910 Geografie, Reisen
Refine
Has Fulltext
- yes (46)
Year of publication
Document Type
- Journal article (27)
- Doctoral Thesis (12)
- Book (6)
- Preprint (1)
Keywords
- Germany (4)
- Nachhaltigkeit (4)
- Einzelhandel (3)
- time series (3)
- Central Asia (2)
- Deutschland (2)
- Fernerkundung (2)
- Geografie (2)
- Klima (2)
- Klimaänderung (2)
Institute
Schriftenreihe
Sonstige beteiligte Institutionen
- Deutsches Zentrum für Luft- und Raumfahrt (DLR), Deutsches Fernerkundungsdatenzengrum (DFD) (1)
- Geographisches Institut (Humboldt Universität zu Berlin) (1)
- Hochschule für angewandte Wissenschaften München, Fakultät für Tourismus (1)
- Sonderforschungsbereich Re-Figuration von Räumen (Technische Universität Berlin) (1)
Politische Geographie
(1981)
No abstract available
No abstract available
Remote sensing time series is the collection or acquisition of remote sensing data in a
fixed equally spaced time period over a particular area or for the whole world. Near
daily high spatial resolution data is very much needed for remote sensing applications
such as agriculture monitoring, phenology change detection, environmental
monitoring and so on. Remote sensing applications can produce better and accurate
results if they are provided with dense and accurate time series of data. The current
remote sensing satellite architecture is still not capable of providing near daily
or daily high spatial resolution images to fulfill the needs of the above mentioned
remote sensing applications. Limitations in sensors, high development, operational
costs of satellites and presence of clouds blocking the area of observation are some
of the reasons that makes near daily or daily high spatial resolution optical remote
sensing data highly challenging to achieve. With developments in the optical sensor
systems and well planned remote sensing satellite constellations, this condition
can be improved but it comes at a cost. Even then the issue will not be completely
resolved and thus the growing need for high temporal and high spatial resolution
data cannot be fulfilled entirely. Because the data collection process relies on satellites
which are physical system, these can fail unpredictably due to various reasons
and cause a complete loss of observation for a given period of time making a gap
in the time series. Moreover, to observe the long term trend in phenology change
due to rapidly changing environmental conditions, the remote sensing data from
the present is not just sufficient, the data from the past is also important. A better
alternative solution for this issue can be the generation of remote sensing time series
by fusing data from multiple remote sensing satellite which has different spatial and
temporal resolutions. This approach will be effective and efficient. In this method
a high temporal low spatial resolution image from a satellite such as Sentinel-2 can
be fused with a low temporal and high spatial resolution image from a satellite such
as the Sentinel-3 to generate a synthetic high temporal high spatial resolution data.
Remote sensing time series generation by data fusion methods can be applied to
the satellite images captured currently as well as the images captured by the satellites
in the past. This will provide the much needed high temporal and high spatial
resolution images for remote sensing applications. This approach with its simplistic
nature is cost effective and provides the researchers the means to generate the
data needed for their application on their own from the limited source of data available
to them. An efficient data fusion approach in combination with a well planned
satellite constellation can offer a solution which will ensure near daily time series of
remote sensing data with out any gap. The aim of this research work is to develop
an efficient data fusion approaches to achieve dense remote sensing time series.
Nearly a quarter of the Alpine area is covered by a dense network of large protected areas (LPAs) of the four categories national park(NP), biosphere reserve (BR), nature park and world natural heritage site (WNHS). From the time of early industrialization, the Alpine area has undergone a mixed and increasingly polarized demographic development between the poles of immigration and emigration. This article investigates the possible mutual impact of population development and the existence of LPAs. The research design includes a quantitative survey of all Alpine LPAs in terms of their population development and the structure of immigration in the first decade of the 21st century. This will be linked with qualitative expert interviews in four selected NPs. The overall results allow an interpretation of the statistical
correlations between type of LPA and migration.
Snow cover (SC) and timing of snowmelt are key regulators of a wide range of Arctic ecosystem functions. Both are strongly influenced by the amplified Arctic warming and essential variables to understand environmental changes and their dynamics. This study evaluates the potential of Sentinel-1 (S-1) synthetic aperture radar (SAR) time series for monitoring SC depletion and snowmelt with high spatiotemporal resolution to capture their understudied small-scale heterogeneity. We use 97 dual-polarized S-1 SAR images acquired over northeastern Greenland and 94 over southwestern Greenland in the interferometric wide swath mode from the years 2017 and 2018. Comparison of S-1 intensity against SC fraction maps derived from orthorectified terrestrial time-lapse imagery indicates that SAR backscatter can increase before a decrease in SC fraction is observed. Hence, the increase in backscatter is related to changing snowpack properties during the runoff phase as well as decreasing SC fraction. We here present a novel empirical approach based on the temporal evolution of the SAR signal to identify start of runoff (SOR), end of snow cover (EOS) and SC extent for each S-1 observation date during melt using backscatter thresholds as well as the derivative. Comparison of SC with orthorectified time-lapse imagery indicates that HV polarization outperforms HH when using a global threshold. The derivative avoids manual selection of thresholds and adapts to different environmental settings and seasonal conditions. With a global configuration (threshold: 4 dB; polarization: HV) as well as with the derivative, the overall accuracy of SC maps was in all cases above 75 % and in more than half of cases above 90 %. Based on the physical principle of SAR backscatter during snowmelt, our approach is expected to work well in other low-vegetation areas and, hence, could support large-scale SC monitoring at high spatiotemporal resolution (20 m, 6 d) with high accuracy.
Schutzgebiete und insbesondere Nationalparke haben nach den Richtlinien der IUCN ein Doppelmandat bzw. eine doppelte Funktion: Sie sollen zum einen Räume für Natur- und Artenschutz und zum anderen für Erholung, Umweltbildung und Tourismus bieten und durch letztgenanntes zur Stärkung der Regionalökonomie beitragen. Um diesen Spagat zu meistern, sollten sich Schutzgebiete bzw. deren Verwaltungen und kooperierende Destinationsmarketingorganisationen darüber im Klaren sein, welche Besuchersegmente bzw. Tourismusprodukte im Schutzgebiet anzutreffen sind, bzw. angeboten werden und welchen Einfluss diese auf die Erfüllung des Doppelmandates haben. Die deduktiv entworfene Product-based Typology for Nature-based Tourism von ARNEGGER et al. (2010) bietet hierfür einen zweidimensionalen Analyserahmen, der die Angebots- und Nachfrageperspektive auf den Tourismus und dessen Produkte vereint und bisher noch nicht empirisch angewendet wurde, was das vorrangige Ziel dieser Studie ist.
Hierfür wurde von Theorien und empirischen Studien aus dem Kontext von Natur- und Ökotourismus eine Operationalisierung der Typologie abgeleitet, die am Beispiel des Nationalparks Berchtesgaden eingesetzt wurde. Dabei wurden zwei Ansätze verfolgt, eine angebotsseitige und eine nachfrageseitige Abgrenzung von Tourismusprodukten. Zur empirischen Erfassung von Tourismusprodukten wurde eine umfassende Besucherbefragung in der Sommersaison 2014 durchgeführt, bei der Informationen von rund 1.400 Besuchern des Nationalparks gesammelt werden konnten.
Aus Sicht der Nachfrager wurden sechs Produkt-Cluster identifiziert, die sich bezüglich Reiseaktivitäten und Motiven unterscheiden. Das mit der höchsten Naturaffinität ist das Produkt-Cluster der „Naturbildungsurlauber“ bzw. der „Ökotouristen“. Auf der anderen Seite des Spektrums stehen die „Passiven Erholungsurlauber“ mit einer geringen Nationalparkaffinität. Des Weiteren wurden spezifische Tourismusprodukte aus der Angebotsperspektive, wie Exkursionen der Nationalparkverwaltung oder mehrtägige geführte Wanderungen von spezialisierten Nischenreiseveranstaltern, identifiziert.
Nach der empirischen Abgrenzung der Produkte wurden diese dahingehend überprüft, ob sie sich bezüglich ökonomischer und ökologischer Indikatoren unterscheiden, um zu eruieren, inwieweit die Segmente aus Sicht einer nachhaltigen Regionalentwicklung bzw. aus Sicht des Doppelmandats zu beurteilen sind. Auch hier schneiden etwa die Naturbildungsurlauber relativ gut ab, da sie Muster von structured ecotourism aufweisen und sich durch eine hohe Naturaffinität, positive Einstellungen zu nachhaltigem Tourismus und relativ hohe Reiseausgaben auszeichnen. Bei drei Clustern zeigt sich ein gewisser trade-off: Während die Bergsteiger aus ökologischer jedoch nicht aus ökonomischer Perspektive interessant sind, ist dies bei den allgemeinen Vergnügungs- und Naturerlebnisurlaubern und den passiven Erholungsurlaubern genau umgekehrt.
Basierend auf den Ergebnissen werden mögliche Adaptionen der Typologie diskutiert und darauf aufbauend ein Analyserahmen für eine „Typologie für Nachhaltige Park-Tourismus Produkte“ erarbeitet. Zudem werden theoretische und erste praktische Implikationen für das Management von Schutzgebiets-Destinationen diskutiert, um unter Berücksichtigung der trade-offs das Produktportfolio weiterzuentwickeln, das eine Destination auf den Pfad des sogenannten enlightened mass tourism bringen kann.
Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of similar to 4 Mio km(2). The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500-1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.
The Seville Strategy spurred a signifi cant paradigm shift in UNESCO’s MAB Programme, re-conceptualising the research programme as a modern tool for the dual mandate of nature conservation and sustainable development. However, many biosphere reserves failed to comply with the new regulations and in 2013 the ‘Exit Strategy’ was announced to improve the quality of the global network.
This study presents a global assessment of the implementation of the quality enhancement strategies, highlighting signifi cant differences worldwide through 20 country-specifi c case studies. It concludes that the strategies have been fundamental in improving the credibility and coherence of the MAB Programme. Challenges in the implementation were not unique to individual countries but were common to all Member States with pre-Seville sites, and in many states the process has led to a rejuvenation of national biosphere reserve networks.
Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10%–15% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75% are achievable.
Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.