In this study, 215 MERIS images were used to estimate the chlorophyll-a concentration between 2003 and 2012 in Lake Kasumigaura, Japan. The MERIS level 1b data were first processed using an atmospheric correction algorithm. The atmospherically corrected remote-sensing reflectances were then input into a chlorophyll-a retrieval algorithm for estimating chlorophyll-a concentration. Finally, the MERIS-derived chlorophyll-aconcentrations were compared to the measured chlorophyll-a concentrations obtained from the Lake Kasumigaura database. The results showed that the MERIS data in tandem with the atmospheric correction and water quality retrieval algorithms achieved acceptable accuracy for all test sites with a normalized mean absolute error (NMAE) in the range of 24% to 34%, a root mean squared error (RMSE) in the range of 17.16 to 31.30mg m-3, and a correlation coefficient (R) in the range of 0.73 to 0.78. In addition, the MERIS-derived chlorophyll-a concentrations also showed seasonal and yearly variations similar to those of the measured chlorophyll-a concentrations (R between 0.59 and 0.78, p<0.001). These findings show the potential for satellite data to be used instead of field measurements for monitoring water quality.
We can quickly grasp the information of land surface by aerial images. In particular, if the aerial images are integrated with GIS, these images can be used for various purposes. Therefore, the aerial image registration to a map is quite important. For this purpose, GPS/IMU is used in recent years, however all aircraft are not equipped with it. When a wide-scale disaster strikes, if aerial images taken by any ordinary cameras without IMU can be used to the registration to maps, lots of aircrafts can be used to collect the land surface situation linked with geographical information. However, the image based registration between the aerial image and the map is not simple. This is because an aerial image taken by the hand-carried camera will be an oblique aerial image with perspective distortion, and not all objects in the map are in the aerial image, and vice versa. Furthermore, the position of the reference point in the oblique aerial image is different from aircraft’s position measured by GPS. In this paper, we propose an automatic registration method for the oblique aerial image. The IMU is a special device, but GPS is general. Our method uses an oblique aerial movie with GPS data. The method consists of three parts: estimation of major directions at Pseudo-orthogonal image, estimation of the location of the reference point, and estimation error removal by using a digital map image. We implemented our proposed method on a personal computer, and experimented using real data. As a result, the maximum error on the road areas is within six meters, and the results show that the proposed method is useful for several operations after the disasters.
The preparation for the launch of the Greenhouse gases Observing SATellite-2 (GOSAT-2) in fiscal 2017 has been progressing. Although the amount of observation data increases according to the changes, such as the increase in the number of bands in GOSAT-2 TANSO-CAI-2, it is not likely that of realization if the floor spaces to set up a ground system for the steady operational processing of the GOSAT-2 data significantly exceed that of GOSAT. To deal with this problem, we evaluated the usability of the graphics prosessing unit (GPU), which was designed for handling multiple tasks such as image processing simultaneously. Meanwhile the processing time is not usually simply inversely proportional to the processing capacity of the central processing unit (CPU) or GPU. It is necessary to compare different CPUs and GPUs in order to estimate the processing time on the basis of the processing capacity. For this reason, we compared the processing times of GOSAT TANSO-CAI L2 cloud flag processing with OpenMP (CPU), OpenACC (GPU), and CUDA (GPU) using three computers: Computer 0, Computer 1, and Computer 2. The results were as follows: 1) On Computer 2, the CPU multi-threaded code with OpenMP using an Intel Core i7-3820 was the fastest (25 msec). The primary reason that GPU multi-threaded code was slower than CPU multi-threaded code was the presence of a performance bottleneck for transferring processing data between the host and the device memory. Outside of the transfer between the host and the device memory, the GPU multi-threaded code with OpenACC or CUDA was 15.5 times faster than the CPU multi-threaded code. 2) In the case of converting double-precision floating-point variables to single-precision floating-point variables, the CPU+GPU hybrid parallel processing with OpenMP and CUDA on Computer 2 was the fastest (20 msec). The importance of CPU+GPU hybrid parallel processing will increase in the future as the data transfer speed between the host and the device memory becomes faster.