Due to the limited time between earthquake occurrence and a tsunami reaching land, it is important for us to flee temporarily to buildings designated for tsunami escape such as mid-to-high-rise buildings. We attempted the use of aerial LiDAR data to assess the appropriateness of buildings to be used for tsunami escape in Asahi city, Chiba prefecture, which was damaged by the tsunami triggered during the March 11 earthquake. Our calculations were based on the dimensions of buildings designated as possible candidates for tsunami escape using aerial LiDAR data in predicted inundation areas. We then did an investigation to confirm the suitability of candidates. Firstly, the inundation height was estimated using VRS RTK-GNSS survey equipment in filed survey on August 26, 2012. Secondly, to correct aerial LiDAR data observed pre-earthquake, the surface deformation patterns were evaluated with InSAR technique using ALOS/PALSAR data. Thirdly, the spatial information of the predicted inundation area was simulated using a digital elevation model derived from aerial LiDAR data. Fourthly, the safety of the candidates for tsunami escape was calculated from the dimensions of the buildings measured using a digital feature model derived from aerial LiDAR data. Finally, the specific condition of the candidates for tsunami escape was confirmed by field survey on January 6, 2012. In conclusion, we found that candidate buildings for tsunami escape can effectively be determined using aerial LiDAR analysis. Moreover, the influence of ground height to affect the safety of the candidates was found to be a major factor.
Change detection using satellite data is frequently used in cases of natural disasters to identify the location and extent of the damage. However, current practical analysis for disaster monitoring largely depends on manual analysis by experts, creating a bottleneck of data that hinders effective and prompt response. This paper proposes an automatic change detection system that can execute entire processes of change detection without the intervention of human experts. The proposed system employs a knowledge-driven system based on ontology. The system stores knowledge modules, each of which can extract information or make an inference. At the time of change detection, the system selects the appropriate knowledge modules and constructs a Bayesian network for the inference of target change by interpreting the semantics of the target change and knowledge modules using ontology. The system extracts information and makes an inference, yielding probability images that indicate the confidence degree of the inference of the target change. This paper presents a demonstration of the proposed detection system by applying it without any modifications in the system to two cases of disaster monitoring: a mudslide and a flood.
Not only for the temperature measurement, to understand the causes of the measured temperature which is observed the same area by both of camera and thermoradiometer is developed. Presently, the commercial thermoradiometer tends to have the narrow FOV, however the environmental temperature measurement needs the wider FOV for the area averaging. So we develop the wide FOV thermoradiometer for the environmental use. The thermoradiometer has the sufficient accuracy, RMSE 0.2 [K], for use. In addition, a digital camera to take the same area image as the thermoradiometer measurement synchronous with temperature measurement is equipped. Vegetation Coverage Ratio (VCR) and Shadow Content (SC) which affect the temperature is defined from the digital camera image. We measured the vegetation by this equipment. The temperature and SC show the relationship of inverse correlation. Observation temperature is affected from leaf surface temperature in vegetation cover area. VCR was able to quantify the relationship to observation temperature. In conclusion, the more deep understanding of the environmental temperature can be possible to measure VCR and SC at same time as temperature measurement.
Forest fire smoke detection is an important use of remote sensing applications and image interpretation of moderate resolution imaging spectroradiometry (MODIS) true-color images is a common technique for this purpose. However, there are some difficulties in the decision process, particularly in discriminating between smoke and clouds. To overcome this problem, we propose a new MODIS false-color composite image method that combines a smoke reflectance index, mid-infrared reflectance and a water index. The false-color image depicts smoke in a reddish color that is easily distinguished from clouds and land surfaces. A Smoke Aerosol Reflectance Index (SARI) and a Water Index (WI) were developed to create the false-color composite image of R : SARI, G : MODIS channel 7, and B : WI. The SARI is derived from MODIS channels 1 (red) and 3 (blue) using the Aerosol Enhancement (AE) function and exponential transformation to correlate linearly with an Aerosol Optical Thickness (AOT) of 0.55μm over land. The Water Index (WI) is a synthesis of four water indices : the thick cloud index of channel 32 brightness temperature, the Aerosol Vapor Index (AVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Snow Index (NDSI). Some smoke pixels from a false-color image were sampled as a training dataset and overall smoke pixels were successfully detected. In this paper, we report three case studies of forest fires (in Korea, China and Russia, and Australia) and one set of results was compared and validated with those of an existing multi-threshold method. This comparision confirmed that the proposed false-color composite image is useful for detecting smoke plumes more accurately.
Vegetation, which extensively covers the global land area, is closely related to the climate system. According to the predictions of some earth system models, the boreal forest will redistribute northward as a result of climate change that will occur within the next three hundred years. This paper reviews recent remote sensing studies that have investigated the spatio-temporal variability of vegetation in boreal regions with specific reference to the impact of the climate. Based on the time series of the normalized difference vegetation index (NDVI) derived from satellite observations, the decadal change in vegetation can be examined. Increasing trends in the vegetation index and advancing trends in the green-up date have been found in Siberia and in the tundra region in Alaska from the 1980s to the 1990s, although some uncertainties remain. These trends are presumably due to warming in high-latitude regions. The development of an estimation algorithm of vegetation that defines biophysical parameters such as the leaf area index (LAI) and above-ground biomass (AGB) is another important task of remote sensing that can contribute to climate system modeling. The snow cover concealing the forest floor allows us to extract forest overstory greenness data from total greenness data by satellite observation and consequently, the LAI arising only from the forest overstory can be estimated. Additionally, we suggest that the relative sparseness of the boreal forest may be suitable for AGB estimation by microwave radar remote sensing.