Global Forest Monitoring with Synthetic Aperture Radar (SAR) Data

2016 ◽  
pp. 273-297
Author(s):  
Richard Lucas ◽  
Daniel Clewley ◽  
Ake Rosenqvist ◽  
Josef Kellndorfer ◽  
Wayne Walker ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Forest Monitoring ◽  
Synthetic Aperture ◽  
Sar Data ◽  
Aperture Radar

scholarly journals Estimating Forest Stand Height in Savannakhet, Lao PDR Using InSAR and Backscatter Methods with L-Band SAR Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4516
Author(s):  
Helen Blue Parache ◽  
Timothy Mayer ◽  
Kelsey E. Herndon ◽  
Africa Ixmucane Flores-Anderson ◽  
Yang Lei ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Forest Stand ◽  
Forest Monitoring ◽  
Synthetic Aperture ◽  
Use Case ◽  
Important Indicator ◽  
L Band ◽  
Sar Data ◽  
Stand Height ◽  
Aperture Radar

Forest stand height (FSH), or average canopy height, serves as an important indicator for forest monitoring. The information provided about above-ground biomass for greenhouse gas emissions reporting and estimating carbon storage is relevant for reporting for Reducing Emissions from Deforestation and Forest Degradation (REDD+). A novel forest height estimation method utilizing a fusion of backscatter and Interferometric Synthetic Aperture Radar (InSAR) data from JAXA’s Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) is applied to a use case in Savannakhet, Lao. Compared with LiDAR, the estimated height from the fusion method had an RMSE of 4.90 m and an R2 of 0.26. These results are comparable to previous studies using SAR estimation techniques. Despite limitations of data quality and quantity, the Savannakhet, Lao use case demonstrates the applicability of these techniques utilizing L-band SAR data for estimating FSH in tropical forests and can be used as a springboard for use of L-band data from the future NASA-ISRO SAR (NISAR) mission.

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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