scholarly journals Assessment of the impact of Landsat 7 Scan Line Corrector data gaps on Sungai Pulai Estuary seagrass mapping

2015 ◽  
Vol 7 (3) ◽  
pp. 189-202 ◽  
Author(s):  
Mohammad Shawkat Hossain ◽  
Japar Sidik Bujang ◽  
Muta Harah Zakaria ◽  
Mazlan Hashim
Keyword(s):  
Data Gaps ◽  
Landsat 7 ◽  
Seagrass Mapping ◽  
Scan Line ◽  
The Impact

Interpolation Of Data Gaps Of SLC-Off Landsat ETM+ Images Using Algorithm Based On The Differential Operators

2014 ◽  
Vol 6 (2) ◽  
pp. 93-100 ◽  
Author(s):  
Raghvendra Singh ◽  
P. Rama Chandra Prasad
Keyword(s):  
Partial Derivative ◽  
Differential Operators ◽  
Interpolation Method ◽  
Thematic Mapper ◽  
Scientific Applications ◽  
Data Gaps ◽  
Landsat 7 ◽  
Good Technique ◽  
Scan Line

Abstract The scan-line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed in May 2003, and this abnormal functioning of SLC resulted in about 22% of the pixels per scene without being scanned. By filling the un-scanned gap by a good technique will help in more use of ETM+ data for many scientific applications. While there have been a number of approaches developed to fill in the data gaps in ETM+ imagery, each method has shortcomings, especially they require SLC-on (images acquired before SLC-off anomaly) imagery for the same location to fill the gaps in SLC-off (images acquired after SLC anomaly) image. To overcome such shortcomings this study proposes an alternative interpolation method based on the partial derivative. This case study shows that this technique is very much useful to interpolate the missing pixel values in the SLC-off ETM+ data.

scholarly journals Studi Laju Perubahan Garis Pantai di Pesisir Tenggara Bali Menggunakan Citra Satelit Landsat (Studi Kasus Kabupaten Gianyar dan Klungkung)

2017 ◽  
Vol 3 (2) ◽  
pp. 204
Author(s):  
I Nengah Jaya Nugraha ◽  
I Wayan Gede Astawa Karang ◽  
I Gusti Bagus Sila Dharma
Keyword(s):  
Landsat Imagery ◽  
Shoreline Change ◽  
Rate Of Change ◽  
Landsat 8 ◽  
Band Ratio ◽  
Preliminary Information ◽  
Landsat 7 ◽  
Combination Of Methods ◽  
Analysis System ◽  
The Impact

Erosion and abrasion are the events that led to the beach shoreline position changes. The impact of climate change is the rise in sea level also causes changes in the coastline. South East coast of Bali, especially along the coast Gianyar and Klungkung changing coastline. This study aims to identify and calculate the rate of shoreline change in Gianyar and Klungkung from 1995 to 2015. The study was a preliminary information shoreline change and do not analyze the causes such as tides, currents, waves, and wind. The method used remote sensing analysis with the extraction of the coastline from the Landsat 5 satellite images in 1995, Landsat 7 in 2005, and Landsat 8 2015. Landsat imagery analyzed by a combination of methods approach the threshold and band ratio of wave infrared and green. Image processing using software Quantum GIS 2.8 and System for Automated Geoscientific Analyses (SAGA) GIS 2.2, extention Digintal Shoreline Analysis System (DSAS) to make calculations transect coastline. The results of the analysis of overlaying identify coastline in Gianyar and Klungkung change at a rate that varies every village. The rate of change of coastline in Gianyar due to accretion between 0.5096 - 8.6074 m / yr, while due to erosion between -3.7343 to -1.3201 m / yr. The rate of change in Klungkung regency coastline due to accretion between 0.6337 - 2.6875 m / yr, while due to erosion between -8.8795 to -0.8833 m / yr.

scholarly journals Comparison of Methods to Estimate Lake-Surface-Water Temperature Using Landsat 7 ETM+ and MODIS Imagery: Case Study of a Large Shallow Subtropical Lake in Southern Brazil

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 168 ◽  
Author(s):  
Matheus Tavares ◽  
Augusto Cunha ◽  
David Motta-Marques ◽  
Anderson Ruhoff ◽  
J. Cavalcanti ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Single Channel ◽  
Atmospheric Correction ◽  
Ecological Status ◽  
Surface Water Temperature ◽  
Lake Surface ◽  
Subtropical Lake ◽  
Landsat 7 ◽  
The Impact

Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 ° C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 ° C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates.

scholarly journals Understanding and mitigating the impact of data gaps on offshore wind resource estimates

2021 ◽  
Vol 6 (2) ◽  
pp. 505-520
Author(s):  
Julia Gottschall ◽  
Martin Dörenkämper
Keyword(s):  
Wind Energy ◽  
Mesoscale Model ◽  
Measurement Data ◽  
Reanalysis Data ◽  
Offshore Wind ◽  
Data Gaps ◽  
Mean Wind Speed ◽  
Harsh Climate ◽  
Almost All ◽  
The Impact

Abstract. Like almost all measurement datasets, wind energy siting data are subject to data gaps that can for instance originate from a failure of the measurement devices or data loggers. This is in particular true for offshore wind energy sites where the harsh climate can restrict the accessibility of the measurement platform, which can also lead to much longer gaps than onshore. In this study, we investigate the impact of data gaps, in terms of a bias in the estimation of siting parameters and its mitigation by correlation and filling with mesoscale model data. Investigations are performed for three offshore sites in Europe, considering 2 years of parallel measurement data at the sites, and based on typical wind energy siting statistics. We find a mitigation of the data gaps' impact, i.e. a reduction of the observed biases, by a factor of 10 on mean wind speed, direction and Weibull scale parameter and a factor of 3 on Weibull shape parameter. With increasing gap length, the gaps' impact increases linearly for the overall measurement period while this behaviour is more complex when investigated in terms of seasons. This considerable reduction of the impact of the gaps found for the statistics of the measurement time series almost vanishes when considering long-term corrected data, for which we refer to 30 years of reanalysis data.

scholarly journals Hydrological Response of the Imphal River on the Impact of Landuse/ Landcover and Rainfall Patterns

2020 ◽  
Vol 66 (2) ◽  
pp. 126-135
Author(s):  
Kh. Pradipkumar Singh ◽  
◽  
Priyalina Sapam ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Land Cover ◽  
Remotely Sensed Data ◽  
Land Use Pattern ◽  
Rural And Urban ◽  
Landsat 7 ◽  
Spatio Temporal ◽  
Surrounding Areas ◽  
The Impact

The river regime is one of the important parameters in studying the physical attributes in a region. The influence of a river passing in a region is immense. The nature of the stream reflects the natural and cultural set up of the surrounding areas. For instance soil factors, Land use/Land cover and vegetation, habitat, settlements, etc. Everywhere land use/ land cover is often altered during the process of economic and social development and eventually, the morphology and structure of river systems are unconsciously or consciously influenced along with the land-use change. The changes in land use/ land cover have a large amount of impact on the nature of runoff and associated hydrological characteristics. Availability of remotely sensed data has made convenient and accurate to map and monitor the Spatio-temporal variation of land use/ land cover at regional or local scales. The present paper highlights the changing of land use pattern in the Imphal River catchment. To identify the changes, Landsat 5 TM and Landsat 7 ETM+ obtained in 2005 and 2016 have been used and categorize the images into 16 major land use/ land cover. It has been found that over the periods both rural and urban built-up area has increased more than 24 Km2 and decreased in forests cover area by more than 113 Km2 . Further, the study also focused on the rainfallrunoff response through regression analysis. The integration of the analyses demonstrates the effect of land use/ land cover change on discharge characteristics of the study area.

scholarly journals Assessment of BSRN radiation records for the computation of monthly means

2010 ◽  
Vol 3 (5) ◽  
pp. 4423-4457
Author(s):  
A. Roesch ◽  
M. Wild ◽  
A. Ohmura ◽  
E. G. Dutton ◽  
C. N. Long ◽  
...  
Keyword(s):  
Missing Data ◽  
Global Radiation ◽  
Longwave Radiation ◽  
Surface Radiation ◽  
High Time Resolution ◽  
Monthly Means ◽  
Radiation Fluxes ◽  
Data Gaps ◽  
Downwelling Longwave Radiation ◽  
The Impact

Abstract. The integrity of the Baseline Surface Radiation Network (BSRN) radiation monthly averages are assessed by investigating the impact on monthly means due to the frequency of data gaps caused by missing or discarded high time resolution data. The monthly statistics, especially means, are considered to be important and useful values for climate research, model performance evaluations and for assessing the quality of satellite (time- and space-averaged) data products. The study investigates the spread in different algorithms that have been applied for the computation of monthly means from 1-min values. The paper reveals that the computation of monthly means from 1-min observations distinctly depends on the method utilized to account for the missing data. The intra-method difference generally increases with an increasing fraction of missing data. We found that a substantial fraction of the radiation fluxes observed at BSRN sites is either missing or flagged as questionable. The percentage of missing data is 4.4%, 13.0%, and 6.5% for global radiation, direct shortwave radiation, and downwelling longwave radiation, respectively. Most flagged data in the shortwave are due to nighttime instrumental noise and can reasonably be set to zero after correcting for thermal offsets in the daytime data. The study demonstrates that the handling of flagged data clearly impacts on monthly mean estimates obtained with different methods. We showed that the spread of monthly shortwave fluxes is generally clearly higher than for downwelling longwave radiation. Overall, BSRN observations provide sufficient accuracy and completeness for reliable estimates of monthly mean values. However, the value of future data could be further increased by reducing the frequency of data gaps and the number of outliers. It is shown that two independent methods for accounting for the diurnal and seasonal variations in the missing data permit consistent monthly means to within less than one Wm−2 in most cases. The authors suggest using a standardized method for the computation of monthly means which addresses diurnal variations in the missing data in order to avoid a mismatch of future published monthly mean radiation fluxes from BSRN.

scholarly journals Assessment of BSRN radiation records for the computation of monthly means

2011 ◽  
Vol 4 (2) ◽  
pp. 339-354 ◽  
Author(s):  
A. Roesch ◽  
M. Wild ◽  
A. Ohmura ◽  
E. G. Dutton ◽  
C. N. Long ◽  
...  
Keyword(s):  
Missing Data ◽  
Global Radiation ◽  
Longwave Radiation ◽  
Surface Radiation ◽  
High Time Resolution ◽  
Monthly Means ◽  
Radiation Fluxes ◽  
Data Gaps ◽  
Downwelling Longwave Radiation ◽  
The Impact

Abstract. The integrity of the Baseline Surface Radiation Network (BSRN) radiation monthly averages are assessed by investigating the impact on monthly means due to the frequency of data gaps caused by missing or discarded high time resolution data. The monthly statistics, especially means, are considered to be important and useful values for climate research, model performance evaluations and for assessing the quality of satellite (time- and space-averaged) data products. The study investigates the spread in different algorithms that have been applied for the computation of monthly means from 1-min values. The paper reveals that the computation of monthly means from 1-min observations distinctly depends on the method utilized to account for the missing data. The intra-method difference generally increases with an increasing fraction of missing data. We found that a substantial fraction of the radiation fluxes observed at BSRN sites is either missing or flagged as questionable. The percentage of missing data is 4.4%, 13.0%, and 6.5% for global radiation, direct shortwave radiation, and downwelling longwave radiation, respectively. Most flagged data in the shortwave are due to nighttime instrumental noise and can reasonably be set to zero after correcting for thermal offsets in the daytime data. The study demonstrates that the handling of flagged data clearly impacts on monthly mean estimates obtained with different methods. We showed that the spread of monthly shortwave fluxes is generally clearly higher than for downwelling longwave radiation. Overall, BSRN observations provide sufficient accuracy and completeness for reliable estimates of monthly mean values. However, the value of future data could be further increased by reducing the frequency of data gaps and the number of outliers. It is shown that two independent methods for accounting for the diurnal and seasonal variations in the missing data permit consistent monthly means to within less than 1 W m−2 in most cases. The authors suggest using a standardized method for the computation of monthly means which addresses diurnal variations in the missing data in order to avoid a mismatch of future published monthly mean radiation fluxes from BSRN. The application of robust statistics would probably lead to less biased results for data records with frequent gaps and/or flagged data and outliers. The currently applied empirical methods should, therefore, be completed by the development of robust methods.

scholarly journals Understanding and mitigating the impact of data gaps on offshore wind resource estimates

2020 ◽  
Author(s):  
Julia Gottschall ◽  
Martin Dörenkämper
Keyword(s):  
Wind Energy ◽  
Mesoscale Model ◽  
Measurement Data ◽  
Reanalysis Data ◽  
Offshore Wind ◽  
Data Gaps ◽  
Mean Wind Speed ◽  
Harsh Climate ◽  
Almost All ◽  
The Impact

Abstract. Like almost all measurement datasets, wind energy siting data are subject to data gaps that can for instance originate from a failure of the measurement devices or data loggers. This is in particular true for offshore wind energy sites where the harsh climate can restrict the accessibility of the measurement platform, which can also lead to much longer gaps than onshore. In this study, we investigate the impact of data gaps and its mitigation by correlation and filling with mesoscale model data. Investigations are performed for three offshore sites in Europe, considering two years of parallel measurement data at the sites, and based on typical wind energy siting statistics. We find a mitigation of the data gaps' impact by a factor of ten on mean wind speed, direction and Weibull scale parameter, and a factor of three on Weibull shape parameter. With increasing gap length, the gaps' impact increases linearly for the overall measurement period while this behaviour is more complex when investigated in terms of seasons. This considerable reduction of the impact of the gaps found for the statistics of the measurement time series almost vanishes when considering long-term corrected data, for which we refer to 30 years of reanalysis data.

scholarly journals Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019

2021 ◽  
Vol 15 (3) ◽  
pp. 1663-1675
Author(s):  
Daniel Cheng ◽  
Wayne Hayes ◽  
Eric Larour ◽  
Yara Mohajerani ◽  
Michael Wood ◽  
...  
Keyword(s):  
Sea Level ◽  
Landsat Imagery ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Automated Method ◽  
Regional Trends ◽  
Current Implementation ◽  
Landsat 7 ◽  
Scan Line ◽  
Automated Methods

Abstract. Sea level contributions from the Greenland Ice Sheet are influenced by the rapid changes in glacial terminus positions. The documentation of these evolving calving front positions, for which satellite imagery forms the basis, is therefore important. However, the manual delineation of these calving fronts is time consuming, which limits the availability of these data across a wide spatial and temporal range. Automated methods face challenges that include the handling of clouds, illumination differences, sea ice mélange, and Landsat 7 scan line corrector errors. To address these needs, we develop the Calving Front Machine (CALFIN), an automated method for extracting calving fronts from satellite images of marine-terminating glaciers, using neural networks. The results are often indistinguishable from manually curated fronts, deviating by on average 86.76 ± 1.43 m from the measured front. Landsat imagery from 1972 to 2019 is used to generate 22 678 calving front lines across 66 Greenlandic glaciers. This improves on the state of the art in terms of the spatiotemporal coverage and accuracy of its outputs and is validated through a comprehensive intercomparison with existing studies. The current implementation offers a new opportunity to explore subseasonal and regional trends on the extent of Greenland's margins and supplies new constraints for simulations of the evolution of the mass balance of the Greenland Ice Sheet and its contributions to future sea level rise.

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