2005 dec icoqsia
DESCRIPTION
2005TRANSCRIPT
Satellite-Based Approach for Multi-temporal PM10 Analysis Over Malaysia
Asmala Ahmad, Mazlan Hashim, M. Nizam Ayof, Agus Setyo Budi
ICOQSIA 2005, 6 8 December, Penang, Malaysia.
SATELLITE-BASED APPROACH FOR MULTI-TEMPORAL
PM10 ANALYSIS OVER MALAYSIA
ASMALA AHMAD, MAZLAN HASHIM, M. NIZAM AYOF, AGUS SETYO BUDI
Dept. of Science and Mathematics, Centre of Academic Services (CAS), National Technical University, College of Malaysia (KUTKM), Locked Bag 1200, Ayer Keroh, 75450 Malacca, Malaysia
[email protected]. of Remote Sensing, Faculty of Geoinformation Science and Engineering, Universiti Teknologi Malaysia (UTM),
Locked Bag 791, 80990 Johor Bahru,Malaysia.
[email protected]. of Science and Mathematics, Centre of Academic Services (CAS), National Technical University, College of Malaysia (KUTKM), Locked Bag 1200, Ayer Keroh, 75450 Malacca, Malaysia
Dept. of Science and Mathematics, Centre of Academic Services (CAS), National Technical University, College of Malaysia (KUTKM), Locked Bag 1200, Ayer Keroh, 75450 Malacca, Malaysia
Abstract: This study aims to analyse one of the haze major constituent, particulate matter sizing less than 10 micron (PM 10) from space-borne platform. Seven dates of NOAA-14 AVHRR satellite were successfully recorded for this purpose. These remotely sensed data represented the seven days during the September 1997 thick haze episode in Malaysia. Five locations of air pollution station were selected where PM10 has been measured. Visible (0.58 - 0.68 (m) and Near Infrared (0.725 - 1.00 (m) of AVHRR band was utilised for this purpose. Relationship between the satellite reflectance and the corresponding PM 10 ground measured API was determined using regression analysis. Variation of PM 10 concentration for seven days was then analysed using the obtained model. Finally, accuracy of the result was assessed using RMSE technique. The result proven that remote sensing technique using NOAA-14 AVHRR data was capable of quantifying PM 10 concentration spatially and continuously. Complementarily, DMSP and Earth Probe TOMS satellite data were also used as comparison.
Keywords: Haze, PM 10, API, NOAA-14 AVHRR, regression
1. Introduction
In recent times, reduced visibility has become a recurrent phenomenon across areas of Southeast Asia, resulting from biomass burning in Kalimantan and Sumatra. Haze events have been recorded in 1983, 1990, 1991, 1994, 1997 and 1998 (Radojevic and Hassan 1999, Muraleedharan et al. 2000). Koe et al. (2001) noted that reduced visibility in Malaysia during the 1997 haze episode was the result of long range transport of smoke from Sumatra. Haze from biomass burning contains large amount of trace gases (e.g., CO, NO2, SO2) and particles matter (e.g., organic matter, graphitic carbon), which are hazardous to health. Lung and eye deceases have been identified as the common immediate effect of haze. In long term, haze is capable of increasing the atmospheric greenhouse effects besides affecting the tropospheric chemistry. In big cities, emission from industrial and vehicle have acted as local contributors that worsen the situation.
Main constrains over conventional monitoring instruments are that they could not detect an early sign of haze and is impractical for large areas or for continuous monitoring. Besides that they are also logistically expensive and time consuming. The 1997 haze events prompt the need for searching of an alternative method for determining and monitoring haze in Malaysia.
Remote sensing offers a cheap, fast and more reliable alternative to monitor haze continuously over any given areas and at any time. The current study is an extension of previous work that earlier discussed on techniques for determination and mapping of haze from NOAA-14 AVHRR scene (Asmala and Mazlan, 1997, 2000, 2002).
2. Materials
Materials used in this study are as follows:
2.1 Ground-truth data
Conventional measurements of haze were complementarily used throughout performing multi-temporal analysis of haze. API (Air Pollution Index) measurements for PM 10 from 1st to 30th September 1997 area were obtained from ASMA (Alam Sekitar Malaysia Sdn. Bhd.) to represent the actual haze intensity over the study area the Peninsular Malaysia (Figure 1).
Figure 1 : (a) Study area, and (b) Combination of band 1 (0.58 - 0.68 (m), 2 (0.725 - 1.00 (m) and 4 (10.30 - 11.30 (m) of NOAA-14 AVHRR data dated 22 September 1997, were used to differentiate between haze (orange), low clouds (yellow) and high clouds (white).
2.2 Satellite data
Seven sets of NOAA-14 AVHRR data dated 22, 23, 25, 2, 28, 29 and 30 September 1997 acquired from SEAFDEC (Southeast Asia Fishery Development Centre) receiving station were used. NOAA-14 AVHRR was suitable for haze study as it offers high spectral and temporal resolution with a minimum cost. Some useful characteristics of NOAA-14 AVHRR satellite are shown in Table 1.
2.3 Ancillary data
Meteorological information over study area, including visibility, air temperature, pressure, relative humidity, wind, etc were obtained from MMS (Malaysian Meteorological Service).
Table 1: NOAA-14 AVHRR sensor and spectral characteristics
AVHRR Sensor characteristics
Swath width
2399km
Resolution at nadir
1.1km approx.
Altitude
833km
Quantisation
10 bit
Orbit type
Sun synchronous
No. of orbits per day
14.1 (approx.)
AVHRR Spectral characteristics
Channel No
Wavelength (microns)
Typical use
1
0.58 - 0.68
Daytime cloud and surface mapping
2
0.725 - 1.00
Land-water boundaries
3
3.55 - 3.93
Night cloud mapping, sea surface temperature
3A
N/A
Snow and ice detection
3B
N/A
Night cloud mapping, sea surface temperature
4
10.30 - 11.30
Night cloud mapping, sea surface temperature
5
11.50 - 12.50
Sea surface temperature
(Source: Kidwell et al., 1995)
3. Method
Four modules incorporated in this study are (a) Derivation of haze model, (b) Sampling and Regression analysis, (c) Multi-temporal analysis, and (d) Visual analysis from other satellite data.
3.1 Derivation of haze model
Prior to further data processing, post launch calibration of visible and near infrared channel was earlier implemented in order to compensate data degradation due to extreme temperature change before and after launching of AVHRR sensor to space (Rao et al., 1996). Clouds and haze were successfully differentiated using thresholding technique (Baum et al., 1997). This to ensure both were not being misinterpreted between each other. Model used in this study is based on Siegenthaler and Baumgartner (1996), which make use of skylight to indicate the existence of haze. Skylight is an indirect radiation, which occurs when radiation from the sun being scattered by elements within the haze layer. It is not a direct radiation, which is dominated by pixels on the earth surface. Figure 2 shows electromagnetic radiation path propagating from the sun towards the NOAA-14 AVHRR satellite penetrating through a haze layer. Path number 1, 3 and 4 are skylight caused by direct radiation, whereas path 2 is indirect radiation.
(Source : Modified after Siegenthaler and Baumgartner, 1996)
Figure 2 : Model used in this study is based on the skylight parameter
This model can be described by:
( - R = L V
(1)
where,( :reflectance recorded by satellite sensor,
R:reflectance from known object from earth surface ,
L:skylight, and
V:lost radiation caused by scattering and absorption.
3.2 Sampling and regression analysis
From the NOAA-14 AVHRR data, calibration pixels were sampled within a radius of 2.5 km from each of the air pollution stations in order to execute regression analysis. The best regression models for PM10 is shown below where the R2 (coefficient of determination) obtained is 0.5098 (Figure 3).
Figure 3 : Regression model for PM 10
3.3 Multi-temporal analysis
Multi-temporal analysis was performed at two selected cities, Prai Station in Penang and Pasir Gudang Station (Figure 4) in Johor. Results of the analysis were shown as graphs of haze intensity variation (API vs time) for all the five major components throughout the seven NOAA-14 AVHRR acquisition dates (Figure 6). Root-mean-square error (RMSE) analysis was carried out where ground-truth measured API and model derived API were used as input. Obtained RMSE both cities are shown in Table 2.
(2)
where:
n
:sample size,
APIcalculated:Air Pollution Index calculated by model
APImeasured:Air Pollution Index measured by air station
3.4 Visual analysis from other satellite data
Visually, the scenario was also observed from other satellite data such as DMSP (Defence Meteorological Satellite Program) satellite (Figure 7) and Earth Probe TOMS (Total Ozone Mapping Spectrometer) satellite (Figure 8).
4. Results and Discussion
Result of this study was presented as PM 10 concentration map for the whole Peninsular Malaysia. PM 10 condition for 26 September 1997, 7.29 GMT is shown in Figure 5. RMSE for selected big cities; Prai and Pasir Gudang are 31 and 27 respectively. It was believed that the relatively high gained RMSE was due to limited number of air pollution stations used. Future study will consider of using more air pollution stations as well as higher resolution satellite data in order to obtain better accuracy.
Table 2 : RMSE for PM 10 concentration at Penang and Johor Bahru
LocationPM10 (API)
Prai, Penang31
Pasir Gudang, Johor Bahru27
5. Conclusion
The study shows that remote sensing technique is capable of monitoring and quantifying PM 10 concentration continuously with minimum cost and time. These are useful in order to provide haze early warnings, so that necessary measures could be taken effectively by both government authorised party as well as public.
(a)
(b)
Figure 4 : Multitemporal trend of calculated and measured PM 10 at (a) Prai, Penang, and (b) Pasir Gudang, Johor.
PM10(API)
0 (Awan)
151-200
1-50 201-250
51-100
251-300
101-150
Figure 5 : PM 10 concentration map of Peninsular Malaysia for 26 September 1997, 7.29 GMT
Figure 6 : Haze visual analysis from band 1, 2 and 4 of NOAA-14 AVHRR satellite data for March 1997 (a)22nd, (b)23rd, (c)25th, (d)26th, (e)28th, (f)29th and (g)30th .
Landcover Water StableFire CloudBoundary N/A
(Source : National Geophysical Data Center, NOAA, USA)
Figure 7 : Haze visual analysis from DMSP satellite data for March 1997 (a)22nd, (b)23rd, (c)25th, (d)26th, (e)28th, (f)29th and (g)30th .
(Source: Goddard Space Flight Center, USA)
Figure 8 : Haze visual analysis from Earth Probe TOMS satellite data for March 1997 (a)22nd, (b) 23rd, (c)26th, (d)28th, (e)29th and (f)30th .
References
Alam Sekitar Malaysia Sd. Bhd. (1998). Measurement of Air Pollution Index (API). ASMA: Unpublished.
Asmala Ahmad and Mazlan Hashim (2002). Determination of Haze Using NOAA-14 Satellite Data. Proceedings on The 23rd Asian Conference on Remote Sensing 2002 in Kathmandu, Nepal, 25-29 November 2002.
Asmala Ahmad, and Mazlan Hashim, (1997). Determination of Haze from Satellite Remotely Sensed Data : Some Preliminary Results. Proceedings on The 18th Asian Conference on Remote Sensing 1997 at Nikko Hotel, Kuala Lumpur.
Asmala Ahmad, and Mazlan Hashim, (2000). Determination of Haze Air Pollution Index from Forest Fire Emission during the 1997 Thick Haze Episode in Malaysia using NOAA AVHRR Data. Malaysian Journal of Remote Sensing & GIS. 1 : 77-84.
Baum, B.A. (1997). Discrimination between clouds and smoke/fires in daytime AVHRR data. NASA Langley Research Center Atmospheric Sciences Division Radiation Sciences Branch Homepage [Online] Available, http://asd-www.larc.nasa.gov/~baum/Pathfinder/smoke.htmlDefense Meteorological Satellite Programme (DMSP) Website, http://dmsp.ngdc.noaa.gov/dmsp.htmlEarth Probe TOMS Website, http://toms.gsfc.nasa.gov/eptoms/ep.htmlKidwell, K. B., comp. and ed., 1995, NOAA-14 Polar Orbiter Data (TIROS-N, NOAA-14-6, NOAA-14-7, NOAA-14-8, NOAA-14-9, NOAA-14-10, NOAA-14-11, NOAA-14-12, and NOAA-14) Users Guide: Washington, D.C., NOAA-14/NESDIS.
Koe, L. C. C, Arellano, A. F, and McGregor, J. L. (2001). Investigating the haze transport from 1997 biomass burning in Southeast Asia: its impact on Singapore. Atmospheric Environment 35, 27232734,.
Malaysian Meteorological Service (1997a). : Upper Air Data for September 1997. Kuala Lumpur : MMS.
Malaysian Meteorological Service (1997b). : Visibility for September 1997. Kuala Lumpur : MMS.
Muraleedharan, T. R., Rajojevic, M., Waugh, A., and Caruana, A.: Chemical characteristics of haze in Brunei Darussalam during the 1998 episode. Atmospheric Environment 34, 27252731, 2000.
Rao, C.R.N. and Chen, J. (1996), Post-launch calibration of the visible and near-infrared channels of the Advanced Very High Resolution Radiometer on the NOAA-14 spacecraft International Journal of Remote Sensing, 17, 2743-2747.
Radojevic, M. and Hassan, H. (1999). Air quality in Brunei Darussalam during the 1998 haze episode. Atmospheric Environment 33, 35613568.
Siegenthaler, R., and Baumgartner, M.F. (1996). Haze and Miss Phenomena in the Swiss Lowlands Analysed with Spaceborne (NOAA-14 AVHRR) and Airborne (Imaging Spectrometry) Data in Parlow, E. Progress in Environmental Remote Sensing Research and Application. 453-459.
Station
A.
Kuala Lumpur, Wilayah
P
ersekutuan
B
.
Prai,
Penang
C
.
Pasir
Gudang
,
Johor
D.
Bukit
Rambai
,
Melaka
E
.
Bukit
Kuang
,
Terengganu
(a)
(b)
Measurement from station
Calculated from model
Measurement from station
Calculated from model
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(g)
(f)
(e)
(d)
(c)
(b)
(a)
EMBED Excel.Sheet.8
27
_1034474970.bin
_1192027107.unknown
_1192026187.xlsPM10_Chart
260
260
260
260
260
260
260
260
260
260
260
260
260
113
113
113
113
113
113
113
113
113
113
113
113
113
56
56
56
56
56
56
56
56
56
56
56
56
56
118
118
118
118
118
118
118
118
118
118
118
118
118
82
82
82
82
82
82
82
82
82
82
82
82
82
146
146
146
146
146
146
146
146
146
146
146
146
146
118
118
118
118
118
118
118
118
118
118
118
118
118
63
63
63
63
63
63
63
63
63
63
63
63
63
46
46
46
46
46
46
46
46
46
46
46
46
46
70
70
70
70
70
70
70
70
70
70
70
70
70
199
199
199
199
199
199
199
199
199
199
199
199
199
124
124
124
124
124
124
124
124
124
124
124
124
124
120
120
120
120
120
120
120
120
120
120
120
120
120
194
194
194
194
194
194
194
194
194
194
194
194
194
131
131
131
131
131
131
131
131
131
131
131
131
131
231
231
231
231
231
231
231
231
231
231
231
231
231
186
186
186
186
186
186
186
186
186
186
186
186
186
80
80
80
80
80
80
80
80
80
80
80
80
80
142
142
142
142
142
142
142
142
142
142
142
142
142
169
169
169
169
169
169
169
169
169
169
169
169
169
153
153
153
153
153
153
153
153
153
153
153
153
153
140
140
140
140
140
140
140
140
140
140
140
140
140
124
124
124
124
124
124
124
124
124
124
124
124
124
160
160
160
160
160
160
160
160
160
160
160
160
160
97
97
97
97
97
97
97
97
97
97
97
97
97
100
100
100
100
100
100
100
100
100
100
100
100
100
98
98
98
98
98
98
98
98
98
98
98
98
98
88
88
88
88
88
88
88
88
88
88
88
88
88
109
110
111
112
113
114
115
116
117
118
119
120
121
64
64
64
64
64
64
64
64
64
64
64
64
64
85
85
85
85
85
85
85
85
85
85
85
85
85
55
55
55
55
55
55
55
55
55
55
55
55
55
61
61
61
61
61
61
61
61
61
61
61
61
61
49
49
49
49
49
49
49
49
49
49
49
49
49
44
44
44
44
44
44
44
44
44
44
44
44
44
pm10
Band 1 (Reflectance)
PM10 (API)
PM10 VS Band 1
PM10 = -0.2410*(Band 1)2+0.007*(Band 1)3+108.3328R2 = 0.5098
pm10_new
Jalur 1Jalur 2pm10
4035260
4336260
4236260
4135260
4236260
4135260
4337260
4034260
4034260
4337260
4135260
4434260
4135260
3836113
3634113
3735113
3735113
3836113
3634113
3836113
3634113
3533113
3937113
3735113
3933113
3735113
312756
332956
322856
322856
332856
312756
332956
312756
302656
343056
322856
342656
322756
4030118
3828118
3929118
3929118
4030118
3828118
4030118
3828118
3727118
4131118
3929118
4127118
3928118
282582
292782
292682
282682
292782
282582
302782
272582
272482
302882
292682
312482
282582
3230146
3432146
3331146
3331146
3432146
3230146
3432146
3230146
3129146
3533146
3230146
3230146
3531146
3228118
3126118
3228118
3127118
3228118
3127118
3329118
3026118
3026118
3329118
3026118
3026118
3327118
312963
323163
323163
313063
323163
313063
333263
302963
302963
333263
302963
302963
333063
353346
333246
343246
343246
353346
333146
353346
333146
323046
363446
333146
323146
363246
262170
272370
272370
262270
272370
262270
282470
252170
252170
282470
252170
252170
282270
3533199
3735199
3634199
3634199
3735199
3533199
3735199
3533199
3432199
3836199
3735199
3932199
4133199
3834124
3732124
3733124
3733124
3834124
3632124
3934124
3632124
3531124
3935124
4034124
3831124
3932124
3733120
3935120
3935120
3834120
3935120
3834120
4036120
3733120
3733120
4036120
3935120
4133120
4134120
4032194
3831194
3932194
3931194
4032194
3831194
4033194
3830194
3730194
4133194
4232194
4030194
4131194
3630131
3832131
3731131
3731131
3832131
3630131
3832131
3630131
3529131
3933131
3832131
4030131
3931131
4037231
4238231
4138231
3937231
4238231
3937231
4239231
3836231
3936231
4139231
4136231
4340231
4137231
4236186
4134186
4236186
4235186
4236186
4135186
4337186
4134186
4034186
4437186
4132186
4336186
4135186
322780
342980
342880
322880
342880
322780
342980
312780
312680
343080
332780
353080
332780
3633142
3332142
3533142
3532142
3533142
3532142
3634142
3431142
3331142
3834142
3430142
3634142
3432142
3233169
3435169
3434169
3234169
3435169
3233169
3535169
3133169
3232169
3436169
3333169
3537169
3333169
3434153
3635153
3535153
3534153
3635153
3434153
3636153
3333153
3333153
3636153
3634153
3533153
3334153
3331140
3229140
3330140
3330140
3231140
3329140
3431140
3229140
3128140
3132140
3427140
3028140
3129140
3230124
3431124
3431124
3330124
3431124
3330124
3532124
3129124
3229124
3532124
3430124
3329124
3130124
4134160
3933160
4133160
4133160
3934160
4132160
4234160
4032160
3931160
4035160
4231160
3832160
3933160
322797
342997
342897
332897
332997
322797
342997
312797
312697
343097
342797
322697
312797
3432100
3634100
3533100
3533100
3634100
3432100
3634100
3332100
3331100
3635100
3632100
3432100
3632100
323298
313098
323198
323198
313298
323098
333298
313098
302998
303398
303098
333098
303198
272388
292588
292588
282488
292588
282488
302688
262388
272388
342688
292388
272388
352488
3429109
3228110
3329111
3328112
3229113
3428114
3430115
3327116
3127117
3230118
3227119
3527120
3228121
322964
333164
333064
323064
333164
322964
343164
302964
312864
333264
332964
312864
342964
323085
353185
343085
333085
343185
332985
353185
322985
352885
323285
323185
322985
353085
353155
332955
343055
343055
333155
352955
333155
352955
322855
363255
333155
322955
323055
293161
313361
303261
303261
313361
293161
313361
293161
353061
283461
293361
293161
363261
322949
302749
302849
312849
302949
312749
292949
322749
292649
333049
302949
292749
292849
302344
312644
312544
302444
312544
302444
322644
292344
322344
292644
292644
292344
322444
_935242903.doc