scholarly journals Comparison of Surface Characteristics of the Antarctic Ice Sheet with Satellite Observations (Abstract)

1987 ◽  
Vol 9 ◽  
pp. 253
Author(s):  
N. Young ◽  
I. Goodwin
Keyword(s):  
Surface Topography ◽  
Accumulation Rate ◽  
Ice Sheet ◽  
Ground Truth ◽  
Surface Characteristics ◽  
Small Scale ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Additional Information ◽  
Wilkes Land

Ground surveys of the ice sheet in Wilkes Land, Antarctica, have been made on oversnow traverses operating out of Casey. Data collected include surface elevation, accumulation rate, snow temperature, and physical characteristics of the snow cover. By the nature of the surveys, the data are mostly restricted to line profiles. In some regions, aerial surveys of surface topography have been made over a grid network. Satellite imagery and remote sensing are two means of extrapolating the results from measurements along lines to an areal presentation. They are also the only source of data over large areas of the continent. Landsat images in the visible and near infra-red wavelengths clearly depict many of the large- and small scale features of the surface. The intensity of the reflected radiation varies with the aspect and magnitude of the surface slope to reveal the surface topography. The multi-channel nature of the Landsat data is exploited to distinguish between different surface types through their different spectral signatures, e.g. bare ice, glaze, snow, etc. Additional information on surface type can be gained at a coarser scale from other satellite-borne sensors such as ESMR, SMMR, etc. Textural enhancement of the Landsat images reveals the surface micro-relief. Features in the enhanced images are compared to ground-truth data from the traverse surveys to produce a classification of surface types across the images and to determine the magnitude of the surface topography and micro-relief observed. The images can then be used to monitor changes over time.

scholarly journals Comparison of Surface Characteristics of the Antarctic Ice Sheet with Satellite Observations (Abstract)

1987 ◽  
Vol 9 ◽  
pp. 253-253
Author(s):  
N. Young ◽  
I. Goodwin
Keyword(s):  
Surface Topography ◽  
Accumulation Rate ◽  
Ice Sheet ◽  
Ground Truth ◽  
Surface Characteristics ◽  
Small Scale ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Additional Information ◽  
Wilkes Land

Ground surveys of the ice sheet in Wilkes Land, Antarctica, have been made on oversnow traverses operating out of Casey. Data collected include surface elevation, accumulation rate, snow temperature, and physical characteristics of the snow cover. By the nature of the surveys, the data are mostly restricted to line profiles. In some regions, aerial surveys of surface topography have been made over a grid network.Satellite imagery and remote sensing are two means of extrapolating the results from measurements along lines to an areal presentation. They are also the only source of data over large areas of the continent. Landsat images in the visible and near infra-red wavelengths clearly depict many of the large- and small scale features of the surface. The intensity of the reflected radiation varies with the aspect and magnitude of the surface slope to reveal the surface topography. The multi-channel nature of the Landsat data is exploited to distinguish between different surface types through their different spectral signatures, e.g. bare ice, glaze, snow, etc. Additional information on surface type can be gained at a coarser scale from other satellite-borne sensors such as ESMR, SMMR, etc. Textural enhancement of the Landsat images reveals the surface micro-relief.Features in the enhanced images are compared to ground-truth data from the traverse surveys to produce a classification of surface types across the images and to determine the magnitude of the surface topography and micro-relief observed. The images can then be used to monitor changes over time.

scholarly journals The Surface Topography of Large Ice Masses from Landsat Imagery

1987 ◽  
Vol 33 (113) ◽  
pp. 16-23 ◽  
Author(s):  
Julian A. Dowdeswell ◽  
Neil F. McIntyre
Keyword(s):  
Surface Topography ◽  
Landsat Imagery ◽  
Ice Sheet ◽  
Surface Characteristics ◽  
Drainage Basins ◽  
Landsat Images ◽  
Antarctic Ice Sheet ◽  
Ice Caps ◽  
Ice Surface ◽  
The Antarctic

AbstractApparent ice-surface topography is observed at several scales on Landsat multi-spectral scanner (MSS) imagery. Digitally enhanced MSS scenes from Antarctica and Nordaustlandet, Svalbard, are compared with ice-surface elevations from aircraft altimetry (relative accuracy 2–3 m) to show that this apparent topography is real. Apparent ice divides on Landsat images fit closely with divides on altimetric records. Ice-surface irregularities within drainage basins are also shown to be real. On Byrd Glacier, Antarctica, apparent “flow lines” coincide with ridges on altimetric records. Synoptic Landsat data, calibrated by information from aircraft altimetric flight lines, are used to classify the surface roughness of the ice caps on Nordaustlandet and 40% of the Antarctic ice sheet. On Nordaustlandet, the roughest ice is of amplitude 15–25 m and wavelength 3–4.5 km. Drainage basins with such rough surface characteristics may be associated with ice streams or possibly past surge activity. The most rough Antarctic terrain is up to 60 m in amplitude, with wavelengths of <10 km. The roughness of the Antarctic ice sheet increases with distance from ice divides, reflecting changes in the parameters affecting the transfer of basal stresses to the ice surface.

scholarly journals The Surface Topography of Large Ice Masses from Landsat Imagery

1987 ◽  
Vol 33 (113) ◽  
pp. 16-23 ◽  
Author(s):  
Julian A. Dowdeswell ◽  
Neil F. McIntyre
Keyword(s):  
Surface Topography ◽  
Landsat Imagery ◽  
Ice Sheet ◽  
Surface Characteristics ◽  
Drainage Basins ◽  
Landsat Images ◽  
Antarctic Ice Sheet ◽  
Ice Caps ◽  
Ice Surface ◽  
The Antarctic

AbstractApparent ice-surface topography is observed at several scales on Landsat multi-spectral scanner (MSS) imagery. Digitally enhanced MSS scenes from Antarctica and Nordaustlandet, Svalbard, are compared with ice-surface elevations from aircraft altimetry (relative accuracy 2–3 m) to show that this apparent topography is real. Apparent ice divides on Landsat images fit closely with divides on altimetric records. Ice-surface irregularities within drainage basins are also shown to be real. On Byrd Glacier, Antarctica, apparent “flow lines” coincide with ridges on altimetric records. Synoptic Landsat data, calibrated by information from aircraft altimetric flight lines, are used to classify the surface roughness of the ice caps on Nordaustlandet and 40% of the Antarctic ice sheet. On Nordaustlandet, the roughest ice is of amplitude 15–25 m and wavelength 3–4.5 km. Drainage basins with such rough surface characteristics may be associated with ice streams or possibly past surge activity. The most rough Antarctic terrain is up to 60 m in amplitude, with wavelengths of <10 km. The roughness of the Antarctic ice sheet increases with distance from ice divides, reflecting changes in the parameters affecting the transfer of basal stresses to the ice surface.

scholarly journals A Multispectral Bayesian Classification Method for Increased Acoustic Discrimination of Seabed Sediments Using Multi-Frequency Multibeam Backscatter Data

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 455 ◽  
Author(s):  
Timo Gaida ◽  
Tengku Tengku Ali ◽  
Mirjam Snellen ◽  
Alireza Amiri-Simkooei ◽  
Thaiënne van Dijk ◽  
...  
Keyword(s):  
Ground Truth ◽  
Single Frequency ◽  
Qualitative Comparison ◽  
Ground Truth Data ◽  
Geological Interpretation ◽  
Additional Information ◽  
Triple Frequency ◽  
Acoustic Discrimination ◽  
Multiple Frequencies ◽  
Seabed Sediments

Multi-frequency backscatter data collected from multibeam echosounders (MBESs) is increasingly becoming available. The ability to collect data at multiple frequencies at the same time is expected to allow for better discrimination between seabed sediments. We propose an extension of the Bayesian method for seabed classification to multi-frequency backscatter. By combining the information retrieved at single frequencies we produce a multispectral acoustic classification map, which allows us to distinguish more seabed environments. In this study we use three triple-frequency (100, 200, and 400 kHz) backscatter datasets acquired with an R2Sonic 2026 in the Bedford Basin, Canada in 2016 and 2017 and in the Patricia Bay, Canada in 2016. The results are threefold: (1) combining 100 and 400 kHz, in general, reveals the most additional information about the seabed; (2) the use of multiple frequencies allows for a better acoustic discrimination of seabed sediments than single-frequency data; and (3) the optimal frequency selection for acoustic sediment classification depends on the local seabed. However, a quantification of the benefit using multiple frequencies cannot clearly be determined based on the existing ground-truth data. Still, a qualitative comparison and a geological interpretation indicate an improved discrimination between different seabed environments using multi-frequency backscatter.

scholarly journals Vegetation Cover Change Detection and Assessment in Arid Environment Using Multi-temporal Remote Sensing images and Ecosystem Management Approach

2016 ◽  
Author(s):  
Anwar Abdelrahman Aly ◽  
Abdulrasoul Mosa Al-Omran ◽  
Abdulazeam Shahwan Sallam ◽  
Mohammad Ibrahim Al-Wabel ◽  
Mohammad Shayaa Al-Shayaa
Keyword(s):  
Remote Sensing ◽  
Ecosystem Management ◽  
Vegetation Cover ◽  
Secondary Data ◽  
Ground Truth ◽  
Arid Environment ◽  
Management Approach ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Multi Temporal

Abstract. Vegetation cover (VC) changes detection is essential for a better understanding of the interactions and interrelationships between humans and their ecosystem. Remote sensing (RS) technology is one of the most beneficial tools to study spatial and temporal changes of VC. A case study has been conducted in the agro-ecosystem (AE) of Al-Kharj, in the centre of Saudi Arabia. Characteristics and dynamics of VC changes during a period of 26 years (1987–2013) were investigated. A multi-temporal set of images was processed using Landsat images; Landsat4 TM 1987, Landsat7 ETM+ 2000, and Landsat8 2013. The VC pattern and changes were linked to both natural and social processes to investigate the drivers responsible for the change. The analyses of the three satellite images concluded that the surface area of the VC increased by 107.4 % between 1987 and 2000, it was decreased by 27.5 % between years 2000 and 2013. The field study, review of secondary data and community problem diagnosis using the participatory rural appraisal (PRA) method suggested that the drivers for this change are the deterioration and salinization of both soil and water resources. Ground truth data indicated that the deteriorated soils in the eastern part of the Al-Kharj AE are frequently subjected to sand dune encroachment; while the south-western part is frequently subjected to soil and groundwater salinization. The groundwater in the western part of the ecosystem is highly saline, with a salinity ≥ 6 dS m−1. The ecosystem management approach applied in this study can be used to alike AE worldwide.

On the potential and challenges of using machine-learning for automated quality control of environmental sensor data

2020 ◽  
Author(s):  
Lennart Schmidt ◽  
Hannes Mollenhauer ◽  
Corinna Rebmann ◽  
David Schäfer ◽  
Antje Claussnitzer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Ground Truth ◽  
Sensor Data ◽  
Small Scale ◽  
Ground Truth Data ◽  
Starting Point ◽  
Environmental Sensor ◽  
Spatio Temporal ◽  
Automated Quality Control

&lt;p&gt;With more and more data being gathered from environmental sensor networks, the importance of automated quality-control (QC) routines to provide usable data in near-real time is becoming increasingly apparent. Machine-learning (ML) algorithms exhibit a high potential to this respect as they are able to exploit the spatio-temporal relation of multiple sensors to identify anomalies while allowing for non-linear functional relations in the data. In this study, we evaluate the potential of ML for automated QC on two spatio-temporal datasets at different spatial scales: One is a dataset of atmospheric variables at 53 stations across Northern Germany. The second dataset contains timeseries of soil moisture and temperature at 40 sensors at a small-scale measurement plot.&lt;/p&gt;&lt;p&gt;Furthermore, we investigate strategies to tackle three challenges that are commonly present when applying ML for QC: 1) As sensors might drop out, the ML models have to be designed to be robust against missing values in the input data. We address this by comparing different data imputation methods, coupled with a binary representation of whether a value is missing or not. 2) Quality flags that mark erroneous data points to serve as ground truth for model training might not be available. And 3) There is no guarantee that the system under study is stationary, which might render the outputs of a trained model useless in the future. To address 2) and 3), we frame the problem both as a supervised and unsupervised learning problem. Here, the use of unsupervised ML-models can be beneficial as they do not require ground truth data and can thus be retrained more easily should the system be subject to significant changes. In this presentation, we discuss the performance, advantages and drawbacks of the proposed strategies to tackle the aforementioned challenges. Thus, we provide a starting point for researchers in the largely untouched field of ML application for automated quality control of environmental sensor data.&lt;/p&gt;

scholarly journals Monitoring and assessment of forest cover disturbance in the Middle Volga region of Russia using Landsat images

2021 ◽  
Vol 932 (1) ◽  
pp. 012007
Author(s):  
O N Vorobev ◽  
E A Kurbanov ◽  
S A Lezhnin ◽  
D M Dergunov ◽  
L V Tarasova
Keyword(s):  
Forest Cover ◽  
Forest Disturbance ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Volga Region ◽  
Landsat Images ◽  
Middle Volga Region ◽  
Ground Truth Data ◽  
Hybrid Strategy ◽  
Middle Volga

Abstract The knowledge of the disturbance effect on the forest ecosystems is crucial for sustainable development on the global level. It is important to quantify, map and monitor forest cover resulting from natural and anthropogenic disturbances. This research presents spatio-temporal trend analyses of forest cover disturbance in the Middle Volga region of Russia, using a time series of Landsat images. We generated a series of image composites at different year intervals between 1985 and 2018 and utilized a hybrid strategy consisting of Tasseled Cap transformation, sampling ground truth data and post-classification analyses. For validation of the disturbance maps, we used a point-based accuracy assessment, using local forest inventory reports and ground truth sample plots data for 2016-2018. The produced Landsat 1985, 2001 и 2018 thematic maps for 7 classes of forest cover show that coniferous area decreased by 4%. At the same time, there is a decrease in small-leaved (19%), mixed (8%) and an increase in young stands (23%). A significant disturbed forest area 85,120 ha was observed between 2014-2018, where much of the loss occurs due to severe wildfires. More research is needed with the inclusion of the additional number of anthropogenic and natural factors to increase the accuracy of monitoring and detection of forest disturbance of the region.

scholarly journals Understanding Mn-nodule distribution and related deep-sea mining impacts using AUV-based hydroacoustic sensing and optical observations

2017 ◽  
Author(s):  
Anne Peukert ◽  
Timm Schoening ◽  
Evangelos Alevizos ◽  
Kevin Köser ◽  
Tom Kwasnitschka ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Deep Sea ◽  
Ground Truth ◽  
Optical Observations ◽  
Small Scale ◽  
Ground Truth Data ◽  
Mining Impacts ◽  
Multibeam Data ◽  
Quantitative Assessments

Abstract. In this study ship- and AUV-based multibeam data from the German Mn-nodule license area in the Clarion-Clipperton Zone (CCZ; eastern Pacific) are linked to ground truth data from optical imaging. Photographs obtained by an AUV enable semi-quantitative assessments of nodule coverage at a spatial resolution in the range of meters. Together with high resolution AUV bathymetry this revealed a correlation of small-scale terrain variations (

scholarly journals ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 489-492
Author(s):  
S. A. R. Hosseini ◽  
H. Gholami ◽  
Y. Esmaeilpoor
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Ground Truth ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Sensing Technology ◽  
Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

scholarly journals Spatio-Temporal Variation of Vegetation in Godavari Eastern Delta

2019 ◽  
pp. 1-7
Author(s):  
G. Kishore Kumar ◽  
M. Raghu Babu ◽  
A. Mani ◽  
M. Matin Luther ◽  
V. Srinivasa Rao
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Land Use Changes ◽  
Satellite Image ◽  
Ground Truth ◽  
Likelihood Method ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Wide Range

Spatial variability in land use changes creates a need for a wide range of applications, including landslide, erosion, land planning, global warming etc. This study presents the analysis of satellite image based on Normalized Difference Vegetation Index (NDVI) in Godavari eastern delta. Four spectral indices were investigated in this study. These indices were NIR (red and near infrared) based NDVI, green and NIR based GVI (Green Vegetation Index), red and NIR based soil adjusted vegetation index (SAVI), and red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for 2011-12 kharif, rabi and 2016-17 kharif, rabi of Godavari eastern delta. Different threshold values of NDVI are used for generating the false colour composite of the classified objects. For this purpose, supervised classification is applied to Landsat images acquired in 2011-12 and 2016-17. Image classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from satellite images of 2011-12 and 2016-17. There was 11% and 30% increase in vegetation during kharif and rabi seasons from 2011-12 to 2016-17. The vegetation analysis can be used to provide humanitarian aid, damage assessment in case of unfortunate natural disasters and furthermore to device new protection strategies.

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