Applying pixel-based object detection on high resolution NDSM

The detection of small biotopes or ecotones has been extensively studied. Current methods for detecting small biotopes rely mostly on very high spatial resolution images (Bunting and Lucas, 2006; Cousins and Ihse, 1998; Hirschmugl et al., 2007; Pouliot et al., 2002). Cousins and Ihse (1998) made a first step towards a national landscape monitoring system including small biotopes and linear elements based on color infrared (CIR) aerial photographs. However, the CIR aerial photographs needs manual interpretation and this is time- consuming. Levin et al. (2009) mapped scattered trees using a combination of spectral and segmentation based methods from Landsat and SPOT images. This procedure is applicable only for a limited size of trees, and tree groups may appear as single trees on satellite images. As more fine spatial resolution remote sensing data becomes available, more advanced procedures and spectral measures have been developed for tree delineation. However these methods are mostly applied to specific spectral information and the application processes are complicated and time-consuming (Bunting and Lucas 2006,

Sheeren et al. 2009, Larsen et al. 2011). Lechner et al. (2009) showed the limitations of the use of remote sensing images without additional data for the accurate detection of small and linear landscape elements. Only using high-resolution images, the segmentation of the small biotopes is problematic because of the varying reflectance characteristics of the small biotopes and the existence of surrounding pixels with similar reflectance value. In this sense the combination of other data with remote sensing images may supply a solution for this problem, such as lidar data which has been often used in mapping tree crowns and measuring individual tree structure (Brandtberg et al., 2003; Holmgren et al., 2008; Lee et al., 2010; Morsdorf et al., 2004).

In this research, a methodology combing object-based and pixel-based image analysis is proposed using the high resolution NDSM (derived from lidar system) and multispectral images (RapidEye data). It demonstrates a data fusion aspect for land use classification and fine-scale landscape elements detection. Two steps are separately implemented on different data sources. First, the RapidEye images have been used in the object–based classification of the vegetation mask. Second, overlaying the vegetation mask with NDSM small biotopes have been detected within the field area based on their morphological features. Using the high resolution NDSM (1 m) the morphological features can be measured at the level of pixel size. Therefore, the pixels are chosen as the basic unit for further detection. It is assumed that these small woody biotopes are the only elevated objects existing in the field. In this case, object height is used as the only factor for the preliminary segmentation of small biotopes. A pixel-based “buffering and shrinking” procedure is developed specially for modifying the outline of the linear elements (i.e. tree row). The differentiation of the small biotopes is based on their height and shape attributions (see Table 3.2) and the overall accuracy is 80 % (see Table 4.6). The proposed hybrid method allows spectral and shape information to be successively used to extract the small biotopes. This improves the applicability of the approach, as it is not limited to the combination of data sources. But it also has limitations. For example, the accuracy of the base map (land uses derived from RapidEye images) will affect the detection results; and some other elevated elements inside the field area (e.g. telegraph poles, hunter cabins) can be misclassified as small biotopes. . The correction of such elements may be achieved by incorporation of very high resolution aerial images or on site field investigation. As a result, the detected small biotopes and the land-use maps classified from RapidEye data show that it is possible to update official land- use data like the German digital landscape model (ATKIS) partly by using multispectral data from sensors like RapidEye in combination with high resolution elevation data.

For the ecotone analysis different methods have been developed depending on the focus of ecotone research, such as moving split window (Senft, 2009), probability mapping (Hill et al.,

2007), or wombling techniques (Fortin et al., 2000). But the variable and the non-exclusive use of the term “ecotone” can be a source of confusion when interpreting and comparing studies (Hufkens et al., 2009). In this work, the difference in height is chosen to represent the environmental gradient between forest and field at a fine spatial scale. This enables the ecotone to be explored in three dimensional space, not treating height as the only variable but also taking surrounding patches into account using the moving window analysis. A pixel- based ecotone detection method based on NDSM has been introduced in chapter 3.2.2. And the result of applying this method in Rathen shows ecotones in this region are mostly in the form of transitional boundary between forest and field (Figure 4.12). But there are also other forms of ecotones that could be mixed with small biotopes. For example, a copse or hedge located closely to forest patches may have the ecotonal feature as elevation gradient on forest/field boundary (see in Figure 4.13). In this case, the small biotopes with ecotonal feature will be classified as ecotones since ecotones are considered more influential than individual biotopes in landscape connectivity analysis (see detail explanation in chapter 5.2.2.2). Within the object-based environment, some meaningful attributes can be directly calculated for the transitional boundary between forest and field, e.g. length, width, average height, standard deviation of height, and curvilinearity (Figure 5.3). These attributes have particular ecological meaning and can be used in further landscape structure analysis, for example the curvilinearity of the transitional boundary strongly influences wildlife usage and movement (Forman, 1995).

Figure 5.3: Comparison between the conceptual model and the detection result for the transitional boundary in 2D (a) and 3D (b) (reproduced according to Forman, 1995).

In summary, the incorporation of the third dimension for detailed landscape elements detection has proved to be promising and practicable. Using the pixel-based algorithm small biotopes can accurately be delineated and their outline can be adjusted. This would ensure that they can be detected based on the shape features. In terms of ecotone, the height gradient model enables the ecotone to be explored from the third spatial dimension and yields valuable information on forest/field boundary. It can be expected that this detailed representation of the landscape pattern will enhance the landscape structure analysis and result in a more realistic simulation of landscape pattern.