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Berlin Environmental Atlas

01.02 Impervious Soil Coverage (Sealing of Soil Surface) (Edition 2007)

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Methodology

The Berlin University of Technology's Institute for Landscape Architecture and Environmental Planning, in cooperation with the Geographical Institute of the Humboldt University of Berlin and the company Digitale Dienste Berlin were contracted to design and implement a hybrid mapping procedure, with the goal of developing a homogeneous city-wide database which would be current and precise enough to ascertain the impervious coverage situation and provide a means for changing it. After evaluation of a test area, the procedure was developed further and applied to the entire municipal area of Berlin. The evaluation procedure is based on the use of ALK (Automated Map of Properties) 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.
The development of the procedure was carried out with a SPOT5 scene. Relevant information from the Environmental Atlas, the Urban and Environmental Information System (ISU) and the Berlin Water Works (BWB data) are incorporated into the classification process. The ISU statistical blocks serve as reference surfaces.
The mapping procedure consists of three evaluation steps:

  • Mapping of impervious built-up sections,
  • Mapping of impervious non-built-up sections,
  • Ascertainment 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 Study on the mapping of impervious coverage 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 method

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 summer houses in allotment-garden areas are frequently missed, partially or entirely. In the future, there is a good chance that the data base can be completed.

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. The method took into account the following requirements:

  • 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,
  • Integration of the mapping results into the ISU.

The satellite-image evaluation consists of the following five major 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).

Tab. 2: Remote-Sensing-Relevant Section-Type Categories
Section-Type Categories (KAT) Mean Impervious Coverage [%]*) Effect Factors
  Total Built-up Non-
Built-up
Buildings height Vegetation height Spectral reflection Hetero-
genity
Densely built-up core, commercial and mixed areas; block structure 1 >80 (>66) >66 >10 (>33) hoch / sehr hoch mittel mittel mittel / sehr hoch
Imperial-era block-edge buildings 2 >66 (>80) >66 (>33) >10 hoch hoch mittel hoch
Block edge buildings of the '20s/'30s, linear structure (no concrete-plate housing) 3 >66 >10 >10 hoch hoch mittel hoch
High buildings 4 >66 >10 >10 sehr hoch mittel gering gering
Low and village-type buildings with gardens, tree nurseries/ horticulture, water sports 5 >10 >10 >10 gering sehr hoch mittel mittel
Traffic areas, urban squares/ promenades, sports facilities 6 >66 (>80) >10 >66 mittel sehr gering / sehr hoch gering / sehr hoch mittel
Public facilities/ special facilities (except traffic areas) 7 >33 >10 >10 mittel / hoch mittel / hoch mittel / hoch mittel / hoch
Forest 8 >1 < 1 >1 sehr gering mittel sehr gering sehr gering
Farmland 9 >1 < 1 >1 sehr gering sehr gering gering gering
Parks, cemeteries, camp grounds 10 >10 >1 >10 sehr gering hoch mittel mittel
Allotment gardens 11 >10 >10 (>1) >10 (>1) sehr gering sehr hoch mittel / hoch sehr hoch
Fallow areas 12 >1 >1 >1 mittel mittel hoch hoch
Slightly built-up areas w/ primarily commercial/ industrial use 13 >66 >10 >33 hoch mittel mittel / hoch mittel
Schools 14 >33 >10 >33 mittel / hoch mittel / hoch mittel / hoch mittel
Sports facilities 15 >33 >1 >33 gering gering mittel / hoch gering
Rail yards without track beds; Track beds 16 >80 >7 >66 sehr gering gering / hoch hoch gering
Supply/ waste disposal areas 17 >66 >10 >33 mittel / hoch sehr gering mittel / hoch gering
Airports 18 >80 <10 >80 gering sehr gering hoch gering

Reduction of map precision
sehr gering  very low gering  low mittel  medium hoch  high sehr hoch  very high
*) according to Environmental Atlas data as of 2001

Tab. 2: Remote-Sensing-Relevant Section-Type Categories

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 1,300 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 coefficient 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 and 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 coverage of impervious areas by treetops 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 graduations 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 signature mixtures 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 future, it is to be possible to evaluated track gravel differently depending on the use of the data on impervious coverage. In some contexts, it is considered impervious, for others, they will be assigned to the "pervious sections" category. Therefore, they were classed separately within rail yards. 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 covered 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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