Within the context of disaster risk reduction, including climate change adaptation, significant thematic discourse has been dedicated to the difficulty of implementing research-based knowledge in policy and practise. Not only has the discussion focused on the causes of this issue, but many recommendations for enhancing the use of information and knowledge have also been made. The authors first frame the knowledge challenges and, second, introduce a systematic means to identify the factors hindering the use of information and knowledge. The approach proposed allows determining core barriers in the co-production, exchange, and use of knowledge. Subsequently, we illustrate where further advancement is needed in the field of knowledge development, means of transmission and use for disaster risk reduction. We suggest a method that analyses cases considering the success or failure of information flows from and to different stakeholder groups. The aim is to identify causes for knowledge fragmentation at different phases in the disaster management continuum, and, subsequently, to strengthen both individual and institutional learning, as well as to determine social and functional changes required to address pressing issues of disaster risk reduction, including climate change adaptation, in a competent manner

Accurate mapping of landslides and the reliable identification of areas most affected by landslides are essential for advancing the understanding of landslide erosion processes. Remote sensing data provides a valuable source of information on the spatial distribution and location of landslides. In this paper we present an approach for identifying landslide-prone “hotspots” and their spatio-temporal variability by analyzing historical and recent aerial photography from five different dates, ranging from 1944 to 2011, for a study site near the town of Pahiatua, southeastern North Island, New Zealand. Landslide hotspots are identified from the distribution of semi-automatically detected landslides using object-based image analysis (OBIA), and compared to hotspots derived from manually mapped landslides. When comparing the overlapping areas of the semi-automatically and manually mapped landslides the accuracy values of the OBIA results range between 46% and 61% for the producer’s accuracy and between 44% and 77% for the user’s accuracy. When evaluating whether a manually digitized landslide polygon is only intersected to some extent by any semi-automatically mapped landslide, we observe that for the natural-color images the landslide detection rate is 83% for 2011 and 93% for 2005; for the panchromatic images the values are slightly lower (67% for 1997, 74% for 1979, and 72% for 1944). A comparison of the derived landslide hotspot maps shows that the distribution of the manually identified landslides and those mapped with OBIA is very similar for all periods; though the results also reveal that mapping landslide tails generally requires visual interpretation. Information on the spatio-temporal evolution of landslide hotspots can be useful for the development of location-specific, beneficial intervention measures and for assessing landscape dynamics.