Yuzo Suga

Comparison of SAR and optical sensor data for monitoring of rice plant around Hiroshima

Abstract Through the research on the application of the multi-temporal data acquired with Landsat-5 TM, SPOT-2 HRV, SPOT-4 HRVIR and RADARSAT-1 SAR to monitoring of rice field following features are clarified. Vegetation indices NDVI (Normalized Difference Vegetation Index) and EVI (Extended Vegetation Index) derived from different optical sensors TM, HVR, HRVIR indicate nearly same characteristics suggesting validity of calibration of respective sensors. It was found that by combined use of multi-temporal vegetation indices NDVI and EVI together with SAR (Synthetic Aperture Radar) data it was found that rice planted area can be estimated with the error less than 2%.

Monitoring of a rice field using landsat-5 TM and landsat-7 ETM+ data

Abstract Through the research on the application of the multi temporal data acquired with Landsat-5 TM and Landsat-7 ETM+ to monitoring of rice field the following features are clarified Two vegetation indices NDVI (Normalized Difference Vegetation Index) and EVI (Extended Vegetation Index) obtained from Landsat-5 TM data of 7 July 2000 and that from Landsat-7 ETM+ data of 6 July 2000 show almost the same feature proving the validity of calibration of both sensors. NDVI computed from satellite data increases corresponding to the growth of rice plants until the flowering stage while EVI further continues to increase until the fructification stage. The vegetation indices computed from the in situ survey data with a portable multispectral radiometer do not coincide with those computed from satellite data. This is because that the reflectance of the background such as soil and water is included in the satellite data.

Detection of urban disaster using InSAR. A case study for the 1999 Great Taiwan Earthquake

The authors studied the applicability of interferometric SAR (InSAR) using ERS-2 SAR data for detecting damaged urban areas by the Great Taiwan Earthquake (Chi-Chi Earthquake) occurred on Sep. 21, 1999. Three temporal ERS-2 SAR data acquired by Taiwan ground station on Jan. 21, May 6, and Sep. 23, in 1999 were used as the test data. By overlaying two SAR multi-look intensity images before (May 6) and after (Sep. 23) the earthquake, it was found that the damaged urban areas by the earthquake were hardly detected only by the intensity change. On the other hand, the overlay of the two coherence images from the pairs (Jan.-May) and (May-Sep.) indicated that the urban areas damaged severely (e.g. Dongshi) resulted in clear decrease of coherence by the earthquake occurrence, while the coherence in the urban areas not damaged severely (e.g. Taichung) did not change even after the earthquake. The result of this study suggests that InSAR technology has a great potential for detecting urban disasters by earthquake.

Application of ERS-2/SAR data for the 1999 Taiwan earthquake

Abstract The applicability of interferometric SAR (InSAR) for detection of urban damages and land displacements caused by The 1999 Great Taiwan earthquake (Chi-chi earthquake) occurred on Sep. 21, 1999 were studied based on the data acquired on ERS-2 repeated pass. Three ERS-2 SAR data acquired on Jan. 21, May 6, and Sep. 23 in 1999 were used, the former two were acquired before the earthquake occurrence and the third was acquired two days after the earthquake. Two major results are obtained from the study. The first is the verification on the effectiveness of coherence information for detecting the damaged urban areas by building collapse, and the other is the effectiveness of differential interferograms to extract land displacement patterns. The latter result also proves that the extracted land displacement pattern by InSAR is consistent in general with the result of GPS survey.

Comparison of various SAR data for vegetation analysis over Hiroshima city

Abstract The backscattering intensity values for various targets in and around Hiroshima are investigated using the data obtained from the SARs of Japanese JERS-1, European ERS-1, and Canadian RADARSAT. The result of the analysis indicates the backscattering intensity values for the same target vary with the wavelength and incidence angle of SAR. The comparison between SAR and the optical sensor data of ADEOS AVNIR, in terms of the capability for differentiating vegetation coverage conditions, indicates some capability of SAR data to detect vegetation coverage conditions with the correlation between SAR backscatter levels and the normalized difference vegetation index derived from AVNIR data.

Comparative study for flood detection using JERS-1 SAR and Landsat TM data

The authors verified the applicability of spaceborne SAR data for flood detection by the comparative study using JERS-1 SAR and Landsat TM data. For the purpose of verification of flood detection by SAR data, the inundated areas were extracted using the change of normalized difference vegetation index (NDVI) of Landsat TM data taken in almost same period as that for SAR data. Then the performance of SAR data for flood detection was investigated by comparing the extracted areas by both of SAR and TM data. The performance by SAR was evaluated by two indices, the coincidence rate of inundated areas by SAR for inundated areas extracted by TM (true production rate: TPR) and the rate of non-inundated areas by TM for inundated areas by SAR (false production rate: FPR). The result of the study verified the effectiveness of spaceborne SAR data for flood monitoring by remote sensing.

Monitoring of new plantation development in tropical rain forests using JERS-1 SAR data

Abstract The authors studied the applicability of multi temporal SAR data obtained from JERS-1 SAR for monitoring the change in tropical rain forest conditions due to plantation development. The test site was the southern part of Sumatra Island, Indonesia. A total of seven JERS-1 SAR and one ERS-2 SAR data from 1992 to 1997 were analyzed together with two optical data, MOS-1 MESSR in 1990 and SPOT HRV in 1997. The result of this study verified that the backscatter change in multi temporal JERS-1 SAR data can be used effectively to monitor the process of plantation development in the test site, which accompanies the burning of felled tree trunks after deforestation, one of the causes for forest fire and serious smoke.

Extraction of rice-planted area using a self-organizing feature map

We introduce a neural network of self-organizing feature map (SOM) to classify remote-sensing data, including microwave and optical sensors, for the estimation of areas of planted rice. This method is an unsupervised neural network which has the capability of nonlinear discrimination, and the classification function is determined by learning. The satellite data are observed before and after rice planting in 1999. Three sets of RADARSAT and one set of SPOT/HRV data were used in Higashi–Hiroshima, Japan. The RADARSAT image has only one band of data and it is difficult to extract the rice-planted area. However, the SAR back-scattering intensity in a rice-planted area decreases from April to May and increases from May to June. Therefore, three RADARSAT images from April to June were used in this study. The SOM classification was applied the RADARSAT and SPOT data to evaluate the rice-planted area estimation. It is shown that the SOM is useful for the classification of satellite data.

Higher resolution images for visible and near infrared bands of LANDSAT-7 ETM+ by using panchromatic band

Abstract Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) simultaneously acquires multispectral image data with three different spatial resolutions; 30 meters for Band 1–5 and 7 (visible, near infrared and short wave infrared bands), 60 meters for Band 6 (thermal band) and 15 meters for Band 8 (panchromatic band). The spectral range of Band 8 is 520–900 rum and those of Bands 2, 3, and 4 are 530–610, 630–690 and 780–900 run respectively. Multispectral Band 2, 3 and 4 data at a 15 meter spatial resolution were generated by fusing with the higher resolution Band 8 data. Four methods are tested i.e. HSI (Hue, Saturation, Intensity) transformation, Brovey transformation and two proposed methods which utilize the statistical features of the multispectral and panchromatic data. The result indicates that in HSI and Brovey transformations the fusion of Band 8 data has reduced the numerical values of the average and the standard deviations of the original data while the proposed methods have generated the data of almost same color tone with nearly same values of average and standard deviations of the original data.

Rice crop monitoring using X, C and L band SAR data

Rice crop is one of the most important agricultural products in Asia. It is necessary to monitor rice in wide area for the food control and the adjustment of food. This study focuses on the validation for monitoring of rice crop growth and extraction of rice-planted area using the German TerraSAR-X (X-band), ENVISAT-1/ASAR (C-band) and ALOS/PALSAR (L-band). TerraSAR-X is an advanced satellite which is able to observe 1m resolution with single polarization (HH or VV) or 2m resolution with dual polarization (HH/VV) in SpotLight mode. Also ASAR and PALSAR have single and dual polarization mode. Multi-temporal SAR data of each satellite are processed and analyzed to investigate temporal change of SAR backscattering coefficient of rice-planted area during the rice growing cycle with different wavelength, polarization and resolution in the test site of Hiroshima, Japan. Ground truth data are measured simultaneously with satellite observation such as height of plant, vegetation cover and Leaf Area Index (LAI) corresponding to SAR observation, and also the correlation between SAR backscattering coefficient and those parameters of rice crop growing were analyzed. SAR backscatter shows the significant change in early stage of rice growing cycle. Therefore, rice-planted area extraction is conducted with multi-temporal SAR data based on a classification technique using maximum likelihood method (MLC). In conclusion, rice crop growth and rice-planted area extraction can be successfully monitored using multi-temporal and multi-wavelength SAR data.