Assessment of Landsat 7 Scan Line Corrector-off data gap-filling methods for seagrass distribution mapping

Hossain, Mohammad Shawkat; Bujang, Japar Sidik; Zakaria, Muta Harah; Hashim, Mazlan

doi:10.1080/01431161.2015.1007257

Cited by 28 publications

(11 citation statements)

References 33 publications

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“…GERALDINE masks cloud cover using the GEE built-in "simple cloud score" function (Housman et al 2018). This pixel-wise cloud probability score allows fast and efficient identification of clouds, suitable for large-scale analysis (Housman et al, 2018), and has been previously applied and well-justified for use in glacial environments (Scherler et al, 2018). A 20 % threshold is applied to every image, thereby excluding any pixel with a cloud score >20 % from the image.…”

Section: Cloud Maskingmentioning

confidence: 99%

“…The dual Landsat 5/7 constellation increases tool area accuracy further to 69 %. However, a decrease in mean area accuracy is evident after the failure of the Landsat 7 Scan Line Corrector in May 2003 (Markham et al, 2004), decreasing tool areal accuracy by 4 %, due to images missing up to 20 %-25 % of their data per image in the stack (Hossain et al, 2015). We find that a number of Landsat 7 scenes also feature stripes of no data, pre-dating the scan line corrector failure, and can inaccurately cause "stripes" of new debris in tool outputs.…”

Section: Validationmentioning

confidence: 99%

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GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new tool for identifying and monitoring supraglacial landslide inputs

et al. 2020

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Abstract. Landslides in glacial environments are high-magnitude, long-runout events, believed to be increasing in frequency as a paraglacial response to ice retreat and thinning and, arguably, due to warming temperatures and degrading permafrost above current glaciers. However, our ability to test these assumptions by quantifying the temporal sequencing of debris inputs over large spatial and temporal extents is limited in areas with glacier ice. Discrete landslide debris inputs, particularly in accumulation areas, are rapidly “lost”, being reworked by motion and icefalls and/or covered by snowfall. Although large landslides can be detected and located using their seismic signature, smaller (M≤5.0) landslides frequently go undetected because their seismic signature is less than the noise floor, particularly supraglacially deposited landslides, which feature a “quiet” runout over snow. Here, we present GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new free-to-use tool leveraging Landsat 4–8 satellite imagery and Google Earth Engine. GERALDINE outputs maps of new supraglacial debris additions within user-defined areas and time ranges, providing a user with a reference map, from which large debris inputs such as supraglacial landslides (>0.05 km2) can be rapidly identified. We validate the effectiveness of GERALDINE outputs using published supraglacial rock avalanche inventories, and then demonstrate its potential by identifying two previously unknown, large (>2 km2) landslide-derived supraglacial debris inputs onto glaciers in the Hayes Range, Alaska, one of which was not detected seismically. GERALDINE is a first step towards a complete global magnitude–frequency of landslide inputs onto glaciers over the 38 years of Landsat Thematic Mapper imagery.

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Section: Cloud Maskingmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new tool for identifying and monitoring supraglacial landslide inputs

et al. 2020

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“…Because the scan line corrector (SLC) in the enhanced thematic mapper plus (ETM+) instrument of Landsat-7 failed on May 31, 2003, approximately 22% of the data in the Landsat-7 scene were missing. 39) Therefore, some strips in the Landsat-7 image, part of the original data, are missing. To effectively evaluate the data detection results, we applied mask processing to the hyperspectral data to make it consistent with the Landsat-7 data, as shown in Fig.…”

Section: Description Of the Datasetsmentioning

confidence: 99%

A Cooperative Framework Based on Active and Semi-supervised Learning for Sea Ice Classification using EO-1 Hyperion Data

Han

Zhao

Zhang

et al. 2019

Trans. Japan Soc. Aero. S Sci.

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In the classification of remote-sensing sea ice images, labeled samples are difficult to acquire. To adequately utilize the massive number of unlabeled samples, which contain abundant information, we propose a cooperative framework based on active learning (AL) and semi-supervised learning (SSL) for sea ice image classification. We acquire the most valuable samples using AL and make full use of the abundant information contained in the unlabeled samples using SSL, and then conduct a label consistency verification procedure to further ensure the quality of the pseudo-labeled samples obtained through cooperation between AL and SSL. In the AL part, we adopt a sampling strategy that integrates uncertainty and diversity criteria to acquire the most valuable samples to label. In the SSL part, we utilize the SSL sampling strategy to choose the unlabeled samples with the most information and little redundancy, and use the transductive support vector machine (TSVM) as the classification model. The cooperation between AL and SSL ensures the accuracy of the pseudo-labeled samples through a consistency verification procedure. We conduct comparative experiments using the method proposed and other methods on two hyperspectral images obtained from the Earth Observation Satellite 1 (EO-1). The proposed method achieves the highest classification accuracy for both datasets and can be effectively applied to sea ice classification.

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“…The upper left-hand corner of this area is 120°45′12′′ E, 41°39′7′′ N, and its bottom right-hand corner is 121°13′9′′ E, 39°44′42′′ N. The validation data consisted of Landsat-7 (with a spatial resolution of 15 m) sea ice data from the same location acquired on 26 January 2008. Because the scan line corrector (SLC) in the enhanced thematic mapper plus (ETM+) instrument of Landsat-7 failed on 31 May 2003, approximately 22% of the data in the Landsat-7 scene were missing [ 28 ]. Therefore, some strips in the Landsat-7 image, part of the original data, are missing data, as shown in Figure 8 b.…”

Section: Experimental Analysismentioning

confidence: 99%

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

Han

Zhang

et al. 2017

Sensors

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Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection.

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Assessment of Landsat 7 Scan Line Corrector-off data gap-filling methods for seagrass distribution mapping

Cited by 28 publications

References 33 publications

GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new tool for identifying and monitoring supraglacial landslide inputs

GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new tool for identifying and monitoring supraglacial landslide inputs

A Cooperative Framework Based on Active and Semi-supervised Learning for Sea Ice Classification using EO-1 Hyperion Data

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

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