Search

The utilization of multi-sensor and constellation satellite data with appropriate geometric and radiometric calibration and validation is required for effective satellite data applications for various monitoring tasks. In this paper, we present the research and development of optical calibration sites using both natural surface and ground point-source-mirror reflectors constructed at The Center for Research and Application of Satellite Remote Sensing of Yamaguchi University (YUCARS), Japan. The YUCARS calibration sites experimented with GRUS-1A (Axelspace Corporation, Tokyo, Japan) and PlanetScope (Planet Lab, San Francisco, CA, USA) images for the verification of radiometric and geometric performance following the harmonized reflectance product. The top of atmosphere (TOA) radiance and reflectance of optical micro-satellite imageries were simulated by MODTRAN6 based on the in-situ data of the ground point-source-mirror reflector, ground surface and atmospheric measurements. The YUCARS mirror arrays were used to verify geometric accuracy and better band co-registration. The TOA reflectance derived from the ground measurements and acquired by satellite instruments were correlated to derive harmonization coefficients. The results show an improvement in image accuracy and harmonization of the different sensor data for the multi-temporal application. The preliminary results show that the mirror-arrays method can efficiently solve the limits of the external environment, time, and space. Furthermore, it can be used for improving radiometric performance and image quality using deblurring from a point spread function created from YUCARS mirror arrays.

Timely detection of flooding is paramount for saving lives as well as evaluating levels of damage. Floods generally occur under specific weather conditions, such as excessive precipitation, which makes the presence of clouds very likely. For this reason, radar-based sensors are most suitable for near-real-time flood mapping. The public dataset Sen1Floods11 recently released by the Cloud to Street is one example of ongoing beneficial initiatives to employ deep learning for flood detection with synthetic aperture radar. The present study used this dataset to improve flood detection using well-known segmentation architectures, such as SegNet and UNet, as networks. In addition, this study provided a deeper understanding of which set of polarized band combination is more suitable for distinguishing permanent water, as well as flooded areas from the SAR image. The overall performance of the models with various kinds of labels and a combination of bands to detect all surface water areas were also assessed. Finally, the trained models were tested on a completely different location at Kerala, India, during the 2018 flood for verifying their performance in the real-world situation of a flood event outside of the given test set in the dataset. The results prove that trained models can be used as off-the-shelf models to achieve an intersection over union (IoU) as high as 0.88 in comparison with optical images. The omission and commission error were less than 6%. However, the most important result is that the processing time for the whole satellite image was less than 1 min. This will help significantly for providing analysis and near-real-time flood mapping services to first responder organizations during flooding disasters.

Earthquakes are one of the most devastating types of natural disasters, and happen with little to no warning. This study combined Landsat-8 and interferometric ALOS-2 coherence data without training area techniques by classifying the remote sensing ratios of specific features for damage assessment. Waterbodies and highly vegetated areas were extracted by the modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI), respectively, from after-earthquake images in order to improve the accuracy of damage maps. Urban areas were classified from pre-event interferometric coherence data. The affected areas from the earthquake were detected with the normalized difference (ND) between the pre- and co-event interferometric coherence. The results presented three damage types; namely, damage to buildings caused by ground motion, liquefaction, and landslides. The overall accuracy (94%) of the confusion matrix was excellent. Results for urban areas were divided into three damage levels (e.g., none–slight, slight–heavy, heavy–destructive) at a high (90%) overall accuracy level. Moreover, data on buildings damaged by liquefaction and landslides were in good agreement with field survey information. Overall, this study illustrates an effective damage assessment mapping approach that can support post-earthquake management activities for future events, especially in areas where geographical data are sparse.