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391.
Dirk Tiede 《制图学和地理信息科学》2014,41(3):227-234
Traditional geographic information system (GIS)-overlay routines usually build on relatively simple data models. Topology is – if at all – calculated on the fly for very specific tasks only. If, for example, a change comparison is conducted between two or more polygon layers, the result leads mostly to a complete and also very complex from–to class intersection. A lot of additional processing steps need to be performed to arrive at aggregated and meaningful results. To overcome this problem a new, automated geospatial overlay method in a topologically enabled (multi-scale) framework is presented. The implementation works with polygon and raster layers and uses a multi-scale vector/raster data model developed in the object-based image analysis software eCognition (Trimble Geospatial Imaging, Munich, Germany). Advantages are the use of the software inherent topological relationships in an object-by-object comparison, addressing some of the basic concepts of object-oriented data modeling such as classification, generalization, and aggregation. Results can easily be aggregated to a change-detection layer; change dependencies and the definition of different change classes are interactively possible through the use of a class hierarchy and its inheritance (parent–child class relationships). Implementation is exemplarily shown for a change comparison of CORINE Land Cover data sets. The result is a flexible and transferable solution which is – if parameterized once – fully automated. 相似文献
392.
Peatlands play a major role in the global carbon cycle but are largely overlooked in current large-scale vegetation mapping efforts. In this study, we investigated the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) to capture the extent and distribution of peatlands in the St. Petersburg region of Russia by analyzing the relationships between peatland cover fractions derived from reference maps and 1-km resolution MODIS Nadir BRDF-Adjusted Reflectance (NBAR) data from year 2002.First, we characterized and mapped 50 peatlands from forest inventory and peat deposit inventory data. The peatlands represent three major nutritional types (oligotrophic, mesotrophic, eutrophic) and different sizes (0.6–7800 ha). In addition, parts of 6 peatlands were mined for peat and these were mapped separately. The reference maps provided information on peatland cover for 1105 MODIS pixels. We performed regression analysis on 50% of the pixels and reserved the remainder for model validation. Canonical correlation analysis on the MODIS reflectance bands and the peatland cover fractions produced a multi-spectral peatland cover index (PCI), which served as the predictor in a reduced major axis (RMA) regression model. The results suggest a high potential for mapping peatlands with MODIS. The RMA regression models explained much of the variance in the PCI (r2 = 0.74 for mined and r2 = 0.81 for unmined peatlands). Model validation showed high correlation between observed versus predicted peatland cover (mined: r = 0.87; unmined: r = 0.92). We used the models to derive peatland cover estimates for the St. Petersburg region and compared the results to current MODIS land cover maps. 相似文献
393.
Dynamic downscaling of global climate projections for Eastern Europe with a horizontal resolution of 7 km 总被引:1,自引:1,他引:1
Dirk Pavlik Dennis Söhl Thomas Pluntke Andriy Mykhnovych Christian Bernhofer 《Environmental Earth Sciences》2012,65(5):1475-1482
Climate change is one of the key factors influencing the quantity and quality of water resources in hydrologically sensitive
regions. In order to downscale global climate simulations from horizontal resolutions of about 125–200 km to about 7 km, a
double nesting strategy was chosen. The modelling approach was implemented with the Regional Climate Model CCLM (COSMO-Climate
Local Model) with a first nesting covering a central part of Europe and with a second nesting covering parts of Poland, Belarus,
and the Ukraine. A control run—driven by global reanalysis data—was evaluated by comparing the model results with corresponding
reference data. Long-term yearly and monthly mean differences of temperature and precipitation were statistically assessed.
As reference data for the first nesting, the gridded CRU data set with a horizontal resolution of about 55 km was used. Station
data of the NOAA and ECA databases were interpolated to provide an appropriate reference data set for the second nesting.
Both nestings overestimated long-term yearly precipitation means. Seasonal evaluation of the first nesting showed positive
precipitation biases for spring and winter months and negative biases in summer. Furthermore, differences in the spatial precipitation
patterns occured, especially in the high mountain area of the Carpathians. The second nesting overestimated precipitation
in spring and summer with smaller biases than in the first nesting. Long-term area means of temperature were properly reproduced.
The first nesting showed an overestimation for all months with maximal deviations in summer and spring. In contrast, the second
nesting was slightly too warm for summer and autumn and too cold for winter and spring. 相似文献