Geoportal  

 

Berlin Environmental Atlas

01.02 Impervious Soil Coverage (Sealing of Soil Surface) (Edition 2017)

map view Text in Deutsch verfuegbar content    back forward

Methodology

The evaluation procedure was based on the use of ALKIS and additional building data for impervious built-up areas, and on the analysis of high-resolution multi-spectral satellite-image data for the impervious non-built-up areas.

A Sentinel-2A 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 Waterworks (BWB data) were incorporated into the classification process.

The mapping procedure consists of three evaluation steps:

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

The mapping of impervious coverage concentrates on block and block segment areas; 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 areas.

The complete Final Report of the Impervious Coverage Mapping Procedure 2016 can be downloaded from the chapter Literature (Coenradie & Haag 2016) as a pdf-file (5.8 MB, only in German).

Figure 2
Fig. 2: Diagram of the hybrid mapping procedure

Mapping of Built-up Impervious Areas

In Edition 2017, the delimitation of the built-up impervious areas was carried out based on two data bases for the first time. On the one hand, the ALKIS building data was used. Since it contains gaps, especially regarding allotments, so-called Non-ALK data from the Environmental Atlas Map "Building and Vegetation Heights" (06.10, SenStadtUm 2014) was used on the other hand. Integrating building data into the mapping process constituted the first component of the hybrid method approach. For these areas, no evaluation has been carried out via satellite-image data.

The use of additional building data (Non-ALK) also impacts upon the mapping of changes between 2011 and 2016 and is especially noteworthy. Based on the improved data base of the building stock, the proportion of the built-up area also changes for blocks that actually remain unchanged (pseudo-changes). This involves 718 blocks. Major changes in ISU block geometry affected 424 block and block segment areas between 2010 and 2015, resulting in area size changes of more than 10 %. Here too, pseudo-changes may occur in the impervious coverage mapping.

Mapping of Non-built-up Impervious Areas

For the mapping of the impervious non-built-up areas, a classification approach was used in which satellite-image data (Sentinel-2A) and geo-data (building data, ISU) were incorporated and combined.

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

Categorization of Area Types Relevant for Remote Sensing

To improve the mapping results, a categorization of ISU area 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 classifications, and an optimized choice of methodology respectively. Eighteen categories were defined.

Spectral Classification of Non-Built-Up Areas

The satellite-based remote-sensing data was 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 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 photosynthetically active vegetation canopy" will tend to positive values close to 1 (e.g. Hildebrandt 1996).

Particularly relevant surface materials, such as sand, ash and tamped soil, 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
Fig. 3: Diagram of the spectral classification of non-built-up areas

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 areas (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 (degree of vegetation: 100 %), such as forests or grasslands 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 areas (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 area type or area-type category.

Based on this approach, 12 NDVI categories were established (cf. Table 2).

In the context of the process of the mapping of changes, the degrees of impervious coverage in 2011 are to be compared with those in 2016, for which purpose the spectral properties and phenological properties of the satellite image scenes have to be comparable. One advantage of the 2011 and 2016 impervious coverage maps is that both scenes were taken in May and are similar in their phenology. The satellite images of 2016 could thus be adapted both geometrically and radiometrically to the existing reference system of 2011, 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 areas" 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 area-type categories "Railyards without track areas" and "Track areas". Moreover, the K5 rail route network was used, which made it possible to detect tracks 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 areas were aggregated with the impervious built-up building areas to form a classified combined-block area.

The category "Shade" remained separated from the other categories.

map view Text in Deutsch verfuegbar content    back forward

umweltatlas_logo_klein