scholarly journals Thành lập nhanh bản đồ lũ bằng chỉ số lũ khác biệt chuẩn hóa NDFI và chỉ số khác biệt lũ trong vùng thực vật thấp NDFVI sử dụng lợi thế của hệ thống Vietnam Data Cube

2020 ◽  
pp. 25-33
Author(s):  
Thị Thu Hằng Lê ◽  
Minh Hải Phạm ◽  
Anh Tuấn Vũ ◽  
Hồng Quảng Nguyễn
Keyword(s):  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Data Cube ◽  
Synthetic Aperture ◽  
Viet Nam ◽  
Flood Index ◽  
Aperture Radar

Trong bối cảnh biến đổi khí hậu, việc có được một bản đồ lũ kịp thời và chính xác ngay khi thiên tai là thực sự cần thiết trong việc lập kế hoạch quản lý khẩn cấp nhằm giảm thiểu rủi ro thiên tai một cách hiệu quả.Nghiên cứu áp dụng một phương pháp lập bản đồ lũ lụt nhanh dựa trên hai chỉ số khác biệt lũ chuẩn hóa (NDFI – Normalized Difference Flood Index) và chỉ số khác biệt lũ trong vùng thực vật thấp (NDFVI - Normalized Difference Flood inshort Vegetation Index) từ chuỗi dữ liệu của ảnh Radar khẩu độ tổng hợp (SAR –Synthetic Aperture Radar) Sentinel-1.Hai chỉ số này được tính toán, trên các cảnh ảnh trước lũ và trong lúc có lũ, từ đó lập bản đồ các khu vực ngập nước lũ và các khu vực có nước ngập thảm thực vật thấp bằng phương pháp phân ngưỡng. Dữ liệu SAR băng tần C của vệ tinhSentinel-1 được sử dụng trong nghiên cứu này do đây là nguồn dữ liệu miễn phívà có tần suất chụp ảnh khá tốt (12 ngày). Tuy vậy, phương pháp cũng có thể được áp dụng cho tất cả các dữ liệu vệ tinh SAR băng C khác để tăng cao tần suất quan sát, một điều rất cần thiết trong theo dõi lũ. Áp dụng phương pháp trên khu vực thử nghiệm tỉnh Đồng Tháp năm 2018 cho thấy có độ tin cậy cao và hiệu quả của phương pháp qua việc đánh giá, so sánh với dữ liệu thực địa gồm143 điểm. Hiện nay, toàn bộ ảnh Sentinel-1 đã được thu thập và liên tục cập nhật trong hệ thống Vietnam Data Cube do Trung tâm Vũ trụ Việt Nam vận hành cho phép áp dụng phương pháp trên toàn lãnh thổ Việt Nam

scholarly journals Synthetic Aperture Radar (L band) and Optical Vegetation Indices for Discriminating the Brazilian Savanna Physiognomies: A Comparative Analysis

2005 ◽  
Vol 9 (15) ◽  
pp. 1-15 ◽  
Author(s):  
Edson E. Sano ◽  
Laerte G. Ferreira ◽  
Alfredo R. Huete
Keyword(s):  
Comparative Analysis ◽  
Synthetic Aperture Radar ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Primary Response ◽  
Synthetic Aperture ◽  
L Band ◽  
Vegetation Structures ◽  
Aperture Radar

Abstract The all-weather capability, signal independence to the solar illumination angle, and response to 3D vegetation structures are the highlights of active radar systems for natural vegetation mapping and monitoring. However, they may present significant soil background effects. This study addresses a comparative analysis of the performance of L-band synthetic aperture radar (SAR) data and optical vegetation indices (VIs) for discriminating the Brazilian cerrado physiognomies. The study area was the Brasilia National Park, Brazil, one of the test sites of the Large-Scale Biosphere–Atmosphere (LBA) experiment in Amazonia. Seasonal Japanese Earth Resources Satellite-1 (JERS-1) SAR backscatter coefficients (σ°) were compared with two vegetation indices [normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] over the five most dominant cerrados’ physiognomies plus gallery forest. In contrast to the VIs, σ° from dry and wet seasons did not change significantly, indicating primary response to vegetation structures. Discriminant analysis and analysis of variance (ANOVA) showed an overall higher performance of radar data. However, when both SAR and VIs are combined, the discrimination capability increased significantly, indicating that the fusion of the optical and radar backscatter observations provides overall improved classifications of the cerrado types. In addition, VIs showed good performance for monitoring the cerrado dynamics.

scholarly journals Tropical Peatland Identification using L-Band Full Polarimetric Synthetic Aperture Radar (SAR) Imagery (Study Case: Siak Regency, Riau Province)

2019 ◽  
Vol 26 (2) ◽  
pp. 63
Author(s):  
Desti Ayunda ◽  
Ketut Wikantika ◽  
Dandy A. Novresiandi ◽  
Agung B. Harto ◽  
Riantini Virtriana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Error Matrix ◽  
Tropical Peatland ◽  
Average Value ◽  
Sar Imagery ◽  
Aperture Radar

From previous research reported that tropical peatland is one of terrestrial carbon storage in Earth, and has contribution to climate change. Synthetic Aperture Radar (SAR) is one of remote sensing technology which is more efcient than optical remote sensing. Its ability to penetrate cloud makes it useful to monitor tropical environment. This research is conducted in a tropical peatland in Siak Regency, Riau Province. This research was conducted to identify tropical peatland in Siak Regency using polarimetric decomposition, unsupervised classifcation ISODATA, and Radar Vegetation Index (RVI) from SAR data that had been geometrically and radiometrically corrected. Polarimetric decomposition Freeman-Durden was performed to analyze radar backscattering mechanism in tropical peatland, which shows that volume and surface scattering was dominant because of the presence of vegetation and open area. Unsupervised classifcation ISODATA was then performed to extract “shrub class”. By assessing its accuracy, the class that represents shrub class in reference map was selected as the selected “shrub class”. RVI then was calculated using a certain formula. Spatial analysis was then conducted to acquire certain information that average value of RVI in tropical peatland tend to be higher than in non-tropical peatland. By integrating selected “shrub class” and RVI, peat classes were extracted. The best peat class was selected by comparing with peatland referenced map which is acquired from the Indonesian Agency for Agricultural Resources and Development (IAARD) using error matrix. In this research, the best peat class yielded 73.5 percent of Producer’s Accuracy (PA), 81.6 percent of User’s Accuracy (UA), 66.1 percent of Overall Accuracy (OA), and 0.1079 of Kappa coefcient (Ks).

scholarly journals JAXA Annual Forest Cover Maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and Auxiliary Data

2019 ◽  
Vol 11 (20) ◽  
pp. 2412 ◽  
Author(s):  
Truong ◽  
Hoang ◽  
Cao ◽  
Hayashi ◽  
Tadono ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Forest Cover ◽  
European Space Agency ◽  
Synthetic Aperture ◽  
National Scale ◽  
Forest Mapping ◽  
Multi Temporal ◽  
Mosaic Images ◽  
Aperture Radar

Monitoring the temporal changes of forests is important for sustainable forest management. In this study, we investigated the potential of using multi-temporal synthetic aperture radar (SAR) images for mapping annual change in forest cover at a national scale. We assessed the robustness of using multi-temporal Phased Array L-band Synthetic Aperture Radar-2/Scanning Synthetic Aperture Radar (PALSAR-2/ScanSAR) mosaic images for forest mapping by comparison with single-temporal PALSAR-2 mosaic images for three test sites in North, Central, and Southern Vietnam. We then used a combination of multi-temporal PALSAR-2/ScanSAR images, multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) images, and Shuttle Radar Topography Mission (SRTM) images to map annual forest cover for mainland Vietnam during 2015–2018. Average overall accuracies of our forest/non-forest (FNF) maps (86.6% ± 3.1%) were greater than recent maps of Japan Aerospace Exploration Agency (JAXA, (77.5% ± 3.2%)) and European Space Agency (ESA, (85.4% ± 1.6%)). Our estimates of mainland Vietnam’s forest area were close to that of the Vietnamese government. A comparison of the spatial distribution of forest estimated from JAXA and ESA FNF maps showed that our FNF map in 2015 agreed relatively well with the ESA map, with 77% of pixels being consistent. This study demonstrates the merit of using multi-temporal PALSAR-2/ScanSAR images for annual forest mapping at a national scale.

scholarly journals Complementing Optical Remote Sensing with Synthetic Aperture Radar Observations of Hail Damage Swaths to Agricultural Crops in the Central United States

2020 ◽  
Vol 59 (4) ◽  
pp. 665-685 ◽  
Author(s):  
Jordan R. Bell ◽  
Esayas Gebremichael ◽  
Andrew L. Molthan ◽  
Lori A. Schultz ◽  
Franz J. Meyer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Growing Season ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Hail Damage ◽  
Sar Imagery ◽  
Moisture Conditions ◽  
Aperture Radar

AbstractThe normalized difference vegetation index (NDVI) has been frequently used to map hail damage to vegetation, especially in agricultural areas, but observations can be blocked by cloud cover during the growing season. Here, the European Space Agency’s Sentinel-1A/1B C-band synthetic aperture radar (SAR) imagery in co- and cross polarization is used to identify changes in backscatter of corn and soybeans damaged by hail during intense thunderstorm events in the early and late growing season. Following a June event, hail-damaged areas produced a lower mean backscatter when compared with surrounding, unaffected pixels [vertical–vertical (VV): −1.1 dB; vertical–horizontal (VH): −1.5 dB]. Later, another event in August produced an increase in co- and cross-polarized backscatter (VV: 0.7 dB; VH: 1.7 dB) that is hypothesized to result from the combined effects of crop growth, change in structure of damaged crops, and soil moisture conditions. Hail damage regions inferred from changes in backscatter were further assessed through coherence change detections to support changes in the structure of crops damaged within the hail swath. While studies using NDVI have routinely concluded a decrease in NDVI is associated with damage, the cause of change with respect to the damaged areas in SAR backscatter values is more complex. Influences of environmental variables, such as vegetation structure, vegetation maturity, and soil moisture conditions, need to be considered when interpreting SAR backscatter and will vary throughout the growing season.

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