Radiometric and geometric assessment of data from the RapidEye constellation of satellites

Chander, Gyanesh; Haque, Md. Obaidul; Sampath, Aparajithan; Brunn, Andreas; Trosset, G.; Hoffmann, Dietrich; Roloff, S.; Thiele, Michael; Anderson, Cody

doi:10.1080/01431161.2013.798877

Cited by 29 publications

(21 citation statements)

References 13 publications

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“…Sensor-internal RapidEye co-registration ( Figure 6) has revealed a steadily improving spatial fit between the datasets since the start of operational data acquisition in 2009, cumulating in offsets of one pixel or less for most of the images acquired in 2011 and 2012. These results are in accordance with the findings of a study assessing the geometric accuracy of the RapidEye constellation [32].…”

Section: Applicability Of Approachsupporting

confidence: 82%

“…RapidEye data have been acquired in the frame of the RESA (RapidEye Science Archive) program, allowing customized tasking of data acquisition during pre-defined time periods. Due to the five independent satellites of the RapidEye system [32], a database of high spatial and temporal resolution could be created for the whole region of interest. In total, the database comprises 503 Level 3A standard orthorectified data products characterized by a 5-m pixel size, resulting from cubic convolution resampling of the original 6.5-m RapidEye data.…”

Section: Satellite Remote Sensing Databasementioning

confidence: 99%

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Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection

et al. 2014

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Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution. Correlation-based co-registration uses world-wide available terrain corrected Landsat Level 1T time series data as the spatial reference, ensuring global applicability. The developed approach has been applied to a multi-sensor time series of 592 remote sensing datasets covering an approximately 12,000 km 2 area in Southern Kyrgyzstan (Central Asia) strongly affected by landslides. The database contains images acquired during the last 26 years by Landsat (E)TM, ASTER, SPOT and RapidEye sensors. Analysis of the spatial shifts obtained from co-registration has revealed sensor-specific alignments ranging between 5 m and more than 400 m. Overall accuracy assessment of these alignments has resulted in a high relative image-to-image accuracy of 17 m (RMSE) and a high absolute accuracy of 23 m (RMSE) for the whole co-registered database, making it suitable for multi-temporal landslide detection at a regional scale in Southern Kyrgyzstan.

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Section: Applicability Of Approachsupporting

confidence: 82%

Section: Satellite Remote Sensing Databasementioning

confidence: 99%

Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection

et al. 2014

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“…These specifications were already confirmed in the literature [45,46] and were largely met in the TDS.…”

Section: ‚ Geometrysupporting

confidence: 70%

Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2

et al. 2015

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Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 2015,7, mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10-20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the "Sentinel-2 for Agriculture" project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of "Sentinel-2 like" time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the "Joint Experiment for Crop Assessment and Monitoring" network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future "Sentinel-2 for Agriculture" system.

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“…Against this background, the methodological goal of this study is the development of an automated approach for object-oriented landslide mapping of large areas which is suitable for generating multi-temporal landslide inventories, including the possibility for regular monitoring of spatiotemporal landslide activity. For this purpose, a multi-temporal RapidEye [38] remote sensing database of 5 m spatial resolution has been established for a 7500 km 2 landslide-affected area in southern Kyrgyzstan for the time period between 2009 and 2013 with up to six multi-temporal acquisitions per year between April and July ( Figure 1c). The database contains standard orthorectified Level-3A data products to enable operational applicability of the approach requiring efficient analysis of large amounts of data independent from the availability of ground truth information.…”

Section: Introductionmentioning

confidence: 99%

Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

et al. 2014

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Abstract:In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information. The approach builds on analyzing temporal NDVI-trajectories for the separation between landslide-related surface changes and other land cover changes. To accommodate the variety of landslide phenomena occurring in the 7500 km 2 study area, a combination of pixel-based multiple thresholds and object-oriented analysis has been implemented including the discrimination of uncertainty-related landslide likelihood classes. Applying the approach to the whole study area for the time period between 2009 and 2013 has resulted in the multi-temporal identification of 471 landslide objects. A quantitative accuracy assessment for two independent validation sites has revealed overall high mapping accuracy (Quality Percentage: 80%), proving the suitability of the developed approach for efficient spatiotemporal landslide mapping over large areas, representing an important prerequisite for objective landslide hazard and risk assessment at the regional scale. OPEN ACCESSRemote Sens. 2014, 6 8027

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Radiometric and geometric assessment of data from the RapidEye constellation of satellites

Cited by 29 publications

References 13 publications

Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection

Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection

Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2

Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

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