scholarly journals Snow density retrieval using SAR data: algorithm validation and applications in part of North Western Himalaya

2013 ◽  
Vol 7 (3) ◽  
pp. 1927-1960 ◽  
Author(s):  
P. K. Thakur ◽  
R. D. Garg ◽  
S. P. Aggarwal ◽  
P. K. Garg ◽  
J. Shi ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Western Himalaya ◽  
Synthetic Aperture ◽  
Snow Density ◽  
Ground Truth Data ◽  
Array Type ◽  
Runoff Modeling ◽  
Sar Data ◽  
Aperture Radar

Abstract. The current study has been done using Polarimetric Synthetic Aperture Radar (SAR) data to estimate the dry snow density in Manali sub-basin of Beas River located in state of Himachal Pradesh, India. SAR data from Radarsat-2 (RS2), Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR) and Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR) have been used. The SAR based inversion models were implemented separately for fully polarimetric RS2, PALSAR and dual polarimetric ASAR Alternate polarization System (APS) datasets in Mathematica and MATLAB software and have been used for finding out dry snow dielectric constant and snow density. Masks for forest, built area, layover and shadow were considered in estimating snow parameters. Overall accuracy in terms of R2 value and Root Mean Square Error (RMSE) was calculated as 0.85 and 0.03 g cm−3 for snow density based on the ground truth data. The retrieved snow density is highly useful for snow avalanche and snowmelt runoff modeling related studies of this region.

scholarly journals An approach for detecting changes related to natural disasters using Synthetic Aperture Radar data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 819-826 ◽  
Author(s):  
N. Milisavljevic ◽  
D. Closson ◽  
F. Holecz ◽  
F. Collivignarelli ◽  
P. Pasquali
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Ground Truth Data ◽  
Synthetic Aperture Radar Data ◽  
Before And After ◽  
The Impact ◽  
Aperture Radar

Land-cover changes occur naturally in a progressive and gradual way, but they may happen rapidly and abruptly sometimes. Very high resolution remote sensed data acquired at different time intervals can help in analyzing the rate of changes and the causal factors. In this paper, we present an approach for detecting changes related to disasters such as an earthquake and for mapping of the impact zones. The approach is based on the pieces of information coming from SAR (Synthetic Aperture Radar) and on their combination. The case study is the 22 February 2011 Christchurch earthquake. <br><br> The identification of damaged or destroyed buildings using SAR data is a challenging task. The approach proposed here consists in finding amplitude changes as well as coherence changes before and after the earthquake and then combining these changes in order to obtain richer and more robust information on the origin of various types of changes possibly induced by an earthquake. This approach does not need any specific knowledge source about the terrain, but if such sources are present, they can be easily integrated in the method as more specific descriptions of the possible classes. <br><br> A special task in our approach is to develop a scheme that translates the obtained combinations of changes into ground information. Several algorithms are developed and validated using optical remote sensing images of the city two days after the earthquake, as well as our own ground-truth data. The obtained validation results show that the proposed approach is promising.

scholarly journals Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

2019 ◽  
Vol 11 (20) ◽  
pp. 2351 ◽  
Author(s):  
Yusupujiang Aimaiti ◽  
Wen Liu ◽  
Fumio Yamazaki ◽  
Yoshihisa Maruyama
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Ground Truth Data ◽  
Active Sensor ◽  
Optical Images ◽  
Comparison Results ◽  
Landslide Mapping ◽  
Aperture Radar

Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth’s surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (Mw 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively.

Multi-Temporal Synthetic Aperture Radar Data for Paddy Crop Area Estimation in Eastern part of Godavari Delta, Andhra Pradesh, India.

2020 ◽  
Vol 8 (3) ◽  
pp. 208-218
Author(s):  
S.K. Tiwari ◽  
Prasada Rao G
Keyword(s):  
Synthetic Aperture Radar ◽  
Cloud Cover ◽  
Andhra Pradesh ◽  
Synthetic Aperture ◽  
Crop Acreage ◽  
The Third ◽  
Godavari Delta ◽  
Sar Data ◽  
Paddy Crop ◽  
Aperture Radar

In the present study, an attempt is made to estimate the area under paddy crop during Rabi, 2013-14 using Microwave satellite data in the eastern part of Godavari delta. Clouds veil nearly the entire sky in both (Kharif & Rabi) seasons of Andhra Pradesh and hinder the estimation of crop acreage through optical satellite sensors. Microwaves can penetrate clouds and be used to detect crops during the day and night, regardless of cloud cover. Radar Imaging SATellite-1 (RISAT-1), microwave sensor, dual-polarization Horizontal-Horizontal (HH), Horizontal-Vertical (HV), Medium Resolution scanSAR Mode (MRS) data (18 m pixel spacing and 37° incidence angle) of three different dates (in December, January, and February) with 25 days interval was used. The backscatter (dB) values of the early, mid, and late-season transplanted stages of paddy crop were used to estimate the paddy crop acreage coupled with ground truth information during different stages of the crop. It was observed that the dB values at the transplanting stage rapidly increased with plant growth in the early season sown areas and mid-season sowed paddy illustrate a dip in dB values in the second date due to change in transplantation and increased backscatter coefficient values in the third date because of crop growth after transplantation. The backscatter signature value of late sowing paddy crop showed first and second dates with high backscatter due to previous crop/vegetation and then a sudden dip in the third date as submerged field ready for transplantation. The dB values of the above stages were used in decision-based classifier to estimate paddy crop acreage. The paddy area was compared at Mandal (sub-district level) estimates observed the significant coefficient of determination (R² = 0.89) between traditional estimates and Synthetic Aperture Radar (SAR) data assessment. This study robustly suggests the utilization of SAR data in agricultural crop monitoring during cloud cover.

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