry Season

MODIS SIWSI Image of Taiwan in Wet Season

SIWSISIWSI

Nu

mb

er of P

ixelsN

um

ber o

f Pixels STD

MeanMean

Statistics and T-test Table of SIWSI Values of grasslands in Dry and Wet Seasons (MODIS Images)

Statistics and T-test Table of SIWSI Values of grasslands in Dry and Wet Seasons (MODIS Images)

SIWSI Differences SIWSI Differences for Sample Sites of for Sample Sites of Grasslands in Dry Grasslands in Dry and Wet Seasons, and Wet Seasons, based on MODIS based on MODIS ImagesImages

SIWSI Differences SIWSI Differences for Sample Sites of for Sample Sites of Grasslands in Dry Grasslands in Dry and Wet Seasons, and Wet Seasons, based on MODIS based on MODIS ImagesImages

MODIS SIWSI Images

Number of Samples

SIWSI Mean P(T<=t) differences

Dry Season 78 -0.290.3214 Insignificant

Wet Season 78 -0.31

ConclusionConclusion

This research indicates MODIS images with 36 bands have substantial potential in drought sensing. It is hereby possible to replace the NOAA-AVHRR satellite images with MODIS images, for more precise image data/information.

This research indicates MODIS images with 36 bands have substantial potential in drought sensing. It is hereby possible to replace the NOAA-AVHRR satellite images with MODIS images, for more precise image data/information.

The MODIS Band 22 at the spatial resolution of 1,000 m is the most sensitive thermal bands to drought. And the NTI is the unique index of sensing thermal energy only available in MODIS images. Furthermore, this research hence utilizes the important wave bands which are chosen from the Band 22 to calculate NTI.

The MODIS Band 22 at the spatial resolution of 1,000 m is the most sensitive thermal bands to drought. And the NTI is the unique index of sensing thermal energy only available in MODIS images. Furthermore, this research hence utilizes the important wave bands which are chosen from the Band 22 to calculate NTI.

We can conclude that the NDVI, NDWI and NTI are sensitive to the monitoring of surface vegetation status, water content of vegetation and surface temperature respectively. As a result, they hereby have practical usages for drought forecast and monitoring.

We can conclude that the NDVI, NDWI and NTI are sensitive to the monitoring of surface vegetation status, water content of vegetation and surface temperature respectively. As a result, they hereby have practical usages for drought forecast and monitoring.

The EndThe End