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Scale Effect of Vegetation Index Based Thermal Sharpening: Scale Effect of Vegetation Index Based Thermal Sharpening:

Scale Effect of Vegetation Index Based Thermal Sharpening: - PowerPoint Presentation

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Scale Effect of Vegetation Index Based Thermal Sharpening: - PPT Presentation

XH Chen a Y Yamaguchi a J Chen b YS Shi a a Graduate School of Environmental Studies Nagoya University Nagoya 4648601 Japan b State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Beijing 100875 China ID: 384672

ndvi spatial tsharp extent spatial ndvi extent tsharp image resolution slope relationship improved thermal sharpening regressed effect local coarse

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Slide1

Scale Effect of Vegetation Index Based Thermal Sharpening: A Simulation Study Based on ASTER Data

X.H. Chen

a

, Y. Yamaguchi

a

, J. Chen

b

, Y.S. Shi

a

a

Graduate School of Environmental Studies, Nagoya University, Nagoya, 464-8601, Japan

b

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, ChinaSlide2

Outlines

Introduction

1

TsHARP

2

Scale Effect of NDVI-

T

Relationship

3

Improved

TsHARP

Method

4

6

Discussion and Conclusion

5Slide3

1. INTRODUCTION

Thermal infrared (TIR) band imagery has been widely applied in many studies (e.g. evapotranspiration

esitimation

; urban heat island; drought monitoring, etc.)

Unfortunately, the spatial resolution of TIR bands is usually coarser than that of

visble-near infrared (VNIR) bands

Several thermal sharpening methods have been developed for sharpening spatial resolution of TIR band by using VNIR bandSlide4

Vegetation Index Based Thermal Sharpening

TsHARP

(

Kutas et al, 2003) was intensively studied

Negative correlation between NDVI and surface temperature (T)NDVI-

T Relationship established on coarse resolution is applied on fine resolution.Previous studies found that the spatial resolution does not affect NDVI-T relationship largely;However, another factor, spatial extent, was largely neglected in the previous studies.Our study aims to: Investigate the scale effect of NDVI-TImprove TsHARP by considering the effect of

spatial extentSlide5

2. TsHARP

Establish relationship between

T

and NDVI on the coarse resolution

The

regression relationship is applied to the NDVI at their finer resolution (

NDVI

high). Then, the divergence of the retrieved temperatures from the observed temperature field is due to spatial variability in T driven by factors other than vegetation cover, and can be assessed at the coarse resolution

This coarse-resolution residual field is added back into the sharpened map

The slope is key parameter for sharpening resultSlide6

3. SCALE EFFECT OF NDVI-

T

3.1 Data

A subset image (256×256 pixels) with 90m

resolution

of ASTER captured in

the grassland in Inner

Mongolia, China (44.6ºN, 116.0ºE), on the date of July 16th, 2010, was used for study. A subset image (256)

VNIR band

NDVI

Surface TemperatureSlide7

SCALE EFFECT OF NDVI-T

Two aspects of “Scale”

Spatial

Resolution (size of a pixel)

Spatial Extent (size of

study area)

90m

720m

1440mSlide8

NDVI-T Relationship on Different Resolutions

NDVI and

T

images were resampled to different spatial resolutions (90m to 2880m

) by

linear aggregation.

Slope (

a) of NDVI-T on different resolutions were investigated

The regressed slope increases slightly with increasing of spatial resolutionSlide9

Spatial Extent of

m

pixels

Original image is divided into N/(

m×m

) windows.Average the values of the pixels in each windowLocal difference image is derived by subtracting the original image with the averaged image

Regression is conducted on the local difference images of NDVI and T

Local Difference Image

NDVI-

T

Relationship on Different ExtentsSlide10

Regressed slope (

a

) increases with the increasing of spatial extent following a power function

Compared with spatial resolution, spatial extent affects regressed slope more largely.

(a)

(b)

Spatial extent (m)Slide11

4. IMPROVED TsHARP

Sharpening

T

image is equal to retrieving the local difference image of

T on extent of a thermal pixel.

The regression relationship should be established on the spatial extent of one thermal pixel

Spatial Extent

Slope

Slope on extent of whole image (

a

)

Slope on extent of one thermal pixel (

a

local

)

: Unkown without high resolution T image

Slope on extent of 2×2 thermal pixels

We use

the power function of (spatial extent -regressed slope) to estimate the slope (

a

local

) on the

extent of one thermal pixel

Improved TsHARP replaces a with

a

localSlide12

Algorithm Test

T

image with 900m resolution was generated.

The coarse

T image was sharpened to 90m using TsHARP

and improved TsHARP respectively

TsHARP

(a)

Improved

TsHARP

(alocal)Spatial extent (m)

(23040m, 38.1)Slide13

Sharpened Result

Image sharpened by Improved

TsHARP

is smoother than that by original

TsHARP

(c)

TsHARP

Improved TsHARP

True

T

image

Coarse

T

imageSlide14

Accuracy Assessment

Improved TsHARP

TsHARP

Best slope

The best value of slope is around 15.9

Improved method acquired higher sharpening accuracy

Original

TsHARP

over-sharpens the

T

image

Actual

T

image with 90m is used for accuracy assessmentSlide15

15

5. DISCUSSION and CONCLUSION

Why spatial extent affects the NDVI-

T

relationship?

Other than NDVI, soil moisture also affects surface temperature. Assuming that

Since NDVI is somehow positively correlated with soil moisture, when T is regressed with only NDVI, the regressed slope becomes (for convenience, we assume the data is standardized)As spatial pattern of moisture is smoother than NDVI, when spatial extent increases, the correlation between NDVI and Moisture increases, consequently the regressed slope also increases.Slide16

Conclusion

Spatial extent is an important factor affecting the NDVI-

T

relationship, and should not be neglected in the related studies

Improved

TsHARP considers the effect of spatial extent and can acquire better sharpening result in this case of study.Slide17

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