Impervious Soil Coverage (Sealing of Soil Surface) 2021


The impervious soil coverage mapping of the block (segment) areas and the road space was carried out separately based on two different methods. They were later combined to an overall evaluation of impervious coverage. Bodies of water were not included in this mapping.

Fig. 2: Diagram of the hybrid mapping procedure

Degrees of Impervious Coverage of Block (Segment) Areas

The analysis process of the block (segment) areas 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 (cf. Figure 2).

A Sentinel-2A scene recorded on June 7, 2021 was employed. Relevant information from the Environmental Atlas, the Urban and Environmental Information System (ISU), and the already ascertained corrective factors, which were developed based on the data of the Berlin Waterworks (BWB data), were incorporated into the classification process. The mapping process is divided into three stages of analysis:

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

A detailed description of the methodological approach may be found in the final report on the impervious soil coverage mapping of 2021 (only in German).

The built-up impervious areas contained in the 2021 dataset were defined based on two data bases. This involved, on the one hand, the use of ALKIS building data. As it is incomplete, especially regarding allotments and newly built-up areas, NOT-ALKIS data was used in addition on the other hand (cf. Statistical Base). Integrating building data into the mapping process constituted the first component of the hybrid method approach. These areas were therefore not analysed with reference to the satellite-image data.

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

The evaluation of the satellite image focuses on the following evaluation criteria.

Categorisation of Area Types Relevant for Remote Sensing

To improve the mapping results, the ISU area types were categorised according to criteria relevant for remote sensing, i.e. building height, vegetation height, reflective properties, heterogeneity and relief, as well as the average degree of impervious coverage of the existing data stock (2001). Eighteen categories were defined. This permitted spatially separated sub-classifications with an optimised methodological choice in each case.

Spectral Classification of Non-Built-Up Areas

The data collected by the satellite’s sensor was further processed with the aid of an automatic classification system. First, the degree of vegetation of the non-built-up areas was calculated using the Normalised Difference Vegetation Index (NDVI) per pixel of 2.5 × 2.5 m².

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 much more, relatively, in the near infrared range (wavelength from about 700 to 1300 nm). Normalisation results in a range of values between -1 and +1, with positive values close to 1 indicating “many healthy, photosynthetically active plants per area” (e.g. Hildebrandt 1996).

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

  • There is a linear connection between NDVI and the degree of vegetation: the higher the NDVI, the more (vital) vegetation is present.
  • There is a high negative correlation between the degree of vegetation and the 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 healthy vegetation (degree of vegetation: 100 %), such as forests or grasslands are largely reflected by high to very high indices. These areas were classified as pervious.

The problem of local obscuring by treetops of impervious areas cannot be solved based on the evaluation of satellite-image data with its top view. To correct for this “error”, context-related correction factors were ascertained and implemented, with the aid of ISU data. Detecting and distinguishing between graduations of the degree of vegetation (degree of vegetation: >0 % and 0 %). They were assigned to different degrees of impervious coverage in the rule-based classification system, depending on the area type or area-type category.

Based on this approach, 12 NDVI categories were established.

Drawing on the impervious coverage data, it should be possible to evaluate *track grave*l flexibly. In some contexts, these areas are considered impervious, in other contexts, they are considered to rather fall into the pervious area category. Therefore, “track gravel” was classed separately in the “railyard” category.

Due to their reflective properties, surface materials such as sand, ash and tamped soil as well as artificial surfaces were further separated into mappings based on objects. This step was taken to ensure a more targeted consideration of their impervious properties in the evaluation process and to minimise mapping errors.

The classification components were merged into a pixel-based data set, which formed the basis for the subsequent rule-based classification. Areas mapped as sand, ash or tamped soil, artificial surface or track gravel were aggregated with the impervious built-up building areas to form a classified combined block area.

The “Shade” class remained separated from the other classes and was addressed further when calculating the degrees of impervious coverage.

Rule-Based Classification

In the rule-based classification, the results of spectral classification were combined with ISU data (area types) to yield degrees of impervious coverage derived by pixel. For this purpose, an existing set of rules was initially repeated and applied as is, and a preliminary mapping was carried out for 2021.

In order to improve the comparability of two rule-based classifications derived using a mono-temporal approach, a second step was carried out involving a multi-temporal change analysis of satellite image data between 2016 and 2021.

The new rule-based classification of 2021 and the previous one of 2016 were thus available as an intermediate result. The objective was to obtain reliable information on changes in the degree of impervious coverage at block or block-segment level by linking these mappings with each other and with the current ISU5 of 2020.

Methodologically, the following aspects had to be taken into account in this process:

  • Recording of changed areas and automated elimination of pseudo-changes by means of multi-temporal change mapping,
  • Comparability of the blocks in terms of geometry and area type category.

For the reliable recording of suspected areas, indicating a change in impervious coverage, the existing satellite image data of 2016 and 2021 was analysed for the non-built-up areas on the one hand. For this purpose, relevant NDVI channels were compared with each other following normalisation and suspected areas were derived that indicate changes in impervious coverage. On the other hand, the building data was examined with regard to possible changes within the built-up impervious areas.

Another set of rules taken from the rule-based classification of 2016 and the intermediate result of 2021 were used to derive the final rule-based classification of 2021. For unchanged block (segment) areas, the 2016 classification was retained. The rule-based classification of 2021 was adopted in the following cases:

  • Changed block (segment) areas (changes of the ISU area type, or major changes of block geometry),
  • Suspected areas within unchanged block (segment) areas (changes in spectral properties, taking into account the phenology),
  • Previously built-up areas, which, according to the current ALKIS building stock, no longer contain any structures (demolition).

The final result of the rule-based classification system of 2021 for the non-built-up area was also the final result of the satellite-image classification process. The category “non-built-up impervious area” has been described in the classification with the 12 categories of the degree of impervious coverage, a Shade class and a Track-gravel class (cf. Figure 3).

Figure 3 shows the 12 categories representing the degree of impervious coverage, the Shade and Track-gravel classes, and the built-up impervious areas from building data on a grid basis. Drawing on this intermediate result (grid data), the mean degrees of impervious coverage were then calculated by block area (cf. Calculation of the Degrees of Impervious Coverage). After final plausibility checks, any distinct pseudo changes were located, and relevant blocks were excluded from further analysis.

Fig. 3: Uncorrected degrees of impervious coverage of 2021 (grid data) - intermediate result of the rule-based classification

Fig. 3: Uncorrected degrees of impervious coverage of 2021 (grid data) - intermediate result of the rule-based classification

The intermediate result published in the FIS Broker as the 2021 “Impervious Coverage Map (uncorrected degrees of impervious coverage, grid data)(Link to the Map illustrates the distribution of impervious coverage within the block (segment) areas. The effect of shade in the various block (segment) areas is also presented. However, it is both a grid map as well as an uncorrected intermediate result of the impervious coverage mapping, i.e. a satellite data result based on a rule-based classification. At the grid level of 2.5 m x 2.5 m, twelve classes representing the degree of impervious coverage are displayed for the non-built-up area. Furthermore, the buildings are mapped based on various building data, i.e. built-up impervious areas, as well as track-gravel and shaded areas.

For the impervious coverage map of the Environmental Atlas, the grid-level information was aggregated at block (segment) area level. It was then further processed and corrected where necessary. The black shaded areas presented here were assigned a degree of impervious coverage in a subsequent classification based on their immediate surroundings and their area types. The Environmental Atlas map “Impervious Soil Coverage”, on the other hand, illustrates the mean degree of impervious soil coverage by block (segment) area.

Calculation of Degrees of Impervious Coverage at Block and Block Segment Level

The goal of the impervious-coverage mapping process is the derivation of the degrees of impervious coverage at block and block segment level in absolute and relative area numbers. A distinction is made between three degrees of impervious coverage (IC):

  1. IC – built-up impervious area (calculated from building data),
  2. IC – non-built-up impervious area (calculated from satellite data),
  3. IC – total (sum of 1 and 2).

For the calculations, the results of the pixel-based satellite-image classification were collated with the block (segment) areas from the 2020 ISU5 block map. For this purpose, a summation by category of degree of impervious coverage was initially carried out for each block (segment) area, based on the grid-based intermediate result. Subsequently, a degree of impervious coverage was assigned to the shaded areas based on their immediate surroundings and their area types.

Correction factors were applied to individual residential-land-use types characterised by greenery in order to further improve the mapping results. For this purpose, the impervious coverage data of the BWB (Berlin Waterworks) was used, providing information on non-built-up impervious block areas and thus allowing for a look beneath the canopy cover.

The “Track gravel” class was maintained as a data field of its own, and could optionally be included in the calculations either as non-built-up impervious (100 %) or non-built-up pervious (0 %). This ensures that gravel is evaluated according to the subject matter at hand. In the map shown, track gravel is considered 100 % impervious.

Adoption of the Pavement Types from 2001

The pavement types of the non-built-up impervious block segments (walkways, courtyard areas etc.) were grouped into four pavement-type classes, from concrete to grass trellis stones. Their respective distribution was investigated using selected test areas. The results were then applied to all areas of the same area type. The pavement type distribution by area type was not updated for the current map but was adapted to the new ISU area types of 2020 (SenSW 2020b) instead. It is based on a survey from 1988 (AGU Arbeitsgemeinschaft Umweltplanung (Environmental Planning Working Group) 1988). The actual pavement types are not shown in the map; however, they may be displayed via the factual data display by block (segment) area in the Geoportal (only in German).

Tab. 2: Pavement classes of the non-built-up impervious area by area type (Goedecke & Gerstenberg 2013)

Tab. 2: Pavement classes of the non-built-up impervious area by area type (Goedecke & Gerstenberg 2013)

Degrees of Impervious Coverage of Roads

For the first time, the impervious coverage mapping of the road space was carried out based on differentiated road survey data (as of 2014) (SenUVK 2014). This data set contains information on 17 different materials, which served as a basis for allocating the different road surfaces to the following seven different pavement classes:

Tab. 3: Overview of the pavement classes of road areas

  • Pavement class 0:
    Non-built-up, pervious

    Degree of impervious coverage:

  • Pavement class 1:
    Asphalt, concrete, paving stones with joint sealer or concrete substructure, synthetic surface materials

    Degree of impervious coverage:
    extremely high

  • Pavement class 2:
    Artificial stone and plates (edge length > 8 cm), concrete-stone composites, clinker, medium and large-sized paving stones

    Degree of impervious coverage:

  • Pavement class 3:
    Small-stone and mosaic paving (edge length < 8 cm)

    Degree of impervious coverage:

  • Pavement class 4:
    Grass trellis stones, water-bound pavement (e.g. ash, gravel or tamped ground), gravel lawn

    Degree of impervious coverage:

  • Pavement class 5:
    Pavement unknown

    Degree of impervious coverage:

  • Pavement class 6:
    Tram track area in separate track bed

    Degree of impervious coverage:
    track area

  • Pavement class 7:

    Degree of impervious coverage:

In the calculation of the degree of impervious coverage, pavement classes 1-4 are considered 100 % non-built-up impervious.

Areas with pavement class 5 “n/a” are not covered by the road survey data due to geometric deviations between the road survey data and the ISU5 road space. For those areas, the mean degree of impervious coverage of all areas included in the road survey data of the borough in question was used.

Areas with pavement class 6 “track area” are included in the calculation once as 0 % impervious and once as 100 % impervious, in line with the approach for block (segment) areas. These two scenarios allow an optional weighting of “track area” in the impervious coverage calculation, depending on the subject at hand.

All areas with pavement class 7, which were determined based on the ALKIS building data, represent the built-up impervious proportion. These built-up impervious areas are mostly building edges that are part of the road space, due to a scale-related imprecise delineation of the block (segment) areas.

The proportions of the different types of pavement and the total degree of impervious coverage were calculated for each individual road section. For this purpose, the road sections (approx. 32,000 sections) newly and systematically formed as part of the road space classification were used for the first time (LUP GmbH 2022).

See the final report of the impervious soil coverage mapping of 2021 (only in German) for more information.