Landslides and debris flow events triggered by substantial precipitation in typhoon events often cause enormous damage. It is crucial to rapidly acquire, process, and distribute the most updated information of the disaster areas, particularly optical images with high-spatial resolution. An unmanned aerial vehicle (UAV) provides an innovative approach to remote sensing that is much cheaper, safer, and more flexible for deployment in a small area, ranging from a few tens of square kilometers. This paper demonstrates the application of a low-cost UAV to rapid landslide assessment in difficult access and climatic conditions, as well as presents two examples of rapid response to the natural disaster events at Mudan and Manjhou triggered by Typhoon Nanmadol on August 29, 2011. Various approaches were successfully integrated and implemented in an automatic mission planning and image processing system to plan a flight mission and generate three levels of georeferenced products. Comparing with 14 check points identified on the 25-cm resolution aerial orthophoto, the accuracy of the orthophoto product can achieve a root mean square error of less than 2.81 pixels. All orthophotos are further processed to one seamless, color-balanced, and georeferenced mosaic that can be published on the free-access Google Earth within 24 h.
An expert system was developed to integrate all useful spatial information and help the interpreters determine the landslide and shaded areas quickly and accurately. The intersection of two spectral indices, namely the normalized difference vegetation index and the normalized green red difference index, as well as the first principle component of the panchromatic band, is employed to automatically determine the regional thresholds of nonvegetation and dark areas. These boundaries are overlaid on the locally enhanced image and the digital topography model to closely inspect each area with a preferred viewing direction. The other geospatial information can be switched on and off to facilitate interpretation. This new approach is tested with 2 m pan-sharpened multispectral imagery from Formosat-2 taken on August 24, 2009, for several disaster areas of Typhoon Morakot. The generated inventory of landslide and shadow areas is validated with the one manually delineated from the 25 cm aerial photos taken on the same day. The production, user, and overall accuracies are higher than 82%, 85%, and 98%, respectively. The fall in production and user accuracies mainly comes from the differences in resolution. This new approach is as accurate as the general approach of manual delineation and visual interpretation, yet significantly reduces the required time.
Light emerging from the sea surface carries information of the water constituents. General empirical methods to derive in-water ocean color algorithms use measurements near the sea surface to relate the emerging radiative signals to the water contents. Problems with existing algorithms are frequently reported and there is no single algorithm adopted for Case 2 waters. There is a general trend in investigators moving from pure empirical methods to model-based techniques to solve the inverse problem. Among these techniques, the non-linear optimization approach (NOA) offers the highest accuracy without any dependency on the simulated or training data, but generally requires substantial computation time. Our research presents an approach to substantially decrease the computation time of the NOA by using a look-up-table (LUT) technique to correct the effects of inelastic scattering. A series of sensitivity tests was made to determine the critical factors required to accurately simulate the remote sensing reflectance. Results show that the inherent optical properties (IOPs) and inelastic scattering play a significant role, while variations of the ambient optical environment and surface wind speed are negligible. A LUT was then derived from numerous forward simulations using the Hydrolight radiative transfer model. All processes of inelastic scattering were considered and a set of three-variable (chlorophyll concentration, CDOM ratio and backscattering fraction) biooptical models, was used to yield a flexible parameterization of IOPs. This new approach was validated against in situ measurements. To examine its application to a large variety of water types, an extensive model-to-model comparison was made for a wide range of combinations of IOPs. Results show that our model provides both fast and accurate retrievals of chlorophyll concentration, CDOM ratio and backscattering fraction for an optically homogeneous water body. This new inversion approach may accelerate the use of ocean color remote sensing.