Abstract
Geographic object-based image analysis (GEOBIA) is a primary remote sensing tool utilized in land-cover mapping and change detection. Land-cover patches are the primary data source for landscape metrics and ecological indicator calculations; however, their application to visual landscape character (VLC) indicators was little investigated to date. To bridge the knowledge gap between GEOBIA and VLC, this paper puts forward the theoretical concept of using viewpoint as a landscape imageability indicator into the practice of a multi-temporal land-cover case study and explains how to interpret the indicator. The study extends the application of GEOBIA to visual landscape indicator calculations. In doing so, eight different remote sensing imageries are the object of GEOBIA, starting from a historical aerial photograph (1957) and CORONA declassified scene (1965) to contemporary (2018) UAV-delivered imagery. The multi-temporal GEOBIA-delivered land-cover patches are utilized to find the minimal isovist set of viewpoints and to calculate three imageability indicators: the number, density, and spacing of viewpoints. The calculated indicator values, viewpoint rank, and spatial arrangements allow us to describe the scale, direction, rate, and reasons for VLC changes over the analyzed 60 years of landscape evolution. We found that the case study nature reserve (“Kózki”, Poland) landscape imageability transformed from visually impressive openness to imageability due to the impression of several landscape rooms enclosed by forest walls. Our results provide proof that the number, rank, and spatial arrangement of viewpoints constitute landscape imageability measured with the proposed indicators. Discussing the method’s technical limitations, we believe that our findings contribute to a better understanding of land-cover change impact on visual landscape structure dynamics and further VLC indicator development.
Highlights
The movement of remote sensing software from pixel-based approaches to geographic object-based image analysis (GEOBIA) [1] led to improved workflows for imagery processing, especially land-cover classification [2] and land-cover change detection [3]
Scale signatures are presented as ROC-local variance (LV) graphs (Figure 3) with scale ranges from 1–150
The most significant scale levels were identified for the grayscale CORONA imagery, which provides a good example for our selection method
Summary
The movement of remote sensing software from pixel-based approaches to geographic object-based image analysis (GEOBIA) [1] led to improved workflows for imagery processing, especially land-cover classification [2] and land-cover change detection [3]. The contribution of land-use patches to landscape indicator calculation is already a well-recognized topic in landscape ecology [21], landscape quality assessment [22,23], and cultural ecosystem services [24], so far, no comprehensive long-term study exists on the complex relationships between land-cover patch changes and landscape visual structure. The accuracy of automated viewpoint calculations depends on segmentation quality; finding optimal SP thresholds for each time period of our analysis is essential This can be a challenging task, solved either through a trial-and-error approach [33,34,35] or a more automated method such as using estimation scale parameter (ESP) software [36]. The plug-in was successfully used in several studies and is regarded as a credible tool [47,48,49,50] for automating image segmentation at three levels of detail
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