Effect of landcover types on ambient air temperatures

Table 3-2 indicates that surface temperatures vary with landcover types. Built-up areas with paved roads and residential and factory buildings have significant higher surface temperatures than other landcover types, while water ponds have the lowest surface temperatures. Surface temperatures of paddy fields are 3-4° higher than that of other vegetations. This may be due to the fact that rice crops in the paddy fields have not formed full canopy coverage at the time of this study. However, with regard to the living environment, it is the ambient air temperature that is of major concern.

Although the built-up landcover has a significantly higher surface temperature than other landcover types, the corresponding air temperature differences are smaller. This is because the built-up landcover has the largest vertical temperature gradient. On 16 March 2005 the maximum air temperature difference (between the built-up areas and the water ponds) was about 3°C, while on 4 April 2005 the maximum difference was about 4.2°C. It is worthy to mention that the differences between landcover-specific surface and air temperatures are also dependent on local climatological condition.

The data in Table 3-2 imply that if an area of NOAA pixel size (1.1km×1.1km) with a complete water ponds coverage is converted to a complete built-up coverage, the ambient air temperature will be raised by about 3°C (29.34-26.35) to 4°C (27.23-23.05). Other kinds of landcover conversions will result in smaller changes on ambient air temperatures. Similarly, if the same area is converted from a full paddy coverage to a complete built-up area, the ambient air temperature rise will be about 0.8°C (29.34-28.51) to 2°C (27.23-25.17). Such arbitrarily hypothesized landcover conversions between two landcover types are referred to as the blind landcover conversions since it may not reflect the actual landcover conditions of the study area.

For example, in our study there is no pixel with complete paddy or water coverage.

We argue that landcover conversions will not arbitrarily occur and the likely conversions are often restricted by the local or regional conditions of resources availability, transportation, etc. Within an area of NOAA pixel size, landcover conversions are amore likely to take place among several landcover types, instead of mutual conversion between two landcover types. Prevalent landcover conversions area related to climatological, geographical, economical, sociological, and other factors, and should be considered in assessing the effect of landcover types on ambient air temperatures. Such prevalent conversions often are too complicated to be

characterized by a generally applicable model and should be considered as a local or regional phenomenon. Thus, a locally based assessment of the effect of landcover types on ambient air temperatures is presented below.

Apart from comparing the landcover-specific air temperatures based on blind landcover conversions, another way of assessing the effect of landcover types on ambient air temperatures is by evaluating average air temperatures with respect to coverage ratios of certain landcover types within individual NOAA pixels. Such assessment is similar to Yokohari et al. (1997), although in their study temperature differences between 50m×50m cells and a reference urban area with respect to paddy coverage ratios were evaluated.

Figure 3-10 illustrates relationships between pixel-average air temperatures (Ta) and within-pixel coverage ratios (CR) of different landcover types. All regression lines, particularly the one associated with the built-up landcover type, are very significant, suggesting well-established landcover patterns in the study area. It should be emphasized that, for any given value of average air temperature (e.g., 28.8°C), the sum of corresponding coverage ratios of different landcover types (determined by the regression lines) is always very close to 100%. It indicates the regression lines shown in Figure 3-10 are inter-related and collectively they characterize the existing landcover pattern within the study area. For example, a pixel with 60% built-up coverage is likely to have about 27 and 13% coverage of vegetations and paddy fields, respectively, The maximum coverage ratio of paddy fields within a NOAA pixel is about 26% whereas the maximum coverage ratio of built-up areas reaches near 100%

due to the dense population and fully developed industrial and manufactural parks. It can also be observed that water ponds only exist in areas which are agriculture (paddy fields and other vegetations combined) dominant since they are used as irrigation

water supply. Another important observation of Figure 3-9 is that reduction in paddy and vegetation coverages ten to occur contemporaneously due to decline in agricultural activities, and such reductions are converted to increase of built-up areas.

For regions with no well-established landcover pattern, co-existence of the landcover-specific regression lines in Figure 3-9 will not appear.

The increasing (or decreasing) trend of ambient air temperatures with respect to increasing within-pixel coverage ratio of built-up areas (or other landcover types) is apparent. Under the existing landcover pattern (i.e., the pattern of inter-related regression lines in Figure 3-9), the ambient air temperature will rise by 1.7°C (from 27.8 to 29.5 on 16 March) to 3.1°C (from 24.3 to 27.4 on 4 April) if the coverage ration of paddy fields decreases from its maximum of 26% to none. It may seem unreasonable that this amount of ambient air temperature rise is higher that the 0.8-2°C rise under the blind landcover conversion. This can be explained by considering the existing landcover pattern in the region.

Under the existing landcover pattern of the study area, a pixel with 26% paddy coverage is likely to have 19% water ponds, 51% vegetations, and only 5% of built-up area, resulting in a pixel-average air temperature of 27.8°C (16 March 2005) which is lower than the landcover-specific air temperature of paddy fields (28.51°C). Similarly, when the paddy coverage is reduced to zero, the vegetation coverage will also decrease due to decline of agricultural activities in the area. Reduced coverages of paddy fields and vegetations are converted to built-up areas, causing the pixel-average air temperatures to reach around 29.5°C (very close to the landcover-specific air temperature for built-up areas, 29.34°C). The will-established existing landcover pattern reflects the complex local conditions that sustain the totality of living environment in the region. Blind landcover conversion ignores such existing

Figure 3-10 Empirical relationships between within-pixel coverage ratios of different landcover types and pixel-average air temperature. The inter-related regression lines collectively characterize the prevalent landcover conversion pattern of the study area.

landcover pattern and will only yield imaginary assessment results. For example, assessing the effect of turning huge areas (several pixels) of all-paddy fields into water ponds is unrealistic since such all-paddy pixels do not exist in the study area.

Scenarios contradicting the existing landcover pattern should not be presented for assessment.

We may further consider the situation of changing from an existing landcover condition to a forced landcover condition. Suppose a pixel with existing landcover condition of 26% paddy fields is forced to become 50% of built-up areas and 50% of paddy fields. Under such forced landcover conversion, the average air temperature will be raised from 27.8 to 28.93°C (using landcover-specific air temperatures on 16 March 2005), an increase of 1.13°C. In contrast, if a prevalent landcover conversion (conversion following the existing landcover pattern) is taken, a pixel with 50%

built-up coverage (corresponding o 16% and 32% coverages of paddy fields and other vegetation, respectively) has an average air temperature of 28.63°C. The forced conversion results in a higher temperature increase than would be under prevalent conversion.

The concept of different landcover conversions can be better illustrated in a coverage-ratio space as shown in Figure 3-11. Scattering of actual landcover ratios of individual NOAA pixels (points marked by ▲ except C and D) exhibits a pattern which characterizes the existing landcover conditions. Landcover condition of point C is unrealistic and thus landcover conversion from point C to B is a blind conversion.

Point A represents an existing landcover condition and conversions from point A to B and D are respectively the prevalent and forced landcover conversions. A forced landcover conversion contradicts the existing landcover pattern and may cause complicated consequences. For instance, conversion from point A to D will force

Figure 3-11 Illustrative example of the prevalent, blind and forced conversion.

Numbers represent coverage ratios in percentage. Point marked by ▲ (except C and D) represent actual landcover coverage ratios of NOAA pixels in the study area.

Scattering of these points characterizes the existing landcover pattern.

landcover types of vegetation and water ponds to disappear and increase the coverage of paddy fields and built-up areas. It may encounter problems such as not having enough water for irrigation and low intention of local farmers to transform from vegetation growing to paddy culture. Additional resources allocation and incentives may need to be introduced in order to ensure a successful forced conversion.

Several final remarks should also be mentioned:

(1) We recognize that environmental changes are dynamic processes and the existing landcover pattern may gradually change over time. Therefore, long-term monitoring of landcover changes should be pursued.

(2) Analyses and results of this pilot study were based on data collected in only 2 days of field investigation. It may not reflect the complete range of temperature changes during the full growing period of paddy rice. Therefore, continuation of this pilot study is necessary for a more complete assessment of landcover effect on ambient air temperature in the study area.

(3) Spatial scattering of different landcover types within a NOAA pixel may also affect the pixel-average air temperatures. Yokihari et al. (1997) studied the effect of segmentation of paddy fields on air temperature and found that, for intermediate coverage ratios (30-70%) of paddy fields, segmentation level of paddy fields has strong influence on air temperatures is more complicated and should be pursued in future study.

3.8 Conclusions

In this study we present a new method for assessment of landcover effect on ambient air temperature using remote sensing images. The proposed method takes into account the existing landcover pattern. A few concluding remarks are drawn as

follows.

Landcover-specific empirical relationships exist between within-pixel coverage ratios and pixel-average air temperatures. These empirical relationships are inter-related and they collectively characterize the existing landcover pattern of the region. Landcover conversions tend to follow a local prevalent pattern which sustains the totality of the living environment in the region.

In our study, under the prevalent landcover conversion pattern, reduce the coverage ratio of paddy fields from its maximum of 26% to none will result in an ambient air temperature rise of 1.7-3.1°C. Increasing the coverage ratio of built-up areas (or decreasing coverage of paddy fields, other vegetation, and water ponds) will result in rise of ambient air temperature (measured at 2m height). In the study area, reductions in paddy and vegetation coverage tend to occur contemporaneously due to the decline in agricultural activities, and such reductions are converted to increase of built-up areas.

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