The radiative impact of aerosols emanating from biomass burning ...

The radiative impact of aerosols emanating from biomass burning, through the Minnaert constant

V. Karathanassi, V. Andronis, and D. Rokos Laboratory of Remote Sensing, Department of Rural and Surveying Engineering, National Technical University of Athens, Heroon Polytechniou 9, Zographos 15780, Athens, Greece, tel. 01-7722593, 01-7722695, Fax: 01-7722594, e-mail: [email protected] Short tittle: The radiative impact of aerosols through the Minnaert constant

Abstract. Forest fires releases gases, ashes and particulate matter into the atmosphere, affecting the atmospheric radiation budget. The increment of aerosols by particles emanating from biomass burning, affects solar radiation mainly in the troposphere by multiple scattering and interactions between molecule and aerosol scattering, for several days after the fire. Among the topographic normalization methods, the Minnaert method assumes the anisotropic reflectance of surfaces by introducing the Minnaert constant, the value of which is affected by the atmospheric diffusion light. In this paper, a methodology is developed in order to study atmospheric effects on this value. This is based on the optimum value of the Minnaert constant, which is the value that leads to the higher classification accuracy. The methodology has been tested on four Landsat TM and three SPOT XS images respectively. The optimum Minnaert value presented a high decrement (0.1-0.2 instead of 0.8-0.9) for two images. Both of them had been captured shortly after fires, without being visibly affected by smoke and atmospheric perturbation effects. The proposed methodology has successfully revealed fires through the Minnaert constant. This methodology has also revealed the quality of the image. Sahara dust events are also to be investigated through the Minnaert constant.

1.

Introduction

Forest fires have increasing regional and global impact on the environment and especially the atmosphere. Fire releases gases and particulate matter into the atmosphere. Wood, after heating, releases volatile organics into the atmosphere. Smoke plumes from forest fires carry large amounts of atmospheric pollutants, including excessive emissions of CO2, CO, NOx, N2O, CH4, non-methane hydrocarbons and aerosols. Andreae and Goldammer (1992) estimated that 2200 teragrams (Tg) of carbon are being emitted annually to the atmosphere from tropical fires. Smog-like photochemistry produces

ozone

concentrations.

According

to

Goldammer

(1997),

photochemical reactions in the plumes of vegetation fires may be responsible for as much as one third of the global input of ozone into the troposphere. The consequences in the atmosphere last long after the fire. In Kalimantan, Indonesia, for example, ten weeks were needed to reduce atmospheric effects owing to clouds and smoke, after the big fire of 1997, which burned approximately 13. 18 million ha of forest land (Fuller and Fulk 2001). Fire also affects the atmospheric radiation budget. The increment of aerosols by particles emanating from biomass burning, affects solar radiation mainly in the troposphere by multiple scattering and interactions between molecule and aerosol scattering, for several days after the fire. The impact on the regional radiation budget of biomass burning has been studied but there are difficulties in evaluating this effect due to uncertainties over the particles’ characteristics (Lenoble, 1991), especially their single – scattering albedo. Moreover, precise evaluation requires knowledge of aerosol content, which is difficult to attain. 1

Thus, atmospheric correction models usually rely on empirical data (atmospheric correction databases) (Richter 1997), due to lack of in situ measurements. These models produce ambiguous results in cases where atmospheric perturbations occur on the date of capture of the remotely sensed data. Over land surfaces, increment of aerosols: a) reduces ground contribution because less radiation reaches the ground, due to troposphere scattering, and b) increases aerosol contribution to the radiation budget. Consequently, changes in the scattering characteristics of atmosphere affect the quality of remotely sensed data. In some cases, this is visible as a blurring effect or a reduction in the contrast among the different parts of the observed scene (Lenoble 1993). Based on blurring effects, Richter (1996) has proposed an atmospheric correction method. However, there are cases in which atmospheric impact cannot be distinguished in an image, captured after fire and obliteration of the smoke plumes. This occurs when the part of the scene to be processed presents land uses of similar spectral signatures, like forest areas. If such a scene is captured after a fire, and given the following constraints: •

fire databases are not always precise and available at a convenient time, and

•

the time period and spatial distribution of radiative impact of aerosols and particulate matter emanating from biomass burning, which depend on fire intension and duration, air direction and intention, and proximity to the fire’s ignition point, are usually not known,

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then, the clearness of the atmosphere cannot be estimated and the quality of remotely sensed data cannot be evaluated. This seriously affects the accuracy of processing results, as well as decisions on the pre-processing chain. The contribution of atmosphere in the radiation budget could be studied through the value of the Minnaert constant introduced in the topographic normalization method. According to the Minnaert assumption, the terrain reflects equal amounts of light in all directions, foreshortening in the direction of observation which is taken into account (Teillet 1986). The Minnaert constant accounts for the anisotropic reflectance of the surfaces and depends on spectral band, phase angle, and surface cover type. It is a measure of how close a surface is to an ideal diffuse reflector (Lambertian assumption), for which k=1. In situ measurements of the surface’s spectral reflectance show its spectral distributions, which assign to the Minnaert constant a value of less than one. The spectral distributions are contaminated by atmospheric effects, but anisotropic reflectance is still visible, if measurements are taken by aircraft, even after an oil fire smoke (Soulen et al. 2000). In this paper, contamination of the surfaces’ reflectance distributions by atmospheric effects isdemonstrated, through the optimum value of the Minnaert constant required for the topographic normalization of a remotely sensed scene. Experiments show that this value is indicative for the: 1.

contribution of the atmosphere in the radiance balance,

2.

detection of fires whichoccured shortly before the date that the scene is captured, and

3.

quality of the remotely sensed data.

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2. Atmospheric effects through the topographic normalization methods

Topographic shading creates an ambiguity among scene components having similar hues and saturation but different intensities, leading to a reduction in classification accuracy (Itten and Meyer 1993, Franklin et al. 1986). The bidirectional reflection distribution functions (BDRF), which are a measure of the forest canopy directional reflection, are likely to be affected by topography, as well as, by canopy characteristics. Uncorrected topographic shading has hindered the attempt to use forest canopy roughness as a parameter to differentiate forest stands of different species and ages (Gu and Gillespie 1998). Thus, recovery of true surface reflectance by removing topographic effects is an important task for successful forest studies (Conese et al. 1993, Proy et al. 1989). Several topographic normalization methods have been proposed, among them the Lambertian cosine correction, the Minnaert, a statistical-empirical correction, and the C correction. Each method marginally meets success criteria because it presents weaknesses in the full parameterisation and reliable adjustment of the model parameters, which are valid for large-scale applications (Teillet et al. 1982). However, the Minnaert and the C-correction methods yield the best results and lead to substantial (Itten and Meyer 1993) or non substantial improvements (Karathanassi et al. 2000). Drawbacks of the Lambertian cosine correction have been mainly attributed to:

4

•

approximation of the real diffuse reflectors as isotropic ones which satisfy Lambert’s law,

•

neglect of diffuse solar flux.

For remotely sensed images, the latter is the most significant. The cosine correction model usually underestimates the reflectance of sun-facing slopes and overestimates the reflectance of slopes facing away from the sun, resulting in the appearance of negative or inverted topography in corrected images. This effect may be reduced by subtraction of the downwelling atmospheric irradiance, which may contribute up to 10% of total irradiance in NIR and up to approximately 20% in visible bands (Deering et al. 1994). On the other hand, some topographic effects also remain in the images corrected by the Minnaert method, either as residual topography or negative topography. These residual effects have been attributed to: •

oversimplification by the photometric model,

•

neglect of diffuse illumination from sky and/or environment, and

•

inaccuracy of digital terrain models (DTM) (Civco 1989).

For remotely sensed scenes of the same area, differences in residual effects are due to the neglect of the diffuse illumination. Adjustment of the optimum value of the Minnaert constant compensates these effects. In this case, changes of the optimum value of the Minnaert constant do not reflect changes in the area’s reflectance properties, but changes of diffused skylight. The optimum value of the Minnaert constant can be estimated by (Karathanassi et al. 2000):

5

•

applying the Minnaert slope-aspect correction method ten times for each remote sensing scene, each time with a different value of the k constant, ranging from 0.10 to 1 and increasing by a step of 0.10,

•

performing ten classifications, one on each corrected data set, and

•

evaluating the classification results.

In this study, the proposed methodology is applied on Landsat TM images, captured in the 1995 – 1998 period. On four geometrically corrected images, the blurring effect is tested by: a) photo-interpretation inspection, and b) applying the criterion based on the tasseled cap transformation (Richter 1996).

Then, the optimum value of the Minnaert constant is estimated

through classification accuracy, as described above. A relation between atmospheric conditions and value of the Minnaert constant is established.

3.

Case study

3.1

Study area and satellite data

The study area is a mountainous forest area in the north-east of the Athens agglomeration, which covers an area of about 30 km2. It is a typical Mediterranean forest area where forest pines alternate with shrubs, rockoutcrops and bare soil. The geographical coordinates (Lat/Long) of its center are φ = 38ο 04’ and λ = 23ο 54’. Vegetation in this area consists mainly of pinus halepensis, quercus coccifera, and pistacia lentiscus. The area has intensive topographic relief with elevations ranging from 10 to 1104 meters. Pine forest and bushes are found on elevations above 375 meters. Most of the pine forest is encountered around 550 meters height. 23.4% of the terrain

6

of the study area has smooth slopes (0-10%), 56.9% has gently rugged slopes (11-20%), 15.9% has a rugged slope (20-30%) and 3.9% has highly rugged slopes (>30%). Four LANDSAT TM images were used in this application. Visual inspection of images did not reveal any smoke or flame. The images have the scene parameters summarized in table 1: [Insert table 1] Images have been geometrically corrected. The Digital Elevation Model (DEM) of the area, required by the topographic normalization methods, has also been produced. For this purpose, seven topographic maps, at scale 1: 5000, were used. These maps were originally compiled from vertical aerial photographs taken in 1945, at scale 1:42000, and photogrammetrically updated in 1972. The DEM was constructed by digitizing the 4-m contour lines of these maps. The ARC-INFO 7.0.3 software was used to construct the 2-m resolution grid DEM. The projection of the DEM is the Greek National Geodetic System (EGSA 1987).

3.2

Examination of the blurring effect

The images were first visually interpreted in terms of haze. Several colour composites, as well as histograms were produced, in order to locate the blurring effect. No significant reduction in contrast was observed in any of them. Figure 1 shows the histogram of the first band of each image. [Insert Figure 1]

7

The tasseled cap transformation was also applied on the images and since our area does not include any urban uses, the first threshold criterion established by Richter (1996), has been performed on each of the channels of the four images: DN > μTC + T3 x σ ΤC (1) where DN is the digital number of a pixel, μTC and σ ΤC are mean and standard deviation of the tasseled cap transformation, and T3 is a threshold parameter, ranging from 0.5 to 0.9 in our case. The tasseled cap haze component TC for LANDSAT TM is computed by (Richter, 1996): TC = 0.846 x TM1 - 0.464 x TM3 (2) where TM1 and TM3 are the digital numbers in TM bands 1 and 3, respectively. According to this criterion, bands 1,4, and 5 of each image contain haze, whereas bands 2,3, and 7 do not. Consequently, all images have been considered to be of equal quality.

3.3

Estimation of the optimum value of the Minnaert constant

The Minnaert topographic normalization method was performed on each image data set: LH = LT (cosθs / cos i) k (3) where, LT is the radiance observed on a tilted surface, LH is the radiance observed on a horizontal surface, θs is the sun’s zenith angle, i is the incidence angle and k is the Minnaert constant. Angle i is computed by: cosi = cosθs cos α + sinθs sin α cos (φs – φα) (4)

8

where, α is the terrain’s slope angle, and φs and φα are the sun’s azimuth angle and the terrain’s aspect angle respectively. For this purpose, slope and aspect images were generated from the DEM, and the angle i was calculated for every pixel in the image of the study area. The method was applied 10 times for each image data set, each time with different value of the k constant, ranging from 0.10 to 1 and increasing by a step of 0.10. Results were input in the classification procedure. The Maximum Likelihood classification algorithm was applied on the original image data, as well as, on radiometrically corrected image data, in order to determine the optimum value of the Minnaert constant. To achieve this, 44 supervised classifications -11 for each date- were performed by, , classifying the land cover of the study area into 6 categories: burned areas; quarries; rock outcrops; bare soil; bush; pine. For this purpose, the six bands of TM and a set of 11 training sites were used. The training sites were selected using photointerpretation methods and techniques on air – photographs taken in the 1995 – 1999 period. Photointerpretation was supported by ancillary data collected in situ. The in situ data concern 23 training and test sites for the vegetation types present in the study area in September 1999. The training sites are small homogeneous plots of about 1.4 ha and present an average slope of 13%, 17%, 18%, 20%, 24%, and an average aspect of 265, 245, 315, 190 and 120, respectively. Changes in land cover types during the 1995-1999 period were checked and taken into consideration. Validation of the classification images were performed using a different set of 14 test sites, selected using a) photointerpretation methods and techniques

9

on air – photographs taken in the 1995 – 1999 period and b) the ancillary data collected in situ. Seven of the test sites belong to the pine category but have differing topographic characteristics (slope and aspect). Figure 2 presents overall accuracy for each of the 40 classifications performed on the radiometrically corrected image data. The line presenting the classification accuracy for each image dataset has a positive slope, except for that of the image dataset captured on 95/05/31, which presents a negative slope. The minimum classification performance is observed on the image which requires a low optimum value of the Minnaert constant. [Insert Figure 2] Table 2 shows the optimum value of the Minnaert constant for each image dataset, which gives the optimum classification results. The values of 0.80 and 0.90 produce the highest accuracy for the scenes captured in 1998 and 1997.

On the contrary, the value of 0.20 gives the highest classification

accuracy for the scene taken in 1995. [Insert Table 2] In previous research (Karathanassi et al. 2000), the optimum values of the Minnaert constant were estimated for the same test area, using three SPOT XS image datasets. We also observe in table 3 that the Minnaert constant value of 0.90 produces the highest accuracy for the scenes captured in 1987 and 1986. On the contrary, a value of 0.10 gives the highest classification accuracy for the scene captured in 1992. [Insert Table 3]

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Table 4 shows the overall accuracy and the value of the kappa coefficient for classifications based on a) original LANDSAT and SPOT data, and b) data which have been radiometrically corrected using the optimum value of the Minnaert constant. We observe that the Minnaert correction method has in most cases improved the classification results, with improvements up to 0.13%. [Insert Table 4]

4.

Evaluation of the results. Discussion

Tables 2 and 3 indicate a sharp decrease of the optimum value of the Minnaert constant for the images captured on 95/05/31 and 92/08/05, respectively. Inquiry in the fire database regarding the study area, showed that a big fire erupted on the 92/07/27 and burned 113 ha, and another erupted on the 95/05/31 and burned 14.7 ha. The inquiry yielded no results concerning possible fire eruption before the dates of the other datasets. Sahara dust events databases have also been examined. No Sahara dust events were found to have occurred on the dates of data collection. Adopting the assumption that the optimum value of the Minnaert constant is indicative for the amount of the diffused skylight, the following conclusions can be derived: •

In clear atmospheric conditions, real reflectors almost satisfy Lambert’s law. The optimum value of the Minnaert constant, ranging from 0.8 to 0.9, indicates both divergence between real and ideal Lambertian

11

reflectors, and variation of the atmospheric radiative parameters regarding non-diffused atmosphere. Conclusions on the contribution of the diffused skylight cannot be drawn if the anisotropic reflectance of the surfaces towards the sensor direction is not known. •

The radiative impact of aerosols and particulate matter emanating from biomass burning can be revealed, by the optimum value of the Minnaert constant. If this value ranges from 0.1 to 0.2, it can be concluded that the atmosphere is perturbed by a fire event. Sahara dust events may also assign similar optimum values to the Minnaert constant but these were beyond the scope of this study.

•

Blurring does not affect images which were captured several days after a big fire, e.g. the case of the SPOT XS image captured on92/08/05, or a few hours after a small fire, e.g. the LANDSAT TM image captured on 95/05/31. .

•

Big fires affect the atmosphere for a longer period than small fires do. This is also reflected on the optimum value of the Minnaert value in relation to the date of fire eruption.

•

Classification performance is reduced for images presenting a low optimum value of the Minnaert constant. This is due to the lower image quality of the remotely sensed data.

5.

Conclusions

In this study, atmospheric effects of fire have been revealed through the optimum value of the Minnaert constant, i.e. the value which yields the best

12

classification results if image datasets which have been radiometrically corrected by the Minaert method, are used. Although the Minnaert constant is considered to account for the anisotropic reflectance of surfaces, this study has shown that it also accounts for the amount of the diffused skylight, especially in cases in which the atmospheric optical thickness increases beyond clear atmosphere values. Anisotropic reflectance of the natural surfaces assigned a value of 0.90 to the Minnaert constant, which means that natural surfaces almost approach the ideal diffuse reflector behaviour. On the contrary, an increase in the diffused skylight, significantly affected the optimum value of the Minnaert constant, assigning values close to 0.1. Increases in the diffused skylight occur after specific events, such as fires and Sahara dust events. Thus, when the optimum value of the Minnaert constant is close to 0.1 this implies perturbation in the atmosphere by fire events or Sahara dust events. It also indicates low quality of the image dataset and reduced classification performance, even if images do not present blurring effect. The time and size parameters of a fire event can also be taken into account through the Minnaert constant. Even small fires, erupting few hours before the time of satellite data capture, can be revealed through the Minnaert constant. Two fire cases were revealed in this study: a big fire which took place 12 days before data capture and a small fire which took place few hours before data capture. In summary, the results of this study can be exploited in: a) determining fire events, on the basis of a single dataset, without requiring multi-temporal data, even if a fire database is not accessible or available, and

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b) evaluating image radiometric quality. Further research may consider a) effects of Sahara dust events on the Minnaert constant, and b) the relationship between air-pollution over urban areas and the Minnaert constant.

References ANDREAE, M.O., and GOLDAMMER, J.G., 1992, Tropical wildland fires and other biomass burning: Environmental impacts and implications for land use and fire management. In Conservation of West and Central African Rainforests. World Bank Environment, Paper 1, edited by K.Cleaver (Washington:The World Bank), pp.79-109. CIVCO, L.D., 1989. Topographic Normalization of Landsat Thematic Mapper Digital Imagery. Photogrammetric Engineering & Remote Sensing, 55(9), 1303-1309. CONESE, C., GILABERT, M.A., MASELI, F. and BOTTAI, L., 1993, Topographic normalization of TM scenes through the use of an atmospheric correction method and digital terrain models. Photogrammetric Engineering and Remote Sensing, 59, 1745-1753. DEERING, D.W., MIDDLETON, E.M. and ECK, T.F., 1994, Reflectance anisotropy for a spruce-hemlock forest canopy. Remote Sensing of Environment, 47, 242-260. FRANKLIN, J., LOGAN, T.L., WOODCOCK, C.E., and STRAHLER, A.H., 1986, Coniferous forest classification and inventory using Landsat and digital terrain data. IEEE Transactions on Geoscience and Remote Sensing, GE24(1), 139-149.

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FULLER, D.O., FULK, M., 2001, Burned area in Kalimantan, Indonesia mapped with NOAA-AVHRR and Landsat TM imagery. International Journal of Remote Sensing, 22(4), 691-697. GOLDAMMER, J.G., 1997, Overview of fire and smoke management issues and options in tropical vegetation. AIFM International Conference, 189-217. GU, D., and GILLESPIE, A., 1998, Topographic Normalization of Landsat TM images of forest based on subpixel Sun-Canopy-Sensor geometry. Remote Sensing of Environment, 64(2), 166-175. ITTEN, K.I. and MEYER, P., 1993, Geometric and radiometric correction of TM data of mountaineous forested areas. IEEE Transactions on Geoscience and Remote Sensing, 31, 764-770. KARATHANASSI, V., ANDRONIS, V. and ROKOS, D., 2000, Evaluation of the topographic normalization methods for a Mediterranean forest area, XIX Congress of ISPRS, Vol. XXXIII, 16-23 July 2000, pp. 654-661. LENOBLE, J., 1991, The particulate matter from biomass burning: a tutorial and critical review of its radiative impact. In Global biomass burning: climatic and biospheric implications, edited by J.S. Levine,(Massachusetts, USA: MIT press), pp.381-386. LENOBLE, J., 1993, Atmospheric Radiative Treansfer, (Hampton, USA: A.Deepak Publishing). PROY, C., TANRE, D., and DESCHAMPS, P.Y., 1989, Evaluation of topographic effects in remotely sensed data. Remote Sensing of Environment, 30, 21-32.

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RICHTER, R., 1996, Atmospheric correction of satellite data with haze removal including a haze/clear transition region. Computers & Geosciences, 22(6), 675-681. RICHTER, R., 1997, Correction of atmospheric and topographic effects for high spatial resolution satellite imagery. International Journal of Remote Sensing, 18(5), 1099-1111. SOULEN, P.F., KING, M.D., TSAY, S.-C., ARNOLD, G.T., LI, J.Y., 2000, Airborne spectral measurements of surface-atmosphere anisotropy during the SCAR-A, Kuwait oil fire, and TARFOX experiments. Journal of Geophysical Research D: Atmospheres, 105(8), 10,203-10,218. TEILLET, P.M., 1986, Image correction for radiometric effects in remote sensing. International Journal of Remote Sensing, 7(12), 1637-1651. TEILLET, P.M., GUINDON, B., and GOODENOUGH, D.G., 1982, On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing, 8, 84-93.

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Scene Parameters Date Azimuth

98/10/30 angle 57.30

98/07/26

97/05/04

95/05/31

62.4

61.1

61.3

153.2

151.1

151.4

10:28

10:27

10:29

(degrees) Elevation angle 151.8 (degrees) Time

10:27

Table 1. LANDSAT TM parameters

i

LANDSAT TM 31/05/1995




Figure 1. Histograms of the 1st band of the LANSAT TM images

ii

Landsat 5 TM 30/10/98 Landsat 5 TM 26/07/98 Landsat 5 TM 04/05/97 Landsat 5 TM31/05/95 0,95

0,90

Overall Accuracy

0,85

0,80

0,75

0,70

0,65

1,00

0,90

0,80

0,70

0,60

0,50

0,40

0,30

0,20

0,10

0,60

Value of the Minnaert constant

Figure 2. Overall accuracy of the classifications produced by different values of the Minnaert constant

TM 98/10/30

TM 98/07/26

TM 97/05/04

TM 95/05/31

0.80

0.80

0.90

0.20

Table 2. The optimum value of the Minnaert constant for each LANDSAT TM image dataset

SPOT XS 86/05/16

SPOT XS 87/08/01

SPOT XS 92/08/05

0.90

0.90

0.10

Table 3. The optimum value of the Minnaert constant for each SPOT XS image dataset

iii

Original data Overall Accuracy

Kappa Coefficient (%)

LANDSAT 5 TM 98/10/30

0,84

79,60


0,83

79,50


0,77

69,70


0,70

59,90

SPOT XS 86/05/16

0,70

60,00

SPOT XS 87/08/01

0,86

82,20

SPOT XS 92/08/05

0,75

68,70

Radiometrically corrected data using the Minnaert optimum value Overall Accuracy

Kappa Coefficient (%)


0,93

89,40


0,93

89,40


0,89

85,70


0,83

79,50

SPOT XS 86/05/16

0.71

61,00

SPOT XS 87/08/01

0.89

85,40

SPOT XS 92/08/05

0.75

68,60

Table . Overall accuracy and kappa coefficient for the original and the radiometrically corrected data produced by the use of the optimum value of the Minnaert constant

iv