The RADARSAT Constellation Mission in Support of Environmental Applications

Dabboor, Mohammed; Iris, Steve; Singhroy, V.

doi:10.3390/ecrs-2-05136

Cited by 15 publications

(13 citation statements)

References 6 publications

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“…With the launch of the RADARSAT Constellation Mission (RCM) planned in early 2019, an increased number of SAR C-band acquisitions will be potentially available soon [51]. When combined with S-1 data, the time period could be substantially shorter to receive the number of required acquisitions for a windthrow detection of similar quality.…”

Section: Discussionmentioning

confidence: 99%

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

2019

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Storm events are capable of causing windthrow to large forest areas. A rapid detection of the spatial distribution of the windthrown areas is crucial for forest managers to help them direct their limited resources. Since synthetic aperture radar (SAR) data is acquired largely independent of daylight or weather conditions, SAR sensors can produce temporally consistent and reliable data with a high revisit rate. In the present study, a straightforward approach was developed that uses Sentinel-1 (S-1) C-band VV and VH polarisation data for a rapid windthrow detection in mixed temperate forests for two study areas in Switzerland and northern Germany. First, several S-1 acquisitions of approximately 10 before and 30 days after the storm event were radiometrically terrain corrected. Second, based on these S-1 acquisitions, a SAR composite image of before and after the storm was generated. Subsequently, after analysing the differences in backscatter between before and after the storm within windthrown and intact forest areas, a change detection method was developed to suggest potential locations of windthrown areas of a minimum extent of 0.5 ha—as is required by the forest management. The detection is based on two user-defined parameters. While the results from the independent study area in Germany indicated that the method is very promising for detecting areal windthrow with a producer’s accuracy of 0.88, its performance was less satisfactory at detecting scattered windthrown trees. Moreover, the rate of false positives was low, with a user’s accuracy of 0.85 for (combined) areal and scattered windthrown areas. These results underscore that C-band backscatter data have great potential to rapidly detect the locations of windthrow in mixed temperate forests within a short time (approx. two weeks) after a storm event. Furthermore, the two adjustable parameters allow a flexible application of the method tailored to the user’s needs.

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Section: Discussionmentioning

confidence: 99%

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

2019

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“…In an ideal case, optical and radar images are acquired at the same date. With the increasing availability of both radar (e.g., the upcoming Radarsat constellation mission [138] plus many of the planned SmallSAT missions [139]) and multi-spectral sensors (e.g., Planet Scope [140]), this will be more likely in the near future. Nonetheless, a minimum number of image pairs over the investigated period is required, as well as a budget to cover the acquisition costs of commercial satellite data.…”

Section: Discussionmentioning

confidence: 99%

Refugee Camp Monitoring and Environmental Change Assessment of Kutupalong, Bangladesh, Based on Radar Imagery of Sentinel-1 and ALOS-2

2019

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Approximately one million refugees of the Rohingya minority population in Myanmar crossed the border to Bangladesh on 25 August 2017, seeking shelter from systematic oppression and persecution. This led to a dramatic expansion of the Kutupalong refugee camp within a couple of months and a decrease of vegetation in the surrounding forests. As many humanitarian organizations demand frameworks for camp monitoring and environmental impact analysis, this study suggests a workflow based on spaceborne radar imagery to measure the expansion of settlements and the decrease of forests. Eleven image pairs of Sentinel-1 and ALOS-2, as well as a digital elevation model, were used for a supervised land cover classification. These were trained on automatically-derived reference areas retrieved from multispectral images to reduce required user input and increase transferability. Results show an overall decrease of vegetation of 1500 hectares, of which 20% were used to expand the camp and 80% were deforested, which matches findings from other studies of this case. The time-series analysis reduced the impact of seasonal variations on the results, and accuracies between 88% and 95% were achieved. The most important input variables for the classification were vegetation indices based on synthetic aperture radar (SAR) backscatter intensity, but topographic parameters also played a role.

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“…The presently available and upcoming C-band SAR satellite constellation missions (e.g., Sentinel-1 of the Copernicus Programme [35][36][37] and RADARSAT Constellation Mission [38,39]), along with the existing free open-source SAR processing toolboxes (e.g., SNAP [40] and POLSARPRO [41]), can provide academic centers, environmental agencies, nongovernmental organizations, and the petroleum industry itself with the required support to build libraries with the category of oil slicks in the vicinities of offshore oil and gas facilities. The scrutiny of these oil-slick category records is of great interest not only from the political-economic perspective (i.e., discovery of new exploration frontiers based on the identification of oil seeps observed in the surface of the ocean coming from active petroleum systems), but is also relevant from the social-ecological viewpoint (i.e., it can assist in reducing headline uproar inquires with reliable timely information with the detection of oil spills using more efficient environmental surveillance techniques).…”

Section: Introductionmentioning

confidence: 99%

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

et al. 2019

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A novel empirical approach to categorize oil slicks’ sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved ~70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (σ°, β°, γ°), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, log10). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks’ size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses—cubist or random forest—to attempt to further improve oil-slick category discrimination.

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The RADARSAT Constellation Mission in Support of Environmental Applications

Cited by 15 publications

References 6 publications

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

Refugee Camp Monitoring and Environmental Change Assessment of Kutupalong, Bangladesh, Based on Radar Imagery of Sentinel-1 and ALOS-2

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

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