Keishi Iwashita

Assessment of air pollution of the forests using satellite data in the neighborhood of cities and industrial complex

Abstract Analysis of the data of in situ observation of spectral reflectance of the polluted cedar leaves and Landsat TM data indicate following interesting features. (1) The level of pollution caused by sulfur dioxide in the forest area can be assessed by the reflectance ratio (NIR/R) of the spectrum of red (R) and (TM band-3) and near infrared (NIR) band (TM band-4) calculated from the Landsat TM data. (2) It is possible to visualize the degree of atmospheric pollution by inputting results of the analysis of the Landsat TM data, the findings of ground truth and growing conditions of forestal trees together with the results of the chemical analysis of sulfur dioxide.

Evaluation of Atmospheric Effects on Interferograms Using DEM Errors of Fixed Ground Points

High-resolution synthetic aperture radar (SAR) data are widely used for disaster monitoring. To extract damaged areas automatically, it is essential to understand the relationships among the sensor specifications, acquisition conditions, and land cover. Our previous studies developed a method for estimating the phase noise of interferograms using several pairs of TerraSAR-X series (TerraSAR-X and TanDEM-X) datasets. Atmospheric disturbance data are also necessary to interpret the interferograms; therefore, the purpose of this study is to estimate the atmospheric effects by focusing on the difference in digital elevation model (DEM) errors between repeat-pass (two interferometric SAR images acquired at different times) and single-pass (two interferometric SAR images acquired simultaneously) interferometry. Single-pass DEM errors are reduced due to the lack of temporal decorrelation and atmospheric disturbances. At a study site in the city of Tsukuba, a quantitative analysis of DEM errors at fixed ground objects shows that the atmospheric effects are estimated to contribute 75% to 80% of the total phase noise in interferograms.

Estimation of phase noise by time series analysis of ALOS-2 PALSAR-2 data

PALSAR-2 on ALOS-2 developed by JAXA, is a follow-on L-band SAR sensor from PALSAR on ALOS. The mission objectives defined by JAXA include contribution to disaster monitoring. To establish the methodology to acquire the damaged area for disaster monitoring, we need to improve our understanding of the relationships between the sensor parameters (e.g., coherence and errors of the interferogram) and specification. This study conducted InSAR analysis using several pairs of PALSAR-2 data of the Tsukuba study site, and quantitatively analyzed the coherence and errors of the DEM of flat targets. In addition, we developed a method to evaluate the phase noise and revealed that the phase noise of PALSAR-2 was about 2% (half) that of the noise of PALSAR.

Landsat analysis for evaluating spatiotemporal changes of regional-scale landscapes

This paper evaluates spatiotemporal changes of regional-scale landscapes using Landsat data, which are able to acquire large amount of data since 1972. The data provides high-quality imagery for many different applications such as land cover mapping and change detection. In this study Landsat TM images on November 1985 and October 1997 were used to evaluate quantitative landscape changes of the Chubu and Kinki regions in Japan. Land classification images were analyzed using simple landscape metrics and sequential analysis method. Consequently, we identified that the urban area grew spatially and aggregated around an existing urban core area. This study demonstrates that remote satellite images provide the desired information such as urban planning and regional planning for a spatiotemporal assessment of various regional-scale landscape changes. Keywords-Landsat; landscapes; landscape metrics; spatiotemporal