Berlin Environmental Atlas

01.02 Impervious Soil Coverage (Sealing of Soil Surface) (Edition 2012)

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The evaluation procedure was based on the use of ALK data for impervious built-up sections, and on the analysis of high-resolution multi-spectral satellite-image data for the impervious non-built-up sections.
Once again, a SPOT5 scene was used. Relevant information from the Environmental Atlas, the Urban and Environmental Information System (ISU), and the already ascertained corrective factors developed from the data of the Berlin Water Works (BWB data) were incorporated into the classification process. The ISU statistical blocks serve as reference surfaces.
You to the new satellite-image seen and the changes in the ISU section types in 2010, the mapping procedure had to be slightly adapted, and now consists of three evaluation steps:

  • Mapping of impervious built-up sections
  • Mapping of impervious non-built-up sections
  • Derivation of the degree of impervious coverage.

The mapping of impervious coverage concentrates on the areas of the statistical blocks; transportation routes and bodies of water are not considered. The following illustration shows the use of the various data from the agencies and from geo- and satellite image data in the Berlin mapping procedure for impervious sections.

The complete Final Report of the Impervious Coverage Mapping Procedure 2011 can be downloaded from the chapter Literature as a PDF file (in german).

Figure 2
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Fig. 2: Diagram of the Hybrid Mapping Procedure

Mapping of Built-Up Impervious Sections

The delimitation of the built-up impervious sections was carried out exclusively on the basis of ALK data. Their integration into the mapping process constituted the first component of the hybrid method approach. For these sections, no evaluation has been carried out via satellite-image data.
With regard to the mapping precision of the built-up impervious sections, the familiar problems with regard to the topicality of ALK data must be considered. Particularly buildings on industrial and commercial areas, as well as urban rail stations, are frequently missed, partially or entirely. Due to a change in the definition of buildings, summer cottages in allotment garden areas are no longer included in the current ALK. The share of built-up impervious space in allotment garden areas therefore had to be calculated separately.

Mapping of Impervious Non-Built-Up Sections

For the mapping of the impervious non-built-up sections, a classification approach was used in which satellite-image data (SPOT5) and geo-data (ALK, ISU) were incorporated and combined. This procedure took into account the following criteria:

  • Mapping of the entire municipal area
  • Low expenditure of time and effort for the pre-processing of the satellite-image data
    • use of geo-coded, system corrected data
    • coverage of the municipal area with as few scenes as possible
  • Low expenditure of time for the analysis of the satellite-image and geo-data
  • Restriction of use of terrestrial photos, or controls to ensure they be kept to a minimum
  • Flexible sensor and scene selection,
  • Realization of a high degree of automation
  • comparability with existing degrees of imperviousness from 2005
  • Integration of the mapping results into the ISU.

The satellite-image evaluation consists of the following evaluation focuses.

Categorization of Section Types Relevant for Remote Sensing

To improve the mapping results, a categorization of ISU section types according to the remote-sensing-relevant criteria building height, vegetation height, reflection quality, heterogeneity and relief, as well as the average degrees of impervious coverage (2001) was carried out. This permitted spatially separate segment classification, and optimized choice of methodology. Eighteen categories were defined (Table 2), which had to be adapted to the new ISU section types of adopted in 2010.

Some adaptations have also affected the ascertainment of changes between 2005 and 2011, and required special consideration. In the course of the updating of ISU section types in 2010, uses were not only updated, but also corrected. In the course of the automated evaluation process, unchanged block sections were thus assigned to different impervious coverage categories (pseudo-changes). This involved 718 block sections. Major changes in ISU block geometry affected 244 block sections between 2005 and 2010, i.e., the section sizes had changed by more than 10 %. Here too, pseudo-changes in impervious coverage mapping could result.

Spectral Classification of Non-Built-Up Areas

The satellite-based remote-sensing data were further processed by means of a machine-based, automatic classification procedure. First, the degree of vegetation coverage of non-built-up areas was ascertained via the Normalized Differenced Vegetation Index (NDVI).
This index is based on the fact that healthy vegetation reflects relatively little radiation in the visible spectral range (wavelengths of approx. 400 to 700 nm), and relatively much more in the subsequent near-infrared range (wavelengths of approx. 700 to 1300 nm). In the near-infrared range, this reflection is strongly correlated with the vitality of a plant: the greater the vitality, the higher the increase of the reflection coefficient in this spectral range. Other surface materials, such as soil, rock or even dead vegetation, show no such distinctive difference in reflection coefficients for these two ranges. This fact can thus serve on the one hand to distinguish areas covered with vegetation from bare areas, and also to obtain information on photosynthetic activity, vitality and density of vegetation cover. This standardization yields a range of values between -1 and +1, where "an area containing a dense vegetation canopy" will tend to positive values (say 0.3 to 0.8) (Wikipedia 2007)

Particularly relevant surface materials, such as sand, ash and tamped soil, railway-track gravel, artificial surfacing, as well as shaded areas, which are frequently evaluated faultily, must continue to be examined with special care.

Figure 3 shows the spectral classification procedure, which consists of 6 partial evaluations.

Figure 3
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Fig. 3: Diagram of the Spectral Classification of Non-Built-Up Sections

The degrees of impervious coverage are obtained step-by-step from the degrees of vegetation coverage per pixel ascertained. The method is based on the following assumptions:

  • There is a linear connection between NDVI and degree of vegetation coverage: the higher the NDVI value, the more vital vegetation will be present.
  • There is a high negative correlation between degree of vegetation coverage and degree of impervious coverage.

Vegetation-free spaces (degree of vegetation: 0 %) are reflected by low to very low index values. More detailed distinctions between impervious and pervious sections are not possible via NDVI.
Areas completely covered by green vegetation, such as forests or grasslands (degree of vegetation: 100 %) are largely reflected by high to very high index values. These areas were classified as pervious.
The problem of the local obscuring by treetops of impervious areas is not soluble via the evaluation of satellite-image data. To correct for this "error", context-related correction factors were ascertained and used, with the aid of ISU data. The ascertainment and distinction process of the graduation of degrees of vegetation coverage (degree of vegetation coverage: >0 % and <100 %) was methodologically demanding. Medium index values predominated. The fact that identical index values could result from different mixtures of signatures had to be taken into account.
The present procedural development made use of these differences: NDVI values which indicate partial vegetation coverage of sections (vegetation degree >0 %) were considered in a differentiated manner, and assigned to different degrees of impervious coverage in the rule-based classification system, depending on section type or section-type category.
Based on this approach, 12 NDVI categories were established (cf. Table 3).

In the context of the process of the mapping of changes, the degrees of impervious coverage in 2005 are to be compared with those in 2011, for which purpose the spectral properties and phenological properties of a satellite image scene taken in May and of one taken in September are to be considered and rendered comparable. To that end, the satellite images of 2011 were adapted both geometrically and radiometrically to the existing reference system of 2005, the so-called "master scene".

Track gravel was to be evaluated differently in the context of the use of the data on impervious coverage. In some contexts, it is considered impervious, for others, it is assigned to the "pervious sections" category. Therefore, such areas were classed separately within railyards. A "track gravel" category was created, which can be assigned optionally to either of the two impervious coverage categories.
The spatial proximity of the materials iron, gravel and in some cases the wood of the rail ties yielded a largely characteristic reflection of track gravel. Here, ascertainment was more difficult, due to a category-typical spectral heterogeneity. Particularly distinction from such impervious surfaces as streets was not always possible for certain. To avoid mis-mapping, the mapping of track gravel was carried out exclusively within the section-type categories "Railyards without Track Beds" and "Track Beds". Moreover, the K5 route network was used, which made it possible to detect tracks of secured by treetops as well.

The corrected classification components were brought together into a pixel based data set, which formed the basis for the subsequent rule-based classification system. The mapped sand, artificial-surface and track-gravel sections were aggregated with the impervious built-up building sections from the ALK to form a classified combined-block section.
The category "shaded" remained separated from the other categories.

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