Analysis of Light Intensity Data by the DMSP/OLS Satellite ...akiyama-lab.jp/yuki/paper/2012/ISPRS2012_ppt_YukiAkiyama.pdfSignificance levels of analyses in each area are 95%. Using

EDITORIA, The University of Tokyo

Analysis of Light Intensity Data by the DMSP/OLS Satellite Image Using Existing Spatial

Data for Monitoring Human Activity in Japan

XXII ISPRS Congress 2012 Melbourne, Australia Aug. 25- Sep. 1, 2012

Yuki Akiyama

The University of Tokyo, Japan Earth Observation Integration & Fusion Research Institute


Outline

1. Background and objective

2. Study area

3. Data development and Analyses

4. Conclusion


Outline


2. Study area

3. Data development and analyses

4. Conclusion


1.Background and objective

・Monitoring for location and shape of urban areas are very significant task for planning urban management in broad metropolitan areas.

・However, it is often the case that developments of basic spatial data are poor especially in developing countries.

↓ Many studies try to monitor locations and shapes of urban areas and human activity without dependence on existing spatial data. One of such method is to use nighttime images (DMSP/OLS) by NOAA/NGDC.

1



Examples of urban monitoring by the DMSP

2

Urban monitoring by the DMSP

・Estimation of damaged urban areas by earthquakes (Kohiyama et al.(2000))

・Estimation of building distribution (Takahashi and Hayashi (2001))

・Expansion monitoring of urban areas in some major cities around the world (Small et al. (2005))

Relationship analysis between the DMSP and other data

・Relationship analysis between DMSP night light intensity with business activity or electricity consumption (Elvidge et al. (1997))

・Relationship analysis with population distribution (Dobson et al. (2000))

・Relationship analysis with GDP (Ghosh et al. (2009))



We can use DMSP data for free

3 http://www.ngdc.noaa.gov/dmsp/downloadV4composites.html

Cloud-free image data made using all the available archived DMSP-OLS smooth resolution data for calendar years (30 arc second grids, 180 to 180 degrees longitude and -65 to 75 degrees latitude.).

http://www.ngdc.noaa.gov/dmsp/downloadV4composites.html



4

Many studies have monitored distributions of urban areas and estimate human activities using DMSP/OLS images. ↓ On the other hand…

Which kinds of human activities are especially reflected in the DMSP/OLS images? are not clear. Because…

Detailed and reliable spatial data about human activity or urban facilities are not developed adequately.



Objective

5

It is clear that DMSP data reflect which kinds of human activities to compare some existing spatial data in Japan. We use 3 kinds of micro-accurate spatial data in Japan as existing spatial data. 1) Road network data 2) Building polygon data 3) Dynamic population data DMSP and these 3 data are aggregated into same size grids and compare by single and multiple regression analyses.


Contents


2. Study area


4. Conclusion


2.Study area

6

Japan Tohoku region

・Total area :Approx. 6.69km2 ・Population: Approx. 9.2 million There are relatively broad suburban areas adjoined major cities and broad depopulated rural areas and mountain forest areas.

Sendai

Koriyama

Morioka


Contents


2. Study area


4. Conclusion


3.Data development and analyses

7

DMSP image data (Raster data)

Road network data (Line data)

Building data (Polygon data)

Dynamic population data

existing spatial data of Japan

Reaggregation into same size gird polygons (1km square grid called “Japanese standard regional mesh Level 3”)

There are 69,814 girds in Tohoku region



7










Reaggregation of DMSP data

8

・Grid size of DMSP data is 30arc seconds (about 820m in Tohoku region). ・Japanese regional mesh is 1km square. →DMSP values are reaggregated by following method.

Calculation of reaggregating DMSP value in mesh i

i

iSR

VSDVSDVSDVSDNV 44332211



Reaggregating DMSP data

9 Image opportunities of Japan are approximately 8:00~9:00PM .



7










Reaggregation of road data

10

・Road network data was developed by the national census map data in 2005 provided by Center for Spatial Information Science, The University of Tokyo. ・ This data contains line data of highway, main road and other road. →Load length grid data can be developed to split this line data by the Japanese regional mesh. ・Japan road association set the luminance level of road in Japan as following table.

Urban Suburban Rural

Highway 1.0 1.0 0.5

Main road 1.0 0.7 0.1Other road 0.7 0.1 0.1

Region typeRoad Type

How to define urban, suburban and rural areas of each grid?



How to define urban, suburban and rural areas?

11

・ National Land Information of Japan in 2006 contains polygon data of urbanization promoting areas, urbanization control areas and other areas.

・In this study, urbanization promoting areas are defined as urban area, urbanization control areas are suburban area and others are rural area. ・To overlay this polygon data with the regional mesh, all regional meshes gain regional information.



How to define urban, suburban and rural areas?

12

Reaggregating DMSP Zonal classification




13

Mesh i (in Suburban area) 1. Calculate road length of each kinds of road

Highway (Red) = 120[m] Main road (Blue) = 210[m] Other road(green) = 250[m]

Urban Suburban Rural

Highway 1.0 1.0 0.5

Main road 1.0 0.7 0.1Other road 0.7 0.1 0.1

Region typeRoad Type

Luminance level of road in Japan

2. Multiple luminance level

Highway = 120[m] * 1.0 = 120 Main road = 210[m] * 0.7 = 147 Other road = 250[m] * 0.1 = 25

3. Get road distribution data

120 + 147 + 25 = 292




14

Reaggregating DMSP

Road data



15

Correlation analysis between DMSP and road data

Independent variable(X): road grid data Dependent variable(Y): DMSP grid data

Number ofmesh

Correlationcoefficient ( R )

Coefficient ofdetermination(R2)

Whole area 69814 0.6234 0.3886

Urban area 2245 0.5745 0.3301Suburban area 5765 0.8122 0.6597Rural area 61804 0.6144 0.3775

Correlation in suburban area is relatively large.



7










Reaggregation of building data

16

・Digital residential maps in 2008 (ZmapTOWN II, by ZENRIN CO., LTD) were used as building distribution data. ・Locations, shapes, areas, kinds of building, and use of almost all rooms everywhere in Japan can be collected using this data.




17

Mesh i 1. Classify building use by attribute data Residential buildings: red Shops and office buildings: blue Multi-use buildings: green

Room type number of rooms

Personal house 10

Shop and office 2Others 3Total 15

Room information

100㎡ 100㎡

80㎡

400㎡ 500㎡ 2. Calculate light intensity values by buildings without multi-use building

S = 280 V = 280 *0.5 = 140 S = 400 V = 400 * 2 = 800

3. Calculate light intensity values by multi-use building

S = 500 V= 500 * {(0.5*10 + 2*2)/15} = 300

4. Get building distribution data

140 + 800 + 300 = 1240




18

Reaggregating DMSP Bldg distribution data



19

Correlation analysis between DMSP and bldg data

Independent variable(X): building distribution grid data Dependent variable(Y): DMSP grid data

Correlation in suburban area is relatively large.

Number ofmesh



Whole area 69814 0.6153 0.3786

Urban area 2245 0.5076 0.2577

Suburban area 5765 0.7476 0.5589

Rural area 61804 0.6123 0.3749



7











20

・The Agoop Data in 2009 (AGOOP Corp.) were used as distribution data of estimating dynamic population. ・Estimating dynamic populations in each month and each hour can be monitored by the unit of the regional mesh. ・Estimating dynamic population are calculated to integrate GPS logs by mobile phones with some kinds of statistical data. ・In this study the Agoop Data between 20:00 and 21:00 were used because image opportunities of the DMSP are also between 20:00 and 21:00.



Dynamic population data (20:00-21:00)

21

Reaggregating DMSP Dynamic population data



22

Correlation analysis between DMSP and dynamic population data

Independent variable(X): dynamic population data Dependent variable(Y): DMSP grid data

Correlations in each area are not large. >DMSP cannot monitor actual distribution of population appropriately.

Number ofmesh



Whole area 69814 0.3615 0.1307

Urban area 2245 0.2584 0.0668Suburban area 5765 0.5336 0.2847Rural area 61804 0.3552 0.1262

EDITORIA, The University of Tokyo 23









DMSP grid data (1km grid)

Road grid data (1km grid)

Building grid data (1km grid)


(1km grid)


Dependent variable (Y)

Independent variables (X1, X2, X3)

Multiple regression analyses



24

Result of multiple regression analyses Significance levels of analyses in each area are 95%.

Using these values, we can monitor which independent values have the strongest impact for DMSP images.

Whole area Urban area Suburban area Rural area

0.6735 0.5959 0.5224 0.4826

0.4536 0.3551 0.2729 0.23290.4536 0.3542 0.2725 0.23289.6745 11.6262 13.0607 7.076169814 2245 5765 61804

Intercept 6.863 35.843 22.612 4.910Road 0.0018865 0.0008254 0.0015749 0.0016717Building 0.0002392 0.0000814 0.0005255 0.0029940Dynamic population 0.0000726 0.0000501 0.0001448 0.0003788Intercept 175.329 79.017 96.831 23.315Road 95.1163943 18.0746061 19.2717919 51.6165023Building 87.0567910 8.8593756 17.8187962 56.2256234Dynamic population 23.4419122 2.0332598 0.2761974 17.1764813Road 1.2723 0.9840 1.3376 0.9639Building 0.6691 0.3857 0.4162 1.3328Dynamic population 0.1990 0.0940 0.1674 0.5092

Critical F-value 0 7.7643E-213 0 0

Adjusted R2

Correlation coefficient Coefficient of determination

Standard deviationNumber of mesh

Coefficient

Standard paritalregression coefficient

t-value



25

Result of multiple regression analyses

・Impacts by road distribution are strong in urban and suburban areas. ・ Impacts by building distribution become strong in suburban and rural areas. ・There are few affects by dynamic populations.

Whole area Urban areaSuburban

areaRural area

Road 1.2723 0.9840 1.3376 0.9639

Building 0.6691 0.3857 0.8162 1.3328

Dynamic population 0.1990 0.0940 0.1674 0.5092

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

1.4000

1.6000

Stan

dar

d p

arti

alre

gre

ssio

n c

oe

ffic

ien

t



DMSP grid data (1km grid)

Road grid data (1km grid)

Building grid data (1km grid)


(1km grid)


Dependent variable (Y)

Independent variables (X1, X2, X3)

Regression formulas in each area

Estimating DMSP values



Estimating DMSP data by the results of multiple regression analyses

27

Real DMSP Est. DMSP



Regression analysis between estimating DMSP and real DMSP

28



Map of Real DMSP / Estimating DMSP values

29

・Blue areas Grid values of real DMSP are much smaller than est. DMSP.

・Green areas Difference between them are small. ・Red areas Grid values of real DMSP are much larger than est. DMSP.




30

They locate on roads and small villages in rural areas.

・Blue areas Grid values of real DMSP are much smaller than est. DMSP.




30

・Red areas Grid values of real DMSP are much larger than est. DMSP.

They locate around cities like ring shape because of light saturation.




30





31


Misawa airbase

Ski resorts

Airport and large factories

Nuclear power plants (Fukushima-daiichi and Fukushima-daini)

Human activity in these areas are small, However light intensity is large.

Other reasons


Contents


2. Study area

3. Data development and Analyses

4. Conclusion


4.Conclusion

32

・This study shows how much can be monitored various human activities using nighttime images of the DMSP/OLS in Tohoku region, Japan. ・Light intensity by the DMSP/OLS correlates strongly with road distribution in urban and suburban areas and building distribution in rural areas. The correlation with dynamic population is weak in all areas. ・Results of multiple regression analysis show that road distribution has a major effect on the light intensity of the DMSP/OLS in urban and suburban areas and building distribution in rural areas.


4.Conclusion

33

・Estimating light intensity based on 3 factors can explain actual light intensity by the DMSP/OLS relatively well. > It means that the light intensity by the DMSP/OLS can monitor various kinds of lights radiated by human activities appropriately. ・The DMSP/OLS cannot monitor distributions of some roads and small towns and villages in rural areas. ・Effects of saturating light in suburban areas from city centers and facilities radiating strong light in suburban and rural areas should be considered.


Thank you for your kind attention!

Contact

Yuki Akiyama

Email: [email protected]

Phone: +81-3-5452-6417

Acknowledgment

Documents

Analysis of Light Intensity Data by the DMSP/OLS Satellite ...akiyama-lab.jp/yuki/paper/2012/ISPRS2012_ppt_YukiAkiyama.pdfSignificance levels of analyses in each area are 95%. Using