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The chapter is an experience in transdisciplinarity illustrated by the case of the Upper Otztal, part of the Tyrolean LTSER Platform in the Austrian Alps. In this effort, the search was for an effective framework for integrated monitoring that would not be limited to observing and monitoring the state of nature alone, but one that would assess and guide overall (regional) sustainability with a focus on the interaction between the natural and the social realms. To this end, the chapter proposes an integrated monitoring and sustainability assessment scheme that has been developed in the context of biosphere reserves by our team and might potentially be useful for many LTSER Sites. Applying this scheme to the Upper Otztal, this chapter offers various scientific insights into the social, interaction and natural sphere of the study area. The transdisciplinary component is captured in the scenario workshop where these insights were discussed with local stakeholders to better understand their views, interests and developmental perspectives. Despite challenges that underlie transdisciplinary processes, the chapter highlights the relevance of engaging local communities as part of a self-organising and self-maintaining socio-ecological system when it comes to addressing questions of regional sustainability.

Landscape pattern displays the interaction between natural and cultural forces (Baudry, 1989; Naveh, 1995; Burgi, Hersperger, & Schneeberger, 2004; Haber, 2004). In times of changing land-use systems (intensification and unification), climate change and biodiversity loss it is necessary and helpful to know how landscapes were built up in former times to develop potential scenarios for the future (Antrop, 2004, 2005). This change is, as is typical for most landscapes, a slow ongoing process which can only be seen by comparing data from different centuries. Various studies have dealt with this issue, but most of them tried to picture changes over decades (e.g. Jenerette & Wu, 2001; Nelson, Soranno, & Qi, 2002; Crews-Meyer, 2004) not over centuries (e.g. White & Mladenoff, 1994; Tasser, Teutsch, Noggler, & Tappenier, 2007). Also the Seewinkel, an area east of Lake Neusiedl has been partly analysed (Kohler, Rauer, & Wendelin, 1994).

High mountain ecosystems are sensitive to climate change (Box 1). Historical records of the flora on high summits in the Alps provide an important baseline against which climate-induced effects on high mountain ecosystems can be assessed. Reinvestigations of these old “monitoring summits” have shown that mountain plants have migrated upwards during the 20th century. An increase of atmospheric temperatures since the late 19th century is the most likely cause of this upward shift (Gottfried et al. 1994; Grabherr et al. 1994; 1995; 2001a; Pauli et al. 1996; 2001a). This “summit study” underlined the importance of long-term monitoring for assessing climate change effects on mountain ecosystems and initiated the establishment of extensive monitoring networks in mountain environments.

GLORIA, a Global Observation Research Initiative in Alpine Environments, aims to establish an urgently required global indicator network to detect climate-induced changes in high mountain regions. High mountains appear to be particularly appropriate for such a global initiative, because they still comprise low-temperature determined, natural ecosystems in a world wide distribution. Evidence of upward migrations of vascular plants was found at high mountain peaks in the Alps — most likely resulting from the climate warming since the 19th century. GLORIA is aligned to “target regions” in alpine or nival environments of all principal vegetation zones from polar to tropical latitudes. The “Multi Summit-Approach”, part of the proposed GLORIA-network, shall provide an effective method to compare mountain ecosystems and their climate-induced changes by using summits of different altitude in each target region. Reasons why high mountain summits can be particularly beneficial as indicator environments are pointed out. The sampling design and the method — already tested in field — are outlined along with first results. The final part gives some notes on the implementation of the research initiative. A close co-operation among international research co-ordinators and high mountain ecologists appears to be crucial for a globally active and successful indicator network.

Satellite images or aerial photos are often the most appropriate method of gaining a first general view of large areas. In order to detect different spatial patterns (e.g. vegetation patterns, soil patterns), aerial or satellite images can assist ecological studies. The revealed patterns could provide the basis for the selection of sampling points. Until recently, there has been much debate on how to optimally design sampling programs at large spatial scales. The problem of locating the sampling points in the area of interest, and how many sampling points should be taken, remains largely unresolved. Random sampling is too laborious for comprehensive surveys, whereas traditional subjective sampling, which has been commonly used in e.g. vegetation ecology, violates basic scientific principles for quantitative assessments (e.g. reproducibility, comparability, statistical analysis). One compromise is stratified random sampling. This process of stratification divides the space into subunits (strata) based on factors like precipitation, land management, or habitat constants, e.g. habitat type or elevation. Modern tools for computer-assisted data handling, especially Geographical Information System (GIS) and programs for image processing, have greatly simplified the selection of strata. We present a case study which aims to describe meadow vegetation in a 150 Km2 area of the Prealpine region of Lower Austria based on stratified random sampling to minimize field work, and to maximize the reliability of the result, i.e. the description of the variability, the character of the vegetation in the whole research area based on a vegetation distribution model. Main emphasis was to establish monitoring system for detection of land use change effects in this unique meadow vegetation in the mountain belt. The procedure of stratification was based on satellite data (LandsatTM with a pixel size of 28.5 m × 28.5m) and the use of a digital elevation model (DEM) with a grid size of 250 m × 250 m. The classification of all the input data-sets resulted in a total 20 strata. Each stratum consists of several disconnected sub areas so called sampling regions. Within each Stratum five sampling regions were randomly selected for further analysis. Within these sampling regions, several relevs were subjectively selected resulting in 30 plant communities. These group were found to closely resemble community types revealed from a previous vegetation survey of the meadow-land of that area. The applied satellite images, as well as the coarse grained DEM-data (commercially available data sets), provide sufficient information to delineate sampling regions but not to fix relev locations in the regions. Whether the application of satellite images with a pixel size of less than 10 meters will lead a new approach, which allow a point centered sampling design in contrast to the sampling regions concept, will be shown in the near future. Based on the relevs and the derived plant community types with high degree of representation, a monitoring system which covers the different ecological situations in a particular area, was established.