Cornelius Senf

Learn More
We developed and evaluated a new approach for mapping rubber plantations and natural forests in one of Southeast Asia's biodiversity hot spots, Xishuangbanna in China. We used a one-year annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and shortwave infrared (SWIR) reflectance data to develop(More)
Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help ensuring sustainability. Remote sensing has(More)
Broad scale and continuous land-use/cover mapping is important for research in the context of global and climate change. We have therefore developed a method based on MODIS time-series and Random Forest classification to map forested, non-forested and plantation areas in SouthEast Asia. Our approach is optimized for regions with frequent cloud cover and(More)
BACKGROUND Because it has earlier been shown that exercise 24 or two hours pre-dive may suppress the appearance of venous gas bubbles (VGB) in connection with the dive, we studied whether exercise before or during N2 elimination would influence the rate of the latter. Nitrogen elimination was recorded in eight volunteers breathing a normoxic O2+argon(More)
Forest insect outbreaks are influenced by ecological processes operating at multiple spatial scales, including host-insect interactions within stands and across landscapes that are modified by regional-scale variations in climate. These drivers of outbreak dynamics are not well understood for the western spruce budworm, a defoliator that is native to(More)
Global biodiversity change creates a need for standardized monitoring methods. Modelling and mapping spatial patterns of community composition using high-dimensional remotely sensed data requires adapted methods adequate to such datasets. Sparse generalized dissimilarity modelling is designed to deal with high dimensional datasets, such as time series or(More)
The attribution of forest disturbances to disturbance agents is a critical challenge for remote sensing-based forest monitoring, promising important insights into drivers and impacts of forest disturbances. Previous studies have used spectral-temporal metrics derived from annual Landsat time series to identify disturbance agents. Here, we extend this(More)