The Insulin-like Growth Factor (IGF) system in breast and colorectal carcinogenesis: dietary intervention and molecular studies

Leeuwen, F.E. van [Promotor]Veer, L.J. [Copromotor]van 't Kampman, E. [Copromotor

Evaluating erosion from space: a case study near Uberlândia

Satellites can offer important spatial data for the assessment of soil erosion. This study was conducted to explore how satellite imagery could be used for evaluating erosion in a 10*10 km area in the Brazilian Cerrados. Products obtained from a variety of satellite sensors were analyzed for the purpose of (1) detecting erosion features; and (2) qualitatively mapping erosion risk. Erosion detection was done through visual image interpretation. Optical TerraASTER images allowed for a better detection and delineation of major gullies as ENVISAT ASAR imagery. Gully dynamics could be assessed by jointly interpreting aerial photos of 1979 and a high-resolution QuickBird image of 2003. QuickBird also allowed for the detection of smaller erosion features, like rills. Erosion risk mapping was performed for the complete study area with a simple qualitative method integrating information on slope and vegetation cover. Slope was calculated from the SRTM DEM, and NDVI, being indicative of vegetation cover, was obtained from a wet-season ASTER image. Both factors were automatically classified based on their relative susceptibility to erosion. The erosion risk map was constructed by combining both classifications with the minimum-operator. The accuracyof the map was good (75 %) when compared to field estimates of erosion risk. The method presented therefore allowed for a quick and proper indication of spatial differences of erosion risk in the study area, particularly concerning rill and sheet erosion

Improved drought detection to support crop insurance models: powerpoint

Anomaly assessment for drought monitoring, as required for index insurance applications, is commonly done by comparing actual NDVI measurements against their historical records on a pixel-by-pixel basis. Limited years of satellite records with operational real-time availability result in time-series with a relative low count in annual repeats, e.g., the VEGETATION sensor onboard SPOT and Proba-V has completed at present only 19 full annual repeats. This number is too low for agricultural index insurance models that require accurate assessments of impacts of perils (e.g. of drought) on crop performance during a specific growing season. Generally, they want to see at least 30 annual repeats. Then, considering that the index models typically focus on the left-tail part of the NDVI frequency-distribution, to extract NDVI-thresholds that correspond to drought incidence statistics, the obtainable model accuracy further drops. Typical trigger points used by index insurance models concern the 15th and 5th percentile statistics, which implies that even with thirty annual repeats, these percentiles cannot be robustly estimated. Derived results are thus hardly acceptable to actuaries of insurance companies and financial institutes alike. In this paper, we propose an innovative way to assess NDVI anomalies that significantly increases the statistical basis for their calculation. Rather than focusing only on a single pixel, we make use of the fact that large clusters of pixels respond relatively homogeneously to anomalies in weather patterns. These clusters have a similar land cover and land use, which are determined by climatic conditions, including its variability, most notably drought frequency, duration and severity. The clusters can be derived with unsupervised classification by analyzing the similarity in their long-term NDVI-profiles. Using NDVI-measurements of all pixels in a cluster and all their annual repeats, allows accurate extraction of needed left-tail percentile statistics. Subsequently, seasonal anomalies for individual pixels are then compared against these percentiles. Based on this logic, we have designed an index insurance model that is currently implemented in Ethiopia, and that utilizes real-time Proba-V data as broadcasted through EUMETCast. The model showed an excellent correspondence with surveyed data on farmers experiences. An added value is that the presented approach can easily be scaled to other regions, and that it can also be utilized to benefit real-time drought early warning schemes