Abstract:In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information. The approach builds on analyzing temporal NDVI-trajectories for the separation between landslide-related surface changes and other land cover changes. To accommodate the variety of landslide phenomena occurring in the 7500 km 2 study area, a combination of pixel-based multiple thresholds and object-oriented analysis has been implemented including the discrimination of uncertainty-related landslide likelihood classes. Applying the approach to the whole study area for the time period between 2009 and 2013 has resulted in the multi-temporal identification of 471 landslide objects. A quantitative accuracy assessment for two independent validation sites has revealed overall high mapping accuracy (Quality Percentage: 80%), proving the suitability of the developed approach for efficient spatiotemporal landslide mapping over large areas, representing an important prerequisite for objective landslide hazard and risk assessment at the regional scale.
OPEN ACCESSRemote Sens. 2014, 6 8027
Riparian forests are assumed to play a crucial role in the global carbon cycle. However, little data are available on C stocks of floodplains in comparison to other terrestrial ecosystems. In this study, we quantified the C stocks of aboveground biomass and soils of riparian vegetation types at 76 sampling sites in the Donau‐Auen National Park in Austria. Based on our results and a remotely sensed vegetation map, we estimated total C stocks. Carbon stocks in soils (up to 354 t ha–1 within 1 m below surface) were huge compared to other terrestrial ecosystems. As expected, soils of different vegetation types showed different texture with a higher percentage of sandy soils at the softwood sites, while loamy soils prevailed at hardwood sites. Total C stocks of vegetation types were significantly different, but reflect differences in woody plant biomass rather than in soil C stocks. Mature hardwood and cottonwood forests proved to have significantly higher total C stocks (474 and 403 t ha–1, respectively) than young reforestations (217 t ha–1) and meadows (212 t ha–1). The C pools of softwood forests (356 t ha–1) ranged between those of hardwood/cottonwood forests and of reforestations/meadows. Our study proves the relevance of floodplains as possible C sinks, which should be increasingly taken into account for river management. Furthermore, we conclude that plant‐species distribution does not indicate the conditions of sedimentation and soil C sequestration over the time span of interest for the development of soil C stocks.
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