@phdthesis{Arnegger2014,
  author    = {Arnegger, Julius},
  title     = {Protected Areas, the Tourist Bubble and Regional Economic Development - Two Case Studies from Mexico and Morocco},
  publisher = {W{\"u}rzburg University Press},
  isbn      = {978-3-95826-001-6},
  doi       = {10.25972/WUP-978-3-95826-001-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-100928},
  school      = {W{\"u}rzburg University Press},
  pages     = {248},
  year      = {2014},
  abstract  = {Nature-based tourism and ecotourism experienced a dynamic development over the past decade. While originally often described as specialized post-Fordist niche markets for ecologically aware and affluent target groups, in many regions they are nowadays characterized by a heterogeneous structure and the presence of a wide product range, from individual travels to package tours. The present dissertation analyzes the structure and economic importance of tourism in two highly frequented protected areas in middle income countries, the Sian Ka'an Biosphere Reserve (SKBR) in Mexico and the Souss-Massa National Park (SMNP) in Morocco. Both areas are situated in close proximity to the most important package tour destinations Canc{\´u}n (Mexico) and Agadir (Morocco) and are subject to high touristic use and development pressure. So far, the planning of a more sustainable tourism development is hampered by the lack of reliable data. Based on demand-side surveys and income multipliers calculated with the help of regionalized input-output models, the visitor structure and economic impact of tourism in both protected areas are described. With regional income effects of approximately 1 million USD (SKBR) and approximately 1.9 million USD (SMNP), and resulting income equivalents of 1,348 and 5,218 persons, both the SKBR and the SMNP play an important—and often undervalued—role for the regional economies in underdeveloped rural peripheral regions of the countries. Detailed analyses of the visitor structures show marked differences with regard to criteria such as travel organization, nature/protected area affinity and expenditures. With regard to planning and marketing of nature-based tourism, protected area managers and political decision-takers are advised to focus on ecologically and economically attractive visitor groups. Based on the results of the two case studies as well as existing tourism typologies from the literature, a classification scheme is presented that may be used for a more target-oriented development and marketing of nature-based tourism products.},
  subject      = {{\"O}kotourismus},
  language  = {en}
}
@phdthesis{Cord2012,
  author    = {Cord, Anna},
  title     = {Potential of multi-temporal remote sensing data for modeling tree species distributions and species richness in Mexico},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-71021},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {Current changes of biodiversity result almost exclusively from human activities. This anthropogenic conversion of natural ecosystems during the last decades has led to the so-called 'biodiversity crisis', which comprises the loss of species as well as changes in the global distribution patterns of organisms. Species richness is unevenly distributed worldwide. Altogether, 17 so-called 'megadiverse' nations cover less than 10\% of the earth's land surface but support nearly 70\% of global species richness. Mexico, the study area of this thesis, is one of those countries. However, due to Mexico's large extent and geographical complexity, it is impossible to conduct reliable and spatially explicit assessments of species distribution ranges based on these collection data and field work alone. In the last two decades, Species distribution models (SDMs) have been established as important tools for extrapolating such in situ observations. SDMs analyze empirical correlations between geo-referenced species occurrence data and environmental variables to obtain spatially explicit surfaces indicating the probability of species occurrence. Remote sensing can provide such variables which describe biophysical land surface characteristics with high effective spatial resolutions. Especially during the last three to five years, the number of studies making use of remote sensing data for modeling species distributions has therefore multiplied. Due to the novelty of this field of research, the published literature consists mostly of selective case studies. A systematic framework for modeling species distributions by means of remote sensing is still missing. This research gap was taken up by this thesis and specific studies were designed which addressed the combination of climate and remote sensing data in SDMs, the suitability of continuous remote sensing variables in comparison with categorical land cover classification data, the criteria for selecting appropriate remote sensing data depending on species characteristics, and the effects of inter-annual variability in remotely sensed time series on the performance of species distribution models. The corresponding novel analyses were conducted with the Maximum Entropy algorithm developed by Phillips et al. (2004). In this thesis, a more comprehensive set of remote sensing predictors than in the existing literature was utilized for species distribution modeling. The products were selected based on their ecological relevance for characterizing species distributions. Two 1 km Terra-MODIS Land 16-day composite standard products including the Enhanced Vegetation Index (EVI), Reflectance Data, and Land Surface Temperature (LST) were assembled into enhanced time series for the time period of 2001 to 2009. These high-dimensional time series data were then transformed into 18 phenological and 35 statistical metrics that were selected based on an extensive literature review. Spatial distributions of twelve tree species were modeled in a hierarchical framework which integrated climate (WorldClim) and MODIS remote sensing data. The species are representative of the major Mexican forest types and cover a variety of ecological traits, such as range size and biotope specificity. Trees were selected because they have a high probability of detection in the field and since mapping vegetation has a long tradition in remote sensing. The result of this thesis showed that the integration of remote sensing data into species distribution models has a significant potential for improving and both spatial detail and accuracy of the model predictions.},
  subject      = {Fernerkundung},
  language  = {en}
}