Extracting icebergs freeboard from the shadows in Landsat-8 panchromatic images

Mapping Intimacies ◽

10.5194/egusphere-egu2020-12810 ◽

2020 ◽

Author(s):

Zhenfu Guan ◽

Yan Liu

Keyword(s):

Large Scale ◽

Accuracy Assessment ◽

Elevation Angle ◽

Absolute Error ◽

Landsat 8 ◽

High Precision Measurement ◽

Solar Elevation ◽

The Mean ◽

The Antarctic ◽

Solar Elevation Angle

Abstract:&#160;The iceberg freeboard is an important geometric parameter for measuring the thickness of the iceberg and then estimating its volume. Based on the fact that the iceberg can cast elongated shadow on the surface of sea ice in winter, this paper proposes a method to measure the iceberg freeboard using shadow length and the predefined or estimated solar elevation angle. Three Landsat-8 panchromatic images are selected to test our method, with center solar elevation angle of respectively 5.43&#176;, 7.49&#176;and 11.01&#176; on August 29, September 7, and 16 September in 2016. Shadow lengths of five isolated tabular icebergs are automatically extracted to calculate the freeboard height. For the accuracy assessment, we use the matching&#160;points at the different time as cross validation. The results show that the measurement error of shadow length is less than one pixel. When the sun elevation angle is lower than 11.01&#176;, the Root Mean Square Error (RMSE) of the iceberg freeboard from the panchromatic 15 m image is less than 2.0 m, and the Mean Absolute Error (MAE) is less than 1.5 m. Such experiment shows that: under the angle of low solar elevation in winter, the landsat-8 panchromatic 15 m image can be used for high-precision measurement of the iceberg freeboard, and has the potential to measure the Antarctic iceberg freeboard in large scale.Key words:&#160;Antarctic, icebergs, freeboard, shadow altimetry, Landsat-8&#160;

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Accuracy Assessment of TanDEM-X 90 and CartoDEM Using ICESat-2 Datasets for Plain Regions of Ratlam City and Surroundings

Engineering Proceedings ◽

10.3390/ecsa-8-11441 ◽

2021 ◽

Vol 10 (1) ◽

pp. 59

Author(s):

Unnati Yadav ◽

Ashutosh Bhardwaj

Keyword(s):

Mean Absolute Error ◽

Accuracy Assessment ◽

Absolute Error ◽

Terrain Analysis ◽

Mean Square ◽

High Quality ◽

Ice Cloud ◽

The Mean ◽

Cut And Fill ◽

Statistical Results

The spaceborne LiDAR dataset from the Ice, Cloud, and Land Elevation Satellite (ICESat-2) provides highly accurate measurements of heights for the Earth’s surface, which helps in terrain analysis, visualization, and decision making for many applications. TanDEM-X 90 (90 m) and CartoDEM V3R1 (30 m) elevation are among the high-quality openly accessible DEM datasets for the plain regions in India. These two DEMs are validated against the ICESat-2 elevation datasets for the relatively plain areas of Ratlam City and its surroundings. The mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) of TanDEM-X 90 DEM are 1.35 m, 1.48 m, and 2.19 m, respectively. The computed ME, MAE, and RMSE for CartoDEM V3R1 are 3.05 m, 3.18 m, and 3.82 m, respectively. The statistical results reveal that TanDEM-X 90 performs better in plain areas than CartoDEMV3R1. The study further indicates that these DEMs and spaceborne LiDAR datasets can be useful for planning various works requiring height as an important parameter, such as the layout of pipelines or cut and fill calculations for various construction activities. The TanDEM-X 90 can assist planners in quick assessments of the terrain for infrastructural developments, which otherwise need time-consuming traditional surveys using theodolite or a total station.

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Multi-temporal analysis of radiometric changes in satellite images of forest edges to infer selective-logging areas in the Amazon forest

Acta Amazonica ◽

10.1590/1809-4392201401073 ◽

2015 ◽

Vol 45 (1) ◽

pp. 35-44 ◽

Cited By ~ 2

Author(s):

Paulo Maurício Lima de Alencastro GRAÇA ◽

Francisco Dario MALDONADO ◽

João Roberto dos SANTOS ◽

Edwin Willem Hermanus KEIZER

Keyword(s):

Satellite Images ◽

Near Infrared ◽

Elevation Angle ◽

Temporal Analysis ◽

Selective Logging ◽

Amazon Forest ◽

Forest Edges ◽

Solar Elevation ◽

Multi Temporal ◽

Solar Elevation Angle

Radiometric changes observed in multi-temporal optical satellite images have an important role in efforts to characterize selective-logging areas. The aim of this study was to analyze the multi-temporal behavior of spectral-mixture responses in satellite images in simulated selective-logging areas in the Amazon forest, considering red/near-infrared spectral relationships. Forest edges were used to infer the selective-logging infrastructure using differently oriented edges in the transition between forest and deforested areas in satellite images. TM/Landsat-5 images acquired at three dates with different solar-illumination geometries were used in this analysis. The method assumed that the radiometric responses between forest with selective-logging effects and forest edges in contact with recent clear-cuts are related. The spatial frequency attributes of red/near infrared bands for edge areas were analyzed. Analysis of dispersion diagrams showed two groups of pixels that represent selective-logging areas. The attributes for size and radiometric distance representing these two groups were related to solar-elevation angle. The results suggest that detection of timber exploitation areas is limited because of the complexity of the selective-logging radiometric response. Thus, the accuracy of detecting selective logging can be influenced by the solar-elevation angle at the time of image acquisition. We conclude that images with lower solar-elevation angles are less reliable for delineation of selecting logging.

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Modelling of a carbon removal biological aerated filter doing partial nitrification during large-scale secondary treatment

Water Quality Research Journal ◽

10.2166/wqrjc.2014.045 ◽

2014 ◽

Vol 49 (3) ◽

pp. 245-257 ◽

Cited By ~ 2

Author(s):

Jean Bernier ◽

Vincent Rocher ◽

Sabrina Guerin ◽

Paul Lessard

Keyword(s):

Large Scale ◽

Absolute Error ◽

Carbon Removal ◽

Plant Optimization ◽

The Mean ◽

Hourly Concentration ◽

Aerated Filter ◽

Media Data ◽

Soluble Components ◽

Composite Samples

A wastewater biofiltration model is used to assess the potential of modelling plant-sized secondary carbon removal biofilter units. Two distinct datasets collected at the Seine-Centre biofiltration plant (Colombes, France) are used. The model is first calibrated on multiple grab samples taken at different heights inside the filter media. Data from 24 hour composite samples at the unit influent and effluent over a 2 year period are then simulated. Additional data are used to estimate hourly concentration profiles from composite samples in order to correctly use both composite and grab samples during modelling. The calibrated model is in most cases able to correctly predict the general nutrient behaviour for both datasets. The results of statistical scores such as the mean error and the mean absolute error are low for soluble components and remain correct for particles during years 2008–2009. Only one parameter set containing few heavily modified values is used to obtain these results. Modelling plant-sized biofilters appears to be practical and can be useful for easily evaluating plant optimization scenarios.

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Effectively Extracting Iceberg Freeboard Using Bi-Temporal Landsat-8 Panchromatic Image Shadows

Remote Sensing ◽

10.3390/rs13030430 ◽

2021 ◽

Vol 13 (3) ◽

pp. 430

Author(s):

Zhenfu Guan ◽

Xiao Cheng ◽

Yan Liu ◽

Teng Li ◽

Baogang Zhang ◽

...

Keyword(s):

Southern Ocean ◽

Global Climate ◽

Elevation Angle ◽

Freshwater Flux ◽

Landsat 8 ◽

Solar Elevation ◽

Panchromatic Image ◽

Aerial Vehicle ◽

Data Gap

The freshwater flux from icebergs into the Southern Ocean plays an important role in the global climate through its impact on the deep-water formation. Large uncertainties exist in the ice volume transported by Southern Ocean icebergs due to the sparse spatial and temporal coverage of observations, especially observations of ice thickness. The iceberg freeboard is a critical geometric parameter for measuring the thickness of an iceberg and then estimating its volume. This study developed a new, highly efficient shadow-height method to precisely measure the freeboard of various icebergs surrounded by sea ice using Landsat-8 Operational Land Imager 15-m bi-temporal panchromatic image shadows at low-solar-elevation angles. We evaluated and validated shadow length precision according to bi-temporal measurements and comparison with the measurements from the unmanned aerial vehicle. We determined freeboard precision according to shadow length precision and solar elevation angle. In our case study area, 4832 available freeboard measuring points with shadow length precision better than 2 pixels covered 376 icebergs with sizes ranging from 0.002 to 0.7 km² and with freeboard ranging from 2.3 to 83.4 m. At the solar elevation angles of 5.2°, the freeboard precision of 64.1% data could reach 1 m and 86.9% could reach 2 m. Our proposed method effectively filled in the data gap of existing freeboard measurement methods.

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The Effectiveness of Alternative Preference Elicitation Procedures in Predicting Choice

Journal of Marketing Research ◽

10.1177/002224379303000109 ◽

1993 ◽

Vol 30 (1) ◽

pp. 105-114 ◽

Cited By ~ 42

Author(s):

Joel Huber ◽

Dick R. Wittink ◽

John A. Fiedler ◽

Richard Miller

Keyword(s):

Large Scale ◽

Absolute Error ◽

Accurate Method ◽

Preference Elicitation ◽

National Study ◽

Relative Preference ◽

Full Profile ◽

The Mean ◽

Combining Information ◽

Better Than

In a large-scale national study, the authors evaluated the effectiveness of several preference elicitation techniques for predicting choices. The criteria for accuracy included both individual hit rates and a new measure, the mean absolute error predicting aggregate share using a logit choice simulator. The central finding is that hybrid models combining information from different preference elicitation tasks consistently outperform models based on one task. For example, ACA, a method that combines a self-explicated prior with relative preference measures on pairs, predicts choices better than full-profile conjoint when warmup tasks are lacking. However, there is no difference between the models if ACA's prior is combined with the full-profile information. Further, the most accurate method combines data from all three sources, suggesting that each preference elicitation technique taps a different aspect of the choice process in the validation task. Finally, full-profile conjoint is found to be significantly more accurate after rather than before, other preference elicitation tasks, implying that its performance can be improved with warmup exercises.

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Solar elevation angle probability distribution

Solar Energy ◽

10.1016/0038-092x(80)90308-4 ◽

1980 ◽

Vol 24 (4) ◽

pp. 417-420 ◽

Cited By ~ 3

Author(s):

D.C. Larson ◽

C.R. Acquista

Keyword(s):

Probability Distribution ◽

Elevation Angle ◽

Solar Elevation ◽

Solar Elevation Angle

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Landsat 8 OLI Broadband Albedo Validation in Antarctica and Greenland

Remote Sensing ◽

10.3390/rs13040799 ◽

2021 ◽

Vol 13 (4) ◽

pp. 799

Author(s):

Giacomo Traversa ◽

Davide Fugazza ◽

Antonella Senese ◽

Massimo Frezzotti

Keyword(s):

Narrow Band ◽

Mean Absolute Error ◽

Absolute Error ◽

Landsat 8 ◽

Polar Regions ◽

Landsat 8 Oli ◽

Anomalous Data ◽

The Antarctic ◽

Good Agreement

The albedo is a fundamental component of the processes that govern the energy budget, and particularly important in the context of climate change. However, a satellite-based high-resolution (30 m) albedo product which can be used in the polar regions up to 82.5° latitude during the summer seasons is lacking. To cover this gap, in this study we calculate satellite-based broadband albedo from Landsat 8 OLI and validate it against broadband albedo measurements from in situ stations located on the Antarctic and Greenland icesheets. The model to derive the albedo from raw satellite data includes an atmospheric and topographic correction and conversion from narrow-band to broadband albedo, and at each step different options were taken into account, in order to provide the best combination of corrections. Results, after being cleaned from anomalous data, show a good agreement with in situ albedo measurements, with a mean absolute error between in situ and satellite albedo of 0.021, a root mean square error of 0.026, a standard deviation of 0.015, a correlation coefficient of 0.995 (p < 0.01) and a bias estimate of −0.005. Considering the structure of the model, it could be applied to data from previous sensors of the Landsat family and help construct a record to analyze albedo variations in the polar regions.

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IRANIAN LAND COVER MAPPING USING LANDSAT-8 IMAGERY AND RANDOM FOREST ALGORITHM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-77-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 77-81

Author(s):

M. Amani ◽

A. Ghorbanian ◽

S. Mahdavi ◽

A. Mohammadzadeh

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Land Cover ◽

Large Scale ◽

Accuracy Assessment ◽

Land Cover Classification ◽

Google Earth ◽

Machine Learning Techniques ◽

Land Cover Mapping ◽

Landsat 8

Abstract. Land cover classification is important for various environmental assessments. The opportunity of imaging the Earth’s surface makes remote sensing techniques efficient approaches for land cover classification. The only country-wide land cover map of Iran was produced by the Iranian Space Agency (ISA) using low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and a basic classification method. Thus, it is necessary to produce a more accurate map using advanced remote sensing and machine learning techniques. In this study, multi-temporal Landsat-8 data (1,321 images) were inserted into a Random Forest (RF) algorithm to classify the land cover of the entire country into 13 categories. To this end, all steps, including pre-processing, classification, and accuracy assessment were implemented in the Google Earth Engine (GEE) platform. The overall classification accuracy and Kappa Coefficient obtained from the Iran-wide map were 74% and 0.71, respectively, indicating the high potential of the proposed method for large-scale land cover mapping.

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Latitude and Weather Influences on Sun Light Quality and the Relationship to Tree Growth

Forests ◽

10.3390/f10080610 ◽

2019 ◽

Vol 10 (8) ◽

pp. 610 ◽

Cited By ~ 7

Author(s):

Camilo Chiang ◽

Jorunn E. Olsen ◽

David Basler ◽

Daniel Bånkestad ◽

Günter Hoch

Keyword(s):

Light Quality ◽

Elevation Angle ◽

Spectral Composition ◽

Weather Conditions ◽

Seasonal Adjustment ◽

Light Climate ◽

Solar Elevation ◽

Light Quantity ◽

R:Fr Ratio ◽

Solar Elevation Angle

Natural changes in photoperiod, light quantity, and quality play a key role in plant signaling, enabling daily and seasonal adjustment of growth and development. Growing concern about the global climate crisis together with scattered reports about the interactive effects of temperature and light parameters on plants necessitates more detailed information about these effects. Furthermore, the actual light emitting diode (LED) lighting technology allows mimicking of light climate scenarios more similar to natural conditions, but to fully exploit this in plant cultivation, easy-to-apply knowledge about the natural variation in light quantity and spectral distribution is required. Here, we aimed to provide detailed information about short and long-term variation in the natural light climate, by recording the light quantity and quality at an open site in Switzerland every minute for a whole year, and to analyze its relationship to a set of previous tree seedling growth experiments. Changes in the spectral composition as a function of solar elevation angle and weather conditions were analyzed. At a solar elevation angle lower than 20°, the weather conditions have a significant effect on the proportions of blue (B) and red (R) light, whereas the proportion of green (G) light is almost constant. At a low solar elevation, the red to far red (R:FR) ratio fluctuates between 0.8 in cloudy conditions and 1.3 on sunny days. As the duration of periods with low solar angles increases with increasing latitude, an analysis of previous experiments on tree seedlings shows that the effect of the R:FR ratio correlates with the responses of plants from different latitudes to light quality. We suggest an evolutionary adaptation where growth in seedlings of selected tree species from high latitudes is more dependent on detection of light quantity of specific light qualities than in such seedlings originating from lower latitudes.

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