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利用 PALSAR-2 数据时间序列评估关东地区周边变形趋势。

Evaluation of the Trend of Deformation around the Kanto Region Estimated Using the Time Series of PALSAR-2 Data.

机构信息

College of Industrial Technology, Nihon University, Chiba 2758575, Japan.

Harris Geospatial Co., Tokyo 113-0033, Japan.

出版信息

Sensors (Basel). 2020 Jan 7;20(2):339. doi: 10.3390/s20020339.

Abstract

In the Kanto region of Japan, a large quantity of natural gas is dissolved in brine. The large-scale production of gas and iodine in the region has caused large-scale land subsidence in the past. Therefore, continuous and accurate monitoring for subsidence using satellite remote sensing is essential to prevent extreme subsidence and ensure the safety of residences. This study focused on the small baseline subset (SBAS) method to assess ground deformation trends around the Kanto region. Data for the SBAS method was acquired by the Advanced Land Observing Satellite (ALOS)-2 Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2 from 2015 to 2019. A comparison of our results with reference levelling data shows that the SBAS method underestimates displacement. We corrected our results using linear regression and determined the maximum displacement around the Kujyukuri area to be approximately 20 mm/year; the mean displacement rate for 2015-2019 was -7.9 ± 2.9 mm/year. These values exceed those obtained using past PALSAR observations owing to the horizontal displacement after the Great East Japan Earthquake of 2011. Moreover, fewer points were acquired, and the root mean-squared error of each time-series displacement value was larger in our results. Further analysis is needed to address these bias errors.

摘要

在日本关东地区,大量天然气溶解在卤水中。该地区过去曾大规模生产天然气和碘,导致大规模地面沉降。因此,利用卫星遥感对沉降进行连续、准确的监测对于防止极端沉降、确保住宅安全至关重要。本研究侧重于使用小基线集(SBAS)方法评估关东地区周围的地面变形趋势。用于 SBAS 方法的数据是由先进陆地观测卫星(ALOS)-2 相控阵 L 波段合成孔径雷达(PALSAR)-2 在 2015 年至 2019 年期间获取的。我们的结果与参考水准数据的比较表明,SBAS 方法低估了位移。我们使用线性回归校正了我们的结果,并确定了 Kujyukuri 地区周围的最大位移约为 20mm/年;2015-2019 年的平均位移速率为-7.9±2.9mm/年。这些值超过了过去使用 PALSAR 观测获得的值,这是由于 2011 年东日本大地震后的水平位移。此外,我们的结果中获取的点数较少,每个时间序列位移值的均方根误差也较大。需要进一步分析以解决这些偏差误差。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8668/7013849/7bb051cfb02f/sensors-20-00339-g001.jpg

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