Context and objectives
This study proposes to map microclimatic cold zones using an interferometric SAR (InSAR) Digital Elevation Model (D.E.M.) combined with a simulation of the Sun illumination over the year. This information is of special interest for both urban and forest planning because i) it will allow the mapping of present and future urban areas located inside cold zones, and ii) it will assist the selection of those vegetation types that are best adapted to low temperature and illumination levels.
Specific goals are:
a. to generate an InSAR SAR D.E.M. of the entire Walloon Region;
b. to develop a computer module that simulates the Sun illumination over the year, in order to highlight areas which receive the minimum of direct sunlight;
c. to superimpose the "cold" surfaces to the limits of urban areas defined in the sector or country planning maps.
d. to compare the data from the Sun illumination simulator with the field knowledge of forest planners at the Forest and Nature Division of the Walloon Region Administration.
Expected scientific results
At first glance, the statistical parameters are quite similar in the “classic” and interferometric cases. The colour map of sunlight data, in equal interval classes, gives us the same impression. The extreme values are symmetrically disposed for both images. One of the main differences in the histograms comes from the central part for the “classic” models where a few values are over-represented. Analysis of this data shows that the most important value in the histogram of the “classic” model (13.9% of the total) represents flat areas. The reason it does not exist in the histogram of the interferometric DEM is that they are very few totally flat pixels
The comparison between the houses of the Ferraris maps (1770-77), present urban areas of “plan de secteur” and the light data must be analysed attentively and in detail. In all, we analysed 14 hamlets and villages in the district of Sprimont. We analysed the presence (high = 1point; low = ½ point) of classes of sunlight inside building areas for two kinds of DEMs (“classic” and interferometric) and for two periods (1770-77 and 1970-80).
We observed in all periods and in all models that the mean and the upper mean are best represented. It is only for the «classic» model, during the recent period, that we can find equivalence between lower and upper means. The difference between the lower and the upper mean is always greater for the interferometric model than the “classic” one.
If we are only interested in statistical parameters we conclude that houses, in the case of the old maps, and the urban area, in the “plan de secteur”, must be considered as samples that reflect the global distribution of sunlight. However, the sample represents human settlements and small variations can be explained by human behaviour. In both cases, the pattern of houses reflects the distribution of the more highlighted areas in a very small environment.
The statistical parameters of the global distribution of sunlight for houses give no information about the way the houses are distributed in the field. It might be possible to mathematically describe the relative organisation of the houses. However, the human brain does that more easily than an algorithm.
The differences between “classic” and “Insar” sunlight files can be explained by the nature of the topographic surface. The «classics» DEMs are generated from a complex interpolation of elevation data digitised from contour maps. The Insar DEM represents the retrodiffusion surface of incident wave. Consequently, land use influences the shape of the topographic surface. This is also the case for the “classic” model, but for other reasons. Topographic maps are designed, first, for military use. Therefore, in urban areas for example, the most important information is the relative position of houses, roads and factories. Thus, contours do not appear. Similarly, for extraction zones, the main lines are drawn in order to give the most important feature of the extraction area rather than the correct position of the elevation value through the contour lines. When a “classic” DEM is built, this pool of data is often compensated by the imagination of the person responsible for the digitalisation. In our case study, several quarries were erased!
Twenty percent of urban and career areas, 23% of woods (broad-leaved trees and firs) and 57% of meadows composes the test area. For the meadows, the topographies are the same, except where farms exist. For the woods, the results are roughly similar. For urban areas and quarries, the differences are greater, but it depends on the place. At present, we have not extracted a rule, which allow us to predict places where we can forecast differences. From a statistical point of view, it is thus obvious that the correlation between parameters is near 0.7: good correlation with meadows, plus a small part from wooded areas.
The realisation of a light map for the whole Walloon region could be useful for the evaluation of the correlation between agricultural and forest productivity for different kinds of species and light data; in the framework of sewer planning: definition of the best location for purifying stations which use photosynthetic bacteria; realisation of an epidemiological study in order to check if there exists a relation between number of persons suffering from nervous breakdowns and the location of poorly-lit houses. Comparison between the location of poorly-lit areas and the thermal profile of motorways.