Advanced Land Observing Satellite-2 (ALOS-2) is a Japanese earth observation satellite operated over two years after the launch on May 24, 2014. The mission objective of ALOS-2 are disaster monitoring, environmental monitoring, land monitoring, and technology development. To achieve these missions, ALOS-2 carries the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) that has many improved performances compared to previous L-band SAR satellite in Japan. This paper introduces the ALOS-2 missions, specifications of the satellite and PALSAR-2, current operation status such as data acuisition and emergency observation, and examples of PALSAR-2 applications.
Persistent scatterer interferometry is a method for mapping surface displacements using satellite-based synthetic aperture radar data. Because of its strong advantage in mapping broad surface displacements accurately and with high spatial resolution, persistent scatterer interferometry has been successfully applied to a variety of displacement phenomena such as ground subsidence and landslides. Although previous studies have evaluated the accuracy of the method, to the best of our knowledge, no theoretical evaluation using simulation data for ALOS/PALSAR L-band data has yet been carried out. In this study, we evaluated the accuracy of persistent scatterer interferometry of ALOS/PALSAR data using simulated and real SAR data analyses, and investigated surface displacement in the Kujukuri Plain, Chiba Prefecture, Japan. Our simulation study showed that the RMSE of time-series displacements was 1.14cm and the mean absolute difference of annual displacement velocity was 0.20cm/year. In our real data analysis, the RMSE values of persistent scatterer interferometry results at two GPS stations were 0.50 and 0.65cm, and the absolute differences of annual displacement velocity were 0.07 and 0.08cm/year. This high accuracy and the detailed spatial distribution would be effective for surface displacement monitoring. Moreover, the present evaluation of theoretical accuracy with simulated data would be useful for analyses with a variety of satellite SAR data and areas of interest.
Synthetic Aperture Radar (SAR) is a very useful tool for detecting surface changes and deformation caused by volcano activities because of its independence from weather and daylight conditions. A volcano in Kuchinoerabu Island, Kagoshima, Japan, erupted explosively and pyroclastic flows reached the coastal area on May 29, 2015. In this study, ALOS-2 PALSAR-2 data were used to analyze surface changes and deformations associated with the eruptive activity. The pyroclastic flows around the Shindake crater were observed by the differences of backscattering coefficients and the coherence values. Changed areas were extracted by a threshold value of the coherence, and were compared with a visual interpretation result conducted by the Geospatial Information Authority of Japan (GSI). The ground deformation due to the eruption was detected by the interferometric analysis, and its vertical component was estimated by combining the results from two InSAR pairs.
Rapid and all-weather detection of flood areas is needed to monitor and mitigate flood disasters. This study addressed flood area detection using ALOS-2 PALSAR-2 data acquired during the 2015 heavy rainfall disaster in the Kanto and Tohoku area of Japan. We propose an approach to flood area detection by thresholding amplitude and interferometric coherence images for non-urban and urban areas, respectively. PALSAR-2-derived flood areas were validated using the inundation map provided by the Geospatial Information Authority of Japan (GSI) and showed 75% accuracy and a 0.51 kappa coefficient in flood/non-flood discrimination. The effectiveness of a lower incidence angle (less than 40 degrees) and a high-sensitivity observation mode (6-m resolution mode) for detecting non-urban flooding was also demonstrated by a comparative study. Interferometric phase variation was revealed to be more effective in detecting urban flooding than conventional interferometric coherence. Our results demonstrate the feasibility of PALSAR-2 for rapid flood monitoring and can be used as a reference for possible future flood disasters.
The L-band synthetic aperture radar (SAR) satellite, ALOS-2 (Daichi-2), has a capability for educational purposes. The radar aboard ALOS-2, named PALSAR-2, has a wavelength of 24 cm, compatible with a makeable reflector size for children. In addition, as the spatial resolution of the radar is approximately 3 m, children can easily draw a letter in the radar image by deploying their own reflectors in the schoolyard. We have developed a sufficiently reflective corner reflector (CR) that can be easily built by children. In experiments, we clarified that ALOS-2 can detect reflected waves from a CR with a side length of three times the radar wavelength. We also developed free software that enables children and their educators to analyze most earth observation satellite data including ALOS-2 PALSAR-2 data. In 2014, we established an educational program called “Let's expose ourselves on Daichi-2 data” with the YAC-J (Young Astronauts Club Japan) and EORC (Earth Observation Research Center), JAXA. We have also held seminars for educators in various places in Japan. As a result, more than 20 groups have implemented the program. We conclude that this program enables children and their educators to feel a connection to space and fosters their interest in their native environments.
The purpose of this study was to monitor the growth of rice on a weekly basis by multicopter. The data collected were used to 1) determine whether topdressing was required, 2) assess the potential for lodging risk, 3) estimate yield, and 4) create maps of rice growth for estimated protein contents. The conventional NDVI and 2 G_RBi were both suitable as monitoring indices, and their application revealed the following: 1) The standard deviation of 2 G_RBi values was found to be useful for determining the timing of topdressing, which was estimated to be most effective 10-15 days after maximum standard deviations were recorded. Areas with poor growth could also be identified by using the NDVI values of the non-productive tillering stages and areas where topdressing was needed could be identified. 2) To diagnose lodging, plant length was estimated using the differences between the DSM before the field was prepared for planting and on the monitoring day, and the risk of lodging 14 days before heading was shown for the entire area. 3) Yield was found to be highly correlated with the NDVI values of the heading stage, and yield maps were created using a yield estimation equation. 4) With regard to eating quality, a strong correlation was observed between the protein content of brown rice and NDVI values from the heading stage to the first half of the maturing stage (15 days after the heading stage), and accurate maps of eating quality were created.
The monitoring of rice growth using a multicopter is both safe and cost effective for individual farmers. The findings presented here show that the use of this method to obtain objective data and maps to assess topdressing, lodging risk, yield, and protein content is useful for the detailed management of crop growth in fields.
Photographic surveying using a small-sized UAV (Unmanned Aerial Vehicle) has recently attracted attention. The SfM (Structure from Motion) method makes it possible to create 3D point clouds and a 3D model from multiple 2D images. Furthermore, an orthomosaic photograph and DSM (Digital Surface Model) can be generated from the 3D model. It has been reported that the precision of the point clouds becomes low when the target is vegetation due to insufficient resolution of images, the vegetation moving in the wind, and shadow areas in the images. This study, therefore, created a DSM of a forest crown using nadir+oblique stereo pair images taken by a small-sized UAV.
The study was carried out in the larch forests at the foot of the Yatsugatake Mountains, Yamanashi Prefecture, Japan, in July, 2015. A UAV with a digital camera flew over the study site to acquire crown images in the nadir and oblique directions using an autopilot system. We first generated dense point clouds, from which we then generated orthomosaic photographs and DSMs following three patterns: (1) 70 nadir images taken at an altitude of 100m above the ground; (2) (1) plus 54 nadir images taken at an altitude of 50m above the ground; and (3) (1) plus 54 oblique images taken at an altitude of 50m above the ground.
Under Pattern (1), 17.5% of the total area had no point clouds, while Patterns (2) and (3) showed 12.8% and 9.7%, respectively, with no point clouds. We obtained DSMs with a spatial resolution of 2.0∼2.5cm for all three patterns. Some areas of the DSM of Pattern (1) showed less surface roughness; such areas decreased in Patterns (2) and (3). In conclusion, the present study demonstrates an improvement in the reproducibility of DSMs by adding oblique images in comparison with the use of nadir images alone.
This technical report describes a demonstration experiment for estimating water depth in shallow sea areas using Hodoyoshi-1 multispectral (MS) sensor. Hidoyoshi-1 is the first microsatellite funded by the Japanese Office of Cabinet and Japan Society for the Promotion of Science as part of the FIRST (Funding Program for World-Leading Innovative R&D on Science and Technology) program. Hodoyoshi-1 was developed based on “Reasonably Reliable Systems Engineering” concept. The microsatellite was launched on 6th November, 2014. Hodoyoshi-1 carries an optical MS sensor which provides blue (450 to 520nm), green (520 to 600nm), red (630 to 690nm) and near infrared (780 to 890nm) bands at 6.7 meter spatial resolution. Hodoyoshi-1 is a low-cost satellite imagery product that is expected to boost the remote sensing industry. In this report, Hodoyoshi-1 acquired imagery of several shallow sea areas, which were published on large-scale NOAA nautical charts. A linear algorithm (LA) - which has been successfully used for water depth estimation - achieved high estimation accuracy (determination coefficient is 0.819) using Hodoyoshi-1 MS imagery. The support vector machine (SVM) regression, a well-known robust estimation model, achieved equivalent estimation accuracy (determination coefficient is 0.891). Furthermore, comparison of estimated water depth maps and root mean square error in each depth indicate that SVM is capable of wide and deep coverage. Hidoyoshi-1 MS sensor performed relatively well for extracting the earth surface information as well as estimating water depth based on SVM. However, Hodoyoshi-1 imagery does not have Rational Polynomial Coefficients (RPC). In addition, the imagery has low location accuracy without ground control points (GCPs). As a result, Hodoyoshi-1 failed to produce an estimation model in a complex seafloor topography area. This issue would be resolved by improving location accuracy and performing geometric correction method for Hodoyoshi-1.