IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 41, NO. 6, JUNE 2003

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A Technique for Mapping Thermal Infrared Radiation
Variation Within Land CoverKazuo Oki and Kenji Omasa

Abstract—In this letter, a technique for mapping the thermal infrared radiation variation within land cover using both a classification map and a thermal infrared image is proposed. Furthermore, atechnique to effectively calculate the average thermal radiance foreach land cover from Thematic Mapper (TM)-type sensors, whichaquire high resolution of visible and near-infrared images and lowresolution of thermal infrared images, was also proposed. Thesetechniques provide useful information on processing TM-type sensors for urban heat monitoring, vegetation diagnosis monitoring,forest fire monitoring, and so forth.
Index Terms—Mixed pixel, thermal infrared image, urban heat,vegetation diagnosis.

I. INTRODUCTION

II. ESTIMATING THERMAL INFRARED RADIANCE
OF EACH LAND COVER
A. Linear Mixture Model
A thermal infrared image with low spatial resolution generally contains multiple categories within a pixel (mixed pixels).
Therefore, a thermal infrared image should be analyzed, taking
the influence of each category into consideration.
If atmospheric influences can be ignored and temperaturesin the same category are equal throughout the thermal infrared
image, then the thermal infrared radiance (watts per square
meter steradian micron) of a pixel ( ) can be expressed as the) as
thermal infrared radiance representing each category (follows:

THERMAL INFRARED images observed by remotesensing provide useful information in evaluating theenvironmental conditions of land and water areas. For example,
information regarding plankton and El Niño is used in helping
fisheries [1], [2] and evaluating influences on the global
environment, such as global warming. Information regarding
the condition of areas of land is used in evaluating the urban
heat effect, vegetation transpiration, volcanic activities, andother phenomena. However, current thermal infrared sensors
for satellite remote sensing have low spatial resolutions. As aresult, an observed pixel contains multiple categories (mixedpixels).
Some researchers have reported studies regarding the problems of mixed pixels [3]–[7] and low spatial resolutions in infrared thermal imaging [8]–[11]. These studies are based on
the assumption that temperatures are equal throughout the sameland cover type [8]–[11]. However, this assumption is often nottrue when the selected area is wide. For example, the urban
heat phenomenon may make the ground surface temperature ina metropolitan area different from that in an area categorized as
the same urban area. Even the same species of vegetation may
show different leaf temperatures, depending on a different transpiration rate [12].
In this letter, the thermal infrared radiance from each land
cover in low spatial resolution images using visible and near-infrared (NIR) images of higher spatial resolutions was estimated,
and a technique of extracting abnormal thermal infrared radiation areas based on the fact that temperatures are not equal
throughout the same category was proposed.
Manuscript received June 12, 2002; revised April 2, 2003.
The authors are with the Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Tokyo 113-8657, Japan (e-mail: agrioki@mail.ecc.utokyo.ac.jp; aomasa@mail.ecc.u-tokyo.ac.jp).
Digital Object Identifier 10.1109/TGRS.2003.813697(1)
However(2)(3)
where and are the coverage of each category within a pixelin (1) can be estimated, theand the number of categories. Ifthermal infrared radiance in each category can be analyzed withhigher precision.
B. Determining Thermal Infrared Radiance of Each Category
We used visible and NIR images of Landsat Thematic Mapper(TM) (spatial resolution: 30 m), which has higher spatial resolutions than thermal infrared image of Landsat TM (spatial res. In this letter, we used digolution: 120 m), to determine
ital numbers (DNs) in order to express thermal infrared radiare proportional toance of thermal infrared images. andDNs used in thermal infrared images of Landsat TM, since theTM thermal band is calibrated on a linear relationship between
DN and radiance. Fig. 1 shows a false-color image of the Kanto
Area in Japan observed on April 19, 1993, and Fig. 2 shows thethermal infrared image.
from (1), (thermal infrared radiance ofTo determinea pixel) and (coverage of each category within a pixel) is required. The thermal infrared radiance of a pixel can be obtained
from the observed image. To calculate the coverage of each category within a pixel of a thermal infrared image of Landsat TM,land covers with a number of categories were classified using

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 41, NO. 6, JUNE 2003

Fig. 1. False-color image of the Kanto Area in Japan observed on April 19,1993.

Fig. 3. Image classified by the ISODATA method using six bands of the visible
and NIR images of Landsat TM observed on April 19, 1993.

C. Image of Thermal Infrared Radiance From EachLand CoverFig. 2. Thermal infrared image of Landsat TM.

six bands of the visible and NIR images of higher spatial resolutions. Then, the thermal infrared image was overlaid on theclassified Landsat image. From the overlaid image, the coverageof each category within a pixel was determined for a Landsat
TM thermal infrared image.
From the calculated coverage of each category and theknown thermal infrared radiance of a pixel in the thermal
infrared image, the unknown thermal infrared radiance in eachcategory was estimated by the least squares method using (1)and under the conditions of (4) [13](4)Each category in the classified Landsat image must be ideallythe same land cover type. However, in general, it is difficult todetermine the optimal number of categories within an image,since the user has to subjectively decide the categories or thenumber for the purpose of research.
There may be a more optimal number of categories, but inthis study, the Landsat Image was classified into 15 categories,
in order to separate urban and snow area and to consider eachland cover type or same land cover type within the image. Here,
we assumed that the optimal number of categories is 15. Fig. 3shows the image classified by the ISODATA method using six
bands, the visible and NIR images, of Landsat TM observedon April 19, 1993. Table I lists the estimated thermal infrared
radiance for each category. The classified categories roughly
consist of water, vegetation, urban area, snow area, and baresoil. Ideally, we want to decide the name of land use or land
cover for the 15 categories, but it is difficult to specify what
each category is.

in the classified image in
By substituting the estimatedFig. 3, the resolution of the thermal infrared image shown inFig. 2 is finer. Fig. 4 shows the estimated fine image of thethermal infrared radiance from each land cover.
The estimated fine image in Fig. 4 is based on the assumptions that atmospheric influences can be ignored throughout theimage and that temperatures are equal throughout the same category [3]–[6].
III. EXTRACTING INFORMATION ON ABNORMAL THERMALRADIATION FROM EACH LAND COVERThe estimated fine image shown in Fig. 4 is based on the assumption that the temperature is equal throughout the same category. However, this assumption is often not true, due to factors
such as urban heat phenomenon and plant transpiration. Thus,
a new technique for extracting an area of abnormal thermal radiation was developed, based on the fact that the temperature is
not equal throughout the same category. If several influences
produce a temperature difference even in the same category,then the thermal infrared radiation radiance in a pixel of an infrared-band image should not be expressed as in (1) but as follows:(5)is the difference between the observed and estiwheremated thermal infrared energies based on the assumption thatthe temperatures are equal throughout the same category. In
other words, an area with abnormal thermal radiation in eachcategory can be extracted from a thermal infrared image bywith (6)evaluating(6)
expresses a positive or negative value. A large positive
value indicates high thermal infrared radiance, while a large

OKI AND OMASA: TECHNIQUE FOR MAPPING THERMAL INFRARED RADIATION VARIATION WITHIN LAND COVER

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TABLE IESTIMATED THERMAL INFRARED RADIANCE (DN) FOR EACH CATEGORY

Fig. 4.
cover.

Fig. 5.

1E image calculated from (6).

Fig. 6.

1E image of the central Tokyo.

Estimated fine image of the thermal infrared radiance from each land

negative value indicates low thermal infrared radiance. 0 indiis affected by thecates an average area of thermal radiance.
abnormal thermal radiation in each category but also by othereffects such as atmospheric effects, topographic effects, land
cover classification errors, and nonlinearities in mixed-pixel radepends on the image data itself anddiances. Furthermore,
may not be an inherent land surface parameter. In addition, if, then atmomultitemporal data are to be analyzed using
spheric correction is necessary to compensate for seasonal atmospheric variations, and emissivity is also required to be considered because emissivity for each land cover such as vegetation and urban areas can be different.
image calculated from results of Fig. 2Fig. 5 shows aand Fig. 4 using (6) at a spatial resolution of 120 m. The spatial
image is also 120 m. In Fig. 5, the thermal
resolution of theinfrared radiance varies in vegetation areas. The cause of thisvariation cannot be clarified without a field survey but may be
attributable to the difference in thermal radiation from the transpiration of leaves [12]. In addition, Fig. 6 shows an image ofTokyo enlarged from Fig. 5. From Figs. 5 and 6, it can be seenthat the thermal infrared radiance value of central Tokyo, especially the downtown area, is greater than those in other cities,
value corresponds to major business areas or major
and thevalue in central Tokyo.
roads. The rivers have also a higher
The cause of these variations cannot be clarified without a fieldsurvey but may indicate that central Tokyo is discharging moreis simple andheat than other urban areas. The derivation ofwill give effective information for thermaleasy. Moreover,infrared analysis of remotely sensed imagery.
IV. CONCLUSION
A technique was developed for estimating the thermal infrared radiance of each land cover in a low spatial resolution
image using visible and NIR images of higher spatial resolutions. Also, a technique was developed for mapping the thermal
infrared radiation variation based on the fact that the temperature is not equal throughout the same category. In the future,these techniques can be applied to the diagnosis of vegetationand evaluation of thermal environments inside cities by anain images from season to season.
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