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Impervious Soil Coverage (Sealing of Soil Surface) 2011

Methodology

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).

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Fig. 2: Diagram of the Hybrid Mapping Procedure
Image: Umweltatlas Berlin

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
  • 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.

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

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Fig. 3: Diagram of the spectral classification of non-built-up areas
Image: Umweltatlas Berlin

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 2).

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.

Rule-Based Classification

Under rule-based classification, the results of spectral classification are combined with ISU data (section types) to yield degrees of impervious coverage derived at the pixel level. For this purpose, we first proceeded by using the set of rules developed for the 2007 Edition, and carried out a preliminary mapping process for 2011 was. Figure 4 shows a schematic overview.

In order to improve the comparability between two mono-temporally derived rule-based classifications, a second step was carried out involving a multi-temporal change analysis of satellite image data between 2005 and 2011.

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Fig. 4: Diagram of Rule-based Classification
Image: Umweltatlas Berlin

The classes and the NDVI categories were then assigned to degrees of impervious coverage. A reliable delimitation of completely vegetation-free and completely vegetation-covered areas was achieved in the NDVI categories 1 and 12 (lowest and highest NDVI values, respectively). The corresponding threshold values were derived automatically by means of reference sections.

  • NDVI Category 12 “Vegetation – Certain:” Under the rules, such sections were classified as 0 % impervious. This applied to all section-type categories.
  • NDVI Category 1 “Vegetation-Free – Certain:” Vegetation-free spaces were only considered to be 100 % impervious once they had been determined to not be neither “Sand” nor “Track Gravel.”

The range of values between these NDVI limits is broken down via interval scaling into ten additional NDVI categories of “Vegetation – Uncertain”. In order to obtain a reliable assignment of degrees of vegetation and impervious coverage, they had to be interpreted differently, by section-type category or section type. Thus, three assignment variants were established (Table 2), with the mean percentage value (5 %, 15 %, …, 95 %) the conversion factor for each NDVI and impervious coverage category.

Recommendations from the concept study, the evaluation results of Haag 2006 and findings from aerial image interpretations, and terrain inspections were incorporated, and results of the procedural validation process (cf. Validation, 2007 Edition) were taken into account for the iterative process optimization.

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Tab. 2: Assignment Variants: Degree of Vegetation – Degree of Impervious Coverage
Image: Umweltatlas Berlin

The assignment variants were oriented toward certain section types, which are characterized by the spatial interconnection and proximity of certain surface materials and types of buildings.

  • Assignment Variant A: Vegetation and pervious vegetation-free sections.
    The intermediate stages of the degrees of vegetation coverage (5% – 95%) were interpreted as mixed signatures of vegetation and other pervious surface types. The corresponding sections were therefore classified as pervious.
    Examples: Fallow areas, Forest, Farmland.
  • Assignment Variant B: Vegetation and impervious vegetation-free sections.
    The characteristic surface materials suggest a low share of vegetation-free pervious sections. Intermediate stages of the degrees of vegetation were therefore interpreted as mixed signatures of vegetation and impervious surfaces. The gradual increase in degree of vegetation per category thus corresponded to an adequate drop in degree of impervious coverage.
    Examples: Allotment gardens, traffic areas, block-edge buildings.
  • Assignment Variant C: Vegetation and impervious vegetation-free sections – block type “Airports”.
    A variety of impervious surfaces characterized this block type. Some materials, such as concrete, showed strong spectral coincidences with sand and open soil. Such blocks indicate runways, parking areas etc. Within the airport area; green spaces were largely delimited as separate blocks. To achieve certain separation, it has proved useful to classify sections with low degrees of vegetation as completely impervious (NDVI categories 2 through 6).

The new rule-based classification for 2011 and that of 2005 were thus available as intermediate results. These sets of map data were interlinked, and also linked to the current ISU block map, in order to obtain reliable information on changes of degree of impervious coverage at block level.

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

  • Ascertainment of changed sectors and elimination of pseudo-changes by means of multi-temporal change mapping
  • Comparability of the blocks in terms of geometry and sector type category.

For the reliable ascertainment of suspected sectors, the satellite image data of 2005 and 2011 for the non-built-up sectors were first of all evaluated, and secondly, the ALK data on possible changes in the built-up impervious sectors were examined.

Figure 5 provides an overview of the derivation of the results of the 2011 rule-based classification:

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Figure 5: Diagram of the 2011 Rule-based Classification
Image: Umweltatlas Berlin

The ascertainment of changed sectors was carried out taking the various phenological effects pertaining at the times of photography of the scenes, May and September respectively, into account. The spectral reflections of vegetation-covered services could vary depending on the development and/or vitality of the vegetation. Unchanged surfaces are accordingly described differently in the satellite images, and could therefore lead to mis-mapping in the automated evaluation, due to so-called pseudo-changes.

Using the procedure of principle component transformation (cf. Principal Component Analysis/ PCA, Wikipedia 2012), the respective NDVI channels for 2005 in 2011 were analyzed, and suspected sectors, i.e. those which, based on their NDVI values, indicated possible changes in impervious coverage, were statistically derived.

The conclusive rule-based classification in 2011 was derived from a set of rules from the rule-based classification of 2005, and from the 2011 intermediate results. For unchanged blocks, the 2005 classification was retained. The rule-based classification in 2011 was adopted in the following cases:

  • changed blocks (changes of the ISU sector type, or major changes of block geometry)
  • suspected areas within unchanged blocks (changes in spectral properties, taking into account the phenology, ascertained by means of principle component transformation)
  • previously built-up areas which, according to the current ALK, no longer contain any structures (demolition).

The conclusive result of the rule-based classification system in 2011 for the non-built-up blocks corresponded to the final result of the satellite-image classification process. The category “non-built-up impervious sections” has been described in the classification with the 12 impervious coverage-degree categories, a Shade class and a Track-Gravel class.

Fig. 6 shows the result of the 12 impervious coverage-degree categories, a Shade class and a Track-Gravel class, and the built-up impervious sections from the ALK, on a grid basis. Based on this intermediate results (grid data), the mean degrees of impervious coverage per block section were then calculated (cf. Calculation of the Degrees of Impervious Coverage).

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Fig. 6: Uncorrected Degree of Impervious Coverage (Grid Data) – Intermediate Results of Rule-based Classification
Image: Umweltatlas Berlin

The intermediate result published in the FIS Broker as the 2011 Impervious Coverage Map (uncorrected degrees of impervious coverage, grid data) shows the distribution of impervious coverage within the blocks. The effect of shade in the various blocks can also be seen (Map 01.02, by contrast, shows the mean degree of impervious coverage per block area).

Calculation of Degrees of Impervious Coverage

The goal of the impervious-coverage mapping process is the derivation of the degrees of impervious coverage at block level. The absolute and relative section information was calculated. Three degrees of impervious coverage (IC) were distinguished:

  • IC “Built-up impervious sections” (calculated from the Automated Map of Properties/ ALK data),
  • IC “Non-built-up impervious sections (calculated from satellite data),
  • IC Total (sum of the above).

For the calculations, the results of the pixel-based satellite-image classification were collated with the areas from the block map ISU 5.

First, a summation by category of degree of impervious and block area was carried out. Thus, the grid data of the classification system was no longer necessary for further analyses.

There were thus 15 section-referenced statements in sq. m. for each block and block segment:

  • Built-up areas (from the ALK)
  • 12 categories of degrees of impervious coverage – for non-built-up areas (corresponding to the NDVI categories)
  • Track-gravel areas (optionally either 0 % or 100 % impervious), and
  • Shaded area (unclassified).

For the further improvement in the mapping results, the following additional calculations were carried out.

Optional Assignment of an Impervious-Coverage Value to Track-Gravel Areas

The class “Track Gravel” has been maintained as a data field of its own, and can optionally be included in the calculations either as an impervious non-built-up (100 ) or pervious built-up area (0 %). This ensures the different evaluation of gravel according to the respective question at issue. In the map shown, track gravel is considered 100 impervious.

Classification of Shaded Areas

Shaded areas have been assigned impervious-coverage values at block level in a follow-up classification procedure using ISU data or BWB data. The shaded areas were evaluated depending on section type. For section types with predominantly residential use and adequate BWB data, the latter were used for the classification of the shaded areas. For all other section types, shaded areas were classified in accordance with their block-specific surroundings.

Evaluation of Built-Up and Non-Built-Up Impervious Sections in the Category “Allotment Gardens”

For the category “Allotment Gardens,” the data on impervious soil coverage usually showed only the overall degree of impervious coverage. Since the ALK mapped no summer cottages and hardly any functional buildings, the non-built-up impervious areas could only rarely be distinguished from the built-up impervious areas. Therefore, the degree of impervious coverage was ascertained almost entirely via satellite-image evaluation.

For this impervious-coverage map, the differentiation between built-up and non-built-up areas was carried out with the help of average values from the Department of Urban Development and the Environment, Section I C, Allotment Gardens. A degree of impervious coverage for built-up areas of 9.6 % for West Berlin and 8.6 % for East Berlin was assumed.

Application of Correction Factors

For the further improvement in the mapping results, so-called correction factors were used. The BWB data on impervious soil coverage was used for this purpose. The principle of section-type-referenced corrections is based on the following well-founded assumptions:

  • the BWB data were still largely up-to-date at the time of processing,
  • the BWB data were adequately precise, due to the ascertainment methods (terrestrial inspection, aerial-image interpretation, building-owner information),
  • the one-time calculation of correction factors makes them transferable to future evaluations, since they describe systematic trends in a city-wide comparison.

Due to topicality, overlap problems, differing definitions of impervious coverage, and gaps in impervious coverage ascertainment of some types by the BWB, correction factors could be calculated only for 5 section types (cf. Table 3).

Correction factors were only used on the non-built-up areas.

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Tab. 3: Correction Factor by Section Type
Image: Umweltatlas Berlin

Adoption of the Surface Types from 2001

The surface types of the non-built-up impervious block segments (walkways, courtyard areas etc.) were grouped into four surface-type classes, from concrete to grass pavers. Their respective distribution was investigated via selected test areas, and the results transferred to all areas of the same section type. The type-specific surface type distribution was not updated for the current map, but is based on a survey from 1988 (AGU Arbeitsgemeinschaft Umweltplanung 1988). The surface types are not shown on the map; however, they can be shown via the factual data display by block area.

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Tab. 4: Impervious Coverage Classes in Non-built-up Impervious Areas
Image: Umweltatlas Berlin