Domain-specific class modelling for one-level
gether, the single processes of segmentation and classification were cou- pled in a cyclic approach. Finally, representing the entire scene content in a scale finer than the initial regional level, has accomplished OLR. Build- ing upon the preceding papers, we endeavoured to improve the algorithms for tree crown delineation and also extended the underlying workflow. The transferability of our approach was evaluated by (1) shifting the geo- graphical setting from a hilly study area (National Park Bavarian Forest, South-Eastern Germany) to a mountainous site (Montafon area, Western Austria); and (2) by applying it to different data sets, wherein the latter dif- fer from the initial ones in terms of spectral resolution (line scanner RGBI data vs. false colour infrared orthophotos) and spatial resolution (0.5 m vs.
0.25 m), as well as ALS point density, which was ten times higher in the original setting. Only minor adaptations had to be done. Additional steps, however, were necessary targeting the data sets of different resolution. In terms of accuracy, in both study areas 90 % of the evaluated trees were correctly detected (concerning the location of trees). The following classi- fication of tree types reached an accuracy of 75 % in the first study area. It was not evaluated for the second study area which was nearly exclusively covered by coniferous trees.
1 Introduction
Very high spatial resolution (VHSR) optical sensors and airborne laser scanning (ALS) technology, especially in combination, provide informa- tion on a broad range of possible target features in human-related scale domains. Increased spatial resolution along with additional continuous in- formation (such as derived from ALS) serves to improve our ability to de- tect individual features. But the rich information content of the ‘H-res’
situation (Strahler et al., 1986), where many pixels make up each object, an excess of spatial detail must be dealt with (as in Lang, chapter1.1, Hoffmann et al., chapter 1.2, Castilla and Hay, chapter 1.5). Region-based segmentation techniques are an intuitive, yet empirically improvable means for re-aggregating this detailed information and thus reducing scene complexity. Multi-scale segmentation (Baatz and Schäpe, 2000) in general provides a hierarchical set of scaled representations, adaptable to the re- quired level of detail. In forest applications we usually aim at scaled repre- sentations of the entire scene content, and not at solely feature extraction (i.e. Tiede et al. 2004a). However, for a full representation of the scene content, one single adapted layer produced through segmentation may be desired as an inappropriate trade-off between over- and under-
segmentation. As presented in Lang (2002), specific structural arrange- ments of target features may require class- (or domain-)specific multi-scale segmentation. Assessing relevant scales may be conducted in an analytical way, such as by performing scale-space analysis and deriving characteris- tics on object behaviour while moving through scale (e.g. Hay et al., 2005). The approach discussed in this chapter is complementary and knowledge-driven: we discuss a supervised approach for the process of segmentation and object generation utilizing human a priori knowledge on the specific scale domain of the target features.
A key premise of our approach is that the result should capture the en- tire scene content in a spatially contiguous one-level representation (OLR, Lang and Langanke, 2006). In addition, the approach should be transfer- able to different geographical settings and to data sets with different spatial or spectral resolution. We therefore selected two different study areas, transferred the workflow, and applied the approach with only minor adap- tations.
2 Study Areas and Data sets
Study areas
The first study area is located in the National Park Bavarian Forest (NPBF) in South-Eastern Germany along the border with the Czech Republic (Fig.
1). The NPBF study area covers almost 270 hectares (ha) of near-natural forest, with an elevation between 780 and 1,020 meters above sea level (ASL) and slopes up to 25 degrees. Different forest structures occur with both open and closed forests, and multiple tree canopy layers with varying tree species, ages and sizes. In the 1990s, mountain spruce stands faced se- vere attacks from spruce bark beetle (Ips typograficus) especially, mainly triggered by major storm events.
The second study site is situated in the Montafon area in the federal state of Vorarlberg in Western Austria (see Fig. 1). The study area in the Montafon area is characterized by west-facing slopes ranging from 1,400 to 1,800 meters ASL with slopes ranging between 25 and 40 degrees. 22 ha (460 m x 480 m) in size, the area is dominated by old spruce stands partly thinned out due to windfall caused by heavy storms in the year 1990. Tree patterns typical for mountain forests occur, such as clusters or smaller groups of trees and gaps in between (in German: Rotten). This study site also includes a spruce-pole stand in the north-western part of the
area. Forest coverage on this slope helps prevent hotels and houses in the valley below from damages caused by rock-fall or avalanches. Automati- cally derived forest structure parameters from both ALS and optical data support a forest rehabilitation project carried out in this area to preserve the protection function on a long-term basis (Maier 2005).
Fig. 1. Study areas in South-Eastern Germany (National Park Bavarian Forest) and Western Austria (Montafon area)
Data sets and pre-processing
For the NPBF study area data from the Toposys Falcon system (cf. Wehr and Lohr, 1999, Schnadt and Katzenbeisser, 2004) was available. Survey- ing of the study area was done at three different dates: two leaf-off flight campaigns in March 2002 and May 2002, and a leaf-on campaign in Sep- tember 2002. Both first and last returns were collected during the flights with a pulse repetition rate of 83 kHz, a wavelength of 1560 nm and an av- erage point density of 10 pts per m² in common. An average flight height around 850 m and a maximum scan angle of 14.3 degrees resulted in a swath width of about 210 m and a high overlap due to the three different flights.
The resulting data sets were pre-processed by Toposys using TopPit (TopoSys Processing and Imaging Tool) software. The derived surface model (DSM) and terrain model (DTM) were subtracted from each other to create a normalised crown model (nCM) with 1 m ground sample dis-
tance (GSD). The nCM served as a basis for the single tree-crown delinea- tion. Simultaneously to the ALS measurements, image data were recorded using the line scanner camera of TopoSys. The camera provides four bands (RGBI) at a GSD of 0.5 m: blue (440-490 nm), green (500-580 nm), red (580-660 nm), and NIR (770-890 nm).
For the study area in Montafon ALS data were acquired in December 2002 under leaf-off canopy conditions by TopScan (Germany) on request from the Vorarlberg government. The Optech Airborne Laser Terrain Mapper (ALTM 1225) was used to collect first and last returns. The pulse repetition rate was 25 kHz with a wavelength of around 1000 nm and an average point density on ground of 0.9 points per m². The mean flying height was 1000 m and the maximum scan angle 20 degrees. As a result the swath width was about 725 m and the overlap between flight lines was 425 m (Wagner et al. 2004, Hollaus et al. 2006). The raw data were proc- essed at the Institute of Photogrammetry and Remote Sensing (IPF, TU Vienna). Again, both DTM and a DSM with 1 m GSD were produced. The software package SCOP++ developed by IPF was used, based on the hier- archic robust filtering approach (Kraus and Pfeiffer, 1998). An nCM with 1 m GSD was derived. In addition, a set of false colour infrared (FCIR) ae- rial photos from 2001, recorded separately with a GSD of 0.25 m, were available.
In both study areas visual interpretation was used for validation pur- poses.
3 Methodology
The approach was realised by developing rule sets in Cognition Network Language (CNL) within the Definiens Developer Environment. CNL, similar to a modular programming language, supports programming tasks like branching, looping, and defining of variables. More specifically, it en- ables addressing single objects and supports manipulating and supervising the process of generating scaled objects in a region-specific manner (cf.
Tiede and Hoffmann 2006). By this, the process steps of segmentation and classification can be coupled in a cyclic process; this we call class model- ling. It provides flexibility in designing a transferable workflow from scene-specific high-level segmentation and classification to region-specific multi-scale modelling – in this case of single tree crowns.
High-level segmentation and classification generating a priori information
For initial segmentation (high-level segmentation), we used a region- based, local mutual best fitting segmentation approach (Baatz and Schäpe, 2000). The ‘scale parameter’ (which controls the average size of generated objects) in the two study areas was different due to differences in spatial resolution (see Tab. 1). In the NPBF study, the initial segmentation was built upon the five available dimensions of the input data layers (optical and nCM data). In the Montafon study the segmentation was based only on optical information. This step resulted in a rough delineation of domains with different forest characteristics (see below), and likewise non- vegetated areas (such as roads, larger clearance areas, etc.). The initial re- gions are characterised by homogeneous spectral behaviour and uniform height information. Accordingly they were assigned to image object do- mains, and provided ‘a priori’ information for the subsequent delineation of single trees.
Table 1. High-level segmentation settings (L = Level, SP = scale parameter, SW = shape weighting, CPW = compactness weighting)
Study area L SP SW CPW Remarks
NPBF 1 100 0.5 0.5 nCM was weighted three
times higher than RGBI Montafon 1 300 0.5 0.5 only FCIR were deployed
The Normalized Difference Vegetation Index (NDVI) was used for separating coniferous, deciduous, dead trees, and non-vegetated areas (cf, Lillesand et al., 2004, Wulder 1998). The standard deviation of the nCM data per object served as indicator for the respective forest structure. In the NPBF study we distinguished between five domains: a. coniferous open, b.
coniferous closed, c. deciduous open, d. deciduous closed, and e. mixed forest. The coniferous and deciduous classes were further differentiated based on nCM values, introducing two more sub-categories, namely for- ests below 20 m height and forest above 20 m height. Note that we use the terms ‘open’ and ‘closed’ not in a strict methodological sense; we utilize nCM standard deviation per object as a proxy to differentiate roughly be- tween open and closed forested areas to control the algorithms in the fol- lowing single tree crown delineation.
In the Montafon study only two different domains were identified: a.
coniferous open and b. coniferous closed. Reasons are the prevailing natu- ral conditions which only allows for limited occurrence of deciduous trees.
Bare ground caused by wind throw, gaps or outcrop formed an additional, merged class. Figure 2 shows the results in subsets of both study areas.
Fig. 2. High-level segmentation and initial classification of different domains in subsets of the study areas in the NPBF (left) and the Montafon area (right). Bright values indicate domains with a higher percentage of deciduous trees, dark values are indicative of predominant coniferous trees. Results are regions for supervised multi-scale class modelling.
Optimized multi-scale class modelling - object generation in a domain specific hierarchy
Segmentation algorithms based on homogeneity criteria, like the one used for the initial high-level segmentation, were found not suitable for deline- ating complex and heterogeneous canopy representations in VHSR optical data or ALS data (cf. Tiede et al. 2004b, Burnett et al. 2003). New devel- opments help overcome these limitations through the use of scalable seg- mentation algorithms. These specific object generation algorithms can be adapted to the prevailing scale domains or even to the actual object. Object delineation is therefore controlled by user-specified parameters. The do- mains and their spatial instances, the regions, provide a priori information for the domain-specific embedded scalable algorithms, and they set the spatial constraints for its application. By this, we accomplish an optimized multi-resolution segmentation for single tree delineation.
One crucial element of this approach is to again break down the regions into pixels (‘pixel-sized objects’) within a region’s boundary. These pixel- objects are used to generate objects in a supervised manner. Specific rule- sets used were developed for single tree crown delineation on ALS data by
Tiede and Hoffmann (2006) and have now been adapted to the specific conditions in the study areas and the data sets used. In the underlying rule sets a region growing segmentation algorithm is programmed using a con- tinuity constraint starting from tree tops (local maximum) as seed points.
Table 2 gives an overview of the parameterisation controlled by the pre- classified domains. According to Tiede et al. (2006) the following region- specific parameters were controlled: (1) The search radius for the local maximum method needs to be adapted for each region depending on the assigned domain: taller deciduous trees require a bigger search radius to avoid detecting false positives due to the flat and wide crown structure;
dense coniferous stands require a smaller search radius to detect close standing tree tops. Therefore the search radius varies between 1 m and 4 m (cf. Wulder et al. 2000). (2) The stopping criterion for the region-growing process depends on the underlying nCM data. Candidate objects are taken into account, as long as differences in height between the respective ob- jects not exceed a certain limit. The limits are variable in terms of different tree height and tree types, ranging between 2 m and 9 m height difference.
(3) A maximum crown width is used for preventing uncontrolled growth of tree crown objects and merging with other potential tree crowns. This may happen, if a local maximum was not recognized correctly, for example in dense deciduous stands due to fairly planar tree surface or missing tree top representations in the ALS data. In comparison to Tiede et al. (2006) this criterion was further adapted by introducing a crown width parameter. In addition to the a priori information being used, this parameter is now di- rectly linked to the individual tree height value derived from the ALS data (cf. Pitkänen et al., 2004; Kini and Popescu, 2004; Koch et al., 2006).
Crown width limits range from 4 to 17 m for coniferous trees and 5 to 20 m for deciduous trees, corresponding to the crown width of open grown trees in Austria dependent of tree heights according to Hasenauer (1997).
Table 2. Overview of domain-specific controlled differences in object generation processes. Plus (+) and minus (-) indicate higher or lower values, LMR = Local maximum search radius; SENS = Sensitivity of the stopping criterion value based on the nCM data (continuity criterion); CWL = Crown width limit influenced by the initial region and the local maximum height; further details see text above.
Parameter Finer scale – smaller trees Coarser scale –larger trees LMR - (to detect tree tops in closed
stands)
+ (to avoid false positives) SENS + (small coniferous trees)
++ (for small deciduous trees) -- (large coniferous trees) - (large deciduous trees) CWL -- (small coniferous trees)
- (small deciduous trees)
+ (large coniferous trees) ++ (large deciduous trees) The objects resulting from this procedure are expected to correspond with single tree crowns. However, in both study areas smaller holes occur between the delineated tree crown objects. Causes for this are mainly due to limitations of the available ALS data with only 1 m GSD. Therefore we applied an additional algorithm, which uses object neighbourhood infor- mation to fill these holes up to a certain size with the respective surround- ing object.
Figure 3 shows the complete workflow. Note that for the Montafon study area it was extended. Because of the given resampling method of the data sets used and the different spatial resolutions, the local maximum method was biased. To overcome this problem, the two major steps of the process, i.e. (1) high-level segmentation and (2) region-specific tree crown delineation were separated in two different project settings. The a priori information was integrated by importing the information as a thematic layer to be addressed via cognition network language in the new project setting. That means, re-loading FCIR data was not necessary in this step of the workflow.
Fig. 3. Workflow after Tiede and Hoffmann 2006, extended: (1) High-level seg- mentation and classification of domains with different forest characteristics in an initial phase. The arrow and the dotted line around it indicate an extension of the workflow in the Montafon case. Export and re-import of regions are only neces- sary when multispectral and ALS data have different spatial resolution. (2) Break- down of imported pre-classified forest type domains to small objects (here: pixel objects) and extraction of local maxima. Generation of domain-specific objects us- ing a region growing algorithm (local maxima taken as seed points). (3) Extracted single tree crowns (holes mainly caused by limitations of available ALS data);
cleaning up single tree crown objects using neighborhood information.
For the final classification of single tree objects we used NDVI values and nCM height values. In the NPBF study five classes were differenti- ated: coniferous trees, deciduous trees, dead trees, non-vegetated area and understorey. The latter comprises objects which could not be allocated to a tree crown according to the defined stopping criteria but which still show values indicating vegetated areas. In the spruce dominated Montafon study
area we only differentiated between coniferous trees (i.e. spruce), non- vegetated area, and understorey. Reasons for the use of these classes are different requirements in the study areas: In NPBF there is a need for dif- ferentiation between coniferous trees (mainly spruce), deciduous trees and dead tress, mainly to quantify and predict spruce bark beetle attacks (Ochs et al., 2003). In the Montafon area the main concern is the protection func- tion of the forest. Therefore location, density and height of trees are impor- tant (Maier, 2005).
The validation of the classification process was conducted visually by on-screen digitizing of tree-tops and comparing the results with the auto- matically derived tree crown polygons (i.e. point in polygon analysis).
Ground truth data were only available for the Montafon area but the small subset of measured trees did not contain a representative amount. There- fore we relied on visual interpretation carried out in top-view by experts.
Visual inspection was considered more suited and more effective for evaluating this approach than any other quantitative method; reason being the required quality control not only of the class assignment but also the way of delineation. For the delineation of tree crowns from the given ALS data with a GSD of 1 m, a minimum tree crown size of several pixels is re- quired (cf. Coops et al., 2004). According to Maltamo et al., (2004) and Pitkänen (2001) the local maxima method is mainly suited to find domi- nant trees (dominant tree layer according to Assmann, 1970). Due to the open conditions of the old Montafon spruce stands, we considered trees higher than 5 m above the shrub layer (Schieler and Hauk, 2001) for vali- dation. In NPBF trees of the dominant layer and the middle layer were validated. Because of the more closed forest conditions, understorey (trees smaller than 10 m) was not taken into consideration. ‘Understorey’ was used according to Schieler and Hauk (2001) and IUFRO (1958), who de- fined understorey as the lowest third of the forest total top height or domi- nant height (h100 = average height of the 100 thickest trees per ha, herein replaced by the 100 highest trees per ha (see Assman, 1970, Zingg, 1999 and Hasenauer, 1997)).
4 Results and Discussion
In the NPBF study altogether more than 73,600 trees were extracted and tree crowns delineated, out of which 75 % were classified as coniferous trees, 19 % as deciduous trees and 6 % as dead trees.
In the Montafon study 2,344 single trees were detected in total, for al- most all of which crowns were delineated, even in the dense pole forest in