Nansat: a Scientist-Orientated Python Package for Geospatial Data
                        Processing

Korosov, Anton; Hansen, Morten W.; Dagestad, Knut‐Frode; Yamakawa, Akira; Vines, Aleksander; Riechert, Maik

doi:10.5334/jors.120

Cited by 20 publications

(16 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All preprocessing steps including radiometric calibration, downsampling and geolocation were performed using the Nansat package [29,30].…”

Section: Sentinel-1a Datamentioning

confidence: 99%

“…Valid match-ups were randomly split into two equal parts: one part was used for sensitivity tests (see Section 3.3), another part for independent validation (see Section 3.2). Buoy coordinates in the spherical coordinate system (longitude, latitude) were converted into the Cartesian coordinate system of a matching SAR image (rows, columns) utilizing the georeference information from the corresponding GCPs using Nansat [29]. …”

Section: Sea Ice Driftersmentioning

confidence: 99%

See 1 more Smart Citation

A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data

Korosov¹,

Rampal²

2017

Remote Sensing

View full text Add to dashboard Cite

Sea ice drift strongly influences sea ice thickness distribution and indirectly controls air-sea ice-ocean interactions. Estimating sea ice drift over a large range of spatial and temporal scales is therefore needed to characterize the properties of sea ice dynamics and to better understand the ongoing changes of the climate in the polar regions. An efficient algorithm is developed for processing SAR data based on the combination of feature tracking (FT) and pattern matching (PM) techniques. The main advantage of the combination is that the FT rapidly provides the first guess estimate of ice drift in a few unevenly distributed keypoints, and PM accurately provides drift vectors on a regular or irregular grid. Thorough sensitivity analysis of the algorithm is performed, and optimal sets of parameters are suggested for retrieval of sea ice drift on various spatial and temporal scales. The algorithm has rather high accuracy (error is below 300 m) and high speed (the time for one image pair is 1 min), which opens new opportunities for studying sea ice kinematic processes. The ice drift can now be efficiently observed in the Lagrangian coordinate system on an irregular grid and, therefore, used for pointwise evaluation of the models running on unstructured meshes or for assimilation into Lagrangian models.

show abstract

“…All preprocessing steps including radiometric calibration, downsampling and geolocation were performed using the Nansat package [29,30].…”

Section: Sentinel-1a Datamentioning

confidence: 99%

Section: Sea Ice Driftersmentioning

confidence: 99%

A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data

Korosov¹,

Rampal²

2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…2.2 using Nansat -an open-source Python toolbox for processing 2-D satellite earth observation data (Korosov et al, 2015a(Korosov et al, , 2016.…”

Section: The Amsr-e Lf Data Setmentioning

confidence: 99%

Error assessment of satellite-derived lead fraction in the Arctic

Ivanova¹,

Rampal²,

Bouillon³

2016

The Cryosphere

View full text Add to dashboard Cite

Abstract. Leads within consolidated sea ice control heat exchange between the ocean and the atmosphere during winter, thus constituting an important climate parameter. These narrow elongated features occur when sea ice is fracturing under the action of wind and currents, reducing the local mechanical strength of the ice cover, which in turn impact the sea ice drift pattern. This creates a high demand for a highquality lead fraction (LF) data set for sea ice model evaluation, initialization, and for the assimilation of such data in regional models. In this context, an available LF data set retrieved from satellite passive microwave observations (Advanced Microwave Scanning Radiometer -Earth Observing System, AMSR-E) is of great value, which has been providing pan-Arctic light-and cloud-independent daily coverage since 2002. In this study errors in this data set are quantified using accurate LF estimates retrieved from Synthetic Aperture Radar (SAR) images employing a threshold technique. A consistent overestimation of LF by a factor of 2-4 is found in the AMSR-E LF product. It is shown that a simple adjustment of the upper tie point used in the method to estimate the LF can reduce the pixel-wise error by a factor of 2 on average. Applying such an adjustment to the full data set may thus significantly increase the quality and value of the original data set.

show abstract

“…Remote sensing is an effective tool for detecting damaged areas because it can be used to document damage to large areas without direct access to the affected area (Yamazaki and Matsuoka, 2007;Rathje and Adams, 2008;Dell'Acqua and Gamba, 2012). Immense improvement to the accessibility of remote-sensing imagery data and geospatial data processing tools has been achieved over the last several years (Vuolo et al, 2016;Korosov et al, 2016). A dramatic increase in the number of satellite, aircraft, and unmanned aerial vehicle (UAV) sensors has been observed as well.…”

Section: Introductionmentioning

confidence: 99%

Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan

Moya¹,

Yamazaki

Wen

et al. 2018

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The 2016 Kumamoto earthquake sequence was triggered by an M w 6.2 event at 21:26 on 14 April. Approximately 28 h later, at 01:25 on 16 April, an M w 7.0 event (the mainshock) followed. The epicenters of both events were located near the residential area of Mashiki and affected the region nearby. Due to very strong seismic ground motion, the earthquake produced extensive damage to buildings and infrastructure. In this paper, collapsed buildings were detected using a pair of digital surface models (DSMs), taken before and after the 16 April mainshock by airborne light detection and ranging (lidar) flights. Different methods were evaluated to identify collapsed buildings from the DSMs. The change in average elevation within a building footprint was found to be the most important factor. Finally, the distribution of collapsed buildings in the study area was presented, and the result was consistent with that of a building damage survey performed after the earthquake.

show abstract

Nansat: a Scientist-Orientated Python Package for Geospatial Data Processing

Cited by 20 publications

References 5 publications

A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data

A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data

Error assessment of satellite-derived lead fraction in the Arctic

Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan

Contact Info

Product

Resources

About