Berlin Environmental Atlas

06.10 Building and Vegetation Heights (Edition 2014)

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Object-based classification

Based on the existing digital aerial photography images and the NDSM, it was possible to define certain item classes with the aid of ALK building data. Object based classification is a multi-stage procedure. The particular steps are processed hierarchically along the so-called process tree (cf. Fig. 9), and then stored; they are thus available for use in later procedures (e.g. in the context of monitoring by means of change analyses).

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Fig. 9: Process tree developed for object-based classification (depiction of a segment) (DLR 2013)

The classification is structured for the following areas of evaluation emphasis:

First, two main classes are distinguished (Buildings and Vegetation). In addition, other sub-classes are defined for the vegetation and for the buildings, respectively, in the form of height levels; moreover, the buildings are also broken down into further semantical classes (e.g. garages, sheds, garden houses and Buildings planned or under construction).

Ascertainment of the buildings

In the area of buildings, various item classes are distinguished, which are also to be kept separate in the further procedure, and in the data storage in the GIS:

  • ALK-Level Buildings, in the categories:
    • Existing
    • Planned/under construction
    • Special ascertainment of the following use types: garages, sheds, garden and weekend cottages
    • Special category: "Roof area under trees"
  • ALK-Level Topography (bridges, above-ground railway areas)
  • Above-ground structures which are not part of the ALK, in the categories
    • Sheds/garden cottages under the ALK building key system "Allotment gardens or cottage colonies"
    • Buildings
  • Greened roofs, distinguished by location as:
    • ALK buildings
    • Non-ALK buildings

In a generalized segmentation process, the higher buildings are separated from the lower ones on the basis of certain homogeneity criteria. Next, a more detailed segmentation of the vegetation from the anthropogenic items is carried out. For this purpose, adjacent pixels which fulfil certain homogeneity criteria are compiled into ever greater segments. This is continued until a pre-established homogeneity threshold value is no longer exceeded. Distributed across the entire scene, all segments grow simultaneously, which ensures that they are of equal size, and hence comparable (Baatz, Schäpe 2000).

The classification of the measured buildings is carried out with the aid of the ALK building layer, so that, e.g., even large roof eves ascertained in the aerial photography data can be corrected for. In addition, bridge-type items and above-ground railway areas of various types – especially railway stations and elevated railway tracks – can be ascertained as part of the ALK topography.
Low buildings such as sheds, garages or cottages which can often not be masked out under the interpolation rules of the surface model, can subsequently be ascertained in the ensuing step on the basis of the respective key from the ALK item key catalogue. The later assignment of heights is in this case carried out by reference to type of use (number of stories = 1 * 2.8 m).
The ALK level "Planned buildings" is also a separate item class which includes surface or subsurface buildings planned or under construction. For these items – some 1,400 in the city, according to the ALK as of June 2010 – no conclusive heights can be determined. There are likely candidates for later change analyses (cf. Fig. 10) However, it should be noted in this respect that structural changes or new structures are far more numerous than those documented in the ALK data.

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Fig. 10: Two classification examples for buildings of the item class "Buildings planned or under construction" (as of June 2012); left and middle: certain classified segments, and, right, as compiled items; in the top row is an example of the construction project in progress; below it is a building already completed at the time of the aerial photography flight in September 2010 (DLR 2013)

All building items which are not part of the ALK structures but which are nonetheless structural entities, have been ascertained just as comprehensively. The example of the item class "Sheds/garden cottages" in allotment garden and cottage colonies illustrates the detailed precision of the procedure with which all built-up areas are to be largely classified without error, even prior to the generation of the vegetation mass. In the case of low garden cottages, the shape characteristics have been used to delimit them from road and pathway surfaces (cf. Fig. 11).

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Fig. 11: Classification steps for the determination of the item classes "Garden cottages" outside the ALK system:
a) The classification of the vegetation mask attained prior to this step, with real colour RGB aerial photography data superimposed;
b) Situation-precise section of the surface model NDSM – no heights of garden cottages can be directly derived;
c) Initial classification attempt yields errors in the area of streets;
d) Ensuing classification step, with correction for the street areas
(DLR 2013)

For buildings listed in the ALK, building heights are conclusively presented on the basis of the ALK building outlines and their storey lines. For this purpose, the ALK building polygons are first of all separated into ALK building portions by means of the storey lines. The heights of the building segments are then aggregated proportionately to these ALK building portions (Fig. 12).

Thus, the mean heights of ALK buildings are represented on the basis of ALK geometries. Buildings not listed in the ALK are therefore still presented by means of building segments.

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Fig. 12: Aggregation of heights of building segments to the ALK building portions;
left: building segments; right: ALK building portions

Ascertainment of vegetation

After the classification of buildings, the classification of the vegetation mask is carried out. By means of the preceding multi-resolution segmentation (MRS), the existing items are very finely structured, so that the differentiated diversity of the existing vegetation can be shown. The usual method of vegetation extraction used in digital remote sensing is based on a vegetation index calculated by means of the channels red (R) and infrared (NIR) ascertained by aerial photography. This Normalized Difference Vegetation Index (NDVI) is derived as follows:

NDVI = (NIR - R) / (NIR + R).

The index uses the fact that vital vegetation yields particularly high values in the near-infrared range, and much lower values in the red spectral range, a constellation not found in any other class of items, which makes possible a simple separation between vegetation and other classes (Albertz 2001).

In order to ascertain the existing vegetation structures and heights, a Contrast Split Segmentation (CSS) process is carried out. In this way, the vegetation mask can be subdivided into lower and higher vegetation segments. The resulting detailed results provide a very suitable basis for the ensuing calculations with regard to further differentiation of vegetation height data. For this purpose, the Multi-Threshold Segmentation (MTS) method is used. This procedure uses the pixel values of the NDOM, and subdivides the existing segments according to the stipulated nine height levels, comparable to the drafting of a contour line map (cf. Fig. 13).

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Fig. 13: Results of Multi-Threshold Segmentation (MTS) – Subdivision of the vegetation into nine height levels (DLR 2013)

In order to address the differentiated diversity of vegetation on a still more detailed scale, the existing nine vegetation height layers are further subdivided by taking their height structures into account. With the aid of the already described MRS, it is thus possible to ascertain which different vegetation structures exist (cf. Fig. 14).

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Fig. 14: Results of the subdivision into vegetation structures with the aid of multi-resolution segmentation (b and c) within particular height levels, from the preceding multi-threshold segmentation (a, cf. Abb. 13) (DLR 2013)

Greened roof surfaces

In connection with the measures for the adaptation to climate change or efforts for sustainable concepts for rainwater use [German only] the ecological value of greened roofs has increasingly been discussed. While Berlin has a long tradition of greening roof surfaces, there has to date been no full scale mapping of these types of roofs.

In the initial ascertainment carried out in the context of this project, no differentiation has been made with regard to varying intensities of roof greening, such as intensively used roof gardens or exclusively moss-covered roof surfaces (cf. Fig. 15). The correlation of the vegetation mask with the building layer based on the ALK was to serve the purpose of categorization of surfaces as greened roofs, but it provided no conclusive result. Here, no account was taken of adjacent vegetation which might partially cover the roof and hence distort the result somewhat.

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Fig. 15: Greened roof surfaces, as a result of the correlation of the vegetation mask with the ALK building layer (DLR 2013)

In order to classify the vegetation items which cover roof segments, but are not themselves greened roof surfaces, the class of greened roofs was examined in NDOM and in NDVI for its differences from the adjacent items. If the items in the class "Greened roofs" showed only a slight difference in height with respect to adjacent tree items, and a greater difference in height with respect to the buildings, as well as very similar spectral properties to the trees, they were iteratively shifted to the class "Roof segments covered by vegetation". In Figure 16, the necessity of this formation of classes can be clearly seen with reference to the attached NDOM image. If they were to remain classified as buildings, they would lead to a distortion of the figures for average building heights.

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Fig. 16: Classification results of the class "Roof segments covered by vegetation" (conclusive representation below right) (DLR 2013)

The classification of greened roofs also yielded several further errors which could be largely eliminated via spectral analysis of adjacent segments. The analyses carried out in the context of this project are insufficient to provide a very precise detection of greened roof surfaces with regard to their delimitation, and possibly also there vegetational stock; however, they do provide a largely complete overview of the generally greened roof surfaces. The initial ascertainment in the inner-city area shows that in the 445 sq km area processed, there were approx. 10,000 roof surface areas (a figure which does not equal the number of buildings) which have been greened to various degrees of intensity. Due to the poor resolution of the data obtained from the outlying areas, the information on greened roofs obtained in Project Phase 2 was not of satisfactory precision. For this reason, greened roofs in the outlying areas have not been marked.

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